**Physical Activity and Nutrition Survey and Evaluation for Public Health**

Editors

**Roberto Pippi Carmine Giuseppe Fanelli Matteo Vandoni**

Basel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester

*Editors* Roberto Pippi University of Perugia Perugia, Italy

Carmine Giuseppe Fanelli Perugia University School of Medicine Perugia, Italy

Matteo Vandoni Universita di Pavia ` Pavia, Italy

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Nutrients* (ISSN 2072-6643) (available at: https://www.mdpi.com/journal/nutrients/special issues/physical activity nutrition survey evaluation).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-9156-8 (Hbk) ISBN 978-3-0365-9157-5 (PDF) doi.org/10.3390/books978-3-0365-9157-5**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license.

## **Contents**



### *Editorial* **Physical Activity and Nutrition Survey and Evaluation for Public Health**

**Roberto Pippi 1,\*, Matteo Vandoni <sup>2</sup> and Carmine Giuseppe Fanelli <sup>1</sup>**


Regular physical activity (PA) and healthy nutrition are effective strategies to improve crucial modifiable lifestyle factors that affect health status, both in healthy people and in special populations suffering from metabolic disorders (e.g., obesity and type 2 diabetes, or DM2) or other Non-Communicable Diseases (NCDs) [1]. For this reason, it is mandatory to provide guidance on the proper implementation of a healthy lifestyle by suggesting nutritional medical therapies and prescribing exercise programs to improve people's health. The evaluation of these modifiable lifestyle factors is also a mandatory component of every medical visit or intervention.

In the Nutrients Special Issue "Physical Activity and Nutrition Survey and Evaluation for Public Health" (https://www.mdpi.com/journal/nutrients/special\_issues/physical\_a ctivity\_nutrition\_survey\_evaluation (accessed on 22 September 2023)), we presented some papers to focus on the importance of evaluation through surveys, tests, or other forms of evaluation in different settings (i.e., sport and clinical practice) to better orient interventions for public health improvement. This Editorial aims to summarize the twelve articles (nine original articles, one review, and two systematic reviews) that are published in this Special Issue.

Among the behavioral patterns linked to people's health is the Mediterranean Lifestyle (MLS), which includes a high adherence to the Mediterranean Diet (MD) and regular physical activity. Montero-Sandiego et al. [2] presented a systematic review of different methodologies and tools to assess the MLS, and the authors identified four indexes (MEDiLIFE-index, MEDI-Lifestyle, Total Lifestyle Index, and MedCOVID-19 Score). As a consensus has not yet been reached on a single instrument to comprehensively and reliably measure the MLS that has proven and adequate psychometric properties. The authors emphasized the need to design an instrument for the general population that includes all dimensions of the MLS.

Continuing on the theme of Mediterranean nutritional habits, Béjar [3] presented their results on the difference between weekly and weekend dietary patterns that emerged from the evaluation of Mediterranean diet habits in a group of 361 students (263 women and 98 men) of the University of Seville (Andalusia, Spain). Using the Electronic 12-h Dietary Recall smartphone application and calculating the adherence to the Mediterranean diet index, the researchers observed that the diet quality of the entire sample of Spanish university students was poor in general, especially on the weekends. For this reason, these results could be used to improve public health campaigns to promote healthy eating among Spanish university students.

In another paper published in this Special Issue, Trovato et al. [4] analyzed the relationship between PA, sun exposure, vitamin D, and perceived stress in a sample of the general Italian population. The authors identified an association between sunshine/vitamin D only in those who are physically active, suggesting that PA is necessary to realize the benefits of sunshine/vitamin D supplementation for stress.

**Citation:** Pippi, R.; Vandoni, M.; Fanelli, C.G. Physical Activity and Nutrition Survey and Evaluation for Public Health. *Nutrients* **2023**, *15*, 4139. https://doi.org/10.3390/ nu15194139

Received: 13 September 2023 Accepted: 18 September 2023 Published: 25 September 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Some studies of this Special Issue focused on various PA and nutrition aspects that need to be considered when we talk about PA evaluation and prescription. Regarding PA and exercise prescription for health, Calcaterra et al. [5] emphasized the importance of the modality and volume of exercise required to obtain benefits. The authors assessed clinical, auxological, hemodynamic (i.e., weight, height, waist circumference, waist-to-height ratio, visceral adiposity index, fasting blood glucose, lipid profile), and lifestyle parameters (i.e., PA levels, etc.) in a group of 35 children (14 females/21 males) with obesity participating in 12 weeks of online exercise training. The participants were allocated into two groups based on the volume of performed exercise (above or below 1200 MET·min/week). The authors concluded that improvement in the volume of the structured exercise was associated with a reduction in the arterial pressure percentile, and that progress in metabolic controls was evident. Moreover, the online exercise training program, when designed and progressively adapted according to individual fitness level, was a successful instrument and alternative to promote health improvements because it is sustainable both economically and organizationally.

Another alternative card to play in the clinical field is proposed by Pippi et al. [6] with an article examining an aquatic exercise program in 93 adults (72 women and 21 men) with obesity and/or DM2. The paper utilized the well-established evaluation model developed by the C.U.R.I.A.Mo. Healthy Lifestyle Institute of Perugia University (Umbria, Italy). This study showed the effectiveness of an aquatic exercise program, supervised by experts such as kinesiologists, in improving body mass index, waist circumference, and blood pressure values in obese adults with and without type 2 diabetes. The authors recommended the usefulness of aquatic exercise in managing patients with metabolic diseases who often present with other health impairments, such as musculoskeletal problems or cardiovascular or rheumatic disease that could contraindicate gym-based exercise.

Petri et al. [7] performed a body composition assessment in a group of 68 participants (41 males aged 30.1 ± 9.2 years and 27 females aged 32.1 ± 8.0 years) at the 2021 World Natural Bodybuilding Federation Italian Championships using the vector bioimpedance methodology (BIVA). The study aimed to provide normative references of body composition with the BIVA and to compare BIVA assessments performed on both sides and the upper and lower body. They concluded that BIVA references in bodybuilders could help adjust their training and nutritional programs during the peak week before a competition.

Kasovi´c et al. [8] evaluated the acute effects of a four-week resistance training program on body composition, muscular fitness, and flexibility in 764 Croatian veterans. The participants were subdivided into two groups (50–64 and 65–80 years old). Based on their results which showed that resistance training increased lean mass, muscular fitness, and flexibility and decreased body weight, body mass index, and fat mass across the groups, the authors concluded that even a relatively short resistance training intervention may lead to higher physical fitness levels in men and women aged 50–80 years.

The "lockdown" and social isolation imposed in some world countries during the SARS-CoV-2 virus global health crisis period led to a drastic alteration in lifestyle habits, which was particularly dangerous for people with NCDs. In this Special Issue, two contributions linked to COVID-19 were presented. Ke et al. [9] presented a study that described an online survey result of PA, dietary behavior, and body weight changes during the COVID-19 nationwide lockdown in Taiwan. The results were obtained from the replies of 374 respondents, aged between 20 and 66, and showed a negative impact on all PA levels of participants, with a significant increase in a sedentary lifestyle. However, the authors reported that some good eating habits, including eating fewer snacks or sweets and being less likely to eat as a reward, significantly increased during this period. This may also explain the lack of significant changes in body weight during the lockdown period. Another contribution to determining the training and nutrition plans of professional athletes after infection with COVID-19 was provided by Sliz et al. [10]. The authors described the assessment of the consequences of SARS-CoV-2 virus infection and pandemic restrictions on nutrition and PA among 49 endurance athletes (43 male and 6 female), and its validation through cardiopulmonary exercise testing. This paper underlined the importance for nutritionists considering which eating habits emerge when athletes have limited training opportunities and spend more time at home (i.e., adequate supply of fluids, fruits and vegetables, vitamins, and whole grains), and that trainers should be more aware of body and performance changes after suffering from COVID-19. In addition, another lesson that the COVID-19 era has taught us is the importance of technology. Considering that wearable devices are increasingly popular in the clinical and non-clinical population as a tool for exercise prescription, the monitoring of daily physical activity and nutrition, and health-related parameter management, Natalucci et al. [11] provided a narrative review of the effectiveness of new technologies in physical activity and nutrition. The authors aimed to review the current application of wearable devices in NCDs, highlighting their role in prescribing and monitoring daily PA and dietary habits in a population living with chronic diseases. This study concluded that the benefits obtained from the use of wearable devices are likely to translate into public health and are important tools for the development of prevention plans in everyday life and clinical practice for optimal patient management.

Although proper nutrition and adequate PA are recognized as the most important factors promoting health, the cult of slimness and excessive attention to a healthy lifestyle in the interaction of some factors can sometimes lead to eating disorders. Orthorexia, in particular, is an informally diagnosed condition characterized by an obsessive preoccupation with eating healthily. This condition is frequently associated with high levels of PA to maintain fitness and health in order to achieve the "ideal" body shape that is promoted by mass media. These themes were discussed by Grajek et al. [12] in their paper evaluating the prevalence of orthorexic behaviors in a group of 300 Polish students in terms of their differential health behaviors, such as diet and PA levels. The authors speculated that students who participate intensely in sports may have a significantly higher exposure to orthorexia than those with lower levels of PA, and they confirmed that orthorexia may occur in students with a low BMI index.

Finally, coaches and clinical team professionals caring for the general population or patients with NDC should better understand the various types of barriers people face that prevent them from adhering to a healthy lifestyle. With regard to difficulties for the general population in adhering to PA and healthy nutrition recommendations, Cavallo et al. [13] presented a systematic review to highlight the barriers that patients with chronic-degenerative diseases experience in implementing a healthier diet and an exercise-based therapeutic program. The authors identified barriers for both for exercise and nutrition including lack of time, environmental barriers, health status, and psychological barriers.

**Author Contributions:** Conceptualization, R.P., M.V. and C.G.F.; writing—original draft preparation and writing—review and editing, R.P., M.V. and C.G.F. All authors have read and agreed to the published version of the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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### *Article* **Physical Activity, Sun Exposure, Vitamin D Intake and Perceived Stress in Italian Adults**

**Bruno Trovato 1, Justyna Godos 2,\*, Simone Varrasi 3, Federico Roggio 1,4, Sabrina Castellano 3,† and Giuseppe Musumeci 1,†**


**Abstract:** The last decades of global development have, due to rapid urbanization, pressuring entire populations to changes in lifestyle and dietary habits, led to an increase in the prevalence of mental disorders, including stress. This study explored how lifestyle and dietary factors, such as physical activity, sun exposure, and vitamin D intake are related to perceived stress in a Mediterranean-based population. Physical activity level was evaluated using the international physical activity questionnaires (IPAQ), sun exposure was evaluated using the sunlight exposure measurement questionnaire (SEM-Q), and validated food frequency questionnaires (FFQs) were used to assess dietary intakes. The perceived stress of the study participants was evaluated using the perceived stress scale (PSS). Multivariate logistic regression models were used to test for potential associations. In the most adjusted model, an inverse association between physical activity level, sunlight exposure, vitamin D intake, and high perceived stress was found (OR = 0.72, 95% CI: 0.51, 1.00, OR = 0.72, 95% CI: 0.52, 0.99, OR = 0.69, 95% CI: 0.53, 0.89, respectively). However, when stratifying the population by level of physical activity, the retrieved associations with sunlight exposure and dietary vitamin D intake were significant only among those individuals reporting being moderately to highly physically active (OR = 0.16, 95% CI: 0.08, 0.33 and OR = 0.46, 95% CI: 0.28, 0.76, respectively), while results on low physically active participants were null. In conclusion, this study demonstrated that higher dietary intake of vitamin D and sunlight exposure are associated with a lower likelihood of having high perceived stress among physically active individuals.

**Keywords:** physical activity; vitamin D; sun exposure; perceived stress; mental health; Mediterranean

#### **1. Introduction**

The first documented definition of stress in literature is the one given by Selye in 1956, defining stress as a psychological response of the body dealing with positive or negative non-specific demands [1]. Since then, researchers throughout the world failed to identify a rigorous definition of stress, providing misleading definitions of it [2]. The World Health Organization (WHO) defines stress as a state of worry or mental tension due to a difficult situation to challenge. Nowadays, stress is regarded as a series of experiences that generate frustration or anxiety due to a perceived threat to one's security or the inability of an individual to cope with it [3]. Exposure to chronic or acute stressors endorses the release of catecholamines and cortisol that are fundamental for the fight-or-flight response [4]. However, when their levels remain high throughout the day, they can

**Citation:** Trovato, B.; Godos, J.; Varrasi, S.; Roggio, F.; Castellano, S.; Musumeci, G. Physical Activity, Sun Exposure, Vitamin D Intake and Perceived Stress in Italian Adults. *Nutrients* **2023**, *15*, 2301. https:// doi.org/10.3390/nu15102301

Academic Editor: Aamer Sandoo

Received: 31 March 2023 Revised: 3 May 2023 Accepted: 4 May 2023 Published: 13 May 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

negatively affect psychophysiological well-being [4]. Stressful events are often more easily remembered, and there is a correlation between chronic and acute stress and morphological adaptation/alterations of the amygdala. This indicates that stress has the potential to actively modify a biological system [5]. Furthermore, chronic exposure to stress that elicits a maladaptive response can promote dysregulation of the immune system, inducing migraines and increasing the risk of cardiovascular pathologies, depression, and symptoms related to anxiety [6]. Therefore, targeting modifiable risk factors such as dietary habits and physical activity to improve the resilience of a person is fundamental [7].

Physical activity is one of the most useful strategies to manage stress without the risk of harming an individual with pharmacological treatment. The benefits of physical activity for both healthy and pathological people include reduced risk of metabolic [8], musculoskeletal [9], and neurological diseases [10]. Moreover, the positive effects of being physically active, even at levels below that recommended by the WHO, have also been demonstrated for mental health outcomes [11]. A possible beneficial mechanism through which physical activity may exert an action toward mental health (including tension, anger, and depressive feelings) is outdoor practice [12]. Such activity could increase exposure to sunlight, which can improve mood and has a positive association with cognitive function [13]. Moreover, skin exposure to sunlight starts the metabolism of vitamin D, due to the UVB ray that transforms the 7-dehydrocholesterol in pre-vitamin D3, isomerized later into vitamin D3 [14]. Several studies affirmed that deficiency in levels of vitamin D3 are associated with different health-threatening diseases, such as breast cancer [15], cardio-metabolic conditions [16], musculoskeletal pathologies [17,18], depression [19], and sleep disorders [20]. Low levels of vitamin D may also provoke deficits in strength and degeneration of glycolytic muscle fibers, thus reducing physical performance [21]. The only method to improve the levels of vitamin D, other than the exposition to sunlight, is with nutrition. Currently, in Europe, the intake of vitamin D through nutrition is low and its deficiency in the general population is a main concern for the public health system [22]. Considering that vitamin D supplementation can have positive effects in reducing symptoms of depression and anxiety [23], studying its levels in the general population and promoting a correct intake with nutrition is essential to try to manage stress from different points of view. The aim of this study is to understand the impact of physical activity, sunlight exposure, and vitamin D intake with nutrition on levels of perceived stress in a sample derived from the Italian general population.

#### **2. Materials and Methods**

#### *2.1. Study Design and Population*

The Mediterranean healthy eating, aging, and lifestyles (MEAL) study is an observational study aiming to assess the link between dietary habits, in the context of a cluster of lifestyle behaviors characterizing the Mediterranean basin, and non-communicable diseases. A complete protocol of the MEAL study has been published previously [24]. The cohort and baseline survey was established between 2014–2015 in southern Italy by randomly enrolling a sample of 2044 men and women aged 18 or more years old through the registered records of local general practitioners stratified by sex and 10-year age groups.

For the purpose of providing a specific relative precision of 5% (Type I error, 0.05; Type II error, 0.10), considering an anticipated 70% participation rate, the theoretical sample size was estimated to be 1500 individuals. In brief, a sample of 2405 individuals was invited to participate in the study, out of which 361 individuals declined, leaving 2044 participants as the final sample included, with a response rate of 85%. The aims of the study were exhaustively explained to all of the participants before the acceptance of participation; those who agreed provided a written informed consent. The study procedures were conducted in line with the Declaration of Helsinki (1989) of the World Medical Association. The study protocol has been evaluated and approved by the concerning ethical committee.

#### *2.2. Physical Activity Assessment*

The data were collected by trained personnel via face-to-face, computer-assisted interviews. The physical activity level was evaluated using the international physical activity questionnaires (IPAQ) [25]. The IPAQ consists of questions referring to five domains on time dedicated to physical activity in the last week. In brief, individuals are considered as having a "high physical activity level" if performing strenuous physical activity on no less than three days, reaching a minimum total physical activity of no less than 1500 MET minutes/w or ≥seven days of any combination of walking, moderate-intensity or vigorous-intensity activities reaching a minimum total physical activity of no less than 3000 MET minutes/w, while individuals performing ≥three days of vigorous-intensity activity of at least 20 min/d or ≥five days of moderate-intensity activity or walking of at least 30 min/d or ≥five days of any combination of walking, moderate-intensity or vigorous-intensity activities achieving a minimum of at least 600 MET minutes/w are considered as having "moderate physical activity level". Finally, individuals who do not meet the criteria for being highly or moderately physically active are categorized as having a low physical activity level.

#### *2.3. Sun Exposure Assessment*

Information on sun exposure was evaluated using the sunlight exposure measurement questionnaire (SEM-Q) [26]. The tool consisted of 6 items investigating the occasions and level of exposure to sunlight through assigning different weights (ranging between 0 and 1) according to sun exposure (i.e., using sunscreen creams, % of body exposed, etc.). The final scoring algorithm was generated by multiplying the time (minutes) spent in the sun by the proportions of different domains and then divided into three groups, defined as low, medium, and high exposure.

#### *2.4. Dietary Assessment*

The dietary exposures were assessed through a long and a short version of food frequency questionnaires (FFQs), formerly tested for validity and reliability in Sicilian individuals [27,28]. The intake of seasonal foods was calculated based on the consumption during the time period of their availability and subsequently adjusted by their proportional intake over one year. Dietary intakes of macro- and micronutrients, as well as total energy intake, were determined using food composition tables of the Council for Research in Agriculture and Analysis of Agricultural Economy (CREA) [29]. In detail, the individual food consumption (in mL or g) was calculated for each individual by converting the standard portion sizes and different frequencies of consumption to 24 h intake. After, the databases were screened for average values of the energy content and macro- and micronutrients contained in each food (per 100 mL or g). Lastly, the energy and nutrient intake from foods was calculated by multiplying the content of each variable by the daily consumption of each food. FFQs with missing data or unreliable dietary intakes (<1000 or >6000 kcal/d) were not included in the analyses. Vitamin D data was calculated from dietary intakes (participants were asked to report eventual supplementation).

The quality of an individual's diet was examined by calculating adherence to the Mediterranean diet using a validated literature-based score [30]. In brief, two points were assigned to the highest category of intake of foods typical of the Mediterranean diet (such as vegetables, fruits, legumes, cereals, fish), one point for the middle category, and 0 points for the lowest category of intake. On the contrary, two points were assigned for the lowest category of intake of foods not characteristic of the Mediterranean diet (such as meat and dairy products), one point for the middle category, and 0 points for the highest category of intake. Moderate alcohol intake and regular use of olive oil contributed to the higher adherence. The final score consists of nine food groups with a minimum of 0 points (lowest adherence) and maximum of 18 points (highest adherence), and individuals catalogued in the subsequent tertiles: (i) low, (ii) medium, and (iii) high adherence to the Mediterranean diet [31].

#### *2.5. Covariate Assessment*

In addition to dietary data, information on factors potentially associated with the investigated exposures and outcomes was collected. Data on sex, age, marital and educational (the highest educational degree achieved) status, and smoking status were collected. Marital status was catalogued as (i) unmarried/widowed or (ii) married. Educational status was catalogued as (i) low (primary/secondary), (ii) medium (high school), and (iii) high (university). Smoking status was catalogued as (i) non-smoker, (ii) ex-smoker, and (iii) current smoker. Eating habits comprised questions on skipping breakfast, daily snacking, skipping dinner, and out-of-home eating, with answers categorized as (i) always/often and (ii) seldom/never. Finally, body mass index (BMI) was categorized as normal weight (BMI < 25 kg/m2), overweight (BMI 25 to 29.9 kg/m2), and obese (BMI ≥ 30 kg/m2) [32].

#### *2.6. Perceived Stress Assessment*

The stress symptoms of the study participants were assessed using the perceived stress scale (PSS) [33]. Briefly, the PSS is a 14-item questionnaire used to evaluate perceived stress by examining the level to which situations in an individual's life are perceived as stressful. Each query has five possible answers ranging from zero to four (0 = never, 1 = almost never, 2 = sometimes, 3 = often, and 4 = always). The final score is the sum of the scores of 14 queries and it ranges from 0 (minimum) to 56 (maximum). The sex-specific median value was considered as a cut-off point to define high or low perceived stress.

#### *2.7. Statistical Analysis*

After excluding missing entries from tools or data collection, a total of 1728 individuals were included in the analysis. Categorical variables are reported as frequencies of occurrence and percentages, with the Chi-squared test used to assess differences between physical activity levels. Continuous variables are reported as mean and standard deviations (SDs), with the ANOVA test used to test differences between groups. Multivariate logistic regression models were calculated to determine the cross-sectional association between physical activity level, dietary vitamin D intake, sunlight exposure, and perceived stress. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for different models adjusted for potential confounders, including background characteristics and Mediterranean diet adherence as a proxy of diet quality. An additional analysis via stratifying the sample by low and medium/high category for each variable of exposure was further investigated to test their influencing role on each other. The reported *p* values were based on two-sided tests and compared to a significance level of 5%. All the statistical calculations were performed using SPSS 21 (SPSS Inc., Chicago, IL, USA) software.

#### **3. Results**

The background characteristics of the study sample by level of physical activity are presented in Table 1. Among individuals reporting higher levels of physical activity, there was a higher proportion of younger men, with a higher educational level and more current smokers than those reporting lower physical activity. Concerning eating habits, among those more physically active, there also was a higher prevalence of individuals reporting daily snacking and out-of-home eating, as well as a higher adherence to the Mediterranean diet.

The distribution of dietary vitamin D intake and sunlight exposure by the level of physical activity is presented in Figure 1. There was a significant difference in the distribution of both variables; interestingly, there was a higher proportion of individuals reporting low vitamin D intake and sunlight exposure in the medium category of physical activity level, while higher vitamin D intake and sunlight exposure was more evident in both low and high physical activity level groups, suggesting no clear trends in the distribution of these variables.


**Table 1.** Demographic characteristics of the study participants according to physical activity level (*n* = 1728).

Table 2 presents the results of the logistic regression analyses assessing the association between the variables of interest and perceived stress. After adjusting for potential confounding factors (including diet quality assessed as higher adherence to the Mediterranean diet), individuals in the highest group of physical activity level (OR = 0.72, 95% CI: 0.51, 1.00), sunlight exposure (OR = 0.72, 95% CI: 0.52, 0.99), and vitamin D intake (OR = 0.69, 95% CI: 0.53, 0.89) were less likely to report perceived stress compared to those in the lowest groups; similar findings were retrieved for the medium category of physical activity level and sunlight exposure (Table 2).

The stratification by low and medium/high category of each variable of exposure to test their influencing role on each other is presented in Figure 2. Interestingly, neither vitamin D intake nor sunlight exposure was significantly associated with perceived stress in the low physical activity group, while individuals reporting medium-to-high physical activity levels and being in the highest category of exposure for both vitamin D intake and sunlight exposure were associated with lower odds of having perceived stress (OR = 0.67, 95% CI: 0.44, 1.01 and OR = 0.77, 95% CI: 0.61, 0.98, respectively; Figure 2). Similarly, when considering low exposure to dietary vitamin D and sunlight exposure, there were no evidence associations for the other variables, while in individuals with higher vitamin D intake there was a significant association of both high physical activity

and high sunlight exposure (OR = 0.68, 95% CI: 0.50, 0.93 and OR = 0.75, 95% CI: 0.60, 0.94, respectively); as additionally, individuals with higher sunlight exposure had a significant association of both high physical activity and high vitamin D intake with lower likelihood of having perceived stress (OR = 0.64, 95% CI: 0.42, 0.95 and OR = 0.52, 95% CI: 0.30, 0.92, respectively; Figure 2).


**Figure 1.** Distribution of dietary vitamin D intake, sunlight exposure, and physical activity level in the study sample (*n* = 1728). Gray color identifies low category, orange color identifies medium, and blue color identifies high category of vitamin D intake and sunlight exposure.


**Table 2.** Odds ratios (ORs) and 95% confidence intervals (CIs) of the association between physical activity level, sunlight exposure, vitamin D intake, and perceived stress.

\* Multivariate model 1 was adjusted for BMI (normal, overweight, obese), sex, educational status (low, medium, high), smoking status (never, current, former), age (continuous, years), eating habits (skipping breakfast, daily snacking, skipping dinner, out of home eating), marital status (unmarried/widowed, married). \*\* Multivariate model 2 was further adjusted for adherence to the Mediterranean diet.

**Figure 2.** Odds ratio (ORs) and 95% confidence intervals (CIs) of the association between difference level of exposure to physical activity, vitamin D intake, and sunlight exposure and perceived stress. Multivariate model was adjusted for BMI (normal, overweight, obese), sex, educational status (low, medium, high), smoking status (never, current, former), age (continuous, years), eating habits (skipping breakfast, daily snacking, skipping dinner, out of home eating), marital status (unmarried/widowed, married), and adherence to the Mediterranean diet. L-PA, low physical activity; H-PA, high physical activity; L-Vit D, low vitamin D intake; H-Vit D, high vitamin D intake; L-Sun Exp, low sunlight exposure; H-Sun Exp, high sunlight exposure.

#### **4. Discussion**

In this study, the association between physical activity level, sunlight exposure, dietary vitamin D intake, and perceived stress levels was investigated in a sample of Italian adults. An inverse association between all the variables of exposure and perceived stress was found. However, when stratifying the population by level of physical activity, vitamin D intake, and sunlight exposure, the retrieved associations of each variable with perceived stress were significant only among individuals with higher exposure to the others, suggesting a strict relation between them.

Physical activity has been positively associated with the reduction of musculoskeletal [34] and major non-communicable diseases [35,36]; furthermore, mental conditions [37] can benefit from its regular and tailored practice. Previous studies reported substantial benefits of physical activity on psychological conditions, such as burnout [38,39], anxiety [40,41], or depression [42]. Concerning perceived stress, a previous study conducted on a sample of 537 college students showed that vigorous and moderate physical activity were inversely associated with perceived stress [43]. In line with our results, similar findings have also been reported in intervention studies. A meta-analysis of 13 randomized controlled trials showed that exercise in evidence-based treatments for individuals with stress or anxiety disorders was able to improve symptoms [44].

The stress reduction related to an increase in physical activity detected in our sample could be explained through several mechanisms. Physical activity promotes the secretion of β-Endorphin, a hormone that can positively influence mood states [45]. Moreover, some physical activity can be performed outdoors, thus increasing exposure to sunlight. There is consistent evidence from the scientific literature that performing physical activity outdoors has a higher positive impact on mood and mental well-being compared to indoor activities, possibly related to exposure to sunlight [12,46]. Additionally, in the present study, we found that sun exposure was associated with lower perceived stress, and that the association was significant among individuals with moderate-to-high physical activity levels. In line with our findings, in a study conducted on 948 Korean adults, people with less exposure to sunlight had significantly higher levels of perceived stress than those who were more exposed to sunlight [47]. Mechanistically, it has been hypothesized that mood regulation by the light needs a pathway independent of the suprachiasmatic nucleus that links intrinsically photosensitive retinal ganglion cells to the perihabenular nucleus, which is a mood regulator [48]. Furthermore, sunlight is fundamental for vitamin D metabolism, with UVB rays transforming 7-dehydrocholesterol to pre-vitamin D3, then isomerizing into vitamin D [14]: the positive effects of sunlight on stress could be explained by enhancement in vitamin D levels. We further found that also higher intake of vitamin D was inversely associated with perceived stress, especially in individuals reporting moderate-to-high physical activity levels. A previous investigation conducted on Northern American adults showed that those having lower serum 25(OH)D concentrations had poorer mental health and psychosocial stress compared to those with higher concentrations of serum 25(OH)D [49]. In fact, vitamin D receptors (VDR) and their activating enzymes are present in various regions of the human brain and 25(OH)D can reach the central nervous system by crossing the blood–brain barrier, indicating that it could potentially have autocrine-paracrine functions within the human brain [50]. VDR and 1a-hydroxylase have been found to be distributed in the human brain, both in glial cells and neurons, in a layer-specific pattern, especially in the hypothalamus and neurons within the substantia nigra [50]. Stress activates the hypothalamic–pituitary–adrenal axis (HPA) [51], which functions in connection with the autonomic nervous system and the immune system in managing the hormonal and inflammatory responses to stress [52]. The HPA activation promotes the release of corticotropin-releasing hormone in the hypothalamus and the secretion of adrenocorticotropin from the pituitary cells, mediating adaptive responses that are necessary for survival [52]. However, chronic stress may elicit harmful changes that can bring an alteration of the HPA fostering long-term pathological alterations [53]. It was shown that one of the metabolites of vitamin D,1,25-dihydroxyvitamin D3 has neuroprotective effects, upregulating neurotrophin-3 and neurotrophin-4 [54], and nerve growth factors [55] that can be found in the hippocampus and neocortex. Furthermore, vitamin D influences inflammatory pathways that have been associated with depression [56] by activating anti-inflammatory pathways through VDR-mediated gene transcription and downregulating autoimmune mechanisms that produce proinflammatory cytokines [54,57]. These underlying physiological mechanisms could explain our findings of an inverse association between lower levels of perceived stress in people with higher vitamin D intake.

To the best of our knowledge, this is the first study that analyzes the association between physical activity, sun exposure, vitamin D, and perceived stress in a sample of the general Italian population. Nonetheless, despite the novelty of this study, the results may be subjected to some limitations. Considering the cross-sectional nature of the analysis, the reverse causation cannot be ruled out. Another limitation concerns the dietary assessment method, as the use of FFQs may potentially under- or overestimate food intake due to recall bias. Additionally, despite adjusting for multiple confounding factors, the results may have been influenced by unmeasured variables. Another limitation may be related to the lack of information on whether individuals are primarily engaged in indoor or outdoor sports and, thus, directly exposed to more or less sunlight. The PSS is a good screening tool to assess the prevalence of stress symptoms in large sample sizes; however, it may not be deemed as a clinical evaluation, and thus may over- or underestimate the outcome prevalence. Finally, the findings of this study may not be generalizable to other populations as several factors may affect the intensity of sun exposure, including but not limited to solar zenith angle, altitude, or skin type.

#### **5. Conclusions**

In conclusion, the results of this study highlight that higher sun exposure and dietary intake of vitamin D are associated with a lower likelihood of having high perceived stress among physically active individuals.

**Author Contributions:** Conceptualization, methodology and formal analysis, J.G. and S.C.; writing original draft preparation, B.T., J.G., S.V., S.C. and G.M.; writing—review and editing, B.T., J.G., S.V., F.R., S.C. and G.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** J.G. was supported by the co-financing of the European Union—FSE-REACT-EU, PON Research and Innovation 2014–2020 DM1062/2021; CUP: E65F21002560001.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of CE Catania 2 (protocol code 802/23 December 2014).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data that support the findings of this study are available upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

### *Article* **Bioimpedance Patterns and Bioelectrical Impedance Vector Analysis (BIVA) of Body Builders**

**Cristian Petri 1, Matteo Levi Micheli 2, Pascal Izzicupo 3,\*, Niccolò Timperanza 2, Tommaso Lastrucci 2, Daniele Vanni 2, Massimo Gulisano <sup>2</sup> and Gabriele Mascherini <sup>2</sup>**


**Abstract:** Bodybuilders are athletes characterized by high muscle mass. During competitions, the evaluation is performed based on aesthetic parameters. The study aims to provide normative references of body composition with the vector bioimpedance methodology (BIVA). A second aim is to compare BIVA assessments performed on both sides and the upper and lower body. A group of 68 elite bodybuilders (41 males aged 30.1 ± 9.2 years and 27 females aged 32.1 ± 8.0 years) was enrolled. A BIVA assessment was performed the day before the 2021 World Natural Bodybuilding Federation Italian Championships. As a result, male and female bodybuilders ranked to the left in the BIVA ellipse relative to the general population. Furthermore, unlike females, males also ranked lower than the general athletic population. In addition, in the symmetry assessment, males show a significantly greater upper body than the lower, right, and left parts, while in women, this is observed for the lower part of the body. The differences in the results obtained between males and females can be attributed to the different patterns of endocrine production between the sexes and the different criteria used by the juries to attribute the final score during the competitions. Therefore, BIVA references in bodybuilders could help adjust the training and nutritional program during the peak week before a competition.

**Keywords:** body composition; bodybuilding; muscle mass; muscle hypertrophy

#### **1. Introduction**

Body composition assessment is currently a part of functional assessment in sportspeople, allowing the monitoring of athletes' nutritional and health status and checking the state of physical fitness. The study of the relationship between body composition and sports performance is a growing research area in sports science. In particular, some sports, such as gymnastics and running, have a gravitational component; therefore, reduced fat mass values can favor success in competitions. Alternatively, many combat sports are weight-classified; therefore, athletes must stay within a specific range of body mass. Finally, sports characterized by expressions of strength and/or speed, such as sprints in athletics or weightlifting, benefit from the presence of large muscle masses. However, in some competitions, success is attributed based on the aesthetic characteristics of the participants. In this context, monitoring body composition has become fundamental, and in aesthetic sports can provide additional and helpful information [1].

Bodybuilding is an aesthetic sport, and a prerequisite for a successful bodybuilder is to have the expected muscular morphology [2]. Specifically, bodybuilding is a combined

**Citation:** Petri, C.; Micheli, M.L.; Izzicupo, P.; Timperanza, N.; Lastrucci, T.; Vanni, D.; Gulisano, M.; Mascherini, G. Bioimpedance Patterns and Bioelectrical Impedance Vector Analysis (BIVA) of Body Builders. *Nutrients* **2023**, *15*, 1606. https://doi.org/10.3390/nu15071606

Academic Editors: David C. Nieman and Alessandro Sartorio

Received: 7 March 2023 Revised: 24 March 2023 Accepted: 24 March 2023 Published: 25 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

aesthetic and weight-class sport in which physical appearance, muscular mass, symmetry, shape, and definition are the evaluation parameters with specific references based on the category in which each athlete competes [3]. Therefore, the goal of a bodybuilder is to attain maximal body muscle by focusing on hypertrophy training and, in preparation for a competition, striving for excellent muscular definition [4].

Among the methods developed to assess body composition, bioelectrical impedance analysis (BIA) has long been an easy and viable technique for obtaining qualitative and quantitative assessment in bodybuilders. However, the studies predominantly used the conventional BIA evaluation, which studies and describes the different body tissues (mainly fat-free and fat-mass) [5]. This approach has some limitations, including sensitivity to hydration status, potential inaccuracy of predictive equation when applied to populations with different characteristics, and a minimum standard error of estimation, even when the equation is accurate [6,7].

An innovative use of BIA consists of evaluating the raw bioelectrical parameters, such as resistance (R), reactance (Xc), and phase angle (PhA) through bioelectrical impedance vector analysis (BIVA) that allows the evaluation of the vector position compared with population-specific tolerance ellipses [8]. Furthermore, studying bioelectrical properties in athletes made it possible to create specific references helpful in monitoring body composition during the competitive season [9]. Recently, studies have investigated the positioning within the ellipse of the R-Xc graph of specific sports populations within the athlete's population, in particular, the bioelectrical characteristics of soccer players [10], cyclists [11], handball players [12], synchronized swimmers [13], and rowers [14] have been studied. Furthermore, side-by-side differences in bioelectrical parameters have been found to be more pronounced in tennis players than in the general population, suggesting this method can be useful to identify body asymmetries in those sports with unilateral loading [15].

The study through the BIVA assessment of bodybuilders could increase the knowledge of the body composition of these athletes, providing specific positioning within the R-Xc graph, given the high muscularity associated with low levels of fat mass that characterize this particular study population. This study hypothesizes that bodybuilders have a position far to the left within the R-Xc graph, with a high phase angle (PhA). Therefore, this study aims to perform a BIVA assessment on a sample of bodybuilders to provide the populationspecific tolerance ellipses. A secondary aim of this study is to compare BIVA assessments performed on both sides and on the upper and lower body (hand to hand and foot to foot, respectively). We hypothesize that male and female bodybuilders will show different BIVA patterns according to the sexual dimorphism in body masses distribution.

#### **2. Materials and Methods**

#### *2.1. Participants*

During the Italian championships 2021 of the World Natural Bodybuilding Federation, athletes were required to participate voluntarily in the study. The inclusion criteria were (1) to be a member of the World Natural Bodybuilding Federation Italy, (2) to be over 18 years old, (3) to have finished at least sixth in the final ranking, (4) to not use drugs that can induce muscle hypertrophy, and (5) be of white ethnicity.

A group of 64 elite bodybuilders (37 males aged 30.1 ± 9.2 yrs with body mass index = 24.5 ± 1.5 kg/m2 and 27 females aged 32.1 ± 8.0 yrs with body mass index = 19.8 ± 1.3 kg/m2) was enrolled in the study after receiving written informed consent. The categories in which the male participated were: eleven subjects in Classic Physique, sixteen in Man Physique, and fourteen in Bodybuilding. At the same time, eight females participated in the Figure and nineteen in the Bikini. The study was carried out in conformity with the ethical standards in the 1975 Declaration of Helsinki.

#### *2.2. Procedures*

The recruitment and evaluation of the participants were conducted on the morning of the day before the competition; therefore, during the peak week [2], the athletes showed their optimal body composition (i.e., the lowest fat mass and the highest muscle mass). Weight was measured to the nearest 0.1 kg and height to 0.5 cm (Seca GmbH & Co., Hamburg, Germany). Body mass index (BMI) was then calculated as weight divided by height2 (W/H, expressed as kg/m2).

Whole-body BIVA is the body tissues' opposition to the flow of an electric current. It is the vector sum of the resistance (R, Ω)—the major resistance to the current through intracellular and extracellular ionic fluids—and the reactance (Xc, Ω)—the additional opposition due to the capacitive elements such as cell membranes, tissue interfaces, and non-ionic substances. The raw bioimpedance parameters R, Xc, and Phase Angle (PhA, calculated as the arc tangent of Xc/R × 180◦/π) were obtained using a phase-sensitive segmental bioelectrical analyzer (BIA 101 BIVA PRO, Akern, Florence, Italy) with a current of 250 μA at a single frequency of 50 kHz. It was calibrated every morning using a calibration circuit procedure of known impedance (R = 380 Ohm, XC = 47 Ohm, 1% error) supplied by the manufacturer. After cleaning the skin with isotropy alcohol, four low intrinsic impedance adhesive electrodes (Biatrodes Akern Srl, Florence, Italy) were placed on the hand's back and the other four electrodes on the neck of the corresponding feet, respecting the standard protocol [16]. The proximal hand electrode was between the radial and ulnar styloid processes, and the distal hand electrode was positioned in the center of the third proximal phalanx. The proximal foot electrode was placed directly between the medial and lateral malleoli at the ankle, and the distal foot electrode was proximal to the second and third metatarsophalangeal joints. Therefore, through this procedure, with a single impedance measurement, it was possible to collect all the data to evaluate both the whole body and the body side evaluation.

The whole body BIVA assessment uses R and Xc parameters, standardized for the subject height to remove the effect of conductor length, producing a vector plotted in an R-Xc graph. Vector length indicates the hydration status from fluid overload (reduced R, short vector) to dehydration (increased R, longer vector), and lateral migration of the vector due to low or high Xc indicates a decrease or increase in mass dielectric (membranes and tissue interfaces) of soft tissues. The individual vector can be ranked on the R-Xc point graph against tolerance ellipses representing 50%, 75%, and 95%, according to the values of a reference population [17].

The body sides BIVA assessment was performed on the right side (hand to foot right side), left side (hand to foot left side), upper side (hand to hand), and lower side (foot to foot) of the body with the subjects in a supine position with their arms and legs abducted. The evaluation parameters were:


In order to verify muscle mass through raw bioelectrical values, the Levi Muscle Index, defined as LMI = (PhA × H)/R, was calculated [18]. BIA measurements were all taken by the same-trained investigator to avoid inter-observer errors.

#### *2.3. Statistical Analysis*

The normality of the data was verified by applying the Shapiro–Wilk test and descriptive statistics were calculated for each independent variable, as reported in Table 1. The bioelectrical impedance variables followed the Gaussian distribution. Differences between the BIVA modalities for the raw bioelectrical parameters were evaluated through the analysis of variance with repeated measures (RM-ANOVA) on the within-subject factor (right side, left side, upper side, and lower side). The Bonferroni–Holm method was used for the post hoc test, and the sphericity was corrected using the Greenhouse–Geisser method when the condition of equal variance was violated. Each participant was plotted in the tolerance ellipses (50%, 75%, and 95%) of the Italian reference population of the same

sex [19]. A two-sample Hotelling's T2 test was used to determine the BIA vector differences concerning the reference population [19] and the athletic population [20] and for BIVA modalities (right and left side, upper and lower body) comparisons. The Mahalanobis' test calculated distances between ellipses [21]. A *p*-value < 0.05 was considered significant. IBM SPSS 23.0 (SPSS, Chicago, IL, USA) was used for statistical calculations, and BIVA software [21] was used for plotting and comparing the bioelectrical parameters, as well as for computing the tolerance ellipses (50%, 75%, and 95%) of the investigated sample.

#### **3. Results**

Table 1 describes the general and anthropometric characteristics of the sample for both sexes.


**Table 1.** General and anthropometric characteristics of the sample for both sexes.

The BIVA point graph indicates that male natural bodybuilders mainly fell outside the 75% percentile (Figure 1a). Furthermore, most bodybuilders fell in the lower left quadrant area to the left of the impedance vector, as previously indicated by Campa for the athletic population [20]. Male bodybuilders were statistically different compared with the general male population [19] and athletic reference [20]. Specifically, they fell more to the left than the general population (T<sup>2</sup> < 168.8; D = 2.14; *p* < 0.001) and to the left and down than the athletic population (T2 < 17.7; D = 0.95; *p* < 0.001) (Figure 1b) and their specific the 50%, 75%, and 95% tolerance ellipses are represented in Figure 1c. On the other hand, female bodybuilders compared with the general female population [18] and athletic reference [20] fell to the left of the general population (T2 < 67.3; D = 1.64; *p* < 0.001) but did not differ from the athletic population (T<sup>2</sup> < 1.9; D = 0.27; *p* < 0.40) (Figure 1d,e). The specific 50%, 75%, and 95% tolerance ellipses of female natural bodybuilders are represented in Figure 1f. The LMI value of male bodybuilders is 3.7 ± 0.6, while for female bodybuilders, it is 2.3 ± 0.4.

Bioelectrical characteristics of both sexes, for the right and left sides and the upper and lower body, are shown in Figure 2. Significant differences for both sexes were found for R/h (males: *F*(1.3, 45.4) = 18.0, *p* < 0.001, *partial eta squared* = 0.33; females: *F*(1.3, 34.2) = 30.6, *p* < 0.001, *partial eta squared* = 0.54) and Xc/h (males: *F*(1.2, 43.2) = 30.8, *p* < 0.001, *partial eta squared* = 0.461; females: *F*(1.3, 32.8) = 10.5, *p* < 0.001, *partial eta squared* = 0.29). Post hoc analysis showed that in males, the R/h of the upper body significantly differed from the R/h of the lower body (*p* < 0.02), right (*p* < 0.001), and left (*p* < 0.001) sides, respectively. Similarly, the Xc/h of the upper body significantly differed from the Xc/h of the lower body (*p* < 0.01), right (*p* < 0.001), and left (*p* < 0.001) sides, respectively. Furthermore, the Xc/h of the lower body was significantly different from the left side (*p* < 0.005) (Figure 2a). On the other hand, in females, the R/h of the lower body significantly differed from the R/h of the upper body (*p* < 0.001), right (*p* < 0.001), and left (*p* < 0.001) sides, respectively. In comparison, the Xc/h of the lower body significantly differed from the Xc/h of the upper body (*p* < 0.004), right (*p* < 0.001), and left (*p* < 0.001) sides, respectively (Figure 2b). No differences were found in PA for both sexes.

**Figure 1.** Whole-body BIVA graph of male and female natural bodybuilders. Caption: (**a**) Wholebody BIVA point graph of the male bodybuilders plotted on the tolerance ellipses of the athletic reference. (**b**) Whole-body BIVA mean graph for the whole-body mean impedance vectors of the male bodybuilders and the athletic and general reference population. (**c**) Specific 50%, 75%, and 95% tolerance ellipses of male natural body builders. (**d**) Whole-body BIVA point graph of the female bodybuilders plotted on the tolerance ellipses of the athletic reference. (**e**) Whole-body BIVA mean graph for the whole-body mean impedance vectors of the female bodybuilders and the athletic and general reference population. (**f**) Specific 50%, 75%, and 95% tolerance ellipses of female natural body builders.

**Figure 2.** Bioelectrical characteristics of the sample for both sexes, for right and left sides and upper and lower body. (**a**) males. (**b**) females. caption: R/h = resistance/height; Xc/h = reactance/height. \* Significantly different from the corresponding upper body bioelectrical parameter; § Significantly different from the corresponding lower body bioelectrical parameter.

Figure 3a compares the BIVA vectors assessed on the right and left sides and on the upper and lower body in males. The Hotelling's T2 test indicates that the upper body bioelectrical vector was significantly shorter than the corresponding vectors assessed on the right (T2= 76.6; F = 38.8; D = 1.44; *p* < 0.001) and left (T2 = 76.6; F = 38.8; D = 1.44; *p* < 0.001) sides. Figure 3b compares the BIVA vectors assessed on the right and left sides and on the upper and lower body in females. The Hotelling's T<sup>2</sup> test indicates that the lower body bioelectrical vector was significantly shorter than the corresponding vectors assessed on the right (T2= 76.6; F = 38.8; D = 1.44; *p* < 0.001) and left (T<sup>2</sup> = 76.6; F = 38.8; D = 1.44; *p* < 0.001) sides, and upper body (T2 = 76.6; F = 38.8; D = 1.44; *p* < 0.001).

**Figure 3.** Bioelectrical characteristics of the sample for both sexes, for right and left sides and upper and lower body. Caption: (**a**) BIVA vectors of the right and left sides and on the upper and lower body in males. (**b**) BIVA vectors of the right and left sides and on the upper and lower body in females.

#### **4. Discussion**

This study has two aims: (1) to provide for the first time the population-specific tolerance ellipses for BIVA assessment in bodybuilders, and (2) to compare bioelectrical parameters of the upper, lower, right, and left sides.

The hypothesis of the first aim has been verified, as the body structure, endowed with high muscularity, places the study sample in the left quadrant of the ellipse. In detail, male bodybuilders differ from the general population and athletes in being more left-leaning [19] and, therefore, with higher PhA. However, they also rank lower in BIVA ellipses than athletes: they take a higher water content to increase muscle mass due to higher hydration status. The results of the female bodybuilders report differences compared to the general population that is superimposable to the male counterpart [19]. On the other hand, no differences are described concerning the population of athletes. The sample of female athletes described by Campa et al. [20] showed a higher body mass index than the female bodybuilders of this study. Therefore, it is possible to speculate about the search for a slimmer shape by female bodybuilders, probably to have advantages in the judging phase based on the aesthetic character of the competition.

Generally, in peak week, bodybuilders increase aerobic exercise in their training program [22] and re-modulate the carbohydrate loading to increase muscle glycogen storage [23,24] due to the imminence of competition. In particular, a reduction in carbohydrates from 5.3 to 3.8 g/kg/day has been described in the transition from the non-competitive to the competitive phase [25]. However, reducing carbohydrates of this magnitude close to the competition should only happen briefly to avoid muscle loss [26]. Furthermore, approaching the competitive phase, a 5–6% reduction in body fat mass is sought; therefore, the total energy intake is reduced, and proteins and fats are modulated [27]. Increasing the protein content during diets with energy deficit regimes allows the maintenance of muscle

mass. Therefore, on average, bodybuilders close to competition are advised to maintain protein values up to 3.1 g/kg of lean body weight during severe calorie restriction [28]. Fat intake is generally the lowest of the three macronutrients and, like carbohydrates, reduces over time in favor of protein maintenance [29]. The optimal strategy of combining training and nutrition for the peak week has yet to be established [29]. Peak week goals are to maximize the volume of muscle mass with the most defined aspect. Therefore, maintaining resistance training levels and increasing protein intake is an established procedure. However, the increase in aerobic exercise [27] for greater muscle definition may lead to a depletion of muscle glycogen with a consequent reduction in the volume of muscle masses. At the same time, the reduction in carbohydrate intake could have the same effect as the reduction in muscle volume due to the reduced fluid recall exerted by the lower muscle glycogen content [24].

The study's second aim was to analyze the different BIVA patterns based on the upper, lower, right, and left body sides. Currently, there are few studies targeting BIVA by body segments. Unfortunately, this paucity in the literature may not allow an appropriate comparison with the results of the present study because the other studies used tools with different work frequencies [30] or did not show all vector impedance parameters [31]. A recent study indicates that BIA discriminated more pronounced asymmetries of bioelectrical parameters in tennis players than in the general population, suggesting this method can be useful in assessing the body characteristics of athletes competing in sports with a dominance of unilateral loading [15]. Thus, the authors believe that evaluating the single body side could be a new direction of study in an aesthetic discipline where symmetry and proportion are reported as a parameter of judgment for the final score between the right and left and between the upper and lower sides. The results of the present study show that the upper body is more relevant in males (Figure 3a). In contrast, the lower body is more relevant for female bodybuilders (Figure 3b): this could be due to the greater muscle mass expressed in different body parts between the sexes. In addition, male and female hormone action differences play a decisive role in final body composition outcomes [32].

The study of body composition using bioimpedance recently has a new orientation toward the vector evaluation of raw bioelectrical parameters: this has promoted the possibility of independent evaluation from formulas in which sex, age, weight, and ethnicity are parameters of these predictive equations [6]. Furthermore, the elaborations of the reference ellipses for different sporting and non-sporting populations were followed. In this context, providing bodybuilders' BIVA references for the first time will be helpful for (1) comparison with athletes from other sports given the muscularity that characterizes this specific sports population; (2) providing training feedback to bodybuilders themselves; therefore, they can adjust the combination of nutrition and training to reach their optimal level. In this context, the evaluation of muscle mass using the Levi Muscle Index (LMI), a new parameter based on raw bioelectrical parameters, is also useful. In detail, the male bodybuilders in this study show higher values than male elite soccer players (3.7 ± 0.6 vs. 3.08 ± 0.35, respectively) [18].

Some studies [14,33,34] link bioelectrical parameters, mainly phase angle [35], with sports performance. However, the physical performance of bodybuilders would not be directly related to the probability of victory, as the aesthetic nature characterizes bodybuilding. Therefore, morphological aspects influence the final score more than higher strength values.

Currently, the bioimpedance assessment performed on some specific body areas does not have a single denomination. Instead, there are simultaneously terms such as "regional," "segmental," and "district," which in some cases are used as synonyms, but each has its specific meaning. Therefore, greater homogeneity in the terminology of this specific area is advisable to ensure correct scientific progress in this field of research.

Future directions of study could be:

1. increase the sample size of bodybuilders in the BIVA study, which would make it possible to provide both normative reference values for this specific population and a left border of the ellipse in the R-Xc graph of the sports population that are more reliable from a statistical point of view.

2. Deepen the evaluation of body sides in sports where symmetry is a parameter that can determine success in a competition. This could be done by increasing the number of bodybuilders evaluated and by carrying out symmetry assessments on both large body areas (e.g., upper–lower or right–left sides) and small body areas (e.g., right–left tight or right–left arm) and then using the localized impedance measurement technique.

#### *Strengths and Limitations of the Study*

This study has strengths. The first is the sample size, which aligns with the studies on larger numbers of bodybuilders. Second, the enrolled athletes participated in the national championship and were all enrolled as competitive athletes, unlike some studies of amateur athletes. Thirdly, it is the first study to perform total bioelectrical and body side assessment on bodybuilders.

However, this research is not without its limitations. First, the subjects are from the same country; therefore, the results may only be generalizable to some athletes worldwide with different ethnicity. Secondly, the muscle mass values recorded in the present study's sample may be lower than in other studies (the BMI value falls within the normal weight range). However, the authors have chosen this federation to avoid drug use that could compromise the reliability of the physiological hypertrophic response. Third, enrolled athletes fall into different categories, following different metrics that could influence training goals; however, this heterogeneity could also provide a broader picture of possible responses to bodybuilding practice.

#### **5. Conclusions**

In summary, this study provided, for the first time, the BIVA ellipses for the athletic bodybuilder population. In addition, the high muscularity that characterizes these athletes allows us to provide the hypothetical left margin of the BIVA ellipse of the general athletic population and therefore have a reference.

Furthermore, this study highlights asymmetries between the upper and lower body in both sexes. This aspect could compromise the competition result, as the symmetry, intended not only between the right and left sides, is part of the judgment parameters for the final score.

**Author Contributions:** Conceptualization, M.L.M. and G.M.; methodology, M.L.M. and D.V.; software, P.I.; validation, G.M. and M.L.M.; formal analysis, P.I.; investigation, D.V., N.T. and T.L.; data curation, D.V., N.T. and T.L.; writing—original draft preparation, G.M. and C.P.; visualization, M.L.M. and D.V.; supervision, M.L.M. and M.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki. Ethical review and approval were waived for this study because this research is part of an international project entitled "The International Bio-impedance Analysis Multi-center Collaboration project" which the first published article is Campa F, Thomas DM, Watts K, Clark N, Baller D, Morin T, Toselli S, Koury JC, Melchiorri G, Andreoli A, Mascherini G, Petri C, Sardinha LB, Silva AM. Reference Percentiles for Bioelectrical Phase Angle in Athletes. Biology (Basel). 8 February 2022; 11 (2): 264. doi: 10.3390/biology11020264.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data can be obtained from Gabriele Mascherini on a reasonable request gabriele.mascherini@unifi.it.

**Acknowledgments:** The authors express their gratitude to all the participants involved in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

## *Article* **Effects of Endurance Exercise Intensities on Autonomic and Metabolic Controls in Children with Obesity: A Feasibility Study Employing Online Exercise Training**

**Valeria Calcaterra 1,2, Giuseppina Bernardelli 3,4, Mara Malacarne 5, Matteo Vandoni 6, Savina Mannarino 7, Vittoria Carnevale Pellino 6,8, Cristiana Larizza 9, Massimo Pagani 3, Gianvincenzo Zuccotti 1,10 and Daniela Lucini 3,5,\***

	- <sup>4</sup> DISCCO Department, University of Milan, 20122 Milan, Italy
	- <sup>10</sup> Department of Biomedical and Clinical Science, University of Milano, 20157 Milan, Italy
	- **\*** Correspondence: daniela.lucini@unimi.it

**Abstract:** Exercise is one of the major determinants of a healthy lifestyle, which is particularly important in childhood and serves as a powerful preventive tool. On the other hand, obesity and arterial hypertension rates are increasing in children, representing a huge risk for developing major cardiovascular and metabolic diseases in adult life. Of fundamental importance is the modality and volume of exercise required to obtain benefits. In this feasibility study, we considered a group of obese children, studied before and after a 12-week online exercise training program, and subdivided the participants into two groups considering the volume of exercise performed (above or below 1200 MET·min/week). This threshold level was applied in two different ways: subdivision A considered the total weekly physical activity volume (considering both time spent walking for at least 10 min consecutively and time spent performing structured exercise) and subdivision B considered only the weekly volume of structured exercise. We assessed autonomic and metabolic control and auxological and lifestyle parameters. We observed that the improved volume of structured exercise was associated with reduced arterial pressure percentile only in subdivision B and an improvement in markers of vagal and metabolic control was evident. Moreover, the 12-week online exercise training program, defined considering individual fitness level and progressively adapted as the goal was reached, proved to be sustainable from an economical and organizational point of view.

**Keywords:** exercise intensity; training; autonomic nervous system; heart rate variability; metabolic control; children; online exercise training; obesity

### **1. Introduction**

Childhood and youth are ideally the best periods in life to instill healthy behaviors [1], thereby fostering wellbeing and preventing chronic non-communicable diseases (CNCD). On the other hand, overweight, obesity, and the prevalence of hypertension are increasing, particularly in the initial phases of life [2,3], thus determining a dramatic increase in the risk of diseases such as diabetes, coronary artery disease, and cancer [3,4]. Unhealthy lifestyles, such as poor nutrition and sedentariness, contribute to this phenomenon. Additionally, recent data show that obesity [5] and an unhealthy lifestyle, in particular sedentariness [6],

**Citation:** Calcaterra, V.; Bernardelli, G.; Malacarne, M.; Vandoni, M.; Mannarino, S.; Pellino, V.C.; Larizza, C.; Pagani, M.; Zuccotti, G.; Lucini, D. Effects of Endurance Exercise Intensities on Autonomic and Metabolic Controls in Children with Obesity: A Feasibility Study Employing Online Exercise Training. *Nutrients* **2023**, *15*, 1054. https:// doi.org/10.3390/nu15041054

Academic Editor: Aloys Berg

Received: 20 January 2023 Revised: 9 February 2023 Accepted: 16 February 2023 Published: 20 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

are linked to poor prognosis in COVID-19 patients, thus expanding the role of lifestyle in determining health status further to CNCD.

Physical activity is one of the major determinants of a healthy lifestyle, in addition to healthy nutrition, not smoking, good sleep hygiene, and the capability to manage stress [7,8]. Regular physical activity is particularly important in childhood and youth as it grants immediate (improvement of wellbeing, scholastic performance, social relationships [9], etc.), and long-term benefits (prevention/treatment of many diseases), and it may be considered a sustainable tool [10] for the individual and environment. On the other hand, epidemiological data from the USA show that only 28.3% of youth aged 6 to 11 years are active for ≥60 min every day of the week, thus meeting more recent guideline recommendations [3,11–13], and this decreases to 16.5% of youth aged 12 to 17 years [4]. European Commission data show that 32.4% of Italian children aged 8–9 years are estimated to meet sufficient physical activity levels (considering WHO guidelines), and this percentage decreases to 11.9% of children aged 11 years old and further decreases to 6.8% of children aged 15 years old [14].

International guidelines [11–13] indicate that children and adolescents should perform moderate-to-vigorous intensity, mostly aerobic, physical activity at least an average of 60 min every day of the week and that vigorous-intensity aerobic activities, as well as activities that strengthen muscle and bone, should be incorporated at least 3 days a week. A recent paper [15] reported that a minimum of 20 min/day of vigorous endurance exercise may be best for maximizing cardiorespiratory fitness in adolescence, a parameter that a growing body of epidemiological and clinical evidence [16] considers to be a potentially strong predictor of mortality, showing that the intensity of endurance exercise may be a determinant of the beneficial effect of exercise programs.

Recent data on the association between walking pace and telomere length [17] (a parameter associated with chronic diseases and proposed as a marker of biological age) underlined the importance of exercise intensity, showing that only brisk walkers had significantly longer telomeres than slow walkers.

An exercise program, and a lifestyle program in general, may be considered efficacious only if it leads to an improvement of the underlying pathogenetic mechanisms that determine a disease or risk of developing a disease. The mechanisms that are potentially improved by exercise are multifarious and complex, ranging from improved immunological and metabolic profiles to an improvement in hemodynamics [1,4,7,8,11,16,18]. An amelioration in cardiac autonomic regulation (CAR, autonomic control of cardiovascular system) may also play an additional, but little recognized, beneficial role [19], generating a "risk factor gap" above and beyond usual treatments. Several chronic diseases, such as arterial hypertension and diabetes, are characterized in adulthood [20,21] and also in childhood and adolescence [22,23] by autonomic nervous system (ANS) impairment, which may be reversed by exercise training and/or healthy nutrition programs [10,22,24–27].

The literature is particularly rich with scientific data demonstrating that exercise may positively interfere with many mechanisms underlying chronic diseases, rendering it a well-recognized preventive and therapeutic tool [1,4,7,8,11,16,18,22,24–28]. However, the implementation of efficacious exercise programs is less defined [7,8,12,29]. Generally, programs that group children together, such as school-based programs or participation in team sports [12], are considered more effective than home-based exercise programs, since they also foster socialization and play an educational role. On the other hand, these approaches may present some economical and organizational barriers [30,31], such as the need for transportation to the gym or sports field, lack of parental support, cost, and family time management. These barriers may have a particular impact on families of low socio-economical level or in a country, such as Italy, where time spent at school is limited. Moreover, other important barriers, such as lack of skills, motivation, enjoinment, and peer support, as well as feeling shy about physical activity in public [31,32], need to be accounted for in the case of obese children and adolescents. Home-based exercise intervention programs may help in overcoming some of these barriers [33–35]. As previously reported [36], remote physical activity programs may provide a valuable strategy for fostering compliance with physical activity guidelines, representing an opportunity for pediatric subjects with obesity to stay healthy. On the other hand, their effectiveness may vary [12,33,37] according to the level of support granted by exercise professionals and the level of personalization of the proposed program.

The goal of this feasibility study was to verify the effectiveness on cardiac autonomic regulation and metabolic parameters of a supervised, home-based exercise program specifically designed to meet obese children's needs and be enjoyable and sustainable from an economical and organizational point of view in order to facilitate acceptance and compliance. In particular, we assessed the autonomic and metabolic effects considering the volume of physical activity actually performed by children.

#### **2. Materials and Methods**

We consecutively enrolled 35 Caucasian children and adolescents (14 females/21 males) admitted to our pediatric outpatient clinic for obesity by their general practitioner or by their primary care pediatric consultant between March 2021 and December 2021. We studied them before (T0) and after (T1) a 12-week online exercise training protocol.

To be included in our study, the children must have been aged between 8 and 13 years, have a body mass index (BMI) z-score ≥ 2 (according to World Health Organization [38]), and have Italian language competency. The exclusion criteria were: known secondary obesity conditions, no comprehension of Italian language, cardiovascular and respiratory chronic diseases, orthopedic problems, and absolute contraindications to practicing physical activity.

This study was approved by the institutional ethics committee (Milano Area 1 protocol number 2020/ST/298, approval date 2 December 2020) and conducted in accordance with the Helsinki Declaration of 1975, as revised in 2008.Written consent was obtained by all participants or their responsible guardians once they were well informed about the study.

#### *2.1. Clinical Evaluations*

All children underwent the following assessments.

#### 2.1.1. Clinical, Auxological, and Hemodynamic Assessment

In all patients, weight, height, waist circumference (WC), pubertal stage according to Marshall and Tanner [39,40], BMI, waist-to-height ratio (WHtR) [41], triponderal mass index [42], and visceral adiposity index (VAI) [43] were considered as adiposity indexes related to cardiometabolic risk [44]. A basal musculoskeletal assessment was employed in order to exclude the presence of musculoskeletal limitations to the exercise program. Weight was measured with patients not wearing shoes and in light clothing, standing upright in the center of the scale platform (Seca, Hamburg, Germany), facing the recorder, hands at the sides, and looking straight ahead. Standing height was measured using a Harpenden stadiometer (Holtain Ltd., Cross-well, Crymych, UK) with a fixed vertical backboard and adjustable headpiece [44].

WC was performed in the horizontal plane midway between the lowest ribs and iliac crest, using a flexible inch tape [44].

BMI was calculated as body weight (kilograms) divided by height (meters squared) and was transformed into BMI z-scores using WHO reference values [38].

Other adiposity indexes were calculated as follows [42,43]:

$$\text{WHtR} = \text{WC} / \text{Ht}$$

TMI = weight (kg)/height (m)3

$$\sf{Val}$$

Male = [WC/(39.68 + (1.88 × BMI))] × (TG/1.03) × (1.31/HDL-C);

Female = [WC/(36.58 + (1.89 × BMI))] × (TG/0.81) × (1.52/HDL-C)

Pubertal stages according to Tanner were classified as follows: prepubertal stage 1 = Tanner 1; middle puberty stage 2 = Tanner 2–3; and late puberty stage 3 = Tanner 4–5 [39,40].

Systolic arterial pressure (SAP) and diastolic arterial pressure (DAP) were measured twice, in the supine position, using an electronic mercury sphygmomanometer (A & D Medical, Tokyo, Japan) with an appropriately sized cuff on the right arm after 5 min of rest [45]; the second measurement was used for analysis. We determined the blood pressure percentile for each child, following recent guidelines [46–48]. Moreover, every child underwent a basal electrocardiogram and echocardiogram in order to verify the absence of main cardiac diseases that might contraindicate or limit exercise training, particularly at high intensities.

#### 2.1.2. Metabolic Assessment

A blood sample for the evaluation of fasting blood glucose (FBG), total cholesterol (TOT Cho), high-density lipoprotein (HDL C) cholesterol, triglycerides (TG), and insulin was obtained in fasting state between 8:30 and 9:00 a.m. and analyzed the same morning by standard methods (Advia XPT, Siemens Healthcare). As a surrogate of insulin resistance (IR), we considered the homeostasis model assessment (HOMA-IR) calculated as insulin resistance = (insulin × glucose)/22.5 [49] and the triglyceride and glucose (TyG) index was calculated as ln[fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2 ]) [50].

#### 2.1.3. Lifestyle Assessment

An ad hoc questionnaire was employed to quantify lifestyle [51–55]:


We considered the following levels: brisk walking (≈3.3 METs), other activities of moderate intensity (≈4.0 METs), and activities of vigorous intensity (≈8.0 METs).

In accordance with current guidelines [11,12], these levels were used to assess the weekly exercise volume, using the following equations:


The population of our study was subdivided into two groups: those reaching the physical activity goals suggested by the latest guidelines [11,12], corresponding to at least an average of 60 min/day of moderate-to-vigorous intensity, mostly aerobic, physical activity (above 1200 MET·min/week, and those who did not reach the physical activity goals (below 1200 MET·min/week. Accordingly, we considered the physical activity volume reached at the end of the training.

The threshold level of 1200 MET·min/week was obtained in two different ways:


The results considering these two different subdivisions are reported respectively in Tables 1 and 2.

**Table 1.** Summary of descriptive data of the study population subdivided into two groups considering total volume of physical activity (METsTOT) (Subdivision A).



**Table 1.** *Cont.*

Abbreviations: T0 = before intervention; T1 = after intervention; HR = heart rate; RR= RR interval; RRTP = RR total power (RR interval variance); LF = low frequency component of RR variability; HF = high frequency component of RR variability; nu = normalized unit; LF/HF = RRLF on RRHF ratio; SAPpc = percentile of systolic arterial pressure; DAPpc = percentile of diastolic arterial pressure; AHA score = American Heart Association Nutrition Score; METsTOT = total weekly physical activity volume; METsMV = weekly physical activity volume calculated only considering other activities of moderate intensity and activities of vigorous intensity volume (i.e., volume of structured exercise); BMI= body mass index; WHtR = waist-to-height ratio; FBG = fasting blood glucose; HOMA-IR = homeostasis model assessment—insulin resistance; TG = triglycerides; TOT Chol = total cholesterol; HDL C = HDL cholesterol; TMI = triponderal mass index; VAI = visceral adiposity index; TyG = triglyceride and glucose index; 6MWT = 6-min walk test; PAQ-C = physical activity questionnaire—children; IFIS = international fitness scale; [.] = arbitrary units. Significant values are evidenced in bold.


**Table 2.** Summary of descriptive data of the study population subdivided into two groups considering weekly volume of structured exercise (METsMV) (Subdivision B).


Abbreviations: T0 = before intervention; T1 = after intervention; HR = heart rate; RR = RR interval; RRTP = RR total power (RR interval variance); LF = low frequency component of RR variability; HF = high frequency component of RR variability; nu = normalized unit; LF/HF = RRLF on RRHF ratio; SAPpc = percentile of systolic arterial pressure; DAPpc = percentile of diastolic arterial pressure; AHA score = American Heart Association Nutrition Score; METsTOT = total weekly physical activity volume; METsMV = weekly physical activity volume calculated only considering other activities of moderate intensity and activities of vigorous intensity volume (i.e., volume of structured exercise); BMI= body mass index; WHtR = waist-to-height ratio; FBG = fasting blood glucose; HOMA-IR = homeostasis model assessment—insulin resistance; TG = triglycerides; TOT Chol = total cholesterol; HDL C = HDL cholesterol; TMI = triponderal mass index; VAI = visceral adiposity index; TyG = triglyceride and glucose index; 6MWT = 6-min walk test; PAQ-C = physical activity questionnaire—children; IFIS = international fitness scale; [.] = arbitrary units. Significant values are evidenced in bold.

We considered these two different subdivisions in order to unveil the possible different effects associated with structured exercise (generally of higher intensity) or with physical activities of less intensity, such as walking.

All children were equipped with an activity tracker (Fitbit Charge 2 ©, Fitbit Inc, San Francisco, CA, USA) to monitor their heart rate during the day and during the supervised training sessions.

#### 2.1.4. Physical Fitness (PF) Assessment

Prior to starting the training program, all the children were tested for cardiovascular fitness, as follows:


**Table 2.** *Cont.*


#### 2.1.5. Cardiac Autonomic Regulation (CAR) Assessment

On the day of autonomic evaluation, all subjects arrived at the clinic avoiding caffeinated beverages since awakening as well as heavy physical exercise in the preceding 24 h. Recordings were performed between 04:00 am and 06:00 pm in an air conditioned, quiet room. After a preliminary 10-min rest period in the supine position, electrocardiogram (ECG) and respiratory activity (piezoelectric belt) were continuously recorded over a minimum 5-min period using a two-way radiotelemetry system (Marazza, Monza Italy). Subsequently, subjects were asked to stand up unaided and remain in the upright position for 5 min, during which recordings were maintained. Data were acquired with a PC at 250 samples/second using a custom built software tool (HeartScope) that automatically provided a series of indices describing heart rate variability (HRV) in the time domain: RR interval (in msec) and RR interval variability (RRTP) (assessed as total power, i.e., variance, in msec2), taken as simple classifiers typical of vagal control [21,61,62]. A series of indices were also provided in the frequency domain: autoregressive spectral components both in the low frequency (LF, center frequency ≈ 0.1 Hz) and high frequency (HF, centered with respiration, ≈0.25 Hz) (assessed in msec2 as well as in normalized units [nu]) and markers of prevalent sympathetic and vagal activities, indicating sinoatrial node function [21,62].

Based on all of these assessments before starting the training, a medical exercise prescription defining the modality, intensity, duration, frequency, and progression of exercise was created for each participant, who were individually followed by exercise physiologists using a Zoom online platform.

#### *2.2. Exercise Training Protocol*

Children performed a 12-week online training program supervised by two sport specialists using the Zoom online platform (California, USA). The training protocol consisted of three 60-min sessions per week, over 12 weeks, for a total of 36 sessions. Each session was streamed in real-time using the Zoom platform, which allowed live interaction among the instructors and children. Two sport specialists supervised every training session, which usually consisted of different typology and intensity of exercises, defined considering individual fitness levels and progressively adapted as each individual reached the goal. In detail, all sessions were divided into three sub-sessions: an initial warm-up of approximately 5 min to ensure the correct preparation of the children for the subsequent exercises, the main training that consisted of a combination of aerobic and muscular routines for approximately 50 min, and a final part of cool down of approximately 5 min to ensure the return of the body to the rest condition. The exercise physiologist recorded that the training session was actually performed by the child. All the proposed exercises were playful and recreative activities; for example, we proposed motor and active fabulation, dancing to

music, imitation games, circuit training in a playful way, playing with small objects such as balls, books, pencils, and paper, and paths through the furniture. The activities started from low intensity and progressed through the training to moderate and high intensities with values of maximum heart rate ranging from 50% to 80%, as calculated using the Tanaka equation [63] after clinical verification that no cardiovascular contraindications were present for the execution of vigorous exercise. Shorter bouts of yoga and mindfulness were proposed as cool-down activities [64]. The main goal of the intervention was to administer a simple and enjoyable online training sessions with interaction and adaptable loads that were affordable and sustainable for families and had no cost or transportation difficulties. Starting 30 min from the beginning of the session, the trainers acquired the children's heart rates in order to control the intensity of the training using the Fitbit Charge 2 © (Fitbit Inc, San Francisco, CA, USA).

To better comply with the guidelines [11,12] recommending 60 min of moderate-tovigorous exercise per day for children and adolescents, we provided a dedicated YouTube channel "LAMA Junior" with adapted exercise routines for days without supervised training [34]. See Figure 1.


**Figure 1.** Exercise program.

Throughout the exercise program, patients were asked to maintain a healthy diet but no standard modifications were made.

#### *2.3. Statistical Analysis*

Data are presented as the median (25–75 percentile). The significance of differences was assessed with repeated measures GLM (General Linear Model), considering group and intervention as factors. Differences in pubertal stage between T0 and T1 were assessed using the Chi-squared test. Bivariate Pearson's correlation analysis was employed with two-tailed significance.

Computations were performed using IBM SPSS Statistics for Windows, version 27 (IBM Corp., Armonk, NY, USA) with a PC (DELL). A value of *p* < 0.05 was taken as the threshold for statistical significance.

#### **3. Results**

Throughout the program period, no significant changes in pubertal stage were noted. (Stage 1 = 19 vs. 17, Stage 2 = 6 vs. 8, Stage 3 = 10 vs. 10, *p* = 0.82).

As specified in the methods, we subdivided the population into two groups: those reaching the physical activity goals suggested by the latest guidelines (corresponding to at least an average of 60 min/day of moderate-to-vigorous intensity, mostly aerobic, physical activity, i.e., above 1200 MET·min/week) and those who did not reach the physical activity goals (i.e., below 1200 MET·min/week).

Table 1 reports the results of subdivision A considering the total weekly physical activity volume (METsTOT) (i.e., both time spent walking and time spent performing structured exercise), while Table 2 reports the results of subdivision B considering only the weekly volume of structured exercise (METsMV). The tables report data before (T0) and after (T1) exercise intervention.

#### *3.1. Clinical, Auxological, Hemodynamical and Metabolic Data*

Subdivision A showed that significant differences were present between conditions in clinical auxological parameters (BMI z-score *p* = 0.004 and TMI *p* < 0.001) and between groups in metabolic parameters (total cholesterol *p* = 0.048). No significant differences were noted regarding systolic arterial pressure, diastolic arterial pressure percentile, and heart rate. Similarly, there was no difference in autonomic parameters for RRV.

Subdivision B showed significant differences between conditions in clinical auxological parameters (BMI z-score *p* = 0.033, TMI *p* = 0.003, and VAI *p* = 0.012) and metabolic parameters (total cholesterol *p* = 0.048), with an interaction for HOMA-IR (*p* = 0.05) and VAI (*p* = 0.048). No significant differences were noted regarding systolic arterial pressure, diastolic arterial pressure percentile, and heart rate. Borderline significance was noted in RR interval (*p* = 0.062).

#### *3.2. Lifestyle Data*

No significant differences were noted in subdivisions A and B regarding hours sleep/day and school performance. Quality of sleep and health perceptions were slightly different between groups in subdivision A. Hours of sedentariness/week was slightly different between groups in subdivisions A and B. The AHA score (quality of nutrition index) was slightly different between groups only in subdivision B, and it improved after training. As expected, the volume of performed physical activity was significantly increased after intervention in both subdivisions A and B (*p* = 0.008 and *p* < 0.001, respectively). Interestingly, in both subdivisions, the subjects who performed higher volumes of physical activity after intervention were also characterized by higher volumes of physical activity before intervention (*p* < 0.001 and *p* = 0.012, respectively), with significant interaction (*p* = 0.030 and *p* = 0.012, respectively), suggesting that the increase was significantly higher in group 2 for both subdivisions.

#### *3.3. Physical Fitness Assessment Data*

The covered distance of 6MWT and PAQ-C score were significantly increased after intervention in groups 1 and 2, for both subdivisions A and B. No significant differences between groups or conditions were observed in the IFIS score for both subdivisions A and B.

#### *3.4. Cardiac Autonomic Regulation Data*

No significant differences between groups or condition were observed in any of the autonomic variables in subdivision A.

Subdivision B unveiled an interaction effect in RRHFnu (marker of prevalent vagal modulation of the sinoatrial node) and LF/HF ratio, being slightly increased only in those subjects who performed higher volumes of structured exercise.

Table 3 reports the Pearson's correlations among changes (Δ) between T0 and T1 for the major variables. Notably, ΔMETsTOT was correlated, as expected, with ΔRRTP (change in total variance of heart rate variability), while ΔMETsMV was significantly correlated not only with ΔRRTP but also with ΔRR (change in RR interval) and ΔHFnu. Moreover, ΔMETsMV was significantly correlated (r = −0.400, *p* = 0.017) with Δ SAPpc (change in percentile of systolic arterial pressure), suggesting that only structured exercise was associated with a reduction of the percentile of SAP. Additionally, ΔRRTP and ΔRR were significantly correlated with ΔSAPpc.

**Table 3.** Pearson's correlations among changes between T0 and T1 of main variables.


Abbreviations: Δ = change between T0 and T1; METsTOT = total weekly physical activity volume; METsMV = weekly physical activity volume calculated only considering other activities of moderate intensity and activities of vigorous intensity volume (i.e., volume of structured exercise); RR = RR interval; RRTP = RR total power (RR interval variance); LF = low frequency component of RR variability; HF = high frequency component of RR variability; nu= normalized unit; LF/HF= RRLF on RRHF ratio; SAPpc= percentile of systolic arterial pressure. BMI= body mass index. Significant values are evidenced in bold. \*\*: correlation is significant at the 0.01 level (2-tailed). \*: correlation is significant at the 0.05 level (2-tailed).

#### **4. Discussion**

In this feasibility study, we observed that only the improvement in the volume of structured exercise was associated with the reduction in arterial pressure percentile and improvement of markers of vagal control in obese children. Moreover, structured exercise improved the auxological and metabolic parameters. The increase in volume of structured exercise was obtained through a 12-week exercise training protocol delivered online by exercise physiologists [34,54], using an individualized exercise prescription that was progressively adapted as each individual reached their goal and was specifically designed to meet the needs of obese children and their families, while being economically and organizationally sustainable.

The beneficial effects of exercise are well reported in the literature and exercise is now considered a mandatory preventive and therapeutic strategy in all CNCD, particularly in youth [11–13], as underlined in the introduction. Of fundamental importance is the dose/volume of physical activity required to obtain such benefits. While international guidelines [11–13] clearly indicate the volume of required physical activity and the need to perform moderate and vigorous exercise at a young age, their practical translation into everyday life is sometimes less specific. Simply walking or practicing sports, without paying attention to the volume and particularly the intensity of exercise, is considered efficacious. This phenomenon is well emblemized by the importance given to the number of steps walked every day [65] independent of the speed of walking. On the contrary, the literature shows that the benefits of endurance exercise are also linked to the level of cardiorespiratory fitness [15,16] in children and that only brisk walkers had significantly longer telomeres than slow walkers among adults [17], thereby showing a direct correlation between exercise intensity and longevity. In this paper, we observed that only the volume of exercise due to structured moderate-to-vigorous intensity exercise was associated with improved cardiac autonomic control, reduced systolic arterial pressure, and improved auxologic and metabolic parameters. Of particular interest is the reduction in the HOMA-IR index, which suggested reduced insulin resistance after training, confirming the relevant influence of different types of exercise on insulin resistance [66]. In contrast, we observed only a slight change in the auxologic parameters, such as BMI z-score and TMI, considering that the MET count also included METs linked to simple walking for more than 10 min.

The data in our study, albeit preliminary, suggest that only structured exercise was capable of inducing an improvement in autonomic and metabolic control. Of particular interest is the observation that the change in the SAP percentile with training was associated with the change in structured exercise volume and changes in ANS variable markers of vagal control. Arterial hypertension is a condition that characterizes childhood obesity [2,3,6] and increases the risk of major cardiovascular disease over the lifespan. Many studies have demonstrated that exercise is a powerful tool to prevent/treat arterial hypertension and obesity [11–13]. The data in the current study corroborate previous observations in which improved autonomic cardiac regulation was among the benefits of exercise in the management of hypertension and obesity [10,22,24–27].

Another interesting point to discuss is the methodology employed to help children with obesity become physically active. Participation in team sports or programs that generally group children together are considered more effective, but they may present some practical barriers (economical, organizational, and psychological), as reported in the introduction. In this paper, we observed that a home-based, online exercise training program could overcome, albeit in part, these barriers, and could be enjoyable and sustainable from an economical and organizational point of view, thus facilitating acceptance and compliance of children and their families. This observation was corroborated by 86% of children in our study remaining in the program after completing the final evaluations.

In addition, the possibility of starting exercise at home, thus avoiding interaction with peers while improving their physical performance, may result in an improvement of self-efficacy [67] and, consequently, motivate obese children to exercise in a different environment close to their peers. A fundamental characteristic of the employed protocol [34] was the balance between the possibility of being delivered online and tailoring the proposed modality/intensity of exercises to the child's needs, preferences, and reached goals [54]. An individualized clinical assessment and exercise prescription was essential to the achievement of this goal, which required exercise physiologists to interact online with children, design enjoyable exercises, and adjust the program during the training period. In contrast [12,37,68], home-base exercise programs that were not tailored to individual needs showed poor or no efficacy in previous studies.

In this observational study, we also observed that children with obesity who significantly increased their exercise volume through the intervention program were already characterized by higher exercise volumes and less sedentary hours (considering both subdivisions) before starting the intervention program. This finding suggests that other factors, in addition to the methodology employed in the exercise program, need to be considered, such as psychological and social conditions [9,67]. Although we do not have data to support this hypothesis, we observed that the group of children who actually increased their

exercise volume in subdivision A were characterized before the intervention by slightly higher health and quality of sleep perceptions.

Throughout the exercise program, children with obesity were asked to maintain a healthy diet but no standard modifications were made. Nevertheless, the group of subjects who significantly increased the volume of structured exercise was also characterized by a higher AHA score, suggesting an improvement in the quality of nutrition. Further supporting the hypothesis expressed in the previous paragraph, we also observed that this group was characterized by better AHA scores before the intervention.

Some limitations of this study must be acknowledged. First, this study was an observational feasibility study and the group size was small, thus limiting the interpretative value of the results. Additionally, we did not consider a control group (subjects studied before and after sham intervention). Nevertheless, we observed in other studies that sham interventions did not induce modifications of the autonomic nervous system per se [69,70], corroborating the idea that the observed changes were attributable to functional remodeling. Second, only 19 children (54.29%) wore the activity tracker both during the day and during the training sessions and were considered for the specific analysis. Interestingly, we observed a significant correlation (r = 0.445, *p* = 0.049) between the total steps recorded by the tracker and the total volume of structured exercise (METsMV), while no significant correlation (r = 0.235, *p* = 0.3.19) was observed with the total volume of physical activity (METsTOT). Third, the children's diets were not standardized, allowing for a potential bias. Moreover, we did not have data regarding immunological control (another main control system that plays an important role in cardiometabolic conditions) and psychological and social aspects. These are important factors to consider when studying the effects of intervention programs in subjects with obesity. In addition, the intervention lasted only 12 weeks and we do not have data regarding the long-lasting effects of our model. Therefore, in view of the small number of subjects and overall feasibility of the design, generalizing the results is not possible.

#### **5. Conclusions**

In conclusion, this study showed the feasibility of an online exercise program as a practical tool to improve cardiac autonomic control and hemodynamic, metabolic, and auxological parameters in a group of children with obesity, rendering it a valuable strategy for fostering compliance with physical activity guidelines and providing an opportunity for pediatric subjects with obesity to stay healthy, thus managing the increased risk of CNCD. Moreover, by guaranteeing that an adequate level of exercise intensity was reached, this study underlines the importance of structured exercise in obtaining significant health benefits. The benefits of exercise overcome those of "simple" weight reduction (which in this study was present when considering subdivision A), which are also linked to improvements in important endocrine and autonomic control mechanisms (which in this study was evident when considering subdivision B). In addition, the ability of exercise to affect control mechanisms, particularly cardiac autonomic control, seems only to be evident if exercise reaches an adequate intensity, i.e., even at the same exercise volume (1200 METs), the effect may differ depending on the intensity of the exercise performed. With this in mind, we have to consider that this was a feasibility study. Therefore, the capability of the exercise program to affect CAR and its economical/organizational sustainability must be verified. On the basis of the present observations, larger and longer lasting studies aimed at confirming these preliminary satisfactory data seem justified.

**Author Contributions:** Conceptualization, D.L., V.C. and M.V.; methodology, D.L., M.P., M.V., V.C.P., V.C. and G.Z.; software, D.L., M.P., M.V. and C.L.; formal analysis, D.L., G.B. and M.M.; investigation, D.L., G.B., M.M., V.C.P., V.C. and S.M.; resources, D.L., G.Z., M.V., D.L., M.M. and G.B.; writing—original draft preparation, D.L. and M.P.; writing—review and editing, D.L., G.B., M.M., M.V., V.C.P., V.C., S.M., C.L., G.Z. and M.P.; supervision, D.L., V.C., G.Z. and M.V.; project administration, M.V. and D.L.; funding acquisition, D.L. and G.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partially funded by the Italian Ministry of Health.

**Institutional Review Board Statement:** This study were approved by the institutional ethics committee (Milano Area 2 proto-col number 2020/ST/298, approval date 2 December 2020).

**Informed Consent Statement:** Written consent was obtained by all participants or their responsible guardians once they were well informed about the study.

**Data Availability Statement:** Data will be available on justified request. We have not yet uploaded the data because they are part of an ongoing study on the effects of exercise in children and other papers may be prepared using them.

**Acknowledgments:** We thank Fitbit Inc., San Francisco, CA, USA to have furnished activity trackers (Fitbit Charge 2 ©).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **The Influence of Nutrition and Physical Activity on Exercise Performance after Mild COVID-19 Infection in Endurance Athletes-CESAR Study**

**Daniel Sli ´ z˙ 1,2,\*, Szczepan Wiecha 3,\*, Jakub S. G ˛asior 4, Przemysław Seweryn Kasiak 5, Katarzyna Ulaszewska 5, Marek Postuła 6, Łukasz A. Małek <sup>7</sup> and Artur Mamcarz <sup>1</sup>**


**Abstract:** COVID-19 and imposed restrictions are linked with numerous health consequences, especially among endurance athletes (EA). Unfavorable changes in physical activity and nutrition may affect later sports and competition performance. The aims of this study were: (1) to assess the impact of COVID-19 infection and pandemic restrictions on the nutrition and physical activity of EAs and (2) to compare them with the results of cardiopulmonary exercise testing (CPET). In total, 49 EAs (nmale = 43, nfemale = 6, mean age = 39.9 ± 7.8 year., height = 178.4 ± 6.8 cm, weight = 76.3 ± 10.4 kg; BMI = 24.0 <sup>±</sup> 2.6 kg·m−2) underwent pre- and post-COVID-19 CPET and fulfilled the dietary and physical activity survey. COVID-19 infection significantly deteriorated CPET performance. There was a reduction in oxygen uptake and in heart rate post-COVID-19 (both *p* < 0.001). Consuming processed meat and replacing meat with plant-based protein affected blood lactate concentration (*p* = 0.035). Fat-free mass was linked with consuming unsaturated fatty acids (*p* = 0.031). Adding salt to meals influenced maximal speed/power (*p* = 0.024) and breathing frequency (*p* = 0.033). Dietary and Fitness Practitioners and Medical Professionals should be aware of possible COVID-19 infection and pandemic consequences among EA. The results of this study are a helpful guideline to properly adjust the treatment, nutrition, and training of EA.

**Keywords:** COVID-19; endurance athletes; nutrition; diet; physical activity; sport performance; cardiopulmonary exercise test; health promotion; lifestyle interventions; exercise

### **1. Introduction**

As reported by the World Health Organization, the coronavirus disease-2019 (COVID-19) pandemic has affected the lives of all humanity due to its rapid spread and high mortality rate (https://covid19.who.int (accessed on: 22 November 2022)). Despite the passage of time, a fully effective method of preventing and treating COVID-19 infection has not been found [1]. In addition, a large part of the population remains reluctant to be vaccinated [2], and full vaccination does not completely protect against COVID-19 complications, especially those affecting the circulatory and respiratory systems [3]. A mounting problem for caregivers is the high mutation ability of the virus, which can take various

**Citation:** Sli ´ z, D.; Wiecha, S.; G ˛ ˙ asior, J.S.; Kasiak, P.S.; Ulaszewska, K.; Postuła, M.; Małek, Ł.A.; Mamcarz, A. The Influence of Nutrition and Physical Activity on Exercise Performance after Mild COVID-19 Infection in Endurance Athletes-CESAR Study. *Nutrients* **2022**, *14*, 5381. https://doi.org/ 10.3390/nu14245381

Academic Editors: Roberto Pippi, Carmine G. Fanelli and Matteo Vandoni

Received: 30 November 2022 Accepted: 16 December 2022 Published: 18 December 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

forms [4]. Mutations consist of changes in proteins' structure and modification of bonds. Due to the unpredictability of the directions of changes, an effective approach to limit spreading and provide sufficient protection in public places is a matter of vital importance.

The period of the pandemic has negatively affected the lifestyle of people around the world. Many people have experienced adverse lifestyle changes [5]. One of the key aspects is changes in physical activity and amount of sitting time [6]. An important aspect related to imposed restrictions is their negative impact on nutrition [7], which may have very serious consequences in the future, such as being overweight, weakening of physical capacity, and civilization diseases [8]. Physical activity and nutrition are both important factors influencing immunity. A potentially better prognosis with COVID-19 infection is achieved by patients consuming more fiber and plants. Such a diet has a positive effect on the gut microflora [9].

The lockdown has affected the performance and lifestyle of professional athletes [10], which may affect later performance in sports. Being ill with COVID-19 has also reduced the number of training days in this population [11], even though athletes are not considered to be at high risk of severe infection and pass it mainly mildly [12]. Studies show that the pandemic has also affected the nutrition of athletes. For example, adverse changes such as increased caloric intake and decreased fruit intake were shown in a group of Rugby players [13]. Moreover, eating disorders associated with mental health deterioration have been noticed among young athletes during the pandemic [14], another study described that the pandemic could have a negative impact on people with eating disorders [15].

COVID-19 disease through multifactorial pathological mechanisms may be accompanied by acute cardiovascular conditions, such as myocarditis, acute myocardial infarction, and cardiomyopathy [16]. The infection may have a long-term negative impact on systemic health status, including the respiratory system due to the risk of respiratory failure, pulmonary thromboembolism, pulmonary embolism, and pneumonia [17]. Due to frequent long-term abnormal lung function, fatigue, shortness of breath, chest pain, and coughing, the disease can be severe for athletes or physically active people [18]. In one study, it was described that post-COVID-19 patients examined by cardiopulmonary exercise testing (CPET) show breathing and decreased peripheral oxygen extraction or decrease in maximum oxygen uptake (VO2max) [19].

CPET is a test that can be performed on a treadmill or cycle ergometer and is used to assess aerobic capacity. Is a useful tool to assess progress and plan training [20]. There is a study that shows that the use of CPET as a test to determine persistent symptoms of COVID-19 infection can be helpful in testing endurance athletes [21]. Another study found that patients with persistent dyspnea post-COVID-19 have decreased VO2max as well as present circulatory exercise limitations [22]. An interesting aspect is the influence of nutrition on performance results. For example, a paper comparing a ketogenic diet to a well-balanced diet shows that in the second case even with reduced caloric intake increases in endurance could be achieved [23]. Another study shows that severe deficiencies of micro- and macronutrients such as folic acid and vitamin B12 cause anemia and reduce the performance of endurance work [24]. Thus, paying attention to nutrition among endurance athletes remains a highly important aspect.

We stipulate that previous COVID-19 infections, even with a mild course, paired with imposed restrictions and overall shifts in public life negatively influenced lifestyle and physical performance among endurance athletes. We also hypothesize that those changes are linked together. The aims of this study were: (1) to assess dietary habits, nutrition, and physical activity with their changes caused by mild COVID-19 infection and imposed restrictions, (2) to measure differences in physical performance caused by mild COVID-19 infection, and (3) to compare changes in both areas together and find whether they are correlated.

#### **2. Materials and Methods**

#### *2.1. Study Design*

This was a prospective study using data from CPET performed between June 2021 and December 2022. Procedures were conducted in the Tertiary Care Sports Diagnostics Clinic SportsLab (SportsLab, Warsaw, Poland). Participants underwent double CPET preand post-COVID-19 infection and had to fulfill a health-assessing questionnaire.

The study group comprised amateur and professional endurance athletes. Inclusion criteria were: (1) CPET performed at the Sportslab Clinic no earlier than 3 years before COVID-19 infection, (2) previous COVID-19 infection (documented by a PCR mRNA test or a positive antigen test) in a period of 14 days to 6 months prior to re-test of CPET, (3) mild course of COVID-19 infection (mandatory no hospitalization), (4) completing each part of the questionnaire, (5) declared lack of actual long-lasting COVID-19 infection consequences, (6) current negative PCR test for COVID-19. The health status of the participants, based on the declared information, was assessed by a medical professional in internal medicine or cardiology before post-COVID-19 CPET. Exclusion criteria were: (1) any respiratory diseases (COPD, poorly controlled bronchial asthma, blood saturation <95%), (2) any cardiovascular diseases (cardiac arrhythmias in the ECG, myocardial ischemia, prolongation of the QT interval in the ECG, structural disorders of the heart in echocardiography, decompensated hypertension with blood pressure >160/100 mmHg), (3) neurological and psychiatric conditions, (4) musculoskeletal disorders, and (5) significant deviations in CBC (leukocytosis >10,000·mm−3, anemia with HB levels <10 g·dL−1). The flowchart of study procedures is presented in Figure 1.

Each athlete underwent CPET on the same modality pre- and post-COVID-19 infection because each modality was previously linked with different performance results [25]. During the post-COVID-19 CPET, athletes received a questionnaire covering selected areas of general health assessment.

#### *2.2. Survey Tool*

Subjects fulfilled a questionnaire assessing general health and lifestyle during the CPET post-COVID-19 infection. A validated questionnaire from the PaLS study (Pandemic against LifeStyle project) was used [26–28]. The questions covered 3 general areas: demographic information and infection course, diet and eating habits, and physical activity.

Physical activity was rated by the International Physical Activity Questionnaire- Short Form. This part examines three levels of activity: low-, moderate- and vigorous-intensity. Additional questions relate to the period of sitting every day calculated in minutes and hours. Questions related to diet and eating habits were prepared based on Polish National Institute of Public Health recommendations (https://www.pzh.gov.pl/; accessed on 13 November 2022). EAs were asked about the ingredients included in their diet and the intake of particular nutrients (i.e., vegetables and fruits, milk and dairy, processed meat, animal fats and trans fatty acids, sweetened drinks and fruit juices, salt). Moreover, we included questions about eating behaviors (i.e., eating in front of the screen, reading labels of the products, and number of meals pre- and post-COVID-19 pandemic). EAs chose one of the following responses: once a week and less often, 2–3 times a week, most days of the week, every day. "Dietary product" was defined as a food included in the diet (not a dietary supplement) and "Dietary habit" was defined as the habitual decision of individuals regarding what foods they eat. Participants received information about the definition of the above-mentioned terms at the beginning of the survey. Their proper understanding was a mandatory requirement. Demographic and infection-related questions were the author's questions and assessed, among others, primary sports discipline, sports competition experience, long-lasting COVID-19 consequences, etc.

An additional original question was added to each assessed area, in which respondents rated the impact of the COVID-19 pandemic and previous infection on a given part of their lifestyle or health via a −5/0/+5 scale. Negative (−5/−1) values represented a negative impact, 0 indicated no impact, and +1/+5 values a represented positive impact (values were adjusted for impact strength).

**Figure 1.** Study procedure. Abbreviations: EA, endurance athlete; PCR, polymerase chain reaction; COVID-19, coronavirus disease 2019; CPET, cardiopulmonary exercise test; ECG, 12-lead electrocardiogram; ECHO, echocardiography examination; CBC, complete blood count; COPD, Chronic obstructive pulmonary disease.

#### *2.3. CPET and Somatic Measurements*

Body composition was assessed using the Tanita device (Tanita, MC 718, Japan) before each of the double CPETs. Multifrequencies of 5 kHz/50 kHz/250 kHz were used. Body composition analysis and CPET were conducted under the same environmental conditions: ventilated room, an ambient temperature of 20–22 degrees Celsius, and humidity between 40 and 60%. Participants received information on how to properly prepare a few days before the stress test: (1) eat a high-carbohydrate energy meal 2–3 h before, (2) stay hydrated with isotonic sports drinks, (3) avoid intense physical exercise a few days before the test, and (4) be familiar with the test protocol and exercise characteristics on a mechanical treadmill or stationary cycle ergometer.

Cycle CPET was performed on the Cyclus ergometer (RBM elektronik-automation GmbH, Leipzig, Germany). Running CPET was performed on the Cosmos treadmill (h/p/Cosmos quasar, Germany). Cardio-pulmonary measurements were obtained via a Cosmed Quark CPET device (Rome, Italy). Exercises began with a 3–5-min warm-up. It consisted of slow running or walking at a conversational pace (between 7–12 km·h−1) or pedaling without resistance. The initial load was individually adjusted for each participant. The speed was then increased every 2 min by 1 km·h−<sup>1</sup> (constant inclination of 1%), while the load was increased every 2 min by 20 Watts for females and 30 Watts for males. The test was considered terminated if: (1) the participant declared further inability to continue the effort or (2) the VO2max plateau was reached (no increase in the curve of oxygen uptake with increasing exercise intensity). Athletes were encouraged to continue CPET and achieve the best possible result by their supervisor.

The following somatic parameters were measured: weight, height, body fat percentage (BF), fat mass (FM), and fat-free mass (FFM). The following exercise parameters were measured: speed (S) for treadmill or power (P) for cycle ergometry, relative oxygen uptake (VO2), absolute oxygen uptake (VO2a), heart rate (HR), pulmonary ventilation (VE), breathing frequency (fR), and blood lactate concentration (Lac). Each exercise variable was measured at anaerobic threshold (AT), respiratory compensation point (RCP), and maximal exertion. CPET indices were obtained using the breath-by-breath method and a Hans Rudolph V2 Mask (Hans Rudolph Inc, Shawnee, Kansas). VO2max was calculated as the average value of the 10 s period immediately preceding the termination, while HR was considered as the highest value during the stress test. Blood for Lac was collected: (1) before the test, (2) after each increase in intensity, and (3) 3 min after exercise. A 20 μL blood sample was taken each time. The first few drops were drained into a cotton swab before collecting the proper sample. A Super GL2 analyzer (Müller Gerätebau GmbH, Freital, Germany) was used for the analysis, which was calibrated for each participant.

AT was evaluated after meeting the following parameters: (1) the VE/VO2 plot was rising with a constant plot of VE/VCO2 and (2) the end-tidal partial pressure of oxygen increased at a constant end-tidal partial pressure of carbon dioxide. RCP was evaluated after meeting the following parameters: (1) the VE/VCO2 plot had reached its lowest value and started to increase, (2) the decline in the end-tidal partial pressure of carbon dioxide had begun after maximal exertion, (3) there was a rapid increase in VE (2nd deflection), and (4) there was an increase in VCO2 relative to VO2 (these variables lost their linear relationship) [29].

#### *2.4. Ethics*

The athletes were tested in accordance with the Declaration of Helsinki. The study protocol was approved by the Bioethics Committee of the Medical University of Warsaw (approval no. KB/50/21 from 19th April 2021). The subjects had to give written consent to participate in the study, published obtained results in the scientific literature and were informed about the potential risks and nuisances related to the procedures. Personal data has been anonymized and does not allow for the identification of the subjects.

#### *2.5. Statistical Analysis*

Basic data are presented as mean with standard deviation (SD) for continuous variables and numbers (n) with percentage (%) for categorical variables. The data are presented in accordance with APA guidelines (https://apastyle.apa.org/; accessed on 26 November 2022). Normal distribution was assessed using the Shapiro–Wilk test. Between-group differences comparing data from in vivo CPET with declared results from questionnaires were analyzed with Kruskal–Wallis rang's ANOVA. Differences between CPET and somatic measurements were obtained from Student's t-test for independent variables.

The total sample size was determined using the G∗Power program (version 3.1.9.2; Düsseldorf, Germany). The significance level was set at *p* = 0.05. The total number of people required is the minimum effective value.

The basic analyses were performed using Excel software (Microsoft Corporation, Washington, USA), and the advanced statistics were conducted in the STATISTICA software

(version 13.3, StatSoft Polska Sp. z o.o., Kraków, Poland) and SPSS software (version 28; IBM SPSS, Chicago, IL, USA).

#### **3. Results**

#### *3.1. Basic Population Data*

In total, 49 endurance athletes who were referred for CPET and received a survey were included in the study. The baseline description is presented in Table 1. The cohort was 43 (87.8%) males and six (12.2%) females. The average age was 39.9 ± 7.8 years. A total of 31 (62.8 %) endurance athletes completed CPET on the treadmill and 18 (37.7%) on the cycle ergometer. The interval between CPETs was 591.7 ± 282.2 days, while the interval between pre-COVID-19 CPET and negative PCR (indicated as the end of the infection) was 436.4 ± 290.4 and the interval between post-COVID-19 CPET and negative PCR was 155.3 ± 82.52. Twenty (40.8%) athletes declared themselves runners, 14 (28.6%) cyclists, and the remaining 15 (30.6%) declared other disciplines (triathlon, football, and martial arts) as their primary sport. Training experience was 1–2 years for four (8.2%) athletes, 3–5 years for 14 (28.6%) athletes, 6–10 years for 19 (38.8%) athletes and 12 (21.3%) athletes declared >10 years of training experience. COVID-19 infection resulted in waived competition for 23 (46.9%) participants. On a 0–5 scale subjects declared their overall health as 4.8 ± 0.5 before infection and as 4.1 ± 0.5 after. Ten (20.4%) participants suffered from long-lasting COVID-19 consequences for >14 days.


**Table 1.** Participants' characteristics.

Abbreviations: COVID-19, coronavirus disease 2019; BMI, body mass index; FFM, fat-free mass; BF, body fat; FATM, fat mass. Data are presented as means with standard derivations (±) for continuous variables and numbers with percentages (%) for categorical variables.

#### *3.2. CPET Characteristics*

We found significant differences between CPET performance conducted pre- and post- \COVID-19 infection. The most significant differences were noted for VO2 at AT, RCP, and maximum (each *<sup>p</sup>* < 0.001). The mean VO2 before infection was 35.0 ± 6.4 mL·kg·min−1, 43.9 ± 7.3 mL·kg·min<sup>−</sup>1, and 47.8 ± 7.8 mL·kg·min<sup>−</sup>1, respectively, for AT, RCP, and maximal. Meanwhile, post-infection values were 32.4 ± 5.9 mL·kg·min<sup>−</sup>1, 40.5 ± 6.6 mL·kg·min<sup>−</sup>1, and 45.0 ± 7.0 mL·kg·min<sup>−</sup>1, respectively, for AT, RCP, and maximal. In HR measurements there was also an aggravation at AT and RCP (both *p* < 0.001). The peak HR before infection

was 145.1 ± 10.8 bpm for AT and 168.8 ± 9.0 bpm for RCP. Post-infection values were 141.1 ± 10.0 bpm for AT and 165.1 ± 9.7 bpm for RCP. Lacmax and VE only deteriorated for RCP. Their precise values are presented in Table 2.

**Table 2.** Differences in CPET performance before and after COVID-19 infection.


Abbreviations: COVID-19, coronavirus disease 2019; VO2AT, oxygen uptake at the anaerobic threshold; VO2ATa, absolute oxygen uptake at the anaerobic threshold; HRAT, heart rate at the anaerobic threshold; VEAT, pulmonary ventilation at the anaerobic threshold; SAT, speed at the anaerobic threshold; PAT, power at the anaerobic threshold; fRAT, breathing frequency at the anaerobic threshold; VO2RCP, oxygen uptake at the respiratory compensation point; VO2RCPa absolute oxygen uptake at the respiratory compensation point; HRRCP, heart rate at the respiratory compensation point; VERCP pulmonary ventilation at the respiratory compensation point; SRCP, speed at the respiratory compensation point; PAT, power at the respiratory compensation point; fRRCP, breathing frequency at the respiratory compensation point; LacRCP, blood lactate concentration at the respiratory compensation point; VO2max, maximal oxygen uptake; VO2maxa, absolute maximal oxygen uptake; HRmax, maximal heart rate; VEmax, maximal pulmonary ventilation; Smax, maximal speed, Pmax, maximal power; fRmax, maximal breathing frequency; Lacmax, maximal blood lactate concentration. Data are presented as means with standard derivations (±). Speed is presented for treadmill CPET (*n* = 29) and power is presented for cycle ergometer CPET (*n* = 18).

#### *3.3. Nutrition and Eating Habits*

Descriptive results of the survey along with mean rangs have been presented in Table 3. Participants received information about the definition of "Dietary product" and "Dietary habit" at the beginning of the survey. Due to a high number of possible combinations, only significant results (with *p* < 0.05) have been shown. A Kruskal–Wallis H-test showed significant differences between the frequency of particular dietary products, eating habits, and CPET scores. Precise results are presented in Table 4. Consuming products containing

processed meat significantly affected Lacmax [H(3) = 8.9; *p* = 0.030]. The highest Lacmax was noted for participants who chose "Majority of days within a week" compared to other responses (mean rang= 20.1). However, replacing meat with protein-rich plant products such as nuts and legumes influenced both Lacmax [H(3) = 8.6; *p* = 0.035] and maximal HR [H(3) = 8.6; *p* = 0.036]. Athletes who again selected "Majority of days within a week" had the highest Lacmax (mean rang= 19.2) and HRmax (mean rang= 32.4). It is worth mentioning that FFM was linked with consuming products that are sources of unsaturated fatty acids [H(3) = 8.9; *p* = 0.031]. Adding salt to meals influenced maximal speed or power [H(3) = 9.4; *p* = 0.024] and fR at RCP [H(3) = 13.7; *p* = 0.033].


**Table 3.** Nutrition and dietary habits of participants.

Abbreviations: COVID-19, coronavirus disease 2019; na, Lacmax, maximal blood lactate concentration; HRmax, maximal heart rate; fRRCP, breathing frequency at the respiratory compensation point; Smax, maximal speed; Pmax, maximal power. Data are presented as numbers (N) with percentages (%) of the whole population and in the −5/0/+5 scale question data are presented as means with standard deviations (±). The mean rang has been added only for significant differences (*p* < 0.05) between a particular group and CPET performance. The mean rang was calculated by the Kruskal–Wallis H-test.


**Table 4.** Relationships between nutrition, dietary habits, and CPET performance.

Abbreviations: Lacmax, maximal blood lactate concentration; HRmax, maximal heart rate; FFM, fat-free mass; Smax, maximal speed; Pmax, maximal power; fRRCP, breathing frequency at the respiratory compensation point. Due to a high number of possible combinations, only significant results (with *p* < 0.05) have been shown.

#### *3.4. Physical Activity*

Declared results of physical activity questions are presented in Table 5. There were not any significant differences (each *p* > 0.05) between the frequency of selected answers in the International Physical Activity Questionnaire—Short Form and CPET performance.

**Table 5.** Results of physical activity questions.


Abbreviations: IQR, interquartile range; COVID-19, coronavirus disease 2019. Data are presented as medians with the interquartile range due to their non-normal distribution.

#### **4. Discussion**

The detrimental effect of COVID-19 infection on nutrition and eating habits and CPET results in the group of endurance athletes was shown. The main findings were: (1) among changes in eating habits, the most commonly reported were drinking sweetened drinks and fruit juices instead of water, more salting of food, and consumption of more meals per day compared to the pre-pandemic time; (2) post-COVID-19 status influenced outcomes of CPET such as VO2 at AT, RCP, and maximum, as well as HR at AT and RCP (each *p* <0.001); (3) eating habits during the pandemic influenced the achieved results of CPET, e.g., Lacmax increased in people who more often consumed products containing processed meat and Lacmax and HRmax were affected by replacing meat with protein-rich plant products such as nuts and legumes.

There are studies describing the impact of COVID-19 on CPET scores and the physical performance of patients. One study shows that post-COVID-19 people had a reduced VO2 score compared to the control group, and they also showed more respiratory failure (VE/VCO2) [30]. This means that this study, like our study, did not achieve the expected result at peak exercise, which may be due to the long-term effects of the disease on the cardiovascular or respiratory systems [31]. In a study in a sample of elite swimmers that compared CPET results in athletes who had the infection and athletes who did not have the disease, no evidence of the effect of the disease on the variability of results was found [32]. A similar study on volleyball players showed results typical of detraining, but without access to the patient's results before the onset of the disease, it is difficult to assess [33]. The conclusions differ from our results, but the reason for this may be the different course of the disease and its impact on healthy people, especially athletes who are not considered to be at risk [34]. The next study assessed the exercise capacity of mildly symptomatic patients using an ergometer, comparing the results before and after contracting COVID-19. Slight differences in MET and VO2peak results were noted [35]; median VO2max in the cited study was 21,857 pre-COVID-19 and 21,699 post-COVID-19, while in our study pre-COVID-19 it was 3623.47 and post-COVID-19, 3406.00 (ml·kg·min−1), which proves that athletes, despite the disease, have a greater capacity than other patients. Another study claims that symptomatic patients with the post-COVID-19 syndrome were more likely to show lower main VO2peak, less likely to reach the AT, and symptoms of the syndrome worsened during examinations [36]. In patients with long-term symptoms after infection, 55% had reduced pVO2peak in CPET, and 31% had reduced pVO2peak secondary to limitation and muscular impairment [37]. The data from these studies are worrying because they show the long-term impact on patients' health. Reduced performance deteriorates the quality of life of any patient, especially professional athletes. It was recognized that VO2max could be used as a clinical tool for estimating the severity of COVID-19 infection [38]. It turns out that the VO2 result can also be a good predictor of mortality in patients with SARS-CoV-2 [37]; therefore, the use of the CPET test in our work reliably showed the impact of COVID-19 on the health and performance of patients. The probable explanation why we observed the decrease in HR among the EAs may be due to age-related changes in the cardiovascular system [39]. Although some time had elapsed between both CPETs, time-related impact is justified. It is worth noting here that Komici et al. observed a change in forced expiratory volume in one second examined in spirometry in EAs who had survived COVID-19 [40].

There is no doubt that proper nutrition is one of the most important components of human health and life. Professional athletes in their daily diet care for the building of muscles, but also for the proper development of other tissues (such as the vascular system and central nervous system) [41] and providing the right amount of energy to the body. Adequate nutrition for endurance athletes is difficult because it depends on many factors, such as the amount and type of training, time to competition, and type of exercise performed [42]. Even more so, the period of the COVID-19 pandemic and disease did not contribute positively to the improvement of nutrition [28]. Evidence supports that high carbohydrate intake provides muscle glycogen stores and prevents hypoglycemia [43], and there is debate about fluid intake as water poisoning has occurred in the ultra-resistant environment [44].

We recommend further similar studies that include analysis adjusted with participants' competition results. It is worth mentioning that one study conducted on Olympic athletes did not affect the results achieved during the competition [45], but more research should be performed to deeply investigate those findings. Many competitions were canceled during the pandemic and EAs had no chance to face them. Returning to competition after a long break should be well thought out [46]. Moreover, other competitions had to be adapted to the new circumstances. In the case of a long-term break from training, it is worth using rehabilitation aimed at improving respiratory efficiency. In addition to physical preparation, mental and dietary preparation are also important [47,48]. Thus, EAs during and after illness require special attention to their mood and nutrition.

Our respondents found that more frequent consumption of processed meat increases Lacmax, which is not desired by professional athletes [49]. Excessive consumption of processed meat can even be carcinogenic [50]. As described in the Results, it was pointed out that up to 59.2% of participants eat processed meat more than two times per week. In a study that investigated the effect of a plant-based diet on cardiovascular disease, it turned out to be positive because it lowered blood pressure, HR, and insulin levels [51]. Furthermore, our respondents, who were more likely to use plant-based protein products, had an aggravation in HR and Lacmax. Among studies examining the effect of unsaturated fats on health, one of them showed that the consumption of such fats is associated with a reduction in the risk of cardiovascular disease, and also increases the share of lean body mass [52], and this was also achieved by the athletes in our survey. Another study in mice showed that eating unsaturated fats compared to saturated fats provides more energy and endurance [53]. Excessive salt added to food can have long-term effects such as the risk of high blood pressure, stroke, kidney, and heart disease [54]. There are studies that show health problems in athletes who consume too little and too much sodium, and salt should be replaced after exercise, remembering to hydrate reasonably [55]. These and other aspects of nutrition are very important for professional athletes due to their impact on body structure and endurance, so it is important to take care of meals even in the case of illness and the COVID-19 pandemic. A proper diet introduced after recovering from COVID-19 can reduce the long-term effects of the disease [56]. However, the explanation of the physiological basis of the obtained results is beyond the scope of this paper. Thus, we recommend it for further research.

#### *Limitations*

Probably due to the small sample size and structure of questions, we did not observe any significance in the results of the physical activity questionnaire. The limitation of our questionnaire is the possibility that the physical activity and nutrition of the respondents changed during the pandemic; in addition, they may not remember their old habits. There was a relatively long period of time between the two CPET tests, so this may have influenced the changes in parameters. We are aware of the existing limitations, therefore we recommend carefully evaluating the results.

#### **5. Conclusions**

EAs require special professional care to be ready for sports competitions. Training loads and nutrition status, especially considering the period after infection, should be carefully adjusted. The results of this study may be used in determining the training and nutrition plans of professional athletes. Nutritionists should consider what eating habits emerge when athletes have limited training opportunities and spend more time at home. It is important to remember the adequate supply of fluids, fruits and vegetables, vitamins, and whole grains. Thanks to these results, doctors and trainers will be more aware of how performance and the body changes after suffering from COVID-19. Gradual adaptation of EAs to loads after a break in training is crucial to staying in shape and avoiding injury. Rehabilitation may also be required in the event of greater damage to health and condition. This will allow them to choose the right treatment and tests to avoid long-term complications. Each EA should be treated individually and comprehensive care should be implemented to facilitate recovery.

**Author Contributions:** Conceptualization, D.S. and S.W.; methodology, D. ´ S. and P.S.K.; validation, ´ D.S. and S.W.; formal analysis, D. ´ S., J.S.G. and P.S.K.; investigation, D. ´ S., S.W. and A.M.; resources, D. ´ S. ´ and S.W.; data curation, D.S., S.W., P.S.K., K.U., M.P. and Ł.A.M.; writing—original draft preparation, ´ D.S., P.S.K. and K.U.; writing—review and editing, D. ´ S., S.W., J.S.G., P.S.K. and K.U.; visualization, ´ D.S., S.W., J.S.G. and P.S.K.; supervision, D. ´ S., S.W. and A.M.; project administration, D. ´ S., S.W. ´ and A.M.; funding acquisition, D.S. All authors have read and agreed to the published version of ´ the manuscript.

**Funding:** This research was funded by the affiliated university—the Medical University of Warsaw (2022/EL12489).

**Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Medical University of Warsaw (approval no. KB/50/21 from 19 April 2021).

**Informed Consent Statement:** Written informed consent was obtained from the patients to publish this paper.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to not obtaining consent from respondents to publish the data.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Supervised Exercise in Water: Is It a Viable Alternative in Overweight/Obese People with or without Type 2 Diabetes? A Pilot Study**

**Roberto Pippi 1,\*, Matteo Vandoni 2, Matteo Tortorella 3, Vittorio Bini <sup>4</sup> and Carmine Giuseppe Fanelli <sup>1</sup>**


**Abstract:** The study of the effects of a water-based exercise program in overweight/obese people with or without type 2 diabetes is a topic of relatively recent interest. This type of exercise presents some advantages in reducing the risk of injury or trauma, and it can be a valuable therapeutic card to play for sedentary or physically inactive patients who have chronic metabolic diseases. This work aims to make a contribution showing the effects of a water-based exercise intervention, supervised by graduates in sports sciences, in a group of overweight/obese people with or without type 2 diabetes. In total, 93 adults (age 60.59 ± 10.44 years), including 72 women (age 60.19 ± 10.97 years) and 21 men (age 61.95 ± 8.48 years), were recruited to follow a water-based exercise program (2 sessions/week, for 12 weeks) at the C.U.R.I.A.Mo. Healthy Lifestyle Institute of Perugia University. Results showed an improvement in body mass index (−0.90 ± 1.56, *p* = 0.001), waist circumference (−4.32 ± 6.03, *p* < 0.001), and systolic (−7.78 ± 13.37, *p* = 0.001) and diastolic (−6.30 ± 10.91, *p* = 0.001) blood pressure. The supervised water-based intervention was useful in managing patients with metabolic diseases who often present with other health impairments, such as musculoskeletal problems or cardiovascular or rheumatic disease that could contraindicate gym-based exercise.

**Keywords:** exercise; overweight/obesity; type 2 diabetes; water-based exercise

#### **1. Introduction**

Type 2 diabetes (DM2) is an ever-increasing, noncommunicable disease usually associated with overweight, obesity, and a sedentary lifestyle. The beneficial effects of physical activity and exercise in people with overweight/obesity with or without DM2 are well documented by solid evidence [1–4]. Guidelines from the World Health Organization (WHO) [5], the American College of Sports Medicine (ACSM) [6], and the American Diabetes Association (ADA) [7], as well as Italian national standards of care [8], state that adult people should be engaged in at least 150–300 min per week of moderate aerobic physical activity or at least 75–150 min of vigorous aerobic physical activity, or an equivalent combination, in addition to muscle-strengthening activities on two or more days per week [5]) to obtain substantial health benefits. Despite this, many people are sedentary or physically inactive (people who do not engage in at least 150 min per week of MVPA are defined as inactive by WHO), with increased health risks [9]. Physical activity level and sedentary behaviors affect people's health status [10], especially people with obesity and DM2 [11], who frequently are also affected by one or more osteoarticular or micro- or macroangiopathic complications.

In water-based exercise, the effect of body weight is limited due to the partial weight support the water provides. This in turn reduces weight-bearing stress on skeletal joints [12].

**Citation:** Pippi, R.; Vandoni, M.; Tortorella, M.; Bini, V.; Fanelli, C.G. Supervised Exercise in Water: Is It a Viable Alternative in Overweight/ Obese People with or without Type 2 Diabetes? A Pilot Study. *Nutrients* **2022**, *14*, 4963. https://doi.org/ 10.3390/nu14234963

Academic Editor: Hirofumi Tanaka

Received: 10 November 2022 Accepted: 20 November 2022 Published: 23 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

As a result, people with overweight/obesity can more easily engage in water-based exercise rather than ground-based exercise, which can cause more physical discomfort due to the effect of gravity [13]. By reducing the risk of injury or trauma, aquatic exercise can be a valuable therapeutic card to play in place of total weight-bearing exercises or aerobic activity, such as walking on land [14], which patients are less likely to enjoy and, in some cases, are associated with greater risk of injury or long-term disability. According to Bonifazi (2005), water-based exercise is a "type of activity that includes performing exercises to improve muscle tone and joint mobility" [15].

Although some authors have highlighted how physical activities practiced in water are useful in managing patients with health impairments such as musculoskeletal damage, cardiovascular diseases, and rheumatic and metabolic diseases [16–21], the effect of a waterbased exercise intervention on health has not been comprehensively studied in people with overweight or obesity with or without DM2. In particular, the efficacy in weight reduction and metabolic improvements is not clear/well documented.

For these reasons, this work aims to show the effects of a water-based supervised exercise intervention on weight management and metabolic panel in overweight/obese people with or without DM2.

#### **2. Materials and Methods**

#### *2.1. Participants*

Among 1464 patients enrolled between January 2010 and February 2014 in the C.U.R.I.- A.Mo. trial a total sample (Figure 1) of 93 adults (mean age of 60.59 ± 10.44 years), including 72 women (age 60.19 ± 10.97 years) and 21 men (age 61.95 ± 8.48 years), was recruited to follow a multidisciplinary intervention at the C.U.R.I.A.Mo. Healthy Lifestyle Institute of Perugia University. The intervention included educational classes with endocrinologists and nutritionists as well as a water-based exercise program (2 sessions/week, for 12 weeks). Particularly at the beginning, the exercise program entailed water-based drills which were aimed to improve both patients' relationship with water and their swimming technique [22].

Inclusion criteria were: age between 37 and 78 years; BMI ≥ 25 kg/m2; the presence of musculoskeletal disorders or other health conditions that could contraindicate gym exercise or Nordic walking activities. The exclusion criteria were: the presence of clinical conditions that could seriously reduce life expectancy or ability to participate in the study; no written informed consent to study participation. According to these criteria, this study involved 90 adults (mean age of 60.86 ± 9.50 years), including 69 women (age 60.52 ± 9.82 years) and 21 men (age 61.95 ± 8.48 years). Furthermore, a selection was performed to include only participants (*n* = 38 overweight/obese people, of which *n* = 20 with diabetes and 18 without diabetes) with baseline (T0) and follow-up (T1) data on every measured variable. Finally, we studied a small group of participants (*n* = 23 overweight/obese people, of which *n* = 13 with diabetes and 10 without diabetes) who presented all data for anthropometric and clinical variables at T0, T1, and T2.

#### *2.2. Study Design*

This is anuncontrolled pilot study involving a subsample of subjects who participated in the C.U.R.I.A.Mo. (Centro Universitario di Ricerca Interdipartimentale Attività Motoria) trial [22]. The C.U.R.I.A.Mo. trial has been registered in the Australian New Zealand Clinical Trials Registry (a Primary Registry in the WHO registry network) with the number ACTRN12611000255987. Anthropometric and clinical as well as other haematobiochemical variables were assessed at the beginning (T0), and after three months (T1) of the waterbased exercise program using a quasiexperimental study design. For a small group of participants (*n* = 23, those patients who have adhered to all the follow-up visits foreseen by the C.U.R.I.A.Mo. program), data after one year (T2) from the end of the intensive period of activity are also available.

#### *2.3. The C.U.R.I.A.Mo. Clinical Model*

All the participants have been assessed through the C.U.R.I.A.Mo. evaluation model composed of some clinical steps: (1) a clinical examination, conducted by an endocrinologist, to exclude the existence of any clinical conditions that could contraindicate the exercise and to prescribe blood sample tests according to national standards of care [8]; (2) a brief motivational interview, conducted by a psychologist, to assess participants psychological status; (3) a nutritional assessment conducted to assess the nutritional habits of the participants; (4) a water-based exercise intervention consisting of three-month repeated cycles of water exercises, as specified below.

#### *2.4. The Exercise Intervention*

The exercise program was a water-based exercise intervention in a 25 m swimming pool lane. The protocol consisted of a 12-week water-based exercise program of two sessions per week lasting fifty (50) minutes each.

Each session was composed of an initial warm-up of twenty (20) minutes, in which each patient performed light water-based movements and some breathing exercises. The warm-up procedures were adapted to each participant's skill level and involved swimming unaided or with aids (noodles, floating belts). Each participant was asked to start slowly and increase exercise intensity subjectively to a moderate intensity during which, ideally, they could still talk to each other.

The second part of the session was resistance-oriented and based on upper and lower limb exercises lasting fifteen (15) minutes. During this section, upper and lower limbs were exercised through water-resistance movements. Those participants that showed improvement through the weeks had extra resistance added by introducing floating dumbbells for the upper limbs and noodles for the lower limbs.

After the resistance section, a cool-down of ten (10) minutes and some light stretching for five (5) minutes were provided. During the cool-down, each participant was required to execute some light and continuous water exercise at a lower intensity than the warm-up. Stretching was executed in the water and involved mainly upper and lower limbs.

After the first follow-up medical visit, we offered the patients the opportunity to repeat the three-month cycle of water-based exercise.

#### *2.5. Clinical Assessments*

Before (T0) starting, at the end of the three-month exercise program (T1) and after one year (T2), all the participants underwent a clinical assessment of anthropometric and clinical variables, as follows:

#### 2.5.1. Anthropometric Variables

Anthropometric measurements of weight, body mass index (BMI), and waist circumference (WC) were collected. Weight was assessed using a Tanita body composition analyzer BC-420MA (Tokyo, Japan), and BMI was calculated as weight (kg)/height (m)2. According to the World Health Organization and the International Diabetes Federation (IDF) tips [23], WC was measured in the horizontal plane midway between the lowest ribs and the iliac crest, using the ergonomic circumference measuring tape Seca® 201 model.

#### 2.5.2. Clinical Variables

Systolic (SBP) and diastolic (DBP) blood pressure and blood sample variables, such as fasting blood glucose, glycated hemoglobin (HbA1c), total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides, and uric acid were also collected.

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) values were measured three times during the first medical visit with one minute between consecutive measurements 5 min of sitting at rest using an upper arm blood pressure monitor (UM-101

mercury-free sphygmomanometer, A&D Medical, Naples, Italy) on the right upper arm using a size-appropriate cuff.

Biochemical blood test variables were collected from tests performed by patients in routine clinical practice.

#### *2.6. Statistical Analysis*

Descriptive analyses in terms of means, standard deviations, or percentages were computed for each investigated variable at baseline (T0). Participants' data were then split into two subgroups according to the pathological condition diagnosed by the endocrinologist (overweight/obesity with or without DM2), and an unpaired t-test was conducted to compare all variables at baseline (please see Table S1). To evaluate the effects of the water-based exercise program, parameters at baseline and after three months of exercise intervention were compared through a t-test for paired samples (Table 1). Delta (Δ) changes (T1-T0) are presented as means ± standard deviations. Moreover, to evaluate the effects of water-based exercise programs over a longer period (1 year), parameters at baseline, after 3 (T1), and after 12 months (T2) were compared through a linear mixed model repeated measures analysis of variance in a small group of participants (*n* = 23) who presented all data for anthropometric and clinical variables collected both before and after the intervention. Means ± standard errors for all data and delta changes (using a method of multiple testing correction with Bonferroni adjustment) were presented (Table 2). *p*-value < 0.05 was indicated as statistically significant. Finally, to study the delta changes trend of declining, we calculated the B coefficient with SE for all the variables using linear regression with time as the independent variable. All analyses were performed with IBM SPSS® version 25.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY, USA: IBM Corp.).

**Table 1.** Anthropometric, blood pressure, and blood chemistry parameters values at baseline (T0) in the entire sample (*n* = 38), overweight/obese with diabetes (*n* = 20), and overweight/obese without diabetes (*n* = 18) subjects. Results are presented as mean ± standard deviation. Statistical significance was set for *p* values ≤ 0.05.


Legend: BMI = body mass index; WC = waist circumference; SBP = systolic blood pressure; DBP = diastolic blood pressure; HbA1c = glycosylated haemoglobin; Hdl = high-density lipoprotein; LDL = low-density lipoprotein.


**Table 2.** Anthropometric, blood pressure, and blood chemistry parameters values at baseline (T0) and after 3 months (T1) and 12 months (T2) post-intervention. Results are presented as mean ± standard errors (SE) in *n* = 23 subjects. Statistical significance was set for *p* values ≤ 0.05.

Legend: BMI = body mass index; WC = waist circumference; SBP = systolic blood pressure; DBP = diastolic blood pressure; HbA1c = glycosylated haemoglobin; Hdl = high-density lipoprotein; LDL = low-density lipoprotein.

#### **3. Results**

#### *3.1. Baseline Results*

Anthropometric and clinical variables values did not show statistically significant differences between overweight/obesity with or without DM2 at baseline (please see Table S1), except for fasting blood glucose (*p* = 0.003), HbA1c (*p* = 0.004), and LDL cholesterol (*p* = 0.005).

#### *3.2. Follow-Up Results*

At the first follow up (T1), statistically significant improvements in weight (*p* = 0.013) and BMI (*p* = 0.001) and reductions in WC (*p* < 0.001), SBP (*p* = 0.001), and DBP (*p* = 0.001) were observed in the entire sample (Table 1). Clinical effects, although not statistically significant, were observed in the differential values of blood sample variables such as fasting blood glucose (−1.49 mg/dL), HbA1c (−0.28%), total cholesterol (−8.00 mg/dL), HDL (+1 mg/dL), LDL cholesterol (−8.55 mg/dL), triglycerides (−8.62 mg/dL), and uric acid (−0.10 mg/dL). In the overweight/obese with diabetes, data showed statistically significant variations in weight (*p* = 0.037) and BMI (*p* = 0.008) and reductions in WC (*p* < 0.001), SBP (*p* = 0.028), and DBP (*p* = 0.002), while in patients without diabetes, we observed reductions in SBP (*p* = 0.016) and fasting blood glucose (*p* = 0.039).

Data analyzed from all patients with an available one-year (T2) follow-up evaluation (*n* = 23, 1 male and 22 female, mean age 58.78 ± 9.17 years) confirmed a statistically significant change (Table 2) in the following outcomes: weight (*p* = 0.023), BMI (*p* = 0.020), WC (*p* < 0.001), SBP (*p* = 0.013), and DBP (*p* = 0.019). Furthermore, the time factor effect was observed in total cholesterol (*p* = 0.013) and LDL (*p* = 0.024).

#### **4. Discussion**

This study aimed to show the effects of a water-based exercise program of 24 biweekly sessions on some anthropometric (i.e., weight, BMI, and WC) and clinical (i.e., SBP and DBP, lipids, and glycemic variables) outcomes in a group of overweight/obese adults with and without DM2. Preliminary results showed an improvement in BMI, WC, SBP, and DBP in all the participants that exercised for 12 weeks.

Previously, contrasting results were obtained in similar water-based exercise programs. A Tanaka's review article [24] reported that few studied have addressed the effects of swimming exercise on bodyweight and them stated no changes in weight [25,26]. Recent studies conducted by Rezaeipour et al. [27] and Cugusi et al. [2] observed a significant reduction in weight and BMI, as we observed in this study. Conversely, Rezaeipour, M. (2020) [28] reported that pool workouts are ineffective in terms of weight loss and changes in body composition, as also stated by Gubiani et al. [29]. In an epidemiological study, other authors [30] observed that regular physical activity (including swimming exercise) is associated with an attenuation of weight regain over a middle–long period (10 years). Interestingly, our study results showed that weight reduction was observed not only after a short period of a water-based exercise program but also after 1 year (respectively, −3.12 kg and −6.64 kg).

Previous studies showed contrasting metabolic effects of water-based exercise training. Some authors evaluated anthropometric and cardiometabolic parameters in women [31,32] and adult men with obesity and DM2 [2]. While the first observed no significant improvements in body weight and body composition, Cugusi et al. showed a significant improvement in the cardiovascular system and metabolic profile (body weight, WC, BMI, and blood pressure improvements). Some authors [33–35] have studied the impact of regular exercise on lipid and lipoprotein plasma levels, especially HDL, as the main tool for reducing the risk of cardiovascular diseases. Other authors [36] have specifically studied the effects of water exercise on blood lipid outcomes. Rezaeipour et al. [33] found that there was not a significant alteration of blood lipid parameters, and we did not observe a statistically significant improvement in total cholesterol, HDL, LDL cholesterol, and triglycerides (*p* > 0.005), after the intensive phase of the exercise program (12 weeks). These results could probably be due to the short period of observation and nutritional habits. In fact, at T2 (Table 2) data seems to show encouraging results regarding lipid control.

Water-based exercise effects on blood pressure were assessed in previous studies. A recent meta-analysis [37] reported that water-based exercise had positive effects on BP values. Particularly, SBP decrease was estimated equal to −8.4 mmHg, while DBP decrease was estimated equal to −3.3 mmHg. After three months of water-based exercise in our sample, we found a lower improvement in SBP (−7.78 mmHg), but a higher improvement of DBP (−6.30 mmHg). Although Delevatti et al. [38] concluded that "aerobic training in an aquatic environment provides effects similar to aerobic training in a dry-land environment in patients with type 2 diabetes", the result of the present study seems to be promising compared to previous studies conducted in small samples of people with overweight/obesity who exercised for three months in a gym-based program [39].

Certainly, this study has some limitations. First, we did not include a control group that could have better clarified the extent of the results; the original C.U.R.I.A.Mo. intervention model did not foresee it. Second, we enrolled only a small group of participants. This was due mainly to technical and methodological reasons. It was possible to provide access to the pool for only a small number of patients for each exercise group. Both for the limited space available and for the need to guarantee personalized supervision of the activity, we decided to set up groups no larger than five participants each. Additionally, a water exercise-based program encountered various critical issues in the adherence of patients, who have reported difficulties related to the time necessary to carry out activities in the water (i.e., long preparation times due for example to the fact of having to dry the hair, etc.). Furthermore, many key measures (i.e., waist circumference, blood pressure) were derived using manual methods subject to investigator bias, even if measurements at T0 and T1 were always conducted by the same operator to minimize operator variability and all operators were specifically trained in accordance with standard measurement procedures. Additionally, in this study, we did not show data referring to nutritional habits and pharmacological treatment. In future studies, an analysis of eating habits should

be carried out together with insights into the drugs taken, given that these components are relevant in this type of patient with metabolic diseases. Our results showed that in a relatively short period of educational training it is possible to obtain positive effects which have a significant impact on people's health. Reductions in anthropometric (such as BMI and WC) and clinical (such as blood pressure and cholesterol) variables values are linked to obesity and cardiovascular risks [40]. These aspects are particularly important for people with health conditions for whom weight-bearing exercise is contraindicated or for the elderly [41,42].

#### **5. Conclusions**

The supervised water-based intervention was effective in improving weight, BMI, waist circumference, and blood pressure. Although it showed lower results on short-term glucometabolic control, this type of exercise practiced in water was useful in managing patients with health problems—i.e., musculoskeletal problems, cardiovascular disease, rheumatic and metabolic diseases—that could contraindicate gym-based exercises. Longer periods of intervention and observation, which may involve larger numbers of participants, are desirable for future research.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/nu14234963/s1, Table S1: Anthropometric, blood pressure, and blood chemistry parameters values at baseline (T0) in all the sample (*n* = 38), over-weight/obese with diabetes (*n* = 20) and over-weight/obese without diabetes (*n* = 18) subjects.

**Author Contributions:** Conceptualization, R.P., M.V. and M.T.; data curation, R.P., V.B., M.V. and M.T.; formal analysis, R.P. and V.B.; funding acquisition: C.G.F.; investigation, R.P., M.T. and C.G.F.; methodology, R.P., M.V. and C.G.F.; project administration, R.P. and C.G.F.; software, R.P., M.T. and V.B.; supervision, M.V. and C.G.F.; writing—original draft, R.P., M.V. and M.T.; writing—review and editing, R.P., M.V., M.T., V.B. and C.G.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** C.U.R.I.A.Mo. project has been registered in the Australian New Zealand Clinical Trials Registry (a Primary Registry in the WHO registry network) with the number ACTRN12611000255987. Moreover, the study was conducted in compliance with the guidelines in the Declaration of Helsinki.

**Informed Consent Statement:** Written informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The datasets used during the current study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Physical Activity, Dietary Behavior, and Body Weight Changes during the COVID-19 Nationwide Level 3 Alert in Taiwan: Results of a Taiwanese Online Survey**

**Hao-Ting Ke 1, Chi-Lin Hsieh <sup>2</sup> and Wei-Jen Cheng 1,3,4,\***


**Abstract:** This cross-sectional study aimed to explore the influence of the COVID-19 pandemic on physical activity (PA) and dietary habits, and their impact on body weight changes during the Level 3 alert period that resulted in the lockdown in Taiwan. The study was conducted between 1 July 2021 and 15 July 2021, using a Google Forms online survey platform. Personal data, anthropometric information, PA information, and dietary habit information were collected before and during the alert period. Exactly 374 respondents, aged between 20 and 66, were included in the study. The results indicate that the lockdown during the alert period negatively impacted all levels of PA, including vigorous and moderate activities and walking. Additionally, respondents showed a sedentary lifestyle, with an increased daily sitting time of 22%. However, body weight and dietary behavior were not significantly affected, and some dietary questions achieved significant differences, including eating three meals less regularly, among others. During the pandemic, exercise was still one of the most important ways to maintain health; therefore, we hope to bring more attention to the prevention of sedentary lifestyles and dietary abnormalities in Taiwan during a pandemic.

**Keywords:** COVID-19; physical activity; eating habits; lifestyle; bodyweight

### **1. Introduction**

Coronavirus disease 2019, or COVID-19, is a novel coronavirus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1,2]. Since the first case reported in Wuhan, China, in December 2019, COVID-19 has affected more than 60 countries worldwide, with over 6000 cases and 106 deaths, only two months after the outbreak [3]. Despite its proximity to and its dense economic activities with China, Taiwan took rapid responses and quickly instituted specific approaches for a containment strategy and resource allocation to control the outbreak [4–6]. Thus, there were only 443 cases and seven deaths as of 7 June 2020. However, on 19 May 2021, due to the surge in the number of indigenous cases and the increasing number of deaths, Taiwan's Central Epidemic Command Center (CECC) raised the COVID-19 alert to Level 3 nationwide. The control measures of Level 3 alerts include mandatory wearing of masks in public; closing entertainment venues; banning physical activities in open spaces, gyms, and sports centers; and food vendors offering only takeout services [7]. While these restrictions helped to reduce the rate of infection, such limitations have led to numerous behavioral changes, such as social distancing and prolonged homestay, which caused negative effects by reducing physical activity (PA) and restricting access to many forms of exercise (e.g., closed civil sports centers and gyms, restrictions on walking distance, and insufficient space for exercise at home) [8–10]. Exercise is one of the most important risk factors for major disease morbidity. Multiple studies in humans and animals have demonstrated that regular exercise is positively linked to immune

**Citation:** Ke, H.-T.; Hsieh, C.-L.; Cheng, W.-J. Physical Activity, Dietary Behavior, and Body Weight Changes during the COVID-19 Nationwide Level 3 Alert in Taiwan: Results of a Taiwanese Online Survey. *Nutrients* **2022**, *14*, 4941. https:// doi.org/10.3390/nu14224941

Academic Editors: Roberto Pippi, Carmine G. Fanelli and Matteo Vandoni

Received: 20 October 2022 Accepted: 18 November 2022 Published: 21 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

function [11]. Additionally, the benefits of exercise may reduce the COVID-19 fatality rate due to its positive effect on COVID-19-related comorbidities (e.g., cardiovascular disease, hypertension, type-2 diabetes, and obesity) [12].

In addition to limiting access to PA, banning indoor dining and limiting the number of people in supermarkets and grocery stores may change eating habits because it is less easy to obtain food than usual. Additionally, limited access to fresh food can have a negative effect on overall health both physically and mentally [13]. Combined with a structural barrier to maintaining a physically active lifestyle during the lockdown period, impaired nutritional habits could produce a state of positive energy balance, in which caloric intake exceeds energy expenditure, which can cause weight gain due to increased body fat [10]. Therefore, this study aimed to investigate the effect of the COVID-19 pandemic on physical activity and dietary behavior, and to further understand the relationship between physical activity, dietary behavior, and changes in body weight during the COVID-19 nationwide Level 3 alert period in Taiwan.

#### **2. Materials and Methods**

#### *2.1. Study Design*

This cross-sectional study investigated the changes in physical activity and dietary habits, and their impact on body weight during the COVID-19 pandemic in Taiwan.

#### *2.2. Participants*

Participants were recruited through social media platforms, including FacebookTM (Meta Platforms Inc., Menlo Park, CA, USA), WhatsAppTM (Meta Platforms Inc.), InstagramTM (Meta Platforms Inc.), and LineTM Corporation, Tokyo, Japan). We included people older than 20 years who were living in Taiwan during the nationwide Level 3 COVID-19 alert period. All participants were fully informed about the study requirements, and that the results could be used for research purposes. Each participant could answer only once. They could withdraw their participation in the survey at any point before submission, and incomplete responses could not be saved. To maintain and protect the confidentiality of participants, their personal information and data were anonymized.

#### *2.3. Survey Development and Privacy*

The questionnaire was administered during the nationwide Level 3 COVID-19 alert period in Taiwan, using the Google Forms® online survey platform. A link to the survey was shared on social media (FacebookTM, WhatsAppTM, InstagramTM, and LineTM). Participants completed the questionnaire directly connected to the Google platform, and the final database was downloaded as a Microsoft Excel spreadsheet.

This study was approved by the Institutional Review Board (IRB) of the Chang Gung Medical Foundation. As participation was voluntary and survey responses were anonymous, the IRB waived the need for informed consent.

#### *2.4. Survey Questionnaires*

The questionnaire included three sections with 46 questions: (1) personal data (including gender, age, level of education, marital status, and employment status) and anthropometric information (including height and weight); (2) physical activity information, using the Taiwanese version of the International Physical Activity Questionnaire Short Form (IPAQ-SF) [14]; and (3) dietary habit information, using the Diet Behavior Questionnaire from Health Promotion Administration, Ministry of Health and Welfare, Taiwan (HPA-MOHW) [15]. This was a self-report survey that asked participants to recall information regarding body weight, physical activity, and dietary habits before the Level 3 alert was launched (19 May 2021) and over the past 7 days. No actual measurements were taken. The entire questionnaire was completely anonymous and the respondents provided replies for both timeframes in one survey.

#### 2.4.1. Sociodemographic Characteristics

Information on age, sex, level of education, marital status, employment status, height, and weight before and during the Level 3 alert was collected.

#### 2.4.2. Physical Activity Levels

Changes in physical activity levels due to Level 3 alerts were identified using the Taiwanese version of the IPAQ-SF. The CVI of the IPAQ-Taiwan version was higher than 0.9, indicating good content validity. The retest reliability was 0.67 and the concurrent validity was 0.63, which was similar to other language versions, and it can be used as a tool to compare the physical activity levels of people around the world [16]. According to the official IPAQ-SF guidelines, data from the IPAQ-SF were summed within each category, including vigorous activity (V), moderate activity (M), and walking (W), to estimate the total amount of time engaged in PA per week [17,18]. Moderate activities are defined as walking with more than five kilograms of weight on one's back (carrying children or heavy items), more laborious household tasks, tai chi, etc. Vigorous activities refer to weight training, hiking, long-distance running, playing ball, aerobic dance or riding a spinning bike, etc. The questionnaire referred to the PA of the last 7 days, and each activity had to take at least 10 min to be included. The respondents' total weekly PA was estimated in metabolic equivalents of tasks performed in minutes per week (MET-min/week). This was calculated by multiplying three parameters: the MET value of an activity, the duration of the activity (in minutes), and the number of times the activity was carried out (V = 8 METs, M = 4 METs, W = 3.3 METs). We then added the MET minutes achieved in each category to obtain the total MET minutes of PA per week. The time spent sitting was also recorded in the questionnaire in order to assess sedentary behavior.

#### 2.4.3. Dietary Habits Information

The impact of Level 3 alerts on dietary habits was assessed using the Diet Behavior Questionnaire from the HPA-MOHW. This is an online survey sourced from "Overcoming Obesity: A Weight Loss Guide" published by the National Health Service, Ministry of Health and Welfare in 1997 [15]. The questionnaire had 12 questions, namely "Q1. Eating three meals regularly", "Q2. No snacks or sweets", "Q3. Eat slowly and chew carefully", "Q4. No sugary drinks", "Q5. Avoid high-fat foods", "Q6. Eat fruits every day", "Q7. Eat green vegetables every day", "Q8. Eat late-night snacks", "Q9. Eat while watching TV or reading books", "Q10. Emotional eating behavior", "Q11. Eat as a reward", and "Q12. Eat only when hungry".

The response choices and the designated scores for the first seven questions were as follows: "Always" = 3; "Most of the time" = 2; "Sometimes" = 1; "Never" = 0. The response choices and the designated scores for the eighth to twelfth questions were as follows: "Always" = 0; "Most of the time" = 1; "Sometimes" = 2; "Never" = 3. The total score of this questionnaire corresponded to the sum of the scores in the 12 questions ranging from 0 to 36, where "0~12" represents very unhealthy dietary behavior, "13~20" represents fine dietary behavior, "21~30" represents good dietary behavior, and "31~36" represents very healthy dietary behavior.

#### *2.5. Data Analysis*

Descriptive statistics were used to determine the demographic and personal characteristics and anthropometric parameters of the study sample. Data are represented as numbers and percentages (*n* (%)) for categorical variables or as mean and standard deviation (M ± SD) for continuous variables. To calculate the significant differences in responses before and during the Level 3 alert period, paired-sample *t*-tests were used.

To determine the magnitude of the change in the score, Cohen's ds was calculated as the effect size for the comparison between two means. This was interpreted as 0.2 to be considered a "small" effect size, 0.5 for a "medium" effect size, and 0.8 for a "large" effect size. All of the statistical analyses were performed using GraphPad Prism 9.4.1 and Microsoft Excel 2019. The significance level was set at *p* < 0.05.

#### **3. Results**

#### *3.1. General Characteristics of the Participants*

The nationwide Level 3 COVID-19 alert in Taiwan began on 19 May 2021, and lasted until 27 July 2021. The web survey was launched on 1 July 2021, and was concluded on 15 July 2021. In total, the data of 374 respondents (243 female and 131 male participants) aged between 20 and 66 years were analyzed, with four of the original 378 respondents excluded due to missing data.

Overall, 65.0% of the participants were female. The general characteristics, including education level, marital status, and employment status, are presented in Table 1.


**Table 1.** Participant characteristics.

*3.2. Anthropometrics Information before and during the Level 3 Alert Period*

Table 2 presents the changes in body weight. The results indicated no significant differences in body weight before and during the Level 3 alert period.



#### *3.3. Physical Activity Level Changes during the Level 3 Alert Period*

The responses to the physical activity questionnaire recorded before and during the Level 3 alerts are shown in Table 3. There was a significant decrease in all of the physical activity levels. The effect of the COVID-19 Level 3 alert on different gender and age groups for all of the PA MET values are shown in Tables 4 and 5. All gender and age groups, except for 60–69 years, showed significant differences.


**Table 3.** Effect of the COVID-19 Level 3 alert on the physical activity level.

**Table 4.** Effects of COVID-19 Level 3 alert on gender for all of the PA MET values.


**Table 5.** Effects of the COVID-19 Level 3 alert on different age groups for all of the PA MET values.


The number of times/week of vigorous activity decreased by 38.0% during the Level 3 alert, compared with before (*p* < 0.001, *d* = 0.409) and the number of minutes/time decreased by 48.7% (*p* < 0.001, *d* = 0.555). In addition, the MET values of vigorous activity were 47.5% lower during the Level 3 alert period compared with before (*p* < 0.001, *d* = 0.418). As for moderate activity, the number of times/week and minutes/time decreased by 17.6% (*p* < 0.001, *d* = 0.187) and 26.1% (*p* < 0.001, *d* = 0.275), respectively, compared with before. In addition, the MET values of the moderate activity were 27.0% lower during the Level 3 alerts (*p* < 0.001, *d* = 0.212). As for walking, the number of times/week and minutes/time during the Level 3 alerts, compared with before, decreased by 39.2% (*p* < 0.001, *d* = 0.713) and 37% (*p* < 0.001, *d* = 0.432), respectively. Additionally, the MET values of walking were 48.3% lower during the Level 3 alerts (*p* < 0.001, *d* = 0.476). Overall, the MET values of all PA were 44.1% lower during the Level 3 alerts (*p* < 0.001, *d* = 0.523). In terms of the effects of the COVID-19 Level 3 alert on different gender and age groups, male participants and those aged 20–29 years were impacted the most, as their PA MET values were 46.1% (*p* < 0.001, *d* = 0.606) and 48.4% (*p* < 0.001, *d* = 0.605) lower during the Level 3 alerts, respectively. Regarding the hours of sitting per day, the results increased by 22.2% during the Level 3 alert period (*p* < 0.001, *d* = -0.399).

#### *3.4. Dietary Behavior Changes during the Level 3 Alert Period*

#### 3.4.1. Total Score of Diet Behavior Questionnaire

With regard to the dietary behavior status, no significant difference was observed between the total scores of the dietary behavior results during the Level 3 alert compared to before (Table 6). The subgroups of the dietary behavior status before and during the Level 3 alert period are presented in Table 7.

**Table 6.** Participants' total scores of dietary behavior before and during the Level 3 alert period.



"0~12" represents a very unhealthy dietary behavior, "13~20" represents a fine dietary behavior, "21~30" represents a good dietary behavior, and "31~36" represents a very healthy dietary behavior.

#### 3.4.2. Score of Each Dietary Question

The recorded scores in each response to the dietary behavior questionnaire before and during the alert period are shown in Figure 1. The Q1 score (eat three meals regularly) decreased significantly during the period (*t* = 2.528, *p* = 0.0119, *d* = 0.17). The Q2 score (no snacks or sweets) increased significantly during the period (*t* = 3.054, *p* = 0.0024, *d* = −0.19), and the Q11 score (eat as reward) was significantly higher during the period (*t* = 2.192, *p* = 0.0290, *d* = −0.14). As for the remaining questions, no significant difference was found before and during the alert period.

**Figure 1.** Participants' scores in response to the related diet behavior questions. \* Significant differences between "before" and "during" the COVID-19 Level 3 alert period.

#### **4. Discussion**

Different studies and surveys that have been conducted worldwide suggest that the COVID-19 lockdown affected the population's daily lifestyle [19–23]. This cross-sectional study provides information regarding the impact of the COVID-19 lockdown on body weight, physical activity, and dietary behavior changes among Taiwanese residents during the Level 3 alert period. There were 374 replies, and the female respondents were about twice the number of males; 81.1% of the respondents were 20–49 years; 93.3% of the respondents were educated to college degree or above. According to a report on the age structure of the Taiwanese population in 2020 [24], the percentage of the population aged 20–49 years was about 52.5%. Statistics from Taiwan's Ministry of the Interior, Department of Statistics showed that 47.3% of citizens aged over 15 years had a college degree or above by the end of 2020 [25]. Those aged 25–29 years had the highest percentage (81.42%) of people with a college degree or above, and 63.90% had a college degree. Our demographic data did show some bias toward younger populations and higher education levels. A review study comparing FacebookTM recruitment of participants with traditional recruitment methods showed an over representation of females, young adults, and people with higher education levels and incomes [26], which was similar to our survey population.

The findings from the online survey confirmed that the COVID-19 Level 3 alert negatively affected all levels of PA, including vigorous and moderate activity, and walking, as was initially expected. Furthermore, we found that respondents had a sedentary lifestyle with an increased daily sitting time of 22%. While the effect size for most values was small to medium, a 39.2% reduction in the number of times per week on walking, a 48.7% reduction in the number of minutes per week on vigorous activity, and a 44.1% reduction in MET minutes per week on overall PA were medium to large. This result is consistent with

recent studies, indicating that COVID-19 home confinement could greatly impact activities in life, including participation in sports activities and PA engagement [19]. We also saw a 46.1% reduction and 48.4% reduction in MET values on overall PA in the male group and the group aged 20–29 years, respectively, showing that these groups were impacted the most. This result is consistent with a study from Bangladesh [27], which showed that the prevalence of physical inactivity during the COVID-19 pandemic was significantly higher among young adults compared with other groups. A possible explanation could be the closure of schools, which led to a lack of physical education courses, meaning that young adults were more prone to spend more time on the internet and electronic devices, leading to a decrease in all levels of PA. In terms of gender, it is traditionally believed that females tend to have more household chores compared with males. More time spent at home during the Level 3 alert could have led to more housework, which could be one possible reason why females showed less of a decrease in overall PA MET values than males. Yet, our results are inconsistent with those of previous studies conducted in Taiwan on investigating exercise behavior during the COVID-19 pandemic. In this study, the results showed that most Taiwanese maintained their exercise frequency, duration, or intensity during the COVID-19 pandemic, with only 20% of individuals decreasing their exercise frequency [28]. However, it is worth noting that the study was conducted from 7 April to 13 May 2020, and the COVID-19 Level 3 alert in Taiwan was not launched at that time. Although Taiwan did not have a large-scale lockdown compared with other parts of the world, Level 3 alert restrictions, such as shutting most business and public places, banning group gatherings and physical activities in open spaces, gyms, and sports centers, may still have created a barrier to maintaining exercise behavior among Taiwanese individuals [7]. While public life restrictions associated with the pandemic were usually of a limited duration, some studies have shown that even a short-term increase in physical inactivity and sedentary behavior can have significant adverse effects [29]. For example, 14 days of step reduction can lead to insulin resistance as well as increased central and liver fat and dyslipidemia, while reducing cardiorespiratory fitness [30]. In another study, 5 days of bed rest was seen to lead to a significant decrease in endothelial function, increased arterial stiffness, and elevated diastolic blood pressure [31]. Although most of these changes are reversible by a resumption of habitual physical activity in younger individuals, this is less true for the elderly and for individuals with metabolic disease [32]. Therefore, identifying ways to maintain or even enhance PA during the confinement periods seems to be of the utmost importance.

Before the COVID-19 pandemic, insufficient physical activity had already been recognized as a public health problem globally. Out of 168 countries, 27.5% of adults do not achieve the levels of physical activity necessary for good health [33]. Home confinement created a structural barrier to maintaining a physically active lifestyle; with a drastic drop in PA and exercise levels, while dietary behaviors remain unchanged or fail to counteract this inactivity lifestyle, leading to a positive energy balance that will gradually cause weight gain [10,34]. However, no significant changes in body weight were observed in our study, inconsistent with a previous study showing an increase in self-reported body weight among obese patients. Even when adjusted for physical activity, age, and gender, the habit of taking snacks remained a risk factor [35]. Additionally, another study showed weight gain between 0.5 and 1.8 kg (±2.8 kg) after just 2 months of quarantine, and risk factors included increased eating in response to sight and smell, emotional eating, and increased snacking frequency [36,37]. Contrary to our expectations, there was no significant difference between the total scores of dietary behavior during and before the COVID-19 Level 3 alert period in our study. However, some dietary questions achieved a significant difference, including eating three meals less regularly, eating fewer snacks or sweets, and being less likely to eat as a reward. The negative changes in regular eating can be attributed to remote work from home and distance learning. When work and study activities were shifted to home, the lack of a clear routine of time and space caused a free work schedule, thus leading to irregular meals. Some good eating habits, including eating fewer snacks

or sweets and being less likely to eat as a reward, were significantly increased during this period. A possible explanation for this could be the differences in living habits between Taiwan and Western countries. Owing to the popularity of the Internet, consumer electronics (cell phones, TVs, PCs, play devices, etc.), developed delivery services, and online shopping in Taiwan, it is easy to purchase food and daily necessities, thus reducing anxiety and stress from the shortage of supplies. In addition, Taiwanese people tend to believe that one should eat healthier before and after receiving COVID vaccines. A healthy diet prevents hyper-inflammation and might help to protect against inflammation from virus infections [38]. The concept of a health regimen based on traditional Chinese medicine is deeply rooted in the minds of the Taiwanese people, which could be a possible explanation for why they consumed fewer snacks during the COVID pandemic.

Our study had several limitations. First, the sample size was small and mostly comprised of females, aged 20–59 years, and individuals with a higher education levels, which may not be representative of the population. Second, the data were collected through a webbased survey, which might have limited the participation of certain groups (e.g., groups that do not have Internet access or who do not use the Internet often). However, the Internet use rate among Taiwanese people aged 12 years and above reached 83.0% in 2020 [39], suggesting high Internet usage by the Taiwanese. Moreover, the Internet use rate was over 90% among those aged below 55 years, 86.2% among those aged 55–59 years, and 72.8% among those aged 60–64 years, but only 42.7% among those aged 65 years and above, which could explain our respondents' age distribution. Third, because of the cross-sectional design of the study and the use of a self-report questionnaire, the existence of recall bias cannot be ruled out, which may have resulted in an over or underestimation of the results. However, the IPAQ is an international tool for the assessment of PA and time spent sitting, and is widely used because of its simplicity and inexpensiveness, and its validity and reliability have been proven in numerous studies [40–42]. Fourth, we asked participants to recall their body weight, physical activity, and dietary habits before the Level 3 alert was launched (19 May 2021); however, we did not clarify the length of the recall period, meaning that it could be any amount of time before the alert. Additionally, the body weight was self-reported, so the data might not be accurate. However, it is very common for patients in Taiwan to measure their blood pressure as well as height and weight before visiting a clinic or hospital, and it is also common to have a weight scale at home. Therefore, it should not be too difficult to measure one's body weight at home.

#### **5. Conclusions**

The results of this cross-sectional study indicate that the lockdown period during the COVID-19 Level 3 alert had a negative impact on all PA levels of Taiwanese adults, with a significant increase in sitting time, leading to a more sedentary lifestyle. However, body weight and dietary behavior were not significantly affected, probably because the changes in PA level could not be reflected in weight changes over such a short period. Furthermore, the body composition was not measured; therefore, we could not determine the relative variation in total fat and skeletal muscle mass proportion. In addition, eating three meals irregularly could be attributed to remote work from home and distance learning during the Level 3 alert period, which is a bad eating habit that can easily lead to obesity. Nevertheless, some good eating habits, including eating fewer snacks or sweets and being less likely to eat as a reward, were significantly increased during this period. This may also explain the lack of significant changes in body weight during the lockdown period.

During the pandemic, exercise is still one of the most important ways to effectively maintain health [2]. Government health organizations should be aware that reducing the possibility of performing PA during Level 3 alerts might have negative consequences on the health status of the population. According to the American College of Sports Medicine, 150–300 min of moderate-intensity aerobic exercise and two sessions of resistance exercise per week are recommended to maintain a healthy body [43]. Although adverse effects on short-term physical inactivity are reversible by habitual PA in younger individuals, this is

less true for the elderly. Therefore, it is important to identify ways to enhance PA during confinement periods.

**Author Contributions:** Conceptualization, H.-T.K.; methodology, H.-T.K. and C.-L.H.; validation, H.-T.K., C.-L.H. and W.-J.C.; investigation, H.-T.K. and C.-L.H.; data curation, H.-T.K.; writing—original draft preparation, H.-T.K.; writing—review and editing, H.-T.K., C.-L.H. and W.-J.C.; visualization, H.-T.K.; supervision, H.-T.K., C.-L.H. and W.-J.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by grants from the Chang Gung Memorial Hospital (CORPG5L0031, CORPG5K0031, CMRPG5K0151, and CMRPG5K0152), Taiwan. The funding agencies played no role in this research.

**Institutional Review Board Statement:** This study was approved by the Institutional Review Board (IRB) of the Chang Gung Medical Foundation (IRB No. 202201282B0).

**Informed Consent Statement:** As participation was voluntary and survey responses were anonymous, the IRB waived the need for informed consent.

**Data Availability Statement:** All of the experimental data are included in the figures and manuscript. The data presented in this study are available upon request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Acute Responses to Resistance Training on Body Composition, Muscular Fitness and Flexibility by Sex and Age in Healthy War Veterans Aged 50–80 Years**

**Mario Kasovi´c 1,2, Lovro Štefan 1,2,3,\* and Zvonimir Kalˇcik <sup>4</sup>**


**Abstract:** Background: Although evidence suggests that resistance training should be prescribed as a method to enhance or maintain physical fitness, these findings are mostly based on research on younger men. Studies investigating responses by sex and age to resistance training, especially in war veterans aged ≥50 years, are lacking. Therefore, the main purpose of this study was to examine whether a 4-week resistance training program would have similar effects on body composition, muscular fitness, and flexibility in men and women aged 50–80 years. Methods: Seven-hundred and sixty-four participants were recruited and categorized into two groups each of men and women aged 50–64 and 65–80 years. The training intervention lasted 4 weeks and consisted of three 60 min sessions per week. All participants were tested for each of the following physical fitness components: body composition, push-ups in 30 s, chair-stands in 30 s, sit-ups in 30 s, and a sit-and-reach test. Results: Over the intervention period of 4 weeks, body weight (*p* = 0.002) and the percent of fat mass (*p* < 0.001) decreased, while the percent of lean mass (*p* < 0.001) in push-ups in 30 s (*p* < 0.001), chair-stands in 30 s (*p* < 0.001), sit-ups in 30 s (*p* < 0.001), and sit-and-reach (*p* < 0.001) increased. Significant time\*age interactions were shown for push-ups in 30 s (*F1,763* = 4.348, *p* = 0.038) and chair-stands in 30 s (*F1,763* = 9.552, *p* = 0.002), where men and women aged 50–64 years exhibited larger time-induced changes compared to their older (65–80 yr) counterparts. Effect sizes were similar between sex- and age-specific groups. Conclusions: The 4-week resistance training produced similar pronounced positive effects on body composition, muscular fitness, and flexibility, while men and women aged 50–64 years displayed significantly larger improvements in upper and lower muscular fitness compared with their 65–80-year-old counterparts.

**Keywords:** aging; performance; sex differences; age differences; strength; intervention-induced changes

#### **1. Background**

The prevalence of middle-aged and older adults is expanding worldwide [1]. The process of aging predominantly leads to physiological decline, often accompanied by reductions in lean mass [2,3], power [4], and strength [5]. These declines have been associated with negative health-related outcomes, such as an increased risk of falls [6], hip fractures [7], functional limitations [8], and all-cause mortality [9,10].

Due to structural and physiological losses during aging, previous studies examined efficient methods to increase or even postpone such changes [11]. Studies have documented that resistance training serves as a safe intervention that can increase the level of physical fitness from young to old in both sexes [12–18]. Furthermore, several studies have

**Citation:** Kasovi´c, M.; Štefan, L.; Kalˇcik, Z. Acute Responses to Resistance Training on Body Composition, Muscular Fitness and Flexibility by Sex and Age in Healthy War Veterans Aged 50–80 Years. *Nutrients* **2022**, *14*, 3436. https:// doi.org/10.3390/nu14163436

Academic Editors: Roberto Pippi, Carmine G. Fanelli and Matteo Vandoni

Received: 17 July 2022 Accepted: 18 August 2022 Published: 21 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

shown that resistance training has multiple health-related benefits for overall health [19,20]. However, whether there are sex- and age-specific adaptations to the same training is still unknown [13,18,21].

When considering sex, studies have found that men can increase absolute strength more than women [22,23], although increases in relative strength and hypertension have been similar between the sexes [24,25]. Others have found that women have a greater relative strength increase than men [26–28]. The most recent systematic review and metaanalysis showed that men and women adapt to resistance training with similar effect sizes for hypertrophy and lower-body strength, yet women have a larger effect for relative upper-body strength, due to differences in the skeletal muscle level between the sexes [18]. However, well-designed studies with a larger sample size are relatively scare, and sexrelated responses to resistance training need to be interpreted with caution.

Age-specific responses to resistance training have been the topic of a few previous studies [12,13,26,29]. Ivey et al. [26] found that young women experience a significantly greater muscle-quality response to resistance training than young men, older men, and older women. In the same study, all groups, except older women, retained resistancetraining-induced increases in muscle quality for 31 weeks after the cessation of training [26]. Furthermore, another study demonstrated that young subjects exhibited greater increases in one-repetition maximum compared with the older subjects [12], while Kittilsen et al. [13] found that older adults improved their one-repetition maximum to the same extent as younger groups.

Findings regarding sex and age responses to resistance training are still inconsistent. Men and women of different age groups have different rates of fatiguability and neuromuscular performance, partially because of differences in anatomy and physiology [30]. Moreover, older individuals may require shorter rest intervals to recover from resistance training, explained by the increase in type I fibers and atrophy of type II fibers occurring with aging [31]. Although resistance training has been recommended for all age groups, its positive effects on neuromuscular impairments and the maintenance of functional performance are especially observed in individuals aged over 50 years [32,33]. By examining sex- and age-specific responses to resistance training, health-related professionals and trainers would be able to plan and program protocols consisting of different training modes, frequencies, and durations to achieve maximum efficiency.

Therefore, the main purpose of the study was to examine whether a 4-week resistance training program would have similar effects on body composition, muscular fitness, and flexibility in middle-aged (50–64 yr) and older (65–80 yr) men and women. We hypothesized that all sex and age groups would have different acute responses to resistance training.

#### **2. Methods**

#### *2.1. Study Participants*

Seven hundred and sixty-four middle-aged (range 50–64 yr, mean age ± SD 56.4 ± 5.3 yr) and older (range 65–80 yr, 69.3 ± 4.0) men and women volunteered to participate in this study. All participants went through a rehabilitation center program established by the Ministry of Croatian Veterans (*N* = 2500). The main goal of the center is to improve and enhance the care system and quality of life. The second goal is to reintegrate them into everyday activities, such as social gatherings or sports. The accommodation program usually includes the use of services for up to 30 days. Before entering the study, participants needed to meet the following inclusion criteria: 1) being without chronic diseases, 2) having no psychiatric symptoms or diagnoses, and 3) being without locomotor disabilities (being able to perform exercises with a full range of motion and without pain and discomfort), in order to perform exercises in the4-week intervention program. Of the 2500 participants whose activity was measured, 764 met the inclusion criteria. Such a sample size (*N* = 764) with a two-tailed test, *p*-value set at 0.05, and statistical power of 0.95 would be able to detect a minimum effect size of 0.13. Not all the participants engaged in physical activity in the past 12 months, so the inactive ones were categorized as being sedentary. Prior to the intervention protocol, all participants

gave written informed consent for participation, and they all agreed that the data generated from this study could be used for scientific purposes. During the study, the procedures were anonymous and each participant received a unique coded number. We followed the methods of the principles of the Declaration of Helsinki [34], and the Ethical Committee of The Home of War Veterans approved the study (Ethics code number: 2017/4).

#### *2.2. Body Composition*

Body composition was estimated using bioelectrical impedance analysis (Omron BF500 Body Composition Monitor, Omron Medizintechnik, Hamburg, Germany). The participants needed to stand on metal footpads barefoot and grasp a pair of electrodes fixed on a handle with arms extended in front of the chest [35]. The software used preprogrammed regression equations to predict lean mass and fat mass in percentages. Each participant was instructed not to consume food or water 2 h before the testing procedure. A Seca portable 202 scale (Seca, Hamburg, Germany) and a digital scale (Seca, model 769) were used to measure the standing height (cm) and weight (kg) with a precision of 0.1 cm and 0.1 kg. The body mass index was calculated using the following formula: weight (kg)/height (m2).

#### *2.3. Muscular Fitness*

To assess muscular fitness, three tests were applied: (1) push-ups in 30 s, (2) chairstands in 30 s, and (3) sit-ups in 30 s. The push-up test measures dynamic muscular endurance [36]. If the participant was not able to regularly perform the test, the test was carried out on the knees, as recommended by previous studies [36]. The final score was recorded as the number of push-ups in 30 s. The chair-stand test estimates lower body strength [37]. The measurer recorded the number of standing up and sitting down sets from a standard chair 40 cm in height, while the participant looked straight ahead with their back in an upright position, legs fully extended and arms folded across the chest. The final score involved counting the number of stand-ups in 30 s. The sit-up test was performed to assess repetitive upper body strength [38]. Before the testing, each participant laid on a soft mat in a supine position, holding the knees bent at an angle of 90◦ and placing the arms on the chest with the hands on opposite shoulders. The correct attempt included performing a full sit-up to the upright position with elbows touching the thighs and returning to the initial position when shoulders touched the mat surface [38]. The final score was the number of correct sit-ups completed in 30 s.

#### *2.4. Flexibility*

To assess flexibility, we used the sit-and-reach test [39]. The participant was instructed to reach forward with the arms overlapping along the centimeter tape taped on the floor, while sitting with legs straight under an angle of 90◦. The test was measured three times and the best score was recorded in centimeters.

#### *2.5. Resistance Training Program*

All participants took part in a 4-week resistance training program performed 3 times per week with at least 1 day of rest between each session. All training sessions were supervised by 2 trained exercise specialists who had finished master studies in sports science and had experience in testing of >5 years. Both instructors were familiar with the training exercises and practical lessons given to the participants. The instructors' role was to supervise the exercises and track the maintenance of intensity. The resistance training was designed to increase lean mass, muscular fitness, and flexibility. Before starting the program, all participants attended two familiarization sessions, as in previous studies [40]. Following familiarization and prior to the intervention, the participants completed the initial one-day testing. Body composition was measured in the morning hours between 9:00 and 11:00 h. Muscular fitness and flexibility were assessed using four tests in the afternoon between 17:00 and 19:00 h. The rest interval between each test was set to 5 min to

avoid fatigue. The testing was performed in groups of 15 participants. The same principles were applied to post-intervention testing with similar testing hours (± 1 h) as for the initial testing.

Each resistance training session consisted of a general warm-up period for 5–10 min at moderate intensity, including cycling or performing leg presses. After the warm-up, participants performed 5 different exercises. The exercises included leg presses, knee flexions and knee extensions, horizontal chest presses, seated rows, back pulls, arm curls, and arm extensions. An indirect measurement of the maximum load was calculated, based on the training load at 50% and 60% of the one-repetition maximum. During the first 2 weeks, the participants exercised with ≈50% of the one-repetition maximum in 3 sets (10 repetitions), while in the second 2 weeks, the volume-load increased to ≈60% of the one-repetition maximum in 4 sets (8 repetitions). The pause between the sets was 2 min. All sessions were performed at the same time between 15:00 and 18:00 h with an airconditioned room temperature of 22 ◦C and 24 ◦C. The training sessions were conducted at the same time during the day inside the rehabilitation facility. Two gyms with standardized equipment (machines, free weights, elastic bands, small/big balls, sticks, and mattresses) were used for the testing protocols and training.

#### *2.6. Statistical Analysis*

Basic descriptive statistics of the study participants are presented as mean and standard deviation. Kolmogorov–Smirnov and Levene's tests were applied to examine the normality of the distribution and homogeneity of variance for all data before the testing. Within-group changes (initial to final) were tested with the repeated measures ANOVA, while univariate ANOVA was used to calculate between-group differences. If a significant *p*-value was noted, the Bonferroni post hoc test was used to determine the differences among groups. The intervention-induced changes, presented in percent, were analyzed as follows: ((final score-initial score)/initial score). To establish the magnitude of the group difference in body composition, muscular fitness and flexibility, Cohen's *d* effect sizes were calculated with the following classification: (i) <0.2 (trivial), (ii) 0.2–0.5 (moderate), (iii) 0.5–0.8 (large), and (iv) >0.8 (very large) [41]. Two-sided *p*-values were used, and the significance was set at α < 0.05. All the analyses were calculated in Statistical Packages for Social Sciences v.23 (SPSS, Chicago, IL, USA).

#### **3. Results**

The number of participants within each sex and age group who undertook the initial and final measurements is given in Table 1.


**Table 1.** The number of the participants (*N*) with both initial and final measurement, according to sex (men/women) and age in years (yr).

The basic descriptive statistics of the study participants before and after the 4-week training intervention are presented in Table 2. Over the 4 weeks, significant decreases were observed for body weight (*F1,763* = 9.257, *p* = 0.002), and the percentage of fat mass (*F1,763* = 63.271, *p* < 0.001), while the increases in the percentage of lean mass (*F1,763* = 21.134, *p* < 0.001), push-ups in 30 s (*F1,763* = 122.417, *p* < 0.001), chair-stands in 30 s (*F1,763* = 200.653, *p* < 0.001), sit-ups in 30 s (*F1,763* = 54.627, *p* < 0.001), and sit-and-reach test (*F1,763* = 40.172, *p* < 0.001) were obtained. Significant time\*age interactions were shown for push-ups in 30 s (*F1,763* = 4.348, *p* = 0.038) and chair-stands in 30 s (*F1,763* = 9.552, *p* = 0.002), where men and women aged 50–64 years exhibited larger time-induced changes compared with their older (65–80 yr) counterparts. No significant time\*sex, time\*age or time\*sex\*age interactions for other variables were noted.

**Table 2.** Body composition, muscular physical fitness, and flexibility for men and women of different ages before and after the 4-week training intervention.


Figures 1 and 2 show sex- and age-specific intervention-induced changes (%) and effect sizes. After the 4 weeks, women exhibited significantly larger changes for chair-stands in 30 s compared with men in both age groups (24.3% and 16.0% vs. 18.8% and 13.7%, respectively). Additionally, women aged 65–80 years performed better in sit-ups in 30 s (18.6%) than 65– 80-year-old men (13.6%). Similarly induced changes in body composition and flexibility in both sex and age groups were observed (*p* > 0.05). The largest effect sizes for muscular fitness were obtained (ranging from 0.15 for sit-ups in 30 s in men aged 65–80 yr to 0.59 for chair-stands in 30 s in women aged 50–64 yr), followed by flexibility (ranging from 0.17 in men and women aged 65–80 yr to 0.30 in men and women aged 50–64 yr). For body composition, the largest effect sizes were observed for the percent of lean mass in men and women aged 65–80 years (0.39 and 0.37, respectively), while for fat mass, the effect sizes ranged from 0.11 in 65–80-year-old men to 0.18 in 65–80-year-old women.

**Figure 1.** Intervention-induced changes (%) for body composition, muscular fitness, and flexibility before and after the 4-week resistance training across the age groups in years (yr).

**Figure 2.** Effect sizes for body composition, muscular fitness and flexibility before and after the 4-week resistance training across the age groups in years (yr).

#### **4. Discussion**

The main purpose of this study was to examine the effects of a 4-week resistance training program on body composition, muscular fitness, and flexibility in middle-aged (50–64 yr) and older (65–80 yr) men and women. The main findings were: (1) significant time changes in all sex and age groups were observed, (2) 50–64 year-old men and women had somewhat larger changes in push-ups in 30 s and chair-stands in 30 s tests compared with their older counterparts, and (3) similar intervention-induced changes (%) and effect sizes between groups occurred.

Studies found that relative upper-body strength increased by 29% in women compared with 17% in men, whereas the increases in lower-body strength were similar [42], which are comparable to our findings in this study. The increases in muscular fitness may be explained by the increases in lean mass and decreases in fat mass, as in previous studies [12]. This statement can be confirmed, because we found significant correlations between lean mass, fat mass, and muscular fitness (*p* < 0.05). Studies showed that different neuromuscular adaptations are one factor contributing to the larger increases in upper-body strength for women, whereas acute recovery following resistance training may be slower in men [18]. Women generally have lower initial levels of physical fitness, causing a ceiling effect for motor skills and better training adaptations compared with men [18]. Although we did not find significant time\*sex interactions for any of the study variables, women exhibited larger intervention-induced changes for chair-stands in 30 s. This may be explained by two potential mechanisms. First, women have a greater proportion of type I fibers in the vastus medialis and biceps brachii, indicating better muscular endurance [43,44]. Second, studies showed that men have longer-lasting muscle soreness than women [45]. Most recently, a study by Roberts et al. [18] showed no significant differences between men and women in hypertrophy (effect size = 0.07 ± 0.06, *p* = 0.31) or lower-body strength (effect size = −0.21 ± 0.16, *p* = 0.20); yet, there was a significant effect favoring women for upperbody strength (effect size = −0.60 ± 0.16, *p* = 0.002). Although the changes for push-ups in 30 s were larger for women in our study, unfortunately, we did not collect additional data regarding biological and physiological characteristics, although previous evidence suggests that women tend to have larger strength gains and more muscle growth during the follicular phase [46]. However, our sample size was based on women aged ≥50 yr, and it was reported that the menopausal age starts roughly at the age of 50, dismissing the effects of the menstrual cycle on resistance training [47].

We also found that younger men and women (50–64 yr) had larger effects in push-ups in 30 s and chair-stands in 30 s compared with their older (65–80 yr) counterparts, which is in line with the findings of previous studies [12,48]. Specifically, Lemmer et al. [12] found that younger participants increased their one-repetition maximum strength significantly more than older participants (34% ± 3% vs. 28% ± 3%). Another study showed that young participants had better performance in specific strength for knee extensions and experienced less fatigue across repetitions compared with old participants [48]. Older adults experience physiological changes in terms of age-associated loss of type II fiber number and size, which significantly contribute to the ability to increase strength in response to resistance training [12]. On the other hand, one study reported the same improvements in one-repetition maximum in all age groups from young adults in their twenties and thirties, middle-aged in their forties and fifties and up, to older adults in their sixties and seventies [13]. Along with mechanical and architectural factors associated with the same sex and age responses to resistance training, previous studies acknowledged that the mechanism for similar training-induced increases in muscle quality lies in the increases in motor unit recruitment and decreases in the coactivation of the antagonist muscle groups following training in older adults [26]. Moreover, the ratio between type I and type II fibers may help improve neuromuscular adaptations that enhance the contractile properties of the muscles [26].

Our findings support previous evidence of similar acute responses to resistance training, irrespective of sex and age. Researchers have well-documented that the differences

are largely influenced by neural, muscular, and motor learning adaptations [18]. Nevertheless, it is important to understand how resistance training may affect body composition, muscular fitness, and flexibility in men and women of different ages in practice.

This study is not without limitations. First, although we found significant time changes, the 4-week resistance training intervention may have been relatively short compared with those in previous studies with longer study durations (between 6 and 24 weeks) [18]. Second, we did not collect data on the participants' biological, physiological, or sociodemographic characteristics, which can mediate the resistance training effects. Furthermore, we did not collect data on the level of physical activity prior to the intervention; thus, it is possible that physically active individuals might have had different basal activity compared with untrained individuals. Future research should collect more detailed information regarding the anthropological and health-related status of the participants. In addition to our finding significant changes in physical fitness after the 4-week resistance training, it would be clinically relevant to study the appropriate intervention duration and training mode for producing the largest effects on health-related physical fitness.

#### **5. Conclusions**

To the best of our knowledge, this is the first study to report marked improvements in body composition, muscular fitness, and flexibility in a large sample of middle-aged and older adults categorized as war veterans, according to sex and age. We found significant changes in all sex and age groups for all variables. Resistance training produced larger improvements in younger men and women in push-ups in 30 s and chair-stands n 30 s compared with older men and women. To conclude, resistance training for 4 weeks increased lean mass, muscular fitness, and flexibility and decreased body weight, body mass index, and fat mass across the groups. Therefore, even a relatively short resistance training intervention may lead to higher physical fitness levels in men and women aged 50–80 years.

**Author Contributions:** Conceptualization, L.Š. and Z.K.; methodology, L.Š.; software, L.Š.; validation, L.Š.; formal analysis, L.Š.; investigation, L.Š. and Z.K.; resources, L.Š. and Z.K.; data curation, L.Š.; writing—original draft preparation, M.K., L.Š. and Z.K.; writing—review and editing, L.Š.; visualization, L.Š.; supervision, L.Š. and Z.K.; project administration, Z.K.; funding acquisition, Z.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was approved by the Ethical Committee of The Home of War Veterans (Ethics code number: 2017/4).

**Informed Consent Statement:** Informed consent was voluntarily signed by the participants.

**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** The authors would like to thank all the participants and training supervisors for their enthusiastic participation in the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **Prevalence of Orthorexia in Groups of Students with Varied Diets and Physical Activity (Silesia, Poland)**

**Mateusz Grajek 1,2, Karolina Krupa-Kotara 3,\*, Krzysztof Sas-Nowosielski 2, Ewa Misterska <sup>4</sup> and Joanna Kobza <sup>1</sup>**


**Abstract:** (1) Background: The literature emphasizes the role of many factors influencing the onset of eating disorders (EDs) and their mutual influence on each other. Therefore, this study aimed to evaluate and compare the prevalence of orthorexic behaviors in groups of health-related and non-health-related students in terms of their differential health behaviors—diet and physical activity levels. (2) The study included 300 individuals representing two equal groups of fields of study, which for the study were called the health-related field (HRF) and the non-health-related field (NRF). (3) Results: Based on the results of the dietary assessment, it was found that the best dietary model was characterized by the HRF group; in this group, 97.2% of students were characterized by a very good and good dietary mode. The NRF group, on the other hand, was dominated by a sufficient dietary mode for 64.4% of all cases in this group (94 people), while the dietary model marked as "good" was less popular, at 24.6% of this group (36 people). (4) Conclusions: Based on the cited self-research and information from the literature, it can be concluded that the problem of orthorexia is still a new issue at the level of social sciences, medical sciences, and health sciences. The psychometric tools used in this study allowed us to demonstrate the prevalence of the aforementioned eating disorders in the sample groups of students.

**Keywords:** eating disorders; eating behaviors; students; nutrition; orthorexia; lifestyle; diet; physical activity; health-related; non-health-related

#### **1. Introduction**

Proper nutrition and adequate physical activity are the most important factors that promote health. However, sometimes, the interaction of certain factors leads to the manifestation of eating disorders (EDs). The literature emphasizes the role of many factors influencing the onset of eating disorders and their mutual influence on each other, the most frequently mentioned being physiological, psychological, and socio-cultural factors [1,2]. Eating disorders form a group of behavioral disorders that involve excessive weight control and focusing thoughts on food. According to the American Psychiatric Association's classification (DSM-V), there are two primary groups of eating disorders—specific and nonspecific [3]. Specific eating disorders include anorexia (mental anorexia), bulimia (mental gluttony), and sometimes a mixed disorder (bulimorexia) [4]. Nonspecific eating disorders form a group of behaviors not classified in the DSM-V, but this does not mean that these individuals do not cause serious health consequences for the person who suffers from them [5]. Nonspecific eating disorders include pathological eating behaviors such as emotional eating or food fanaticism. According to the International Statistical Classification of

**Citation:** Grajek, M.; Krupa-Kotara, K.; Sas-Nowosielski, K.; Misterska, E.; Kobza, J. Prevalence of Orthorexia in Groups of Students with Varied Diets and Physical Activity (Silesia, Poland). *Nutrients* **2022**, *14*, 2816. https://doi.org/10.3390/nu14142816

Academic Editors: Roberto Pippi, Carmine G. Fanelli and Matteo Vandoni

Received: 20 June 2022 Accepted: 7 July 2022 Published: 8 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Diseases and Health Disorders (ICD-10), eating disorders are classified as mental disorders from the group of behavioral disorders, associated with physiological disturbances and psychological factors. Eating disorders are classified as compulsive disorders due to the compulsion to restrict or overindulge in food. Among the most characteristic symptoms of these disorders are anxiety, depression, obsessive-compulsive disorder, self-aggression, or reduced quality of life [1,2]. Major risk factors for EDs include [6,7]:


Namysłowska [8], on the other hand, indicates that factors influencing the development of eating disorders should be considered in terms of a causal sequence or vicious circle. The first group of factors mentioned by the author are predisposing factors, among which are the individual characteristics of the person at risk of developing the disorder, but also family and social issues (excessive ambitions, aggression, violence, social expectations). The second category of factors are triggered, and here behavioral disturbances within the desire for self-monitoring of body weight and the drive for self-acceptance are indicated. In the third category are the maintenance factors, which are defined by the effect of drastic dieting and physical activity in the form of starvation, which drive to maintain such a state of affairs—the person with an eating disorder feels satisfaction through achieving a given effect, and this causes the problem to worsen. Eating disorders are a significant public health problem. Their prevalence in the general population is quite high, despite difficult epidemiological studies. EDs are estimated to affect 2–4% of the population, and the prevalence of eating disorders is significantly higher in some age and social groups than in others [7,9]. Diagnosing eating disorders is a complex process, primarily due to the presence of nonspecific somatic symptoms that mask the underlying illness, thus prolonging the diagnosis process and making it more costly. In addition, the treatment of eating disorders is a lengthy and multi-step process that requires collaboration between the patient's case manager, a psychologist or psychotherapist, and a nutritionist [2,4].

Nowadays, with the cult of slimness and care for a healthy lifestyle, there is a lot of talk about health risks associated with healthism, i.e., excessive attention to health. One can say that the eating disorder resulting from such behavior is orthorexia. Orthorexia nervosa is an informally diagnosed condition characterized by an obsessive preoccupation with eating healthy and "clean" food. In this condition, it is the quality, not the quantity, of food that is crucial [10,11]. Orthorexia nervosa, sometimes called radical eating, is a condition in which a pathological obsession with eating the "right" food is manifested. It is more commonly diagnosed in women and affluent individuals regardless of gender. Orthorexia affects 7% of the general population [1], and research by Dunn and Bartman [12] suggests that the problem may affect up to 90% of members of certain social groups, as the problem is more common in people with high levels of physical activity to maintain fitness and health, as well as in people who strive to achieve the "ideal" body shape that is promoted by mass media [13]. In this disorder, as in classic anorexia, there is an obsessive, systematic focus on counting the caloric value of the foods consumed and giving up all fatty and hard-to-digest foods [13,14]. Unlike anorexia, on the other hand, the focus of the sufferer is not only on the quantity consumed and weight reduction, but more importantly on the quality of the food consumed and on achieving and maintaining

health [15,16]. Individuals with orthorexia think obsessively about food, planning meals, imposing dietary discipline on themselves, and enforcing punishments for breaking the rules they have established. Such behavior stems from the conviction that only eating healthy, easily digestible food and strict adherence to a diet will allow them to prevent the occurrence of diseases characteristic of modern societies-chronic non-communicable diseases [12]. Therefore, the desire to live a healthy and long life, to stay healthy and fit, and to focus on the quality of the food consumed rather than the quantity of the food consumed makes this disease not easy to diagnose [10,17]. There are also cases where patients who eliminate more products start consuming only water while believing that only drinking strictly selected water will ensure full health [5]. Health consequences of chronic orthorexia include, but are not limited to, broken social ties along with avoidance of going out to eat, malnutrition secondary to elimination and reduction diets (including deficiencies of protein, vitamin B12, iron, sodium), rhabdomyolysis (muscle breakdown), metabolic acidosis, and elevated laboratory indicators (mainly liver enzymes and bilirubin) [18]. Due to the lack of unambiguous diagnostic criteria, and thus the very great difficulty in making the diagnosis, the exact number of people with orthorexia is still unknown (apart from the aforementioned estimates, which are based on small group studies). People with orthorexia are often treated by their environment as caring too much about their health, and their relatives do not recognize the features of the disorder in such behavior. The lack of accurate information on the size of the condition is also due to the reluctance of sufferers to report to specialists and their failure to notice such problems [11,19]. Therapy for orthorexia, like therapy for anorexia, consists primarily of compensating for any nutritional deficiencies and psychotherapy. Its goal is to show patients that not all foods they reject carry health risks [12].

Therefore, the main objective of this study was to evaluate and compare the prevalence of orthorexic behaviors in groups of health-related and non-health-related students in terms of their differential health behaviors—diet and physical activity levels.

The following research hypotheses were posed in preparation for the study:


#### **2. Materials and Methods**

#### *2.1. Study Organization and Eligibility Criteria*

The study included 300 individuals representing two equal groups of fields of study, which for the study were called the health-related field (HRF) and the non-health-related field (NRF).

The HRF group consisted of 150 final-year sophomore students with majors in dietetics and physical education. The key to selecting this group was the fact of having in-depth and professional knowledge in the field of rational nutrition and physical activity. The NRF group consisted of 150 students in their final year of second-degree studies with majors in management and computer science. The key to selecting this group was the fact that they did not have in-depth and professional knowledge in the field of rational nutrition and physical activity, at least at the university level. The assumption for the selection of these majors was that the gender groups were more or less equal. Such majors as dietetics and management are more often chosen by females, and physical education or computer science by males.

Individuals in the NRF group showing concurrent education (or past education) in a health-related field were excluded from the study. Individuals who had applied knowledge and skills in rational nutrition and physical activity in their professional work were treated similarly. The physiological state of the respondent was also taken into account. Persons suffering from diseases that influence the diet and/or physical activity of the respondent (e.g., allergies, food intolerances, metabolic diseases, tumors, etc.) were excluded from the research. The same was done with subjects who represented a specific dietary model (elimination diet or pregnancy and puerperium).

The final analyses included 290 (144 subjects in the HRF group and 146 subjects in the NRF group) correctly completed questionnaires that addressed the inclusion criteria described above. All participants were informed about the purpose and scope of the study and gave informed consent to participate.

The study was approved by the Bioethics Committee of the Medical University of Silesia in Katowice, in light of the Act on Medical and Dental Professions of 5 December 1996, which includes a definition of medical experimentation. The study participants consciously agreed to participate in the study.

#### *2.2. Study Procedure and Research Tool*

A diagnostic survey method was used in this study. The survey was conducted using an online form, which is an acceptable method in psychological research. The research was conducted using a survey technique. The survey consisted of a survey questionnaire and a scrapbook of sample foods and dishes. To record information from the research, a research tool was used, which included the following:


#### *2.3. Interpretation of the Tools Used*

Body mass index was calculated using the formula: BMI [kg/m2] = body weight [kg]/height [m]2. The results were then interpreted using a scale [27]: >30 kg/m2—obesity (alarming score); 25–29.9 kg/m2—overweight (elevated body weight); 20–24.9 kg/m2 normal body weight; 17–19.9 kg/m2—underweight (underweight); <17 kg/m2—malnutrition (alarming score).

In the assessment of dietary intake, the author's tool based on nutrition standards for the Polish population [18,27] was used, which included 20 dietary indices (e.g., frequency of consumption of individual product groups, number of meals during the day, regularity of meals during the day, snacking, fluids drunk). One point was awarded for each correct answer (in accordance with the applied standards), so the highest possible total score was 20. To prioritize the results, the following scale was adopted: 18–20 points—very good nutrition; 14–17 pts.—good; 10–13 pts.—moderate, ≤9 pts.—poor nutrition (bad).

Based on the physical activity score in the questionnaire, respondents were assigned a physical activity index (PAL), which takes the following values, based on current recommendations for physical activity, respectively [21,28]: 1.2—no physical activity; 1.4—low physical activity (approximately 140 min per week); 1.6—medium physical activity (approximately 280 min per week); 1.8—high physical activity (approximately 420 min per week); 2.0—very high physical activity (approximately 560 min per week).

The EMI-2 questionnaire consists of 56 variables grouped into 14 categories corresponding to motives for exercising. In each subscale, the respondent could receive 5 points, where 0 means the highest priority for the motivator and 5 the lowest; the lower the scores for a given motivator, the higher the motivation [29].

The ORTO-15 questionnaire consists of 15 questions, each to be answered on a 4-point scale (always, often, rarely, or never). Answers indicating a tendency toward orthorexic behavior receive 1 point, while those corresponding to normal eating receive 4 points. According to the creators, the cutoff point is a score of 40 points; scoring below this score indicates a tendency toward orthorexia [13].

#### *2.4. Statistical Compilation*

Tables and figures were prepared for all extracted data from the survey questionnaire and descriptive statistics (percentages (%), counts (N; n), mean (X) or median (M)values, standard deviations (SD), minimum and maximum values (MIN and MAX)) were included. Detailed statistical analyses were conducted, regarding the demonstration of differences between the represented behaviors (pro-health or anti-health) and the occurrence of orthorexia in the sample group. To analyze the above material, the χ<sup>2</sup> (chi-square) test for nonparametric variables and the V-Cramér coefficient of the strength of the relationship (with Yates and Fisher's correction) were used, as well as the nonparametric equivalent of the one-way analysis of variance, the Kruskal–Wallis test with the coefficient ε2. A probability level of *p* = 0.05 was assumed for the study.

#### **3. Results**

In terms of gender distribution, the sample groups were as follows: women, 60.0%— 174 persons (HRF: 47.1%, n = 82; NRF: 52.9%, n = 92); men, 40.0%—116 persons (HRF: 53.4%, n = 62; NRF: 46.6%, n = 54). All respondents were students in the final year of their master's degree (second year of their second degree). Based on the medical history, it was observed that 5.2% (15 persons) had been diagnosed with chronic diseases, although these were seasonal allergies (pollen, house dust mite, insect venom), i.e., diseases that do not significantly affect their lifestyle. The age of the respondents was 26 years (±2 years). Dietetics was studied by 48.6% of subjects (n = 70) and physical education by 51.4% of subjects (n = 72)—these subjects constituted the HRF group (144 subjects). Management was studied by 47.3% of individuals (n = 69) and computer science by 52.7% (n = 77) these individuals constituted the NRF group (146 individuals). Among the respondents, 249 people (85.9%) lived in large cities (over 100,000 inhabitants), 23 people (7.9%) lived in smaller cities (under 100,000 inhabitants), and 18 people (6.2%) lived in villages. As far as their occupation was concerned, only 13.1% of the respondents (38 persons) had permanent employment, i.e., in telecommunication, service, and administration-office sectors. The main addiction in the surveyed groups was smoking tobacco, with 3.8% of students (11 persons) admitting to this habit. There were no subjects who compulsively consumed alcohol or took other psychoactive drugs.

None of the subjects in the study were characterized as malnourished. More than 15.2% of the subjects were characterized as underweight (44 subjects in the HRF group). Normal weight was a characteristic of 178 subjects (61.3%). Overweight and obesity were present only in the NRF group with a total of 68 subjects (23.4%). These differences were statistically significant (T = 11.281; V = 0.621, *p* = 0.001). The results of the calculations are presented in Table 1.


**Table 1.** Body mass indexes (BMI) of the students (N = 290).

HRF—health-related field students; NRF—non-health-related field. Bold values are statistically significant.

Based on the results of the dietary assessment, it was found that the best dietary model was characterized by the HRF group; in this group, 97.2% of students were characterized by a very good and good dietary mode (84.0%—121 persons, 13.4%—19 persons, respectively). The NRF group, on the other hand, was dominated by sufficient dietary mode, at 64.4% of all cases in this group (94 people). Less popular was the dietary model marked as "good", with only 24.6% of this group (36 people). It should be emphasized that an incorrect dietary pattern was represented only by people from the HRF group (3.9% of the total number of subjects—11 people). Detailed results of the students' dietary assessment are shown in Figure 1.

**Figure 1.** Students' dietary assessment (N = 290): HRF—health-related field students; NRF—nonhealth-related field.

The next stage of the study was to assess the physical activity and motivation of the respondents to undertake physical activity. The level of physical activity was assessed by answering four questions regarding the fact of exercise, type of exercise, frequency, and duration of physical activity. Based on the data obtained, it was observed that 98.6% (142 persons) of the HRF group and 83.6% (122 persons) of the NRF group were physically active. Correspondingly, 2 individuals from the HRF and 24 individuals from the NRF did not engage in any physical exercise daily (1.4% vs. 16.4%)—these individuals were not included in Figure 2 which categorizes physical activity levels by PAL index.

**Figure 2.** Students' physical activity level ratings (N = 264).

Low physical activity was characteristic for 46.2% of respondents (122 persons), and most often chosen by persons from the NRF group (79.5%—97 persons). Medium physical activity was observed in 25.7% of the respondents (68 persons); this activity concerned both the HRF group (33.8%—48 persons) and the NRF group (16.4%—20 persons). Physical activity at a high and very high level concerned 28.1% of the students (75 persons), these were mainly persons from the HRF group (48.4%—70 persons) (Figure 2).

The level of motivation to undertake physical exercise and its reasons in the sample group varied, and were assessed using the EMI-2 questionnaire. In the sample group, the pleasure derived from physical activity was the most important motivation to undertake it (1.79 ± 1.78). Psychological regeneration (1.81 ± 1.73), maintaining health (1.91 ± 1.82), building strength and endurance (1.93 ± 1.81), taking care of appearance (1.96 ± 1.86), and avoiding ill health (1.99 ± 1.71) were also strong motivators. The least important motivation for exercise was health pressure (2.77 ± 2.22), followed by social recognition (2.62 ± 1.93) and the desire to belong to a group (2.37 ± 2.02). There were no statistically significant differences between groups. The results were comparable in both groups of students (Table 2).

**Table 2.** EMI-2 subscales by group (N = 290).


Before evaluating the size and caloric value of the portions using the albums of exemplary products and dishes, each participant had to pass a visual perception test consisting of the evaluation of six images divided into two groups: (1) Ishihara boards assessing the perception of colors, and representing in turn: the number 12, the number 96 and the absence of a specific shape; and (2) optical illusion boards—assessing the perception of space (size, length, and shape). Each correct indication in the visual perception test was scored one point, and a total of six points could be gained. In the sample group, 89.6% (n = 282) scored from five to six points, and 10.4% (n = 30) scored three to four points. The greatest difficulty for the subjects turned out to be the Ishihara board, which does not represent a specific shape, and the optical illusion board, which assesses shape perception (assessing the straightness of the lines depicted). Due to the overall good performance on the test, it was not decided to exclude any participant in the remainder of the study.

The visual perception assessment was followed by the actual test of estimating portion sizes and calories. Each participant received individual albums of sample products and dishes, which contained 12 photographs of food products and ready meals. The evaluator's task was to estimate the size and caloric value of the given item using the variants listed under the photographs (four variants for size (g), and four for energy supply (kcal)). The variants were selected so that one indicated the correct value; one smaller or larger in the range of 8–12% from the correct value, but still treated as a correct answer; and two incorrect variants, underestimated and overestimated in the range of 28–32% relative to the correct value. Based on the data obtained, it was determined what percentage of respondents correctly and incorrectly (over- or underestimated) the size and calorie content of the servings.

Figure 3 shows the results of the portion size estimation test.

Based on the test on the ability to estimate the size of the portions based on photographs, it was found that 32.4% (94 people) overestimated the size of the portions of products and dishes indicated in the photographs. In this group, there were mostly people studying in health faculties—57.6% (83 people); less often, there are people from other faculties—7.5% (11 people). In the case of underestimation (33.8%, n = 98), the situation was reversed—people from the NRF group mainly underestimated the size of products and dishes—56.2% (n = 82); in the HRF group, much fewer people underestimated (11.1%, n = 16). The remaining persons correctly indicated the size of the portion—33.8% (98 people).

Figure 4 shows the results of the caloric estimation test for portions.

**Figure 3.** Level of portion size estimation across student groups (N = 290).

**Figure 4.** Level of calorie estimation of servings in student groups (N = 290).

Analyzing the results of the test on the ability to estimate the calorie content of portions based on photographs, it was observed that 35.8% (104 people) overestimated the calorie content of the products and dishes indicated in the photographs. This group included mainly health-related people (58.3%—84 people), and less frequently, non-health-related people (13.0%—19 people). On the other hand, in the case of underestimation of the energy of dishes (35.2%, n = 102), people from the NRF group mostly underestimated the caloric value of products and dishes presented in the album (55.5%, n = 81); in the HRF group, such cases were much less (15.3%, n = 22). The remaining persons correctly indicated the calorie content of the portion—29.0% (84).

Using the ORTO-15 questionnaire, it was found that, among the subjects, 44.5% (129 subjects) scored below 40, indicating an increased risk of orthorexia. This was more frequent in the HRF group than in the NRF group (63.5% vs. 25.8%), with 92 and 37 subjects, respectively, from the student groups. Based on statistical inference, it was found that low BMI values occur in individuals from the HRF group, which indicates the presence of a statistically significant relationship between the indicated characteristics (T = 13.238; V = 0.723; *p* = 0.0001). Similarly, also in the case of diet, individuals representing good (27.6%) and very good (16.2%) nutrition were more likely to belong to the group of people with an increased risk of orthorexia. In this case, as well, a statistically significant correlation with HRF group membership was shown (T = 10.984; V = 0.683; *p* = 0.0001) (Table 3).


**Table 3.** Relationship between BMI and diet and the group at increased risk for orthorexia (n = 129).

Bold values are statistically significant.

Next, it was decided to verify the relationships between the occurrence of orthorexic behavior and the represented level of physical activity. Based on the statistical inference performed, it was found that high PAL values occur in the HRF group, which indicates the presence of a statistically significant relationship between the indicated characteristics (T = 8.117; V = 0.597; *p* = 0.002) (Table 4).

**Table 4.** Relationship between physical activity level and the group at increased risk for orthorexia (n = 129).


Bold values are statistically significant.

The last verification concerned the relationship between the occurrence of orthorexic behaviors in the sample group and the ability of the respondents to estimate portion sizes and calories. Based on the results presented in Table 5 and the statistical analyses performed, it was concluded that there is a statistical relationship both in the case of estimation of portion size and caloricity of meals; individuals from the HRF group characterized by orthorexic behavior are more likely to overestimate the size and caloricity of the meal (*p* < 0.05).

**Table 5.** Relationship between demonstrated ability to estimate portion size and group at increased risk for orthorexia (n = 129).


Bold values are statistically significant.

#### **4. Discussion**

Several studies indicate that an occupational group with an increased risk of orthorexic behaviors are people who practice sports (representing various sports disciplines, including those that require a very slim and limber body). Due to the extreme difficulty in making a definitive diagnosis, the exact number of people with orthorexia is still unknown. Dunn and Bartman [12] estimate that between 6 and 90% of people may be affected depending on their social group (this spectrum is dictated by the different prevalence of the condition in different populations). People with orthorexia are very often treated by their environment as caring too much about their health, and their loved ones do not find features of the disorder in such behavior. The lack of accurate information on the size of the condition is also due to the reluctance of affected individuals to report to specialists and their failure to notice such problems.

Although orthorexia is not commonly recognized as an eating disorder, there is ongoing research on the condition of overeating, limiting food intake, and leading a pathologically pro-healthy lifestyle. The following is a review of studies that look at the relationship between psychological state, occupation/field of study, practicing physical activity, or anthropometric indices, and the occurrence of orthorexic behavior. In recent years, there have been several papers regarding the condition of orthorexia. Current scientific evidence is presented below and compared with the results of our research.

In our own study, the occurrence of orthorexia depended on the field of study chosen by the participants of the study. Of course, the authors do not suggest that it is the field of study that influences the incidence of orthorexia, but rather the personality conditions that determine its choice, which also influence the development of orthorexic behavior. In the current study, it was observed that orthorexia occurs twice as often in people from the health-related group than in non-health-related people (63.5% vs. 25.8%). In addition, Kinzl et al. [30] in their study checked the prevalence of orthorexia among 283 Austrian female dietitians. They used the German version of the FEV questionnaire, which examines three dimensions of eating behavior, and the ORTO-15 questionnaire to verify the presence of orthorexia. In the sample group, more than one-third were individuals who had recently changed their eating habits. Among the respondents, some orthorexic behavior was presented in 34.9% of people, while orthorexia was found in 12.8% of people. Individuals in the last group were more likely than others to have experienced eating disorders recently. From the FEV questionnaire, it was found that 40% of individuals significantly controlled their eating behavior. A study by Haddad et al. [31] analyzed a group of 811 Lebanese people, from each province. The paper also used the ORTO-15 questionnaire and adopted the same cut-off point as in our study. The study found that 75.2% of the respondents exhibited tendencies and behaviors indicative of orthorexia. There was a significant association of orthorexia in the group of women, with people starving themselves to lose weight, urging others to "go on a diet", and claiming that eating out is unhealthy.

The current study confirmed that people with higher physical activity are more likely to engage in orthorexic behavior. The demonstrated dependence showed that the higher the PAL index, the higher the risk of orthorexic behaviors. Moreover, the PAL index at the level of 2.0 was present only in the group of people with a positive ORTO-15 result. Malmborg et al. [32] investigated health status, physical activity, and frequency of orthorexia among physical education and management students. Respondents completed the Short Form Quality of Life Assessment Questionnaire (SF-36), the International Physical Activity Questionnaire (IPAQ), and the ORTO-15 questionnaire. Of 188 students, 144 (76.6%) had an ORTO-15 score indicating orthorexia, of which 84.5% were physical education students. Orthorexia combined with high levels of physical activity was more commonly observed in male physical education students than in female physical education students (45.1% vs. 8.3%). This would support the hypothesis posed in our study—those who participate intensively in sports may have a significantly higher exposure to orthorexia than those with lower levels of physical activity. This hypothesis is also supported by other research conducted by the author of the present study; in the 2020 study, the risk of orthorexia estimated based on ORTO-15 was determined for three-quarters of the sample group (300 out of 400 ballet schoolgirls in Silesian province) [33].

In addition, in our own study, it was shown that there is a true relationship, which indicates that orthorexia may occur in the group of people with a low BMI index; however, it was first of all noticed that it is appropriate for the group of people with normal body weight, regardless of the field of study they represented (HRF vs. NRF–16.6% vs. 12.8%). Similarly, Agopyan et al. [34] conducted a study to determine the relationship between orthorexia and the body composition of female students at Marmara University. The participants of the study were female students whose scores on the ORTO-15 questionnaire and EAT-40 eating attitude test indicated the presence of orthorexia. Evaluation of the respondents' body composition indices (bioelectrical impedance on a Tanita SC-330 device) showed that there was no significant difference between the EAT-40 and ORTO-15 scores in terms of body composition. The vast majority of female respondents (70.6%) had high ORTO-15 scores, and there was a significant negative correlation (*p* < 0.05) between EAT-40 and ORTO-15 scores. The final results of the data analysis showed that although abnormal tendencies were common among the female students, they were able to maintain normal body composition. Similarly, a study by Grammatikopoulou et al. [35] showed that health sciences students, especially nutrition and dietetics students, have a higher prevalence

of eating disorders. A total of 176 undergraduate students from the Faculty of Nutrition and Dietetics in Greece participated in the study. The study monitored food intake and examined the frequency of eating as a result of emotion and stress (EADES). Among the students participating in the study, 4.5% showed food addiction, and 68.2% showed orthorexia. No differences were observed between males and females in food addiction and orthorexia. Students with orthorexia showed increased BMI. Orthorexics consumed more low-energy foods, including vegetables, and less high-energy foods (meat and fat). Multiple linear regression analysis showed that orthorexic behavior was associated with increased BMI, waist circumference, and energy intake. Lower BMI was associated with the ability to cope with EADES.

Farchakh et al. [36] conducted a study to assess the association between orthorexic behaviors (ORTO-15), eating habits (EAT-40), and anxiety levels (HAM-A) among a representative sample of medical students in Lebanese universities. A total of 627 medical students participated in the study. Linear regression results showed that a higher EAT-40 questionnaire score was significantly associated with lower ORTO-15 scores (more orthorexic behaviors), while a higher HAM-A anxiety score was significantly associated with higher ORTO-15 scores (less orthorexic behaviors). The results obtained in this study suggest an association between eating habits indicative of an eating disorder and orthorexic behaviors. In addition, individuals with orthorexia were shown to be less likely to experience anxiety. The self-reported study also highlighted the fact that there is an association between eating patterns and the occurrence of specific disorders. Barnes and Caltabiano [37] examined the correlations between perfectionism, body image, attachment style, self-esteem, and the occurrence of orthorexia. In total, 220 subjects completed a questionnaire consisting of the ORTO-15 questionnaire, the Multidimensional Perfectionism Scale (MPS), the Multidimensional Body Image Questionnaire (MBSRQ-AS), the Relationship Scale Questionnaire (RSQ), and the Rosenberg Self-Esteem Scale (RSES). The study confirmed a significant association of orthorexia prevalence among individuals with scores indicating perfectionism, appearance orientation, preoccupation with overweight and selfesteem, and aversion to attachment. However, the association of orthorexia prevalence with self-esteem was not confirmed. In our study, the occurrence of orthorexia nervosa was not compared with the mental state of the respondents, but an important correlation was shown that people suffering from this disorder more often overestimate the energy and volume value of food portions, which may indicate that among these people, similarly to other EDs, there are cognitive disorders associated with the incorrect perception of not only your own body, but also food intended for consumption. This condition should be the subject of further research on this topic.

#### *Strengths and Limitations*

The research on the prevalence of orthorexic behaviors among students of different majors allowed us to understand the basic mechanisms, cause–effect relationships, and determinants of the occurrence of the indicated disorders. Conducting the research required a lot of work and preparation in the form of developing research tools and becoming familiar with existing psychometric tools measuring the risk of orthorexia. Of course, the paper does not suggest that it is the field of study that influences the development of the disorder, but rather that individuals who choose it are characterized by certain traits that predispose them to it. This should be understood in the way that, thanks to the results of the study, it is possible to detect groups of people who should be included in the observation in terms of the control and safety of their lifestyle.

An important limitation of the study is that causation cannot be described as it is a cross-sectional study. The main difficulties during the conduct of the study were access to the student group because the study was conducted in the period of May–June 2021, and it should be emphasized that this was the period immediately adjacent to the lifting of the COVID-19 pandemic restrictions, so it was necessary to use the method of online surveying using Google package forms. In addition, data such as height or weight, because the study was conducted using the CAWI method, were provided by the respondents themselves, which may result in a mistake caused by the self-assessment of these indicators by the participants. The use of an Internet survey still raises some doubts among researchers as to the reliability of filling out the questionnaires. It is worth emphasizing here that despite many reservations about online surveys, it is currently one of the main and most acceptable forms of conducting them. The second part of the research, which involved the use of food and dishes photo albums, was conducted face-to-face. This approach was chosen because of the extensive nature of the research tools and concerns about respondents' reluctance to fill out the extensive questionnaire. In addition, contemporary reports confirm that people are more willing to participate in research when the researcher provides a comfortable research experience (online methods, neutral environment, interesting setting) [38]. The research material presented in this study, in light of scientific reports, appears to be a rather novel approach to the topic of nonspecific eating disorders, as research rarely uses methods from psychology and dietetics simultaneously. This approach has allowed for a comprehensive analysis and elaboration of the collected research material.

#### **5. Conclusions**

Based on the cited self-research and information from the literature, it can be concluded that the problem of orthorexia is still a new issue at the level of social sciences, medical sciences, and health sciences. The mechanisms of development of these disorders are still poorly understood, and any data resulting from the study are only a signpost for further scientific investigation. The psychometric tools used in this study allowed us to demonstrate the prevalence of the aforementioned eating disorders in the studied groups of students, along with their determinants, and the photo album of selected foods and dishes allowed us to estimate the respondents' skills in estimating the weight and energy of food intake. Nevertheless, it is worth noting that the methods used were characterized by certain limitations; therefore, in the future, it would be worth extending the scope of the study to other social groups, a wider range of research tools, and deepening the existing ones.

The conducted research allowed us to verify the set research hypotheses and to formulate the following conclusions: orthorexic behaviors are characteristic of a group of people who are associated with health, and who take care of their lifestyle, diet, and participate in physical activity. Individuals who are at increased risk for orthorexia tend to overestimate the size and caloric content of their meals. The dietary condition discussed in the paper should be under constant control, and all health promotion activities should focus on the nutrition education and psychoeducation of young adults.

In conclusion, it should be noted that a necessary aspect of further research on the topic of nonspecific eating disorders should be the development of actions aimed at the prevention of eating disorder development in young adults. This can be achieved by including mental problems of nutritional nature in the National Health Program and the National Mental Health Program, or by planning and implementing nationwide health policy programs that include such tasks as comprehensive health education, with particular emphasis on nutritional education and psychoeducation from the early school years. It should be noted that eating disorders are an important multidisciplinary problem. Apart from the well-known disorders, the less-known ones, whose background may be found in the trend for a "fit life", are more frequently discussed. Therefore, the mentioned education should be provided not only by psychologists, but also by pedagogues, dieticians, personal trainers, and representatives of other professions connected with the problem.

**Author Contributions:** Conceptualization, M.G.; methodology, M.G., K.S.-N. and E.M.; validation, K.S-N.; formal analysis, M.G.; investigation, M.G.; resources, M.G. and K.K.-K.; data curation, M.G.; writing—original draft preparation, M.G.; writing—review and editing, M.G. and K.K.-K.; visualization, K.K.-K.; supervision, K.S.-N. and J.K.; project administration, E.M., M.G. and K.K.-K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

**Acknowledgments:** The authors would like to thank all the participants in the study.

**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

#### **References**


### *Article* **Weekend–Weekday Differences in Adherence to the Mediterranean Diet among Spanish University Students**

**Luis M. Béjar**

Department of Preventive Medicine and Public Health, School of Medicine, University of Seville, 41004 Seville, Spain; lmbprado@us.es; Tel.: +34-954-551-771

**Abstract:** Daily routines may influence eating patterns; however, differences in intake on weekdays and at weekends have rarely been explored. Furthermore, these differences have not been analyzed among university students (a particularly interesting group among the younger generations). The aim of the study was to evaluate weekend–weekday variation in the Mediterranean diet among Spanish university students, while investigating the potential influence of age, gender, studies, body mass index, smoking status and physical activity status. A repeated-measurement 28-day cross-sectional observational study with self-reported dietary intake collected using the e12HR app was conducted. There were 361 participants: average age 20.6 years; 72.9% women; 58.2% students of Pharmacy; average BMI 21.9 kg/m2; 91.4% nonsmokers; 77.6% performed <sup>≥</sup>150 min/week of physical activity. Outcome measurements were adherence to the Mediterranean diet (AMD) index and percentage of participants meeting recommendations for each food group on weekdays and at weekends. In all subgroups, Spanish university students' global diet was associated with low AMD, with poorer diet quality (>12% reductions in mean scores of AMD index and >26% reductions in adequate adherence scores (≥9)) at weekends. In conclusion, weekend health behaviors of Spanish university students displayed less favorable eating behavior, making the weekend an important target for public health interventions aiming to improve dietary intake.

**Keywords:** Mediterranean diet; food; feeding behavior; nutrition assessment; university; smartphone; mobile applications; information technology

#### **1. Introduction**

In 2004, the World Health Organization (WHO) adopted the Global Strategy on Diet, Physical Activity and Health, the overall goal of which was to promote and protect health through diet and physical activity [1]. Since then, improving diet quality has been essential for health promotion strategy.

Within the international debate on changing diets and food systems to more adequate ones, the Mediterranean diet (MD) is potentially the best diet based on evidence as it is at once healthy and sustainable. The MD combines: 1. Important health benefits: a growing body of evidence supports the protective role of the MD, in terms of primary and secondary prevention, against cardiovascular disease, arteriosclerosis, cancer, diabetes mellitus, metabolic syndrome, excess weight/obesity, respiratory disease (asthma and sleep apnea), mental disorders (cognitive decline and depression) and renal disease [2–6]. Non-nutritional aspects, linked in one way or another to food consumption, have been suggested to contribute to the beneficial effect of the MD; these include, among others, physical activity [7]; and 2. Sustainability [6,8–13]: an aspect which has been under debate in recent years regarding dietary patterns is that they should not only be beneficial for all people but also for the environment, for all countries and, as such, for the planet [6,8]. The MD does this as it has a low environmental impact and richness of biodiversity, a high sociocultural value is placed on the foods, and it provides positive local economic benefits [6].

**Citation:** Béjar, L.M. Weekend–Weekday Differences in Adherence to the Mediterranean Diet among Spanish University Students. *Nutrients* **2022**, *14*, 2811. https:// doi.org/10.3390/nu14142811

Academic Editors: Roberto Pippi, Carmine G. Fanelli and Matteo Vandoni

Received: 16 June 2022 Accepted: 6 July 2022 Published: 8 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The MD (with its health benefits and sustainability) is faced with great difficulties in its implementation in other geographic and cultural regions and, paradoxically, it is even struggling to stay alive in traditionally Mediterranean regions (influenced by unhealthy dietary habits as a result of global acculturation) [4] where it is being abandoned, mainly by younger generations [12].

People's day to day lives are punctuated by work, education, domestic chores, sleep and food. Alterations in daily patterns contribute significantly to changes in dietary patterns [14]. Characterization of food consumption across the days of the week can help target health promotion initiatives that are striving to improve dietary intake of the population [15]. However, while much research has focused on the identification of dietary patterns, less attention has been paid to temporal variation of dietary intake [15].

A limited number of studies have analyzed weekly variation dietary patterns in diverse populations (e.g., children/adolescents [15–31] and adults [15,29–37]) residing in different countries (e.g., United States [16–19,26,29,32–34,36,37], Denmark [15,20,21], Australia [25,28], Canada [31,35], Brazil [30], New Zealand [22], France [24], Korea [27], Belgium, Cyprus, Estonia, Germany, Hungary, Italy, Spain and Sweden (multicentric study) [23]).

In general, findings have previously reported less healthful dietary intakes at weekends in comparison to those at weekdays. Compared with weekdays, weekend days have shown higher energy intake [15,20,29,30,32–35], higher percentage of energy from fat [16,17,29,30,32,34], higher consumption of added sugar [15,23], alcohol [15,32,34,35], discretionary foods [15,25,32], sugar-sweetened beverages [15,20–22,30,32], sweets [20], chocolate [20], white bread [20], and hot chips [22,30], lower consumption of dietary fiber [15,32], vegetables [15,17,20,21,24,32,34], fruit [15–17,20,21,24,32,34], milk/yogurt [24,34] and wholegrain products [15,20,24,34], a consistent pattern of more frequent consumption and larger portions of unhealthy foods and beverages [19], and an increase in the prevalence of fast-food and full-service restaurant consumption [32]. Additionally, some studies have provided lower dietary quality scores on weekends than on weekdays, such as the Dietary Guidelines Index for Children and Adolescents score [25] and the Healthy Eating Index [34,35]. However, the nature of weekday–weekend variation in dietary intake among university students (a particularly interesting group among the younger generations) has remained unknown.

The university phase may be the first phase of life when most teenagers start making their own food choices. For this reason, universities may provide an ideal forum for reaching out to many young adults through nutrition education programs that may positively influence students' eating habits [38]. Research on the timing of dietary behaviors is essential for understanding the complexity of dietary patterns in this novel group and necessary to inform nutrition-related health policies and recommendations, which, regarding the MD, could help reverse the abandonment observed especially in the younger generations.

Therefore, to our knowledge, this paper serves as the first attempt to evaluate weekend– weekday differences in the MD among Spanish university students through 28 days of dietary recording, while investigating the potential influence of age, gender, studies, body mass index (BMI), smoking status and physical activity status as determinants for weekly variation. We hypothesized that individuals would display a less healthy diet during the weekends compared with that of the weekdays and furthermore that these differences were heterogeneous across population subgroups under examination.

#### **2. Materials and Methods**

#### *2.1. Participant Recruitment*

A repeated-measurement 28-day cross-sectional observational study was conducted. The recruitment of participants took place in April 2022.

The study took place with students at the Faculties of Medicine and Pharmacy at the University of Seville (Andalusia, Spain, South of Europe), selecting 4 random classrooms in each school.

A member of the research team presented the project to the students providing information on:


After receiving an e-mail from the interested students, a member of the research team responded with another e-mail that contained the following documents and information:


This procedure was implemented to promote participation in the study, avoiding unnecessary travel in order to sign or fill in documents, as well as to avoid the unnecessary use of paper and to promote conservation.

Participation in the study was incentivized with a raffle for school materials (valued at 500 euros) among the participants who completed the study.

#### *2.2. e-12HR App*

e-12HR is a previously validated application that allows for long-term collection of data on dietary intake of food groups [39–43].

After downloading, the first time that a participant used the e-12HR mobile application, they had to activate it by introducing a personally assigned alphanumeric code. After this step, the participants registered the number of standard portions consumed during the day for each of the 18 food groups included in the study: fruits, vegetables, breakfast cereals, pasta, rice, bread, olive oil, milk and dairy products, nuts, fermented beverages (wine and beer), potatoes, legumes, eggs, fish, white meat, red meat and sweets. The application also allowed users to register the number of minutes of moderate and intense physical activity performed throughout the day.

The participants were instructed to use the app after finishing their last meal of the day [44,45]. The app could only be completed between 8PM and 4AM, a time range that might seem strange at first glance but was chosen to provide users with sufficient time to complete the task. Taking into account that the university students, young adults, who made up the sample often go out in the evening and eat and drink until late at night, this time period allowed users to also register those foods/drinks in the application.

At the end of each day of monitoring, an alert appeared on the mobile phone of the participant to let them know it was time to use the app (each participant was allowed to establish the time for the alert according to their own preference). From that moment on, the participant could access the task and register the number of standard servings consumed throughout the day for each of the previously mentioned food groups, and the number of minutes of physical activity.

In order to assist in estimating the number of standard servings consumed, each food group was accompanied by an explanatory text with different homemade measurements (as the research team considered that it would be more appropriate and easier to follow for people without experience in dietetics). The standard servings used by e-12HR are based on a semiquantitative food frequency questionnaire (FFQ) previously validated for the Spanish population [46]. For example, when using the app, participants would see the following: How many servings of fruits (orange, apple, pear, peach, strawberry, watermelon, etc., including fresh juice) have you consumed today? One serving = 150–200 g. Homemade measures: 1 serving = A medium-sized piece of apple, pear or orange, a cup of cherries or strawberries, two slices of melon, a glass of natural juice. Participants would introduce the corresponding number in the "Answer" section and then they would tap the "Next" button to continue on to the following food group. The app also allowed participants to use decimals to better estimate the number of portions consumed. If an error occurred when registering information, participants could return to the previous page by tapping the "Previous" button, and they could begin the process again (Figure 1).

**Figure 1.** Screenshots from the e-12HR app.

To make the app easier to use, the list of food groups appeared in the same order every day (Appendix A, Table A1) and each food group was accompanied by a representative image.

After completing the daily questionnaire on the app, the collected information was automatically sent to the website of the study administrators, so the participants could not change their previous responses nor access the application until the following day when they would complete the next questionnaire.

Registration of dietary data using the e-12HR app was scheduled for twenty-eight consecutive days. Participants could know if they had completed the study period as the application presented a counter with the number of days the task was successfully completed.

#### *2.3. Usability Rating Questionnaire for e-12HR*

Finally, at the end of the 28-day study period, a member of the research team sent an e-mail to each participant that contained a usability rating questionnaire [43,47–49] for the e-12HR app, comprised of five questions about the completion the daily e-12HR task (Appendix A, Table A2).

Each participant was required to complete and return this usability rating questionnaire to the same e-mail address.

#### *2.4. Adherence to Mediterranean Diet (AMD) Assessment*

To calculate the adherence to Mediterranean diet (AMD) index, it took into account previously established rules [50] which consider:


However, it was necessary to make some modifications in order to adapt them to the characteristics of e-12HR (Table 1).


**Table 1.** Mediterranean diet Serving Score for e-12HR.

Cereals: breakfast cereals, pasta, rice and bread. Olive oil: used on salads or bread or for frying. Milk and dairy products: milk, yogurt and cheese. Fermented beverages: wine and beer. White meat: poultry. Red meat: pork, beef and lamb. Sweets: sugar, candies, pastries, sweetened fruit juices and soft drinks.

For food groups that have scores greater than 1, the scoring rules are as follows [51]:


For each food group, if the indicated recommendations were not followed, a value of zero was assigned for that group.

Scorings for the AMD index were calculated manually by the research team (using the data sent by the application to the study website and the rules shown in Table 1).

The information from each participant was analyzed considering three time periods: 1. Total monitoring period; 2. Weekdays (Monday–Thursday); and 3. Weekend days (Friday–Sunday and holidays).

For each period of time, for each individual food group, the average daily number of standard portions registered throughout the period in question was first added all together, and the result was then divided by the number of days for which the task was completed during that same period. For each period of time, for food groups which used the weekly averages for the number of standard servings, as opposed to daily, the result from the previous operation was multiplied by 7.

Obvious errors produced during data entry were modified (as it was considered that the data must have been introduced as grams or milliliters instead of standard servings). For example, on one occasion, a value of 150 was introduced for the question "How many servings of vegetables (tomato, carrot, bell pepper, lettuce, zucchini, etc.) have you consumed today?". The research team considered that this value indicated a consumption of 150 g, which is the equivalent of one serving. In any case, the data were modified by the research team on only 1080 occasions out of a total of 196,452 registered data points (0.55%).

To complete the process, all of the values were added up, and scoring of the AMD index was generated, which could vary between zero and twenty-four.

On top of this, the score on the AMD index was related with one of three levels of AMD [51]: low (0–8 points), moderate (9–15 points) or high (16–24 points).

#### *2.5. Statistical Analysis*

Discrete variables are presented as a number followed by percentages. Continuous variables are presented using means and standard deviations and median and interquartile range (IQR).

The data rwee tested for normality using the nonparametric Kolmogorov–Smirnov test.

For unpaired samples, Students *t*-test or the nonparametric Mann–Whitney U-test were used for the analysis of quantitative variables, and the chi-square test was used for the comparison of proportions.

For paired samples, Students *t*-test or the nonparametric Wilcoxon test were used for the analysis of quantitative variables, and McNemar's test was used for the comparison of proportions.

The results were considered significant if *p*-value < 0.05.

Statistical analyses were performed using the SPSS statistical software package version 26.0 (SPSS Inc., Chicago, IL, USA).

#### **3. Results**

#### *3.1. Database*

The informed consent forms were signed by 439 students who fulfilled the inclusion requirement for the study. Of them, 78 were considered as nonresponsive (as they completed the task on the app for less than 14 days); the data for these individuals was not included in the later statistical analysis. There were no significant statistical differences in the variables studied between the participants who completed the study and those who did not. The study response rate was 82.2% (361 of 439 students completed the task on the app for at least 14 days).

As has been mentioned previously, dietary data registration through the e-12HR app was scheduled for twenty-eight consecutive days. The majority of the responsive participants, 79.2%, completed the monitoring period and of note, more than a third of them, 39.6%, continued using the app for more than thirteen days on their own initiative. The vast majority of the participants completed the application at least seven days both on weekdays and weekend days. The information on the number of days for which the task was completed is shown in Table 2.


**Table 2.** Characteristics of study participants.

\* Not applicable.

The average age of the participants was 20.6 years old. The gender distribution of women/men was 72.9%/27.1%. More than half of them were students at the School of Pharmacy, 58.2%. The average BMI was 21.9 kg/m2. The majority of the participants were nonsmokers, 91.4%, and performed 150 min or more of moderate/intense physical activity each week, 77.6% (Table 2).

The participants who completed the study registered their daily consumption for the 18 food groups included in the study for 10,914 days taken together (representing a collected total of 196,452 data points on daily consumption for the food groups).

#### *3.2. Scores and Levels of the AMD Index*

Table 3 shows the scores and levels of the AMD index in the total monitoring period in the whole study sample and in different subgroups thereof (by age, gender, studies, BMI, smoking status and physical activity status). The mean score of the AMD index was 8.7 (SD = 2.9), by subgroups varying between 7.6 (<150 min/week) and 9.2 (males). There were statistically significant differences in the subgroups by gender, BMI and physical activity status: adherence scores were higher in subgroups of males (9.2), <25 kg/m2 (8.8) and ≥150 min/week (9.0), respectively. An adequate adherence score (≥9) was observed in 47.9% of the participants (although of them, only 1.9% (seven subjects) presented a high level (16–24 points)). By subgroups, the lowest percentage of an adequate AMD score was found among <150 min/week (30.9%), and the highest percentage among ≥150 min/week (52.9%). There was a statistically significant difference in the subgroups by physical activity status.

**Table 3.** Scores and levels of the AMD index (total monitoring period).


SD: standard deviation. IQR: interquartile range. \* Evaluated by the Mann–Whitney U-test. \*\* Evaluated by the chi-square test.

Tables 4 and 5 report the scores and levels of the AMD index on weekdays and weekend days. The differences were statistically significant, considering the scores and levels of the AMD index, in the whole study sample and in all subgroups thereof (except in smoking status yes for the level of the AMD index).

Compared with the weekday average, the Mediterranean diet Serving Score (MDSS) for the weekend average was 1.2 points lower (12.6% reduction). By subgroups, the reduction varied between 18.1% (smoking status yes) and 12.0% (smoking status no) (Table 4).

Compared with that of the weekdays, the adequate adherence score (≥9) for weekends was 17.8 percentage points lower (31.8% reduction). By subgroups, the reduction varied between 26.3% (≥20 years) and 41.2% (<150 min/week) (Table 5).


**Table 4.** Scores of the AMD index (weekdays–weekends).

SD: standard deviation. IQR: interquartile range. \* Evaluated by t-test. \*\* Evaluated by the Wilcoxon test.

**Table 5.** Levels of the AMD index (weekdays–weekends).


\* Evaluated by McNemar's test.

#### *3.3. Stratification of AMD Items (Food Groups)*

Table 6 shows the stratification of AMD items (food groups) in the total monitoring period. Fruits, vegetables, cereals, olive oil, milk and dairy products and nuts (food groups which contribute the highest scores to the MDSS index, Table 1) were consumed according to recommendations by less than 25% of the participants, in the whole study sample and in all subgroups thereof (except for cereals in males, milk and dairy products in smoking status yes and physical activity status ≥150 min/week). Other food groups that were also consumed according to the recommendations by less than 25% of the participants were red meat (in the whole study sample and in all subgroups thereof), sweets (in subgroups <20 years, females, BMI ≥25 kg/m2, smoking status yes and physical activity status <150 min/week) and legumes (in smoking status yes). There were statistically significant differences in the subgroups by gender (eight food groups), physical activity status (five food groups), age and smoking status (two food groups), studies and BMI (one food group).

**Table 6.** Stratification of adherence to MD items (food groups) (total monitoring period).



#### **Table 6.** *Cont.*

\* Statistically significant differences. Evaluated by the chi-square test.

Table 7 reports the stratification of AMD items (food groups) on the weekdays and weekend days. The consumption on weekends was similar to that on the weekdays in the whole study sample and in all subgroups thereof for the food groups which contribute the highest scores to the MDSS index (fruits, vegetables, cereals, olive oil and nuts (except for olive oil and nuts among student of Medicine)) and fish. A lower percentage of participants complied with the recommendations in consumption at weekends than on weekdays in sweets (in the whole study sample and in all subgroups), fermented beverages (in the whole study sample and 11 subgroups), potatoes and legumes (in the whole study sample and 10 subgroups), milk and dairy products (in the whole study sample and eight subgroups), red meat (in the whole study sample and six subgroups), eggs (in the whole study sample and five subgroups) and white meat (in the whole study sample and three subgroups).

**Table 7.** Stratification of adherence to MD items (food groups) (weekdays–weekends).




**Table 7.** *Cont.*

\* Statistically significant differences. Evaluated by McNemar's test.

*3.4. Usability Rating Questionnaire for e-12HR*

The usability rating questionnaire was answered by 174 participants. The responses of the users are shown in Table 8.

**Table 8.** Responses of the study participants to the usability rating questionnaire for the e-12HR app.


**Table 8.** *Cont.*


#### **4. Discussion**

To our knowledge, this study is the first to evaluate weekend–weekday differences in diet among university students (specifically, MD and Spanish university students at the Faculties of Medicine and Pharmacy). As we hypothesized, dietary intake in this sample was found to fluctuate during the week, with weekend days displaying lower dietary quality compared with that of weekdays; however, the differences were very similar across the population subgroups under examination.

A rationale for the existence of distinct, temporally conditioned behavioral patterns, as observed in this study, could be provided by the fact that human behavior during the weekdays is highly structured and generally dictated by time spent at work/in school [14], and many health promotion initiatives target the weekdays. A change in daily structures during the weekend is likely to have consequences for habitual behaviors such as eating [15].

Spanish university students are a particularly interesting group among the younger generations for various reasons. University students are one of the populations with a high risk of having unhealthy habits [52]; generally, young adults, especially university students, present an important challenge due to the coincidence of a series of emotional, physiological and environmental changes. They select their food, are very receptive to fashionable trends such as following slimming diets, skipping meals or consuming snacks, soft drinks and other new products [53], and leading an unhealthy lifestyle, and the risk that these habits are preserved during adulthood represents a great inconvenience for these individuals, especially for students in fields related to Health Sciences [54]. The promotion of healthy habits in the population at large, such as maintaining a good diet, is one of the primary tasks of these future professionals. Therefore, they must not only know the basics of these habits, but also practice them [55].

Spanish university students at the Faculties of Medicine and Pharmacy (this study sample) have compulsory and optional subjects related to nutrition and dietetics: for students of Medicine, the subjects Preventive Medicine and Public Health (compulsory) and Health Promotion (optional); for Pharmacy students, the subjects Public Health (compulsory), Nutrition and Bromatology (compulsory) and Nutrition, Dietetics and Dietotherapy (optional). This training in nutrition/dietetics may have had an influence on the dietary habits of the participants. For this reason, future research will focus on determining weekend–weekday differences in diet among other non-Health Science students (without training in health or nutrition) (see future research related to the current study section).

In general, in the present study, in the whole study sample and in all subgroups thereof (by age, gender, studies, BMI, smoking status and physical activity status): A. In the total monitoring period: Adherence to the MD (by mean scores of the AMD index and adequate adherence score (≥9)) was low (Table 3). This is similar to previous recent literature among Spanish university students (although using other AMD indexes) [56–59]. Among the categories, gender, BMI and physical activity status provided statistically significant differences (although physical activity status was the only one to show any statistically significant differences, in both the AMD index and adequate adherence score (≥9); gender and BMI only in the AMD index) (Table 3). The AMD index was higher in males compared to that in females, which was a finding that follows the trend shown

in these recent studies [58,59]. Also in line with one of these previous studies was that maintaining low levels of physical activity was associated with a diet of worse quality [56]. In contrast, in these recent papers among Spanish university students, the relationship of a low score of the AMD index with overweight/obesity could not be observed [56,58,59]. The food groups which were consumed according to recommendations by less than 25% of the participants were fruits, vegetables, cereals, olive oil, milk and dairy products, nuts, red meat and sweets (Table 6). B. During weekend days, compared with the weekdays: Poorer diet quality was observed. This study confirmed findings from previous research in children/adolescents [15–31] and adults (not specifically university students) [15,29–37]: substantial reductions in following dietary guidelines were observed, with >12% reductions in mean scores of the AMD index and >26% reductions in adequate adherence scores (≥9) (Tables 4 and 5). There was no percentage variation in consumption of fruits, vegetables, cereals, olive oil, nuts and fish (Table 7). There was a lower percentage of participants who complied with the recommendations in consumption of sweets, fermented beverages, potatoes, legumes, milk and dairy products, red meat, eggs and white meat (Table 7).

The Spanish university students' diet was associated with poor diet quality in general and, especially at weekends, in the whole study sample and in all subgroups thereof. Among Spanish university students, policy interventions and public health campaigns are warranted to promote healthy eating focused on all time periods, on food groups that were consumed according to recommendations by less than 25% of the participants (fruits, vegetables, cereals, olive oil, milk and dairy products, nuts, red meat and sweets). In these food groups, the percentage of university students that meets the recommendations was below 25% and, additionally, no differences were observed between the weekends and the weekdays. These food groups (except red meat and sweets) contribute the highest scores to the MDSS index (Table 1), so an improvement in their consumption would be associated with notable increases in the AMD index. At the same time, focus should be placed on the weekends and food groups with a lower percentage of participants which complied with the consumption recommendations (sweets, fermented beverages, potatoes, legumes, milk and dairy products, red meat, eggs and white meat). An improvement in their consumption at the weekends could be associated with increases in the global AMD index. In this sense, future interventions should be applied to all subgroups by age, gender, studies, BMI, smoking status and physical activity status (with very similar results across subgroups under examination).

Only 174 of 361 (48.2%) participants answered the usability rating questionnaire. Regarding this point, on the one hand, the recruitment of the participants took place in April 2022. On the other, the monitoring period was 28 days, and only at the end of these 28 days of study was the email sent to the participants (with the usability rating questionnaire). Taking into account the participant recruitment period (April 2022) and the duration of the study (28 days), the email was received by the participants from the beginning of May onwards, close to the second semester exams. This fact could have influenced the low response rate to the usability rating questionnaire, since the students were already focused on preparing for exams at that time.

The strengths of this study include, among others, the 28-day dietary intake data collection period for each participant. While those participants who completed the app task for at least 14 days were considered responders, the majority of the participants repeated the task for at least 28 days, (Table 2). The decision to use the 14-day limit was due to other studies that included repeated dietary intake data collections for 14 days or less (2 days [25,26,29,30,32,37], 3 days [17,24,28], 5 days [33], 7 days [15,16,20,21], 14 days [18]), except for a single study that included a larger number of repetitions, specifically 52 repetitions in total [34]. In order to determine habitual dietary intake (or average long-term consumption) using short-term tools, it is necessary to repeat these measures multiple times, and more repetitions provide a clearer picture of habitual dietary intake. This has been possible thanks to the use of e-12HR. This app is, basically, a modified 24 h recall (24HR) which is completed once a day during the study period and does not require making photographs of the food groups

consumed [39–43]. These characteristics result in a low workload for users of e-12HR and, at the same time, has allowed the research team to schedule a longer monitoring period. The majority of the participants in this study reported that the e-12HR app was easy and interesting to complete and contained understandable questions; that they would be willing to complete e-12HR app again; and that the task took 3 min or less per day to complete (Table 8); Another important strength of the study was the various subgroups that were analyzed (age, gender, studies, BMI, smoking status and physical activity status).

Nevertheless, a few limitations should be noted. e-12HR is a self-reporting method and, as a consequence, it presents the limitations inherent in this type of tool (amply described in [47,60–65]); among others, the dependence on the memory of each participant and, mainly, the difficulty of estimating the size of servings consumed. As far as the participant's memory is concerned, e-12HR reduces this disadvantage, as it only requires short-term memory, being completed at the end of each day. Indeed, the name e-12HR (electronic 12-Hour Dietary Recall) is a reference to how it differs from a 24HR. While 24HR registers consumption of food groups from the day before, e-12HR allows users to register their consumption during the same day (normally with a maximum period of time between consumption of the food and it being registered of 12 h). Regarding the difficulty of estimating the size of the servings consumed, we must take into account that AMD assessment does not require a precise estimation of the size of the foods consumed, but rather a precise recognition of the food groups consumed together with an approximate estimation of the servings [51]. In this sense, e-12HR allows for collecting data on consumption for all the food groups needed to calculate AMD. Newer alternative methods for determining dietary intake include audio signal processing, inertial sensing, image processing, non-intrusive near-infrared scanning and gesture recognition interfacing [66–69]. Some authors maintain that more research is needed to develop these and other tools, which are more objective and precise, and that resources should be invested to this end [65]. Until these alternatives are available and in spite of their limitations, digital technologies for self-reporting methods can, and must, be developed and utilized [39] as an improvement on traditional self-reporting methods, the progress of which constitutes one of the most important challenges to the field of nutritional epidemiology [48,70] in this day and age. The small number of individuals in some of the subgroups analyzed (e.g., smoking status yes (n = 31)) is another of the limitations of the study.

#### *Future Research Related to the Current Study*

Future research will focus on determining weekend–weekday differences in diet among Health Science students during nonschool periods as well as among other students (non-Health Science students). This will allow the research team to establish if the results observed in the present study are specific to Health Science students and to the school period or if, on the contrary, they are maintained in other periods (e.g., the summer holidays) and among other students (without training in health or nutrition).

#### **5. Conclusions**

This study has been carried out using the e-12HR application. This app is characterized by a low workload for users (allowing the research team to schedule a long monitoring period) as it only requires short-term memory use and allows for collecting data on consumption for all the food groups needed to calculate AMD. Preliminary evidence suggests that Spanish university students' diet was associated with poor diet quality in general and especially at the weekends in the whole study sample and in all subgroups thereof. These results could be used to improve public health campaigns to promote healthy eating among Spanish university students. In a practical way, the interventions could be focused, on the one hand, on all times periods, on food groups that were consumed according to recommendations by less than 25% of the participants (fruits, vegetables, cereals, olive oil, milk and dairy products, nuts, red meat and sweets), and on the other, on weekends, on food groups with a lower percentage of participants who complied with the consumption

recommendations (sweets, fermented beverages, potatoes, legumes, milk and dairy products, red meat, eggs and white meat). Finally, future interventions should be applied to all subgroups by age, gender, studies, BMI, smoking status and physical activity status (with very similar results).

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University of Seville, internal code 2813-N-21 (on 30 March 2022).


**Informed Consent Statement:** Written informed consent has been obtained from the patient(s) to publish this paper.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The author would like to thank the participants in this study.

**Conflicts of Interest:** The author declares no conflict of interests.

#### **Appendix A**

**Table A1.** Questionnaire used in e-12HR.


**Table A1.** *Cont.*


fast, competitive sports and games, etc.




**Table A2.** *Cont.*

**Table A3.** Mediterranean diet Serving Scores (MDSS).


Main meal: breakfast, lunch and dinner. Cereals: breakfast cereals, pasta, rice and bread. Olive oil: used on salads or bread or for frying. Milk and dairy products: milk, yogurt and cheese. Fermented beverages: wine and beer (one glass for females and two glasses for males). White meat: poultry. Red meat: pork, beef and lamb. Sweets: sugar, candies, pastries, sweetened fruit juices and soft drinks.

#### **References**


## *Review* **The Effectiveness of Wearable Devices in Non-Communicable Diseases to Manage Physical Activity and Nutrition: Where We Are?**

**Valentina Natalucci 1,\*, Federica Marmondi 2, Michele Biraghi <sup>3</sup> and Matteo Bonato 3,4**


**Abstract:** Wearable devices are increasingly popular in clinical and non-clinical populations as a tool for exercise prescription, monitoring of daily physical activity and nutrition, and health-related parameters management. In this regard, smart devices not only assist people in pursuing a healthier lifestyle, but also provide a constant stream of physiological and metabolic data for management of non-communicable diseases (NCDs). Although the benefits of lifestyle-based interventions (exercise and nutrition) for NCDs are well known, the potential of wearable devices to promote healthy behaviors in clinical populations is still controversial. In this narrative review, we aimed to discuss the current application of wearable devices in NCDs, highlighting their role in prescribing and monitoring daily physical activity and dietary habits in the population living with chronic diseases. None of the studies considered specifically addressed the efficacy of the use of wearable devices, and limited are those that incorporate monitoring of both physical activity and nutrition for NCDs. However, there is evidence that such devices have helped improve physical activity levels, physical fitness, body composition, and metabolic and psychological parameters. Therefore, the authors believe that the benefits obtained from the use of wearable devices are likely to translate to public health and represent one of the important tools for the development of prevention plans in everyday life and clinical practice for optimal patient management.

**Keywords:** physical activity; exercise; nutrition; wearable devices; lifestyle assessment; health; non-communicable diseases

#### **1. Introduction**

The major non-communicable diseases (NCDs), also known as chronic diseases, continue to become exceedingly ingrained in everyday life and represent the main killers in the modern era. Although chronic disease is not contagious, its long duration and progress slowly has a negative impact on the population's health, accounting for 74% of all deaths globally [1] and representing a high cost to healthcare systems worldwide [2]. The risk factors underlying NCDs include unhealthy behaviors, such as smoking, overeating, alcohol consumption, and sedentary lifestyle, all behaviors that are considered modifiable risk factors. The reversal of these bad health habits can be achieved through leading a healthier lifestyle, with the aim to extend the period of being free from NCDs (primary prevention), to improve managing the disease (secondary prevention), and increasing the quality of life in people with NCDs (tertiary prevention) [3]. To achieve increased adoption of healthy behaviors in the three levels of prevention, integrated action by all clinical and non-clinical stakeholders that looks beyond the traditional model of healthcare and is increasingly in step with the social context is needed.

**Citation:** Natalucci, V.; Marmondi, F.; Biraghi, M.; Bonato, M. The Effectiveness of Wearable Devices in Non-Communicable Diseases to Manage Physical Activity and Nutrition: Where We Are? *Nutrients* **2023**, *15*, 913. https://doi.org/ 10.3390/nu15040913

Academic Editor: Joerg Koenigstorfer

Received: 9 January 2023 Revised: 28 January 2023 Accepted: 7 February 2023 Published: 11 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

In the last 10 years, wearable technology has gained in popularity, both in the general population and in people with NCDs, for physical activity and health monitoring, representing nowadays one of the most popular trends in health and fitness [4]. It is well known that physical activity is associated with numerous health benefits, preventing the onset of non-communicable diseases, and resulting in a lower risk of mortality [5]. Recently, Strain et al. [6] showed that higher volumes of device-based physical activity energy expenditure was associated with reduced mortality rates. The public health relevance of such devices is increasing and may impact areas such as physical activity, wellbeing, cardiovascular health, mortality risk, and dietary habits [4]. In fact, commercial wearable devices have the potential to allow for population-level measurement of physical activity and large-scale behavior change. A common example is represented by the self-monitoring of physical activity in terms of step numbers and minutes of daily activity, as well as physiological parameters (e.g., heart rate) or dietary intake (e.g., energy expenditure), depending on the characteristics of the device. Some products also remind one to stop sedentary activities or introduce meals through notification messages [7]. All this data can be used by users to plan, organize, and monitor their daily physical activity or dietary habits, but can also provide immediate feedback that can eliminate the barriers between the patient and healthcare professional. Moreover, wearable technology can also aid researchers in planning study designs with the aim to use physical activity monitor devices, especially in people with NCDs [8].

In this regard, the actual wide use of wearable devices connected to the internet, social media, and databases, increases the use of real-world data in the medical and healthcare field to monitor patient health status [9]. In fact, new advances in technology for objective assessment of physical activity using wearable devices in research enable testing the efficacy of interventions and give the opportunity to prescribe and monitor patients' home-based physical activity and its effect on health. As technology advances, these devices took the top spot in the American College of Sports Medicine (ACSM) list in 2017, 2019, 2020, and 2022, with nutrition devices also growing in popularity over the past 3 years [10–12].

Although the use of wearable devices has consistently entered society, currently there is a lack of sufficient evidence and data on how wearable devices can be implemented in the NCDs population in both the clinical and non-clinical setting. Therefore, the aim of this narrative review is to discuss the current application of wearable devices in NCDs, highlighting their role in prescribing and monitoring daily physical activity and dietary habits in the population living with chronic diseases. In the following sections, we will first provide an overview of the relationships between NCDs and major, modifiable, behavioral risk factors, such as exercise and nutrition. We will examine the utility of wearable devices for prescribing and monitoring modifiable lifestyle behaviors (exercise and nutrition) and provide an overview of randomized-controlled studies that have included the use of wearable devices for prescribing and monitoring exercise and nutrition habits in the population living with NCDs. We will end with a discussion of the effectiveness and potential role of emerging technologies for prescribing and monitoring daily physical activity and dietary habits, in addition to discussion of the barriers to the integration of the technology into the population and into research and clinical settings.

#### **2. Relationships between Non-Communicable Diseases and Modifiable Behavioral Risk Factors**

#### *2.1. Sedentary Risks or Unhealthy Physical Activity Levels*

In clinical and non-clinical populations, insufficient physical activity levels in all stages of life are a well-documented public health issue [13]. Between 2001 and 2016, the levels of insufficient physical activity decreased marginally globally in high-income countries, which reported a 5% increase in the prevalence of physical inactivity [14]. Moreover, during the COVID-19 pandemic, due to contagion reduction policies based on home confinement and social distancing, a reduction of 33% from 108 to 72 min per week and an increment of 28% of time spent sitting from 5 to 8 h per day was observed in different chronic diseases [15–18]. High levels of sedentary behavior have a negative impact on the human body at the level of the muscular, cardiovascular, metabolic, endocrine, and nervous systems [19], resulting in an increased risk of developing a variety of chronic diseases, including cardiovascular diseases, type 2 diabetes, cancers, and mental illness [20]. To date, people with low levels of physical activity use significantly more healthcare services than active people [21]. The novel aspect to counteract the extraordinary burden of insufficient physical activity and improve understanding of its negative effects lies in the application and monitoring of physical activity components in a new human evolutionary context different from that of our ancestors (hunter-gatherers), who were forced to be physically active to survive.

Physical activity is defined as "any bodily movement produced by the contraction of skeletal muscles that results in energy expenditure" [22]. In this regard, growing evidence suggests a clear dose–response relationship between the volume of physical activity and the difference in mortality rates for chronic disease [23]. The updated current adults physical activity guidelines of the World Health Organization (WHO) recommend undertaking 150–300 min of moderate intensity or 75–150 min of vigorous intensity aerobic physical activity, or some equivalent combination of moderate-intensity and vigorous-intensity aerobic physical activity per week [24]. The same guidelines suggest that adults should also perform moderate- or higher-intensity muscle-strengthening activities involving all major muscle groups 2 or more days a week. In addition, an increase in attention is also directed to daily steps as an important part of preventing chronic disease [25–28]. Even if the guidelines refer to the general healthy population for primary prevention, different thresholds have been recommended for secondary and tertiary prevention for different chronic conditions considering the several components (i.e., the FITT principle [frequency, intensity, time, and type]) of physical activity. There is currently a lack of knowledge regarding the minimum amount of exercise needed to prevent and mitigate different chronic diseases, but promising evidence exists on how individual components of FITT affect the development and progression of chronic diseases. Today, we can talk about 'exercise medicine', in which the response to exercise is specific and predictable for optimal adjuvant treatment of over 30 chronic diseases [29]. The expanded understanding of exercise physiology and molecular biology has provided strong evidence on how different components and combinations of exercise (i.e., frequency, intensity, time, and type of exercise) may play a critical role in the persistent, low-grade inflammation that characterizes chronic diseases [30–34]. Evidence from epidemiological studies and clinical research demonstrates that the exercise component also exerts its effects on mental diseases. As with other chronic diseases with an impact on physical function, mental diseases include symptoms that, while they are different for everyone, can benefit from exercise [35–37].

Although the link between exercise and signaling pathways at the cellular and molecular levels can induce beneficial effects on physical and mental health, more than 80% of people do not meet physical activity guidelines [38].

#### *2.2. Dietary Risks or Unhealthy Foods Consumption*

When macronutrients are consumed in appropriate proportions to support energetic and physiologic needs without excess intake, while also providing sufficient micronutrients and hydration to meet the physiologic needs of the body, it is called a 'healthy diet' [39]. However, over the past 40 years, population diets have shifted toward a greater consumption of processed and ultra-processed foods that are low in nutrients and high in energy [40], with major public health consequences [41].

Unhealthy diet patterns, including the intake of foods high in saturated fat, refined carbohydrates, and excess sodium, coupled with lower physical activity levels, have resulted in higher prevalence rates of obesity, cardiovascular disease, type 2 diabetes, sitespecific cancers, and respiratory diseases [42–47]. According to data from the NCD Risk Factor Collaboration and the Global Burden of Disease Study, NCDs, including those listed above, are responsible for almost 70% of all deaths worldwide, while changes in eating habits play a crucial role in preventing up to 80% of major NCDs [48].

In the modern era, unhealthy nutritional behaviors continue to be recorded in people with and without NCDs. These behaviors are not only limited to extreme social conditions, such as the latest COVID-19 pandemic [49]. Indeed, there is compelling evidence that diet can be influenced by many social and economic factors that evolve over time and interact in a complex manner (e.g., income, food prices, individual preferences and beliefs, cultural traditional, geographical, and environmental aspects). While there is no clear model that can pinpoint the people who adopt an unhealthy diet, on the other hand, the effects that the high consumption of trans-fatty acids has on the deterioration of health conditions are well document. The first consequence is low-grade systemic inflammation, which is positively correlated with type 2 diabetes, cardiovascular disease, cancer, and premature death [50,51]. In addition, particular attention is also paid to the consequences of intra-abdominal fat accumulation as an important risk factor for the promotion of insulin resistance, which can lead to glucose intolerance, elevated triglycerides, and low high-density lipoprotein, as well as hypertension [52–54].

With the aim to counter global unhealthy diets and to prevent a range of NCDs, WHO has developed evidence-based guidelines [55]. As with the exercise guidelines, the exact composition of a healthy diet also depends on individual characteristics (e.g., age, gender, lifestyle, and level of physical activity), cultural context, locally available foods, and dietary customs. Following a healthy diet could be easier in those countries where certain nutritional models, such as the Mediterranean diet (i.e., Euro-Mediterranean countries), are rooted. Paradoxically, that is not the case, and there is compelling evidence that most people from industrialized countries do not meet the current dietary recommendations [56].

#### **3. The Utility of Wearable Devices on Lifestyle Behaviors**

The use of smart technology has a dual role in the clinical and non-clinical context (healthcare and social). Particularly, it represents an important prevention tool, characterized by the possibility of the individual to measure and monitor health parameters because of his lifestyle-related behaviors. Furthermore, it also represents an important continuous monitoring tool, through which clinicians and non-clinician professionals can personalize the exercise prescription and nutritional intake of the individual living with or without NCDs.

#### *3.1. Wearable Devices for Prescription and Monitoring Physical Activity*

To counteract sedentary behavior, defined as an insufficient physical activity level, according to WHO recommendations, is physical activity. Humans' evolutionary history suggests that today, people must be active, but paradoxically find themselves in an increasingly industrialized context, forced to be sedentary [57]. In fact, the effects of the competing demands of activity and inactivity on the body represent an evolutionary paradox that could be countered by the evolutionary thrust of technology.

Advances in 21st century technology have introduced the use of commercial off-theshelf activity trackers that allow users to self-monitor their daily physical activity [58]. These devices allow objectively monitoring physical activity using several consumer-based activity trackers equipped with different sensors, including accelerometers, heart rate (HR), Global Positioning System (GPS) technology, gyroscope, barometer and altimeters, and algorithms built into smartphones or wearable technology (e.g., bracelets, bands, rings, etc.), which determine a device output, such as step-count, distance traveled, energy expenditure, activity intensity, and HR [59]. This need arises from the consequences that self-reported questionnaires and diaries suffer from participants' biases as self-report measures, such as social desirability or imprecise recall [60]. According to Chan et al. [61], wearable devices represent a promising intervention tool for population-wide physical activity promotion, providing real remote monitoring of users' physical fitness. In particular, activity trackers offer a significant source of personalized physical activity data from users that would provide insights into healthcare analytics and user feedback on health status, assisting clinicians and experts in the field (e.g., sport scientists) in providing a more holistic care [62].

#### *3.2. Wearable Devices for Prescription and Monitoring Dietary Intake*

As with exercise, wearable technology, websites, and smartphone applications could estimate the nutritional intake of individuals. In the modern era, the self-reported method using smartphones has discouraged the use of conventional paper-based self-assessment methods both for more practical reasons and for reasons of data accuracy [63–65]. The network capacity of a smartphone also allows this modern approach to have more features, such as a response from clinicians and real-time self-monitoring [66]. The new systems allow participants to record entire days of meals and to monitor their intake of kilocalories and macronutrients. In addition, depending on the applications, there are also other features that allow the consumer to set goals, plans meals, or even track micronutrients.

Over time, research has also demonstrated that the timing and frequency of selfmonitoring dietary intake are significantly related to weight outcomes [67]. These findings related to the importance of the frequency of self-monitoring within each day, as well as the consistency throughout a month, amplify the importance of increasingly accessible and practical tools that enable the development of very specific self-monitoring dietary goals.

#### *3.3. Wearable Devices in Non-Communicable Diseases: Where We Are*

In the last decade, researchers have begun to take advantage of this wearable technology by incorporating these devices in their research studies.

In this regard, here we have reviewed all the longitudinal RCTs from 2016 to 2022 that used wearable devices, focusing on the outcomes that could be relevant for improving physical activity and health-related outcomes in people with NCDs (Table 1). To date, 23 RCTs have been published [68–90]. The NCDs studied were cancer (31%), obesity (26%), cardiovascular diseases (22%), metabolic syndrome (13%), diabetes, and COPD (4%) (Figure 1A). Study protocols had a mean duration of 22 weeks, ranging from 2 to 96 weeks. Participants were a mean of 59 years old, ranging from 31 to 72 years old. The physical activity protocols performed during the RCTs were mainly aerobic (87%), followed by the combination of aerobic and resistance training (13%). None of the RCTs analyzed used a resistance training protocol alone (Figure 1B). The wearable devices used during the studies were Fitbit (44%), Garmin (13%), Polar (9%), and others (30%) (Figure 1C). Drop-out rates during the studies were a mean of 11%, ranging from 0% to 60%. Analyzing the RCTs, all the physical activity protocols showed an increase of physical activity levels with an increase of self-reported physical activity, daily steps, moderate to vigorous physical activity, and motivation to perform physical exercise, with a general reduction of sedentary time. This increase in physical activity levels improved cardiorespiratory fitness, with a reduction of the parameters associated with body composition (body weight, body mass index, fat mass, and waist circumference) and metabolic parameters (glycated hemoglobin and systolic and diastolic blood pressure), and an improvement in psychological parameters (anxiety and depression scores). Regarding nutrition, only eight studies considered this aspect. Particularly, 3 studies monitored diet during the experimental protocol using a 3-day [77] or 7-day food diary [71,79]. Three studies provided nutritional counselling for self-monitoring food intake, planning specific educational sessions with weekly and monthly phone recall [69,82,87]. One study assessed with a specific questionnaire the adherence to the Mediterranean diet [88]. Finally, only one study prescribed a calorierestricted diet. To date, this was linked with an improvement of body composition and metabolic parameters in obese postmenopausal women [86].

















Age values are presented as mean ± standard deviation, unless otherwise noted. \* Range reported, rather than standard deviation. Abbreviation: RCT = randomized controlled trial; HR = heart rate; BMI = body mass index; COPD = chronic obstructive pulmonary disease; ↔ = no changes; ↓ = decrease; ↑ = increase.

**Figure 1.** Pie charts of non-communicable diseases (**A**), type of physical activity (**B**), and wearable devices (**C**) used in the RCTs from 2016 to 2022.

#### **4. Discussion**

This review analyzed 23 RCTs in a narrative manner to highlight the role of wearable devices in prescribing and monitoring daily physical activity and dietary habits in people with NCDs. In particular, it emerged that the prevalent use of wearable devices is aimed at the prescription and monitoring of the physical activity protocol to increase physical activity levels. Wearable devices have been used to prescribe and monitor different types of exercise. As reported in Figure 1B, aerobic exercise was prescribed the most (87%) [69–76,78–81,83– 85,87–90], followed by combined exercise (13%) (i.e., aerobic and resistance) [68,77,82,86]. However, it is well documented in the literature that resistance exercise alone can bring about significant health changes in numerous chronic conditions [91]. This lack, which we find in the studies analyzed, could depend on the current low propensity of wearable devices to monitor functional parameters, such as the muscle strength of the lower and upper limbs. In fact, although by apps it is possible to prescribe resistance exercises to the subject, monitoring is poor due to the lack of accurate wearable devices. To date, the main specific metrics that can be derived from exercise monitoring wearables are shifted towards physiological parameters, such as the number of steps, distance traveled, and cardiometabolic parameters (e.g., HR, energy expenditure, maximum oxygen consumption, oxygen saturation, and blood pressure).

In pursuing physical activity with health implications, tracking daily step count is an invaluable component. The Fitbit was the most used wearable technology in the studies analyzed (Figure 1C). However, as reported in a 2020 systemic review [8], wearable devices are accurate for measuring step count in the laboratory, but exhibit a wider range of inaccuracy in free-living environments. In addition, another major problem is the estimation of the number of steps: some devices overestimate, while others underestimate, and such variability in terms of reliability exists intra-device (i.e., the step count may differ not only within the same company, but also within the same device).

These issues were also found in standard measurements of maximum oxygen consumption (*V*O2max). Though *V*O2 estimation has become more common in wearable devices, the validity and reliability among wearable devices measuring this parameter is still controversial, and none of the popular smartwatches has been well validated [4,92]. If we consider that *V*O2max has proven to be a powerful indicator of health and has recently been proposed as a clinical vital sign by the American Heart Association [93], it is disheartening to note that among the studies analyzed, this parameter was only considered in one study [90]. In fact, the major outcomes of the studies analyzed were: (i) number of daily or weekly steps [70,72,74,75,77,78,83–85,87,89]; (ii) physical activity levels in terms of intensity increments (e.g., from moderate to vigorous), total minutes of daily activity, reduction in sedentary time, and daily walking time [68,69,71,73,76,77,80,81,87,88]. In two studies, the outcomes were also measured subjectively through questionnaires for daily

physical activity levels: the Freiburger Physical Activity Questionnaire and work ability index [71] and the Walking Impairment Questionnaire [86].

Less commonly, clinical outcomes, such as blood pressure [77], metabolic syndrome [79], and physical function [82,83], or psychological parameters, such as anxiety and depression [88] and exercise motivation [81], were explored.

The selection of suitable activity trackers in populations living with NCDs should be able to offer patients and healthcare professionals the reading of the main outcomes of interest that should be monitored for health purposes, without excluding anyone. Therefore, collaboration with activity tracker companies to overcome existing discrepancies remains crucial and should focus on the accuracy and validity of the device. In fact, the accuracy in the monitoring of the different parameters needs to be more precise to collect the physiological and activity outcomes. While wearable activity trackers offer considerable promise for helping people's lifestyle behaviors, there are numerous barriers researchers and users find when using them. First, prolonged use of activity trackers may cause study participants to experience behavioral problems, such as health status monitoring-induced anxiety. Secondly, breakage or loss of the device and technical difficulties with the accompanying device/software were noted as other barriers [94]. Consequently, these barriers may negatively impact study participants' adherence to wearing trackers [95]. Another important barrier is also represented by the reading of the results themselves. In fact, understanding the results could be an incentive for wearable device users, while, as highlighted in a survey, understanding the data is often unknown. Device users find themselves asking for the help of a physician or health coach to guide them in understanding the data of their wearable technology to make lifestyle changes. Moreover, they are often willing to pay for this service, as well [4]. In the field of exercise, an expert in sport sciences with a master's degree in Preventive and Adapted Physical Activity is essential for the correct prescription and correct monitoring of physical activity in population living with NCDs. His role lies in being able to accompany the subject with chronic disease step by step in achieving his health goals by managing the continuous comparison and readjustment of the exercise proposals. Furthermore, it can represent valid support both for healthcare professionals (medical specialists) and for the patient in reading the objective data derived from wearable devices.

Regarding nutrition, only eight studies [69,71,77,79,82,86–88] considered and analyzed nutritional aspects in their trials. Unlike exercise prescription and monitoring, nutritional aspects were predominantly delivered through face-to-face or remote (e.g., by telephone) educational counselling and monitored through a food diary analyzed and reviewed by dietitians. The lack of precision control for eating habits certainly represents a bias in reading the results. In fact, poor monitoring of nutritional aspects can influence the observation of possible improvements in physical fitness, body composition, and metabolic parameters. In this regard, articles that have considered nutritional aspects, in addition to physical activity, have stated that this combination can lead to greater health outcomes than those of exercise alone or nutrition alone. Therefore, accurate and objective monitoring of both lifestyle aspects is desirable for the development of appropriate health promotion interventions.

To date, evidence indicates that wearable devices have not yet reached such a level of development to effectively help researchers or clinicians in monitoring nutrition and, consequently, caloric intake. For both exercise and nutritional aspects, collaboration and regular feedback from exercise specialists and dietitians with other health professionals and device users is still critical to accurately monitoring lifestyle behaviors. For this reason, an integration between health and non-health professionals should be encouraged to limit this shortage and promote a healthy lifestyle in people living with chronic diseases.

#### **5. Current Lifestyle Models and Future Directions**

Despite the benefits of a healthy lifestyle based on correct physical activity levels and healthy foods being well documented, an unhealthy lifestyle is rapidly becoming a major global concern, with health, economic, environmental, and social consequences [96]. Nowadays, any lifestyle intervention should consider physical activity and nutrition as two parallel lines of intervention. The first intervention on physical activity has a twofold objective: the first one aims to reduce sedentary behavior, with daily strategies for carrying out physical activity (e.g., using the stairs instead of the lift, parking away from the workplace, etc.); and the second aims to introduce a structured exercise program into daily life with specific goals [28]. Similarly, the intervention on nutrition also has a twofold objective: the first aims to introduce healthy foods into everyday life; and the second aims to introduce a nutritional program with specific objectives in the presence of NCDs.

The emerging literature contains many examples of interventions, in which physical activity and nutrition are part of the same model, some of which are applied to populations living with NCDs, and the results demonstrate that this can be a model to scale up [97–99]. The success and feasibility of introducing this new model of care, which complements standard care, requires the implementation of specific action plans that may differ from country to country. This may depend on who is authorized to prescribe the exercise or diet, but also on the health system of the country where the model is to be implemented. In this regard, to be effective, this model needs to overcome the barriers of each country and become integrated into a delivery system; this is often guaranteed by the involvement of different health professionals (general practitioners, specialist doctors, dietitians, exercise specialists, and psychologists), who create the so-called multidisciplinary team, in which technology also plays an important role. However, in many countries, benefits stop outside research programs.

To bypass this problem, healthcare institutions and policy makers should make it easier to use wearable devices, which are often very expensive, and make the reality of a multidisciplinary team at the service of the patient more robust. The results of this narrative review may be useful for the development of new remote healthcare services via mobile devices that researchers or clinicians could consider for population health.

#### **6. Strengths and Limitations**

This is an emerging field of investigation, and our RCT analysis work provides the best evidence, to our knowledge, to shed light on the benefits of wearable devices in NCDs to date. Compared to other studies, our approach takes a holistic view of wearable device usage. Indeed, we reviewed the two most important lifestyle aspects (exercise and nutrition) to enable: (i) a better understanding of the use of wearable devices, in NCDs, in lifestyle change; and (ii) the development of new questions for future research. In this regard, a strength of this work is that it can help advance the field by clarifying the use of different devices in prescribing and monitoring healthy lifestyles and by offering, albeit preliminarily, sufficient data to understand the applicability to the different NCDs, on which wearable devices for monitoring and prescribing healthy behaviors have already been applied. The overview provided a solid foundation, upon which to direct future studies to achieve health improvements in clinical populations and age groups. The magnitude of the strength of this literature review is of clinical significance, and based on early results, the gains appear to be promising. We also have some limitations of our work. First, this is a narrative and not a systematic review; therefore, it did not allow drawing firm conclusions on the efficacy of wearable devices to prescribe and monitor physical activity protocols and nutritional aspects to reach beneficial health effects. Second, studies utilizing smartphone apps are not incorporated in this review. Although smartphone apps are gaining much attention in research, many are still not validated for usage in scientific research [100].

#### **7. Conclusions**

With the growing interest in the use of wearable devices and the global need to reduce the health burden of people living with chronic diseases, this study highlighted that more efforts are needed for wearable technologies to integrate them reliably into the field of prevention.

This study has shown that wearable devices have the promise to be an effective method for physical activity monitoring for the prevention, treatment, and improvement of the health and quality of life in people with NCDs. However, the devices are predominantly used to monitor and intervene in physical activity, rather than other daily activities (e.g., diet and sleep) or physiological parameters. Taken together, findings support wearable devices as a valuable tool to reduce the workload of medical personnel and hospitals, allowing, where possible, autonomous management of daily habits in terms of physical activity and nutrition. In the near future, there is a need for innovative measurement tools that are accurate, reliable, and effective for both exercise-related and nutritional aspects, as well as better training for their use. To date, these devices cannot do that without a continuous dialogue between the patient and healthcare professionals.

**Author Contributions:** Conceptualization, V.N. and M.B. (Matteo Bonato); methodology, V.N. and M.B. (Matteo Bonato); investigation, M.B. (Matteo Bonato), V.N. and M.B. (Michele Biraghi); data curation, M.B. (Matteo Bonato), V.N. and M.B. (Michele Biraghi); writing—original draft preparation, V.N. and M.B. (Matteo Bonato); writing—review and editing, V.N., M.B. (Matteo Bonato), F.M. and M.B. (Michele Biraghi); visualization, M.B. (Matteo Bonato); supervision, M.B. (Matteo Bonato). All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Italian Ministry of Health (Ricerca Corrente).

**Institutional Review Board Statement:** Non applicable.

**Informed Consent Statement:** Non applicable.

**Data Availability Statement:** Non applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## **Unraveling Barriers to a Healthy Lifestyle: Understanding Barriers to Diet and Physical Activity in Patients with Chronic Non-Communicable Diseases**

**Massimiliano Cavallo 1,†, Giovanni Morgana 2,†, Ivan Dozzani 3, Alessandro Gatti 4, Matteo Vandoni 4, Roberto Pippi 5,\*, Giacomo Pucci 1, Gaetano Vaudo 1,‡ and Carmine Giuseppe Fanelli 5,‡**


**Abstract:** Although the important contribution of nutrition and physical activity to people's health is known, it is equally well known that there are many barriers to adherence to healthy habits (i.e., of an organizational, economic, and/or psychological nature) experienced by the general population, as well as by people with non-communicable diseases. Knowledge of these barriers seems essential to the implementation of the activities and strategies needed to overcome them. Here, we aim to highlight the most frequent barriers to nutrition and exercise improvement that patients with chronic-degenerative diseases experience. Drawing from the Pubmed database, our analysis includes quantitative or mixed descriptive studies published within the last 10 years, involving adult participants with non-communicable diseases. Barriers of an organizational nature, as well as those of an environmental, economic, or psychological nature, are reported. The study of patients' barriers enables healthcare and non-health professionals, stakeholders, and policymakers to propose truly effective solutions that can help both the general population and those with chronic pathologies to adhere to a healthy lifestyle.

**Keywords:** barriers; physical activity; healthy nutrition

#### **1. Introduction**

It is widely recognized that nutrition and physical activity (PA) are two of the main determinants of maintaining optimal health for the individual [1,2]. Many studies show that following an unbalanced diet [3,4] and being inactive (defined as an accumulation of fewer than 150 min a week of moderate or vigorous PA [5]) are two risk factors for many cardiovascular and chronic degenerative pathologies. However, it is equally well known that many barriers prevent the general population from achieving constant adherence to healthy nutritional and PA habits [6] and that the main barriers encountered are those of an organizational, economic, and/or psychological nature [7]. Indeed, a recent analysis found that more than 27% of the world global population did not reach the suggested guidelines for PA [8], and as reported by Liu et al. [9], more than 85% of American adults consume

**Citation:** Cavallo, M.; Morgana, G.; Dozzani, I.; Gatti, A.; Vandoni, M.; Pippi, R.; Pucci, G.; Vaudo, G.; Fanelli, C.G. Unraveling Barriers to a Healthy Lifestyle: Understanding Barriers to Diet and Physical Activity in Patients with Chronic Non-Communicable Diseases. *Nutrients* **2023**, *15*, 3473. https://doi.org/10.3390/ nu15153473

Academic Editor: Hayato Tada

Received: 14 July 2023 Revised: 2 August 2023 Accepted: 3 August 2023 Published: 5 August 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

junk food daily. Several nutrition and PA strategies have been adopted internationally to encourage the achievement of a healthier lifestyle [10,11]. While these issues may initially appear to be primarily related only to healthy adults, they are equally significant in patients with chronic non-communicable diseases (NCDs). In fact, for individuals with NCDs, proper dietary habits together with regular engagement in PA represent key non-pharmacologic therapeutic strategies [10,12]. A well-balanced diet and regular PA support optimal bodily functions, strengthen the immune system, and promote overall well-being [13–15]. Additionally, they help control blood sugar levels, manage weight, reduce inflammation, and improve cardiovascular health [15–17]. Among well-balanced diet patterns, the Mediterranean diet (MED) has been proposed as an ideal nutritional model against cardiovascular problems [17,18]. MED is considered the gold standard for treatment for preventing and treating NCDs such as diabetes [19], some types of cancer [20], obesity [21–23], and neurodegenerative pathologies in old people [24,25]. Moreover, a positive association between adherence to the Mediterranean diet (MED) and muscle strength was observed in the elderly [16]. MED, as shown by Martínez-González et al. [17], has been also strongly associated with a reduced risk of developing coronary heart disease and ischemic stroke, while also promoting improved cardiovascular health.

More in detail, MED is a nutritional model characterized by a balanced combination of seasonal fruit and vegetables, fish, whole grains, legumes, and extra virgin olive oil, with moderate consumption of white meat, dairy products, and red wine [26].

Concurrently promoting the MED and PA is likely to provide an opportunity for metabolic risk reduction [27] and is a strategic key to both prevent and control the development of NCDs [28]. Therefore, understanding and identifying the barriers to a healthy diet and regular PA becomes imperative in order to develop effective interventions to overcome them.

For these reasons, our study aims to highlight the barriers that patients with chronicdegenerative diseases experience in implementing a healthier diet and an exercise-based therapeutic program, by using a quantitative approach to develop a point-by-point list of the most frequent barriers to both nutritional improvement and exercise. By recognizing and addressing these barriers, it is possible to empower patients with NCDs to adopt healthier lifestyles, enhance their quality of life (QoL), and reduce the burden of NCD management. In the last ten years, there has been a significant advancement in the treatment of patients with NCDs. To ensure that we have the latest and most relevant information, we analyzed only studies published between 2013 and 2023. We decided to consider this period with the aim of providing a recent cross-section of the barriers experienced by the world population, to provide ideas for solving current problems. Moreover, we choose to consider adult people because they conduct very similar lifestyles with the aim of making the population homogeneous.

#### **2. Materials and Methods**

This study used the PRISMA guideline and methodology for systematic review [19]. This review was not registered.

#### *Search Strategy*

The search for studies was conducted by drawing from the Pubmed database using the following search string "(physical activity OR exercise OR diet) (prescription OR participation) AND barriers NOT rehabilitation". To be eligible, studies must have the following characteristics:

Quantitative or mixed descriptive studies.

Adult population with chronic pathologies of a non-neurological or psychiatric nature. Interventions of a non-rehabilitative nature.

Period of publication not exceeding 10 years.

The search produced 432 results (Figure 1). The selection of the studies was performed firstly by reading the titles, then by reading the abstracts and excluding duplicates, and finally, the selection of the remaining studies (Table 1) took place with the aid of the Mixed Methods Appraisal Tool (MMAT) version 2018.

**Figure 1.** PRISMA [29] 2020 flow diagram for new systematic reviews.



**1.**ofthepopulationcharacteristicsofeachstudyandrelative

**Table 1.** *Cont.*


#### **3. Results**

#### *3.1. Barriers to a Healthy Diet*

The five selected studies enrolled a total of 1855 subjects. These studies are nearly unanimous in indicating as barriers to maintaining adequate compliance with a healthy diet: lack of time, organizational and family problems, the high (or presumed high) cost of a healthy balanced diet, concerns about one's body/figure/physical health, and motivational difficulty in changing one's lifestyle.

Gender-related differences. Excepting from the work of Clark-Cutaia et al. [38] in which women represent 37% of the studied population with a limited cohort not letting a deep comparison between male and female, in all the other examined studies the majority of the involved population is composed by women (100% in Bernard-Davila et al. [39] consisting in breast cancer Hispanic survivors; in Mofleh et al. [41], 97.7% of a population of educators from USA; 87% in Mendonça et al. [40] from the Health Academy Program in Brazil; 58% in Miller et al. [37] consisting in breast cancer survivors). Among women, as a synthetic result of all the analyzed studies, differences in the encountered barriers are especially linked to own's body perception, grade of education, being employed, need to take care of families, and economic concerns. Being younger and with lower income, for example, represents a risk factor for food insecurity in Mofleh et at. [41].

Ethnicity-related differences. In Clark-Cutaia et al. [38] the studied population is quite equally divided between Caucasian and African-American but the sample is too little to understand differences in terms of ethnicity. Bernard-Davila et al. [39] choose Hispanic people according to their being the main minority in the US but, at the same time, one of the less involved communities in clinical studies. In her work, Spanish monolingualism is one of the main determinants of lack of enrollment in the protocol together with being unemployed and considering the study cost-consuming. Ethnicity seems to not represent a statistically significant element in terms of adherence to a nutritional intervention with or without in-person counseling in Miller et al. [37]. In Mofleh et al. [41], being white or not does not represent a statistically significant condition for food insecurity in a population of educators from Pennsylvania.

Lack of time. Clark-Cutaia et al. [38] enrolled 30 patients (mean age 63.3 ± 13.3 years) with end-stage renal failure on hemodialysis replacement therapy who underwent semistructured telephone interviews to understand the barriers to following the recommended diet recognized as assisting substitution therapy in the treatment of the underlying disease. Among the main barriers indicated were those related to the time required. The long dialysis sessions, which included the transfer time between home and the dialysis center, led patients to skip a main meal with consequent difficulty in preparing healthy meals and a reduced desire to consume them.

In the work of Bernard-Davila et al. [39], albeit indirectly, it emerges that having a job represents a barrier to adherence to a dietary improvement intervention by a group of Hispanic breast cancer survivors (about 38.2% agreement). Similar factors can also be deduced from the work of Mendonça et al. [40] who also adds the time necessary to look after the family as another time constraint. Moreover, in the study by Mendonça et al. [40], although adherence to the intervention implemented at the Brazilian Primary Health Care service was high (possibly due to subject-related factors, the research scenario [health care service: Health Academy Program], the intervention methodology, and/or the bond-building among participants, professionals and the research team), adherence did seem to have been reduced due to 15 issues related to the incompatibility of situations that included work, self-care, care for the other(s), and service working hours (morning).

In contrast, Farmer Miller et al. [37] showed that for cancer survivors an eight-week course of nutrition education, with guaranteed face-to-face psychological support, was more effective, even though more time-consuming than the same intervention carried out electronically with printed material.

Environmental barriers. In the work of Clark-Cutaia et al. [38], the distance from the hemodialysis site combined with the need to utilize various means of transportation represented a further barrier to maintaining adequate adherence to the recommended diet.

Mendonça et al. [40] emphasize in their paper that an adequate setting for nutritional visits is a factor that promotes adherence.

Reading Miller's [37] work from another point of view, we can see how face-to-face interaction represents a fundamental motivational factor in increasing adherence to a healthy diet in a group of cancer survivors.

Bernard-Davila et al. [39] recognized the monolingualism of Hispanic cancer survivors in an English-speaking setting as a barrier to participation in the dietary intervention envisaged by the study.

Economic and financial barriers. Clark-Cutaia et al. [38] point out that approximately 50% of their enrolled patients said they did not have adequate finances to ensure the maintenance of a suitable diet for end-stage renal failure. Patients reported that dietary recommendations were expensive both in terms of the quantity and quality of the foods recommended. Another common patient concern was the greater ease of obtaining groceries at large stores with the possibility of finding discounted or less expensive items more easily. Some said that they sometimes had to give up drugs due to economic constraints, consequently making food choices immediately subject to direct economic sustainability.

In the work of Mofleh et al. [41], it emerged that in a population of educators of school-age children, the greatest degree of food insecurity manifested itself in younger subjects (31.5% of the population examined), with BMIs compatible with grade I obesity, with fewer years of teaching, and with lower salary levels.

Bernard Davila et al. [39], acknowledge unemployment and concern about the possible costs of the protocol as barriers to enrollment in the nutritional program provided for Hispanic women who survived breast cancer (about 64.3% strongly agreed that it would cost too much).

In contrast, Mendonça et al. [40] in their study aimed to improve fruit and vegetable consumption, found that instead of economic factors, work, having to care for others, and self-care were greater barriers to fruit and vegetable consumption.

Health status. Clark-Cutaia et al. [38] showed that many patients with end-stage renal failure considered their disease status and its consequences (hemodialysis with its timing and related distress) as a barrier to adherence to the best diet regimen.

A reported higher BMI (32.4 mean) in statistically significant terms characterized educators with greater food insecurity in the study by Mofleh et al. [41].

A lower degree of satisfaction with one's body image was a factor significantly related to lower adherence to the nutritional intervention proposed in the work of Mendonça et al. [40]. This was regardless of the general state of health (presence of metabolic and cardiovascular comorbidities).

Among the factors that affected participation in the nutritional intervention planned by the group of Bernard-Davila et al. [39], was concerned about one's own health status and degree of disease (Hispanic breast cancer survivor patients). A total of 37% of the participants thought that a dietary intervention could lead to side effects that doctors cannot predict. On the other hand, perceived health status was not a factor significantly correlated with the greater success of an in-person versus remote nutritional education protocol in the cancer survivor population in the work of Miller et al. [37].

Psychological barriers/insights. In the work of Clark-Cutaia et al. [38], hemodialysis patients reported that poor personalization of dietary intervention as a set of general rules to follow represents a barrier to adherence to the intended diet.

An adherence of more than one year to the nutritional intervention outlined in the work of Mendonça et al. [40] was correlated with better outcomes indirectly, how the proactive attitude towards lifestyle change is fundamental.

Bernard-Davila et al. [39] found that improved confidence in the positive outcome of the research appears to be an inducing factor in participation in the nutritional intervention.

#### *3.2. Barriers to Exercise*

Out of a total population of 1560 subjects tested through a questionnaire regarding the barriers encountered in undertaking physical exercise, 339 declared that they did not have enough time to dedicate to it (22.3%). Another very frequent problem cited was the distance from the gym or unfavorable weather conditions (8.63%, *n* = 131). A total of 242 attributed their state of health as an obstacle to embarking on a healthier life (15.95%). Of these 242, 123 belong to a very fragile category of patients who were undergoing dialysis therapy; a therapy that very often leads to symptoms related to hypotension, fever, joint pain, and fatigue. Furthermore, 33% of the patients involved in the studies had to interrupt the activity due to problems that required hospitalization. A total of 107 complained of mood disorders and showed little motivation to undertake a course in PA (7.05%). Finally, 71 (5.15%) refused to participate in the study as they did not consider it advantageous for their state of health.

*Gender-related differences.* Several studies suggested that women with NCDs tend to experience greater barriers to PA than men. For example, Pereira et al. [37] found that women with NCDs were less likely to participate in PA than their male counterparts. Pereira et al. [42] proposed several reasons for this gender disparity, including greater involvement in household tasks such as childcare, meal preparation, and cleaning [43]. In addition, women face other barriers, such as a lack of support from their partners, who may not encourage them to engage in PA, safety concerns, and fear of violence in some places used for PA [44]. In addition, Venditti et al. [32] showed that women with NCDs perceive more barriers to PA and weight loss than men. These barriers include internal cues (such as thoughts and moods), social cues and time management, physical events (such as injury or illness), and challenges related to access and weather conditions.

*Ethnicity-related differences.* The study conducted by Venditti et al. [32], while exploring the primary barriers to PA, showed a strong association between barriers to PA and ethnicity. For example, American Indians viewed "self-control" as a greater challenge to participating in PA, while white adults saw "social cues" as a more crucial barrier. Moreover, Pereira et al. [42] revealed differences in PA levels between non-white and white Brazilian adults with NCDs. Non-white adults were found to be less active than their white counterparts. However, Rosa et al. [34] did not find any differences in PA levels based on skin color among adults undergoing hemodialysis.

*Lack of time.* The questionnaire results allow us to make a series of important conclusions regarding the perception of PA as a therapeutic tool and the barriers that prevent patients from utilizing it. First, it is immediately evident that one of the main motivations preventing patients from undertaking a more active lifestyle is worries about not having enough time available daily [31,32,34,36]. Exercise therapy requires an important weekly commitment with training sessions varying from three to five sessions of approximately 45–60 min each. Finding time to dedicate to PA during the day, especially for people busy with work [31,32,34,36], can, therefore, be extraordinarily complex. It is one of the main problems to consider when prescribing exercise.

In Rosa et al. [34] it emerged that 39.8% of patients did not have enough time for dedicated PA. This seems to be correlated with a sedentary lifestyle, since only 33% of those who already performed recreational PAreferred to lack of time as an effective barrier.

*Environmental barriers.* The difficulty in reaching the site for PA is another frequent type of barrier, which overlaps with the inconveniences of daily life. This also includes physical distance and adverse weather conditions which can discourage less motivated individuals [30,34,35].

*Health status.* Another interesting fact that we found is the low rate of recruitment to some protocols (30% in the case of Rogers et al. [31]; 20.3% in Koutoukidis et al. [36]). This rate was influenced by some characteristics of the population including youthful age, having one or more chronic conditions, and awareness of having a low level of PA [43,44]. Those who declined participation in the protocols mostly claimed to have a sufficient level of weekly activity [34]. This suggests that the suffering or sedentary patient perceives

the possibility that movement can benefit health. In the study conducted by Sheshadri et al. [30], 73% of participants expressed willingness to participate in the protocol, believing that they could obtain a benefit of some kind that was related to QoL (30%) or be able to achieve a condition such that they could meet the criteria for renal transplantation (27%).

In cancer patients, the perception of pain and fatigue, also related to therapies, affected more than half of the cases undertaking an exercise-based protocol [30].

The results also show that deterrents to undertaking a therapeutic path based on exercise strongly depend on the type of disease involved, since, in the context of chronic pathologies such as diabetes or hypertension, lack of time or access to facilities limits those subjects [45], while in the contexts of more complex pathologies, the most prevalent deterrents are concerns for one's health and low motivation [30,44,45].

Sheshadri et al. [30] found that older age and a higher BMI were associated with increased motivational barriers related particularly to the presence of symptoms.

*Psychological barriers/insights.* From the data analyzed by Venditti et al. [32], another issue emerges in the maintenance of optimal levels of PA after the end of the experimental protocols. The six-month protocol completion rate by participants was an impressive 96%. In subsequent re-evaluations, however, participants had difficulty maintaining sufficient levels of PA; they reported this was related to poor time management, daily life commitments, and self-monitoring problems that prevented them from recording progress. This helps us understand that searching for and removing barriers is on a continuum that must also include the search for a way to ensure adequate maintenance of fitness levels. Additionally, the problem of a person's approach to physical exercise seems to be directly proportional to a person's degree of fitness and familiarity with the type of activity. In fact, in a study conducted by Thomson et al. [33], it emerged that the perceived barriers are greater in those who have had only dietary intervention and have not performed any type of PA. The study demonstrated that the extent of these barriers decreased over time only in the group that performed PA, while the group with only nutritional intervention did not show significant variation.

An interesting fact in Rosa et al. [34] is the degree of satisfaction derived from the exercises performed. Approximately 25.5% of the population involved in the study reported as a barrier the lack of propensity to perform certain exercises which could demotivate specific populations (such as people undergoing hemodialytic treatment) from following a therapeutic PA program.

*Communication errors between Clinicians and Patients.* Doctor–patient communication is perhaps the most important component of a treatment regimen [46]. A patient must trust the doctor and be well-informed by the doctor; a trusting, well-informed relationship often leads the person needing treatment to follow the doctor's advice to the letter. It is not surprising that if patients with a chronic-degenerative disease such as diabetes, are not educated to have a healthy awareness and acceptance of the condition through a broad and exhaustive informative interview, some may find themselves hesitant about what to do to deal with it. The need to develop a healthier lifestyle is often a topic dealt with superficially by the treating doctor, with little specific direction given as to methods for doing so.

#### **4. Discussion**

It should be noted that in literature it is easier to find qualitative rather than quantitative studies that evaluate the barriers to acquiring a healthier lifestyle through an adequate diet and regular physical activities. This is one of the peculiarities that makes this literary review innovative. In this discussion, we compare the result of our review of quantitative mixed studies and qualitative ones.

Patients suffering from chronic NCDs experience a condition of quoad vitam persistence of their morbid condition. The indefiniteness of the duration of the disease makes them particularly exposed to the risk of not adhering to recommended treatment, even in the context of a therapeutic alliance between doctor and patient. Although it is widely known that a healthy diet and maintenance of an adequate level of PA represent a fundamental component of the management and improvement of cardiovascular health [17] and most chronic NCDs in both clinical terms and QoL, it is equally commonly known that patients with NCDs experience numerous barriers to maintaining these prescriptions. Regarding adherence to recommendations, some elements must be considered. There could be an impact depending on the region of origin of the authors' study and the patients involved [17]. For example, many of the investigators who supported MED have born or lived in Mediterranean countries and this could have contributed to the adoption of their opinions on the benefits of MED [47]. This potential bias is not supported by the results of solid studies conducted in non-Mediterranean populations that have found similar benefits in adhering to MED [17]. Similarly, it was observed that adherence to WHO PA guidelines was lower among adults from Southern and Central European countries (Romania, Poland, Croatia, Cyprus, and Malta) and the USA than among Northern European countries (Iceland, Sweden, The Netherlands, and Denmark). Moreover, PA adherence was higher in men than in women [48]. Furthermore, our findings highlighted the presence of ethnicity and gender differences that frequently limit the participation of adults with NCDs in PA.

In our literature review, various similar barriers to healthy diet and exercise emerged. Among these, first, the time needed to be devoted to these two determinants of health is often considered unsustainable, especially for those who are engaged in work activities, who are caring for the family, or for those for whom the chronic disease itself involves considerable consumption of time (i.e., hemodialysis for chronic renal failure, time for therapy, and visits in cancer).

These results appear in agreement with that from qualitative research about these barriers. In Suderman et al. [49], for example, the lack of time is a barrier that remains both at the beginning of the intervention and at the end of it. In particular, workers, that tend to be younger than patients with NCDs seem to encounter this barrier more often. Considering that the number of younger people affected by NCDs is constantly growing in the last decades, especially due to the more effective screening campaign, the more effective diagnostic tools, and the greater sensitivity to their own health, it is particularly important to know this barrier in order to offer useful solutions to overcome them mitigating the severity of disease, especially in a pre-clinical phase or in an early stage in people employed in working activities. Taking care of their own family represents, also in qualitative studies, such as that of Attwood et al. [50] and Sebire et al. [51], an important barrier.

The importance of personalized counselling aimed at maintaining high motivation to achieve appropriate levels of PA and a balanced diet is consistently recognized as a crucial requirement for a broad range of individuals with chronic diseases. Even the accessibility of the exercise or dietary counselling site appears to be a vital element in increasing adherence to behavioral prescriptions. Economic barriers seem to detract more from maintaining adequate dietary levels. Moreover, the perception of a higher cost and a greater effort in procuring food hinders adherence to dietary recommendations. One's own perception of one's health and disease are a common barrier to diet and exercise. Patients with a negative subjective sense of well-being have more difficulty maintaining optimal levels of behavioral prescriptions. A close relationship between therapists and patients affected by NCDs, in terms of engagement and therapeutic alliance, appears to be a factor that can promote adherence to dietary recommendations and implementation of PA. In particular, an in-person individualized approach with repeated adherence assessments seems to represent the most effective strategy for supporting and strengthening the motivation to change [52,53]. Knowledge of the specific and common barriers to two key behavioral prescriptions (diet and PA) offers a fundamental opportunity to develop concrete approaches, individual and group, aimed at overcoming these barriers to improvement of the QoL and long-term control of NCDs.

All of these data seem to be similar to that from qualitative studies. In fact, among barriers frequently encountered in qualitative studies, consistent with the results of the review, there are those of an environmental nature and in particular, as noted by Suderman et al. [49], the fear of not finding competent personnel who are able to manage training programs for patients with chronic diseases. In this case, the issue concerns not only the problem of training competent personnel in the matter but also the scarce dissemination of information on the matter and the little trust that patients place both in the type of intervention and in who should be responsible for accompanying and supporting the patient along the course of care. Finally, the note reported by Suderman et al. [49] is noteworthy, in relation to the judgment of training programs, often considered punitive by the patients themselves. In Attwood et al. [50], instead, a good part of patients perceive such programs as constraints to change their lifestyle.

Comparing results from our literary review and data from qualitative studies, more similarities than differences could be appreciated. Due to the seminal importance of keeping an adequate diet and physical activity to counteract the ominous effects of NCDs, it is important to keep in mind that a lifestyle intervention of this type has a considerable impact on the patient's quality of life and for this reason, the prescription must necessarily take into consideration the needs and expectations of the patient himself. Compliance, in this type of patient, is a fundamental element for the success of the intervention, so in our opinion, the moment in which these therapeutic options are proposed is of fundamental importance. The patient must be aware of both the advantages and disadvantages and limitations that such an intervention can represent. The engagement of patients in their therapeutical plan is a decisive moment in the management of people suffering from NCDs and this could not take place without a complete and deep knowledge of the main behavioral barriers.

In relation to gender differences, women tend to report greater barriers, especially towards increasing levels of physical activity and these barriers are more frequently attributable to psychological problems, environmental barriers, and low motivation than men [28]. In dietary intervention, there are no differences in gender. Ethnic differences affect specifically diet-based interventions, and this is due to sociocultural and economic differences. Hispanic and African-American ethnicity, in the US context, do not enjoy high economic possibilities and often find themselves living in communities where mutual support is essential [50]. Clark-Cutaia [38] highlights how the assumption, by these patients, of a different lifestyle, would tend these subjects to exclude themselves from the community in which they live and to lose the social support of friends and relatives.

An increase in the number of barriers to diet has also been reported by Mofleh et al. [41], whose results show that there are differences regarding insecurities in one's own eating habits precisely in relation to ethnic and cultural differences. In physical activity intervention, there are differences in race too. If we consider the study taken by Venditti et al. [32] regarding non-Caucasian people, there are about 10–20% barriers more than among Caucasian people and this appears to be related to the socio-economic differences and to the difficulty to access the dedicated structure. In Sheshadri et al. [30], instead, there are no differences in gender in a pedometer intervention, this is probably attributed to the specific population (dialysis patients) and the type of intervention that does not require any gym or specific tools to be practiced.

Finally, as reported by Mendonça [40] and Clark-Cutaia [38], in Brazil and Iran, the level of education seems to be a determining factor for the adherence by these subjects to a healthier lifestyle, probably in relation to a degree of higher literacy.

It must be remembered that the time frame examined in this study (last ten years) also includes one of the greatest global health crisis periods in recent history. In fact, in February 2020 the World Health Organization (WHO) declared a public health emergency due to the SARS-CoV-2 virus, which is particularly dangerous for people with NCDs. To avoid the spread of the virus, containment measures were adopted with the ban to get away from their home. A "lockdown" and social isolation were imposed on some world countries, and it led to a drastic alteration of lifestyle habits (i.e., going to the grocery store was much more difficult and it was forbidden to go to eat meals in restaurants, with a consequent increase in meals cooked and eaten at home [54]. Moreover, PA outdoors are stopped, and exercising at home remained the only possibility to stay active during the COVID-19 pandemic [55]. These changes in habits lead to a reduction of PA levels [56] affecting psychological well-being, and worse nutritional habits [57] (i.e., an increase in the number of snacks consumed, an increase in comfort foods, and alcohol consumption). Anyway, during the COVID-19 pandemic were referred some specific barriers to healthy nutrition, such as, i.e., not having sufficient money to buy groceries and worrying about getting COVID-19 at the store [58]. While regarding PA barriers [59], laziness and fatigue, lack of motivation", and lack of time were the most prevalent, together with a lack of appropriate facilities, equipment, or space.

*Strengths and Limitations.* A considerable strength is that the large group of authors allowed us to carefully evaluate and double-check the entirety of all the articles. In fact, in the first phase, one group dealt with diet and another with physical activity, based on the authors' degrees and areas of expertise. Subsequently, the articles selected were evaluated by all the authors. This allowed us to better identify some categories of barriers common to both nutritional and PA recommendations.

The most important limitation, in our opinion, is that only one relevant database was searched (PubMed). Secondarily, only articles available in full-text versions were evaluated for inclusion in our review, which could restrict search opportunities.

Finally, the keywords used to study research were very detailed and could have conditioned the open-field search strategies.

#### **5. Conclusions**

Although it has been a consistent subject of research in recent years, the study of the barriers that prevent patients from adopting a healthy nutritional and PA lifestyle remains a crucial research focus. Greater knowledge of the various types of barriers patients face would allow clinical team professionals who care for patients with NDCs to more accurately understand the obstacles that hinder patients' adherence to a healthy lifestyle. Early identification of which barriers patients experience would improve awareness of their difficulties and enable healthcare and non-health professionals to propose truly effective solutions. Given the complex nature of some barriers, early identification would allow the various stakeholders to collaborate in the creation of environments, policies, and intervention programs that can help the general population as well as those with chronic pathologies to adhere to a healthy lifestyle, an effective tool for the prevention and treatment of NCDs. Tailoring lifestyle plans is a seminal element to improve NCD patients' compliance. Every patient, in fact, presents his/her own needs and motivations that turn away from spending time and resources to obtain a healthier lifestyle. All the members of the community could be engaged in social and political interventions having the aim of stimulating awareness of the real efficacy of a healthier lifestyle in improving global quality of life. An imperative role of health professionals has to educate, ameliorating people's awareness about NCDs and the most effective actions to counteract the ominous effects of them in terms of health and quality of life. Only a real paradigm shift from a simple taking care of health problems to a global approach to the person in terms of motivation, engagement, and deep knowledge of both physical and psychological barriers to a healthy lifestyle could reduce the burden of NCDs and consequent morbidity, mortality, and disability.

**Author Contributions:** Conceptualization, M.C. and G.M.; methodology, M.C. and G.M.; software, M.C. and G.M.; data curation, M.C. and G.M.; writing—original draft preparation, M.C., G.M., I.D., A.G. and R.P.; writing—review and editing, M.C., G.M., I.D., A.G., R.P., M.V., G.P., G.V. and C.G.F.; supervision, G.V. and C.G.F.; project administration, G.V. and C.G.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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### *Systematic Review* **Assessment Strategies to Evaluate the Mediterranean Lifestyle: A Systematic Review**

**Elisabet Montero-Sandiego, Rosario Ferrer-Cascales \*, Nicolás Ruiz-Robledillo \*, Borja Costa-López, Cristian Alcocer-Bruno and Natalia Albaladejo-Blázquez**

> Department of Health Psychology, Faculty of Health Science, University of Alicante, 03690 Alicante, Spain **\*** Correspondence: rosario.ferrer@ua.es (R.F.-C.); nicolas.ruiz@ua.es (N.R.-R.); Tel.: +34-965903400(N.R.-R.)

**Abstract:** The Mediterranean Lifestyle (MLS) has been related to better health and quality of life. However, there is no consensus on how to assess this lifestyle. The main objective of this work was to systematically review the methodology used in different studies on the evaluation of the MLS. The specific objectives were (1) to analyze the MLS components evaluated in previous studies, (2) to explore the assessment instruments available for the analysis of the MLS, and (3) to identify the psychometric properties of these instruments. The search was carried out using the PubMed, Scopus, Web of Science, and ScienceDirect databases with the purpose of identifying those published articles in which the MLS was assessed. The review included 26 studies linked to the assessment of the MLS. Of these studies, only four exclusively used a tool to analyze MLS components globally. These studies included two questionnaires and three different indexes. None of them, however, evaluated all of the recognized MLS components, and food preparation was the least frequently evaluated component. Given the clear importance of analyzing MLS adherence and the lack of consensus in previous research, an evaluation tool needs to be created to comprehensively assess all of the MLS dimensions by means of appropriate psychometric properties.

**Keywords:** Mediterranean Lifestyle; evaluation; questionnaire; index

### **1. Introduction**

Lifestyle (LS) is considered to be a set of repeated behavioral patterns maintained over time that characterize an individual's way of life [1]. The term has gained considerable relevance over recent years due to its close relationship with distinct pathologies and the higher levels of morbidity and mortality [2,3]. Lifestyle factors, such as physical activity, alcohol consumption, smoking, sleep, and diet have been found to be predictors of health status in both the general and the clinical population [4]. However, few studies have performed comprehensive analyses of the different so-called healthy lifestyles, such as the Mediterranean Lifestyle (MLS) [5]. This lifestyle, denominated after the countries situated near the Mediterranean Sea [2], displays the following factors: a high adherence to the Mediterranean Diet (MD); regular physical activity; good hydration and adequate rest (including regular naps); the consumption of seasonal and locally grown products; participation in food preparation and culinary activities; and frequent social interactions [2]. This lifestyle is the result of the interactions between these factors. Not only does the Mediterranean Diet pyramid incorporate recommendations regarding the frequency people should consume certain foods, and the amount of those foods, but it also refers to other dimensions that comprise the traditional Mediterranean life [6]. The MLS extends beyond a simple dietary pattern and is based on the interactions of different aspects of a healthy lifestyle. Different parameters, such as socialization, physical activity, leisure activities, appropriate rest, and, of course, diet, interact to create a healthy lifestyle, as in the case of an MLS [4]. In this way, different studies, although in isolation, have shown how the various components of the MLS are associated with better health and quality of life. For

**Citation:** Montero-Sandiego, E.; Ferrer-Cascales, R.; Ruiz-Robledillo, N.; Costa-López, B.; Alcocer-Bruno, C.; Albaladejo-Blázquez, N. Assessment Strategies to Evaluate the Mediterranean Lifestyle: A Systematic Review. *Nutrients* **2022**, *14*, 4179. https://doi.org/10.3390/ nu14194179

Academic Editors: Roberto Pippi, Carmine G. Fanelli and Matteo Vandoni

Received: 4 September 2022 Accepted: 3 October 2022 Published: 8 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

example, recent research has shown how meals shared with family members are associated with healthy weight and better eating habits [7]. Likewise, culinary activities constitute a behavior of great importance for health promotion, especially among children, as there are studies that show that it favors increased vegetable consumption [2]. In turn, the use of seasonal and locally grown products, another component of the MLS, allows avoiding the consumption of foods that have been processed for their maintenance during transportation and storage. Such processing negatively affects the nutrient content of the food, unlike local products that, after harvesting, are sold in a short time, preserving the freshness, taste, and quality of the product [2].

In relation to the practice of physical activity, there are very many studies that have shown how it is one of the most important protective factors against the development of numerous pathologies, reducing the risk of mortality and increasing life expectancy [8,9]. On the other hand, adequate night rest and characteristic naps are also protective factors against mortality from cardiovascular diseases, considering that both excess as well as lack of rest hours can become detrimental to health [5]. Likewise, socialization and participation in collective activities also provide the same benefits among the population, producing an increase in quality of life [2]. It can therefore be said that the MLS is a complex lifestyle formed by the interaction of different factors that provide great health benefits. Such benefits generally have been attributed only to dietary aspects, in which their protective role against diseases, such as diabetes, cancer, cardiovascular pathologies, obesity, and depression, is confirmed, without taking into account the other factors that configure the MLS [1,7]. Therefore, the MLS can be conceptualized as a complex lifestyle that is based on the interaction of different factors, which provide major combined health benefits. In fact, the MLS is considered a protective factor that reduces the risk of mortality and increases life expectancy [8,9].

According to numerous studies, the benefits of adhering to the MLS include a significant improvement in quality of life [10]. In this regard, many studies have shown that adherence to the MLS acts as a protective factor against chronic non-communicable diseases, including cardiovascular diseases, type 2 diabetes, hypertension [11–15], and metabolic syndrome [16], showing a lower prevalence of these diseases among people with strong adherence to the MLS [16]. In fact, the study by Hershey et al. showed that greater adherence to the MLS can be associated with 41% less mortality from cardiovascular disease [11]. Hence, MLS adherence acts as a protective factor against chronic diseases, especially in the elderly [10], given that adherence to this lifestyle is significantly associated with a lower risk of cardiovascular disease mortality [11–15]. Moreover, as previously indicated, adherence to the MLS is also closely related to a lower prevalence of metabolic syndrome [17] and has been linked to a lower development of glucose disorders in pregnant patients, subsequently reducing the rate of gestational diabetes [18]. In this sense, MLS adherence reduces the development of postpartum glucose disorders by 25%, and specifically by 35% in the rate of development of type 2 diabetes mellitus among women who have had gestational diabetes [18].

As for clinical populations, adherence to the MLS has been associated with an improved health status in renal disease groups that are not dialysis-dependent [16]. Adherence to the MLS among non-dialysis-dependent renal patients prevents progression of the disease [16]. Moreover, in a series of studies, Georgoulis et al. stated that adherence to the MLS is related to an improved cardiometabolic profile in patients with severe obstructive sleep apnea [19–21]. In this sense, a higher adherence to this lifestyle has been related to a reduction in the apnea and hypopnea index in daytime symptomatology, and it has been associated with an improvement in quality of life [19–21].

It should be added that adherence to the MLS not only has physical benefits, but also mental benefits, as it has been shown that adherence to the MLS reduces the risk of depression by 50% [22].

Despite the recognized importance of evaluating adherence to the MLS in different populations given its health benefits, to the best of our knowledge, there is no consensus as to the best strategy to reliably assess it. While some prior studies have used different questionnaires to evaluate the components of the MLS [10,19–21,23–28], others have used a specific index or questionnaire to assess global adherence to the MLS. However, little information exists with regard to the psychometric properties of the questionnaires created to examine the MLS [5]. Moreover, not all dimensions of the MLS have been included in the previously used evaluation strategies.

Given that past studies have relied on numerous heterogeneous assessment strategies and consensus has not yet been reached regarding the most valid and reliable strategy for the analysis of this type of lifestyle, this study attempted to systematically review and analyze the methodologies that were used in different studies to evaluate the MLS for both non-clinical and clinical populations. Moreover, the study was aimed to determine which components of the MLS were most frequently evaluated in past studies and which assessment instruments are currently being used to analyze these MLS components. Finally, the study also attempted to identify the psychometric properties of the available instruments that globally assess the MLS.

#### **2. Materials and Methods**

This study used a systematic review methodology that was based on the PRISMA statement [29].

#### *2.1. Search Strategy*

The main objective of the search strategy was to detect the published studies available in full text. The first step consisted of electronic searches carried out between December 2021 and May 2022 in the following databases: PubMed, Web of Science, Scopus, and Science Direct. This search strategy was designed to obtain original studies published on the assessment methods for MLS. A bulk search strategy was used by applying both descriptors and keywords in the titles and abstracts. Additionally, no date restrictions were applied to the articles' year of publication. Table 1 shows the search strategies used in the different databases.

**Table 1.** Search strategy.


#### *2.2. Inclusion and Exclusion Criteria*

Inclusion criteria were (I) original articles in which at least two of the following MLS components were evaluated following the criteria proposed by Diolintzi et al. [2]: adherence to the Mediterranean Diet (MD), consumption of seasonal and locally grown products, participation in food preparation and culinary activities, regular physical activity, good hydration and adequate rest, including regular naps, and socialization; (II) articles that were available in full text and written in English or Spanish.

Exclusion criteria were (I) articles that were not related to the subject of the study; (II) articles having unreported results; (III) articles that did not refer to MLS assessment; (IV) articles that only referred to the Mediterranean Diet (MD); (V) articles that were reviews and meta-analyses; (VI) documents that were doctoral theses, reports, or conference summaries; and (VII) books and book chapters.

#### *2.3. Selection of Studies*

Once the search was performed in the databases, duplicates were discarded, as well as all works presented at congresses, reports, doctoral theses, and book chapters, among others.

The abstracts that were identified through the bibliographic search were independently evaluated by two authors to confirm whether or not the articles were valid according to the review's inclusion and exclusion criteria. Two authors of this paper evaluated each article independently and discrepancies were resolved by consulting with a third author.

#### *2.4. Assessing the Methodological Quality of the Studies Included in the Review*

The quality of each study was analyzed by applying a commonly used battery of instruments for the evaluation of methodological quality, CADIMA, which was developed at the Joanna Briggs Institute of the University of Adelaide, Australia [30]. This battery provides different evaluation instruments for each type of study design. Specifically, the instrument used for cross-sectional studies includes eight items, the instrument used for cohort studies has eleven items, and the instrument used for randomized trials contains thirteen items. All instruments include a response scale with four response options: "yes", "no", "unclear", or "not applicable". Two authors of this paper independently assessed the quality of each study included in the review and discrepancies were resolved by consulting with a third author. The results reflected an inter-subject reliability of between 0.60 and 0.85, according to Cohen's Kappa statistical parameter.

Tables S1–S3 in the Supplementary Material reveal the methodological quality of the assessment for the cross-sectional studies: randomized and cohort trials that were included in this review.

#### *2.5. Data Extraction*

The sample information extracted from each article consisted of the following elements: sample size, gender, origin, and age of the population under study. Regarding the information on the assessment methods, we extracted the Mediterranean Lifestyle components, the evaluation strategies used to analyze the MLS, the MLS evaluation instruments applied, and their psychometric properties. We also considered the study type and objective.

#### **3. Results**

#### *3.1. Search Results*

Table 2 shows the search strategies used in the different databases and details the corresponding number of total articles extracted per database.

**PubMed Scopus Web of Science Science Direct Total** Mediterranean AND lifestyle (Title/Abstract/keyword) 1654 3536 2240 517 "Mediterranean lifestyle" (Title/Abstract/keyword) 71 89 82 25 "Mediterranean lifestyle" (Title/Abstract/keyword) AND Questionnaire (Title/Abstract/keyword) 10 18 10 0 "Mediterranean lifestyle" (Title/Abstract/keyword) AND Review (Title/Abstract/keyword) 10 17 11 1 "Mediterranean lifestyle" (Title/Abstract/keyword) AND Index (Title/Abstract/keyword) 24 27 22 8 "Mediterranean lifestyle" (Title/Abstract/keyword) AND Evaluation (Title/Abstract/keyword) 58 4 5 "Mediterranean lifestyle" (Title/Abstract/keyword) AND Assessment (Title/Abstract/keyword) 5 22 4 1 Total 1772 3717 2373 557 8419 Total without duplicates 4707

**Table 2.** Bibliographic search strategies.

As can be seen in Figure 1, a total of 8419 articles were obtained. After discarding duplicates, the total number was reduced to 4707 articles. A total of 3680 articles remained after discarding doctoral theses, reports, book chapters, etc. Following a review of the titles and abstracts, an additional 3484 articles were excluded. These articles were discarded mainly because they did not mention the MLS. Instead, they referred generally to lifestyles and included MD as a dietary pattern but did not name nor evaluate a minimum of two MLS components. In many other articles, the main objective was to evaluate the Mediterranean population's lifestyle, but without referencing the MLS as an LS. In these studies, the MD was the only characteristic relating directly to the Mediterranean population's LS, without considering any other characteristic component of the MLS.

**Figure 1.** PRISMA flow diagram of studies evaluated in the systematic review.

After reading the full texts of the articles, an additional 170 were excluded. The reasons for their exclusion were as follows:


Finally, after a rigorous search, a total of 26 articles were included in the review.

#### *3.2. Characteristics of the Studies Included in the Review*

Table 3 lists all of the studies that were used in the final review. The following information is listed per study: authors, study design, year of publication, country, population type, sample size, gender, age, and study objective.

Most of the articles considered a general population living in the Mediterranean Basin. However, some studies included participants having a specific pathology, such as: dementia [23], Chronic Renal Insufficiency [16], obesity and metabolic syndrome or sleep apnea [10,19–21], gestational diabetes [18], type 2 diabetes mellitus [31,32], and fatty liver disease [27].

The article having the largest number of participants had a sample size of 174,209 individuals [28]. In contrast, the study with the smallest sample size had a total of 63 participants [27]. With the exception of two articles, all of the articles specified the gender of the participants.

Most of the articles focused on a middle-aged population, except for one article, which focused on students aged 6 to 18 years old [28]. The study with the largest age range covered <30 to >70 years [33]. Finally, one article did not specify the age of its participants [26].

The main objective of all the articles was to evaluate the MLS in order to analyze the relationship between adherence to this lifestyle and other variables, either with other lifestyles [23,24,33], with the symptomatology of certain pathologies [10–17,22,25,28], or with the effectiveness of an intervention program in which the MLS was implemented in the daily lives of participants, comparing certain variables before and after the implementation of this LS [18–21,27,31,32]. Another study focused on the assessment of changes in adherence to the MLS after the COVID-19 pandemic [34]. Two other studies considered the designing of a questionnaire to measure MLS adherence and the verification of its reliability [5,35].

Of the studies included in this review, Table 3 reveals that nine were cross-sectional [12,16,17,23–25,28], ten were randomized controlled trials [10,15,18–21,27,31,32,35], and the remaining seven were cohort studies [5,11,13,14,22,26,33].

#### *3.3. Conceptual Suitability*

As illustrated in Table 3, the majority of the countries considered in the studies were part of the Mediterranean Basin—e.g., Spain, Italy, Greece, and Croatia—with the exception of the US and Australia, where a total of four studies were conducted [12,16,31,32]. In two other articles, collaboration took place between Mediterranean and non-Mediterranean countries, specifically Spain and Greece and the US [17,28].

The purpose of all of the studies was to evaluate the population's MLS adherence in order to promote this LS, and thereby use the results to demonstrate that high MLS adherence benefits the population's health, acting as a protective factor against various diseases, especially at a cardiovascular level. Therefore, the aim of the articles was both to promote MLS adherence among the population and to demonstrate its protective effect against different pathologies.

#### *3.4. Applicability*

In the study by Katsagoni et al. [28], the population sample was composed of students aged 16 to 18 years. Online questionnaires were used in the students' classrooms and there were trained teachers and/or Information Technology instructors who had been previously trained on the specific guidelines in order to help students correctly complete the questionnaires.

The remainder of the study questionnaires were administered by a trained dietitian, trained personnel, or through online questionnaires, in which the participants responded without the need for any external assistance.

