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Article

Examining the Correlates of Food Habits Among Adolescents in Zimbabwe: A Cross-Sectional Study

by
Ashleigh Pencil
1,*,
Tonderayi Mathew Matsungo
2,
Thomas Mavhu Chuchu
3,
Nobuko Hongu
1 and
Naomi Hayami
1
1
Graduate School of Human Life and Ecology, Osaka Metropolitan University, 3 Chome-3-138 Sugimoto, Sumiyoshi Ward, Osaka 558-8585, Japan
2
Department of Nutrition, Dietetics and Food Sciences (DNDFS), University of Zimbabwe, Mt Pleasant, Harare P.O. Box MP167, Zimbabwe
3
University of Suffolk, Waterfront Building, 19 Neptune Quay, Ipswich IP4 1QJ, UK
*
Author to whom correspondence should be addressed.
Obesities 2025, 5(1), 9; https://doi.org/10.3390/obesities5010009
Submission received: 10 December 2024 / Revised: 28 January 2025 / Accepted: 10 February 2025 / Published: 14 February 2025
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Abstract

:
Introduction: Good eating habits are essential for proper growth and development. The aim of this study was to assess the correlates of eating habits and factors associated with poor food habits (i.e., Food Habits Score <50%) among in-school adolescents. Method: A cross-sectional study was conducted among 423 adolescents enrolled from 10 high schools in Harare, Zimbabwe. A questionnaire was used to collect sociodemographic data, food habits, nutrition knowledge, and physical activity. WHO AnthroPlus was used to calculate weight-for-height z-scores for body mass index values. Pearson’s Chi-square and multinomial logistic regression were used to test for associations and explore factors associated with poor food habits (p < 0.05). Results: There were more girls (n = 225, 53.2%) than boys. Poor food habits were more prevalent than healthy eating habits for both sexes (51.1% and 53.5%, p = 0.619) and in the 14–16-year-old group (59.1%, p = 0.004). Overall, poor food habits were observed across the nutrition status spectrum with a higher percentage among underweight adolescents (58.5%, p = 0.693). Significant associations were found between age groups (14–16-year-olds vs. 17–19-year-olds) and high fat consumption, especially among 14–16-year-olds (54.8%, p = 0.036). Overall, sugar consumption was high among girls and 14–16-year-olds (52.6%, p = 0.278 and 55.1%, p = 0.666), and skipping meals, especially breakfast, was also common among the same sex and age groups (53.1%, p = 0.931 and 55.2%, p = 0.882). Poor food habits were significantly associated with the age group of 14–16 years [OR= 1.582 (1.026–2.440), p= 0.038]; places of residence in particularly high-density areas [OR= 1.816 (1.344–2.454), p = 0.001]; inadequate physical activity [OR = 0.479 (0.311–0.738), p = 0.001]; and inadequate nutrition knowledge [OR = 4.321 (2.242–8.330), p = 0.001]. Conclusions: Food habits were poor across the nutrition status spectrum. Poor food habits were more common among girls and 14–16-year-olds. Age, place of residence, nutrition knowledge, and inadequate physical activity were factors significantly associated with poor food habits.

1. Introduction

Food habits are defined as “intentional, collective, and repetitive behaviors, which lead people to choose, eat, and use certain foods or diets, in response to personal preferences, social, cultural, and economical influences [1]”. Eating habits are formed early in childhood to adolescence through firsthand experiences with food and by learning the eating behaviors of others [2]. Adolescence is the transition phase of life between childhood and adulthood, from ages 10 to 19 [3]. In this phase, adolescents experience rapid physical, cognitive, and psychosocial growth. This stage is an important stage of human development and an important time for laying the foundations of good food habits and healthy nutrition. Unhealthy food habits in this age group can lead to excessive weight gain or weight loss, and increase risks for cardiovascular diseases or other health issues [4]. Ultimately, growing evidence shows that unhealthy eating habits usually develop during adolescence, including the practice of meal skipping, which typically leads to rebound eating that includes a high intake of calories, sugars, sodium, and fats [5,6]. Nutritional inadequacies influence not only adolescents’ health but also their risk for major chronic diseases in adulthood [7].
Unfortunately, teenagers need to be reminded of the urgency of taking care of their wellbeing as they may struggle to conceptualize how their food choices within the short term influence their future and long-term wellbeing and health [8]. Nutrition education campaigns targeting this age group are therefore vital for proper dietary behavior changes in the context of changing food “obesogenic” environments and fast-food advertising. The mushrooming of fast-food outlets, coupled with the availability and affordability of ultra-processed foods from large-scale supermarkets, are key drivers of the obesity problem emerging among adolescents from low-income settings [9].
In Zimbabwe, this is worrying considering that adolescents constitute 22% of the country’s population [10]. Furthermore, the double burden of malnutrition (DBM) characterized by the co-existence of underweight and overweight adolescents within the same population group is prevalent in this age group [7,8]. Despite evidence of the prevalence of the DBM, nutritional and dietary efforts for children provided though schools and local governments are still focusing on underweight individuals only, even though recent evidence now shows that overweight/obesity is now more prevalent than underweight [11,12]. Since both problems are caused by eating habits, it is time to consider methods to tackle both nutrition problems simultaneously. To achieve this, it is important to understand adolescents’ eating habits in order to create informed nutrition education. A focus on the health and nutrition of this group warrants a shift in the narrative of nutrition activities in Zimbabwe, which mostly focuses on children under 5 years old and pregnant mothers [13].
Eating habits and nourishment practices are impacted by a wave of inter-related variables [14]. However, nutrition education is instrumental in improving and maintaining good eating habits [15,16]. Information and, in particular, food-based dietary guidelines motivate young people to explore the complex food system framework, including by promoting their improved understanding of food components and their influence on health and wellness. Therefore, this study was designed to investigate adolescent eating habits and the factors associated with unhealthy eating habits among in-school adolescents aged 14–19 years old from Harare, Zimbabwe. Understanding the factors associated with unhealthy eating habits is instrumental in the development of nutrition education material for the prevention of DBM and cardiometabolic health risks.

2. Materials and Methods

2.1. Study Setting and Participants

The study was carried out among 14–19-year-olds from 10 schools in Harare, the capital of Zimbabwe. The selected schools are registered with the Ministry of Primary and Secondary Education. Study authorization was obtained from The Ministry of Primary and Secondary education, Harae Province, and the schools’ respective District offices. All research procedures were reviewed and approved by Institutional Review Boards before initiating the study (OMU/21-52, MRCZ/A/2857). This manuscript presents data that were part of a bigger parent study entitled The Analysis of Related Factors of Overweight and Obesity among High School Students in Harare, Zimbabwe. The main objective was to assess how eating habits, nutrition knowledge, and physical activity are associated with overweight and obesity among adolescents [17].

2.2. Sample Size and Sampling Technique

The sample size was calculated as described by Charan and Biswas [18]. A total of 437 adolescents registered with the Ministry of Primary and Secondary Education were selected using stratified random sampling and were determined to be a good sample size. The schools were further divided into strata based on their socioeconomic zones, locations, age groups (14–16 years and 17–19 years), and class level (Form 2 to Form 6) based on the Zimbabwean education system. Recruited participants received an orientation about the study’s objectives. After recruitment, the staff members collected the signed consent forms, and the participants were given the date and time for the administration of the questionnaire within their classrooms. After the collection of signed consent forms, 423 adolescents were successfully enrolled in this study.

2.3. Research Instruments and Data Collection Methods

2.3.1. Structured Questionnaire

Previously validated self-administered questionnaires were adapted and used to collect demographic data, food habits, nutrition knowledge, and physical activity data [7,18]. The combined questionnaire had four sections: (1) sociodemographic (10 questions), (2) nutrition knowledge (20 questions), (3) food habits (23 questions), and (4) physical activity (7 questions). These questionnaires were selected because they were previously used among adolescents, easy to understand and answer for this age, and had high validity and reliability scores. A 50% threshold was adopted to simplify classification into poor and good eating behaviors, consistent with median split approaches in behavioral research [19,20].
The questionnaire is explained below.

2.3.2. Food Habits

The Food Habits Score (FHS) questionnaire was adapted from Johnson et al. [7]. It had an internal reliability of Cronbach’s α = 0.82. The Food Habits Score (FHS) was calculated as follows:
FHS = No. of healthy responses × (23 No. of items completed)
Poor food habits were defined as (FHS < 50%) and healthy as (FHS ≥ 50%). An example of a food habits question was “I eat at least three servings of fruits most days”.

2.3.3. Nutrition Knowledge Score (NKS)

The NKS questions were developed by Oz et al. [18]. Cronbach’s alpha coefficient was 0.85 overall. The instrument was a practical and easy-to-administer tool with acceptable reliability among high school students. This section had three subscales: adequate and balanced nutrition, essential nutrients, and malnutrition-related diseases. An example nutrition knowledge question was “We should drink at least 8–10 glasses of water every day” with true, false, and not sure answer options. Nutrition knowledge score (NKS) was categorized as inadequate (NKS < 50%) or adequate (NKS ≥ 50%). An example of a nutrition knowledge question was “According to the nutrition expert, the amount of salt a person consumes in a day should not exceed 6 g”.

2.3.4. Physical Activity (PA)

This questionnaire was adapted from Silva et al. [21]. Cronbach’s alpha was 0.71. The responses were structured in different ways according to each question, each score ranging from 1 to 4, with the maximum score assigned to the healthiest habit. The total score of the PA section was 28; this was categorized as inadequate (PA < 50%) or adequate (PA ≥ 50%). An example of a physical activity question was “Do you practice any form of physical activity?”

2.3.5. Anthropometry

Standing height was measured to the nearest 0.1 cm using a stadiometer (Leceister® Height Measure, Seca, UK). Weight was measured using an electronic bathroom weighing scale (Sunbeam, South Africa). All the equipment was sourced from Johannesburg, South Africa. The nutritional status of the participants was determined using WHO standard protocols for children aged 5–19 years [22]. Body mass index (BMI) (kg/m2) was converted to z-scores using WHO AnthroPlus Version: 1.0.4 (x86). BMI-for-age z-scores were categorized into underweight (<−2 SD), normal (≥−2 to ≤+1SD), overweight (≥1 to +2SD), and obese (>+2SD).

2.4. Statistical Analysis

The data were analyzed using IBM SPSS Statistics for Windows v26 (IBM Corp., Armonk, NY, USA). Shapiro–Wilk tests and Q-Q plot tests were carried out to assess the normality of the data. Continuous data were converted into categorical variables. The study employed diverse response scales (true/false, frequency-based) to maintain alignment with validated instruments tailored for each construct. Responses were subsequently standardized into categorical variables for comparability. Different response scales were converted into dichotomous categories (<50% and ≥50%) based on scoring protocols recommended by Iacobucci et al. [23]. Dichotomization of food habits, nutrition knowledge, and physical activities was conducted after calculating total scores as instructed by Johnson et al. [7], Oz et al. [18] and Silva et al. [21], respectively. Median splits were used to define as; the healthy/unhealthy and adequate/inadequate categories as suggested by Decoster et al. [22]. The data met the conditions for which dichotomization is justifiable as highlighted by Iacobucci and Decoster et al. [22,23]. The association between the categorical variables was evaluated using Pearson’s Chi-square test, and confidence level with p < 0.05 was considered statistically significant. Multinomial logistic regression was used to examine the relationship between poor food habits and demographic factors, nutrition knowledge, and physical activity. The level of significance was p < 0.05. The results for the multinomial logistic regression analyses are presented along with their respective 95% confidence intervals (CIs), signifying precision.

3. Results

3.1. Sociodemographic Characteristics

The sociodemographic characteristics of the adolescents in relation to food habits are summarized in Table 1. There were more girls than boys and poor food habits were more prevalent than healthy eating habits for both sexes (51.1% and 53.5%, p = 0.619). There were more 14–16-year-olds with poor food habits (59.1%, p = 0.004). Most adolescents with unhealthy eating habits came from families whose household head had tertiary education (57.6%, p = 0.005).

3.2. Nutrition Status of Adolescents by Food Habits

The nutritional status of adolescents by food habits is summarized in Figure 1. Overall, poor food habits were observed across the nutrition status spectrum with a higher percentage among underweight adolescents (58.5%, p = 0.693).

3.3. Frequency Distributions of Adolescents Eating Habits

The results in Table 2 show that generally, adolescents did not choose low-fat options (52.7%) or avoid fried foods (74.4%), and they did not try to keep their overall fat intake down (48.3%). Regarding sugar intake, 51.9% stated that they did not try to keep their sugar intake down. There were various responses regarding fruit and vegetable consumptions; 56.6% did not eat at least one serving of fruit a day, but overall, 70% stated that they did not eat at least three servings of fruit most days, 61.5% stated that they ate at least one serving of vegetables or salad excluding potatoes, and 63.8% stated that they tried to ensure they ate plenty of fruits and vegetables. Overall, 76.8% of the adolescents stated that they generally tried to have a healthy diet (Figure 2).

3.4. Food Habits in Relation to Sex and Age

The association between food habits, age, and sex are summarized in Table 3. Significant associations were found between age and high fat consumption, especially among 14–16-year-olds (54.8%, p = 0.036). Fruit and vegetable consumption was adequate among 14–16-year-olds (59.8%, p = 0.057).

3.5. Food Habits, Nutrition Knowledge, and Physical Activity

The associations between food habits, nutrition knowledge, and physical activity are summarized in Table 4. Factors significantly associated with food habits were nutrition knowledge and physical activity, as inadequate nutrition knowledge (66.7%, p = 0.001) and inadequate physical activity both accounted for poor food habits (41.6%, p = 0.001).

3.6. Factors Associated with Poor Food Habits Among Adolescents

Poor food habits were significantly associated with the age group of 14–16 years [OR = 1.582 (1.026–2.440), p = 0.038], particularly high-density areas for places of residence [OR = 1.816 (1.344–2.454), p = 0.001], inadequate physical activity [OR = 0.479 (0.311–0.738), p = 0.001], and inadequate nutrition knowledge [OR = 4.321 (2.242–8.330), p = 0.001] (Table 5).

4. Discussion

4.1. Adolescent Food Habits Overview and Main Findings

  • Sociodemographic factors
This study sought to assess adolescents’ eating habits. Our findings suggest that sociodemographic factors contribute to poor food habits. Although both boys and girls have poor eating habits, special focus should be placed among 14–16-year-old adolescents. At this stage, in addition to puberty, they will be experiencing an increase in academic load as they prepare for their ordinary-level exams, an important national exam administered by the Zimbabwe School Examination Counsil (ZimSEC) and the Ministry of Primary and Secondary Education (MoPSE). Therefore, it is important to prioritize nutrition education at this stage since good nutrition and academic performance go hand in hand [23]. The education level of the household head (tertiary education) and place of residence (high-density areas) are outstanding factors which seem to contribute to poor eating habits. Research shows that adolescents’ eating habits are influenced by their parents, genetic factors, and environment [24,25]. Furthermore, parents’ incomes, which could be influenced by their academic background, have a significant influence on promoting healthy eating habits [26]. Nutrition education for parents might help to improve their dietary behavior and to be good role models to improve their children’s dietary habits [27].
  • Nutrition status and poor food habits
The finding that food habits are poor regardless of nutrition status reinforces the importance of nutrition education in reducing the prevalence of the double burden of malnutrition (DMB) [8]. Poor food habits are a common factor in the DBM. The World Health Organization recommends “Double-duty actions” which address both sides of malnutrition simultaneously through common interventions [28]. This can be an effective strategy to tackle the current prevalence of DBM among urban adolescents in Zimbabwe. Types of intervention include economic and nutrition interventions. While economic interventions are instrumental, they require efforts at the government level [29], whereas nutrition interventions are achievable at the individual and social levels. Potential actions for “double-duty actions” include nutrition education on weight gain [30] and weight loss [31]. Double-duty action-centered nutrition education allows participants to learn about portion control, how to choose food and drinks for good health, reading nutrition fact tables for informed food shopping, and lastly, proper meal plans for healthy weight [32].
  • Frequency distributions of eating and mealtime habits
Although a greater number of adolescents (76.8%) stated that they generally tried to eat a healthy diet, statistical findings suggest otherwise. This is shown by low efforts to avoid fatty and sugary foods while the consumption of fruits and vegetables is relatively low. Although the frequency of avoiding fried food was not measured, its inclusion as a healthy habit reflects its alignment with WHO dietary guidelines recommending lower trans-fat intake [33]. The reason why adolescents’ claims and actual eating habits do not match is unknown and calls for further investigation. This study also shows the need for nutritional education for the improvement of breakfast consumption. Breakfast is usually described as the most important meal of the day, yet adolescents in this study sometimes skipped it. Several studies have assessed whether breakfast habits affect school attendance, academic achievement, and good health in children and adolescents, and most studies have indicated positive effects if breakfast is consumed everyday [34,35,36]. In one study, the additional benefits of consuming breakfast daily included receiving 50 percent more iron, vitamins, receiving a huge amount of fiber, and decreasing body weight and cholesterol levels compared to the breakfast skippers [37].

4.2. Factors Associated with Poor Food Habits

Factors associated with poor food habits are age (14–16 years), inadequate physical activity, and nutrition knowledge. The 14–16 years age group is a vulnerable group as it is marked by the onset of puberty. Most girls enter puberty between the age of 8 and 13 years, while boys enter puberty from the age of 10 to 15 years [38]. Puberty may trigger poor eating, such as by eating too much, not eating enough, or eating a poorly planned diet [22]. At this stage, adolescents may feel hungrier all the time, leading to overeating, or a loss of appetite, leading to undereating and nutrient deficiencies, both of which can be related to hormonal activity during puberty [24].
Zimbabwean adolescents can benefit from pre-adolescent nutrition education. Pre-adolescents are vulnerable, transitioning typically from elementary school to middle school (primary to secondary school) [39]. Pre-adolescent nutrition education can be tailored to equip adolescents with the knowledge they need to navigate puberty while making healthy food choices despite their food environment [40]. For instance, diets rich in fish, vegetables and fruit, cereals, and low-fat dairy products like low-fat milk and yoghurt are positively correlated with the quality and consistency of ovulation among girls [37]. Teaching adolescents to identify foods high and low in fat, saturated fat, cholesterol, sodium, and added sugars is a great method to raise awareness on what is good for their bodies [38]. Additionally, providing nutrition role models, teaching them to increase the value they place on health and their sense of control over food selection and preparation, can provide the empowerment adolescents need to be proactive in making healthy food choices [41]. In Zimbabwe, especially in high-density areas, street food stalls and vendors are a common sight [9]. This could explain the association between place of residence and poor food habits. Improving food environments is an important step towards achieving double-duty actions [24]. This can be achieved by improving infrastructure to sell healthier food options like fruits and vegetables which are perishable foods [39,42].
The finding that poor food habits and inadequate physical activity are significantly related is a novel finding in this population and more studies are required to understand and explain this finding. However, poor food habits and inadequate physical activity are responsible for the disparity between energy intake and usage, contributing to excess weight and increased cardiometabolic risk [43]. The finding that physical activity levels are still low among adolescents is a cause for concern considering that physical activities are compulsory in Zimbabwean schools [44] and that organized sports are popular [29]. There are other types of physical activity that can be explored and implemented in schools. For instance, dance classes are a feasible alternative to traditional physical activity [45]. An American study show that girls enrolled into dance classes had a substantial proportion (29%) of girls’ total moderate-to-vigorous physical activity (MVPA), and girls accumulated 70% more MVPA and 8% less sedentary behavior on program days than on non-program days [46]. There are several categories of dance interventions, including diverse genres of dance (e.g., ballet, jazz, folk, West African, hip hop); exercise dance; and dance and movement therapy (DMT), which is a form of psychotherapy that uses the creative uses of movement and dance and is led by credentialed professionals and conducted either in group or individual sessions [45]. The downside to dance as a form of physical activity among Zimbabwean adolescents is the lack of information about the creation of safe dance protocols, and capable teachers. This may hinder implementation. However, other protocols exist and can be adopted [47,48]. For instance, in Brazil, a dance protocol for the prevention of childhood obesity was developed and tested. This protocol utilized Afro-Brazilian dance training. This strategy showed a significant (p < 0.05) reduction in BMI z-score and waist-to-height ratio in overweight and obese children [47].

4.3. Practical Implications of the Study

The findings of this study have significant practical implications for school authorities, parents, guardians, and healthcare providers. School authorities can benefit from understanding the need to create healthy food environments around and within school premises to make up for a lack of healthy food environments in adolescents’ neighborhoods. Parents and guardians can benefit from understanding the importance of balanced nutrition for their children’s health and can be provided with educational resources to guide healthier food choices, be nutrition role models, and encourage active lifestyles. Parents and guardians can also ensure good nutrition away from home by preparing healthy lunchboxes with more vegetables and correct portions [34,35]. Healthcare providers can use this study’s data to better assess and address the health and nutrition needs of adolescents. Additionally, the study underscores the need for policymakers to support programs that promote healthy eating among adolescents, encouraging schools and community organizations to implement intervention programs that include nutrition education and other alternatives of physical activities which are attractive and fun for adolescents.

4.4. The New Directions for Future Research

We recommend diet quality assessments and data collecting regarding adolescents’ intake of macronutrients (i.e., protein, carbohydrate, and fats) and micronutrients (i.e., vitamins and minerals). Furthermore, healthy eating index scores can be calculated which measure how well dietary intake conforms to the Zimbabwean dietary guidelines in terms of the amounts consumed from each food group. Future longitudinal studies that track changes in dietary intake and health outcomes over time would provide valuable information. Additionally, exploring the impact of specific dietary interventions and alternative forms of physical activity programs tailored to the needs of adolescents could help in developing effective strategies for preventing obesity and promoting health in this population group. It would also be beneficial to assess how psychological factors, such as motivation, emotions, and self-efficacy, influence eating habits. Family income and parental education influence dietary intake. Therefore, dietary interventions targeting adolescents should consider socioeconomic status and sociodemographic factors that influence eating habits in diverse populations.

4.5. Strengths and Limitations of the Study

This study provided some insights on adolescents’ food habits which will help to shape nutrition policies and intervention programs. The main limitation of the study is the cross-sectional design of the study, which limits any conclusion regarding the cause–effect relationship between variables. Furthermore, the variables in this study were developed from existing studies and some parts of the adopted questionnaires had different subscales which may have introduced variability in interpretation. However, this was minimized through standardized scoring. In some instances, synonyms of nouns were used to contextualize the questionnaire. For example, the noun cookie was replaced with biscuits. During data analysis, the dichotomization of variables was used for ease of presentation. In the future, more intense assessments using vitamin biomarkers may be used to measure diet quality. To achieve more accurate results than those of this current study, future research should target a larger population size and include multiple regions in Zimbabwe. Overall, this study adds to the limited literature on adolescents’ eating habits in Zimbabwe with a potential for other low-income African countries to benefit from it.

5. Conclusions

In summary, our findings show that food habits are poor across the nutrition status spectrum. Girls and 14–16-year-olds exhibited higher percentages of poor eating habits. This shows the need for engagements with pre-adolescents’ to co-create social behavior change communication (SBCC)-themed interventions aimed at improving nutrition and weight outcomes. Specifically, positive nutrition messages must be tailored to address identified bad eating habits like skipping breakfast and unhealthy snacking patterns. In addition, the finding that physical activity was low calls for deliberate policy shifts to promote the adoption of healthy nutrition and physical activity-related behaviors.

Author Contributions

Conceptualization, A.P. and N.H. (Naomi Hayami); methodology, A.P.; validation, A.P. and N.H. (Naomi Hayami); formal analysis, A.P. and T.M.C.; investigation, A.P.; resources, A.P. and N.H. (Naomi Hayami); data curation, A.P.; writing—original draft preparation, A.P.; writing—review and editing. N.H. (Naomi Hayami), T.M.M., and N.H. (Nobuko Hongu), visualization, A.P. and T.M.C.; supervision, T.M.M. and N.H. (Nobuko Hongu); project administration, A.P. and N.H. (Naomi Hayami); All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Graduate School of Human Life and Ecology, Osaka Metropolitan University (OMU/21-52), and the Medical Research Council of Zimbabwe (MRCZ/A/2857). Approval was sought at the Ministry of Primary and Secondary Education (MoPSE) and the schools through a consultative engagement process.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

We thank the Ministry of Primary and Secondary Education, School Headmasters, and parents for allowing us to recruit adolescents for this study. We thank the adolescents for showing interest and participating in our study. Without them, we would not have these findings.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Nutrition status of adolescents categorized by food habits.
Figure 1. Nutrition status of adolescents categorized by food habits.
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Figure 2. The distribution of adolescents’ mealtime habits.
Figure 2. The distribution of adolescents’ mealtime habits.
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Table 1. Sociodemographic characteristics of adolescents by food habits.
Table 1. Sociodemographic characteristics of adolescents by food habits.
Food Habits
TotalUnhealthyHealthy
n (%)n (%)n (%)p-Value
SexMale198 (46.8)106 (53.5)92 (46.5)
Female225 (53.2) 115 (51.1)110 (48.9)0.619
Age Group14–16 years229 (54.1)133 (58.1)96 (41.9)
17–19 years194 (45.9)90 (43.8)104 (56.2)0.004 *
Education level of HHNo formal education19 (4.49)11 (57.9)8 (42.1)
Primary education17 (4.08)8 (72.7)9 (27.3)
Ordinary education149 (35.2)61 (40.9)88 (59.1)
Tertiary education244 (57.6)141 (57.8)103 (42.2)0.005 *
Employment status of HHFormally employed228 (59.9)127 (55.7)101 (44.3)
Unemployed22 (5.2)12 (52.2)11 (47.8)
Entrepreneur172 (40.7)82 (47.7)90 (52.3)0.282
Household SizeAverage357 (84.4)190 (52.9)169 (47.1)
Above average64 (15.1)31 (48.4)33 (51.6)0.508
Family StructureBoth parents282 (66.7)153 (54.3)129 (45.7)
Single parent79 (18.8)44 55.7)35 (44.3)
Guardians52 (12.3)20 (38.5)32 (61.5)
Child-headed7 (1.7)4 (57.1)3 (42.9)0.086
Place of ResidenceIntermediate density198 (46.8)159 (0.2)39 (39.8)
High density253 (59.8)112 (44.3)141 (55.7)
Low density172 (40.6)150 (9.4)22 (30.6)<0.001 *
Notes: FHS (Food Habits Score): <50% is Unhealthy and ≥50% is Healthy. The p-value is for Pearson’s Chi-square test. * p-value shows significant differences (p < 0.05). HH = household head. Household size: ≤5 is average and >5 is above average. Place of residence: density describes population size.
Table 2. Frequency distributions of adolescents’ eating habits.
Table 2. Frequency distributions of adolescents’ eating habits.
Food HabitsResponsen%
1. If I am having lunch away from home, I often choose a low-fat option.True13932.9%
False22352.7%
I never have lunch away from home6114.4%
2. I usually avoid eating fried foods.True10825.6%
False31474.4%
3. I usually eat a dessert if there is one available.True30973.2%
False11326.8%
4. I make sure I eat at least one serving of fruit a day.True18343.4%
False23956.6%
5. I try to keep my overall fat intake down.True21851.7%
False20448.3%
6. If I am buying milk, I often choose a low-fat brand.True13030.7%
False22753.7%
I never buy milk6615.6%
7. I avoid eating lots of sausages and burgers.True9823.2%
False30772.6%
I never eat sausages or burgers184.3%
8. I often buy biscuits, donuts, cream puffs or cakes.True25460.2%
False16839.8%
9. I try to keep my overall sugar intake down.True20348.1%
False21951.9%
10. I make sure I eat at least one portion of vegetables or salad a day.True25961.2%
False16438.8%
11. If I am having a dessert at home, I try to have something low in fat.True15937.7%
False20147.6%
I don’t eat desserts6214.7%
12. I rarely eat takeaway meals.True20548.6%
False21751.4%
13. I try to ensure I eat plenty of fruit and vegetables.True27063.8%
False15336.2%
14. I often eat sweet snacks between meals.True18644.1%
False23655.9%
15. I usually eat at least one serving of vegetables (excluding potatoes) or salad with my evening meal.True25961.5%
False16238.5%
16. When I am buying a soft drink, I usually choose a diet drink, e.g., diet coke.True9923.4%
False28366.9%
I never buy soft drinks419.7%
17. When I put butter or margarine on bread, I usually spread it thinly.True23756.0%
False13431.7%
I never have butter or margarine on bread5212.3%
18. If I have a packed lunch, I usually include some chocolate and/ or biscuits.True15035.5%
False22052.0%
I never have a packed lunch5312.5%
19. When I have a snack between meals, I often choose fruit.True17140.4%
False20348.0%
I never eat snacks between meals4911.6%
20. If I am having a dessert in a restaurant, I usually choose the healthiest one.True12730.0%
False20748.9%
I never have desserts in restaurants8921.0%
21. I often have cream on desserts.True17641.6%
False19445.9%
I don’t eat desserts5312.5%
22. I eat at least three servings of fruit most days.True12730.0%
False29670.0%
23. I generally try to have a healthy diet.True32576.8%
False9823.2%
Often6816.1%
Always19145.2%
Table 3. Food habit subscales in relation to sex and age.
Table 3. Food habit subscales in relation to sex and age.
Food HabitsSexAge
Boys
n (%)		Girls
n (%)		p-Value
Value		14–16 Year
n (%)		17–19 Year
n (%)		p-Value
Value
Fruit and vegetable consumptionInadequate93 (45.8)110 (54.2) 101 (50.5)99 (49.5)
Adequate105 (47.7)115 (52.3)0.693128 (59.8)86 (40.2)0.057
Sugar consumptionHigh191 (47.4)212 (52.6) 217 (55.1)177 (44.9)
Normal7 (35.0)13 (65.0)0.27812 (60.0)8 (40.0)0.666
Skipping mealsHigh180 (46.9)204 (53.1) 207 (55.2)168 (44.8)
Normal18 (46.2)21(53.8)0.93122 (56.4)17 (43.6)0.885
Fat consumptionHigh149 (47.9)162 (52.1) 177 (58.4)126 (41.6)
Normal49 (43.8)63 (56.3)0.44952 (46.8)59 (53.2)0.036 *
Notes: FHS (Food habit subscale scores): Fruit and vegetable consumption <50% is inadequate and ≥50%. Sugar and fat consumption: <50% is normal and ≥50% high; skipping meals: <50% normal and ≥50% high. p-value is Pearson’s Chi-squared test. * p-value shows significant differences (p < 0.05).
Table 4. The distribution of food habits, nutrition knowledge, and physical activity.
Table 4. The distribution of food habits, nutrition knowledge, and physical activity.
Food Habits
UnhealthyHealthy
Totaln (%)(n) %p-Values
Overall Nutrition Knowledge (NK)Inadequate171 (40.4)114 (66.7)57 (33.3)
Adequate252 (59.6)107 (42.5)145 (57.5)0.001 *
Adequate and Balanced Nutrition KnowledgeInadequate128 (30.3)70 (54.7)58 (45.3)
Adequate195 (46.0)151 (51.2)44 (48.8)0.508
Essential Nutrients KnowledgeInadequate120 (28.4)67 (55.8)53 (44.2)
Adequate303 (71.6)154 (50.8)149 (49.2)0.353
Malnutrition-Related Disease KnowledgeInadequate128 (30.2)72 (56.3)56 (43.8)
Adequate295 (69.7)149 (50.5)146 (49.5)0.277
Physical ActivityInadequate214 (50.6)89 (41.6)125 (58.4)
Adequate209 (49.4)132 (63.2)77 (36.8)0.001 *
Notes: Physical activity: adequate ≥60 min and inadequate <60 min. For all variables, <50% is inadequate and ≥50% is adequate. The p-value is Pearson’s Chi-squared test; * p-value shows significant differences (p < 0.05).
Table 5. Factors associated with poor food habits among adolescents.
Table 5. Factors associated with poor food habits among adolescents.
BS.E.p-ValueOdds Ratio 95% C.I. for OR
LowerUpper
Sex [Girls]0.0830.2230.7121.0860.7011.682
Age group [14–16 years]0.4590.2210.038 *1.5821.0262.440
Education level of HH [Primary education]−0.0160.1530.9150.9840.7291.328
Employment status of HH [Entrepreneur]0.1460.1150.2041.1570.9241.450
Household size [Average]0.3830.3040.2081.4670.8082.660
Family structure [Both parents]0.2550.1340.0571.2900.9931.677
Place of residence [High-density]0.5970.1540.001 *1.8161.3442.454
Nutrition status [Underweight]−0.0680.2190.7550.9340.6091.433
Physical activity [Inadequate]−0.7360.2210.001 *0.4790.3110.738
Nutrition knowledge [Inadequate]1.4640.3350.001 *4.3212.2428.330
Notes: Goodness of fit: Nagelkerke R2 = 0.218, Cox and Snell test p = 0.163. OR = Odds Ratio, LDS = Low-density suburbs, HH = Household. * Factors significantly associated with poor food habits (p < 0.05). The reference category is the poor food habits category.
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Pencil, A.; Matsungo, T.M.; Chuchu, T.M.; Hongu, N.; Hayami, N. Examining the Correlates of Food Habits Among Adolescents in Zimbabwe: A Cross-Sectional Study. Obesities 2025, 5, 9. https://doi.org/10.3390/obesities5010009

AMA Style

Pencil A, Matsungo TM, Chuchu TM, Hongu N, Hayami N. Examining the Correlates of Food Habits Among Adolescents in Zimbabwe: A Cross-Sectional Study. Obesities. 2025; 5(1):9. https://doi.org/10.3390/obesities5010009

Chicago/Turabian Style

Pencil, Ashleigh, Tonderayi Mathew Matsungo, Thomas Mavhu Chuchu, Nobuko Hongu, and Naomi Hayami. 2025. "Examining the Correlates of Food Habits Among Adolescents in Zimbabwe: A Cross-Sectional Study" Obesities 5, no. 1: 9. https://doi.org/10.3390/obesities5010009

APA Style

Pencil, A., Matsungo, T. M., Chuchu, T. M., Hongu, N., & Hayami, N. (2025). Examining the Correlates of Food Habits Among Adolescents in Zimbabwe: A Cross-Sectional Study. Obesities, 5(1), 9. https://doi.org/10.3390/obesities5010009

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