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Article

Physical Activity and Sedentary Behavior in High School Students: A Quasi Experimental Study via Smartphone during the COVID-19 Pandemic

by
Regina Márcia Ferreira Silva
1,*,
Lauryane Fonseca Terra
1,
Michele da Silva Valadão Fernandes
1,
Priscilla Rayanne E. Silva Noll
1,
Alexandre Aparecido de Almeida
2 and
Matias Noll
1,3,*
1
Department of Education, Federal Institute Goiano, Ceres 76300-000, Brazil
2
Department of Education, Federal Institute of Tocantins, Araguatins 77950-000, Brazil
3
Health Science Graduate Program, Faculty of Medicine, Federal University of Goiás, Goiânia 74001-970, Brazil
*
Authors to whom correspondence should be addressed.
Children 2023, 10(3), 479; https://doi.org/10.3390/children10030479
Submission received: 21 December 2022 / Revised: 3 February 2023 / Accepted: 27 February 2023 / Published: 1 March 2023
(This article belongs to the Special Issue Promoting Physical Activity in the School Setting)
Browse Figure
Review Reports Versions Notes

Abstract

:
The objective of this study was to evaluate whether exposure to information about physical activity and its barriers can increase the level of physical activity and reduce the time exposed to sedentary behaviors in high school students involved in integrated professional and technological education during the coronavirus disease 2019 pandemic. This quasi experimental study was conducted with integrated education high school students, divided into two groups: Intervention Group (IG; n = 59) and Control Group (CG; n = 54). Physical activity and sedentary behavior were identified and measured using the International Physical Activity Questionnaire pre-and post-intervention for both groups. IG students received educational material thrice a week for four weeks. The focus of the material was the importance of physical activity and need to reduce the time exposed to sedentary behavior. The results revealed that IG students showed an average daily reduction of 47.14 min in time exposed to sedentary behaviors, while the CG students showed an increase of 31.37 min. Despite this, the intervention was not effective in improving physical activity levels in the IG and the mean reduction in the time exposed to sedentary behavior was not significant (p = 0.556). The intervention was ineffective in increasing the practice of physical activity and reducing the time exposed to sedentary behavior.

1. Introduction

Physical activity is a concept defined as any movement that results in energy expenditure above resting levels [1]. Recently, it has been extended to people moving, operating, and acting in culturally specific spaces and contexts, influenced by diverse interests, emotions, and relationships [2]. Regular physical activity has been considered a important factor associated with the prevention of chronic non-communicable diseases [3,4,5]. Additionally, studies have recognized physical, psychological, and social benefits associated with regular participation in physical activities [6,7,8,9,10,11,12,13,14,15,16]. Despite this, the prevalence of physical inactivity is above 80% among adolescents worldwide, when those who engage in (moderate to vigorous) physical activity for fewer than 60 min a day are considered physically inactive [17]. In Brazil, this percentage exceeds 83% for this age group [18].
Physical inactivity is characterized by failure to meet current physical activity recommendations [19,20,21], while sedentary behavior is defined as any waking behavior characterized by an energy expenditure ≤1.5 metabolic equivalents (METs); for example, time spent sitting or lying down while awake [20]. The factors that make it difficult or prevent individuals from participating in physical activities are commonly called barriers [22]; therefore, it is possible for a person to be physically active (by complying with the recommendations) and simultaneously spend significant time engaging in sedentary behavior. Barriers can contribute to an increase in physical inactivity and sedentary behaviors. An example of sedentary behavior is the use of electronic devices and prolonged sitting, lying down, or reclining [20].
The barriers can be categorized into four dimensions: environmental; psychological, cognitive, and emotional; sociodemographic; and sociocultural [23,24,25,26,27,28]. In this sense, the coronavirus disease 2019, a disease caused by the SARS-CoV-2 virus that spread into a worldwide pandemic [29], can be considered a barrier to the practice of physical activity, as it directly affected two important public health concerns: physical inactivity and sedentary behavior [30]. The COVID-19 pandemic impacted society in an unprecedented way, characterized by the greatest interruption of the teaching–learning process in the history of world education [31,32]. Thus, educational institutions worldwide needed to adopt emergency remote teaching modalities [33,34].
To promote physical activity, actions are needed that encompass concepts related to physical inactivity, sedentary behavior, and barriers to physical activity. Our study used the ecological framework theory of health behavior, which states that behavioral change is a complex process that can be elicited through a multi-pronged approach targeting intrapersonal, interpersonal, organizational, and socio-community dimensions [35,36]. In this sense, considering that adolescents engage in significant cell phone use during their day [37,38], it can be expected that “positive” messages related to healthy lifestyle habits and physical activity will contribute to a change in sedentary behavior observed in these young people. Subsequently, a line of reasoning highlights the possibility of extrinsic motivation bringing the individual closer to the activity, suggesting an increase in autonomy and an internal incentive [39].
Accordingly, the World Health Organization suggests the use of smartphones to help reduce time spent in sedentary behaviors and increase the practice of physical activity [5]. Recent systematic reviews, including articles of moderate to high quality, identified that smartphone-based physical activity interventions, such as those delivered via application, were effective in increasing an individual’s amount of physical activity [40,41]. Another review noted the need for studies that developed physical activity interventions using mobile health for specific target groups [42]. Several intervention studies used smartphones to promote a healthier lifestyle [43,44,45,46]. In a recent study [44], five weeks of using a mobile application that sent notifications about nutrition and physical activity to Portuguese adolescents improved diet behavior in 28.6% of participants. In addition, a 42.9% increase in the level of physical activity was observed. In another study [43], an application was used to apply individual and collective challenges related to physical activity to Spanish adolescents for 10 weeks. The results showed that the application increased participants’ time spent on physical activity. Finally, an application used for four weeks with female adolescents in Singapore attenuated the decline in physical activity level among participants [45]. Evidence showed that smartphone-based interventions may be a promising strategy for increasing total physical activity time [47,48] and reducing time exposed to sedentary behaviors [49] in adolescents.
Considering the above, an intervention that proposes increasing the level of physical activity and reducing exposure to sedentary behavior during the coronavirus pandemic could be an interesting strategy. Thus, this study aimed to assess whether exposure to information about physical activity and its barriers received via messaging applications can increase physical activity and reduce sedentary behaviors in high school students enrolled in professional and technological integrated education.

2. Materials and Methods

2.1. Study Design and Research Location

This was a quasi-experimental study with a field trial design lasting four weeks. This study was conducted in September 2021 during the school year at a professional and technological education institution located in central Brazil.
At that time, the population of Brazil had already been living with the COVID-19 pandemic for 18 months. The Midwest region of Brazil had the highest COVID-19 mortality rate in the country (301 deaths per 100,000 inhabitants), while the national rate was 249.9 deaths per 100,000 inhabitants [30,50]. Vaccine coverage at that time was just over 20% of the adult population [30,50].
The region was engaged in social isolation, with mandatory use of face protection masks. This state had the seventh lowest rate of social isolation in the country; however, the institution had been practicing emergency remote teaching for over a year [30,50].

2.2. Sample

Participants were recruited among 207 students enrolled in integrated technical courses offered by a federal public institution of professional and technological education. Everyone received the invitation to participate by e-mail. The participants belonged to five groups encompassing the first and third years of technical courses in agriculture, information technology, and the environment. We determined the quantitative sample using a 5.0% margin of error and a 95.0% confidence level, resulting in a sample of 113 participants. We used block randomization [51], which is useful to resolve imbalances in the number of individuals. Each of the five groups corresponded to a randomization block; these blocks were randomized into two groups. Of a total of 113 high school students, 59 belonged to the Intervention Group (IG) and 54 to the Control Group (CG).

2.3. Inclusion and Exclusion Criteria

For the inclusion criteria in the study, students should be properly enrolled in the institution. They should have a smartphone device with functionality and use of a text message application. Finally, they should have the cognitive ability to interpret and answer the questionnaires in the pre-and post-intervention periods. Students who responded inappropriately to questionnaires intended to collect information on these criteria were excluded.

2.4. Intervention

The general focus of the interventions was to improve knowledge about the importance of physical activity and reduce the time exposed to sedentary behaviors. The content of the intervention was based on the strategy developed by the group ‘On Your Feet Britain (10 ways to sit less at work)’ [52] and an intervention carried out with university students [53]. We focused on activities described in the Physical Activity Guide for the Brazilian Population designed for children and young people from 6 to 17 years of age in the domains of free time, displacement, school, and household chores [54].
Currently, mobile technologies are essential to human life, as they bring convenience and practicality to the touch of the screen [55]. A special highlight is the smartphone, which offers various applications that support activities such as study, work, and leisure, among others [56]. One such application is WhatsApp, which has become a widely used communication tool for personal relationships and professional activities [57].
The intervention included sending eight illustrated and colored folders [58] over four weeks. The folders were sent thrice a week through the WhatsApp messaging application. The participants were asked to “reply” to confirm when they had received and read our messages. All folders are included in this study’s supplemental material. Folders were sent on Mondays and Wednesdays, and both folders were resent on Fridays (see Table 1).

2.5. Data Collection

Data collection was performed using two questionnaires and a structured interview with open questions. The International Physical Activity Questionnaire (IPAQ), short version [59,60,61], was used to collect information on the level of physical activity and time exposed to sedentary behavior, and a 10-item questionnaire developed by the authors was used to collect information relevant to the research. The IPAQ was administered three days before and three days after the intervention to both participant groups.
The questionnaire developed by the authors included 10 questions (satisfaction level with the project, language, content and terms; duration and number of questions; if they were encouraged to have a less sedentary week and encouraged to have a week with more physical activities; and score for the project from 0 to 10), and its function was to evaluate the intervention with open and closed answers. Therefore, its use occurred three days after the intervention only in the IG. The two questionnaires were conducted online via Google Forms. The interviews were conducted online with six participants selected from the IG. This amount is recommended for homogeneous samples [62]. Later, the interviews were transcribed and analyzed, and the results were grouped into thematic axes, categories, and indicators.
All enrolled subjects voluntarily participated in this study (with parents’ consent and approval), following ethical principles. Our study was approved by the Ethics Committee of the Instituto Federal Goiano (No. 28163120.4.0000.0036).

2.6. Data Analysis

The level of physical activity was identified through questions 1–3 of the IPAQ and were classified as low, moderate, and high. The physical activity level in the participants was classified as “low” (not meeting the criteria for the “moderate” or “high” categories), “moderate” (at least 20 min of vigorous physical activity three or more days/week; at least 30 min of moderate physical activity or walking five or more days/week; or any combination of walking, moderate, or vigorous physical activity reaching at least 600 metabolic equivalents of task (MET) minutes/week at least five days/week), and “high” (vigorous physical activity reaching at least 1500 MET minutes/week at least three days/week; or any combination of walking, moderate, or vigorous physical activity reaching at least 3000 MET minutes/week at least seven days/week) [34,59].
The time exposed to sedentary behavior (minutes/day) was identified through question 4 of the IPAQ. It was determined from the weighted average of the time sitting on a weekday and a weekend day according to the following equation: [(weekday sitting time ∗ 5 + weekend day sitting time ∗ 2)/7].
We used descriptive and inferential statistics. Average daily exposure to sedentary behaviors in IG and CG before and after the intervention was calculated by delta; absolute delta was calculated by subtracting the average time exposed to sedentary behavior from the post-moment by the pre-intervention moment. The comparison of the mean was carried out with the t-test (SPSS 26.0).
To calculate the sample size, we used G-Power [63], with α = 0.05 (significance level) and β = 0.85 (power of the test) and found that the minimum sample of each group should be 31 participants. Content analysis was used to interpret qualitative data [64] from the interviews. All steps of this analysis were performed by two reviewers with experience in qualitative approaches.

3. Results

The final sample consisted of 80 (26 female; 54 male) participants. After the four-week intervention period, 26 students had dropped out of the IG and three more dropped during the analysis stage. In Figure 1, based on the Consolidated Standards of Reporting Trials (CONSORT), we described the records of the intervention. Four participants dropped out of the CG. This is because they did not respond to the post-intervention questionnaire.
The age of the CG was 15.9 ± 1.15 years, and that of the IG was 16.2 ± 0.94 years (p = 0.225), others characteristics are found in the (Table 2).
The most frequent level of physical activity in the pre-and post-intervention of both groups was high (Table 3). In both groups, there was an improvement in the frequency of low and moderate levels. The high level remained at the same frequency before and after intervention in the IG and increased slightly in the CG.
Regarding the time exposed to sedentary behavior, a reduction in the mean time of 47.14 min per day in the IG and an increase in the mean time of 31.37 min per day in the CG was observed. However, no significant differences were observed in pre-or post-intervention means in both groups (Table 4).
Table 5 presents data on the perception of IG students in relation to participation in the intervention. We also verified whether the intervention contributed in any way to the increase in the practice of physical activity and the reduction in the time exposed to sedentary behaviors of the participants. Through the responses to the intervention evaluation questionnaire, we identified that the majority (n = 24; 80.0%) demonstrated satisfaction in having participated in the project, with the language used and the duration. Only 10% (n = 3) of the participants did not make positive comments.
The interview responses revealed a need for future similar interventions to provide more options, such as sending notifications and inclusion of challenges, photos, and videos:
“I thought the project initiative was very good and really necessary, especially in the time of a pandemic where many stopped exercising, and with the project, people were encouraged to resume practice and even to start it for those who didn’t do anything before the pandemic. Great project, wonderful idea!!”
(P1)
“The experience of this project was incredible because sometimes my routine and obligations end up distracting me from moving more, despite it being something I like. The project helped me to do this and motivated me in moments of fatigue. I loved participating.”
(P2)
“Although I didn’t send photos or videos in the WhatsApp group, the messages reminded me to do some physical activity on days when I was very still. I found the project very interesting. I liked how it was done. Congratulations to those involved!”
(P3)
“I liked receiving the messages and how the project was done in general; however, because I spend a lot of time on computers and in electronic games, almost without observing my notifications, actually, there were no changes in my routine.”
(P4)
“I could see how long I sat while studying; I never stopped to think about it before.” (P5)
The sedentary behavior axis was divided into five categories: concept, the total number of daily hours, number of daily hours (leisure), contributing factors, and positive factors related to the reduction in time exposed to sedentary behavior. In Table 6, the physical inactivity axis includes the knowledge category for the percentage of physically inactive Brazilian adolescents.
In the thematic axis of sedentary behavior, the participants declared that they spent between seven and fifteen hours daily exposed to sedentary behavior (Table 6). Regarding the factors that contribute to sedentary behavior, they mentioned mainly three personal factors (lack of information, lack of interest, and laziness) and three external factors (pandemic, technology, and modern life). Regarding the positive factors caused by the decrease in sedentary behavior, they mentioned the increase in the disposition and prevention of diseases. In the thematic axis of physical inactivity, regarding the knowledge of the percentage of Brazilian adolescents who are physically inactive, the participants reported between 70% and 75%.

4. Discussion

The present study evaluated whether exposure to information about physical activity and its barriers could improve the level of physical activity and reduce the time exposed to sedentary behaviors in high school students enrolled in integrated professional and technological education. The high number of physically inactive adolescents [18] and the large number of health problems that exposure to sedentary behaviors can cause in general [65] demonstrate the need to evaluate efficient ways to reduce the time exposed to sedentary behavior and increase the level of physical activity. After the intervention, a mean decrease of 47.14 (min/day) in exposure to sedentary behavior was observed in the IG. In contrast, in the CG, we verified an increase of 31.37 min per day in the average time in the sedentary behavior activities. While the changes in time exposed to sedentary behavior were not statistically significant between pre-and post-intervention, the findings still have clinical relevance since “every step counts”, no matter how small the increase in physical activity is [5]. Regarding the practice of physical activity, no significant differences were identified between groups, demonstrating that they were homogenous groups in relation to basal condition (before intervention).
Two important factors could be contributed to this absence of significant differences. One was that, however motivational the messages sent to mobile applications were in increasing healthy behavior [66], adherence to social distancing and isolation strategies may have mitigated their effects. Another reason was the strong tendency to sedentary behavior during adolescence. Young adolescents who practice low levels of physical activity in early life tend to maintain low levels of physical activity practices in late adolescence and adult life [67]. As the COVID-19 pandemic was a totally unexpected situation, young people who did not practice adequate levels of physical activity before the establishment of measures to contain the transmission of the virus would not start the practice of physical activities during this period even if stimulated remotely, in this case, by application messages.
The messaging application was chosen first because it is the platform most reportedly used by adolescents between 15 and 17 years old. Approximately 70% of these young people use these technologies in their daily lives [37]. Another factor was the increase in the number of application downloads aimed at physical activity in the home during the pandemic [68,69]. A recent study observed the characteristics a smartphone application should contain to reduce sedentary behavior in adolescents. Among the characteristics mentioned, social relationships, messages, and updates were listed [70] as important. The mobile application used in the present study demonstrated good adherence by the sample analyzed.
The language used was simple and visually appealing; approximately 80% (n = 24) of the students reported being satisfied or very satisfied with the language, terms, and content used in the messages sent to them. These features are important as they arouse interest and facilitates use among the adolescent public. Although the format for disseminating the messages of the present study was well accepted or evaluated by the volunteers, more “relaxed” messages such as pictures or videos may receive more attention and greater dedication to the proposed changes in habits. Additionally, as the application was very popular among adolescents, there may have been a sharing of motivational messages between the experimental and control groups, confounding the interpretation of results.
The duration of the intervention in this research is in line with a similar intervention [71]; both lasted four weeks. More than half of the students who participated in this research (n = 17; 56.6%) reported being satisfied or very satisfied with the duration of this intervention. An interventional study with university students for two, four, and six weeks showed that evidence, advice, reminders, and challenges sent by text messages have the potential to increase non-sedentary patterns [66,72]. Thus, similar interventions of longer duration may show significant effects.
Currently, technologies are essential to human life, as they bring convenience and practicality to the touch of the screen [55]. A special highlight among the technologies is the smartphone, which offers various applications that allow activities such as study, work, and leisure, among others [56]. A widely used application is WhatsApp, which has become a widely used communication tool for personal relationships and professional activities [57].
In research concerning high school students [66,73], similar to this study, an average of two hours a day in sedentary leisure-time behaviors was identified. In accordance with other studies [74,75], the use of technologies was reported as a factor that contributed to exposure to sedentary behavior. Motivating physical activity practices with smartphone or cell phone applications are conflicting. Although these applications may be effective to improve physical activity [76], a meta-analysis review found a non-significant effect [77]. In this sense, we may assume that an uncontrolled confounder in the study may have influenced our results. For example, cell phone use to access messages may stimulate adolescents to continue using their phones, thus increasing the time involved in sedentary behaviors.
To the best of our knowledge, this is the first study that investigated the use of mobile applications to influence increased sedentary behaviors in adolescents during an exceptional condition such as the COVID-19 pandemic. Furthermore, although our study sheds light on some important issues related to adolescent behavior (sedentary behavior and mobile use), it has some limitations. First, an important issue of the research design is that the sampling procedure did not ensure that the participants had same levels of PA experience before the lockdown period. However, in a real school context, it is not possible to evaluate classes of different intervention groups from the same school, to avoid communication bias. Second, we did not follow the post-intervention over time and the use of a self-reported questionnaire. Third, we cannot identify whether sedentary behavior continued to decrease or increase after the intervention. Fourth, during the pandemic, excessive use of the internet and social networking has been shown to contribute to an increase in depressive symptoms [34], which may have influenced the dropout of participants who possibly became “stressed” by so many online activities. We suggest that this be evaluated and considered in future studies. Finally, the lack of access to the data plan of some participants may have influenced the continuity of the intervention, which may have interfered with our results. Despite these limitations, given that knowledge surrounding adolescents’ physical activity during the lockdown and our study’s aim to shed light on the “unknown” period of the COVID-19 pandemic, we believe that the current study still made important advances in the literature targeting adolescents. Adolescents constitute an age group with high rates of sedentary behavior, and many interventions encounter problems in changing behaviors that hinder the increase of physical activity.

5. Conclusions

The intervention was not effective in increasing the practice of physical activity and reducing the time exposed to sedentary behavior in adolescent people. Some factors, such as frequency of sending messages, intervention time, content, and message formats, may have impacted the results and should be further investigated in future research. Future interventions should be improved with options beyond sending folders. We suggest exploring different aspects of feasibility.

Author Contributions

Conceptualization, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; methodology, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; software, R.M.F.S.; validation, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; formal analysis, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; investigation, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; resources, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; data curation, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; writing—original draft preparation, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; writing—review and editing, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; visualization, R.M.F.S., L.F.T., P.R.E.S.N., M.d.S.V.F., A.A.d.A. and M.N.; supervision, A.A.d.A. and M.N.; project administration, A.A.d.A. and M.N. 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, and approved by Ethics Committee of the Instituto Federal Goiano (No. 28163120.4.0000.0036).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study and by the participants’ legal guardian/next of kin.

Data Availability Statement

Additional data and SPSS software code can be obtained from the authors.

Acknowledgments

The authors would like to show their gratitude to Instituto Federal de Goiás, Instituto Federal Goiano, and the Research Group on Child and Adolescent Health (www.gpsaca.com.br (accessed on 30 September 2021)) for the support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Caspersen, C.J.; Powell, K.E.; Christenson, G.M. Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research. Public Health Rep. 1985, 100, 126–131. [Google Scholar] [PubMed]
  2. Piggin, J. What Is Physical Activity? A Holistic Definition for Teachers, Researchers and Policy Makers. Front. Sports Act. Living 2020, 2, 72. [Google Scholar] [CrossRef] [PubMed]
  3. Anderson, E.; Durstine, J.L. Physical activity, exercise, and chronic diseases: A brief review. Sports Med. Health Sci. 2019, 1, 3–10. [Google Scholar] [CrossRef] [PubMed]
  4. Ding, D.; Ramirez Varela, A.; Bauman, A.e.; Ekelund, U.; Lee, I.-M.; Heath, G.; Katzmarzyk, P.T.; Reis, R.; Pratt, M. Towards better evidence-informed global action: Lessons learnt from the Lancet series and recent developments in physical activity and public health. Br. J. Sports Med. 2020, 54, 462–468. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. WHO. Global Action Plan on Physical Activity 2018–2030. 2018. Available online: https://apps.who.int/iris/bitstream/handle/10665/272722/9789241514187-eng.pdf (accessed on 10 August 2021).
  6. Moeini, B.; Rezapur-Shahkolai, F.; Bashirian, S.; Doosti-Irani, A.; Afshari, M.; Geravandi, A. Effect of interventions based on regular physical activity on weight management in adolescents: A systematic review and a meta-analysis. Syst. Rev. 2021, 10, 52. [Google Scholar] [CrossRef] [PubMed]
  7. Chaput, J.-P.; Klingenberg, L.; Rosenkilde, M.; Gilbert, J.-A.; Tremblay, A.; Sjödin, A. Physical Activity Plays an Important Role in Body Weight Regulation. J. Obes. 2011, 2011, 360257. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Bherer, L.; Pothier, K. Physical Activity and Exercise. In Cognitive Training: An Overview of Features and Applications; Strobach, T., Karbach, J., Eds.; Springer: Cham, Switzerland, 2020; pp. 319–330. [Google Scholar] [CrossRef]
  9. Alidadi, A.; Jalili, A. Relationship between Physical Fitness, Body Composition and Blood Pressure in Active and Passive Students. Int. J. Pharm. Biol. Sci. Arch. 2019, 7. Available online: https://www.ijpba.in/index.php/ijpba/article/view/142 (accessed on 12 August 2021).
  10. Lombardi, G.; Ziemann, E.; Banfi, G. Physical Activity and Bone Health: What Is the Role of Immune System? A Narrative Review of the Third Way. Front. Endocrinol. 2019, 10, 60. [Google Scholar] [CrossRef] [Green Version]
  11. Cruz-Jentoft, A.J.; Sayer, A.A. Sarcopenia. Lancet 2019, 393, 2636–2646. [Google Scholar] [CrossRef]
  12. Livingston, G.; Sommerlad, A.; Orgeta, V.; Costafreda, S.G.; Huntley, J.; Ames, D.; Ballard, C.; Banerjee, S.; Burns, A.; Cohen-Mansfield, J.; et al. Dementia prevention, intervention, and care. Lancet 2017, 390, 2673–2734. [Google Scholar] [CrossRef] [Green Version]
  13. Tari, A.R.; Norevik, C.S.; Scrimgeour, N.R.; Kobro-Flatmoen, A.; Storm-Mathisen, J.; Bergersen, L.H.; Wrann, C.D.; Selbæk, G.; Kivipelto, M.; Moreira, J.B.N.; et al. Are the neuroprotective effects of exercise training systemically mediated? Prog. Cardiovasc. Dis. 2019, 62, 94–101. [Google Scholar] [CrossRef]
  14. Dale, L.P.; Vanderloo, L.; Moore, S.; Faulkner, G. Physical activity and depression, anxiety, and self-esteem in children and youth: An umbrella systematic review. Ment. Health Phys. Act. 2018, 16, 66–79. [Google Scholar] [CrossRef]
  15. Donnelly, J.E.; Hillman, C.H.; Castelli, D.; Etnier, J.L.; Lee, S.; Tomporowski, P.; Lambourne, K.; Szabo-Reed, A.N. Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children: A Systematic Review. Med. Sci. Sports Exerc. 2016, 48, 1197. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Dejonge, M.L.; Omran, J.; Faulkner, G.E.; Sabiston, C.M. University students’ and clinicians’ beliefs and attitudes towards physical activity for mental health. Ment. Health Phys. Act. 2020, 18, 100316. [Google Scholar] [CrossRef]
  17. Bull, F.C.; Al-Ansari, S.S.; Biddle, S.; Borodulin, K.; Buman, M.P.; Cardon, G.; Carty, C.; Chaput, J.-P.; Chastin, S.; Chou, R.; et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br. J. Sports Med. 2020, 54, 1451–1462. [Google Scholar] [CrossRef] [PubMed]
  18. Guthold, R.; Stevens, G.A.; Riley, L.M.; Bull, F.C. Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc. Health 2020, 4, 23–35. [Google Scholar] [CrossRef]
  19. Faulkner, G.; White, L.; Riazi, N.; Latimer-Cheung, A.E.; Tremblay, M.S. Canadian 24-Hour Movement Guidelines for Children and Youth: Exploring the perceptions of stakeholders regarding their acceptability, barriers to uptake, and dissemination. Appl. Physiol. Nutr. Metab. 2016, 41, S303–S310. [Google Scholar] [CrossRef] [Green Version]
  20. Tremblay, M.S.; Aubert, S.; Barnes, J.D.; Saunders, T.J.; Carson, V.; Latimer-Cheung, A.E.; Chastin, S.F.M.; Altenburg, T.M.; Chinapaw, M.J.M.; On Behalf Of SBRN Terminology Consensus Project Participants. Sedentary Behavior Research Network (SBRN)—Terminology Consensus Project process and outcome. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 75. [Google Scholar] [CrossRef] [Green Version]
  21. WHO. Global Recommendations on Physical Activity for Health; World Health Organzation: Geneva, Switzerland, 2010; Available online: http://www.who.int/dietphysicalactivity/factsheet_recommendations/en/ (accessed on 12 August 2021).
  22. Cohen-Mansfield, J.; Marx, M.S.; Guralnik, J.M. Motivators and Barriers to Exercise in an Older Community-Dwelling Population. J. Aging Phys. Act. 2003, 11, 242–253. [Google Scholar] [CrossRef] [Green Version]
  23. Ferreira, I.; van der Horst, K.; Wendel-Vos, W.; Kremers, S.; Van Lenthe, F.J.; Brug, J. Environmental correlates of physical activity in youth? A review and update. Obes. Rev. 2007, 8, 129–154. [Google Scholar] [CrossRef]
  24. Silva, R.M.F.; Mendonça, C.R.; Azevedo, V.D.; Memon, A.R.; Noll, P.R.E.S.; Noll, M. Barriers to high school and university students’ physical activity: A systematic review. PLoS ONE 2022, 17, e0265913. [Google Scholar] [CrossRef]
  25. Sallis, J.F.; Prochaska, J.J.; Taylor, W.C. A review of correlates of physical activity of children and adolescents. Med. Sci. Sports Exerc. 2000, 32, 963–975. [Google Scholar] [CrossRef] [PubMed]
  26. Seabra, A.F.; Mendonça, D.M.; Thomis, M.; Anjos, L.A.; Maia, J.A. Biological and socio-cultural determinants of physical activity in adolescents. Cad. Saúde Pública 2008, 24, 721–736. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Van der Horst, K.; Paw, M.J.C.A.; Twisk, J.W.R.; VAN Mechelen, W. A Brief Review on Correlates of Physical Activity and Sedentariness in Youth. Med. Sci. Sports Exerc. 2007, 39, 1241–1250. [Google Scholar] [CrossRef] [PubMed]
  28. Bezerra, R.A.; Oliveira, G.T.A.; Bagni, U.V.; Barbalho, R.; da Rocha, I.M.G.; de Araújo, F.R.; Fayh, A.P.T. Sedentary Behavior and Physical Activity of Schoolchildren from a Low-income Region in Brazil: Associations with Maternal Variables. J. Hum. Growth Dev. 2021, 31, 209–216. [Google Scholar] [CrossRef]
  29. Silveira, E.A.; Noll, M.; Hallal, P.C.; Oliveira, C. The need to use mortality, and not case-fatality, to compare COVID-19 deaths worldwide. Int. J. Prev. Med. 2022, 13, 49. [Google Scholar] [CrossRef]
  30. Silva, R.M.F.; Terra, L.F.; Fernandes, M.D.S.V.; Noll, P.R.E.S.; de Abreu, L.C.; Noll, M. Barriers to Physical Activity among Full-Time Students: A Case Study during the COVID-19 Pandemic. Sustainability 2022, 14, 11896. [Google Scholar] [CrossRef]
  31. Neves, V.N.S.; Valdegil, D.D.A.; Sabino, R.D.N. Emergency remote education during the COVID-19 pandemic in Brazil: State-of-the-art. Práticas Educ. Mem. Oralidades-Rev. Pemo 2021, 3, e325271. [Google Scholar] [CrossRef]
  32. Pacheco, L.F.; Noll, M.; Mendonça, C.R. Challenges in Teaching Human Anatomy to Students with Intellectual Disabilities During the Covid-19 Pandemic. Anat. Sci. Educ. 2020, 13, 556–557. [Google Scholar] [CrossRef]
  33. Rondini, C.A.; Pedro, K.M.; Duarte, C.D.S. COVID-19 pandemic and emergency teaching: Changes in pedagogical practice. Interfaces Cient. 2020, 10, 41–57. [Google Scholar] [CrossRef]
  34. Fernandes, M.D.S.V.; da Silva, T.M.V.; Noll, P.R.E.S.; de Almeida, A.A.; Noll, M. Depressive Symptoms and Their Associated Factors in Vocational–Technical School Students during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 3735. [Google Scholar] [CrossRef] [PubMed]
  35. Chaabna, K.; Mamtani, R.; Abraham, A.; Maisonneuve, P.; Lowenfels, A.B.; Cheema, S. Physical Activity and Its Barriers and Facilitators among University Students in Qatar: A Cross-Sectional Study. Int. J. Environ. Res. Public Health 2022, 19, 7369. [Google Scholar] [CrossRef] [PubMed]
  36. Sallis, J.F.; Owen, N.; Fisher, E. Ecological models of health behavior. Health Behav. Theory Res. Pract. 2015, 5, 43–64. [Google Scholar]
  37. Comitê Gestor da Internet. Survey on Internet Use by Children in Brazil. 2019. Available online: www.cgi.br (accessed on 13 August 2021).
  38. Beliche, T.W.D.O.; Hamu, T.C.D.D.S.; Bizinotto, T.; Porto, C.C.; Formiga, C.K.M.R. The postural control of Brazilian children aged 6 to 9 years using a smartphone is similar to their posture with eyes closed. J. Hum. Growth Dev. 2021, 31, 199–208. [Google Scholar] [CrossRef]
  39. Lamboglia, C.M.G.F.; Silva, C.A.B.; Carvalho, L.M.; Silva, F.V.I., Jr. O Vilão Se Torna Mocinho: Uma Perspectiva Inovadora Da Utilização Das Tecnologias De Entretenimento E Comunicação Para a Promoção E Práticas Em Saúde, 1st ed.; Tecnologias Em Saúde: Da abordagem teórica a construção e aplicação no cenario do cuidado; EdUECE: Fortaleza, Brazil, 2016; Available online: https://efivest.com.br/wp-content/uploads/2019/09/TecnologiaSaude-uece.pdf (accessed on 15 August 2021).
  40. Feter, N.; Dos Santos, T.S.; Caputo, E.L.; da Silva, M.C. What is the role of smartphones on physical activity promotion? A systematic review and meta-analysis. Int. J. Public Health 2019, 64, 679–690. [Google Scholar] [CrossRef] [PubMed]
  41. Romeo, A.; Edney, S.; Plotnikoff, R.; Curtis, R.; Ryan, J.; Sanders, I.; Crozier, A.; Maher, C. Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis. J. Med. Internet Res. 2019, 21, e12053. [Google Scholar] [CrossRef] [PubMed]
  42. Böhm, B.; Karwiese, S.D.; Böhm, H.; Oberhoffer, R. Effects of Mobile Health Including Wearable Activity Trackers to Increase Physical Activity Outcomes Among Healthy Children and Adolescents: Systematic Review. JMIR mHealth uHealth 2019, 7, e8298. [Google Scholar] [CrossRef]
  43. Gil-Espinosa, F.J.; Merino-Marban, R.; Mayorga-Vega, D. Aplicación móvil Endomondo para promocionar la actividad física en estudiantes de educación secundaria. Cult. Cienc. Deporte 2020, 15, 465–473. [Google Scholar]
  44. Villasana, M.; Pires, I.; Sá, J.; Garcia, N.; Teixeira, M.; Zdravevski, E.; Chorbev, I.; Lameski, P. Promotion of Healthy Lifestyles to Teenagers with Mobile Devices: A Case Study in Portugal. Healthcare 2020, 8, 315. [Google Scholar] [CrossRef]
  45. Seah, M.L.C.; Koh, K.T. The efficacy of using mobile applications in changing adolescent girls’ physical activity behaviour during weekends. Eur. Phys. Educ. Rev. 2020, 27, 113–131. [Google Scholar] [CrossRef]
  46. Filho, R.C.D.S.S.; Sasaki, J.E.; Gordia, A.P.; Andaki, A.C.R. Effects of a Physical Exercise Program and Health Advice on Sedentary Behavior of Adolescents. Int. J. Environ. Res. Public Health 2023, 20, 1064. [Google Scholar] [CrossRef] [PubMed]
  47. He, Z.; Wu, H.; Yu, F.; Fu, J.; Sun, S.; Huang, T.; Wang, R.; Chen, D.; Zhao, G.; Quan, M. Effects of Smartphone-Based Interventions on Physical Activity in Children and Adolescents: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth 2021, 9, e22601. [Google Scholar] [CrossRef] [PubMed]
  48. Saucedo-Araujo, R.G.; Huertas-Delgado, F.J.; Barranco-Ruiz, Y.M.; Pérez-López, I.J.; Aznar-Laín, S.; Chillón, P.; Herrador-Colmenero, M. Testing the Mystic School Mobile Application to Promote Active Commuting to School in Spanish Adolescents: The PACO Study. Children 2022, 9, 1997. [Google Scholar] [CrossRef]
  49. Filho, R.C.D.S.S.; E Lemes, T.M.M.A.; Sasaki, J.E.; Gordia, A.P.; Andaki, A.C.R. Sedentary behavior in Brazilian adolescents: A systematic review. Rev. Bras. Ativ. Fís. Saúde 2020, 25, 1–13. [Google Scholar] [CrossRef]
  50. Brasil. Coronavírus Brasil. In Ministério Da Saúde; 2022. Available online: https://covid.saude.gov.br (accessed on 12 October 2020).
  51. Lim, C.Y.; In, J. Randomization in clinical studies. Korean J. Anesthesiol. 2019, 72, 221–232. [Google Scholar] [CrossRef]
  52. On Your Feet Britain. 10 Ways to Sit Less at Work. 2016. Available online: https://onyourfeetday.com/ (accessed on 10 September 2021).
  53. Santos, D.D.A.T.; Galvão, L.L.; Santos, R.G.D.; Viana, R.B.; Santos, E.C.D.O.; Silva, R.R.; Tribess, S.; Júnior, J.S.V.; De Lira, C.A.B. Can smartphone and folder be an alternative to reduce sedentary behavior? Pilot study. Rev. Bras. Ativ. Fís. Saúde 2021, 26, 1–7. [Google Scholar] [CrossRef]
  54. Brasil. Ministério da Saúde. Secretaria de Atenção Primária à Saúde. Departamento de Promoção da Saúde. Guia de Atividade Física para a População Brasileira. 2021. Available online: https://bvsms.saude.gov.br/bvs/publicacoes/guia_atividade_fisica_populacao_brasileira.pdf (accessed on 30 September 2021).
  55. Cha, S.-S.; Seo, B.-K. Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychol. Open 2018, 5, 1–15. [Google Scholar] [CrossRef]
  56. De-Sola Gutiérrez, J.; Rodríguez de Fonseca, F.; Rubio, G. Cell-Phone Addiction: A Review. Front. Psychiatry 2016, 7, 175. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Lee, H.; Kim, J.W.; Choi, T.Y. Risk Factors for Smartphone Addiction in Korean Adolescents: Smartphone Use Patterns. J. Korean Med. Sci. 2017, 32, 1674–1679. [Google Scholar] [CrossRef] [PubMed]
  58. Ferreira Silva, R.M.; Noll, M. Extension Action—No Sedentary Lifestyle. eduCAPES. 2021. Available online: https://educapes.capes.gov.br/handle/capes/703165 (accessed on 30 September 2021).
  59. Craig, C.L.; Marshall, A.L.; Sjöström, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Pratt, M.; Ekelund, U.L.; Yngve, A.; Sallis, J.F.; et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity. Med. Sci. Sports Exerc. 2003, 35, 1381–1395. [Google Scholar] [CrossRef] [Green Version]
  60. Matsudo, S.; Araújo, T.; Matsudo, V.; Andrade, D.; Andrade, E.; Oliveira, L.C.; Braggion, G. International physical activity questionnaire (IPAQ): Study of validity and reliability in Brazil. Ativ. Fís. Saúde 2001, 6, 5–18. [Google Scholar]
  61. Guedes, D.P.; Lopes, C.C.; Guedes, J.E.R.P. Reproducibility and validity of the International Physical Activity Questionnaire in adolescents. Rev. Bras. Med. Esporte 2005, 11, 151–158. [Google Scholar] [CrossRef]
  62. Kuzel, A.J. Sampling in Qualitative Inquiry; Sage: Newbury Park, CA, USA, 1992. [Google Scholar]
  63. Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Bardin, L. Análise de Conteúdo. 3a Reimpressão da 1. São Paulo Edições. 2016, 70. Available online: https://madmunifacs.files.wordpress.com/2016/08/anc3a1lise-de-contec3bado-laurence-bardin.pdf (accessed on 23 September 2021).
  65. Owen, N.; Healy, G.N.; Matthews, C.E.; Dunstan, D.W. Too much sitting: The population health science of sedentary behavior. Exerc. Sport Sci. Rev. 2010, 38, 105–113. [Google Scholar] [CrossRef]
  66. Cotten, E.; Prapavessis, H. Increasing Nonsedentary Behaviors in University Students Using Text Messages: Randomized Controlled Trial. JMIR mHealth uHealth 2016, 4, e99. [Google Scholar] [CrossRef] [Green Version]
  67. Trudeau, F.; Laurencelle, L.; Shephard, R.J. Tracking of Physical Activity from Childhood to Adulthood. Med. Sci. Sports Exerc. 2004, 36, 1937–1943. [Google Scholar] [CrossRef]
  68. Maciel, E.S.; Lima, L.P. The use of apps for physical activity at home during the COVID-19 pandemic. Cent. Pesqui. Avançadas Qual. Vida 2021, 13, 1–10. Available online: https://www.cpaqv.org/revista/CPAQV/ojs-2.3.7/index.php?journal=CPAQV&page=article&op=view&path%5B%5D=629 (accessed on 23 September 2021).
  69. Srivastav, A.; Khadayat, S.; Samuel, A. Mobile-based health apps to promote physical activity during COVID-19 lockdowns. J. Rehabil. Med.-Clin. Commun. 2021, 4, 1000051. [Google Scholar] [CrossRef] [PubMed]
  70. Christofoletti, A.E.M.; Benites, L.C.; Iaochite, R.T.; Dopp, E.V.D.O.; Nakamura, P.M. Relationship between sedentary behavior and application characteristics for smartphone. Rev. Contexto Saúde 2020, 20, 118–129. [Google Scholar] [CrossRef]
  71. Bond, D.S.; Thomas, J.G.; Raynor, H.; Moon, J.; Sieling, J.; Trautvetter, J.; Leblond, T.; Wing, R.R. B-MOBILE—A Smartphone-Based Intervention to Reduce Sedentary Time in Overweight/Obese Individuals: A Within-Subjects Experimental Trial. PLoS ONE 2014, 9, e100821. [Google Scholar] [CrossRef]
  72. Dillon, K.; Rollo, S.; Prapavessis, H. A combined health action process approach and mHealth intervention to reduce sedentary behaviour in university students—A randomized controlled trial. Psychol. Health 2021, 37, 692–711. [Google Scholar] [CrossRef] [PubMed]
  73. Godakanda, I.; Abeysena, C.; Lokubalasooriya, A. Sedentary behavior during leisure time, physical activity and dietary habits as risk factors of overweight among school children aged 14–15 years: Case control study. BMC Res. Notes 2018, 11, 186. [Google Scholar] [CrossRef] [Green Version]
  74. Pearson, N.; Sherar, L.B.; Hamer, M. Prevalence and Correlates of Meeting Sleep, Screen-Time, and Physical Activity Guidelines Among Adolescents in the United Kingdom. JAMA Pediatr. 2019, 173, 993–994. [Google Scholar] [CrossRef] [PubMed]
  75. Vilela, U.N.; Nascimento, V.A. Analysis of the sedentary behavior of students in Ituiutaba, Minas Gerais. Intercursos Rev. Cient. 2018, 17, 37–47. Available online: https://revista.uemg.br/index.php/intercursosrevistacientifica/article/view/3710 (accessed on 3 September 2021).
  76. Coughlin, S.S.; Whitehead, M.; Sheats, J.Q.; Mastromonico, J.; Hardy, D.; Smith, S.A. Smartphone Applications for Promoting Healthy Diet and Nutrition: A Literature Review. Jacobs J. Food Nutr. 2015, 2, 021. [Google Scholar] [PubMed]
  77. Flores Mateo, G.; Granado-Font, E.; Ferré-Grau, C.; Montaña-Carreras, X. Mobile Phone Apps to Promote Weight Loss and Increase Physical Activity: A Systematic Review and Meta-Analysis. J. Med. Internet Res. 2015, 17, e253. [Google Scholar] [CrossRef] [Green Version]
Children 10 00479 g001 550
Figure 1. CONSORT (Consolidated Standards of Reporting Trials) 2010 Flow Diagram.
Figure 1. CONSORT (Consolidated Standards of Reporting Trials) 2010 Flow Diagram.
Children 10 00479 g001
Table
Table 1. The theme of the folders sent to the participants per week.
Table 1. The theme of the folders sent to the participants per week.
WeekTheme of the Folders
1stInformation and concepts related to physical activity and sedentary behavior. Suggestions for physical activities in the “free time” domain and two steps to reduce sedentary behavior.
2ndConcepts and benefits of physical activity. Suggestions for physical activities in the “displacement” domain and two steps to reduce sedentary behavior.
3rdBenefits and barriers to physical activity. Suggestions for physical activities in the “school” domain and two steps to reduce sedentary behavior.
4thBenefits and dimensions of barriers to physical activity. Suggestions for physical activities in the “household chores” domain and two steps to reduce sedentary behavior.
Table
Table 2. Frequencies by group, gender, and age of students.
Table 2. Frequencies by group, gender, and age of students.
CGIG
n (%)n (%)
GenderFemale (n = 26)19 (38.0%)7 (23.3%)
Male (n = 54)31 (62.0%)23 (77.0%)
Table
Table 3. Frequencies of physical activity levels in IG and CG (pre- and post-intervention).
Table 3. Frequencies of physical activity levels in IG and CG (pre- and post-intervention).
Physical Activity LevelPre-InterventionPost-Intervention
n (%)n (%)
CG (n = 50)Low17 (34.0%)13 (26.0%)
Moderate15 (30.0%)18 (36.0%)
High18 (36.0%)19 (38.0%)
IG (n = 30)Low9 (30.0%)6 (20.0%)
Moderate5 (16.7%)8 (26.7%)
High16 (53.3%)16 (53.3%)
Table
Table 4. Average daily exposure to sedentary behaviors in IG and CG before and after the intervention.
Table 4. Average daily exposure to sedentary behaviors in IG and CG before and after the intervention.
GroupPre (min/day)
(Mean ± SD)		Post (min/day)
(Mean ± SD)		Absolute ∆
(min/day)		Relative ∆
(%)		* p
CG485.46 ± 258.99516.85 ± 272.0531.376.46%0.231
IG541.71 ± 158.85494.57 ± 142.27−47.14−8.70%0.556
∆—delta; IG—WhatsApp message intervention group; CG—control group. The absolute ∆ was calculated by subtracting the average time exposed to sedentary behavior from the post-moment by the pre-intervention moment. * t test.
Table
Table 5. Students’ perception of participation in the intervention.
Table 5. Students’ perception of participation in the intervention.
VariablesTotal 30 (100.0%)
Overall satisfaction level with the project
Very satisfied9 (30.0)
Satisfied15 (50.0)
Neutral4 (13.3)
Dissatisfied0 (0.0)
Very dissatisfied2 (6.7)
Satisfaction with language, content and terms
Very satisfied16 (53.3)
Satisfied8 (26.7)
Neutral4 (13.3)
Dissatisfied0 (0.0)
Very dissatisfied2 (6.7)
Satisfaction with the duration and number of questions
Very satisfied4 (13.3)
Satisfied13 (43.3)
Neutral11 (37.7)
Dissatisfied0 (0.0)
Very dissatisfied2 (6.7)
Encouraged to have a less sedentary week
Yes15 (50.0)
Partly11 (36.7)
No4 (13.3)
Encouraged to have a week with more physical activity
Yes16 (53.3)
Partly10 (33.4)
No4 (13.3)
Score for the project from 0 to 10
0 to 30 (0.0)
4 to 60 (0.0)
7 to 813 (43.3)
9 to 1017 (56.7)
Table
Table 6. Thematic analysis of the participants: perceptions.
Table 6. Thematic analysis of the participants: perceptions.
Thematic AxisCategoryIndicators
Sedentary behaviorConcept
-
Not practicing physical activity
-
Sedentary person, remains seated or lying down for a long time
Total number of daily hours in sedentary behavior
-
8 h to 10 h
Number of daily hours in sedentary behavior (leisure)
-
1 h to 2 h
Factors that contribute to sedentary behavior
-
Lack of information
-
Lack of interest
-
Laziness
-
Pandemic
-
Technologies
-
Modern life
Positive factors in reducing sedentary behavior
-
More disposition
-
Disease prevention
Physical
inactivity		Physically inactive Brazilian adolescents
-
From 60% to 75%
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Ferreira Silva, R.M.; Fonseca Terra, L.; da Silva Valadão Fernandes, M.; Noll, P.R.E.S.; de Almeida, A.A.; Noll, M. Physical Activity and Sedentary Behavior in High School Students: A Quasi Experimental Study via Smartphone during the COVID-19 Pandemic. Children 2023, 10, 479. https://doi.org/10.3390/children10030479

AMA Style

Ferreira Silva RM, Fonseca Terra L, da Silva Valadão Fernandes M, Noll PRES, de Almeida AA, Noll M. Physical Activity and Sedentary Behavior in High School Students: A Quasi Experimental Study via Smartphone during the COVID-19 Pandemic. Children. 2023; 10(3):479. https://doi.org/10.3390/children10030479

Chicago/Turabian Style

Ferreira Silva, Regina Márcia, Lauryane Fonseca Terra, Michele da Silva Valadão Fernandes, Priscilla Rayanne E. Silva Noll, Alexandre Aparecido de Almeida, and Matias Noll. 2023. "Physical Activity and Sedentary Behavior in High School Students: A Quasi Experimental Study via Smartphone during the COVID-19 Pandemic" Children 10, no. 3: 479. https://doi.org/10.3390/children10030479

APA Style

Ferreira Silva, R. M., Fonseca Terra, L., da Silva Valadão Fernandes, M., Noll, P. R. E. S., de Almeida, A. A., & Noll, M. (2023). Physical Activity and Sedentary Behavior in High School Students: A Quasi Experimental Study via Smartphone during the COVID-19 Pandemic. Children, 10(3), 479. https://doi.org/10.3390/children10030479

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