Original Article

Effects of a School-Based Behavior Modification Program on Body Composition, Dietary Behavior, and Physical Activity in Overweight High School Students in Rural Northern Thailand: A Randomized Controlled Trial

Please cite this article as follows: Phattharaphakinworakun A, Somdee T, Thongjit S, Yangyuen S. Effects of a school-based behavior modification program on body composition, dietary behavior, and physical activity in overweight high school students in rural northern Thailand: a randomized controlled trial. J Res Health Sci. 2025; 25(2):e00644. doi:10.34172/jrhs.2025.179

Background

Being overweight among high school students represents significant global health concerns with, approximately 1 in 5 experiencing excess weight.¹ The prevalence of overweight was 18.0% in 2020 and is expected to increase to 38.0% by 2035.² Approximately 41.3% of people at high risk for overweight experience health problems related to weight, both physical and psychological. ^3,4 especially contributing to non-communicable diseases (NCDs). Approximately 92 million people worldwide are projected to have premature mortality due to these issues by 2050.⁵

Previous studies have revealed that a behavioral modification (BM) program is a tool designed to change body composition, weight, and lifestyle habits among obese or overweight individuals.⁶ Most BM programs focus on increasing physical activity (PA) levels, decreasing sources of sedentary lifestyles, and improving dietary behavior (DB).^6,7 Additionally, BMs targeting diet and PA changes are the cornerstones of interventions for weight management in overweight and obese populations and are effective in reducing weight and improving health, at least in the short term.⁷ Effective BM methods include self-monitoring, cognitive-behavioral approaches, and problem-solving.⁶ A literature review showed that school-based behavioral modification (SBM) programs are effective in treating and preventing overweight and obesity in adolescents. Examples of these programs include classroom lessons, education programs focused on increasing PA and discouraging sedentary lifestyles, and programs promoting a healthy diet.^8,9 Such SBMs have led to increased PA, improved DB, and changes in triceps skinfold thickness, and body mass index (BMI) Z-scores among overweight or obese adolescents.^8,10 Moreover, BM interventions are more effective when implemented at a younger age. Schools are recognized as health-promoting environments, as adolescents spend a significant part of their day at school.¹¹ Thus, applying SBMs to improve health behaviors and body composition may benefit overweight students.

In Thailand, 35.2% of high school students in rural areas of Northern Thailand are overweight.^12,13 According to the Thai Health Report in 2022, poor eating habits are detrimental to health, with an increase in the consumption of sweetened beverages, processed meats, and semi-prepared foods and high-fat foods (17.3%, 22.9%, and 26.3%, respectively). Additionally, 16.1% of students reported engaging in adequate PA, while 69.5% reported sedentary behavior.^12,14 Thus, high school students may be at risk of being overweight, potentially leading to obesity diagnoses and the prevalence of NCDs.^13,14 BM programs have been used to modify health behavior among overweight and obese students, most of which focus on family- and community-based interventions.¹⁵ However, school-based programs remain limited, primarily focusing on nutritional interventions, and few studies have combined BM techniques addressing both diet and PA.¹⁶ Therefore, this study aimed to examine the effects of an SBM program on health behavior and body composition among overweight Thai high school students, which may help promote healthier behaviors.

Methods

Study design and setting

This randomized controlled trial was conducted from November 2022 to May 2023 among overweight high school students in a rural area in Mae Hong Son Province, Northern Thailand.

Study participants

The sample size was calculated using the formula:

This calculation was based on a study by Duangchan et al,¹⁸ considering a mean difference (µ_con - µ_trt) of 0.35 in average BMI before and after the intervention, with a standard deviation (SD) of body weight in kilograms

n/group = (1.58) [5.337 + 14.063] / (0.35) ² = (1.58) (19.40) / 0.7 = 30.7 / 0.7 = 43.86 ≈ 44

Hence, with a dropout rate of approximately 10%., the total sample size required for each group was 50, and the total sample size was 100. Simple random sampling was used to select 16-17 students per grade from three grade levels for the intervention and control groups. The inclusion criteria were high school students studying in the target schools, overweight students classified with a BMI for their age greater than or equal to the 85th percentile or ≥ + 1 SD.¹⁹ students’ consent to participate in the study, parental permission for their children to participate in the study, and no physical or mental illness.⁵ Exclusion criteria included unwillingness to continue participation, absence from more than three intervention sessions, incomplete questionnaires, and dropout during the study.

Recruitment and randomization

The recruitment and randomization of participants were performed by a researcher who was not involved in the intervention or outcome assessment. The rural high schools in Mae Hong Son province were divided into north and south clusters based on geographical location. Two high schools were then selected from the seven resulting clusters and randomly allocated in a 1:1 ratio to either the control group (south part) or the intervention group (north part) using the cluster sampling method. Then, students within each selected school who met the inclusion criteria were chosen using computer-generated random numbers. Each school consisted of three grade levels, and simple random sampling was used to select 16-17 students per grade for the control and intervention groups. Thus, 100 students entered the study and were randomly assigned to the intervention and control groups, with 50 students in each group.

Data collection tools and techniques

Socio-demographic variables included age, sex, weight, height, and BMI for age. The digital weighing scale was operational and adequately powered. A calibration test was performed to ensure functionality. Thick clothing, including shoes and socks, was removed to minimize excess weight. Participants were required to stand still and upright on the scale for accurate measurement. Weight was recorded with a precision of 0.1 kg, and participants were weighed while fasting, preferably at 15:00 hours. Height was measured using a stadiometer with a width of no less than 5 cm. Measurements were taken in the standard posture, that is, standing erect with the head, back, legs, and heels against the wall. Height was recorded to the nearest 0.1 cm.²⁰ BMI-for-age was calculated using weight (kg) and height (cm) based on the percentile standards developed by the U.S. Centers for Disease Control and Prevention (CDC) for children and adolescents aged 5–19 years. An overweight range was defined as the 85th to less than the 95th percentile, or from + 1 SD to + 2 SD.¹⁹

Body composition evaluation comprised the assessment of subcutaneous fat accumulation, which was assessed using skinfold caliper measurements at four positions, with results expressed in millimeters (mm). Measurements were taken at subcutaneous locations to determine body fat percentage by age and gender. Calculations were based on the equation developed by Durnin and Womersley.²¹ The four skinfold thickness (in mm) were measured as follows:

1. Biceps: Subcutaneous fat at the midpoint of the anterior upper arm muscle (mm);

2. Triceps: Subcutaneous fat at the midpoint of the posterior upper arm muscle (mm);

3. Suprailiac: Subcutaneous fat above the iliac crest, measured diagonally higher than the iliac crest (mm);

4. Suprailiac: Subcutaneous fat below the scapula (mm). ²¹

Measurements were conducted three times at each location to estimate the average of the positions among male and female participants.

Body fat (mm) was calculated using Durnin and Womersley equations:

Body fat = (495/D) – 450

where D = body density, L = log (sum of four skinfolds; biceps, triceps, subscapular, and suprailiac).

For the age group < 17 years, body density was calculated as:

D = 1.1533 - (0.0643 L) for boys

D = 1.1369 - (0.0598 L) for girls

For the age group 17–19 years, the formula was:

D = 1.1620 - (0.0630L) for boys

D = 1.1549 - (0.0678L) for girls.²¹

Waist circumference (cm) was assessed using a standard measuring tape at the midpoint between the lower between the lower rib margin and the top of the iliac crest (cm).²²

Hip circumference (cm) was assessed using a standard measuring tape to measure the circumference of the external pelvic area (cm) at the pelvic position.²²

DB was measured using the Thai Recommended Daily Intake scale (Thai RDI).²³ consisting 36 items with a total score of 216. The DB scores were categorized into three levels²³: good eating habits (157–216 points), moderate eating habits (97–156 points), and poor eating habits (36–96 points). Cronbach’s α of 0.96 indicates good internal consistency.

PA and sedentary behaviors were assessed using the Thailand PA Children Survey scale for recording the PA over the past 7 days, including weekdays and weekends. It comprises 13 questions recording activity patterns and durations (in minutes) per day.²⁴ Cronbach’s α = 0.74 indicates that the scale has good internal consistency. The PA and sedentary behaviors were categorized as follows:

• Adequate PA: This involves an average of 60 minutes of moderate-to-vigorous PA across the week, including aerobic exercises, walking, running, swimming, and sports activities. Vigorous-intensity aerobic activities and muscle-strengthening exercises should be incorporated at least 3 days a week.^2,25

• Prolonged sedentary behavior: Respondents were asked about time spent in sedentary activities per day (e.g., watching television, playing computer/video games, and sitting and reading). Respondents were considered to have prolonged sedentary behavior if they spent more than 2 hours per day on such activities.^2,25

Intervention

After a literature review and discussion with specialists in health education, health behavior, and adolescent psychology, a school-based intervention based on social cognitive theory was developed. The SBM focused on dietandPA, consisting eight sessions with a total of eight activities, each weekly session lasted 80 minutes. The intervention was delivered to the intervention group over eight weeks, followed by a four-week follow-up period (Table 1). Each session was structured as follows:

Session 1: Snack and dessert consumption

Participants learned about the appropriate portion sizes for snacks and desserts, how to read nutritional labels, and how to utilize media from convenience store snack packages.

Session 2: Fast-food and condiment consumption

Participants learned about the appropriate portions for fast food and condiment usage, illustrated using realistic food images. The recommended daily intake, measured in tablespoons, was emphasized.

Session 3: Unhealthy consumption behaviors

Participants were instructed on NCDs and complications arising from unhealthy consumption behaviors, using video media and lifelike disease illustrations.

Session 4: Goal-setting for behavior change

Participants practiced setting personal goals for behavior change, focusing on increasing PA and healthy DB and reducing sedentary behavior.

Session 5: Food records

Participants practiced how to keep food records using a diary to document their main meals (three meals) and snack times.

Session 6: Body composition assessment

Participants were taught how to assess body fat and BMI for age using skinfold calipers, circumference tape, weighing scales, and height measurements.

Session 7: Evaluating PA and sedentary behavior

Participants practiced self-monitoring and evaluating their PA and sedentary behaviors. Additionally, they learned to calculate the time spent on physical activities and sedentary behavior using electronic media and realistic illustrations.

Session 8: Experiences of behavior change

Participants were encouraged to share their experiences of behavior change related to dietary habits and PA with their group members.

During the follow-up period, a 10-15 minute group discussion was conducted once a week for three weeks, which focused on behavior change, troubleshooting challenges, and keeping motivation to continue the effects of the intervention. At the end of week four, a school visit was made for outcome measurements.

Control group

Participants in the control group did not receive any components of the SBM program. Instead, they continued with their regular health education classes, which were offered by the teacher.

Study procedure

Both groups underwent baseline assessments, with the intervention group receiving SBM, while the control group did not. Outcomes were concealed and measured after the intervention and at follow-up using the same data collection methods.

Data analysis

Descriptive statistics were used to analyze the participants’ characteristics and outcomes. Chi-square tests were used for categorical variables, independent-sample t-tests were used to continuously distribute variables with a normal distribution, and the Mann–Whitney U test was used for skewed continuous variables to compare the baseline characteristics of participants between the intervention and control groups. Outcome measurements were compared before intervention, after intervention completion, and at follow-up visits. For within-group comparisons, repeated-measures ANOVA was used to assess changes in normally distributed continuous variables, while Cochran’s Q test was employed for categorical variables. For comparison between the intervention and control groups, independent-sample t-tests and chi-square tests were used for changes in normally distributed continuous variables and categorical variables, respectively. All statistical analyses were carried out using SPSS version 20.0 (IBM Corp., Armonk, NY, USA), with a P value < 0.05 considered statistically significant.

Results

A total of 109 students were assessed for eligibility, of which, nine students were excluded, leaving 100 participants who were enrolled at baseline and completed the 4-week follow-up. Most participants were female (72.0%), with a mean age of 17.03 years (SD = 1.71). Approximately, 51.0% had poor eating habits, 73.0% reported engaging in PA, and 96.0% reported prolonged sedentary behavior. The baseline mean (SD) for body composition parameters were as follows: biceps = 12.64 (4.16), triceps = 13.42 (4.41), subscapular = 23.34 (4.38), suprailiac = 21.39 (5.17), sum of four skinfold thicknesses = 70.79 (12.38), body fat = 29.50 (3.74), waist circumference = 86.80 (10.81), hip circumference = 99.40 (9.88), weight = 76.90 (12.94), height = 165.01 (7.83), and BMI for age (kg/m²) = 28.14 (3.59). The baseline characteristics showed no statistically significant differences between the intervention and control groups (Table 2).

The comparison of outcomes within groups from the intervention to the follow-up visit indicated that the mean body composition (e.g., biceps, triceps, the sum of four skinfold thicknesses, body fat, waist circumference, weight, and BMI for age) and prolonged sedentary behavior in the intervention group decreased significantly (P < 0.05)., However, good DB and adequate PA increased significantly (P < 0.05). Furthermore, no significant changes were observed in the control group (Table 3).

Additionally, the mean body fat and waist circumference at four weeks post-intervention showed a significant decrease (P < 0.05) between the two groups. After eight weeks of intervention, the mean values for biceps, waist circumference, DB, adequate PA, and prolonged sedentary behavior revealed statistical significance (P < 0.05). Moreover, at the follow-up visit, the mean differences between the two groups were statistically significant (P < 0.05) for biceps, triceps, and waist circumference. Likewise, the proportions of good DB, adequate PA, and prolonged sedentary behavior demonstrated statistical significance (P < 0.05), as depicted in Table 4.

Discussion

The results of this trial showed that the SBM was effective in improving body composition (e.g., biceps, triceps, and waist circumference), consistent with prior research. A previous study conducted an eight-week school-based exercise and a nutrition program led by a nutritionist,²⁶ which indicated that combined BM interventions, including diet and PA, are more effective in improving body composition outcomes in overweight adolescents compared to those in the control group. One possible explanation is that SBM focuses on helping students achieve significant changes in their body composition over time by enhancing nutritional knowledge, promoting active behavior, reducing sedentary behavior, and increasing engagement in structured physical activities.^27,28

Our results further suggested that SBM is effective in increasing DB, consistent with previous research.²⁹ A possible explanation is that components of the SBM, including information on nutrition, appropriate food intake proportions, recommendations for daily food intake, and reducing fast food consumption, along with instructions for recording daily food consumption, could support the students in managing their eating habits and modifying their DB.^30-32 Some evidence suggests that increasing the nutritional knowledge of individuals can lead to healthier dietary practices such as choosing healthier food, ensuring adequate nutritional intake, and monitoring food consumption, all of which contribute to healthier habits.^33-36

Furthermore, SBM significantly increased PA and reduced prolonged sedentary behavior, congruent with prior studies.^37,38 Promoting PA or reducing sedentary behavior was associated with significant decreases in overweight and body fat percentages and could change health behaviors, leading to healthier habits. One possible explanation is that the “evaluating PA and sedentary behavior” component of the SBM focuses on the participant’s self-monitoring of PA and sedentary behavior. Intentional surveillance and recording of daily physical activities positively influence self-awareness and personal behaviors.^39,40 Additionally, self-monitoring may help students understand how their daily choices can affect their weight management efforts, highlighting the significant role of PA and reduced sedentary behavior in changing health behaviors.³⁹

This study has some limitations. First, the sample was limited to high school students from two selected colleges in Mae Hong Son province, which may restrict the generalizability of the results to other high school students in other settings. However, it may still reflect health behaviors among overweight individuals within a school-based context. Therefore, further studies should include participants from high schools in different provinces. Second, the SBM was developed for use in a school-based context, specifically in overweight high school students, who may have different experiences with food consumption habits and PA compared to those seeking treatment for overweight or obesity in clinical settings. Therefore, caution should be exercised when generalizing the results to other populations. Despite these limitations, our study demonstrated that the SBM is a feasible intervention that can improve body composition, DB, and PA while reducing sedentary behavior among overweight high school students. Therefore, SBM has the potential to effectively modify students’ health behaviors.

Conclusion

The SBM program effectively improved students’ PA, DB, and body composition compared to the control group, confirming that the program is effective. These findings suggest that the SBM should be implemented to promote healthy behavior among overweight high school students. Future studies should be conducted in different areas. Additionally, integrating the program into health education classes and school curricula can further amplify its impact and can be applied in related research in different contexts and situations.

Acknowledgements

We are grateful to the Faculty of Public Health, Mahasarakham University, for their financial support. We would also like to sincerely thank all subjects for their participation in the study.

Thidarat Somdee (PhD)¹, Supattarayan Thongjit (PhD)², and Suneerat Yangyuen (PhD)¹*

Competing Interests

The authors declare no conflict of interests.

Ethical Approval

This study was approved by the Research Ethics Committee at Mahasarakham University (certification numbers 337-280/2564, October 21, 2021, and 102-128/2566, March 23, 2023). Furthermore, it was registered with the Clinical Trials Registry of Thailand (TCTR) under the auspices of the Medical Research Foundation (MRF), under the registration number TCTR20220606004 on June 6, 2022.

Funding

This research was financially supported by the Faculty of Public Health, Mahasarakham University.

References

1. Zhang X, Liu J, Ni Y, Yi C, Fang Y, Ning Q. Global prevalence of overweight and obesity in children and adolescents: a systematic review and meta-analysis. JAMA Pediatr 2024; 178(8):800-13. doi: 10.1001/jamapediatrics.2024.1576 [Crossref] [ Google Scholar]
2. Aniśko B, Siatkowski I, Wójcik M. Body mass composition analysis as a predictor of overweight and obesity in children and adolescents. Front Public Health 2024; 12:1371420. doi: 10.3389/fpubh.2024.1371420 [Crossref] [ Google Scholar]
3. Lee K. Weight loss attempts increased in Korean adults from 2005 to 2021, excluding those with obesity. Metab Syndr Relat Disord 2024; 22(1):39-48. doi: 10.1089/met.2023.0147 [Crossref] [ Google Scholar]
4. American Diabetes Association Professional Practice Committee. Obesity and weight management for the prevention and treatment of type 2 diabetes: standards of care in diabetes-2024. Diabetes Care 2024; 47(Suppl 1):S145-57. doi: 10.2337/dc24-S008 [Crossref] [ Google Scholar]
5. Zheng J, Zhang H, Shi J, Li X, Zhang J, Zhang K. Association of air pollution exposure with overweight or obesity in children and adolescents: a systematic review and meta-analysis. Sci Total Environ 2024; 910:168589. doi: 10.1016/j.scitotenv.2023.168589 [Crossref] [ Google Scholar]
6. Olateju IV, Ogwu D, Owolabi MO, Azode U, Osula F, Okeke R. Role of behavioral interventions in the management of obesity. Cureus 2021; 13(9):e18080. doi: 10.7759/cureus.18080 [Crossref] [ Google Scholar]
7. Cobal S, Bender DV, Kljusurić JG, Rumora Samarin I, Krznarić Ž. Effect of school-based educational intervention on childhood obesity in Croatian urban and rural settings. Children (Basel) 2024; 11(7):867. doi: 10.3390/children11070867 [Crossref] [ Google Scholar]
8. Demir D, Bektas M. The effect of an obesity prevention program on children’s eating behaviors, food addiction, physical activity, and obesity status. J Pediatr Nurs 2021; 61:355-63. doi: 10.1016/j.pedn.2021.09.001 [Crossref] [ Google Scholar]
9. Pongutta S, Ajetunmobi O, Davey C, Ferguson E, Lin L. Impacts of school nutrition interventions on the nutritional status of school-aged children in Asia: a systematic review and meta-analysis. Nutrients 2022; 14(3):589. doi: 10.3390/nu14030589 [Crossref] [ Google Scholar]
10. Kloek GC, Jongert MW, de Vries SI. Evaluation of a school-based multicomponent behavioural intervention in deprived urban areas for children classified as overweight, obese or at risk for overweight. BMC Res Notes 2021; 14(1):100. doi: 10.1186/s13104-021-05513-y [Crossref] [ Google Scholar]
11. Kaur S, Kumar R, Lakshmi PV, Kaur M. Effectiveness of a school-based behavioural change intervention in reducing chronic disease risk factors in Chandigarh, India: a cluster-randomised controlled trial. Lancet Reg Health Southeast Asia 2024; 21:100353. doi: 10.1016/j.lansea.2024.100353 [Crossref] [ Google Scholar]
12. Mahidol University, Office of the Health Promotion Fund. Thai Health Report 2023. Nakhon Pathom: Institute for Population and Social Research; 2023. p. 136.
13. Widyastari DA, Saonuam P, Pongpradit K, Wongsingha N, Choolers P, Kesaro S. Results from the Thailand 2022 report card on physical activity for children and youth. J Exerc Sci Fit 2022; 20(4):276-82. doi: 10.1016/j.jesf.2022.06.002 [Crossref] [ Google Scholar]
14. Topothai T, Tangcharoensathien V, Suphanchaimat R, Petrunoff NA, Chandrasiri O, Müller-Riemenschneider F. Patterns of physical activity and sedentary behavior during the COVID-19 pandemic in the Thai 2021 National Health Survey. J Phys Act Health 2023; 20(5):364-73. doi: 10.1123/jpah.2022-0528 [Crossref] [ Google Scholar]
15. Sirikulchayanonta C, Sirikulchayanonta V, Suriyaprom K, Namjuntra R. Changing trends of obesity and lipid profiles among Bangkok school children after comprehensive management of the bright and healthy Thai kid project. BMC Public Health 2022; 22(1):1323. doi: 10.1186/s12889-022-13712-w [Crossref] [ Google Scholar]
16. Kanchanachitra M, Angeles G. Impact of a school-based nutrition intervention in preventing overweight and obesity in children in Thailand. J Sch Health 2023; 93(2):140-7. doi: 10.1111/josh.13253 [Crossref] [ Google Scholar]
17. Rosner B. Fundamentals of Biostatistics. 5th ed. California: Duxbury Press; 2000. p. 824.
18. Duangchan P, Yoelao D, Macaskill A, Intarakamhang U, Suprasonsin C. Interventions for healthy eating and physical activity among obese elementary schoolchildren: observing changes of the combined effects of behavioral models. Int J Behav Sci 2010; 5(1):46-59. [ Google Scholar]
19. Hales CM, Freedman DS, Akinbami L, Wei R, Ogden CL. Evaluation of alternative body mass index (BMI) metrics to monitor weight status in children and adolescents with extremely high BMI using CDC BMI-for-age growth charts. Vital Health Stat 1. 2022(197):1-42.
20. Rerksuppaphol S, Rerksuppaphol L. Optimal cut-off points of weight for height, waist circumference and waist-to-height ratio for defining overweight and obesity in Thai school-aged children. J Res Health Sci 2013; 13(1):13-8. [ Google Scholar]
21. Durnin JV, Womersley J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 1974; 32(1):77-97. doi: 10.1079/bjn19740060 [Crossref] [ Google Scholar]
22. Nishida C, Ko GT, Kumanyika S. Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO Expert Consultation on Waist Circumference and Waist-Hip Ratio. Eur J Clin Nutr 2010; 64(1):2-5. doi: 10.1038/ejcn.2009.139 [Crossref] [ Google Scholar]
23. On-Nom N, Promdang P, Inthachat W, Kanoongon P, Sahasakul Y, Chupeerach C. Wolffia globosa-based nutritious snack formulation with high protein and dietary fiber contents. Foods 2023; 12(14):2647. doi: 10.3390/foods12142647 [Crossref] [ Google Scholar]
24. Katewongsa P, Pongpradit K, Widyastari DA. Physical activity level of Thai children and youth: Evidence from Thailand’s 2018 report card on physical activity for children and youth. J Exerc Sci Fit 2021; 19(2):71-4. doi: 10.1016/j.jesf.2020.11.002 [Crossref] [ Google Scholar]
25. Zawdie B, Abate KH, Tamiru D, Belachew T. Abdominal pain of functional gastrointestinal disorders in dietary diversity patterns and its determinants among healthy adults in Jimma city, Southwest Ethiopia. Ethiop J Health Sci 2023; 33(6):1027-38. doi: 10.4314/ejhs.v33i6.13 [Crossref] [ Google Scholar]
26. Bogataj Š, Trajković N, Cadenas-Sanchez C, Sember V. Effects of school-based exercise and nutrition intervention on body composition and physical fitness in overweight adolescent girls. Nutrients 2021; 13(1):238. doi: 10.3390/nu13010238 [Crossref] [ Google Scholar]
27. Wang D, Stewart D, Chang C, Shi Y. Effect of a school-based nutrition education program on adolescents’ nutrition-related knowledge, attitudes and behaviour in rural areas of China. Environ Health Prev Med 2015; 20(4):271-8. doi: 10.1007/s12199-015-0456-4 [Crossref] [ Google Scholar]
28. Epstein LH, Paluch RA, Gordy CC, Dorn J. Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med 2000; 154(3):220-6. doi: 10.1001/archpedi.154.3.220 [Crossref] [ Google Scholar]
29. Jha SS, Dobe M, Taklikar C, Lahiri A. School-based intervention on behavioral intention of adolescents regarding healthy diet in India. Front Public Health 2023; 11:1094960. doi: 10.3389/fpubh.2023.1094960 [Crossref] [ Google Scholar]
30. Sultana M, Nichols M, Jacobs J, Karacabeyli D, Allender S, Novotny R. The range of outcomes and outcome measurement instruments collected in multisectoral community-based obesity prevention interventions in children: A systematic review. Obes Rev 2024; 25(6):e13731. doi: 10.1111/obr.13731 [Crossref] [ Google Scholar]
31. Al-Jawaldeh A, Abbass MMS. Unhealthy dietary habits and obesity: the major risk factors beyond non-communicable diseases in the eastern Mediterranean region. Front Nutr 2022; 9:817808. doi: 10.3389/fnut.2022.817808 [Crossref] [ Google Scholar]
32. Aligol M, Zolfaghari A, Mohammadbeigi A, Gharlipour Z. Factors related to obesity prevention behaviors in female high school students in Qom, Iran based on the social cognitive theory. Qom Univ Med Sci J 2022;16(9):712-23. [Persian].
33. Norman Å, Malek ME, Nyberg G, Patterson E, Elinder LS. Effects of universal school-based parental support for children’s healthy diet and physical activity-the healthy school start plus cluster-randomised controlled trial. Prev Sci 2024; 25(6):963-77. doi: 10.1007/s11121-024-01697-4 [Crossref] [ Google Scholar]
34. Xu H, Li Y, Du S, Zhang Q, Liu A, Sun J. Cost-utility and cost-benefit analyses of school-based obesity prevention program. BMC Public Health 2020; 20(1):1608. doi: 10.1186/s12889-020-09718-x [Crossref] [ Google Scholar]
35. Tanveer M, Asghar E, Tanveer U, Roy N, Zeba A, Al-Mhanna SB. Association of nutrition behavior and food intake with overweight and obesity among school-aged children and adolescents in Pakistan: a cross-sectional study. AIMS Public Health 2024; 11(3):803-18. doi: 10.3934/publichealth.2024040 [Crossref] [ Google Scholar]
36. Ruiz-Hermosa A, Sánchez-López M, Castro-Piñero J, Grao-Cruces A, Camiletti-Moirón D, Martins J. The Erasmus + EUMOVE project-a school-based promotion of healthy lifestyles to prevent obesity in European children and adolescents. Eur J Public Health 2024; 34(5):955-61. doi: 10.1093/eurpub/ckae113 [Crossref] [ Google Scholar]
37. Di Maglie A, Marsigliante S, My G, Colazzo S, Muscella A. Effects of a physical activity intervention on schoolchildren fitness. Physiol Rep 2022; 10(2):e15115. doi: 10.14814/phy2.15115 [Crossref] [ Google Scholar]
38. Shi H, Hu FB, Huang T, Schernhammer ES, Willett WC, Sun Q. Sedentary behaviors, light-intensity physical activity, and healthy aging. JAMA Netw Open 2024; 7(6):e2416300. doi: 10.1001/jamanetworkopen.2024.16300 [Crossref] [ Google Scholar]
39. Olateju IV, Ogwu D, Owolabi MO, Azode U, Osula F, Okeke R. Role of behavioral interventions in the management of obesity. Cureus 2021; 13(9):e18080. doi: 10.7759/cureus.18080 [Crossref] [ Google Scholar]
40. Prates Freitas B, Martins Cândido L, Jakovljevic Pudla Wagner K, Rodrigues Lacerda AC, Amaral Mendonça V, De Micheli R. Sedentary behavior, obesity, and disabilities in community-dwelling older adults: analysis of the Brazilian National Health Survey 2019. Healthcare (Basel) 2024; 12(16):1625. doi: 10.3390/healthcare12161625 [Crossref] [ Google Scholar]