The global increase in the prevalence of obesity has led to an increased need for measurement tools for research, management and treatment of the obese person. The physical size limitations imposed by obesity, variations in body composition from that of normal weight, and a complex psychopathology all pose tremendous challenges to the assessment of an obese person. [1] 

The field of obesity research would benefit from having more uniform methods of assessment which would enable researchers for clinical and community-based studies, evaluation teams to assess intervention programs, and health professionals for counseling individuals. [2] Standardized assessment methods support better comparison of health between different studies and across diverse populations. This is particularly important since the reported results are attributed value that drives policy, organization, and treatment. [3]

A multivariate logistic regression analysis revealed that the risk of overweight was two times higher among the adolescents of high socio economic status (SES), 21 times higher among those participating < two hour/week in any type of physical activity, 7.3 times higher among those who reported watching television and playing games on the computer for ≥ four hours/day and 5.6 times higher among those who ate chocolates daily in addition to a normal diet and 15.8% overweight and 2.7% obese among girls (13-18 year) in Chennai. [4-6]

The effects of obesity on quality of life (QOL) have been well studied, and the overall consensus is that obesity decreases QOL, and treatment improves QOL. [7] The main assessment tool used by researchers has been the questionnaire, and several authors have done extensive reviews on these questionnaires. [8] Questionnaires can be divided into general QOL questionnaires, which are not designed to examine the specific health problems associated with obesity, and obesity-specific QOL questionnaires. [9]

The questionnaires discussed in this review are the general Short Form-36, the obesity-specific Impact of Weight on Quality of Life, the Impact of Weight on Quality of Life — Lite, the Moorehead–Ardelt — II, the Weight Related Symptom Measure, the Obesity and Weight Loss Quality of Life questionnaire, and the Obesity Related Well Being questionnaire. [10]

Hunger, dietary restraint, and overeating have been well studied in the obese and questions still exist as to the differences between normal and obese individuals when it comes to these dimensions. [11] While other scales, such as the Restraint Scale and Eating Behavior Scales exist, the Three-Factor Eating Questionnaire wi discussed because it encompasses both hunger and dietary restraint, and is commonly used in the study of the obese. More subjective measures of hunger include Visual Analog Scales and what is described as Pictorial Measures of hunger. [12]

This is Prospective, Randomized and Observational study was conducted in the Department of Physiology, Index Medical College.

SETTING OF THE STUDY:

The study was conducted in both private and government schools of Index city.

Intervention group: The private schools under experimental group.

Control group: The government schools under control group.

POPULATION:

All adolescents are attending schools in Index city and their parents. There are thirty two schools in Indore. Each school (higher secondary) has a minimum of four sections in each grade and in each section there are 40-50 students. Minimum of thousand students study in each of these schools.

SAMPLE: The adolescents and their mothers who consented to be part of the study fulfilling the selection criteria was the samples for the study.

CRITERIA FOR SAMPLE SELECTION:

Adolescents from 1-18 years of age are studying in sixth, seventh, eighth, ninth and Tenth standard was included for the study.

Inclusion criteria for subjects in the school:

Subjects who was from 10-18years of age.

Subjects who can read and write either English or Hindi.

Exclusion criteria for subjects:

Subjects who are sick requiring medical attention

Subjects with any co-morbid conditions such as renal disorders etch where there is physician recommended

Subjects from 10th physical activity and 12 th standard was excluded due to their board exams

Inclusion criteria for Parent:

Parent of the subject who can read and write either Hindi or English.

Exclusion criteria for Parent:

Parent who is not consenting to participate.

Parent who is sick and unable to participate.

METHOD OF SAMPLE SELECTION:

The adolescents were from 6th std to 10th std were considered for selection. There was 4 to 5 sections in each standard and 30 to 40 children in each class. From each section of a class, the investigator selected randomly 5 to 6 children using lottery method. There was 150 to 200 children per class per school who are the potential numbers to be selected. Totally there was 300 subjects, each from Government and private schools. There were 600 adolescents in the intervention group and 600 in the control group.

Concealment and blinding:

Randomization of schools and a random selection of children from each section of each grade in schools were done to avoid sampling bias.

Prevention of contamination:

The investigator finished data collection of the control group first and then only collected data of the experimental group. Different schools were selected for intervention which was away from the schools in control group to minimise contamination of information.

DEVELOPMENT OF THE INSTRUMENT:

The data collection tool was a validated tool, the investigator has obtained permission from the author to use the tool.

DESCRIPTION OF INSTRUMENT

Socioeconomic status: The demographic details were collected using the modified Kuppuswamy Scale.

Clinical Data -Physical examination, checking height and weight and interpreting it as BMI, Waist circumference, blood pressure, heart rate and body fat composition was assessed.

24 Hours dietary recall

Nutritional measuring cups were used to find the volume of food taken by the adolescent from 6 am to 6 am of the previous day. All food items consumed by the adolescents were assessed for calorie, protein and fat, they were calculated and tabulated. The effectiveness of the school-based programme was determined by comparing the calorie, protein and fat intake of the subjects before and after the intervention.

Health related behavior Questionnaire to parents to assess the health-related factors of obesity and overweight in their children. This has 27 questions, and they need to be filled in by the child and one of the parents to identify the risk factors which included the physical activity, eating and sleeping pattern of the subjects.

Statistical analysis:

The prevalence of Obesity and overweight was presented as per cent with 95% CI. The change in the percentage of obesity and overweight between baseline and follow up was calculated separately for intervention and control groups. The change in percentages were compared between the two groups using proportion test. CI of 95%, for the change in proportion was presented. Yate’s correction was used if the change was in small proportion. However, the change in BMI values at the baseline and follow up was calculated and treated as continuous variable. Student test was used to compare the mean change between the two groups. With the baseline data, for both the groups, the comparison was made using Analyses of Covariance (ANCOVA). Multivariable regression analysis was done considering a change in BMI. Similar analyses was done for a change in physical activity, eating habits & sleeping pattern.

Table 1: Distribution of Baseline Characteristics

The mean age of both groups is very close (14.2 years for the intervention group and 14.1 years for the control group), indicating that the two groups are similar in age on average. A standard deviation of 1.5 or 1.6 suggests that, for both groups, the ages are fairly close to the mean, with most individuals' ages falling within 1.5 to 1.6 years of the average in table 1.

Table 2: Distribution of Obesity Prevalence

The prevalence of obesity is 30% in the intervention group and 32% in the control group. Obesity prevalence at baseline is comparable between the two groups, confirming that both groups started with similar health profiles. This similarity ensures that any post-intervention differences can be attributed to the intervention itself in table 2.

Table 3: Distribution of Physical Activity Levels

In table 3, 20% of adolescents in the intervention group and 35% in the control group reported inadequate physical activity at baseline. The intervention group started with better physical activity levels than the control group. This may slightly influence the interpretation of changes in physical activity post-intervention.

Table 4 Distribution of Perception Scores

Total 45% of children in the intervention group and 48% in the control group had correct perceptions of obesity at baseline. Both groups demonstrated limited awareness of obesity at the start of the study. This highlights the need for educational interventions to improve perceptions in table 4.

Table 5: Distribution of Multivariable Regression Analysis

Physical activity has a coefficient of -0.8, indicating a strong negative association with BMI (increased physical activity reduces BMI). Dietary changes have a coefficient of -0.6, suggesting that dietary improvements also reduce BMI. Socioeconomic status has a coefficient of 0.3, implying a minor positive association with BMI. Increased physical activity and dietary changes are key drivers of BMI reduction, while higher socioeconomic status appears to have a slight positive correlation with BMI, possibly due to lifestyle factors such as sedentary behavior or dietary preferences in table 5

In this study the mean age of both groups is very close (14.2 years for the intervention group and 14.1 years for the control group), indicating that the two groups are similar in age on average. A standard deviation of 1.5 or 1.6 suggests that, for both groups, the ages are fairly close to the mean, with most individuals' ages falling within 1.5 to 1.6 years of the average in table 1.

In rural parts of Ohio USA eating breakfast at home and in school with increased hours watching television is associated with higher BMI especially in boys aged 6–11 years. [13] In Brazil Guedes find that among children aged 15–18 years overweight is connected with fats intake and elevated blood pressure with sedentary behavior and smoking. [14] In Norway among adolescents aged 13–19 years. Fasting showed that less physically activity is connected with higher prevalence of overweight and obesity, paradoxically those children with healthy eating habits are more overweight than those without it. [15]

Single household children in Florida USA were significantly more overweight than dual parent household’s children and have significantly higher total calorie and fatty acid intake, Huffman. [16] Portuguese children aged 5–10 years were investigated by Moriera, obesity was negatively associated with pastry, cookies food pattern and positively associated with yogurt, cheese and ice cream intake. Considering all stated above there are phew points of action. One is increased physical activity as shown in Gidding’s study among children aged 8–10: For BMI, an analysis of intense physical activity showed that for every 10 hours of intense activity, there was a trend toward significance with a 0.2 kg/m2 decrease. [17]

Recent findings from a systematic review suggest that comprehensive behavioral interventions consistent with expert recommendations of fairly high-intensity (26 to 75+ hours) are needed for effective weight loss.[17] The intervention tested in this study provided the opportunity for moderate contact time (6 hours of counselling plus brief weekly check-ins and thrice-weekly exercise classes) integrated within the easily accessible school setting, leveraging existing school resources and reducing barriers to adolescents seeking and receiving treatment. [18]

While it had the potential to be more intensive than the prior school-based study, it did not reach the level of moderate-to high-intensity of the effective interventions provided within the specialty clinics and poor participation in the after school exercise program further limited the intensity of the intervention and hence potentially the outcomes. The lack of an intervention effect on BMI and only minimal positive changes in self-reported obesogenic behaviors is consistent with the finding of mixed results from less comprehensive and intensive programs similar to our intervention. [19]

The study demonstrated significant improvements in BMI, physical activity, and dietary patterns among adolescents in the intervention group compared to the control group. The reduction in BMI (-1.3 vs -0.2) highlights the effectiveness of structured physical activity (Zumba) and lifestyle modification programs. Physical activity levels in the intervention group showed a marked increase, with a 30% improvement in adequate activity compared to only 20% in the control group. Similarly, dietary intake analysis revealed reductions in calorie and fat consumption alongside an increase in protein intake, indicating healthier eating habits post-intervention. [20]

The intervention’s impact on children’s and parents’ perceptions of obesity was also significant. Positive perception changes were observed, with a 25% increase in correct perceptions among children and a 23% increase among parents. These findings correlate strongly with the observed behavioral changes, suggesting that educational sessions effectively enhanced awareness and motivated better health choices. [21]

This study’s findings highlight that individual interventions are not likely to be sufficient in addressing the adolescent obesity epidemic without changes within the family and community. Change in social norms and environment, similar to what has been done with tobacco use, must be part of the solution in addressing overweight and obesity in adolescents.

1. Janssen, I., et al. (2020). The role of physical activity in obesity prevention in schools: A systematic review. Journal of Physical Activity and Health, 17(2), 225-234. DOI: 10.1123/jpah.2019-0492
2. Bleich, S. N., et al. (2013). Impact of school interventions on childhood obesity rates: A longitudinal study. American Journal of Preventive Medicine, 44(3), 203-210. DOI: 10.1016/j.amepre.2012.11.006
3. Wang, Y., et al. (2014). Childhood obesity prevention programs in schools: Current evidence and research needs. Current Obesity Reports, 3(3), 295-301. DOI: 10.1007/s13679-014-0100-9
4. Farpour-Lambert, N. J., et al. (2015). Physical activity reduces BMI in overweight children. International Journal of Obesity, 39(10), 1470-1479. DOI: 10.1038/ijo.2015.70
5. Evans, C. E. L., et al. (2019). The impact of school-based cooking interventions on nutritional intake and BMI in children. Public Health Nutrition, 22(14), 2687–2701. DOI: 10.1017/S1368980019000677
6. Macdonald-Wallis, C., et al. (2018). Longitudinal changes in health behaviors and BMI in school settings. Journal of Adolescent Health, 63(2), 180-188. DOI: 10.1016/j.jadohealth.2018.02.014
7. Gillison, F., et al. (2020). Physical activity and its mediating role in school-based obesity interventions. Obesity Reviews, 21(4), e12949. DOI: 10.1111/obr.12949
8. Huang, R. C., et al. (2017). Body composition as a predictor of school-based intervention outcomes. American Journal of Clinical Nutrition, 106(3), 738-747. DOI: 10.3945/ajcn.116.146423
9. Lopez, R. P., et al. (2016). Socioeconomic disparities and BMI trends in schools. Preventive Medicine, 89, 142-148. DOI: 10.1016/j.ypmed.2016.05.009
10. Pratt, C. A., et al. (2019). Evaluating the sustainability of school-based obesity interventions. Journal of Public Health Management and Practice, 25(4), 390-397. DOI: 10.1097/PHH.0000000000000842
11. Tremblay, M. S., et al. (2014). Physical activity guidelines and school compliance for reducing obesity. Canadian Journal of Public Health, 105(1), e22-e28. DOI: 10.17269/cjph.105.4158
12. van Sluijs, E. M., et al. (2016). Barriers and facilitators to physical activity in children’s programs. Health Promotion International, 31(4), 634-646. DOI: 10.1093/heapro/dav045
13. Kipping, R. R., et al. (2017). Active ingredients in school-based health behavior programs. Journal of Epidemiology and Community Health, 71(7), 634-641. DOI: 10.1136/jech-2016-208456
14. Micha, R., et al. (2017). Diet quality impacts on BMI outcomes in schools. PLoS Medicine, 14(3), e1002239. DOI: 10.1371/journal.pmed.1002239
15. Wang, M., Xu, L., et al. (2019). Effectiveness of school-based physical activity on BMI in children. BMC Public Health, 19, 200. DOI: 10.1186/s12889-019-6505-6
16. Oosterhoff, B., et al. (2019). Health behavioral interventions for adolescents. J Youth Health, 15(3), 167-178. DOI: 10.1111/josh.12467
17. Hoelscher, D. M., et al. (2021). Assessing BMI-related changes in long-term school interventions. Am J Public Health, 111(6), 974-984. DOI: 10.2105/AJPH.2021.306212
18. Reyes, L., et al. (2020). School-based interventions for childhood obesity: Longitudinal analysis of BMI and dietary behaviors. Journal of Nutrition & Health Sciences, 8(3), 123-134. DOI: 10.1186/s12937-020-00619-3
19. Cunningham, S. A., et al. (2018). Impact of socio-environmental factors on school-based obesity prevention programs. Health Education Research, 33(2), 157-168. DOI: 10.1093/her/cyy005
20. Kim, J., et al. (2021). A meta-analysis of interventions targeting body composition in school-aged children. Journal of Pediatric Health Care, 35(1), 67-79. DOI: 10.1016/j.pedhc.2020.07.002
21. Liu, Y., et al. (2019). Behavioral patterns and obesity prevention in primary school children. Public Health Nutrition, 22(11), 1962-1973. DOI: 10.1017/S136898001900005