+91 9063922599
+91 9063922599
submission@cmegeriatricmed.co.uk
|
FAQs
ion The increasing prevalence of overweight and obesity among school-aged children has emerged as a critical public health concern worldwide. This study evaluates overweight and obesity in a school-based program by examining key indicators, including Body Mass Index (BMI), body composition, and health-related behavioral patterns. Behavioral data collected from food frequency questionnaires and activity logs highlighted poor dietary habits, characterized by excessive snack and sugary drink consumption and inadequate vegetable intake. Childhood overweight and obesity have become global public health challenges, leading to an increased need for targeted assessment strategies. Schools are considered key venues for implementing programs to address these issues due to their structured environment and access to children. Material and Methods This is Prospective, Randomized and Observational study was conducted in the Department of Physiology, Index Medical College. The study was conducted in both private and government schools of Index city. Adolescents from 1-18 years of age are studying in sixth, seventh, eighth, ninth and Tenth standard was included for the study. 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. There is one question on sleeping habit, a child who has slept for less than 8 hours during nighttime is at the potential risk of developing obesity. Good sleeping habit was given a score of one, and otherwise, a score of zero was given.Results Intervention Group: The mean BMI is 25.8 and in Control Group: The mean BMI is 26.0 Both the intervention and control groups have similar mean BMI values, with the intervention group being slightly lower (25.8 vs. 26.0). The difference in BMI is quite small (only 0.2), which suggests that, at baseline, the two groups are very similar in terms of BMI. In the intervention group, 60% had good eating habits, 30% had fair habits, and 10% had poor habits. In the control group, the respective percentages were 50%, 35%, and 15%. Intervention group had a baseline blood pressure of 120/80 ± 10 mmHg, while the control group had 122/82 ± 12 mmHg. 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. Conclusion This study found that a school-based intervention including counseling and access to an after school exercise program was feasible for nurses to deliver with high fidelity and acceptable to overweight and obese adolescents, but the majority of adolescents did not participate in the after school exercise program. While such a program delivering weight management counseling to overweight and obese adolescents within the school setting is theoretically appealing and has tremendous public health potential, it was not found to be effective in improving BMI or key obesogenic behaviors.
The increasing prevalence of overweight and obesity among school-aged children has emerged as a critical public health concern worldwide. This study evaluates overweight and obesity in a school-based program by examining key indicators, including Body Mass Index (BMI), body composition, and health-related behavioral patterns. [1] BMI assessments revealed that 25% of the students were overweight or obese, exceeding global health recommendations and indicating a need for targeted interventions. [2] Analysis of body composition through bioelectrical impedance (BIA) identified elevated fat mass levels (28%) and a high fat-to-lean mass ratio in overweight and obese students, underscoring the importance of addressing body composition alongside BMI. [3]
Behavioral data collected from food frequency questionnaires and activity logs highlighted poor dietary habits, characterized by excessive snack and sugary drink consumption and inadequate vegetable intake. [4] Furthermore, physical activity levels, with an average of 5,000 steps per day, fell significantly below the recommended guidelines, while sedentary behaviors such as screen time averaged six hours per day. These findings provide a comprehensive understanding of the multifaceted nature of obesity and its behavioral drivers in a school setting. [5]
The results emphasize the urgent need for holistic interventions that integrate nutritional education, promotion of physical activity, and strategies to reduce sedentary behaviors. [6] By addressing both physiological and behavioral factors, school-based programs can play a pivotal role in mitigating obesity risks and fostering lifelong health and well-being in children. [7]
Childhood overweight and obesity have become global public health challenges, leading to an increased need for targeted assessment strategies. Schools are considered key venues for implementing programs to address these issues due to their structured environment and access to children. [8] Schools play a critical role in the prevention and management of childhood obesity due to their influence on lifestyle behaviors, including diet and physical activity. [9] Research by World Health Organization (WHO) highlights the value of school settings in promoting healthy behaviors and early detection of obesity-related risks. [10]
Body Mass Index (BMI), calculated from weight and height, is a widely used and practical tool for assessing overweight and obesity in school children. Several studies confirm its ease of use, cost-effectiveness, and ability to correlate with risk factors such as cardiovascular disease and diabetes. [11] However, limitations include its inability to differentiate between fat mass and lean body mass. Beyond BMI, direct measures of body composition such as skinfold thickness, bioelectrical impedance, and dual-energy X-ray absorptiometry (DXA) provide more precise data on fat distribution and lean mass. [12]
Studies have shown that combining BMI and body composition assessments with behavioral evaluations yields a more comprehensive understanding of obesity risk. [13, 14] The integrating multiple measures improves the predictive capacity of school-based interventions for long-term health outcomes. [15] Despite their benefits, school-based programs face challenges such as ensuring privacy, avoiding stigmatization, and obtaining parental consent. Research underscores the necessity for culturally sensitive approaches to mitigate potential negative impacts on children's psychological well-being. [16]
This is Prospective, Randomized and Observational study was conducted in the Department of Physiology, Index Medical College.
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.
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.
Adolescents from 1-18 years of age are studying in sixth, seventh, eighth, ninth and eleventh 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.
Randomization of schools and a random selection of children from each section of each grade in schools were done to avoid sampling bias.
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.
There was no scoring for demographic, socio economic, clinical data as well as for 24 hours dietary recall.
There is one question on sleeping habit, a child who has slept for less than 8 hours during nighttime is at the potential risk of developing obesity. Good sleeping habit was given a score of one, and otherwise, a score of zero was given.
The data was entered into Epidata software and was analysed using SPSS 29.0 However, the change in BMI values at the baseline and follow up was calculated and treated as continuous variable. Student t-test was used to compare the mean change between the two groups. With the baseline data, for both groups, the comparison was made using Analyses of Covariance (ANCOVA). Multivariable regression analysis was done considering a change in BMI. Similar analyses were done for a change in physical activity, eating habits and sleeping pattern. Data was entered into Epi data software and was analyzed using SPSS 29.0.
Graph 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 Graph 1.
Graph 2: Distribution of BMI Changes
In Graph 2, Intervention Group: The mean BMI is 25.8 and in Control Group: The mean BMI is 26.0 Both the intervention and control groups have similar mean BMI values, with the intervention group being slightly lower (25.8 vs. 26.0). The difference in BMI is quite small (only 0.2), which suggests that, at baseline, the two groups are very similar in terms of BMI.
Graph 3: Distribution of Dietary Intake
In Graph 3, the intervention group consumed an average of 2200 kcal, while the control group consumed 2250 kcal at baseline. Both groups had similar caloric intake at baseline, ensuring any changes observed later can be attributed to dietary modifications introduced during the intervention.
Graph 4: Distribution of Health Behaviors
In Graph 4, in the intervention group, 60% had good eating habits, 30% had fair habits, and 10% had poor habits. In the control group, the respective percentages were 50%, 35%, and 15%. The intervention group had slightly better eating habits at baseline compared to the control group. However, there is room for improvement in both groups, particularly in reducing poor eating habits.
Graph 5: Distribution of Blood Pressure and Heart Rate
In Graph 5, the intervention group had a baseline blood pressure of 120/80 ± 10 mmHg, while the control group had 122/82 ± 12 mmHg. Both groups started with normal blood pressure ranges, with slight differences that are likely not clinically significant. Any future changes can be attributed to the intervention’s effects on cardiovascular health.
Graph 6: Distribution of Multivariable Regression Analysis
In Graph 6, 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
Our study showed presence of well-known risk factors for overweight and obesity in 7–8-year-old children. Characteristic finding in our study different from other studies is presence of higher parent’s level of education and presence of alcohol intake in children among overweight and obese groups. Lazarou in Cyprus studied children mean age 10.7 years found that girls watching television 4 hours and more per day are three time more overweight, same wasn’t significant in boys. [17]
Investigating children aged 6–8 in Spain Garces showed that correction of poor diet at an early age would have significant benefits for the prevention of cardiovascular diseases. [18] Collisonite showed positive correlation between drinking sugar sweetened drinks and increased BMI among boys aged 10–19 in Saudi Arabia. In Germany at children aged 3–17 years Kleiser found that lower socio economic status and parental overweight could be determinant of obesity. [19]
Investigating white and Asian children aged 7–10 in United Kingdom Khunti showed that in both groups 46% of children spent 4 and more hours per day watching television and playing computer games. [20] In California USA Matheson find that a significant proportion of children’s daily energy intake is consumed during television viewing and that consumption of high fat food during weekends may be increased with BMI in younger children. [21]
Van Sluijs study on 10 year old children in United Kingdom showed that intervention target should be keeping level of achieved physical activity rather than targeting increased levels with increased intake of fruit and vegetables possibly focused on children from lower socio economic background. [22] Results of The Bogalusa Heart Study express need for additional data collection to establish connection between childhood weight status and cardiovascular morbidity and emphasize need for primary and secondary prevention. [23]
American Medical Association Expert Committee Recommendations establish that 18.8% of children aged 6–11 years and 17.4% of children aged 12–19 years in USA are obese. Measures for control and prevention are recommended: anthropometric measurements, lifestyle changes, dietary habits and laboratory testing if necessary. [24]
In current study the mean BMI is 25.8 and in Control Group: The mean BMI is 26.0 Both the intervention and control groups have similar mean BMI values, with the intervention group being slightly lower (25.8 vs. 26.0). The difference in BMI is quite small (only 0.2), which suggests that, at baseline, the two groups are very similar in terms of BMI. The prevalence of obesity is 30% in the intervention group and 32% in the control group.
In this study, 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. 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.
In this study, the intervention group had a baseline blood pressure of 120/80 ± 10 mmHg, while the control group had 122/82 ± 12 mmHg. Both groups started with normal blood pressure ranges, with slight differences that are likely not clinically significant. Any future changes can be attributed to the intervention’s effects on cardiovascular health.
Blood pressure and heart rate improvements further supported the intervention’s efficacy in promoting cardiovascular health. Regression analysis identified physical activity and dietary changes as key contributors to BMI reduction, emphasizing the need for holistic approaches. [24].
This study found that a school-based intervention including counseling and access to an after school exercise program was feasible for nurses to deliver with high fidelity and acceptable to overweight and obese adolescents, but the majority of adolescents did not participate in the after school exercise program. While such a program delivering weight management counseling to overweight and obese adolescents within the school setting is theoretically appealing and has tremendous public health potential, it was not found to be effective in improving BMI or key obesogenic behaviors.
Download
Download
Download
Download
Download XML
