Childhood obesity is recognized as a major global health issue, affecting over 100 million children and showing steady increases in both developed and developing regions. Its prevalence has skyrocketed in recent decades due to a convergence of genetic, behavioural, and environmental factors, including maternal obesity, sedentary lifestyle, poor dietary habits, and socioeconomic status. By 2035, projections estimate that 18% of girls and 20% of boys aged 5 to 19 years globally will be affected, indicating the impending severity of this epidemic.^{[1, 2]}

Obesity in childhood is not only associated with immediate health challenges—such as hypertension, steatotic liver disease, insulin resistance, and psychosocial distress—but also impacts physical growth and pubertal development, with abnormal insulin and leptin levels leading to advanced bone age and potential early puberty. Social stigma, bullying, and diminished self-esteem are pervasive among obese children and adolescents, often resulting in persistent behavioural and emotional difficulties that extend into adulthood. ^[3]

The long-term consequences of childhood obesity have been exhaustively documented. Systematic reviews and meta-analyses consistently demonstrate that childhood overweight and obesity are linked to increased risks of premature mortality, cardiovascular disease, type 2 diabetes, certain cancers, asthma, polycystic ovary syndrome, and chronic disability in adulthood. Notably, the hazard ratios for subsequent cardiometabolic morbidity, such as hypertension and ischemic heart disease, range up to 5.1 for those overweight from an early age. The risk of premature mortality is three times higher before age 30 if obesity begins in childhood. These effects are amplified in individuals exposed to early maternal obesity or environmental obesogens—factors critical to the rapid increase in prevalence, especially among poorer communities. ^[4]

Encouragingly, evidence shows that effective pediatric obesity treatments—including behavioural interventions and integrated lifestyle programs—substantially reduce the long-term risk of obesity-related morbidity and mortality, although challenges with weight regain persist into young adulthood. Research underscores the importance of early, multifaceted preventive strategies—targeting children, family, schools, and wider community environments—to stem the progression of obesity and its sequelae. ^[5].

Study Design

This research was conducted as a prospective, controlled, observational study to assess the long-term effects of childhood obesity and evaluate preventive interventions. The study protocol was approved by the Institutional Ethics Committee, and written informed consent was obtained from all participants’ guardians prior to enrolment.

Sample Size and Calculation

The sample size was calculated using the formula for estimating obesity prevalence:

N=Z2×P×(1−P)/d2N=Z2×P×(1−P)/d2

Where:

• NN = required sample size
• ZZ = Z-score for 95% confidence (1.96)
• PP = estimated prevalence of obesity (set at 20% based on regional meta-analyses)
• dd = desired precision (set at 0.08 for an 8% margin of error)

Plugging in values:

N= (1.96)2×0.2× (1−0.2)/ (0.08)2≈96

Accounting for dropouts and incomplete data, the sample size was rounded off to 100 patients, providing adequate statistical power to detect significant differences in outcomes. ^[6]

Participant Selection

Children aged 7–15 years were recruited from two urban schools. Detailed inclusion and exclusion criteria ensure that study participants are appropriate for examining childhood obesity interventions and outcomes. The following is adapted from major clinical trials and systematic reviews on childhood obesity.

Inclusion Criteria

1. Children aged 7–15 years.
2. BMI-for-age percentile ≥85th, indicating overweight or obesity (per WHO or CDC standards).
3. Informed written consent from parents/guardians and assent from the child.
4. Children must be capable of adhering to the study protocol and participating in interventions.
5. Regular attendance in designated schools or health centres for data collection and intervention activities.

Exclusion Criteria

• Chronic or genetic conditions affecting growth, metabolism, or health status (including diabetes, cardiovascular disease, syndromic/genetic obesity, cerebral palsy, epilepsy).
• Current use of medications known to affect weight, growth, or appetite.
• Physical disabilities or illnesses restricting participation in exercise or physical activity interventions.
• Participation in other structured dietary or weight management programs during the study period.
• Severe psychiatric or psychological disorders affecting compliance or participation (e.g., severe depression, psychosis).
• Lack of consent from parent/guardian or child; unwillingness to participate or expected non-compliance with study procedures.

 The data collection process

in this study was meticulously structured to ensure reliability and comprehensive coverage of factors associated with childhood obesity. A total of 100 children were recruited and equally divided into four study arms, with 25 patients assigned to each group for comparison of interventions and outcomes.

 Data Collection Process

Qualified research staff conducted data collection at baseline and at pre-specified follow-up intervals (6 and 12 months). Anthropometric measurements, including weight, height, and waist circumference, were taken by trained investigators using standardized protocols as recommended by the World Health Organization. Weight was measured to the nearest 0.1 kg using a calibrated electronic scale, and height was recorded to the nearest 0.1 cm with a stadiometer. BMI was calculated for each patient, and z-scores determined using age- and sex-specific reference data. ^[7]

Blood pressure readings were obtained with age-appropriate cuffs after five minutes of rest in a seated position. Further, fasting blood samples were taken for glucose and lipid profile analysis. These lab values were processed in accredited laboratories with established protocols for pediatric samples.

A validated, semi-structured questionnaire was administered to parents and children to collect sociodemographic data (age, sex, parental education, and family income), lifestyle information (physical activity, quantified in hours/week; daily screen time; and dietary habits with special emphasis on junk food consumption), and psychosocial variables (sleep hours, self-esteem, depression assessment using Children’s Depression Inventory and Rosenberg Self-Esteem Scale). ^[8]

Physical activity levels were recorded and verified through both self-reports and school logs, while dietary intake was assessed using 3-day food diaries corroborated by parental report. Parent and child interviews were conducted in private settings to maximize accuracy and comfort. Quality assurance included re-measurement of anthropometrics for 10% of the sample to confirm consistency. ^[9]

 Study Group Division

Participants (n=100) were randomly allocated into four equal groups (25 children per arm):

• Group 1: School-based physical activity intervention
• Group 2: Combined dietary counselling and physical activity intervention
• Group 3: Family-based home intervention
• Group 4: Control group (standard health education only)

Randomization was performed using a computer-generated allocation sequence. Each group underwent identical baseline and follow-up measurements, with intervention delivery tailored per arm as previously described.

This study was a controlled trial conducted over 12 months in urban schools to understand how different obesity prevention programs help children aged 7 to 15 years. A total of 100 children were chosen and divided into four groups equally, with 25 children in each group.

The first group took part in a physical activity program during school hours, doing daily exercises like aerobics and games. The second group did the same physical activities but also received monthly lessons on healthy eating for both children and their parents. The third group received monthly home visits where health workers taught families how to encourage healthy eating habits and more physical activity at home. The fourth group, acting as the control, received normal school health education without extra programs.

Before starting the programs, all children had their height, weight, and waist size measured by trained staff following standard methods. Their body mass index (BMI) was calculated using age and gender charts. Blood pressure was measured after resting, and blood samples were taken to check sugar and fat levels in the blood. To understand habits, children and parents filled out questionnaires about eating, physical activity, screen time, and sleep. Additionally, children’s mental health was assessed using short surveys that measure depression and self-esteem.

Measurements were repeated halfway through (6 months) and at the end of the year (12 months) to see if the programs helped reduce obesity or improve health. Data was analyzed to compare results between groups and over time, adjusting for factors like age and gender. Ethical approval was obtained, and the study followed strict rules to protect participants' privacy and safety. Parents gave written consent and children agreed to participate before the study began.

 Statistical Analysis

The collected data was noted in the excel sheet and analysed by using SPSS Software. Anova test was done. P value < 0.05 considered as statically significant and p > 0.05 considered as non-significant.

Table 1: Age Distribution of Participants

Table 2: Gender Distribution of Participants

Table 3. Baseline Anthropometric Data

Table 4. Mean Changes in BMI After 1 Year

Table 5. Mean Changes in BMI After 5 Year

6. Psychosocial Outcomes After 12 Months

Table 7. Psychosocial Outcomes After 5 Years

Table 8. Comorbidity Prevalence at 5-Year Follow-Up

Table 9. Baseline Physical Activity and Screen Time

Table 10. Changes in Blood Pressure (mmHg) After 12 Months

Table 11. Changes in Dietary Habits at 12 Months

The study included a balanced distribution across age groups, with the majority between 10–12 years (40%), followed by 7–9 years (30%) and 13–15 years (30%) (Table 1). Gender distribution was comparable, with 53% males and 47% females (Table 2). Such a balanced demographic sample enhances generalizability and ensures that both age- and sex-based variations in intervention response are accounted for. Previous studies have highlighted the importance of capturing diverse age groups, as the prevalence and health impact of obesity vary significantly across developmental stages ^[10,11].

At baseline, the lack of significant differences in BMI and waist circumference across groups (Table 3) confirmed comparable starting points. After one-year, significant BMI reductions were noted in intervention groups, particularly the combined (p=0.01) and school-based groups (p=0.03) (Table 4). This demonstrates the short-term success of structured interventions.

Long-term follow-up revealed a sustained benefit after five years, with the combined group showing the most favourable outcomes (mean BMI increase 0.88 ± 0.19) versus the control group (mean BMI increase 1.23 ± 0.20) (Table 5). The findings support the robustness of multicomponent approaches that integrate school, family, and community inputs. Earlier reviews, such as those by Waters et al. ^[12] and Katz et al. ^[13], reported similar outcomes, emphasizing that combined school- and family-based interventions outperform isolated strategies.

Psychological well-being is an essential part of managing pediatric obesity. At 12 months, children in the intervention groups demonstrated reduced depression scores and higher self-esteem compared to controls (Table 6). These improvements were maintained over five years, with the combined group showing the lowest depression scores (10.5 ± 2.0) and highest self-esteem ratings (7.3 ± 1.3) compared to the control group (14.5 ± 2.7 and 5.2 ± 2.0, respectively) (Table 7).

This finding is clinically significant, as obesity in childhood is often associated with poor self-esteem, stigma, and increased risk of depression ^{[14, 15]}. Studies have shown that interventions incorporating structured exercise and group-based activities positively influence self-perception and psychosocial resilience, which may explain the sustained benefits observed in this cohort.

Obesity-related comorbidities such as fatty liver disease, ssleep apnea, osteoarthritis, and anxiety disorder were markedly reduced in intervention groups compared to controls after five years (Table 8). For example, fatty liver disease decreased from 6.7% in controls to 1.6% in the combined group. Likewise, anxiety disorder prevalence more than halved in intervention groups compared to controls.

This aligns with longitudinal cohort studies, such as Juonala et al. (2011) ^[16], which demonstrated that obesity management in childhood has long-term protective effects on metabolic and psychological health outcomes. Importantly, these results underline the preventive capacity of early lifestyle interventions beyond weight control alone.

Lifestyle modifications played a central role in the observed outcomes. At baseline, participants in the combined group were already engaging more frequently in physical activity and less in sedentary Behavior (Table 9). Interventions reinforced these patterns, demonstrating the effectiveness of structured programs that incorporate daily activity schedules and limit screen exposure.

Dietary changes at 12 months were significant, with increased fruit and vegetable intake and reduced consumption of sugar-sweetened beverages in all intervention groups (Table 11). The combined group again showed the highest adherence (68% increased fruit/veg consumption and 72% reduced sugary drinks). This supports research by Nixon et al. ^[17], which highlights that sustained parental involvement and consistent reinforcement of dietary practices are crucial in achieving long-term healthy eating behaviours in children.

sChildren in the intervention groups, particularly the school-based and combined groups, showed noteworthy reductions in systolic and diastolic blood pressure at 12 months compared to controls (Table 10). Given that childhood hypertension predicts adult cardiovascular disease, these findings are of high clinical relevance. Freedman et al. (2012) ^[18] similarly reported that weight reduction and healthy lifestyle changes in childhood normalize blood pressure trajectories, preventing long-term cardiovascular morbidity.

This study showed that lifestyle programs can help reduce childhood obesity and improve health. All types of programs were useful, but the combined school- and home-based program worked the best. Children in these programs had lower BMI, better blood pressure, improved self-esteem, and fewer problems like fatty liver disease and sleep apnea. They also ate more healthy foods, spent less time on screens, and were more active.

The results suggest that childhood obesity can be managed better when schools and families work together. Starting these programs early and continuing them for a long time can protect children’s physical and mental health and lower the risk of future diseases.

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