Internet Based Obesity Prevention Program for Thai School Children- A Randomized Control Trial

Interventions to prevent obesity in children aged 5 to 11 years old

BACKGROUND

Prevention of obesity in children is an international public health priority given the prevalence of the condition (and its significant impact on health, development and well-being). Interventions that aim to prevent obesity involve behavioural change strategies that promote healthy eating or 'activity' levels (physical activity, sedentary behaviour and/or sleep) or both, and work by reducing energy intake and/or increasing energy expenditure, respectively. There is uncertainty over which approaches are more effective and numerous new studies have been published over the last five years, since the previous version of this Cochrane review.

OBJECTIVES

To assess the effects of interventions that aim to prevent obesity in children by modifying dietary intake or 'activity' levels, or a combination of both, on changes in BMI, zBMI score and serious adverse events.

SEARCH METHODS

We used standard, extensive Cochrane search methods. The latest search date was February 2023.

SELECTION CRITERIA

Randomised controlled trials in children (mean age 5 years and above but less than 12 years), comparing diet or 'activity' interventions (or both) to prevent obesity with no intervention, usual care, or with another eligible intervention, in any setting. Studies had to measure outcomes at a minimum of 12 weeks post baseline. We excluded interventions designed primarily to improve sporting performance.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our outcomes were body mass index (BMI), zBMI score and serious adverse events, assessed at short- (12 weeks to < 9 months from baseline), medium- (9 months to < 15 months) and long-term (≥ 15 months) follow-up. We used GRADE to assess the certainty of the evidence for each outcome.

MAIN RESULTS

This review includes 172 studies (189,707 participants); 149 studies (160,267 participants) were included in meta-analyses. One hundred forty-six studies were based in high-income countries. The main setting for intervention delivery was schools (111 studies), followed by the community (15 studies), the home (eight studies) and a clinical setting (seven studies); one intervention was conducted by telehealth and 31 studies were conducted in more than one setting. Eighty-six interventions were implemented for less than nine months; the shortest was conducted over one visit and the longest over four years. Non-industry funding was declared by 132 studies; 24 studies were funded in part or wholly by industry. Dietary interventions versus control Dietary interventions, compared with control, may have little to no effect on BMI at short-term follow-up (mean difference (MD) 0, 95% confidence interval (CI) -0.10 to 0.10; 5 studies, 2107 participants; low-certainty evidence) and at medium-term follow-up (MD -0.01, 95% CI -0.15 to 0.12; 9 studies, 6815 participants; low-certainty evidence) or zBMI at long-term follow-up (MD -0.05, 95% CI -0.10 to 0.01; 7 studies, 5285 participants; low-certainty evidence). Dietary interventions, compared with control, probably have little to no effect on BMI at long-term follow-up (MD -0.17, 95% CI -0.48 to 0.13; 2 studies, 945 participants; moderate-certainty evidence) and zBMI at short- or medium-term follow-up (MD -0.06, 95% CI -0.13 to 0.01; 8 studies, 3695 participants; MD -0.04, 95% CI -0.10 to 0.02; 9 studies, 7048 participants; moderate-certainty evidence). Five studies (1913 participants; very low-certainty evidence) reported data on serious adverse events: one reported serious adverse events (e.g. allergy, behavioural problems and abdominal discomfort) that may have occurred as a result of the intervention; four reported no effect. Activity interventions versus control Activity interventions, compared with control, may have little to no effect on BMI and zBMI at short-term or long-term follow-up (BMI short-term: MD -0.02, 95% CI -0.17 to 0.13; 14 studies, 4069 participants; zBMI short-term: MD -0.02, 95% CI -0.07 to 0.02; 6 studies, 3580 participants; low-certainty evidence; BMI long-term: MD -0.07, 95% CI -0.24 to 0.10; 8 studies, 8302 participants; zBMI long-term: MD -0.02, 95% CI -0.09 to 0.04; 6 studies, 6940 participants; low-certainty evidence). Activity interventions likely result in a slight reduction of BMI and zBMI at medium-term follow-up (BMI: MD -0.11, 95% CI -0.18 to -0.05; 16 studies, 21,286 participants; zBMI: MD -0.05, 95% CI -0.09 to -0.02; 13 studies, 20,600 participants; moderate-certainty evidence). Eleven studies (21,278 participants; low-certainty evidence) reported data on serious adverse events; one study reported two minor ankle sprains and one study reported the incident rate of adverse events (e.g. musculoskeletal injuries) that may have occurred as a result of the intervention; nine studies reported no effect. Dietary and activity interventions versus control Dietary and activity interventions, compared with control, may result in a slight reduction in BMI and zBMI at short-term follow-up (BMI: MD -0.11, 95% CI -0.21 to -0.01; 27 studies, 16,066 participants; zBMI: MD -0.03, 95% CI -0.06 to 0.00; 26 studies, 12,784 participants; low-certainty evidence) and likely result in a reduction of BMI and zBMI at medium-term follow-up (BMI: MD -0.11, 95% CI -0.21 to 0.00; 21 studies, 17,547 participants; zBMI: MD -0.05, 95% CI -0.07 to -0.02; 24 studies, 20,998 participants; moderate-certainty evidence). Dietary and activity interventions compared with control may result in little to no difference in BMI and zBMI at long-term follow-up (BMI: MD 0.03, 95% CI -0.11 to 0.16; 16 studies, 22,098 participants; zBMI: MD -0.02, 95% CI -0.06 to 0.01; 22 studies, 23,594 participants; low-certainty evidence). Nineteen studies (27,882 participants; low-certainty evidence) reported data on serious adverse events: four studies reported occurrence of serious adverse events (e.g. injuries, low levels of extreme dieting behaviour); 15 studies reported no effect. Heterogeneity was apparent in the results for all outcomes at the three follow-up times, which could not be explained by the main setting of the interventions (school, home, school and home, other), country income status (high-income versus non-high-income), participants' socioeconomic status (low versus mixed) and duration of the intervention. Most studies excluded children with a mental or physical disability.

AUTHORS' CONCLUSIONS

The body of evidence in this review demonstrates that a range of school-based 'activity' interventions, alone or in combination with dietary interventions, may have a modest beneficial effect on obesity in childhood at short- and medium-term, but not at long-term follow-up. Dietary interventions alone may result in little to no difference. Limited evidence of low quality was identified on the effect of dietary and/or activity interventions on severe adverse events and health inequalities; exploratory analyses of these data suggest no meaningful impact. We identified a dearth of evidence for home and community-based settings (e.g. delivered through local youth groups), for children living with disabilities and indicators of health inequities.

Interventions to prevent obesity in school-aged children 6-18 years: An update of a Cochrane systematic review and meta-analysis including studies from 2015-2021

Background

Childhood obesity remains a global public health priority due to the enormous burden it generates. Recent surveillance data suggests there has been a sharp increase in the prevalence of childhood obesity during the COVID-19 pandemic. The Cochrane review of childhood obesity prevention interventions (0-18 years) updated to 2015 is the most rigorous and comprehensive review of randomised controlled trials (RCTs) on this topic. A burgeoning number of high quality studies have been published since that are yet to be synthesised.

Methods

An update of the Cochrane systematic review was conducted to include RCT studies in school-aged children (6-18 years) published to 30 June 2021 that assessed effectiveness on child weight (PROSPERO registration: CRD42020218928). Available cost-effectiveness and adverse effect data were extracted. Intervention effects on body mass index (BMI) were synthesised in random effects meta-analyses by setting (school, after-school program, community, home), and meta-regression examined the association of study characteristics with intervention effect.

Findings

Meta-analysis of 140 of 195 included studies (183,063 participants) found a very small positive effect on body mass index for school-based studies (SMD -0·03, 95%CI -0·06,-0·01; trials = 93; participants = 131,443; moderate certainty evidence) but not after-school programs, community or home-based studies. Subgroup analysis by age (6-12 years; 13-18 years) found no differential effects in any setting. Meta-regression found no associations between study characteristics (including setting, income level) and intervention effect. Ten of 53 studies assessing adverse effects reported presence of an adverse event. Insufficient data was available to draw conclusions on cost-effectiveness.

Interpretation

This updated synthesis of obesity prevention interventions for children aged 6-18 years, found a small beneficial impact on child BMI for school-based obesity prevention interventions. A more comprehensive assessment of interventions is required to identify mechanisms of effective interventions to inform future obesity prevention public health policy, which may be particularly salient in for COVID-19 recovery planning.

Funding

This research was funded by the National Health and Medical Research Council (NHMRC), Australia (Application No APP1153479).

Efficacy of Emerging Technologies to Manage Childhood Obesity

Childhood obesity is a widespread medical condition and presents a formidable challenge for public health. Long-term treatment strategies and early prevention strategies are required because obese children are more likely to carry this condition into adulthood, increasing their risk of developing other major health disorders. The present review analyses various technological interventions available for childhood obesity prevention and treatment. It also examines whether machine learning and technological interventions can play vital roles in its management. Twenty-six studies were shortlisted for the review using various technological strategies and analysed regarding their efficacy. While most of the selected studies showed positive outcomes, there was a lack of studies using robots and artificial intelligence to manage obesity in children. The use of machine learning was observed in various studies, and the integration of social robots and other efficacious strategies may be effective for treating childhood obesity in the future.

Impacts of School Nutrition Interventions on the Nutritional Status of School-Aged Children in Asia: A Systematic Review and Meta-Analysis

This review aims to describe school nutrition interventions implemented in Asia and quantify their effects on school-aged children’s nutritional status. We searched Web of Science, Embase, Ovid MEDLINE, Global Health, Econlit, APA PsycInfo, and Social Policy and Practice for English articles published from January 2000 to January 2021. We quantified the pooled effects of the interventions on the changes in body mass index (BMI) and body mass index z score (BAZ), overall and by type of intervention. In total, 28 articles were included for this review, of which 20 articles were multi-component interventions. Twenty-seven articles were childhood obesity studies and were included for meta-analysis. Overall, school nutrition interventions reduced school-aged children’s BMI and BAZ. Multi-component interventions reduced the children’s BMI and BAZ, whereas physical activity interventions reduced only BMI and nutrition education did not change BMI or BAZ. Overweight/obesity reduction interventions provided a larger effect than prevention interventions. Parental involvement and a healthy food provision did not strengthen school nutrition interventions, which may be due to an inadequate degree of implementation. These results suggested that school nutrition interventions should employ a holistic multi-component approach and ensure adequate stakeholder engagement as well as implementation to maximise the effects.

Effectiveness of multiple eHealth-delivered lifestyle strategies for preventing or intervening overweight/obesity among children and adolescents: A systematic review and meta-analysis

OBJECTIVE

To investigate the effect of multiple eHealth-delivered lifestyle interventions on obesity-related anthropometric outcomes in children and adolescents.

METHODS

The Medline (via PubMed), Embase, Cochrane Library, Web of Science, CBM, VIP, CNKI, and Wanfang electronic databases were systematically searched from their inception to March 18, 2022, for randomized controlled trials (RCTs). Meta-analyses were performed to investigate the effect of multiple eHealth-delivered lifestyle interventions on obesity-related anthropometric outcomes (body mass index [BMI], BMI Z-score, waist circumference, body weight, and body fat%). Two independent investigators reviewed the studies for accuracy and completeness. All included studies were evaluated using the Cochrane Risk-of-Bias (ROB) Tool.

RESULTS

Forty trials comprising 6,403 patients were selected for the meta-analysis. The eligible trials were published from 2006 to 2022. Compared with the control group, the eHealth-intervention group was more effective in reducing BMI (weighted mean difference [WMD] = -0.32, 95% confidence interval [CI]: -0.50 to -0.13, I² = 85.9%), BMI Z-score (WMD = -0.08, 95% CI: -0.14 to -0.03, I² = 89.1%), waist circumference (WMD = -0.87, 95% CI: -1.70 to -0.04, I² = 43.3%), body weight (WMD = -0.96, 95% CI: -1.55 to -0.37, I² = 0.0%), and body fat% (WMD = -0.59, 95% CI: -1.08 to -0.10, I² = 0.0%). The subgroup analysis showed that parental or school involvement (WMD = -0.66, 95% CI: -0.98 to -0.34), eHealth-intervention duration of >12 weeks (WMD = -0.67, 95% CI: -0.96 to -0.38), and mobile-based interventions (WMD = -0.78, 95% CI: -1.13 to -0.43) had a significantly greater intervention effect size on BMI.

CONCLUSIONS

This review recommends that multiple eHealth-delivered lifestyle strategies may be useful for preventing or treating overweight and obesity among children and adolescents. However, our results should be cautiously interpreted due to certain limitations in our study.

Effectiveness of Information and Communication Technology on Obesity in Childhood and Adolescence: Systematic Review and Meta-analysis

BACKGROUND

Internet or mobile device use as a form of information and communication technology (ICT) can be more effective in weight loss and weight maintenance than traditional obesity interventions.

OBJECTIVE

The study aims to assess the effectiveness of child-centered ICT interventions on obesity-related outcomes.

METHODS

Articles were retrieved from the Cochrane Central Register of Controlled Trials, Embase, and PubMed web-based databases. We selected randomized controlled trials in which the participants were aged <18 years. The primary outcomes were BMI, body weight, BMI z-score, waist circumference, and percentage body fat.

RESULTS

In total, 10 of the initial 14,867 studies identified in the databases were selected according to the inclusion criteria. A total of 640 participants were included in the intervention group and 619 in the comparator group. Meta-analyses were conducted considering various subgroups (intervention type, comparator type, target participants, mean age, sex, BMI status, and follow-up period). Overall, ICT interventions demonstrated no significant effect on BMI, body weight, BMI z-score, waist circumference, and percentage body fat. Subgroup analyses revealed that the effect of the intervention was statistically significant for the following: web intervention (weighted mean difference [WMD]=-1.26 kg/m2, 95% CI -2.24 to -0.28), lifestyle modification comparator (WMD=-1.75, 95% CI -2.76 to -0.74), intervention involving both boys and girls (WMD=-1.30, 95% CI -2.14 to -0.46), and intervention involving obesity only (WMD=-1.92, 95% CI -3.75 to -0.09).

CONCLUSIONS

The meta-analysis results for children with obesity who used the web intervention program confirmed significant effects on BMI reduction compared with lifestyle modification. Evidence from the meta-analysis identified internet technology as a useful tool for weight loss in children with obesity.

Harnessing technological solutions for childhood obesity prevention and treatment: a systematic review and meta-analysis of current applications

BACKGROUND

Technology holds promise for delivery of accessible, individualized, and destigmatized obesity prevention and treatment to youth.

OBJECTIVES

This review examined the efficacy of recent technology-based interventions on weight outcomes.

METHODS

Seven databases were searched in April 2020 following PRISMA guidelines. Inclusion criteria were: participants aged 1-18 y, use of technology in a prevention/treatment intervention for overweight/obesity; weight outcome; randomized controlled trial (RCT); and published after January 2014. Random effects models with inverse variance weighting estimated pooled mean effect sizes separately for treatment and prevention interventions. Meta-regressions examined the effect of technology type (telemedicine or technology-based), technology purpose (stand-alone or adjunct), comparator (active or no-contact control), delivery (to parent, child, or both), study type (pilot or not), child age, and intervention duration.

FINDINGS

In total, 3406 records were screened for inclusion; 55 studies representing 54 unique RCTs met inclusion criteria. Most (89%) included articles were of high or moderate quality. Thirty studies relied mostly or solely on technology for intervention delivery. Meta-analyses of the 20 prevention RCTs did not show a significant effect of prevention interventions on weight outcomes (d = 0.05, p = 0.52). The pooled mean effect size of n = 32 treatment RCTs showed a small, significant effect on weight outcomes (d = ‒0.13, p = 0.001), although 27 of 33 treatment studies (79%) did not find significant differences between treatment and comparators. There were significantly greater treatment effects on outcomes for pilot interventions, interventions delivered to the child compared to parent-delivered interventions, and as child age increased and intervention duration decreased. No other subgroup analyses were significant.

CONCLUSIONS

Recent technology-based interventions for the treatment of pediatric obesity show small effects on weight; however, evidence is inconclusive on the efficacy of technology based prevention interventions. Research is needed to determine the comparative effectiveness of technology-based interventions to gold-standard interventions and elucidate the potential for mHealth/eHealth to increase scalability and reduce costs while maximizing impact.

Impact of Strategies for Preventing Obesity and Risk Factors for Eating Disorders among Adolescents: A Systematic Review

An effective behavior change program is the first line of prevention for youth obesity. However, effectiveness in prevention of adolescent obesity requires several approaches, with special attention paid to disordered eating behaviors and psychological support, among other environmental factors. The aim of this systematic review is to compare the impact of two types of obesity prevention programs, inclusive of behavior change components, on weight outcomes. "Energy-balance" studies are aimed at reducing calories from high-energy sources and increasing physical activity (PA) levels, while "shared risk factors for obesity and eating disorders" focus on reducing disordered eating behaviors to promote a positive food and eating relationship. A systematic search of ProQuest, PubMed, PsycInfo, SciELO, and Web of Science identified 8825 articles. Thirty-five studies were included in the review, of which 20 regarded "energy-balance" and 15 "shared risk factors for obesity and eating disorders". "Energy-balance" studies were unable to support maintenance weight status, diet, and PA. "Shared risk factors for obesity and eating disorders" programs also did not result in significant differences in weight status over time. However, the majority of "shared risk factors for obesity and eating disorders" studies demonstrated reduced body dissatisfaction, dieting, and weight-control behaviors. Research is needed to examine how a shared risk factor approach can address both obesity and eating disorders.

Health information technology uses for primary prevention in preventive medicine: a scoping review protocol

INTRODUCTION

The use of health information technologies (HITs) has been associated with positive benefits such as improved health outcomes and improved health services. Results from empirical studies reported potential benefits of HITs in preventive medicine measures such as primary prevention. This review will examine the broad range of HITs and their uses and effectiveness in primary prevention.

METHODS AND ANALYSIS

We will conduct searches in relevant databases (MEDLINE, EMBASE, the Cochrane Methodology Register, Cochrane Database of Systematic Reviews, CINAHL, SCOPUS and Web of Science) using Arksey and O'Malley's scoping review methodology. The scoping review will include all study designs to identify the literature on HIT uses. Two reviewers will independently screen the literature following our screening criteria and using a data abstraction form. Findings will be summarised quantitatively (using numerical counts of HITs) and qualitatively (using narrative synthesis).

ETHICS AND DISSEMINATION

The study will synthesise data from published literature and will not require an ethical approval. The results of the review will be disseminated through a peer-reviewed journal.