Digital Interventions for Improving Body Dissatisfaction in Children and Emerging Adults: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Body dissatisfaction is a condition where individuals are dissatisfied with their physical appearance. It has become a global issue, especially among children and emerging adults. A growing number of digital interventions have been developed to address body dissatisfaction in children and emerging adults; however, controversies remain regarding their efficacy, underscoring the need for a comprehensive synthesis of current evidence. Objective: This systematic review aimed to explore the effectiveness of digital interventions in improving body image-related outcomes among children and emerging adults. Methods: From inception to April 24, 2024, a literature search was performed across 7 databases-PubMed, Web of Science, MEDLINE, EBSCO (Elton B Stephens Company), Cochrane Library, CNKI (China National Knowledge Infrastructure), and WANFANG-to identify randomized controlled trials (RCTs) with a predefined set of inclusion criteria. This systematic review was reported in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. Study selection, data extraction, and risk of bias assessment using the Cochrane Risk-of-Bias Tool 2.0 were conducted independently by 2 researchers. Standardized mean differences (SMDs) and 95% CIs from the included RCTs were calculated for the meta-analysis. Heterogeneity was assessed with I² values. A fixed-effects model was used when I²≤50%, and a random-effects model was selected when I²>50%. Results: Twenty RCTs with 5251 participants (2610 in intervention groups and 2641 in control groups) met the inclusion criteria. Digital interventions included web pages, mobile apps, computer-based videos, computer-based sessions, internet-based sessions, internet games, chatbots, podcasts, and social media. Our results indicate that digital interventions could significantly improve body dissatisfaction (SMD=0.38, 95% CI -0.63 to -0.13; I2=55%; P=.003), physical appearance comparison (SMD=-0.24, 95% CI -0.45 to -0.03; I2=0%; P=.003), thin-ideal internalization (SMD=-0.28, 95% CI -0.36 to -0.2; I2=41%; P<.001), self-esteem (SMD=0.14, 95% CI 0.07-0.21; I2=21%; P<.001), self-compassion (SMD=0.55, 95% CI 0.33-0.78; I2=35%; P<.001), and depression (SMD=-0.59, 95% CI -0.97 to -0.21; I2=0%; P=.002), with small to medium effect sizes. Conclusions: While digital interventions improved body dissatisfaction among children and emerging adults, additional well-designed, rigorous, and large-scale RCTs are needed to decisively provide estimates of the effectiveness of digital interventions on body dissatisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".