Effectiveness of Mobile Apps in Promoting Healthy Behavior Changes and Preventing Obesity in Children: Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Mobile apps have been increasingly incorporated into healthy behavior promotion interventions targeting childhood obesity. However, their effectiveness remains unclear. OBJECTIVE: This paper aims to conduct a systematic review examining the effectiveness of mobile apps aimed at preventing childhood obesity by promoting health behavior changes in diet, physical activity, or sedentary behavior in children aged 8 to 12 years. METHODS: MEDLINE, Embase, PsycINFO, CINAHL, and ERIC were systematically searched for peer-reviewed primary studies from January 2008 to July 2021, which included children aged 8 to 12 years; involved mobile app use; and targeted at least one obesity-related factor, including diet, physical activity, or sedentary behavior. Data extraction and risk of bias assessments were conducted by 2 authors. RESULTS: Of the 13 studies identified, most used a quasi-experimental design (n=8, 62%). Significant improvements in physical activity (4/8, 50% studies), dietary outcomes (5/6, 83% studies), and BMI (2/6, 33% studies) were reported. All 6 multicomponent interventions and 57% (4/7) of standalone interventions reported significant outcomes in ≥1 behavioral change outcome measured (anthropometric, physical activity, dietary, and screen time outcomes). Gamification, behavioral monitoring, and goal setting were common features of the mobile apps used in these studies. CONCLUSIONS: Apps for health behavior promotion interventions have the potential to increase the adoption of healthy behaviors among children; however, their effectiveness in improving anthropometric measures remains unclear. Further investigation of studies that use more rigorous study designs, as well as mobile apps as a standalone intervention, is needed.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it