Digital Interventions for Universal Health Promotion in Children and Adolescents: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Digital media has been used mostly to deliver clinical treatments and therapies; however limited evidence evaluates digital interventions for health promotion. The objective of this review is to identify digital interventions for universal health promotion in school-aged children and adolescents globally. METHODS: Eligible articles were searched in PubMed, Embase, Medline, Ovid SP, The Cochrane Library, Cochrane Central Register of Controlled Trials, WHO regional databases, Google Scholar, and reference lists from 2000 to March 2021. Randomized controlled trials and quasi-experimental studies evaluating interventions that promote health in school-aged children and adolescents (5-19.9 years) were included. Methods were conducted in duplicate. Where possible, data were pooled with a random-effects model. RESULTS: Seventy-four studies were included (46 998 participants), of which 37 were meta-analyzed (19 312 participants). Interventions increased fruit and vegetable consumption (servings per day) (mean difference [MD] 0.63, 95% confidence interval [CI] 0.21 to 1.04; studies = 6; P = .003; high quality of evidence), and probably reduced sedentary behavior (MD -19.62, 95% CI -36.60 to -2.65; studies = 6; P = .02; moderate quality of evidence), and body fat percentage (MD -0.35%, 95% CI -0.63 to -0.06; studies = 5; P = .02; low quality of evidence). The majority of studies were conducted in high-income countries and significant heterogeneity in design and methodology limit generalizability of results. CONCLUSIONS: There is great potential in digital platforms for universal health promotion; however, more robust methods and study designs are necessitated. Continued research should assess factors that limit research and program implementation in low- to middle-income countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".