Interventions to Promote Physical Activity and Healthy Digital Media Use in Children and Adolescents: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To identify effective interventions that promote healthy screen time use and reduce sedentary behavior in school-age children and adolescents (SACA) in all settings, over the last 20 years. METHODS: Searches were conducted from 2000 until March 2021 using PubMed, Embase, Medline, PsycINFO, Ovid SP, The Cochrane Library, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, and the WHO regional databases, including Google Scholar and reference lists of relevant articles and reviews. Randomized-controlled trials and quasi-experimental studies assessing interventions to reduce sedentary behaviors and screen time in healthy SACA (aged 5-19.9 years) globally. Data were extracted by 2 reviewers and where possible, pooled with a random-effects model. RESULTS: The review included 51 studies, of which 23 were included in meta-analyses with 16 418 children and adolescents. Nondigital randomized-controlled trials reported a small, but significant reduction of TV-specific screen time (minutes per day) (mean difference, -12.46; 95% confidence interval, -20.82 to -4.10; moderate quality of evidence) and sedentary behavior (minutes per day) (mean difference, -3.86; 95% confidence interval, -6.30 to -1.41; participants = 8920; studies = 8; P = .002; moderate quality of evidence) as compared with control groups. For quasi-experimental studies, nondigital interventions may make little or no difference on screen time (minutes per day) or sedentary behavior (minutes per day), given the high uncertainty of evidence. Most studies were conducted in a high-income country. Generalizability of results to low- and middle- income countries remain limited. CONCLUSIONS: Public health policies and programs will be necessary to reduce excessive sedentary behavior and screen time, especially in the post-coronavirus disease 2019 reality.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".