Digital Interventions for Emotion Regulation in Children and Early Adolescents: Systematic Review and Meta-analysis
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Bibliographic record
Abstract
BACKGROUND: Difficulties in emotion regulation are common in adolescence and are associated with poor social and mental health outcomes. However, psychological therapies that promote adaptive emotion regulation may be inaccessible and unattractive to youth. Digital interventions may help address this need. OBJECTIVE: The aim of this systematic review and meta-analysis was to synthesize evidence on the efficacy, feasibility, and acceptability of emotion regulation digital interventions in children and early adolescents aged 8 to 14 years. METHODS: Systematic searches of Web of Science, MEDLINE, PsycINFO, EMBASE, Education Resources Information Centre, ACM Digital Library, and IEEE Xplore up to July 2020 identified 39 studies, of which 11 (28%) were included in the meta-analyses (n=2476 participants). A bespoke tool was used to assess risk of bias. RESULTS: The studies evaluated digital games (27/39, 69%), biofeedback (4/39, 10%), virtual or augmented reality (4/39, 10%), and program or multimedia (4/39, 10%) digital interventions in samples classified as diagnosed, at risk, healthy, and universal. The most consistent evidence came from digital games, which reduced negative emotional experience with a small significant effect, largely in youth at risk of anxiety (Hedges g=-0.19, 95% CI -0.34 to -0.04). In general, digital interventions tended to improve emotion regulation, but this effect was not significant (Hedges g=0.19, 95% CI -0.16 to 0.54). CONCLUSIONS: Most feasibility issues were identified in diagnosed youth, and acceptability was generally high across intervention types and samples. Although there is cause to be optimistic about digital interventions supporting the difficulties that youth experience in emotion regulation, the predominance of early-stage development studies highlights the need for more work in this area.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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