Digital Interventions for Emotion Regulation in Children and Early Adolescents: Systematic Review and Meta-analysis
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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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".