Smartphone-Based Interventions and Internalizing Disorders in Youth: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Mental health disorders in youth are a global issue that have important implications for the future quality of life and morbidity of affected individuals. In the context of public health initiatives, smartphone-based interventions have been suggested to hold the potential to be an effective strategy to reduce the symptoms of mental health disorders in youth; however, further evaluation is needed to confirm their effectiveness. This systematic review and meta-analysis documents and synthesizes existing research on smartphone-based interventions targeting internalizing disorders in youth populations. OBJECTIVE: This study aims to synthesize existing research on smartphone-based interventions targeting internalizing disorders in youth populations. METHODS: PubMed and SCOPUS were searched in 2019, and 4334 potentially relevant articles were found. A total of 12 studies were included in the final synthesis. We used the Hedges g meta-analysis approach and a random effects model for analysis. RESULTS: The results of this review note that depression and anxiety are the most commonly targeted symptoms, and unlike other similar topics, most studies reviewed were linked to a proven treatment. The overall pooled effect from the meta-analysis showed small but significant effects (κ=12; N=1370; Hedges g=0.20; 95% CI 0.02-0.38) for interventions in reducing the symptoms of internalizing disorders. In total, 4 subgroup analyses examining specific symptoms and intervention styles found varied small significant and nonsignificant effects. CONCLUSIONS: Future research should focus on developing robust evaluative frameworks and examining interventions among more diverse populations and settings. More robust research is needed before smartphone-based interventions are scaled up and used at the population level to address youth internalizing disorders.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".