The efficacy of mobile applications for reducing depression in adolescents and young adults: A meta-analysis of randomized control trials
Bibliographic record
Abstract
Background Mobile applications for mental health have the potential to aid people with mental health disorders, especially depression, by providing them with tools and coping mechanisms. Adolescents and young adults, being at risk of depressive symptoms and leading mobile users, are among the main targets of using mobile applications to alleviate symptoms. Objective This study aimed to evaluate the impact of mobile application–based psychological interventions in reducing depression symptoms in adolescents and young adults compared to those not exposed to the intervention. Methods We conducted a meta-analysis focusing on mobile applications for reducing depressive symptoms. We searched two databases: MEDLINE and EMBASE and included randomized controlled trials conducted in English among participants aged 18–35 years old who were assessed for depressive symptoms using a validated screening measure and used mobile applications–based psychological interventions. Two of six independent reviewers conducted study selection, data extraction, and bias assessment. A pooled mean standardized difference (Cohen's d ) and 95% CI were calculated using random-effects meta-analysis. Risk of bias was assessed using I 2 statistics and forest plot. Egger's test was used for assessing publication bias. Results After screening 740 references, we identified 12 trials with 1869 participants, with a mean age of participants ranging from 14.70 to 25.1 years. The interventions ranged from cognitive behavioral therapy (CBT)-based mobile apps to interactive story-telling apps and apps delivering a mix of CBT, interpersonal psychotherapy for adolescents, and dialectical behavior therapy elements. Control groups included information-only, waitlist, no intervention, and treatment as usual. Seven studies used Patient Health Questionnaire-9 (PHQ-9) to assess the severity of depressive symptoms, while the other five used different scales. There was no evidence of publication bias ( p = 0.325). The mobile applications reduced depression score by 0.08 units of standardized difference more than the control, with a 95% CI of −0.19 to 0.03 ( p = 0.294, I 2 = 15.4%) using standardized mean difference (SMD) as the effect estimate. In a sensitivity analysis including only studies that used PHQ-9, we found a similar trend, SMD −0.72 (95%CI −1.48 to 0.03). However, both findings were not significant. Conclusions Current evidence is insufficient to support mobile applications to relieve depression symptoms in adolescents and young adults. Further trials with larger sample size are needed to confirm our findings of a positive trend. With emerging technologies and the high exposure of apps in this population, mobile applications for depression hold promise for the future of treatment and awareness of mental health disorders in this population.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".