Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Various psychological interventions are effective for reducing symptoms of anxiety when used alone, or as an adjunct to anti-anxiety medications. Recent studies have further indicated that smartphone-supported psychological interventions may also reduce anxiety, although the role of mobile devices in the treatment and management of anxiety disorders has yet to be established. METHODS: We conducted a systematic review and meta-analysis of all randomized clinical trials (RCTs) reporting the effects of psychological interventions delivered via smartphone on symptoms of anxiety (sub-clinical or diagnosed anxiety disorders). A systematic search of major electronic databases conducted in November 2016 identified 9 eligible RCTs, with 1837 participants. Random-effects meta-analyses were used to calculate the standardized mean difference (as Hedges' g) between smartphone interventions and control conditions. RESULTS: Significantly greater reductions in total anxiety scores were observed from smartphone interventions than control conditions (g=0.325, 95% C.I.=0.17-0.48, p<0.01), with no evidence of publication bias. Effect sizes from smartphone interventions were significantly greater when compared to waitlist/inactive controls (g=0.45, 95% C.I.=0.30-0.61, p<0.01) than active control conditions (g=0.19, 95% C.I.=0.07-0.31, p=0.003). LIMITATIONS: The extent to which smartphone interventions can match (or exceed) the efficacy of recognised treatments for anxiety has yet to established. CONCLUSIONS: This meta-analysis shows that psychological interventions delivered via smartphone devices can reduce anxiety. Future research should aim to develop pragmatic methods for implementing smartphone-based support for people with anxiety, while also comparing the efficacy of these interventions to standard face-to-face psychological care.
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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 | 0.029 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.061 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".