Effectiveness of mobile applications in improving insomnia symptoms among adults from multi-community: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: To clarify whether mobile sleep applications alleviate insomnia symptoms among adults from multi-community. PATIENTS/METHODS: A literature search was conducted using the PubMed, Cochrane, PsycINFO, and Web of Science databases for articles related to mobile technology and sleep interventions published between database inceptions and March 27, 2023. We pooled relevant data using a random-effects model, and a meta-analysis was performed using a web version of the Cochrane Review Manager. The effect size was estimated and reported as the combined overall effect (weighted average). Forest plots were created, and the Cochrane risk-of-bias tool and Newcastle-Ottawa Scale were used to evaluate studies. RESULTS: After an initial screening and full-text reviews, seven studies were identified with a total of 10,139 participants (females n = 8844, 87.2 %) recruited from multi-community and not diagnosed with sleep disorders or taking medications. These studies included one cross-sectional study investigating mindfulness meditation apps and six randomized-controlled trials (RCTs), including one with sleep-feedback messaging, one comparing sleep applications with or without a wearable device, and four with multicomponent interventions based on cognitive theory and subsequent behavioral change techniques. In a meta-analysis of three cognitive behavior therapy (CBT)-based RCTs, the intervention group showed statistically significant improvements in insomnia symptoms according to the Pittsburgh Sleep Quality Index but with high heterogeneity, while two CBT-based RCTs showed no significant improvements in the Insomnia Severity Index with low heterogeneity. CONCLUSIONS: A small body of evidence supports the use of CBT-based sleep applications to improve insomnia symptoms among adults from multi-community.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| 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) | 0.003 | 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".