Mental Health Monitoring for Young People Through Mood Apps: Protocol for a Scoping Review and Systematic Search in App Stores
Bibliographic record
Abstract
BACKGROUND: The researchers have used mobile phones to assist in monitoring, analyzing, and managing moods to acquire insight into mood patterns. There is a lack of evidence in their use as clinical tools and interventions, which necessitates a comprehensive review and quality assessment to understand barriers and facilitators for app implementation as an impactful clinical intervention. OBJECTIVE: This review aims to (1) provide an overview of the recent evidence on mobile mood-monitoring apps that are intended for facilitating self-management and support of mental health in children, adolescents, and young people; and (2) investigate the quality of publicly available apps. METHODS: The study will first involve a scoping review of the literature on mood-monitoring apps for children, adolescents, and young people followed by an evaluation of features of the apps available in the marketplace. The scoping review will follow the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and search 6 databases- Embase, CINAHL, PubMed, ACM Digital Library, Scopus, and Springer LNCS-for relevant studies and reviews published in the last 3 years. The author will then screen the references, extract data from the included studies, and analyze them to synthesize the evidence on mood apps. Next, the Apple App Store and Google Play Store will be searched for mood apps. A total of 2 independent reviewers will screen the apps based on eligibility criteria, and disagreements will be resolved through consensus. The features of the selected apps will then be evaluated using the Mobile Health Index and Navigation framework, and descriptive analysis will be used to synthesize the findings. RESULTS: Literature search and screening began soon after submission of the protocol and is expected to be completed by September 2024. The app evaluation will be completed by October 2024. CONCLUSIONS: Combined, the scoping literature review and app evaluation will provide an in-depth overview of the most recent scientific evidence related to mood apps and the quality of apps actually available for use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/56400.
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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 | 0.107 | 0.096 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.021 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.106 | 0.020 |
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".