Evaluating Characteristics and Quality of Mental Health Apps Available in App Stores for Indian Users: Systematic App Search and Review
Bibliographic record
Abstract
BACKGROUND: The mental health app sector in India is expanding rapidly, driven by increasing smartphone usage, growing internet penetration, the popularity of digital initiatives, and heightened recognition of mental health challenges in public discourse. This growth is also influenced by both supply- and demand-side barriers to seeking professional help and the rise of mental health tech startups. While digital mental health solutions provide scalable ways to address unmet needs, concerns persist regarding app quality, privacy, and safety due to rapid market expansion, regulatory challenges, and limited empirical research. We conducted a comprehensive and systematic review of smartphone-based mental health apps accessible to Indian users through app stores. OBJECTIVE: This study aims to describe apps in terms of characteristics such as the nature of their functions, involvement of mental health professionals in development, reference to an empirical basis, and inclusion of nudges to seek professional help, as well as to evaluate app quality. METHODS: This systematic review of mental health apps was conducted using the TECH (Target user, Evaluation focus, Connectedness, and Health domain) approach, along with the PASSR (Protocol for App Store Systematic Reviews) checklist. Fifteen search terms covering mental health conditions and therapies were applied to both Google Play and Apple App Store. Identified apps were screened according to predefined inclusion and exclusion criteria and subsequently downloaded for detailed review. Data were extracted based on prespecified parameters. Additionally, app quality was evaluated using the Mobile Application Rating Scale (MARS). RESULTS: The initial search identified 5827 apps, of which 350 were reviewed in detail after removing duplicates and applying eligibility criteria. Common search terms such as "depression" and "anxiety" yielded nearly a quarter of relevant apps (128/495, 25.9% to 133/497, 26.8%); 62 (17.7%) of the 350 reviewed apps originated from Asia, and 131 (37.4%) focused on a single mental health condition. Multifunction apps (eg, those combining assessment and intervention) constituted the largest category (230/350, 65.7%). Privacy concerns were notable; for example, 54 (15.4%) apps did not mention a data-sharing policy. Most apps were developed by commercial organizations, and 228 (65.1%) did not report involvement of mental health professionals, while 45 (12.9%) mentioned it only cursorily. Only 38 (10.9%) apps referenced empirical research, and more than half did not indicate an empirical basis for their content. Pointers to seek professional help were present in 139 (39.7%) apps, mostly in the form of disclaimers, whereas nudges or motivational prompts to seek help appeared in slightly less than a quarter. Only 105 (30%) apps attempted to dispel mental health myths. Functionality and aesthetics ratings on the MARS were relatively high, but 50 (14.3%) apps scored 3 or lower on the information subscale. CONCLUSIONS: This study is among the first systematic evaluations of mental health apps accessible to Indian users on Google Play and Apple App Store. The findings provide insights to guide future research, app development, and policy making in the digital mental health space. TRIAL REGISTRATION: International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) INPLASY2024100035; https://inplasy.com/inplasy-2024-10-0035/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/71071.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".