Users’ Needs for Mental Health Apps: Quality Evaluation Using the User Version of the Mobile Application Rating Scale
Bibliographic record
Abstract
Background: Mental health is an essential element of life. However, existing mental health services face challenges in utilization due to issues such as societal prejudices and a shortage of counselors. Mobile health is gaining attention as an alternative approach to improving mental health by addressing the shortcomings of traditional services. As a result, various mental health apps are being developed, but there is a lack of evaluation research on whether these apps meet users' needs. Objective: This study aims to evaluate the content and quality of mental health apps from the user's perspective and identify the content features that influence evaluation scores. We also aim to guide future updates and improvements in mental health apps to deliver high-quality solutions to users. Methods: We searched the Google Play Store and iOS App Store using Korean keywords "mental health," "mental health care," "depression," and "stress." Apps meeting the following criteria were selected for the study: relevance to the topic, written in Korean, more than 700 reviews (Android) or more than 200 reviews (iOS), updated within the past 365 days, available for free, nonduplicate, and currently operational. After identifying and defining the primary contents of the apps, 7 users evaluated their quality using the user version of the Mobile Application Rating Scale (uMARS). Correlation analysis was performed to examine the relationships among app content, uMARS scores, star ratings, and the number of reviews. Multiple regression analysis was conducted to identify the factors influencing uMARS scores and each evaluation item. Results: The analysis included a total of 41 mental health apps. Content analysis revealed that reminders (n=29, 71%), recording and statistics features (n=29, 71%), and diaries (n=24, 59%) were the most common app components. The top-rated apps, as determined by uMARS evaluations, consistently provided information about counselors and counseling agencies, and included counseling services. uMARS scores were significantly correlated with the presence of health care provider information (r=0.53; P<.001) and counseling/question and answer services (r=0.55; P<.001). Multiple regression analysis indicated that providing more relevant information was associated with higher uMARS scores (β=.361; P=.02). Conclusions: The quality of mental health apps was evaluated from the user's perspective using a validated scale. To deliver a high-quality mental health app, it is essential to incorporate app technologies such as generative artificial intelligence during development and to continuously monitor app quality from the user's perspective.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".