Users’ Adoption of Mental Health Apps: Examining the Impact of Information Cues
Bibliographic record
Abstract
BACKGROUND: Numerous mental health apps have been developed and made available to users on the current app market. Users may find it difficult and overwhelming to select apps from the hundreds of choices that are available in the app marketplace. Clarifying what information cues may impact a user's selection and adoption of mental health apps is now a critical and pressing issue. OBJECTIVE: The aim of this study was to investigate the impact of information cues on users' adoption of anxiety apps using observational data from the Android app market. METHODS: A systematic search of anxiety apps was conducted on the Android app store by using keywords search. The title and metadata information of a total of 274 apps that met our criteria were collected and analyzed. Three trained researchers recorded the app rankings from the search results page on different dates and Web browsers. RESULTS: Our results show that ratings (r=.56, P<.001) and reviews (r=.39, P<.001) have significant positive correlations with the number of installs, and app prices have significant negative correlations with installs (r=-.36). The results also reveal that lower-priced apps have higher ratings (r=-.23, P<.001) and a greater number of app permission requests (r=.18, P=.002) from the device. For app titles, we found that apps with titles related to symptoms have significantly lower installs than apps with titles that are not related to symptoms (P<.001). CONCLUSIONS: This study revealed a relationship between information cues and users' adoption of mental health apps by analyzing observational data. As the first of its kind, we found impactful indicators for mental health app adoptions. We also discovered a labeling effect of app titles that could hinder mental health app adoptions and which may provide insight for future designs of mental health apps and their search mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".