Availability, readability, and content of privacy policies and terms of agreements of mental health apps
Bibliographic record
Abstract
OBJECTIVE: To assess the availability, readability, and privacy-related content of the privacy policies and terms of agreement of mental health apps available through popular digital stores. MATERIALS AND METHODS: Popular smartphone app stores were searched using combinations of keywords "track" and "mood" and their synonyms. The first 100 apps from each search were evaluated for inclusion and exclusion criteria. Apps were assessed for availability of a privacy policy (PP) and terms of agreement (ToA) and if available, these documents were evaluated for both content and readability. RESULTS: Most of the apps collected in the sample did not include a PP or ToA. PPs could be accessed for 18% of iOS apps and 4% of Android apps; whereas ToAs were available for 15% of iOS and 3% of Android apps. Many PPs stated that users' information may be shared with third parties (71% iOS, 46% Android). DISCUSSION: Results demonstrate that information collection is occurring with the majority of apps that allow users to track the status of their mental health. Most of the apps collected in the initial sample did not include a PP or ToA despite this being a requirement by the store. The majority of PPs and ToAs that were evaluated are written at a post-secondary reading level and disclose that extensive data collection is occurring. CONCLUSION: Our findings raise concerns about consent, transparency, and data sharing associated with mental health apps and highlight the importance of improved regulation in the mobile app environment.
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 | 0.035 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".