Privacy, Data Sharing, and Data Security Policies of Women’s mHealth Apps: Scoping Review and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Women's mobile health (mHealth) is a growing phenomenon in the mobile app global market. An increasing number of women worldwide use apps geared to female audiences (female technology). Given the often private and sensitive nature of the data collected by such apps, an ethical assessment from the perspective of data privacy, sharing, and security policies is warranted. OBJECTIVE: The purpose of this scoping review and content analysis was to assess the privacy policies, data sharing, and security policies of women's mHealth apps on the current international market (the App Store on the Apple operating system [iOS] and Google Play on the Android system). METHODS: We reviewed the 23 most popular women's mHealth apps on the market by focusing on publicly available apps on the App Store and Google Play. The 23 downloaded apps were assessed manually by 2 independent reviewers against a variety of user data privacy, data sharing, and security assessment criteria. RESULTS: All 23 apps collected personal health-related data. All apps allowed behavioral tracking, and 61% (14/23) of the apps allowed location tracking. Of the 23 apps, only 16 (70%) displayed a privacy policy, 12 (52%) requested consent from users, and 1 (4%) had a pseudoconsent. In addition, 13% (3/23) of the apps collected data before obtaining consent. Most apps (20/23, 87%) shared user data with third parties, and data sharing information could not be obtained for the 13% (3/23) remaining apps. Of the 23 apps, only 13 (57%) provided users with information on data security. CONCLUSIONS: Many of the most popular women's mHealth apps on the market have poor data privacy, sharing, and security standards. Although regulations exist, such as the European Union General Data Protection Regulation, current practices do not follow them. The failure of the assessed women's mHealth apps to meet basic data privacy, sharing, and security standards is not ethically or legally acceptable.
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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 | 0.112 | 0.341 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".