Ambiguities in the Privacy Policies of Common Health and Fitness Apps
Bibliographic record
Abstract
With a growing number of health and wellness applications (apps), there is a need to explore exactly what third parties can legally do with personal data. Following a review of the online privacy policies of a select set of mobile health and fitness apps, this chapter assessed the privacy policies of four popular health and fitness apps, using a checklist that comprised five privacy risk categories. Privacy risks, were based on two questions: a) is important information missing to make informed decisions about the use of personal data? and b) is information being shared that might compromise the end-user's right to privacy of that information? The online privacy policies of each selected app was further examined to identify important privacy risks. From this, a separate checklist was completed and compared to reach an agreement of the presence or absence of each privacy risk category. This chapter concludes with a set of recommendations when designing privacy policies for the sharing of personal information collected from health and fitness apps.
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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 | 0.038 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".