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Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach
Luke Hutton1, Blaine A Price1, Ryan Kelly1,2
1Software Engineering and Design Group, School of Computing and Communications, The Open University, Milton Keynes, United Kingdom.
JMIR Mhealth and Uhealth
|October 24, 2018
Summary
Self-tracking apps often fail to protect user privacy, with many not providing full data access or adequate consent controls. Health-focused apps particularly show weaker privacy performance, highlighting the need for better standards.
Area of Science:
- Digital Health
- Human-Computer Interaction
- Data Privacy
Background:
- Self-tracking technologies generate vast personal lifestyle data for health interventions.
- Commercial motivations of vendors raise concerns about sensitive data handling compared to medical data.
- Increasing sophistication of self-tracking apps exacerbates privacy implications.
Purpose of the Study:
- To evaluate the privacy performance of current mass-market self-tracking applications.
- To introduce a heuristic-based framework for assessing privacy in self-tracking services.
Main Methods:
- Analysis of 64 popular self-tracking services using a defined set of privacy heuristics.
- Statistical modeling to identify if app categories correlate with privacy performance.
Main Results:
- Most services lacked full user data access, sufficient consent mechanisms, and adequate third-party disclosure controls.
- App category was not a reliable predictor of privacy.
- Health-related self-tracking apps (e.g., exercise, weight) demonstrated poorer privacy outcomes.
Conclusions:
- Current self-tracking technologies exhibit significant privacy deficiencies.
- There is a critical need for privacy standards in self-tracking app development.
- The developed heuristic method can evaluate existing apps and guide privacy-by-design in future applications.
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