Personal Informatics for Health Behavior Change
Summary
Personal informatics systems combine self-tracking tools, feedback mechanisms and reflective practices to support individuals in monitoring and changing health-related behaviour. These systems typically involve stages of data collection via wearable sensors, mobile applications or manual logs; data processing and visualisation; reflection on patterns; and action-planning to effect sustainable change. Underpinned by theories of behaviour change and persuasive technology, personal informatics seeks to enhance self-awareness and autonomy by delivering timely insights and motivation. Applications span promotion of physical activity, healthy eating, chronic condition management and mental well-being, with practical examples including step-count monitors, nutritional tracking platforms and stress-monitoring wearables. Global adoption has been fuelled by ubiquitous smartphones, advances in sensor accuracy and the rise of machine-learning algorithms that personalise feedback. Despite their promise, these tools face challenges such as long-term engagement, privacy concerns, data overload and potential negative cognitive effects. Interdisciplinary research continues to refine design strategies, combining quantitative and qualitative data representations, social support features and adaptive coaching to maximise adherence and positive health outcomes.
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Personal Informatics for Health Behavior Change publication trend
The graph below shows the total number of articles in personal informatics for health behavior change across all publications each year (not limited to Nature Index journals).
Technical terms
Personal informatics: A framework for collecting and reflecting on personal data to support self-awareness and behaviour change.
Self-tracking: The systematic recording of one’s behaviours or physiological metrics using digital or manual tools.
Data quantification: Numerical representation of tracked metrics, such as step counts or calorie intake, to measure changes over time.
Data qualification: Qualitative or contextual annotations that provide narrative meaning alongside numerical data.
Behaviour change techniques: Evidence-based strategies, such as goal setting and feedback, designed to promote adoption and maintenance of healthy behaviours.
References
- Designing self-tracking experiences: A qualitative study of the perceptions of barriers and facilitators to adopting digital health technology for automatic urine analysis at home. PLOS Digital Health (2023).
- Personal Health Data Tracking by Blind and Low-Vision People: Survey Study. Journal of Medical Internet Research (2023).
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