Align: Reflection Promotion Through Subjective Experiences and Objective Metrics

Inspiration:

Previous work demonstrated the power of self-reflection in enhancing data-driven decision-making. We learned that tools promoting reflection help users connect their personal experiences with measurable health and fitness data, enabling them to better understand their condition or progress.

Based on this, and our discussions with clinical researchers, we believe this approach could benefit many chronic conditions, and even improve athletic training and performance.

[1] Bentvelzen, M., Wozniak, P., Herbes, P. et al (2022). Revisiting Reflection in HCI: Four Design
Resources for Technologies that Support Reflection. Proceedings of the ACM on Interactive, Mobile,
Wearable and Ubiquitous Technologies, 6(1). http://dx.doi.org/10.1145/3517233

Background:

Health-related data is increasingly collected by patients themselves. However, many patients struggle to gain actionable insights from complex data and visualizations. This challenge is particularly evident in individuals with chronic conditions such as diabetes, which requires continuous tracking and making sense of diverse personal data. Reflection can be valuable for gaining insights and motivating behavior change for better health outcomes, but how can we encourage reflection through digital health applications?

For users (i.e. patients) to continue to contribute data, the interfaces they use to do so should be intuitive, informative, and positively contribute to their understanding of their healthcare. Reflection supports self-insight, motivates behavior change, and is desirable for interactive systems. Allowing a patient-facing, reflection-promoting tool that is optimized for a balance between low-effort insight generation, and rich data capture is instrumental for informed, productive self-management.

Building on prior research by Bentvelzen et al. [1], this project aims to develop a personal informatics tool that combines wearable fitness tracker data with users’ own reflections on their biometrics. The tool is designed to help clinical researchers better understand patient experiences and enable patients to reflect more deeply on their own health data. We aim to do so through adaptation of our prototype to a case study coming from DSI Health community collaborators: in type 1 diabetes management.