Personal Health Oracle: Explorations of Personalized Predictions in Diabetes Self-Management
Abstract
The increasing availability of health data and knowledge about computationally modeling human physiology opens new opportunities for personalized predictions in health. Yet little is known about how individuals interact and reason with personalized predictions. To explore these questions, we developed a smartphone app, GlucOracle, that uses self-tracking data of individuals with type 2 diabetes to generate personalized forecasts for post-meal blood glucose levels. We pilot-tested GlucOracle with two populations: members of an online diabetes community, knowledgeable about diabetes and technologically savvy; and individuals from a low socio-economic status community, characterized by high prevalence of diabetes, low literacy and limited experience with mobile apps. Individuals in both communities engaged with personal glucose forecasts and found them useful for adjusting immediate meal options, and planning future meals. However, the study raised new questions as to appropriate time, form, and focus of forecasts and suggested new research directions for personalized predictions in health.
Additional Information
© 2019 held by the owner/author(s). Publication rights licensed to ACM. This work was funded in part by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK090372 and R56DK113189; and the National Library of Medicine (NLM) R01 LM012734-01. Many thanks to the participants, translators, and members of the ARCH Lab who made this work possible.Additional details
- Eprint ID
- 95101
- DOI
- 10.1145/3290605.3300600
- Resolver ID
- CaltechAUTHORS:20190430-074633483
- R01DK090372
- NIH
- R56DK113189
- NIH
- R01 LM012734-01
- NIH
- Created
-
2019-04-30Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field