Abstract
Diabetes self-management continues to present a significant challenge to millions of individuals around the world, as it often requires significant modifications to one’s lifestyle. The highly individual nature of the disease presents a need for each affected person to discover which daily activities have the most positive impact on one’s health and which are detrimental to it. Data collected with self-monitoring can help to reveal these relationships, however interpreting such data may be non-trivial. In this research we investigate how individuals with type 2 diabetes and their healthcare providers reason about data collected with self-monitoring and what computational methods can facilitate this process.
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Acknowledgements
This work was funded by the National Library of Medicine grant “Training in Biomedical Informatics at Columbia University”, T15 LM007079, National Institute of Diabetes and Digestive and Kidney Disease [NIDDK] grant, 1R01DK090372-01A1 and National Library of Medicine grant LM006910.
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Mamykina, L., Levine, M.E., Davidson, P.G., Smaldone, A.M., Elhadad, N., Albers, D.J. (2017). From Personal Informatics to Personal Analytics: Investigating How Clinicians and Patients Reason About Personal Data Generated with Self-Monitoring in Diabetes. In: Patel, V., Arocha, J., Ancker, J. (eds) Cognitive Informatics in Health and Biomedicine. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-51732-2_14
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DOI: https://doi.org/10.1007/978-3-319-51732-2_14
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