From Personal Informatics to Personal Analytics: Investigating How Clinicians and Patients Reason About Personal Data Generated with Self-Monitoring in Diabetes
| Autor: | Matthew E. Levine, Lena Mamykina, David J. Albers, Patricia G. Davidson, Arlene Smaldone, Noémie Elhadad |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Self-management
Activities of daily living Process (engineering) business.industry 05 social sciences Internet privacy 020207 software engineering 02 engineering and technology Disease medicine.disease Data science Personal informatics Analytics Diabetes mellitus 0202 electrical engineering electronic engineering information engineering medicine Self-monitoring 0501 psychology and cognitive sciences business 050107 human factors |
| Zdroj: | Cognitive Informatics in Health and Biomedicine ISBN: 9783319517315 |
| DOI: | 10.1007/978-3-319-51732-2_14 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|