Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysisopen access
- Authors
- Kim, Kyoung Jin; Lee, Jung-Been; Choi, Jimi; Seo, Ju Yeon; Yeom, Ji Won; Cho, Chul-Hyun; Bae, Jae Hyun; Kim, Sin Gon; Lee, Heon-Jeong; Kim, Nam Hoon
- Issue Date
- 6월-2022
- Publisher
- KOREAN ENDOCRINE SOC
- Keywords
- Life style; Diabetes mellitus; type 2; Glycemic control; Fitness trackers; Cluster analysis
- Citation
- ENDOCRINOLOGY AND METABOLISM, v.37, no.3, pp.547 - 551
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ENDOCRINOLOGY AND METABOLISM
- Volume
- 37
- Number
- 3
- Start Page
- 547
- End Page
- 551
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/143411
- DOI
- 10.3803/EnM.2022.1479
- ISSN
- 2093-596X
- Abstract
- Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation???maximization clustering analysis produced two groups: A (n=9) and B (n=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes.
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Collections - Graduate School > Department of Biomedical Sciences > 1. Journal Articles
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