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Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysisopen access

Authors
Kim, Kyoung JinLee, Jung-BeenChoi, JimiSeo, Ju YeonYeom, Ji WonCho, Chul-HyunBae, Jae HyunKim, Sin GonLee, Heon-JeongKim, Nam Hoon
Issue Date
Jun-2022
Publisher
대한내분비학회
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://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61289
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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Bae, Jae Hyun
안암병원 (Department of Endocrinology and Metabolism, Anam Hospital)
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