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KMID : 1141520220370030547
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Endocrinology and Metabolism
2022 Volume.37 No. 3 p.547 ~ p.551
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Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis
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Kim Kyoung-Jin
Lee Jung-Been
Choi Ji-Mi
Seo Ju-Yeon
Yeom Ji-Won
Cho Chul-Hyun
Bae Jae-Hyun
Kim Sin-Gon
Lee Heon-Jeong
Kim Nam-Hoon
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Abstract
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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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KEYWORD
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Life style, Diabetes mellitus, type 2, Glycemic control, Fitness trackers, Cluster analysis
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