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- Title
Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis.
- Authors
Kyoung Jin Kim; Jung-Been Lee; Jimi Choi; Ju Yeon Seo; Ji Won Yeom; Chul-Hyun Cho; Jae Hyun Bae; Sin Gon Kim; Heon-Jeong Lee; Nam Hoon Kim
- 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.
- Subjects
TYPE 2 diabetes; UNHEALTHY lifestyles; GLYCEMIC control; HEART beat; CLUSTER analysis (Statistics)
- Publication
Endocrinology & Metabolism, 2022, Vol 37, Issue 3, p547
- ISSN
2093-596X
- Publication type
Article
- DOI
10.3803/EnM.2022.1479