Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis.
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SUBMITTER: Kim KJ
PROVIDER: S-EPMC9262687 | biostudies-literature | 2022 Jun
REPOSITORIES: biostudies-literature
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Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis.
Kim Kyoung Jin KJ Lee Jung-Been JB Choi Jimi J Seo Ju Yeon JY Yeom Ji Won JW Cho Chul-Hyun CH Bae Jae Hyun JH Kim Sin Gon SG Lee Heon-Jeong HJ Kim Nam Hoon NH
Endocrinology and metabolism (Seoul, Korea) 20220629 3
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 ...[more]
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