- Diabetes, Obesity and Metabolism
- Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis
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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
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Endocrinol Metab. 2022;37(3):547-551. Published online June 29, 2022
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DOI: https://doi.org/10.3803/EnM.2022.1479
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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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Citations
Citations to this article as recorded by  - Rethink nutritional management in chronic kidney disease care
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Frontiers in Nephrology.2023;[Epub] CrossRef - Effect of a Wearable Device–Based Physical Activity Intervention in North Korean Refugees: Pilot Randomized Controlled Trial
Ji Yoon Kim, Kyoung Jin Kim, Kyeong Jin Kim, Jimi Choi, Jinhee Seo, Jung-Been Lee, Jae Hyun Bae, Nam Hoon Kim, Hee Young Kim, Soo-Kyung Lee, Sin Gon Kim
Journal of Medical Internet Research.2023; 25: e45975. CrossRef
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