Endocrinol Metab.
2022 Jun;37(3):547-551. 10.3803/EnM.2022.1479.
Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis
- Affiliations
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- 1Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of MedicineSeoul, Seoul, Korea
- 2Department of Computer Science, Korea University College of Information, Seoul, Korea
- 3Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
- 4Department of Psychiatry, Chungnam National University Sejong Hospital, Sejong, Korea
- KMID: 2531398
- DOI: http://doi.org/10.3803/EnM.2022.1479
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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