4.3 Article

Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis

Journal

ENDOCRINOLOGY AND METABOLISM
Volume 37, Issue 3, Pages 547-551

Publisher

KOREAN ENDOCRINE SOC
DOI: 10.3803/EnM.2022.1479

Keywords

Life style; Diabetes mellitus; type 2; Glycemic control; Fitness trackers; Cluster analysis

Funding

  1. Korea Medical Device Development Fund - Korean government (the Ministry of Science and ICT) [1711139102, KMDF_PR_20210527_0003-202202]
  2. Korea Medical Device Development Fund - Korean government (the Ministry of Trade, Industry and Energy) [1711139102, KMDF_PR_20210527_0003-202202]
  3. Korea Medical Device Development Fund - Korean government (the Ministry of Health Welfare) [1711139102, KMDF_PR_20210527_0003-202202]
  4. Korea Medical Device Development Fund - Korean government (the Ministry of Food and Drug Safety) [1711139102, KMDF_PR_20210527_0003-202202]
  5. Korea Medical Device Development Fund - Korean government (National Research Foundation of Korea) [1711139102, KMDF_PR_20210527_0003-202202]
  6. Korea University Grant

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Factors related to regular sleep patterns may determine lifestyle clustering in patients with type 2 diabetes. Patients with a healthy lifestyle have higher daily step count, lower resting heart rate, and longer sleep duration compared to others.
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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