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

• Published in: Endocrinology and metabolism (Seoul) Vol. 37; no. 3; pp. 547 - 551
• Main Authors: Kim, Kyoung Jin, Lee, Jung-Been, Choi, Jimi, Seo, Ju Yeon, Yeom, Ji Won, Cho, Chul-Hyun, Bae, Jae Hyun, Kim, Sin Gon, Lee, Heon-Jeong, Kim, Nam Hoon
• Format: Journal Article
• Language: English
• Published: Korean Endocrine Society 01.06.2022
• Subjects: Brief Report; cluster analysis; diabetes mellitus, type 2; fitness trackers; glycemic control; life style
• ISSN: 2093-596X; 2093-5978
• DOI: 10.3803/EnM.2022.1479

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.