Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis
- Author:
Kyoung Jin KIM
1
;
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
Author Information
- Publication Type:Brief Report
- From:Endocrinology and Metabolism 2022;37(3):547-551
- CountryRepublic of Korea
- Language:English
- 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.
