Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis.
Endocrinol Metab (Seoul)
; 37(3): 547-551, 2022 06.
Article
in En
| MEDLINE
| ID: mdl-35798553
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Diabetes Mellitus, Type 2
/
Fitness Trackers
Type of study:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Limits:
Humans
/
Middle aged
Language:
En
Journal:
Endocrinol Metab (Seoul)
Year:
2022
Document type:
Article