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

• Kim KJ
• Lee JB
• Choi J
• Seo JY
• Yeom JW
• Cho CH
• Bae JH
• Kim SG
• Lee HJ
• Kim NH

• 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.

Keyword

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

Figure

• 

  Fig. 1. Shapley additive explanations (SHAP) summary plot for expectation–maximization clustering. Red and blue represent high and low levels of each predictor, respectively. The x-axis represents SHAP values. A positive SHAP value means the likelihood of an unhealthy lifestyle, whereas a negative value means the likelihood of a healthy lifestyle. HR, heart rate; SD, standard deviation.

• 

  Fig. 2. Mean changes in hemoglobin A1c (HbA1c) levels from baseline to 9 months.

Reference

1. Tuomilehto J, Schwarz P, Lindstrom J. Long-term benefits from lifestyle interventions for type 2 diabetes prevention: time to expand the efforts. Diabetes Care. 2011; 34(Suppl 2):S210–4.

2. American Diabetes Association. 5. Facilitating behavior change and well-being to improve health outcomes: standards of medical care in diabetes-2021. Diabetes Care. 2021; 44(Suppl 1):S53–72.

3. Georga EI, Protopappas VC, Bellos CV, Fotiadis DI. Wearable systems and mobile applications for diabetes disease management. Health Technol. 2014; 4:101–12.

4. Shin G, Jarrahi MH, Fei Y, Karami A, Gafinowitz N, Byun A, et al. Wearable activity trackers, accuracy, adoption, acceptance and health impact: a systematic literature review. J Biomed Inform. 2019; 93:103153.

5. Tamura T, Maeda Y, Sekine M, Yoshida M. Wearable photoplethysmographic sensors: past and present. Electronics. 2014; 3:282–302.

6. Cho CH, Lee T, Kim MG, In HP, Kim L, Lee HJ. Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: prospective observational cohort study. J Med Internet Res. 2019; 21:e11029.

7. Wu C, Steinbauer JR, Kuo GM. EM clustering analysis of diabetes patients basic diagnosis index. AMIA Annu Symp Proc. 2005; 2005:1158.

8. Rodriguez-Perez R, Bajorath J. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J Comput Aided Mol Des. 2020; 34:1013–26.

9. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017; 30:4768–77.

10. Walker RK, Hickey AM, Freedson PS. Advantages and limitations of wearable activity trackers: considerations for patients and clinicians. Clin J Oncol Nurs. 2016; 20:606–10.

11. Wood WA, Bennett AV, Basch E. Emerging uses of patient generated health data in clinical research. Mol Oncol. 2015; 9:1018–24.

12. Cappuccio FP, D’Elia L, Strazzullo P, Miller MA. Quantity and quality of sleep and incidence of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2010; 33:414–20.

13. Shan Z, Ma H, Xie M, Yan P, Guo Y, Bao W, et al. Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective studies. Diabetes Care. 2015; 38:529–37.

14. Karlsson B, Knutsson A, Lindahl B. Is there an association between shift work and having a metabolic syndrome? Results from a population based study of 27,485 people. Occup Environ Med. 2001; 58:747–52.

15. Zuraikat FM, Makarem N, Redline S, Aggarwal B, Jelic S, St-Onge MP. Sleep regularity and cardiometabolic heath: is variability in sleep patterns a risk factor for excess adiposity and glycemic dysregulation? Curr Diab Rep. 2020; 20:38.

16. Seo JA, Lee DY, Yu JH, Cho H, Lee SK, Suh S, et al. Habitual late sleep initiation is associated with increased incidence of type 2 diabetes mellitus in Korean adults: the Korean Genome and Epidemiology Study. Sleep. 2019; 42:zsz090.

17. Kim KJ. The role of circadian clocks in metabolism. Chronobiol Med. 2019; 1:107–10.