Characterizing Movement Patterns of Older Individuals with T2D in Free-Living Environments Using Wearable Accelerometers

Author:

Yahalom-Peri Tal12ORCID,Bogina Veronika34ORCID,Basson-Shleymovich Yamit25,Azmon Michal16ORCID,Kuflik Tsvi4ORCID,Kodesh Einat7ORCID,Volpato Stefano8,Cukierman-Yaffe Tali12

Affiliation:

1. Division of Endocrinology, Sheba Medical Center, Ramat-Gan 5266202, Israel

2. Epidemiology Department, School of Public Health, Faculty of Health, Tel Aviv University, Tel Aviv 6997801, Israel

3. Department of Industrial Engineering, Tel Aviv University, Tel Aviv 6997801, Israel

4. Department of Information Systems, University of Haifa, Haifa 3498838, Israel

5. Clalit Health Services, Physical Therapy Clinic, Tel Aviv 6423906, Israel

6. Faculty of Health Sciences, Ariel University, Ariel 40700, Israel

7. Department of Physical Therapy, University of Haifa, Haifa 3498838, Israel

8. Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy

Abstract

(1) Background: Type 2 Diabetes (T2D) is associated with reduced muscle mass, strength, and function, leading to frailty. This study aims to analyze the movement patterns (MPs) of older individuals with T2D across varying levels of physical capacity (PC). (2) Methods: A cross-sectional study was conducted among individuals aged 60 or older with T2D. Participants (n = 103) were equipped with a blinded continuous glucose monitoring (CGM) system and an activity monitoring device for one week. PC tests were performed at the beginning and end of the week, and participants were categorized into three groups: low PC (LPC), medium PC (MPC), and normal PC (NPC). Group differences in MPs and physical activity were analyzed using non-parametric Kruskal–Wallis tests for both categorical and continuous variables. Dunn post-hoc statistical tests were subsequently carried out for pairwise comparisons. For data analysis, we utilized pandas, a Python-based data analysis tool, and conducted the statistical analyses using the scipy.stats package in Python. The significance level was set at p < 0.05. (3) Results: Participants in the LPC group showed lower medio-lateral acceleration and higher vertical and antero-posterior acceleration compared to the NPC group. LPC participants also had higher root mean square values (1.017 m/s2). Moreover, the LPC group spent less time performing in moderate to vigorous physical activity (MVPA) and had fewer daily steps than the MPC and NPC groups. (4) Conclusions: The LPC group exhibited distinct movement patterns and lower activity levels compared to the NPC group. This study is the first to characterize the MPs of older individuals with T2D in their free-living environment. Several accelerometer-derived features were identified that could differentiate between PC groups. This novel approach offers a manpower-free alternative to identify physical deterioration and detect low PC in individuals with T2D based on real free-living physical behavior.

Funder

EFSD

Israeli Science Foundation

an investigator-initiated study by Medtronic

Publisher

MDPI AG

Subject

General Medicine

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