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

Tal Yahalom-Peri*, Veronika Bogina, Yamit Basson-Shleymovich, Michal Azmon, Tsvi Kuflik, Einat Kodesh, Stefano Volpato, Tali Cukierman-Yaffe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


(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.

Original languageEnglish
Article number7404
JournalJournal of Clinical Medicine
Issue number23
StatePublished - Dec 2023


FundersFunder number
European Foundation for the Study of Diabetes
Israel Science Foundation262/2017


    • accelerometer
    • movement patterns
    • physical capacity
    • type 2 diabetes


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