Novel Physical Activity Pattern Analysis Using Wrist-worn Wearables: Time-series Clustering (Preprint)

Abstract

Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables have become popular tools for measuring activity levels. However, studies using these devices often rely on a single device model or use improper methods for analyzing the data.

This study aimed to identify methods suitable for analyzing wearable data and determine daily physical activity patterns. The study also explored the association between these physical activity patterns and health risk factors.

We collected personal health data and measured physical activity levels over the course of 1 week in adults with metabolic risk factors who wore wrist-worn wearables. A total of 47 participants were included in the analysis. The TADPole clustering method was used to identify physical activity patterns, while logistic regression models were used to analyze the relationship between activity patterns and health risk factors.

Participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. Logistic regression analysis revealed a significant association between older age (≥ 40 years) and shifting physical activity patterns (OR: 8.68, 95% CI: 1.95–48.85).

This study found that age significantly influenced physical activity patterns. It also suggests a potential role of wrist-worn wearables and the TADPole clustering method in wearable data analysis.