Analyzing Mobility Patterns of Complex Chronic Patients Using Wearable Activity Trackers: A Machine Learning Approach

Abstract

This study suggests using wearable activity trackers to identify mobility patterns in chronic complex patients (CCPs) and investigate their relation with the Barthel index (BI) to assess functional decline. CCPs are individuals who suffer from multiple, chronic health conditions that often lead to a progressive decline in their functional capacity. As a result, CCPs frequently require the use of healthcare and social resources, placing a significant burden on the healthcare system. Evaluating mobility patterns is critical for determining a CCP’s functional capacity and prognosis. To monitor the overall activity levels of CCPs, wearable activity trackers have been proposed. Utilizing the data gathered by the wearables, time series clustering with dynamic time warping (DTW) is employed to generate synchronized mobility patterns of the mean activity and coefficient of variation profiles. The research has revealed distinct patterns in individuals’ walking habits, including the time of day they walk, whether they walk continuously or intermittently, and their relation to BI. These findings could significantly enhance CCPs’ quality of care by providing a valuable tool for personalizing treatment and care plans.

Keywords

• Barthel index
• chronic complex patients
• dynamic time warping
• functional decline
• mobility patterns
• time series clustering