Problems with everyday mobility, such as walking and driving, are common as we age and can negatively affect health and well-being. Our consultations with older adults and caregivers revealed a clear interest in using wearable tracking devices to self-monitor mobility as a vital health indicator. However, there is currently no wearable that comprehensively monitors mobility and that is valid for predicting important health outcomes in older people.

In our research, we will customize a wearable sensing device (i.e., smartwatch) to monitor different aspects of mobility (e.g., steps, distance travelled) in a group of 1500 community-dwelling adults, 65 to 80 years old. Participants will wear the smartwatches for 1-week intervals every few months throughout the 2-year study period. Additional health measures will be captured through in-person assessments, quarterly phone calls, and monthly diaries.

Machine learning and advanced statistical methods will be used to identify trajectories of change in mobility over time and how these trajectories relate to important health outcomes. With input from older adults and caregivers, we will then design a mobility self-monitoring tool. Ultimately, our goal is to develop a tool that can help prevent or delay mobility problems in later life by facilitating early detection and treatment.