Falls are significant health and medical concern among the growing population of older adults (65+ years of age). Falls account for 54% of injury-related deaths, and more generally older adults experience significant health deterioration that can lead to mobility impairments and hospital/nursing home admission following falls. Decreased muscle strength due to loss of muscle mass is the major biomechanical factor associated with the increased risk of falls with aging. Thus, regular physical exercise that strengthens muscles is an essential part of fall prevention intervention.
Personalized programs, such as supervised muscle strengthening exercises, which target the improvement of weakened muscle groups relevant to gait and balance, are safer and more effective than unsupervised at-home exercise training. However, participation in these programs is limited to those who can access and afford them. Existing technology-based exercise programs lack personalization to account for individual functional capabilities, yet such personalization is key to safe and effective remote intervention. Further, remote exercise training programs mainly focus on posture-based assessments (e.g., performance is evaluated based on motion accuracy and completion time). Such programs, though, lack a detailed analysis of musculoskeletal improvements (e.g., muscle strength).
While exercise personalization is recommended for safety and effectiveness, group-based exercise is recommended to increase adherence, social connection, and scalability of resources. Behavioral change interventions that use an approach based on group dynamics help individuals initiate and maintain an exercise routine. Key principles to enhance group dynamics include interaction and communication, goal setting and progress updates, putting people in proximity to each other, and friendly competition. At present, however, methods for remote training at home, such as online videos or remote exercise training, lack social bonding and connectedness. Our fundamental research question is how to use artificial intelligence, biomechanical modeling, and a group-based approach to engage older adults in a remote, group-based physical activity with personalized exercise content to enhance their health and wellness.
Current funding source: Socially-Connected and Ability-Aware Online Physical Training for Older Adults”, FY22 ICTAS EFO-Opportunity Seed Investment Grant, VT. (10/1/21 - 09/30/22). PI: S. Lim, co-PIs: S. Harden, S. Lee, M.A. Nussbaum, and S. Kim.
