The global aging population is rapidly increasing, with associated de-clines in motor and cognitive function leading to higher fall risk and re-duced independence. Exercise interventions improve strength, balance, and cognition, but accessibility barriers remain. While technology as-sistance programs are promising, they often lack clinical perspectives, are difficult to adapt, and lack features that encourage participation. This study aimed to develop and evaluate a motion recognition-based interactive training program designed by a physical therapist to en-hance motor function and physical fitness in older adults. Twenty-eight community-dwelling older adults (≥65 years) were randomly assigned to an experimental group (n=13) or control group (n=15). The experi-mental group completed three weekly sessions of motion recognition exercises with adaptive feedback for 4 weeks, while the control group performed stretching. Outcomes included task performance metrics (attempts, corrections, accuracy) automatically recorded by the program and fitness measures from the senior fitness test. The experimental group showed significant increases in attempts and corrections across all body parts, with notable accuracy improvement in head movements (p<0.05). Flexibility significantly improved between groups, while both groups improved in lower extremity strength and endurance. No signifi-cant changes were observed in upper limb strength or agility. A clinical-ly designed, motion recognition-based interactive program enhanced motor performance and flexibility in older adults. Incorporating adaptive difficulty and real-time feedback, it shows potential as a scalable, cost-effective digital intervention for promoting active aging.
Hong Seok Kim (Tue,) studied this question.