A Smartphone Platform for Remote Motor Fitness Assessment and AI-Generated Personalized Exercise Programs for Older Adults: Randomized Controlled Trial
Abstract
Abstract Background Exercise guidelines for older adults are predominantly “one-size-fits-all,” primarily focusing on aerobic activity with limited emphasis on motor components. Objective We examined the hypothesis that remotely delivered, personalized multicomponent exercise—based on a simple yet highly reliable and accurate smartphone motor fitness assessment and individually tailored using machine learning—can improve balance, flexibility, and strength among older adults, obviating the need for a laboratory or professional supervision. Methods This randomized controlled study recruited community-dwelling healthy older adults aged ≥65 years, with normal cognition, low fall risk, and no hospitalization within the last year for cardiac/neurological illness. Participants were randomly assigned to an experimental 8-week personalized exercise group (5×/wk, multicomponent exercises), an 8-week active-control group (exercise counseling according to World Health Organization guidelines), or a control group (no intervention). Participants were assessed at baseline, 4, 8, and 12 weeks. Measurements were remotely recorded using smartphone sensors and analyzed using machine learning to create each participant’s unique fitness profile. Primary outcomes were fitness profile changes at 8 weeks. Results We assessed 317 volunteers; 239 of them consented and met inclusion criteria (155 women, mean age 72.63, SD 5.38 y). Compared to both controls, the personalized exercise group…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design. Reported size: 317 participants or records.
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