Falls remain the leading cause of injury-related death among adults over 65, yet conventional balance rehabilitation is chronically underused — partly because attending clinic-based programmes is itself a barrier for those most at risk. A large international trial now underway is testing whether AI-guided home rehabilitation can close that gap, and its design offers a revealing look at where digital medicine is heading for vulnerable older populations.
The TeleRehaB DSS platform combines real-time motion tracking with artificial intelligence to deliver personalised multisensory balance rehabilitation entirely at home. The multicentre randomised controlled trial is enrolling 460 community-dwelling adults aged 40–80 across sites in the UK, Europe, and Southeast Asia. Critically, the trial targets four distinct clinical populations simultaneously — stroke survivors, individuals with mild cognitive impairment, those with vestibular dysfunction, and people with long COVID — each of whom experiences fall risk through different physiological pathways. Participants are randomised over nine weeks to either the AI-driven programme (offered in both high-tech and low-tech variants incorporating exergames and cognitive training) or standard care protocols such as the OTAGO exercise programme. Primary endpoints focus on feasibility, acceptability, and safety, with secondary measures including the Functional Gait Assessment and EuroQol quality-of-life instrument.
What makes this trial design particularly instructive is its simultaneous attention to economic outcomes alongside clinical ones — an acknowledgment that telerehabilitation only scales if it demonstrates cost-effectiveness, not just efficacy. The inclusion of a low-tech arm is a pragmatic concession to real-world digital equity, recognising that sensor-rich setups exclude lower-income participants. The four-condition population structure, while ambitious, may dilute statistical power within subgroups unless the analysis plan pre-specifies stratified comparisons. This is a feasibility and protocol paper, meaning definitive efficacy conclusions remain years away. Still, the framework represents a meaningful advance over single-condition telehealth pilots, and if the AI individualisation genuinely adapts to each user's sensory deficits, the platform could shift balance rehabilitation from a clinic-dependent intervention to a scalable community health tool.