Rehabilitation is a critical part of recovery for millions of patients across neurological, orthopedic, and age-related conditions. Yet one of the biggest challenges remains unchanged: patient adherence. Up to 75% of patients do not follow their prescribed home training programs, significantly reducing the effectiveness of rehabilitation and leading to increased healthcare costs and prolonged recovery times.

For clinicians, this lack of adherence creates a blind spot. Once patients leave the clinic, therapists have limited insight into whether exercises are performed correctly or at all. As a result, treatment plans are often based on assumptions rather than real data, limiting the potential for optimal outcomes.

Rebility, a spin-out from Aalborg University, is addressing this fundamental gap by combining motivation, technology, and data into a single, scalable solution.

FROM PASSIVE EXERCISES TO ENGAGING REHABILITATION

Rebility has developed a clinically tested telerehabilitation system called eMotivo that transforms traditional home exercises into an engaging and interactive experience. Through a user-friendly app, rehabilitation is gamified using ball-based exercises, turning repetitive training into something intuitive and motivating for patients.

This shift from passive to active engagement has a measurable impact. The system has demonstrated the ability to increase training motivation by up to 375%, while also improving physical, sensory, and cognitive functions. By making rehabilitation more enjoyable and meaningful, patients are far more likely to stay consistent, leading to better outcomes.

DATA-DRIVEN THERAPY AND BETTER CLINICAL DECISIONS

With eMotivo, Rebility does not only motivate patients; it also equips therapists with something they have historically lacked: real-time insight into patient behavior and progress outside the clinic.

As patients train, data is continuously collected and shared with clinicians, providing a clear picture of adherence, performance, and development over time. This allows therapists to move from static treatment plans to dynamic, data-driven rehabilitation, where exercises can be adjusted based on actual patient progress rather than periodic check-ins.

The result is a more efficient workflow for healthcare professionals, who can spend less time on manual follow-ups while delivering more personalized and effective care.