Sensors, Vol. 26, Pages 4088: The Effectiveness Of Personalized Robot-assisted Rehabilitation On Fall Risk: A Retrospective Controlled Study With A 6-month Follow-up

Sensors, Vol. 26, Pages 4088: The Effectiveness of Personalized Robot-Assisted Rehabilitation on Fall Risk: A Retrospective Controlled Study with a 6-Month Follow-Up

Sensors doi: 10.3390/s26134088

Authors: Letizia Castelli Anna Maria Malizia Alessandra Pedico Sofia Tarquini Claudia Loreti Lorenzo Biscotti Silvia Giovannini

Falls represent a severe public health issue and a primary cause of impairment across both geriatric and adults clinical populations, underscoring the importance of early and tailored prevention. Research evidence shows that technology-assisted rehabilitation may enhance balance and reduce fall risk; however, long-term real-world evidence remains limited. This retrospective study evaluated the impact of personalized balance training by hunova® robotic platform on fall risk in a heterogeneous clinical cohort (n = 355; mean age 58.24 ± 19.63 years) comprising neurological and orthopedic conditions. Fall risk was assessed using the Silver Index, a sensor-based robotic posturographic score to predict 12-month fall probability at baseline (T0), after 6 weeks (T1), and at 6 months (T6). Robotic rehabilitation was provided to 162 patients (TREAT-G), while 193 served as a control group (NOTREAT-G). TREAT-G demonstrated a significant reduction in fall risk over time (p = 0.036), with improvements from T0–T1 (p < 0.001) and T0–T6 (p = 0.009). Domain-specific analysis showed significant increases in limits of stability, sit-to-stand, and gait speed (p < 0.001). Significant time × group interactions were observed for limits of stability (p = 0.004) and sit-to-stand (p < 0.001). Based on the occurrence of falls in the previous six months, the sample was then divided into ‘Fallers’ (at least one fall) and ‘No-Fallers’ (no falls). Subgroup analysis revealed significant improvements in ‘Fallers’ (p = 0.016) and ‘No-Fallers’ (p = 0.034) following treatment in contrast to untreated patients—both ‘Fallers’ and ‘No-Fallers’. These results indicate that tailored robot-assisted therapy with hunova® significantly reduces risk of fall and improves long-term dynamic balance. This study supports the clinical integration of robotic assessment and therapy as a robust tool for proactive fall prevention in diverse clinical settings.