Real-world Balance Assessment while Standing for Fall Prediction in Older Adults

• Published in: IEEE transactions on biomedical engineering 2024-03, Vol.71 (3), p.1-9
• Main Authors: Albites-Sanabria, Jose, Palumbo, Pierpaolo, Helbostad, Jorunn L., Bandinelli, Stefania, Mellone, Sabato, Palmerini, Luca, Chiari, Lorenzo
• Format: Article
• Language: English
• Subjects: Adults; Aged; Algorithms; Assessments; Back; Balance; balance assessment; Computational modeling; fall risk; Falling; Feature extraction; Finite state machines; Humans; inertial sensors; lab-based; Laboratories; Machine Learning; Monitoring; Older adults; Older people; Postural Balance; Posture; Protocols; real-world; Regression models; Resampling; Risk; Sensitivity; Stability analysis; Walking
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2023.3326306

Objective: Postural control naturally declines with age, leading to an increased risk of falling. Within clinical settings, the deployment of balance assessments has become commonplace, facilitating the identification of postural instability and targeted interventions to forestall falls among older adults. Some studies have ventured beyond the controlled laboratory, leaving, however, a gap in our understanding of balance in real-world scenarios. Methods: Previously reported algorithms were used to build a finite-state machine (FSM) with four states: walking, turning, sitting, and standing. The FSM was validated against video annotations (gold standard) in an independent dataset with data collected on 20 older adults. Later, the FSM was applied to data from 168 community-dwelling older people in the InCHIANTI cohort who were evaluated both in the laboratory and then remotely in real-world conditions for a week. A 70/30 data split with recursive feature selection and resampling techniques was used to train and test four machine-learning models. Results: In identifying fallers, duration, distance, and mean frequency computed during standing in real-world settings revealed significant relationships with fall risk. Also, the best-performing model (Lasso Regression) built on real-world balance features had a higher area under the curve (AUC, 0.76) than one built on lab-based assessments (0.57). Conclusion: Real-world balance features differ considerably from laboratory balance assessments (Romberg test) and have a higher predictive capacity for identifying patients at high risk of falling. Significance: These findings highlight the need to move beyond traditional laboratory-based balance measures and develop more sensitive and accurate methods for predicting falls.