Determining Falls Risk in People with Parkinson’s Disease Using Wearable Sensors: A Systematic Review - Maeve Bradley; Sarah O’Loughlin; Eoghan Donlon; Amy Gallagher; Clodagh O’Keeffe; John Inocentes; Federica Ruggieri; Richard B. Reilly; Richard Walsh; Tim Lynch; Daniel G. Di Luca; Conor Fearon (Sensors, 2025)
A prior history of falls remains the strongest predictor of future falls in individuals with Parkinson’s disease (PD). There are limited biomarkers available to identify falls risk before falls begin to occur. The aim of this review is to investigate if features associated with falls risk may be detected by wearable sensors in patients with PD. A systematic search of the MEDLINE, EMBASE, Cochrane, and Cinahl databases was performed. Key quality criteria include sample size adequacy, data collection procedures, and the clarity of statistical analyses. The data from each included study were extracted into defined data extraction spreadsheets. Results were synthesized in a narrative manner. Twenty-four articles met the inclusion criteria. Of these, twelve measured falls prospectively, while the remaining relied on retrospective history. The definition of a “faller” varied across studies. Most assessments were conducted in a clinical setting (18/24). There was considerable variability in sensor placement and mobility tasks assessed. The most common sensor-derived measures that significantly differentiated “fallers” from “non-fallers” in Parkinson’s disease included gait variability, stride variability, trunk motion, walking speed, and stride length.