A Postural Assessment Utilizing Machine Learning Prospectively Identifies Older Adults at a High Risk of Falling

Introduction Falls are the leading cause of accidental death in older adults. Each year, 28.7% of US adults over 65 years experience a fall resulting in over 300,000 hip fractures and $50 billion in medical costs. Annual fall risk assessments have become part of the standard care plan for older adults. However, the effectiveness of these assessments in identifying at-risk individuals remains limited. This study characterizes the performance of a commercially available, automated method, for assessing fall risk using machine learning. Methods Participants (N=209) were recruited from eight senior living facilities and from adults living in the community (five local community centers in Houston, TX) to participate in a 12-month retrospective and a 12-month prospective cohort study. Upon enrollment, each participant stood for 60 seconds, with eyes open, on a commercial balance measurement platform which uses force-plate technology to capture center-of-pressure (60Hz frequency). Linear and non-linear components of the center-of-pressure were analyzed using a machine-learning algorithm resulting in a postural stability (PS) score (range 1-10). A higher PS score indicated greater stability. Participants were contacted monthly for a year to track fall events and determine fall circumstances. Reliability among repeated trials, past and future fall prediction, as well as survival analyses, were assessed. Results Measurement reliability was found to be high (ICC(2,1)[95%CI]=0.78[0.76-0.81]). Individuals in the high-risk range (1-3) were three times more likely to fall within a year than those in low-risk (7-10). They were also an order of magnitude more likely (12/104 vs. 1/105) to suffer a spontaneous fall i.e. a fall where no cause was self-reported. Survival analyses suggests a fall event within 9 months (median) for high risk individuals. Conclusions We demonstrate that an easy-to-use, automated method for assessing fall risk can reliably predict falls a year in advance. Objective identification of at-risk patients will aid clinicians in providing individualized fall prevention care.