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

ABSTRACT: 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

SUBMITTER: Forth KE 

PROVIDER: S-EPMC7772994 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature