Dynamic performance-exposure algorithm for falling risk assessment and fall prevention in community-dwelling older adults

Abstract

This study aimed to design a dynamic performance-exposure algorithm for falling risk assessment and prevention of falls in community-dwelling older adults. It involved a cross-sectional and follow-up survey assessing retrospective and prospective falls and respective performance-related, exposure and performance-exposure risk factors. In total, 500 Portuguese community-dwelling adults participated. Data modelling showed significant (p<0.05) relationships between the above risk factors and selected nine key ordered outcomes explaining falls to include in the algorithm: previous falls; health conditions; balance; lower strength; perceiving action boundaries; fat mass; environmental hazards; rest periods; and physical activity. Respective high-, moderate- and low-risk cutoffs were established. The results demonstrated a dynamic relationship between older adults’ performance capacity and the exposure to fall opportunity, counterbalanced by the action boundary perception, supporting the build algorithm's conceptual framework. Fall prevention measures should consider the factors contributing most to the individual risk of falling and their distance from low-risk safe values