Dynamic Performance-exposure Algorithm for Falling Risk Assessment and Fall Prevention in Community-dwelling Older Adults

Background: Several models and algorithms were designed to identify older adults at risk of falling supported on an intrinsically and extrinsically traditional approach. However, the dynamic interaction between multiple risk factors for falls must be considered. The present study aimed to design a dynamic performance-exposure algorithm for falling risk assessment and fall prevention in community-dwelling older adults. Methods: : The study involved 1) a cross-sectional survey assessing retrospective falls, performance-related risk factors for falls (sociodemographic such as gender and age, cognitive, health conditions, body composition, physical fitness, and dual-task outcomes), exposure risk factors (environmental hazards and (in)physical activity), and performance-exposure risk factors (affordance perception), and 2) follow-up survey assessing prospective falls. Participants were Portuguese community dwellings (≥ 65 years). Data were reported based upon descriptive statistics, curve estimation regression, binary logistic regression, and ROC curve. Results: : The selected and ordered outcomes included in the algorithm and respective cutoffs were: (1) falls in previous year (high risk: n>1, moderated: n=1, low risk: n=0); (2) health conditions (high risk: n >3, moderated: n=3, low risk: n<3); (3) multidimensional balance (high risk: score 33); (4) lower body strength (high risk: rep/30s4); (5) perceiving action boundaries (high risk: overestimation bias, moderated risk: not applied, low risk: underestimation bias); ( 6) fat body mass (high risk: % fat >38, moderated risk: 37≤ % fat ≤38, low risk: % fat 5, moderated risk: n=5, low risk: n4.5, moderated risk: 4≤ hours/day ≤4.5, low risk: hours/day <4); (9) physical activity metabolic expenditure (high risk: MET-min/week 5200, moderated risk: 2300≤ MET-min/week <2800, low risk: 2800≤ MET-min/week ≤5200). Conclusions: : Results demonstrated a dynamic relationship between older adults’ performance capacity and the exposure to falls opportunity, supporting the build algorithm’s conceptual framework. Fall prevention measures should consider the above factors that most contribute to the individual risk of falling, relative weights, and their distance from low-risk value, as proposed in the dynamic algorithm.