CEBALLOS SANCHEZ, JOSE LUIS, RAMOS MUNELL, JAVIER, Reguera-Rodriguez, Manuel , Del Pozo-Cruz, Jesus , Cruz, Borja del Pozo , Alvarez-Barbosa, Francisco
No
Ann Phys Rehabil Med
Article
Científica
1
1
01/05/2026
001722305500001
2-s2.0-105033249270
Background: Falls are a major cause of morbidity, loss of independence, and mortality among older adults, particularly those with osteoarthritis (OA). Traditional fall-risk assessments often rely on categorical thresholds of physical performance tests, which may not capture the continuous and multifactorial nature of fall risk. Objectives: This study aimed to develop a probabilistic model for fall-risk prediction in older adults by examining the association between continuous physical performance measures such as the 5-Times Sit-to-Stand (5-STS) test, gait speed, lower-limb power, and key categorical clinical and demographic variables such as age, sex, OA status, hearing impairment, and pain. Methods: The study provides up to 13 years of follow-up data across multiple waves, with annual sample sizes ranging from approximately 3800 to 8600 participants, totaling 77,464 repeated observations from 18,023 unique participants aged 65 years and older. Data were obtained from the National Health and Aging Trends Study (NHATS), conducted in the United States. Fall occurrence was modeled using a generalized linear mixed model (GLMM) with a binomial distribution. Independent variables included 5-STS duration, 5-STS power, and gait speed, adjusted for age, sex, hearing impairment, pain, and OA. Results: The 5-STS power model provided the best fit (AIC = 17,583.9; R2 = 0.7667), indicating that greater lower-limb strength was protective against falls (OR = 0.87, 95% CI 0.84-0.90). Longer 5-STS duration (OR = 1.03, 95% CI 1.02-1.04) and slower gait speed (OR = 1.45, 95% CI 1.32-1.59) were associated with increased fall risk. Conclusion: Continuous measures of lower-limb performance, 5-STS power, provide superior predictive value for fall risk compared to traditional categorical assessments. These findings support individualized, data-driven approaches to fall prevention among older adults. Future research should validate these findings in external cohorts and explore additional confounders to enhance generalizability and clinical applicability. Database registration: This study is a secondary analysis of publicly available data from the National Health and Aging Trends Study (NHATS, https://www.nhats.org/researcher). No separate database registration or clinical trial registration applies.
Fall-risk prediction; Older adults; Osteoarthritis; Gait speed; Muscle power; Logistic regression