Optimal objective measurement of physical function and its predictive capacity for mortality among community‐dwelling older women

Abstract

AimObjective measurements of physcial function, including gait speed, handgrip strength, and the chair stand test, have been shown to have predictive capacity for negative health‐related outcomes. The aim of this study was to examine campariatively which of these common assessments may be optimal in terms of their predictive capacity for mortality.MethodsA total of 9834 community‐dwelling older women aged 65–89 years from the Study of Osteoporotic Fractures (SOF) were followed for 20 years. Gait speed, handgrip strength, and the chair stand test were measured every 2–4 years on up to 9 visits. All deaths were adjudicated.ResultsAll three measurements of physical function were significantly associated with overall, cardiovascular disease and other mortality. Gait speed had the greatest magnitude of hazard ratios (HRs) for all outcomes of interest. A one‐unit standard deviation increase in gait speed was associated with a 33% (HR = 0.67, 95% confidence interval [95% CI]: 0.64–0.70) lower risk for overall mortality, a 31% (HR = 0.69, 95% CI: 0.64–0.73) lower risk for cardiovascular disease mortality, a 15% (HR = 0.85, 95% CI: 0.78–0.92) lower risk for cancer mortality and a 42% (HR = 0.58, 95% CI: 0.55–0.62) lower risk for other mortality. Further examination of gait speed identified two cut‐points (0.9 and 0.7 m/s) that were strongly indicative of increased mortality risk.ConclusionOur large prospective study indicates that gait speed possesses a better prediction of mortality among older women compared with handgrip strength or the chair stand test. Using cut‐points of 0.9 and 0.7 m/s can help identify older women at higher mortality risk, who may benefit from physical function improvement interventions. Geriatr Gerontol Int 2023; ••: ••–••.

Application of Dynamic Mode Decomposition to Characterize Temporal Evolution of Plantar Pressures from Walkway Sensor Data in Women with Cancer

“…In healthy persons, gait is a well-learned, highly repeatable, and yet highly complex sensorimotor process [ 1 ]. Slower gait speeds are associated with poorer health outcomes in a variety of populations that include adults with cancers [ 2 , 3 , 4 ], but emerging artificial intelligence (AI) applications to gait rely on the clustering of more complex features extracted from gait sensors [ 5 ]. Plantar pressure data from commercial sensor-impregnated walkways are complex data that lend themselves to examination for consistencies and variations within spatiotemporal patterns, both within and across people.…”