Prediction of injurious falls in older adults using digital gait biomarkers extracted from large-scale wrist sensor data.

OBJECTIVES: To determine whether digital gait biomarkers captured by a wrist-worn device can predict injurious falls in older people and to develop a multivariable injurious fall prediction model. DESIGN: Population-based longitudinal cohort study. SETTING AND PARTICIPANTS: Community-dwelling participants of the UK Biobank study aged 65 and older (n = 32,619) in the United Kingdom. METHODS: Participants were assessed at baseline on daily-life walking speed, quality, quantity and distribution using wrist-worn accelerometers for up to 7 days. Univariable and multivariable Cox proportional hazard regression models were used to analyse the associations between these parameters and injurious falls for up to 9 years. RESULTS: Five percent of the participants (n = 1,627) experienced at least one fall requiring medical attention over a mean of 7.0 ± 1.1 years. Daily-life walking speed, gait quality, quantity of walking and distribution of daily walking were all significantly associated with the incidence of injurious falls (P < 0.05). After adjusting for sociodemographics, lifestyle factors, comorbidities, handgrip strength and reaction time; running duration, total step counts and usual walking speed were identified as independent and significant predictors of falls (P < 0.01). These associations were consistent in those without a history of previous fall injuries. In contrast, step regularity was the only risk factor for those with a previous fall history after adjusting for covariates. CONCLUSIONS: Daily-life gait speed, quantity and quality, derived from wrist-worn sensors, are significant predictors of injurious falls in older people. These digital gait biomarkers could potentially be used to identify fall risk in screening programs and integrated into fall prevention strategies.

Cognitive functioning and falls in older people: A systematic review and meta-analysis.

OBJECTIVE: To identify which cognitive functions and specific neuropsychological assessments predict falls in older people living in the community. METHODS: Five electronic databases were searched until 30/08/2022 for studies assessing the association between specific cognitive functions and faller status (prospective and retrospective), in community-dwelling older people. Risk of bias was assessed with the Newcastle-Ottawa Scale. Meta-analyses synthesised the evidence regarding the associations between different neurocognitive subdomains and faller status. RESULTS: Thirty-eight studies (20 retrospective, 18 prospective) involving 37,101 participants were included. All but one study was rated high or medium quality. Meta-analyses were performed with data from 28 studies across 11 neurocognitive subdomains and four specific neuropsychological tests. Poor cognitive flexibility, processing speed, free recall, working memory and sustained attention were significantly associated with faller status, but poor verbal fluency, visual perception, recognition memory, visuo-constructional reasoning and language were not. The Trail Making Test B was found to have the strongest association with faller status. CONCLUSION: Poor performance in neurocognitive subdomains spanning processing speed, attention, executive function and aspects of memory are associated with falls in older people, albeit with small effect sizes. The Trail Making Test, a free-to-use, simple assessment of processing speed and mental flexibility, is recommended as the cognitive screening test for fall risk in older people.