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 participa...
| Publicado en: | Age & Ageing Vol. 52; no. 9; pp. 1 - 9 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Sep2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=172443448&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 172443448 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Sep2023 vid: 52 iid: 9 pid: 622 pub: Oxford University Press / USA artinfo: ui: 172443448 10.1093/ageing/afad179 ppf: 1 ppct: 8 formats: tig: atl: Prediction of injurious falls in older adults using digital gait biomarkers extracted from large-scale wrist sensor data. aug: au: Chan, Lloyd L Y Arbona, Carmen Herrera Brodie, Matthew A Lord, Stephen R affil: Neuroscience Research Australia, Sydney , Australia School of Population Health, University of New South Wales , Sydney, Australia Getafe University Hospital , Madrid, Spain Graduate School of Biomedical Engineering, University of New South Wales , Sydney , Australia School of Population Health, University of New South Wales , Sydney , Australia su: United Kingdom Old age Biomarkers Walking speed Gait in humans Wearable technology Accelerometers Risk assessment Accidental falls Diagnosis Descriptive statistics Independent living Research funding Longitudinal method Proportional hazards models sug: subj: Old age United Kingdom Other Measuring and Controlling Device Manufacturing Biomarkers Walking speed Gait in humans Wearable technology Accelerometers Risk assessment Accidental falls Diagnosis Descriptive statistics Independent living Research funding Longitudinal method Proportional hazards models keyword: accidental falls aged gait speed gait variability older people physical activity real-world gait smartwatch UK Biobank wearable sensors accidental falls aged gait speed gait variability older people physical activity real-world gait smartwatch UK Biobank wearable sensors ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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