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• W4377695353 endingPage "1113.e11" @default.
• W4377695353 startingPage "1106" @default.
• W4377695353 abstract "To determine if digital gait biomarkers captured by a wrist-worn device can predict the incidence of depressive episodes in middle-age and older people.Longitudinal cohort study.A total of 72,359 participants recruited in the United Kingdom.Participants were assessed at baseline on gait quantity, speed, intensity, quality, walk length distribution, and walk-related arm movement proportions using wrist-worn accelerometers for up to 7 days. Univariable and multivariable Cox proportional-hazard regression models were used to analyze the associations between these parameters and diagnosed incident depressive episodes for up to 9 years.A total of 1332 participants (1.8%) had incident depressive episodes over a mean of 7.4 ± 1.1 years. All gait variables, except some walk-related arm movement proportions, were significantly associated with the incidence of depressive episodes (P < .05). After adjusting for sociodemographic, lifestyle, and comorbidity covariates; daily running duration, steps per day, and step regularity were identified as independent and significant predictors (P < .001). These associations held consistent in subgroup analysis of older people and individuals with serious medical conditions.The study findings indicate digital gait quality and quantity biomarkers derived from wrist-worn sensors are important predictors of incident depression in middle-aged and older people. These gait biomarkers may facilitate screening programs for at-risk individuals and the early implementation of preventive measures." @default.
• W4377695353 created "2023-05-24" @default.
• W4377695353 creator A5059030217 @default.
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• W4377695353 date "2023-08-01" @default.
• W4377695353 modified "2023-09-28" @default.
• W4377695353 title "Prediction of Incident Depression in Middle-Aged and Older Adults using Digital Gait Biomarkers Extracted from Large-Scale Wrist Sensor Data" @default.
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• W4377695353 doi "https://doi.org/10.1016/j.jamda.2023.04.008" @default.
• W4377695353 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37236263" @default.
• W4377695353 hasPublicationYear "2023" @default.
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