Daily-life walking speed, running duration and bedtime from wrist-worn sensors predict incident dementia: A watch walk - UK biobank study.

Objective: To determine if wrist-worn sensor parameters can predict incident dementia in individuals aged 60 + years and to compare prediction with other tools.

Design: Observational cohort study.

Setting: Community PARTICIPANTS: The cohort comprised 47,371 participants without dementia, aged 60 + years, who participated in the UK Biobank study (mean age=67 ± 4 years; 52 % female).

Measurements: Nineteen digital biomarkers were extracted from up-to-7-day wrist-worn sensor accelerometry data at baseline. Univariable and multivariable Cox proportional hazard models examined associations between sensor parameters and prospectively diagnosed dementia.

Results: Median follow-up was 7.5 years (interquartile range: 7.0 to 9.0 years), during this time 387 participants (0.8 %) were diagnosed with dementia. Among the gait parameters, slower maximal walking speed had the strongest association with incident dementia (32 % decrease in hazard for each standard deviation increase) followed by lower daily step counts (30 % decrease) and increased step-time variability (17 % increase). While adjusting for age and sex, running duration, maximal walking speed and early bedtime were identified as independent and significant predictors of dementia. The multivariable prediction model performed comparably to the ANU-ADRI and UKB-Dementia Risk Score models in the UK Biobank cohort.

Conclusions: The study findings indicate that remotely acquired parameters from wrist-worn sensors can predict incident dementia. Since wrist-worn sensors are highly acceptable for long-term use, wrist-worn sensor parameters have the potential to be incorporated into dementia screening programs.