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Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45-79 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test-retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test-retest reliability was good to excellent (ICC ≥ 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications.

Older people are at increased risk of many adverse health outcomes, including dementia and depression, that burden the global health system. This paper presents algorithms for the large-scale assessment of daily walking speeds. We hypothesize that (i) data from wrist-worn sensors can be used to assess walking speed accurately; and that (ii) maximal daily walking speed is a better predictor of health outcomes than usual daily walking speed. First, algorithms were developed and tested using data from 101 participants aged 19 to 91 (47 ± 18) years. Participants wore an AX3 accelerometer (Axivity, UK) on their dominant wrist while undertaking daily life activities with electronic walkway data used for ground truth. Subsequently, prediction models for dementia, depression and death were developed using the data of 47,406 participants (≥ 60 years) from the UK Biobank study. Daily walking speeds were derived from 7-day AX3 data with time-to-events using electronic health records. The accuracy of derived walking speeds was assessed using root mean square error (RMSE). Time-to-events were modelled using Cox regression with inverse hazard ratios reported for univariable models and Harrell's concordance for multivariable models. Derived walking speeds had an RMSE of between 3% and 4% depending on arm position. We found that for simple models, maximal walking speed was significantly better than usual walking speed at predicting time to dementia (1.62 vs 1.34), depression (1.29 vs 1.17) and death (1.56 vs 1.27). However, the addition of known risk factors in subsequent multivariable models reduced the apparent benefit of using maximal as opposed to usual daily walking speed as the gait parameter. In summary, walking speed was accurately measured with a wrist-worn device, and maximal daily waking speed may be better than usual daily walking speed at predicting some adverse health outcomes.Clinical Relevance- This study demonstrated the validity of using a simple and unobtrusive wrist-worn sensor to remotely assess daily walking speed. As a single, modifiable and easily understood measure, maximal walking speed was shown to be better than usual walking speed at predicting time-to-dementia, depression and death. Therefore, the inclusion of maximal daily walking speed into screening programs and clinical interventions presents a promising area for further research.

Impaired mobility often co-occurs with depression. However, there is no systematic review evidence as to whether mobility impairments precede the onset of depression. The objective of this systematic review and meta-analysis was to evaluate whether mobility impairment could predict incident depression. A systematic search of cohort studies were performed in MEDLINE, EMBASE, CINAHL and PsycINFO. The target population was people with no depressive symptoms at baseline and follow-up for depression or depressive symptoms of at least three months. Of 1061 identified abstracts, 13 studies met the review eligibility criteria. The majority of included studies (8 out of 13) were of high methodological quality. Follow-up periods ranged from 12 months to 16 years. Gait speed was the most consistently reported mobility measure. Participants with slow gait speed were at higher risk of developing depressive symptoms (pooled OR = 1.93, 95%CI: 1.54 to 2.42, 11 studies). This review shows that slow gait speed is predictive of the onset of depressive symptoms. Systematic review registration number: CRD42020153791., (Copyright © 2020 Elsevier Inc. All rights reserved.)