| Title: | Prediction of Incident Depression in Middle-Aged and Older Adults using Digital Gait Biomarkers Extracted from Large-Scale Wrist Sensor Data |
| Journal: | Journal of the American Medical Directors Association |
| Published: | 23 May 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37236263/ |
| DOI: | https://doi.org/10.1016/j.jamda.2023.04.008 |
Publication 11475
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Abstract
OBJECTIVES: 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.</p>
DESIGN: Longitudinal cohort study.</p>
SETTING AND PARTICIPANTS: A total of 72,359 participants recruited in the United Kingdom.</p>
METHODS: 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.</p>
RESULTS: 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.</p>
CONCLUSIONS AND IMPLICATIONS: 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.</p>
3 Authors
- Lloyd L Y Chan
- Matthew A Brodie
- Stephen R Lord
1 Application
| Application ID | Title |
|---|---|
| 56109 | Prognostic value of movement quality parameters derived from wrist sensor data in a large population |
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