| Title: | Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank |
| Journal: | Medicine & Science in Sports & Exercise |
| Published: | 15 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38768076/ |
| DOI: | https://doi.org/10.1249/mss.0000000000003478 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11402590 |
Publication 12082
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
PURPOSE: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aimed to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort.</p>
METHODS: We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. Thirty-nine individuals will free-living ground-truth annotated step counts were used for model development. An open-source dataset with 30 individuals was used for external validation. Epidemiological analysis was performed using 75,263 UK Biobank participants without prevalent cardiovascular disease (CVD) or cancer. Cox regression was used to test the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders.</p>
RESULTS: The algorithm substantially outperformed reference models (free-living mean absolute percent error of 12.5% vs 65%-231%). Our data indicate an inverse dose-response association, where taking 6430-8277 daily steps was associated with 37% (25%-48%) and 28% (20%-35%) lower risk of fatal CVD and all-cause mortality up to 7 yr later, compared with those taking fewer steps each day.</p>
CONCLUSIONS: We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. The application of this method demonstrated expected associations with CVD and all-cause mortality, indicating excellent face validity. This reinforces public health messaging for increasing physical activity and can help lay the groundwork for the inclusion of target step counts in future public health guidelines.</p>
17 Keywords
- Accelerometry
- Adult
- Aged
- Algorithms
- Cardiovascular Diseases
- Exercise
- Female
- Fitness Trackers
- Humans
- Male
- Middle Aged
- Mortality
- Prospective Studies
- Supervised Machine Learning
- UK Biobank
- United Kingdom
- Wrist
15 Authors
- SCOTT R. SMALL
- SHING CHAN
- ROSEMARY WALMSLEY
- LENNART VON FRITSCH
- AIDAN ACQUAH
- GERT MERTES
- BENJAMIN G. FEAKINS
- ANDREW CREAGH
- ADAM STRANGE
- CHARLES E. MATTHEWS
- DAVID A. CLIFTON
- ANDREW J. PRICE
- SARA KHALID
- DERRICK BENNETT
- AIDEN DOHERTY
1 Application
| Application ID | Title |
|---|---|
| 59070 | Statistical machine learning of wearable sensor data to predict disease outcomes |
Enabling scientific discoveries that improve human health