Journal article
Self-supervised machine learning to characterise step counts from wrist-worn accelerometers in the UK Biobank
- Abstract:
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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 aims 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...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Preview, Accepted manuscript, pdf, 2.2MB, Terms of use)
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- Publisher copy:
- 10.1249/mss.0000000000003478
Authors
Bibliographic Details
- Publisher:
- Lippincott, Williams & Wilkins
- Journal:
- Medicine & Science in Sports & Exercise More from this journal
- Place of publication:
- United States
- Publication date:
- 2024-05-15
- Acceptance date:
- 2024-04-18
- DOI:
- EISSN:
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1530-0315
- ISSN:
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0195-9131
- Pmid:
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38768076
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1998262
- Local pid:
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pubs:1998262
- Deposit date:
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2024-06-25
Terms of use
- Copyright holder:
- American College of Sports Medicine
- Copyright date:
- 2024
- Rights statement:
- © 2024 by the American College of Sports Medicine
- Notes:
- For the purposes of open access, the author has applied a Creative Commons Attribution (CC-BY) public copyright licence to any author accepted manuscript version arising from this submission.
- Licence:
- CC Attribution (CC BY)
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