Joint associations of device-measured step count and sleep duration with incident major adverse cardiovascular events: prospective analysis of the UK Biobank

Journal article

BackgroundThe interaction between physical activity and sleep with cardiovascular disease remains poorly understood, despite both being key risk factors. This study investigated the independent and joint associations of device-measured step count and sleep duration with incident major adverse cardiovascular events (MACE).MethodsProspective analysis of UK Biobank participants who wore a wrist-based accelerometer for seven days between 2013 and 2015. Open-source machine learning algorithms derived daily step count and overnight sleep duration. The outcome was incident MACE (cardiovascular death, non-fatal myocardial infarction or stroke, or revascularisation procedure), identified through electronic health record linkage. Cox proportional hazards models were used to examine independent and joint associations of median daily step count (low [11,000]) and median overnight sleep duration (short [7.5 h]) with incident MACE.FindingsAmong 88,012 participants (mean age 62.2 years [standard deviation, SD 7.8]), 3817 were diagnosed with MACE during follow-up (median 7.9 years [interquartile range, IQR 7.3-8.4]). Low step count and short sleep duration were independently associated with a higher risk of MACE, but there was no evidence of an interaction between step count and sleep duration (P for interaction = 0.42). Compared with the reference group-participants with high step count and intermediate sleep duration-the highest risk of MACE was observed in participants with both low step count and short sleep duration (hazard ratio, HR: 1.84, 95% CI: 1.62-2.10, p < 0.0001).InterpretationThe results of this study show that higher daily step count does not fully attenuate the higher risk of cardiovascular disease associated with short sleep duration, reinforcing the importance of sufficient levels of both daily step count and sleep for the prevention of cardiovascular disease.FundingWellcome Trust (223100/Z/21/Z).

Authors: Yun, J; Brocklebank, L; Harper, C; Doherty, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: EClinicalMedicine
• Volume: 92
• Pages: 103769
• Article number: 103769
• Publication date: 2026-01-29
• Acceptance date: 2026-01-08
• DOI: 10.1016/j.eclinm.2026.103769
• EISSN: 2589-5370
• ISSN: 2589-5370
• Pmid: 41660364
• Language: English
• Keywords: Machine Learning; Cardiovascular disease; Accelerometry; Sleep Duration; Step Count; Uk Biobank