Background: Previous studies on the reproducibility of 7-day accelerometer measurements have been limited by small sample sizes and short follow-up periods. We aimed to assess the long-term reproducibility of accelerometer-derived physical activity and sleep, and to illustrate the impact of regression dilution bias on the association between daily step count and coronary heart disease (CHD) in UK Biobank (UKB). Methods: We analysed data from 3138 UKB participants in the main accelerometry sub-study with up to four repeat accelerometer measurements after 3-4 years. Nine physical activity and sleep phenotypes were extracted to capture different movement behaviours. Reproducibility was assessed using intraclass correlation coefficients (ICCs). The impact on disease associations was illustrated by considering daily step count and incident CHD using Cox regression (87 180 participants; 3899 CHD events), before and after correction for regression dilution. Results: Among the 3138 participants, 51% were women and the mean (SD) age was 63.1 (9.4) years. Reproducibility of phenotypes was moderate to good, with the ICC (95% CI) for overall activity at 0.75 (0.74-0.76), and individual phenotypes ranging from 0.58 (0.56-0.59) for sleep efficiency to 0.69 (0.68-0.70) for sedentary behaviour. In our example, the inverse association between daily step count and CHD showed a 20% lower risk of CHD per 4000 usual steps after correcting for regression dilution, compared to 13% before correction. Conclusions: Accelerometer measurements are moderately reproducible and comparable to measures like blood pressure. Correcting for regression dilution bias is crucial to quantify associations of usual physical activity and sleep with disease risk.

The Nuffield Department of Population Health receives research grants from industry that are governed by University of Oxford contracts that protect its independence and has a staff policy of not taking personal payments from the pharmaceutical and food industries; further details can be found at https://www.ndph.ox.ac.uk/about/independence-of-research.

CZ is supported by the Oxford British Heart Foundation (BHF) Centre of Research Excellence (RE/18/3/34214). HT and BL acknowledge support from UK Biobank, funded largely by the UK Medical Research Council (MRC) and Wellcome. IH is supported by grants to the University of Oxford from the UK MRC, the BHF, and Health Data Research (HDR) UK. RW is supported by a MRC Industrial Strategy Studentship (MR/S502509/1) and by HDR UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations. KW is supported by the UK MRC (MC_UU_00006/4). KSB is supported by Cancer Research UK (C8221/A29017, C16077/A29186) and UKRI (10063259). DB is supported by Novo Nordisk and Swiss Re. SL reports grants from HDR UK Ltd (HDRUK2023.0028) funded by the MRC, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), BHF and Cancer Research UK; outside the submitted work. JCH acknowledges funding from the BHF, National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC), BHF Centre of Research Excellence, and the Nuffield Department of Population Health. AD's research team is supported by a range of grants from the Wellcome Trust (223100/Z/21/Z, 227093/Z/23/Z), Novo Nordisk, Swiss Re, Boehringer Ingelheim, National Institutes of Health's Oxford Cambridge Scholars Program, EPSRC Centre for Doctoral Training in Health Data Science (EP/S02428X/1), and the BHF Centre of Research Excellence (RE/18/3/34214). For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

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