| Title: | Reproducibility and associated regression dilution bias of accelerometer-derived physical activity and sleep in UK Biobank |
| Journal: | International Journal of Epidemiology |
| Published: | 18 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41718664/ |
| DOI: | https://doi.org/10.1093/ije/dyag014 |
Publication 17709
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Abstract
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.</p>
METHODS: We analysed data from 3138 UK Biobank 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 by using intraclass correlation coefficients (ICCs). The impact on disease associations was illustrated by considering daily step count and incident CHD using Cox regression (87 038 participants; 3879 CHD events), before and after correction for regression dilution.</p>
RESULTS: Among the 3138 participants, 51% were women and the mean (SD) age was 63.1 (9.4) years. Reproducibility was good for overall activity, with an ICC (95% confidence interval) of 0.75 (0.74-0.76), and moderate for other phenotypes, with ICCs 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 usual 4000 steps after correcting for regression dilution compared with 13% before correction.</p>
CONCLUSION: Accelerometer measurements are moderately reproducible and comparable to measures such as blood pressure. Correction for regression dilution bias is crucial to quantify associations of usual physical activity and sleep with disease risk.</p>
15 Keywords
- Accelerometry
- Aged
- Bias
- Biological Specimen Banks
- Coronary Disease
- Exercise
- Female
- Humans
- Male
- Middle Aged
- Proportional Hazards Models
- Reproducibility of Results
- Sleep
- UK Biobank
- United Kingdom
15 Authors
- Charilaos Zisou
- Catherine Calvin
- Hannah Taylor
- Ben Lacey
- Imen Hammami
- Rosemary Walmsley
- Tessa Strain
- Katrien Wijndaele
- Nicholas Wareham
- Soren Brage
- Karl Smith-Byrne
- Derrick Bennett
- Sarah Lewington
- Jemma C Hopewell
- 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