Quantifying the Predictive Performance of Objectively Measured Physical Activity on Mortality in the UK Biobank
Open accessAbstract
Background: Objective measures of physical activity (PA) derived from wrist-worn accelerometers are compared with traditional risk factors in terms of mortality prediction performance in the UK Biobank. Method: A subset of participants in the UK Biobank study wore a tri-axial wrist-worn accelerometer in a free-living environment for up to 7 days. A total of 82 304 individuals over the age of 50 (439 707 person-years of follow-up, 1959 deaths) had both accelerometry data that met specified quality criteria and complete data on a set of traditional mortality risk factors. Predictive performance was assessed using cross-validated Concordance (C) for Cox regression models. Forward selection was used to obtain a set of best predictors of mortality. Results: In univariate Cox regression, age was the best predictor of all-cause mortality (C = 0.681) followed by 12 PA predictors, led by minutes of moderate-to-vigorous PA (C = 0.661) and total acceleration (C = 0.661). Overall, 16 of the top 20 predictors were objective PA measures (C = 0.578–0.661). Using a threshold of 0.001 improvement in Concordance, the Concordance for the best model that did not include PA measures was 0.735 (9 covariates) compared with 0.748 (12 covariates) for the best model with PA variables (p-value < .001). Conclusions: Objective measures of PA derived from accelerometry outperform traditional predictors of all-cause mortality in the UK Biobank except age and substantially improve the prediction performance of mortality models based on traditional risk factors. Results confirm and complement previous findings in the National Health and Nutrition Examination Survey (NHANES).
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Existing studies have demonstrated that objectively measured physical activity could be a potential predictor for cardiovascular disease mortality and cancer mortality in the general population.
“In addition, existing studies have demonstrated that objectively measured PA could be a potential predicator for cardiovascular disease (CVD) mortality and cancer mortality in the general population15,16.”
From Accelerometer-derived physical activity and mortality in individuals with type 2 diabetes (2024) · cites this paper as [15]
The Abstract and Discussion (page not provided) report that objectively measured physical activity predicts all-cause mortality among UK Biobank participants over 50. They do not report separate predictions for cardiovascular-disease or cancer mortality, and the studied cohort does not establish the claim for the general population. (LLM, conf=0.87)
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Assessment of physical activity patterns in patients with rheumatoid arthritis using the UK Biobank
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“More recently, measures of physical activity derived from this accelerometer dataset were used to predict mortality [30] as well as investigate the incidence of Parkinson’s disease [31].”
Hou, Y., Cui, E., Lim, K. O., Chow, L. S., Howell, M. J., Ikramuddin, S., & Zhang, R. (2026) · Communications Medicine · doi:10.1038/s43856-025-01372-x · Cited by 5 · see citing works
“This criterion, commonly applied in prior research 19 – 21 , ensured suf fi cient data quality and excluded participants with limited activity records that couldyieldunreliableresults.”
McKee, K. L., Pfeiffer, K. A., Pearson, A. L., & Clevenger, K. A. (2023) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2023-0007 · Cited by 5 · see citing works
Comparing Step Counting Algorithms for High-Resolution Wrist Accelerometry Data in NHANES 2011–2014
Koffman, L. J., Crainiceanu, C., & Muschelli, J. (2024) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003616 · Cited by 5 · see citing works
“Conclusions: Different step counting algorithms provide correlated step estimates and have similar predictive performance that is better than traditional predictors of mortality.”
Self-citationAndrade, A. Q. D., Lim, R., Kelly, T., Parfitt, G., Pratt, N. L., & Roughead, E. E. (2022) · Journal of the American Geriatrics Society · doi:10.1111/jgs.18181 · Cited by 5 · see citing works
“Physical activity measured by actigraphy has been associated with mortality in population studies(9,10) outperforming conventional mortality risk predictors for the general population.(11) Moreover, recent evidence suggests that actigraphy data carries more information about the individual than physical activity patte…”
BackgroundJabbar, K. B. A., Ardle, R. M., Lord, S., Kerse, N. M., Din, S. D., & Teh, R. (2023) · Sensors · doi:10.3390/s23177615 · Cited by 4 · see citing works
“These studies indirectly examine aspects of validation such as construct validity [57] and predictive validity [58], thereby providing some assurances.”
BackgroundImproving Balance and Movement Control in Fencing Using IoT and Real-Time Sensorial Feedback
Nita, V. A., & Magyar, P. (2023) · Sensors · doi:10.3390/s23249801 · Cited by 4 · see citing works
“Traditionally positioned on the hip [9], alternative placements such as the wrist [10,11], thigh [12], knee [13], or even the back [14] are viable options contingent upon the specific demands of a given sport.”
BackgroundChen, X., Zhou, M., Wang, J., Liu, M., Zhu, C., Wu, C., Dong, M., Liu, Y., Ai, S., Feng, H., et al. (2024) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2024.08.005 · Cited by 4 · see citing works
Walker, B., Gibson, J., Jackson, C., Blaikley, J., Jones, S. E., Rutter, M. K., & Sutton, M. (2024) · Chronobiology International · doi:10.1080/07420528.2024.2441192 · Cited by 5 · see citing works
“This is also true of disrupted rest-activity rhythms, which have been identified as a powerful predictor of adverse health outcomes such as mortality risk (Leroux et al. 2021; Xu et al. 2022; Zuurbier et al. 2015).”
Associations between physical activity and CVD-related metabolomic and proteomic biomarkers
Ekblom, Ö. B., Björkbacka, H., Börjesson, M., Ekblom‐Bak, E., Blomberg, A., Caidahl, K., Ehrenborg, E., Engström, G., Engvall, J. E., Erlinge, D., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325720 · Cited by 2 · see citing works
“Also, accelerometer-based variables have been shown to be highly predictive for all-cause and CVD mortality [8,9].”
Filippatou, A. G., Sanjayan, M., Dewey, B. E., Johnson, E., Niyogi, P. G., Fadlallah, Y., Duncan, S., Wyche, C., Moni, E., Zipunnikov, V., et al. (2026) · Multiple Sclerosis Journal · doi:10.1177/13524585261435926 · Cited by 2 · see citing works
Mijnsbergen, M. D., Exadaktylos, V., van Hilten, J. J., Hepp, D. H., & Weijer, R. H. A. (2025) · Journal of Parkinson s Disease · doi:10.1177/1877718x251388890 · Cited by 2 · see citing works
“9 RAR disturbances have also previously been shown in PwPD, 5,10 – 13 indicating that there are disease-related limitations that make it dif fi cult for PwPD to maintain mobility and the performance of ADL, potentially impacting quality of life.”
Han, M., Qu, Y., Zeng, M., Gu, Z., Zhang, Y., Fan, T., Yang, Q., Wang, Z., Gao, P., Chen, S., et al. (2026) · Geriatric Nursing · doi:10.1016/j.gerinurse.2026.103834 · Cited by 2 · see citing works
Kobeissi, E., Dempsey, P. C., Alshallal, A. D., Rennie, K. L., Brage, S., Wijndaele, K., & Strain, T. (2026) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2024-0035 · Cited by 2 · see citing works
Physical activity cut-points for older adults using the Zio XT onboard accelerometer
Etzkorn, L., Davoudi, A., Dooley, E. E., Gabriel, K. P., Chen, L. Y., Crainiceanu, C. M., Schrack, J. A., & Wanigatunga, A. A. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00087-8 · Cited by 2 · see citing works
“Daily hours spent in activity intensity categories have been demonstrated to be important predictors of mortality in other studies which used other research-grade devices [23–25].”
Self-citationJung, G., & Song, G. (2023) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia59966.2023.10326390 · Cited by 2 · see citing works
Twenty‐four‐hour physical behaviour profiles across type 2 diabetes mellitus subtypes
Henson, J. J., Tziannou, A., Rowlands, A. V., Edwardson, C. L., Hall, A. P., Davies, M. J., & Yates, T. E. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15437 · Cited by 2 · see citing works
“43,44 Moreover, RA and MVPA may predict multiple health outcomes better than traditional risk factors or other rest – activity disturbances (e.g., short sleep duration, poor-quality sleep and the total volume of physical activity).”
BackgroundUnderlying control of gait and mobility
Alcock, L., Sigurdsson, H. P., Ray, N. J., Yarnall, A. J., & Rochester, L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21484-4.00001-6 · Cited by 1 · see citing works
Additive value of lung ultrasound to clinical parameters for prognosticating COVID-19
Siddharthan, T., Blair, P. W., Cui, E., Pearce, J., Herrera, P., Liu, G. Y.-G., East, J., Crainiceanu, C., Clark, D. V., Fenstermacher, K., et al. (2023) · ERJ Open Research · doi:10.1183/23120541.00564-2022 · Cited by 1 · see citing works
“005 at both stages to assess the additive accuracy of each variable.(9) The third model was built by two-stage forward selection including clinical parameters at the first stage, followed by LUS parameters with a predefined stopping rule of increasing in AUC < 0.”
MethodSelf-citationComparing Estimators of Discriminative Performance of Time-to-Event Models
Jin, Y., & Leroux, A. (2025) · Journal of Data Science · doi:10.6339/25-jds1163 · Cited by 1 · see citing works
“Accelerometry data was processed in this spirit of the pipeline used by Leroux et al. (2021, 2019) for the NHANES 2003-2006 and the UK Biobank data, respectively.”
MethodSelf-citationWindred, D. P., Burns, A. C., Lane, J. M., Olivier, P. L., Rutter, M. K., Saxena, R., Phillips, A. J. K., & Cain, S. W. (2023) · medRxiv · doi:10.1101/2023.09.08.23295231 · Cited by 1 · see citing works
“This modeling approach provides 286 mechanistic insight into the links between light at night, circadian rhythms, and mortality, and expands upon 287 research that derived circadian metrics from accelerometer data only.(13,14) 288”
BackgroundHou, Y., Cui, E., Ikramuddin, S., & Zhang, R. (2024) · medRxiv · doi:10.1101/2024.11.11.24317124 · Cited by 1 · see citing works
Zhang, R., Hou, Y., Cui, E., Lim, K. O., Chow, L. S., Howell, M. J., & Ikramuddin, S. (2025) · Research Square · doi:10.21203/rs.3.rs-6263507/v1 · Cited by 1 · see citing works
Xu, H., Mu, S., Bao, J., Davatzikos, C. A., Shou, H., & Shen, L. (2025) · BioData Mining · doi:10.1186/s13040-025-00432-1
“Such approaches have been widely adapted to analyze patterns of physical activity data [34, 35].”
Zhang, Y., & Xu, W. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1695462
Hernandez, M., & Crainiceanu, C. (2026) · Journal of Applied Statistics · doi:10.1080/02664763.2026.2619937
Self-citationGu, B., Zhang, H., Yi, M., Jin, S., Xiao, K., Sun, L., Sun, J., Zhao, H., Zhao, O., Ma, Y. X., et al. (2026) · The Journals of Gerontology Series A · doi:10.1093/gerona/glag130
Du, J., Lei, X., Wang, Q., Feng, H., Li, Z., Zhang, R. Q., Liu, Y., Zhang, J., Zhou, Y., & Liang, Y. Y. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.126.049566
- Authors
- Leroux, A., Xu, S., Kundu, P., Muschelli, J., Smirnova, E., Chatterjee, N., & Crainiceanu, C.
- Affiliations
- Johns Hopkins University (US)
- Virginia Commonwealth University (US)
- Corresponding author
- Leroux, Andrew
- ORCID
- Venue
- The Journals of Gerontology: Series A
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-09-30
- Volume
- 76
- Issue
- 8
- Pages
- 1486-1494
- ISSN
- 1079-5006 (ISSN-L)
- 1758-535X
- PMC
- PMC8277083
- OpenAlex
- W3091619687
- Cited by
- 66
- References
- 36
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 15 · 2023: 14 · 2022: 13 · 2021: 5 · 2020: 1
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- The Journals of Gerontology Series A (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke
- National Institutes of Health (R01NS060910)
- National Institute on Aging (T32AG000247)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
- Keywords
- physical activity, mortality prediction, accelerometry, objective physical activity measurement, all-cause mortality, moderate-to-vigorous physical activity, older adults, UK Biobank
- MeSH terms
- Fitness Trackers, Physical Functional Performance, Age Factors, Aged, Female, United Kingdom, Humans, Male, Middle Aged, Mortality, Nutrition Surveys, Predictive Value of Tests, Risk Factors, Exercise, Proportional Hazards Models, Outcome Assessment, Health Care, Risk Assessment, Accelerometry
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