Accelerometry-measured prolonged and interrupted sedentary behavior and cancer incidence and mortality - A cohort study of 91,292 UK Biobank participants

Sep 22, 2026

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Current sedentary behavior guidelines stress total sedentary time, but this cohort study examines how patterns—prolonged versus interrupted sedentary behavior—relate to cancer incidence and mortality. Using accelerometry data from 91,292 UK Biobank participants, the study classifies sedentary behavior via a random-forest two-step approach and estimates hazard ratios with multivariable Cox models across overall cancers, cancer deaths, obesity/diabetes-related cancers, and 23 site-specific cancers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Background

Methods and findings

OPEN ACCESS Citation: Zhou Z, Trost SG, Ryde GC, Parra-Soto S, Fang Z, Xu C, et al. (2026) Accelerometry-measured prolonged and interrupted sedentary behavior and cancer incidence and mortality: A cohort study of 91,292 UK Biobank participants. PLoS Med 23(7): e1004767. [https://doi.org/10.1371/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pmed.1004767](journal.pmed.1004767) Academic Editor: Steven C Moore, National Cancer Institute, UNITED STATES OF AMERICA Received: September 11, 2025 Accepted: May 29, 2026 Published: July 2, 2026 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pmed.1004767 Copyright: © 2026 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, RESEARCH ARTICLE Accelerometry-measured prolonged and interrupted sedentary behavior and cancer incidence and mortality: A cohort study of 91,292 UK Biobank participants Ziyi Zhou1,2,3, Stewart G. Trost4, Gemma C. Ryde5, Solange Parra-Soto6, Zhe Fang2, Chao Xu1, Yujia Lu2, Kai Wang2, Mengxi Du2, Zhi Li7, Yuebin Lv6, Jason M.R. Gill5, Stuart R. Gray5, Carlos Celis-Morales5,8,9, Marc J. Gunter10,11, Edward Giovannucci12, Jill P. Pell1, Mingyang Song2,3,12,13,14‡, Frederick K. Ho1‡* 1 School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom, 2 Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America, 3 Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America, 4 School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia, 5 School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom, 6 Department of Nutrition and Public Health, Universidad del Bío-Bío, Chillan, Chile, 7 China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China, 8 Human Performance Laboratory, Education, Physical Activity and Health Research Unit, Universidad Catolica del Maule, Talca, Chile, 9 High-Altitude Medicine Research Centre (CEIMA), Universidad Arturo Prat, Iquique, Chile, 10 Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France, 11 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom, 12 Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America, 13 Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America, 14 Microbiome Lab, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America ‡ Joint senior authors. * [frederick.ho@glasgow.ac.uk](frederick.ho@glasgow.ac.uk) Abstract Background Current sedentary behavior (SB) guidelines primarily emphasize total time spent sedentary. We explored differences between interrupted and prolonged SB in relation to a range of cancer outcomes. Methods and findings This study included 91,292 UK Biobank participants with valid accelerometer data. Participants were followed for a median of 12.38 years (interquartile range 11.56–13. 15 years) . A two-step approach based on a random forest model was used to classify SB. Multivariable Cox proportional hazards models were applied to overall incident cancers and cancer deaths, plus obesity-related and type-2 diabetes-related cancers, and 23 site-specific cancer

FAQs

What sedentary behavior patterns were compared in this study?

The study compared prolonged sedentary behavior versus interrupted sedentary behavior patterns.

How was sedentary behavior classified from accelerometer data?

A two-step approach based on a random forest model classified sedentary behavior patterns.

What outcomes were analyzed in relation to sedentary behavior?

Overall incident cancers and cancer deaths were analyzed, along with obesity-related, type-2 diabetes-related cancers, and 23 site-specific cancers.

Sep 22, 2026

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