{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:02:56Z","timestamp":1782842576411,"version":"3.54.5"},"reference-count":29,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The association between physical activity (PA) behavior and cardiometabolic risk factors has depended largely on questionnaire-based reporting. More studies are turning to mobile health (mHealth) device solutions to measure PA. While there are differences between self-reported activity levels and objectively measured accelerometer-based activity, how these differences manifest in disease risk is unknown.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n Here, we sought to evaluate these differences between self-reported and mHealth-based PA and to model the impact on their association with cardiometabolic factors. Our study provides a framework to assess the quality of relationships measured by mHealth technologies, which is generalizable to other sensors or activity-measuring devices.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We assessed PA using both wrist-worn accelerometer data and self-reported questionnaires in 16,000 participants of the UK Biobank (UKB) between 2013 and 2015, focusing on walking, sleeping, sedentary, and moderate-to-vigorous physical activity (MVPA). We compared the concordance between self-reported and objective measures of PA. We also compared the association between objectively measured or self-reported PA and future clinical biomarker levels (eg, BMI, pulse rate, glucose control, and cholesterol).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Participants underestimated their weekly sedentary duration on average of 2.86 hours, and the coefficient of correlation (\n r<\/jats:italic>\n ) between subjective and objective activity was 0.12 for sedentary time, 0.16 for MVPA, 0.18 for walking, and 0.13 for sleeping. We found an inverse association between objectively measured MVPA and cardiometabolic biomarkers such as BMI and pulse rate, but found no association between subjectively reported activity and cardiometabolic biomarkers. We estimated that there is a 6% larger association between subjectively measured MVPA and BMI in healthy adults (vs the objective counterpart). We also estimated a 2%-3% difference on a healthy adult heartbeat (healthy range: 60-100 bpm) if relying on subjectively reported observations instead of measured PA.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n These findings suggest that the association based on self-reported activity is likely overestimated and biased compared with objectively measured PA. Therefore, care should be taken when assessing the effects of self-reported PA on key cardiometabolic factors, such as BMI and pulse rate. We emphasize that while the associations are biased when comparing PA modalities, we cannot conclude which method more closely reflects the daily activity load.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/54820","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T21:15:06Z","timestamp":1741900506000},"page":"e54820-e54820","source":"Crossref","is-referenced-by-count":2,"title":["Objectively and Subjectively Measured Physical Activity and Their Associations With Cardiometabolic Risk in the UK Biobank: Retrospective Cohort Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3849-7795","authenticated-orcid":false,"given":"Charlyne","family":"B\u00fcrki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1215-2162","authenticated-orcid":false,"given":"Caiwei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7619-1868","authenticated-orcid":false,"given":"Kenneth","family":"Westerman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8756-8525","authenticated-orcid":false,"given":"Chirag","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"issue":"21","key":"R1","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1001\/jama.298.21.2507","article-title":"Cardiorespiratory fitness and adiposity as mortality predictors in older adults","volume":"298","author":"Sui","journal-title":"JAMA"},{"issue":"3","key":"R2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2015-010038","article-title":"Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233,110 adults from the UK Biobank; the behavioural phenotype of cardiovascular disease and type 2 diabetes","volume":"6","author":"Cassidy","journal-title":"BMJ Open"},{"issue":"5 Suppl","key":"R3","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1177\/1403494810378919","article-title":"Does a variation in self-reported physical activity reflect variation in objectively measured physical activity, resting heart rate, and physical fitness? Results from the Tromso study","volume":"38","author":"Emaus","journal-title":"Scand J Public Health"},{"issue":"11","key":"R4","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1038\/s41591-022-02012-w","article-title":"Association of step counts over time with the risk of chronic disease in the all of us research program","volume":"28","author":"Master","journal-title":"Nat Med"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1001\/jamacardio.2016.4395","article-title":"Feasibility of obtaining measures of lifestyle from a smartphone app: the MyHeart counts cardiovascular health study","volume":"2","author":"McConnell","journal-title":"JAMA Cardiol"},{"issue":"20","key":"R6","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1161\/01.cir.0000435708.67487.da","article-title":"Guide to the assessment of physical activity: clinical and research applications: a scientific statement from the American Heart Association","volume":"128","author":"Strath","journal-title":"Circulation"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1249\/mss.0b013e31815a51b3","article-title":"Physical activity in the United States measured by accelerometer","volume":"40","author":"Troiano","journal-title":"Med Sci Sports Exerc"},{"issue":"5","key":"R8","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1249\/MSS.0b013e3181fdfc32","article-title":"Self-reported and objectively measured activity related to biomarkers using NHANES","volume":"43","author":"Atienza","journal-title":"Med Sci Sports Exerc"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","DOI":"10.1186\/s12966-020-00938-3","article-title":"A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis","volume":"17","author":"Prince","journal-title":"Int J Behav Nutr Phys Act"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","DOI":"10.1186\/1479-5868-5-56","article-title":"A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review","volume":"5","author":"Prince","journal-title":"Int J Behav Nutr Phys Act"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000176","article-title":"Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale","volume":"2","author":"Le Goallec","journal-title":"PLOS Digit Health"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-07743-4","article-title":"GWAS identifies 14 loci for device-measured physical activity and sleep duration","volume":"9","author":"Doherty","journal-title":"Nat Commun"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-26174-1","article-title":"Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants","volume":"8","author":"Willetts","journal-title":"Sci Rep"},{"issue":"6","key":"R14","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1136\/bjsm.2008.048033","article-title":"What factors are associated with physical activity in older people, assessed objectively by accelerometry?","volume":"43","author":"Harris","journal-title":"Br J Sports Med"},{"issue":"5","key":"R15","doi-asserted-by":"publisher","first-page":"e36","DOI":"10.1016\/j.ejim.2011.02.007","article-title":"Significant variation of traditional markers of liver injury after a half-marathon run","volume":"22","author":"Lippi","journal-title":"Eur J Intern Med"},{"key":"R16","unstructured":"Consent Form: UK Biobank. URL: https:\/\/www.ukbiobank.ac.uk\/wp-content\/uploads\/2025\/01\/Participant-original-consent-form.pdf [Accessed 11-08-2025]"},{"key":"R17","unstructured":"UK Biobank Information Leaflet. URL: https:\/\/www.ukbiobank.ac.uk\/wp-content\/uploads\/2025\/01\/Participant-original-information-leaflet.pdf [Accessed 11-08-2025]"},{"issue":"3","key":"R18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","journal-title":"PLoS Med"},{"issue":"2","key":"R19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0169649","article-title":"Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study","volume":"12","author":"Doherty","journal-title":"PLOS ONE"},{"issue":"1","key":"R20","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1249\/MSS.0b013e3182a0595f","article-title":"Comparison of self-reported versus accelerometer-measured physical activity","volume":"46","author":"Dyrstad","journal-title":"Med Sci Sports Exerc"},{"issue":"2","key":"R21","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1249\/MSS.0000000000000760","article-title":"Are self-report measures able to define individuals as physically active or inactive?","volume":"48","author":"Steene-Johannessen","journal-title":"Med Sci Sports Exerc"},{"issue":"1","key":"R22","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.trci.2019.08.007","article-title":"Accelerometer-determined physical activity and cognitive function in middle-aged and older adults from two generations of the Framingham heart study","volume":"5","author":"Spartano","journal-title":"Alzheimers Dement (N Y)"},{"issue":"2","key":"R23","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.jand.2013.09.018","article-title":"Practical guide to measuring physical activity","volume":"114","author":"Sylvia","journal-title":"J Acad Nutr Diet"},{"issue":"8","key":"R24","doi-asserted-by":"publisher","first-page":"e799","DOI":"10.1212\/WNL.0000000000200701","article-title":"Physical and mental activity, disease susceptibility, and risk of dementia: a prospective cohort study based on UK biobank","volume":"99","author":"Zhu","journal-title":"Neurology (ECronicon)"},{"issue":"6","key":"R25","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s00421-009-1000-2","article-title":"Assessment of physical activity: a critical appraisal","volume":"105","author":"Westerterp","journal-title":"Eur J Appl Physiol"},{"issue":"1","key":"R26","doi-asserted-by":"publisher","first-page":"E122","DOI":"10.1152\/ajpendo.00084.2021","article-title":"Exercise effect on insulin-dependent and insulin-independent glucose utilization in healthy individuals and individuals with type 1 diabetes: a modeling study","volume":"321","author":"Romeres","journal-title":"Am J Physiol Endocrinol Metab"},{"issue":"1","key":"R27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s43586-021-00056-9","article-title":"Genome-wide association studies","volume":"1","author":"Uffelmann","journal-title":"Nat Rev Methods Primers"},{"issue":"9","key":"R28","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1038\/s41588-022-01165-1","article-title":"Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention","volume":"54","author":"Wang","journal-title":"Nat Genet"},{"issue":"6","key":"R29","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1038\/s41366-018-0120-3","article-title":"Genome-wide association study of habitual physical activity in over 377,000 UK biobank participants identifies multiple variants including CADM2 and APOE","volume":"42","author":"Klimentidis","journal-title":"Int J Obes (Lond)"}],"container-title":["JMIR mHealth and uHealth"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/mhealth.jmir.org\/2025\/1\/e54820","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:42:00Z","timestamp":1782841320000},"score":1,"resource":{"primary":{"URL":"https:\/\/mhealth.jmir.org\/2025\/1\/e54820"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,27]]},"references-count":29,"URL":"https:\/\/doi.org\/10.2196\/54820","relation":{},"ISSN":["2291-5222"],"issn-type":[{"value":"2291-5222","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,27]]},"article-number":"v13i21e54820"}}