{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T10:07:55Z","timestamp":1789380475483,"version":"build-2803163510"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Chronic Obstructive Pulmonary Disease (COPD) is a major global health challenge. The 6 Minute Walk Test (6MWT) is a simple, cost-effective tool used to assess exercise capacity in COPD patients, but it provides only a single metric of functional performance and doesn't capture the complex physiological changes during exercise. This study aimed to investigate the use of a wearable cardiopulmonary exercise monitoring (WCEM) system with the 6MWT to comprehensively evaluate lung function and exercise capacity in COPD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A clinical trial was conducted on 202 COPD patients, analyzing their baseline pulmonary function, 6MWD, and detailed cardiopulmonary exercise parameters. Patients were stratified into groups based on disease severity and walk distance to identify significant differences in clinical and physiological profiles. A multi-dimensional predictive model for walk distance was also developed using logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study found significant differences in 6MWD and pulmonary function between severe and non-severe COPD patients. Wearable cardiopulmonary parameters, especially carbon dioxide production (VCO2, L\/min) and oxygen consumption per kilogram (VO2\/kg, mL\/min\/kg), showed discriminatory ability for both COPD severity and walk distance. A multi-dimensional logistic regression model combining these parameters predicted walk distance with an overall correct classification rate of 89.1%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>In conclusion, wearable cardiopulmonary exercise monitoring enhances the 6MWT by providing detailed dynamic physiological data that can effectively predict both disease severity and walk distance in COPD patients. This approach makes sophisticated cardiopulmonary exercise testing insights more accessible for routine clinical use, potentially improving the precision of exercise prescriptions and patient management strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1926691","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T09:47:19Z","timestamp":1789379239000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating wearable technology with the 6 minute walk test to evaluate COPD patients"],"prefix":"10.3389","volume":"13","author":[{"given":"Xiaojing","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Qingdao University","place":["Qingdao, China"]},{"name":"Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University","place":["Shanghai, 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