{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T03:45:39Z","timestamp":1777002339504,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"World Athletics"},{"name":"American College of Sports Medicine (ACSM)"},{"name":"American Society of Biomechanics"},{"name":"ACSM Biomechanics Interest Group"},{"name":"De Luca Foundation"},{"name":"Indiana University Graduate and Professional Student Government"},{"name":"Stryd Inc."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3\u201390.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of &lt;1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner\u2019s in-lab data. Researchers and clinicians should consider \u201cborrowing\u201d information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.<\/jats:p>","DOI":"10.3390\/s24092892","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T03:30:49Z","timestamp":1714534249000},"page":"2892","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study"],"prefix":"10.3390","volume":"24","author":[{"suffix":"IV","given":"John J.","family":"Davis","sequence":"first","affiliation":[{"name":"Department of Kinesiology, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9963-2985","authenticated-orcid":false,"given":"Stacey A.","family":"Meardon","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, East Carolina University, Greenville, NC 27858, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1758-8205","authenticated-orcid":false,"given":"Andrew W.","family":"Brown","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John S.","family":"Raglin","sequence":"additional","affiliation":[{"name":"Department of Kinesiology, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaroslaw","family":"Harezlak","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0750-5656","authenticated-orcid":false,"given":"Allison H.","family":"Gruber","sequence":"additional","affiliation":[{"name":"Department of Kinesiology, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s40279-019-01110-z","article-title":"Biomechanical risk factors associated with running-related injuries: A systematic review","volume":"49","author":"Ceyssens","year":"2019","journal-title":"Sports Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vannatta, C.N., Heinert, B.L., and Kernozek, T.W. 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