{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:35:27Z","timestamp":1793295327128,"version":"4.5.0"},"reference-count":43,"publisher":"Human Kinetics","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:p>\n                    <jats:bold>\n                      <jats:italic>Purpose<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic>:<\/jats:italic>\n                    Athlete monitoring has emerged as a common method to track athlete status and adjust training for optimal performance. Despite the influx of monitoring-related resources at the collegiate level, actual training load modifications are largely anecdotal. This prospective cohort study investigated relationships between previous day training load, objective nightly recovery, and next-day subjective wellness and max speed (MS) in elite soccer athletes across a collegiate season.\n                    <jats:bold>\n                      <jats:italic>Methods<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic>:<\/jats:italic>\n                    Twenty-six collegiate female soccer athletes were tracked across the duration of their season (2024\u20132025) for training load (wearable global positioning systems monitors), objective nightly recovery (wearable recovery rings), next-day subjective wellness (morning wellness survey), and MS performance.\n                    <jats:bold>\n                      <jats:italic>Results<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic>:<\/jats:italic>\n                    Previous day training load measures of distance and high-speed running were significant predictors of variables related to nightly objective sleep recovery, next day subjective readiness, and next day speed performance. High-speed running models consistently performed better than total distance for predicting changes in recovery, wellness, and MS.\n                    <jats:bold>\n                      <jats:italic>Conclusions<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic>:<\/jats:italic>\n                    Numerous associations between previous day training load and objective and subjective measures increase accessibility of female athlete monitoring methods for applied practitioners. Although the magnitudes of coefficients vary, the consistency of significant findings across monitoring methods provides utility for continuous and comprehensive athlete monitoring, demonstrating the importance of subjective readiness measures beyond traditional performance metrics, while also addressing the paucity of literature in elite female athletes.\n                  <\/jats:p>","DOI":"10.1123\/ijspp.2026-0076","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:01:06Z","timestamp":1787810466000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Influence of Training Load on Recovery, Wellness, and Performance in Elite Soccer Athletes"],"prefix":"10.1123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0639-3266","authenticated-orcid":true,"given":"Sam R.","family":"Moore","sequence":"first","affiliation":[{"name":"Department of Health and Exercise Science, University of Oklahoma, Norman, OK, USA"},{"name":"Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"name":"Human Movement Science Curriculum, Department of Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3703-7977","authenticated-orcid":true,"given":"Elena I.","family":"Cant\u00fa","sequence":"additional","affiliation":[{"name":"Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5405-304X","authenticated-orcid":true,"given":"Abbie E.","family":"Smith-Ryan","sequence":"additional","affiliation":[{"name":"Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"name":"Human Movement Science Curriculum, Department of Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"name":"Department of Nutrition, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"100","reference":[{"issue":"suppl 2","key":"r1","series-title":"Sports Med","doi-asserted-by":"publisher","first-page":"S139","DOI":"10.1007\/s40279-014-0253-z","article-title":"Monitoring training load to understand fatigue in athletes","volume":"44","author":"Halson SL","year":"2014","unstructured":"Halson SL. 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