{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:30:21Z","timestamp":1792798221091,"version":"4.5.0"},"reference-count":50,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Wu Tsai Human Performance Alliance"},{"DOI":"10.13039\/100000009","name":"foundation for the national institutes of health","doi-asserted-by":"publisher","award":["P41EB027060"],"award-info":[{"award-number":["P41EB027060"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Orthopaedic Journal of Sports Medicine"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Over 1.4 million anterior cruciate ligament (ACL) injuries occur worldwide each year, but studies suggest that they may be preventable through targeted screening and training. Biomechanical factors from squatting, jumping, cutting, and running have been associated with ACL injury risk; however, current movement assessments are lengthy, limiting adoption and compliance. There is no consensus on which activities best assess modifiable biomechanical risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>(1) To identify an optimal set of activities that explain an athlete's full-body, 3-dimensional biomechanical risk factors for ACL injury and (2) to build a framework to determine an individual's ACL injury resilience (AIR) score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design:<\/jats:title>\n                    <jats:p>Descriptive laboratory study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>The authors recruited adolescent female athletes who played soccer, basketball, or volleyball as their primary sport and had no history of injury. Data collection included 3-dimensional motion capture and ground-reaction forces during 5 common ACL injury risk screening activities and the computation of 35 biomechanical risk factors. Column subset selection identified the set of activities that best reconstructed the biomechanical factors from left-out activities. This activity set, with biomechanical thresholds from prospective studies of ACL injuries, was used to build the AIR score framework. The AIR score and the subscores for the trunk, hip, knee, and foot ranked participant biomechanical resilience. Statistical significance was evaluated at \u03b1 = .050.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Twenty-seven females were included in this study (mean \u00b1 SD; age, 15.7 \u00b1 1.2 years; body mass index, 22.19 \u00b1 3.58 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ). Two screening activities\u2014run cuts and single-leg drop jumps\u2014achieved the highest reconstruction performance, predicting the biomechanical factors from the left-out activities with 88.4% accuracy. The AIR score from this 2-activity set strongly agreed with an AIR score generated using all the information from 5 activities (\n                      <jats:italic toggle=\"yes\">r<\/jats:italic>\n                      = 0.90; \u03c4 = 0.72;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The AIR screen and score provided a robust methodological and statistical framework that could be updated as future studies identify and characterize new biomechanical risk factors in larger cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance:<\/jats:title>\n                    <jats:p>The authors\u2019 proposed screening provided an individualized evaluation of ACL injury resilience from just 2 activities, using an objective assessment of 3-dimensional movement. The AIR score framework identified athletes with vulnerable whole-body movement patterns, along with subscores that could be targeted with exercise interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/23259671261433009","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T16:23:17Z","timestamp":1779985397000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantifying Anterior Cruciate Ligament Injury Resilience: A Screening and Composite Score Framework"],"prefix":"10.1177","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1632-5611","authenticated-orcid":false,"given":"Kirsten","family":"Seagers","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Stanford University, Stanford, California, 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