{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:12:25Z","timestamp":1779261145947,"version":"3.51.4"},"reference-count":42,"publisher":"Wiley","issue":"S2","license":[{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["PM&amp;R"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Although numerous studies have examined risk factors and prevention strategies for running\u2010related injuries, few have rigorously tested predictive models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To describe injury patterns throughout marathon training and assess the feasibility of machine learning (ML) models in predicting upcoming weekly running injury status during marathon training with activity logs and runner\u2010reported survey data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>In this prospective observational study, participants completed baseline surveys, 16 weekly interval surveys during marathon training, and shared GPS watch and smartphone\u2010based running logs from Strava. Injury status was summarized and used to train two tabular ML models incorporating baseline, prior surveys, and aggregated training logs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>All data were collected remotely via online platforms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>A total of 643 adult runners (53% female, mean age 43\u2009years) training for the 2022 Tata Consultancy Services New York City Marathon were recruited and had linkable Strava running logs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions<\/jats:title>\n                    <jats:p>N\/A.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main outcome measure<\/jats:title>\n                    <jats:p>Self\u2010reported weekly injury status indicating modification of training.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 643 runners, 307 (48%) experienced at least one injury requiring modification of training during the analysis window. Across 9002 runner\u2010week observations, 75% indicated no injury. Injury status tended to persist, with most runners maintaining the same status week\u2010to\u2010week, and recovery was more common than worsening. For the first model (all runner\u2010week observations), predictive modeling with generalized additive models yielded good performance, specifically an area under the receiver operating characteristic curve (AUROC)\u2009=\u200987% and area under the precision\u2010recall curve (AUPRC)\u2009=\u200952%. For the second model (previously uninjured runner\u2010week observations), performance was poor, yielding AUROC\u2009=\u200967% and AUPRC\u2009=\u20098%. Top features' partial dependency plots often showed nonlinear relationships with injury risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML using survey and running activity data had low discriminatory power in predicting weekly running injury among runners not already modifying their training, though results highlight the importance of the prior week's injury status. Larger samples, more precise injury timing, and additional predictors are likely needed to improve performance and inform future injury prevention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pmrj.70127","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T06:38:16Z","timestamp":1774852696000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting self\u2010reported injury status among runners training for the New York City Marathon"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0529-9035","authenticated-orcid":false,"given":"Mark A.","family":"Fontana","sequence":"first","affiliation":[{"name":"Orthopedic Data Innovation Lab Hospital for Special Surgery  New York New York USA"},{"name":"Department of Population Health Sciences Weill Cornell Medical College  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6288-8058","authenticated-orcid":false,"given":"Jamie S.","family":"Egbert","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics University of Utah  Salt Lake City Utah USA"},{"name":"Department of Physical Medicine &amp; Rehabilitation University of Utah  Salt Lake City Utah USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3857-2779","authenticated-orcid":false,"given":"Brett G.","family":"Toresdahl","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics University of Utah  Salt Lake City Utah USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,29]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ptsp.2019.11.006"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1249\/MSS.0b013e31826900f"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1055\/a\u20100660\u20100061"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/15438627.2019.1586705"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40279\u2010015\u20100331\u2010x"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsm.2006.033548"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0099877"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600\u20100838.2007.00768.x"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsm.17.3.162"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40798\u2010018\u20100164\u2010x"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsm.21.4.148"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ptsp.2011.08.001"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/00913847.2021.1907257"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/036354659302100513"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsports\u20102012\u2010091397"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/sms.12743"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1177\/0363546517736277"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1177\/0363546519892578"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/sms.14313"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjsem\u20102022\u2010001522"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1177\/03635465231222464"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsports\u20102024\u2010108576"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1123\/ijspp.2020\u20100518"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.38124\/ijisrt\/IJISRT24SEP239"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsports\u20102022\u2010105670"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.2519\/jospt.2015.5741"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsports\u20102019\u2010101337"},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103208"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2008.08.010"},{"key":"e_1_2_12_31_1","doi-asserted-by":"crossref","unstructured":"LouY CaruanaR GehrkeJ HookerG.Accurate intelligible models with pairwise interactions. presented at: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining; 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