{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T22:42:29Z","timestamp":1793313749269,"version":"4.5.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100012338","name":"Alan Turing Institute","doi-asserted-by":"publisher","award":["Enrichment Scheme Community Award"],"award-info":[{"award-number":["Enrichment Scheme Community Award"]}],"id":[{"id":"10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["PhD Studentship"],"award-info":[{"award-number":["PhD Studentship"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The causes of endurance running-related injury (RRI) are multifactorial, yet little research has been conducted which utilizes multidisciplinary risk factors for individualized RRI prediction. This paper presents a machine learning (ML)-ready RRI weekly prediction dataset using evidence-based multidisciplinary risk factors. Risk factors in genetic single-nucleotide polymorphisms, history, muscular strength, biomechanics, body composition, nutrition, and training were collected from competitive endurance runners (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009142), who were prospectively monitored for 12 months for RRIs, accumulating 6181 weekly samples. ML models were fitted using (i) risk factors with high-level supporting evidence, and (ii) a broader range of risk factors to establish a performance baseline. Model performance (AUC\u2009=\u20090.784\u2009\u00b1\u20090.014) showed moderate improvement compared to previous RRI prediction modeling. Random forest achieved the best performance (AUC\u2009=\u20090.781\u2009\u00b1\u20090.016, 0.784\u2009\u00b1\u20090.014), which was significantly higher (\n                    <jats:italic>q<\/jats:italic>\n                    \u2009&lt;\u20090.05) than most other algorithms. Only logistic regression achieved significantly improved (\n                    <jats:italic>q<\/jats:italic>\n                    \u2009&lt;\u20090.05) performance when trained using a broader range of risk factors compared to a selection of high-quality risk factors. This study introduces a reproducible methodological framework for future ML sports injury prediction research and a valuable dataset for pooling in larger-scale analytics. Comparisons among different ML methods revealed nuanced insights into the interaction between data structure and model suitability.\n                  <\/jats:p>","DOI":"10.1038\/s41746-026-02413-y","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:47:01Z","timestamp":1770396421000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multidisciplinary prediction of running-related injuries using machine learning"],"prefix":"10.1038","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7166-2695","authenticated-orcid":false,"given":"Han","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3708-4346","authenticated-orcid":false,"given":"Katherine","family":"Brooke-Wavell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9097-7381","authenticated-orcid":false,"given":"Michael R.","family":"Barnes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5356-4227","authenticated-orcid":false,"given":"Zainab","family":"Awan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9553-4886","authenticated-orcid":false,"given":"Sarabjit","family":"Mastana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8292-4191","authenticated-orcid":false,"given":"Sam","family":"Allen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7057-8073","authenticated-orcid":false,"given":"Richard C.","family":"Blagrove","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"2413_CR1","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/j.jshs.2021.04.001","volume":"10","author":"N Kakouris","year":"2021","unstructured":"Kakouris, N., Yener, N. & Fong, D. T. P. A systematic review of running-related musculoskeletal injuries in runners. J. Sport Health Sci. 10, 513\u2013522 (2021).","journal-title":"J. Sport Health Sci."},{"key":"2413_CR2","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1111\/sms.12541","volume":"26","author":"LC Hespanhol Junior","year":"2016","unstructured":"Hespanhol Junior, L. C., van Mechelen, W., Postuma, E. & Verhagen, E. Health and economic burden of running-related injuries in runners training for an event: a prospective cohort study. Scand. J. Med. Sci. Sports 26, 1091\u20131099 (2016).","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"2413_CR3","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1177\/1941738119888504","volume":"12","author":"SC Winter","year":"2020","unstructured":"Winter, S. C., Gordon, S., Brice, S. M., Lindsay, D. & Barrs, S. A multifactorial approach to overuse running injuries: a 1-year prospective study. Sports Health. 12, 296\u2013303 (2020).","journal-title":"Sports Health."},{"key":"2413_CR4","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1136\/bjsports-2015-095850","volume":"50","author":"NFN Bittencourt","year":"2016","unstructured":"Bittencourt, N. F. N. et al. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition\u2014narrative review and new concept. Br. J. Sports Med. 50, 1309\u20131314 (2016).","journal-title":"Br. J. Sports Med."},{"key":"2413_CR5","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1016\/j.jshs.2024.04.011","volume":"13","author":"CK Correia","year":"2024","unstructured":"Correia, C. K. et al. Risk factors for running-related injuries: an umbrella systematic review. J. Sport Health Sci. 13, 793\u2013804 (2024).","journal-title":"J. Sport Health Sci."},{"key":"2413_CR6","first-page":"173","volume":"3","author":"N Maffulli","year":"2013","unstructured":"Maffulli, N. et al. The genetics of sports injuries and athletic performance. Muscles Ligaments Tendons J. 3, 173\u2013189 (2013).","journal-title":"Muscles Ligaments Tendons J."},{"key":"2413_CR7","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1002\/cpt.3152","volume":"115","author":"K Naik","year":"2024","unstructured":"Naik, K. et al. Current status and future directions: the application of artificial intelligence\/machine learning for precision medicine. Clin. Pharm. Ther. 115, 673\u2013686 (2024).","journal-title":"Clin. Pharm. Ther."},{"key":"2413_CR8","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1136\/bjsports-2024-108576","volume":"59","author":"C Leckey","year":"2025","unstructured":"Leckey, C., van Dyk, N., Doherty, C., Lawlor, A. & Delahunt, E. Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis. Br. J. Sports Med. 59, 491\u2013500 (2025).","journal-title":"Br. J. Sports Med."},{"key":"2413_CR9","doi-asserted-by":"publisher","first-page":"101431","DOI":"10.1016\/j.rehab.2020.07.012","volume":"65","author":"P Edouard","year":"2022","unstructured":"Edouard, P., Verhagen, E. & Navarro, L. Machine learning analyses can be of interest to estimate the risk of injury in sports injury and rehabilitation. Ann. Phys. Rehabil. Med. 65, 101431 (2022).","journal-title":"Ann. Phys. Rehabil. Med."},{"key":"2413_CR10","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.1123\/ijspp.2020-0518","volume":"16","author":"SS L\u00f6vdal","year":"2021","unstructured":"L\u00f6vdal, S. S., Den Hartigh, R. J. R. & Azzopardi, G. Injury prediction in competitive runners with machine learning. Int. J. Sports Physiol. Perform. 16, 1522\u20131531 (2021).","journal-title":"Int. J. Sports Physiol. Perform."},{"key":"2413_CR11","doi-asserted-by":"publisher","first-page":"e0201264","DOI":"10.1371\/journal.pone.0201264","volume":"13","author":"A Rossi","year":"2018","unstructured":"Rossi, A. et al. Effective injury forecasting in soccer with GPS training data and machine learning. PLoS One 13, e0201264 (2018).","journal-title":"PLoS One"},{"key":"2413_CR12","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1080\/24733938.2022.2095006","volume":"7","author":"A Hecksteden","year":"2023","unstructured":"Hecksteden, A. et al. Forecasting football injuries by combining screening, monitoring and machine learning. Sci. Med. Footb. 7, 214\u2013228 (2023).","journal-title":"Sci. Med. Footb."},{"key":"2413_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3390\/sports10010003","volume":"10","author":"M Mandorino","year":"2021","unstructured":"Mandorino, M., Figueiredo, A. J., Cima, G. & Tessitore, A. Predictive analytic techniques to identify hidden relationships between training load, fatigue and muscle strains in young soccer players. Sports 10, 3 (2021).","journal-title":"Sports"},{"key":"2413_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.2478\/ijcss-2018-0002","volume":"17","author":"DL Carey","year":"2018","unstructured":"Carey, D. L. et al. Predictive modelling of training loads and injury in Australian football. Int. J. Comput Sci. Sport 17, 49\u201366 (2018).","journal-title":"Int. J. Comput Sci. Sport"},{"key":"2413_CR15","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1055\/a-1502-6824","volume":"43","author":"L Goggins","year":"2022","unstructured":"Goggins, L. et al. Detecting injury risk factors with algorithmic models in elite women\u2019s pathway cricket. Int. J. Sports Med. 43, 344\u2013349 (2022).","journal-title":"Int. J. Sports Med."},{"key":"2413_CR16","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1123\/ijspp.2016-0326","volume":"12","author":"HR Thornton","year":"2017","unstructured":"Thornton, H. R., Delaney, J. A., Duthie, G. M. & Dascombe, B. J. Importance of various training-load measures in injury incidence of professional rugby league athletes. Int. J. Sports Physiol. Perform. 12, 819\u2013824 (2017).","journal-title":"Int. J. Sports Physiol. Perform."},{"key":"2413_CR17","doi-asserted-by":"publisher","unstructured":"Iatropoulos, S., Dandrieux, P.-E., Edouard, P. & Navarro, L. Development and internal validation of machine learning prognostic models of sports injuries using self-reported data in athletics (track and field): The influence of quantity and quality of features. J. Sports Sci. 1\u201315, https:\/\/doi.org\/10.1080\/02640414.2025.2517971 (2025).","DOI":"10.1080\/02640414.2025.2517971"},{"key":"2413_CR18","unstructured":"Spiriev, B., Spiriev, A. World Athletics Scoring Tables of Athletics. https:\/\/worldathletics.org\/about-iaaf\/documents\/technical-information (2022)."},{"key":"2413_CR19","doi-asserted-by":"publisher","unstructured":"Wu, H. et al. A time-sequenced approach to machine learning prognostic modelling with implementation on running-related injury prediction. Preprint at https:\/\/doi.org\/10.1101\/2025.05.07.25327162 (2025).","DOI":"10.1101\/2025.05.07.25327162"},{"key":"2413_CR20","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1002\/bimj.201300297","volume":"56","author":"EW Steyerberg","year":"2014","unstructured":"Steyerberg, E. W., van der Ploeg, T. & Van Calster, B. Risk prediction with machine learning and regression methods. Biometrical J. 56, 601\u2013606 (2014).","journal-title":"Biometrical J."},{"key":"2413_CR21","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-019-1004-8","volume":"19","author":"S Uddin","year":"2019","unstructured":"Uddin, S., Khan, A., Hossain, M. E. & Moni, M. A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inf. Decis. Mak. 19, 281 (2019).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"2413_CR22","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1007\/s41060-024-00523-y","volume":"20","author":"G Papageorgiou","year":"2025","unstructured":"Papageorgiou, G., Sarlis, V. & Tjortjis, C. An innovative method for accurate NBA player performance forecasting and line-up optimization in daily fantasy sports. Int. J. Data Sci. Anal. 20, 1215\u20131238 (2025).","journal-title":"Int. J. Data Sci. Anal."},{"key":"2413_CR23","doi-asserted-by":"publisher","unstructured":"Yang, F. An implementation of naive bayes classifier. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI) 301\u2013306 (IEEE, 2018). https:\/\/doi.org\/10.1109\/CSCI46756.2018.00065.","DOI":"10.1109\/CSCI46756.2018.00065"},{"key":"2413_CR24","doi-asserted-by":"publisher","first-page":"15516","DOI":"10.1109\/ACCESS.2022.3145368","volume":"10","author":"S Senbel","year":"2022","unstructured":"Senbel, S. et al. Impact of sleep and training on game performance and injury in division-1 women\u2019s basketball amidst the pandemic. IEEE Access 10, 15516\u201315527 (2022).","journal-title":"IEEE Access"},{"key":"2413_CR25","doi-asserted-by":"crossref","unstructured":"Goldberg, M. et al. Poor sleep quality is associated with an increased risk of running-related injuries: a prospective study of 339 runners over six months. Scand. J. Med. Sci. Sports 35, e70164 (2025).","DOI":"10.1111\/sms.70164"},{"key":"2413_CR26","doi-asserted-by":"crossref","unstructured":"Xiang, L. et al. Recent machine learning progress in lower limb running biomechanics with wearable technology: a systematic review. Front. Neurorobot. 16, 913052 (2022).","DOI":"10.3389\/fnbot.2022.913052"},{"key":"2413_CR27","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.3390\/nu16071073","volume":"16","author":"TP Theodore Armand","year":"2024","unstructured":"Theodore Armand, T. P., Nfor, K. A., Kim, J.-I. & Kim, H.-C. Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review. Nutrients 16, 1073 (2024).","journal-title":"Nutrients"},{"key":"2413_CR28","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1177\/0363546518773755","volume":"46","author":"SP Messier","year":"2018","unstructured":"Messier, S. P. et al. A 2-year prospective cohort study of overuse running injuries: the runners and injury longitudinal study (TRAILS). Am. J. Sports Med. 46, 2211\u20132221 (2018).","journal-title":"Am. J. Sports Med."},{"key":"2413_CR29","doi-asserted-by":"crossref","unstructured":"Nielsen, R. O. et al. Predictors of running-related injuries among 930 novice runners: a 1-year prospective follow-up study. Orthop. J. Sports Med. 1, 2325967113487316 (2013).","DOI":"10.1177\/2325967113487316"},{"key":"2413_CR30","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1177\/0363546509347985","volume":"38","author":"I Buist","year":"2010","unstructured":"Buist, I., Bredeweg, S. W., Lemmink, K. A. P. M., van Mechelen, W. & Diercks, R. L. Predictors of running-related injuries in novice runners enrolled in a systematic training program. Am. J. Sports Med. 38, 273\u2013280 (2010).","journal-title":"Am. J. Sports Med."},{"key":"2413_CR31","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/S1836-9553(13)70203-0","volume":"59","author":"LC Hespanhol Junior","year":"2013","unstructured":"Hespanhol Junior, L. C., Pena Costa, L. O. & Lopes, A. D. Previous injuries and some training characteristics predict running-related injuries in recreational runners: a prospective cohort study. J. Physiother. 59, 263\u2013269 (2013).","journal-title":"J. Physiother."},{"key":"2413_CR32","first-page":"948","volume":"9","author":"MJ Rauh","year":"2014","unstructured":"Rauh, M. J., Barrack, M. & Nichols, J. F. Associations between the female athlete triad and injury among high school runners. Int J. Sports Phys. Ther. 9, 948\u2013958 (2014).","journal-title":"Int J. Sports Phys. Ther."},{"key":"2413_CR33","first-page":"868","volume":"10","author":"LE Luedke","year":"2015","unstructured":"Luedke, L. E., Heiderscheit, B. C., Blaise Williams, D. S. & Rauh, M. J. Association of isometric strength of hip and knee muscles with injury risk in high school cross country runners. Int. J. Sports Phys. Ther. 10, 868\u2013876 (2015).","journal-title":"Int. J. Sports Phys. Ther."},{"key":"2413_CR34","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s40621-020-00237-2","volume":"7","author":"J Jungmalm","year":"2020","unstructured":"Jungmalm, J. et al. Associations between biomechanical and clinical\/anthropometrical factors and running-related injuries among recreational runners: a 52-week prospective cohort study. Inj. Epidemiol. 7, 10 (2020).","journal-title":"Inj. Epidemiol."},{"key":"2413_CR35","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/1758-2555-1-11","volume":"1","author":"GRD Raissi","year":"2009","unstructured":"Raissi, G. R. D., Cherati, A. D. S., Mansoori, K. D. & Razi, M. D. The relationship between lower extremity alignment and Medial Tibial Stress Syndrome among non-professional athletes. BMC Sports Sci. Med. Rehabil. 1, 11 (2009).","journal-title":"BMC Sports Sci. Med. Rehabil."},{"key":"2413_CR36","doi-asserted-by":"publisher","first-page":"725","DOI":"10.2519\/jospt.2007.2453","volume":"37","author":"MJ Rauh","year":"2007","unstructured":"Rauh, M. J., Koepsell, T. D., Rivara, F. P., Rice, S. G. & Margherita, A. J. Quadriceps angle and risk of injury among high school cross-country runners. J. Orthop. Sports Phys. Ther. 37, 725\u2013733 (2007).","journal-title":"J. Orthop. Sports Phys. Ther."},{"key":"2413_CR37","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1136\/bjsports-2015-094579","volume":"50","author":"IS Davis","year":"2016","unstructured":"Davis, I. S., Bowser, B. J. & Mullineaux, D. R. Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. Br. J. Sports Med. 50, 887\u2013892 (2016).","journal-title":"Br. J. Sports Med."},{"key":"2413_CR38","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1177\/03635465211063909","volume":"50","author":"L Malisoux","year":"2022","unstructured":"Malisoux, L., Gette, P., Delattre, N., Urhausen, A. & Theisen, D. Spatiotemporal and ground-reaction force characteristics as risk factors for running-related injury: a secondary analysis of a randomized trial including 800+ recreational runners. Am. J. Sports Med. 50, 537\u2013544 (2022).","journal-title":"Am. J. Sports Med."},{"key":"2413_CR39","doi-asserted-by":"crossref","unstructured":"Gerlach, K. E., Burton, H. W., Dorn, J. M., Leddy, J. J. & Horvath, P. J. Fat intake and injury in female runners. J. Int. Soc. Sports Nutr. 5, 1 (2008).","DOI":"10.1186\/1550-2783-5-1"},{"key":"2413_CR40","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1001\/archinte.1989.00390110113025","volume":"149","author":"SD Walter","year":"1989","unstructured":"Walter, S. D., Hart, L. E., Mclntosh, J. M. & Sutton, J. R. The Ontario cohort study of running-related injuries. Arch. Intern. Med. 149, 2561 (1989).","journal-title":"Arch. Intern. Med."},{"key":"2413_CR41","doi-asserted-by":"publisher","unstructured":"Dijkhuis, T. B., Otter, R., Aiello, M., Velthuijsen, H. & Lemmink, K. Increase in the acute: chronic workload ratio relates to injury risk in competitive runners. Int. J. Sports Med. https:\/\/doi.org\/10.1055\/a-1171-2331 (2020).","DOI":"10.1055\/a-1171-2331"},{"key":"2413_CR42","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1136\/bjsports-2023-107926","volume":"58","author":"M Lepp\u00e4nen","year":"2024","unstructured":"Lepp\u00e4nen, M. et al. Hip and core exercise programme prevents running-related overuse injuries in adult novice recreational runners: a three-arm randomised controlled trial (Run RCT). Br. J. Sports Med. 58, 722\u2013732 (2024).","journal-title":"Br. J. Sports Med."},{"key":"2413_CR43","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1002\/jgm.2934","volume":"18","author":"FB de Araujo Munhoz","year":"2016","unstructured":"de Araujo Munhoz, F. B. et al. Posterior tibial tendinopathy associated with matrix metalloproteinase 13 promoter genotype and haplotype. J. Gene Med. 18, 325\u2013330 (2016).","journal-title":"J. Gene Med."},{"key":"2413_CR44","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1136\/bjsm.2008.053892","volume":"43","author":"SM Raleigh","year":"2009","unstructured":"Raleigh, S. M. et al. Variants within the MMP3 gene are associated with Achilles tendinopathy: possible interaction with the COL5A1 gene. Br. J. Sports Med. 43, 514\u2013520 (2009).","journal-title":"Br. J. Sports Med."},{"key":"2413_CR45","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1016\/j.jsams.2019.05.021","volume":"22","author":"G Nie","year":"2019","unstructured":"Nie, G. et al. Additional evidence supports association of common genetic variants in MMP3 and TIMP2 with increased risk of chronic Achilles tendinopathy susceptibility. J. Sci. Med. Sport 22, 1074\u20131078 (2019).","journal-title":"J. Sci. Med. Sport"},{"key":"2413_CR46","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s11010-020-03765-z","volume":"471","author":"PRB Nogara","year":"2020","unstructured":"Nogara, P. R. B. et al. Association of estrogen receptor \u03b2 polymorphisms with posterior tibial tendon dysfunction. Mol. Cell Biochem. 471, 63\u201369 (2020).","journal-title":"Mol. Cell Biochem."},{"key":"2413_CR47","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.3390\/genes12071056","volume":"12","author":"K Le\u017anicka","year":"2021","unstructured":"Le\u017anicka, K. et al. Interactions between Gene Variants within the COL1A1 and COL5A1 Genes and Musculoskeletal Injuries in Physically Active Caucasian. Genes (Basel) 12, 1056 (2021).","journal-title":"Genes (Basel)"},{"key":"2413_CR48","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1186\/s13018-018-1020-x","volume":"13","author":"PA Pontin","year":"2018","unstructured":"Pontin, P. A. et al. ER\u03b1 PvuII and XbaI polymorphisms in postmenopausal women with posterior tibial tendon dysfunction: a case control study. J. Orthop. Surg. Res. 13, 316 (2018).","journal-title":"J. Orthop. Surg. Res."},{"key":"2413_CR49","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1177\/1941738112446684","volume":"4","author":"RD Bell","year":"2012","unstructured":"Bell, R. D., Shultz, S. J., Wideman, L. & Henrich, V. C. Collagen gene variants previously associated with anterior cruciate ligament injury risk are also associated with joint laxity. Sports Health. 4, 312\u2013318 (2012).","journal-title":"Sports Health."},{"key":"2413_CR50","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1111\/sms.14077","volume":"32","author":"ECR Hall","year":"2022","unstructured":"Hall, E. C. R. et al. The genetic association with injury risk in male academy soccer players depends on maturity status. Scand. J. Med. Sci. Sports 32, 338\u2013350 (2022).","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"2413_CR51","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1186\/s13018-022-03020-9","volume":"17","author":"R Guo","year":"2022","unstructured":"Guo, R. et al. Association of COL5A1 gene polymorphisms and musculoskeletal soft tissue injuries: a meta-analysis based on 21 observational studies. J. Orthop. Surg. Res. 17, 129 (2022).","journal-title":"J. Orthop. Surg. Res."},{"key":"2413_CR52","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1007\/s00198-008-0606-2","volume":"19","author":"BK Weeks","year":"2008","unstructured":"Weeks, B. K. & Beck, B. R. The BPAQ: a bone-specific physical activity assessment instrument. Osteoporos. Int. 19, 1567\u20131577 (2008).","journal-title":"Osteoporos. Int."},{"key":"2413_CR53","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1002\/(SICI)1098-108X(199904)25:3<349::AID-EAT15>3.0.CO;2-M","volume":"25","author":"KH Luce","year":"1999","unstructured":"Luce, K. H. & Crowther, J. H. The reliability of the eating disorder examination-Self-report questionnaire version (EDE-Q). Int. J. Eat. Disord. 25, 349\u2013351 (1999).","journal-title":"Int. J. Eat. Disord."},{"key":"2413_CR54","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1136\/bjsports-2013-093240","volume":"48","author":"A Melin","year":"2014","unstructured":"Melin, A. et al. The LEAF questionnaire: a screening tool for the identification of female athletes at risk for the female athlete triad. Br. J. Sports Med. 48, 540\u2013545 (2014).","journal-title":"Br. J. Sports Med."},{"key":"2413_CR55","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1016\/S0030-5898(20)30252-2","volume":"13","author":"DM Brody","year":"1982","unstructured":"Brody, D. M. Techniques in the evaluation and treatment of the injured runner. Orthop. Clin. North Am. 13, 541\u2013558 (1982).","journal-title":"Orthop. Clin. North Am."},{"key":"2413_CR56","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1136\/bjsports-2019-101337","volume":"54","author":"B Clarsen","year":"2020","unstructured":"Clarsen, B. et al. Improved reporting of overuse injuries and health problems in sport: an update of the Oslo Sport Trauma Research Center questionnaires. Br. J. Sports Med. 54, 390\u2013396 (2020).","journal-title":"Br. J. Sports Med."},{"key":"2413_CR57","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.jsams.2012.08.002","volume":"16","author":"SW Bredeweg","year":"2013","unstructured":"Bredeweg, S. W., Kluitenberg, B., Bessem, B. & Buist, I. Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J. Sci. Med. Sport 16, 205\u2013210 (2013).","journal-title":"J. Sci. Med. Sport"},{"key":"2413_CR58","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1093\/ecco-jcc\/jjab155","volume":"16","author":"NH Nguyen","year":"2022","unstructured":"Nguyen, N. H. et al. Machine learning-based prediction models for diagnosis and prognosis in inflammatory bowel diseases: a systematic review. J. Crohns Colitis 16, 398\u2013413 (2022).","journal-title":"J. Crohns Colitis"},{"key":"2413_CR59","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/s42452-021-04148-9","volume":"3","author":"M Saarela","year":"2021","unstructured":"Saarela, M. & Jauhiainen, S. Comparison of feature importance measures as explanations for classification models. SN Appl. Sci. 3, 272 (2021).","journal-title":"SN Appl. Sci."},{"key":"2413_CR60","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","volume":"85","author":"RJ Urbanowicz","year":"2018","unstructured":"Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S. & Moore, J. H. Relief-based feature selection: introduction and review. J. Biomed. Inf. 85, 189\u2013203 (2018).","journal-title":"J. Biomed. Inf."},{"key":"2413_CR61","doi-asserted-by":"publisher","first-page":"01046","DOI":"10.1051\/e3sconf\/202235101046","volume":"351","author":"Y Bouchlaghem","year":"2022","unstructured":"Bouchlaghem, Y., Akhiat, Y. & Amjad, S. Feature selection: a review and comparative study. E3S Web. Conf. 351, 01046 (2022).","journal-title":"E3S Web. Conf."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02413-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02413-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02413-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T13:03:25Z","timestamp":1773407005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02413-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,6]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2413"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02413-y","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,6]]},"assertion":[{"value":"29 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"213"}}