{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:08:49Z","timestamp":1764842929209,"version":"3.28.0"},"reference-count":30,"publisher":"Georg Thieme Verlag KG","issue":"CP","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sports Med Int Open"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We introduce a novel approach for predicting running performance, designed to\n                    apply across a wide range of race distances (from marathons to ultras),\n                    elevation gains, and runner types (front-pack to back of the pack). To achieve\n                    this, the entire running logs of 15 runners, encompassing a total of 15,686\n                    runs, were analyzed using two approaches: (1) regression and (2) time series\n                    regression (TSR). First, the prediction accuracy of a long short-term memory\n                    (LSTM) network was compared using both approaches. The regression approach\n                    demonstrated superior performance, achieving an accuracy of 89.13% in contrast,\n                    the TSR approach reached an accuracy of 85.21%. Both methods were evaluated\n                    using a test dataset that included the last 15 runs from each running log.\n                    Secondly, the performance of the LSTM model was compared against two benchmark\n                    models: Riegel formula and UltraSignup formula for a total of 60 races. The\n                    Riegel formula achieves an accuracy of 80%, UltraSignup 87.5%, and the LSTM\n                    model exhibits 90.4% accuracy. This work holds potential for integration into\n                    popular running apps and wearables, offering runners data-driven insights during\n                    their race preparations.<\/jats:p>","DOI":"10.1055\/a-2401-6234","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T11:41:48Z","timestamp":1728560508000},"source":"Crossref","is-referenced-by-count":2,"special_numbering":"S","title":["Win Your Race Goal: A Generalized Approach to Prediction of Running\n                    Performance"],"prefix":"10.1055","volume":"08","author":[{"given":"Sandhyarani","family":"Dash","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Portland State University,\n                    Portland, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s40279-023-01936-8","article-title":"Limits of ultra: towards an interdisciplinary understanding of ultra-endurance\n                    running performance","volume":"54","author":"NJ A Berger","year":"2024","journal-title":"Sports Med"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1123\/ijspp.2020-0766","article-title":"Physiological determinants of ultramarathon trail-running performance","volume":"16","author":"M Coates","year":"2021","journal-title":"Int J Sports Physiol Perform"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.crphys.2022.06.006","article-title":"Could middle- and long-distance running performance of well-trained athletes be\n                    best predicted by the same aerobic parameters?","volume":"5","author":"B S Denadai","year":"2022","journal-title":"Curr Res Physiol"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"7416","DOI":"10.1038\/s41598-023-29626-5","article-title":"Running energy reserve index (RERI) as a new model for assessment and prediction\n                    of world, elite, sub-elite, and collegiate running performances","volume":"13","author":"G Balasekaran","year":"2023","journal-title":"Sci Rep"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1080\/17461391.2021.1995507","article-title":"Effect of ground technicity on cardio-respiratory and biomechanical parameters\n                    in uphill trail running","volume":"22","author":"F Nicot","year":"2022","journal-title":"Eur J Sport Sci"},{"key":"ref7","first-page":"285","article-title":"Athletic records and human endurance: a time-vs-distance equation describing\n                    world-record performances may be used to compare the relative endurance\n                    capabilities of various groups of people","volume":"69","author":"P S Riegel","year":"1981","journal-title":"Am Sci"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s13102-016-0052-y","article-title":"An empirical study of race times in recreational endurance runners","volume":"8","author":"A J Vickers","year":"2016","journal-title":"BMC Sports Sci Med Rehabil"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"e0157257","DOI":"10.1371\/journal.pone.0157257","article-title":"Prediction and quantification of individual athletic performance of runners","volume":"11","author":"DA J Blythe","year":"2016","journal-title":"PLoS One"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"415","DOI":"10.5604\/20831862.1224463","article-title":"Regression shrinkage and neural models in predicting the results of 400-metres\n                    hurdles races","volume":"33","author":"K Przednowek","year":"2016","journal-title":"Biol Sport"},{"volume-title":"Software tools for self-quantifier runners. [M.S. thesis].\n                    Yverdon-les-Bains","year":"2015","author":"A R Admetlla","key":"ref11"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1007\/978-3-319-61030-6_25","article-title":"Running with cases: a CBR approach to running your best marathon","volume":"10339","author":"B Smyth","year":"2017","journal-title":"in Case-Based Reasoning Research and Development"},{"key":"ref13","first-page":"5364","volume-title":"Marathon race planning: a case-based reasoning approach. In: Proceedings of the\n                    Twenty-Seventh International Joint Conference on Artificial Intelligence\n                    (IJCAI\u201918). 2018 Jul 13\u201319; Stockholm, Sweden","author":"B Smyth","year":"2018"},{"key":"ref14","first-page":"369","volume-title":"An analysis of case representations for marathon race prediction and planning.\n                    In: Cox M, Funk P, Begum S (eds). Case-Based Reasoning Research and\n                    Development","author":"B Smyth","year":"2018"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1055\/a-1993-2371","article-title":"Prediction of marathon performance using artificial intelligence","volume":"44","author":"L Lerebourg","year":"2023","journal-title":"Int J Sports Med"},{"key":"ref18","first-page":"293","volume-title":"A better predictor of marathon race times based on neural networks. In: Bramer\n                    M, Petridis M (eds) Artificial Intelligence XXXIV. Lecture Notes in Computer\n                    Science","author":"D C Dracopoulos","year":"2017"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"1096124","DOI":"10.3389\/fspor.2022.1096124","article-title":"Estimating the cost of training disruptions on marathon performance","volume":"4","author":"C Feely","year":"2023","journal-title":"Front Sports Act Living"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1123\/ijspp.2020-0518","article-title":"Injury prediction in competitive runners with machine learning","volume":"16","author":"S S L\u00f6vdal","year":"2021","journal-title":"Int J Sports Physiol Perform"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1515\/jqas-2020-0013","article-title":"TRAP: a predictive framework for the assessment of performance in trail\n                    running","volume":"17","author":"R Fogliato","year":"2021","journal-title":"J Quant Anal Sports"},{"key":"ref22","first-page":"358","volume-title":"Going further with cases: using case-based reasoning to recommend pacing\n                    strategies for ultra-marathon runners. 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Case-Based\n                    Reasoning Research and Development","author":"C McConnell","year":"2019"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"e0279662","DOI":"10.1371\/journal.pone.0279662","article-title":"Prediction of performance in a 100-km run from a simple equation","volume":"18","author":"J B Coquart","year":"2023","journal-title":"PLoS One"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1287\/mnsc.42.7.1082","article-title":"Neural network models for time series forecasts","volume":"42","author":"T Hill","year":"1996","journal-title":"Manage Sci"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1111\/sms.14101","article-title":"Effects of 16 weeks of pyramidal and polarized training intensity distributions\n                    in well-trained endurance runners","volume":"32","author":"L Filipas","year":"2022","journal-title":"Scand J Med Sci Sports"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"e0282838","DOI":"10.1371\/journal.pone.0282838","article-title":"Effects of tapering on performance in endurance athletes: A systematic review\n                    and meta-analysis","volume":"18","author":"Z Wang","year":"2023","journal-title":"PLoS One"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s40798-022-00445-8","article-title":"Reverse periodization for improving sports performance: a systematic review","volume":"8","author":"J M Gonz\u00e1lez-Rav\u00e9","year":"2022","journal-title":"Sports Med Open"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1055\/a-1559-3623","article-title":"Training-intensity distribution on middle- and long-distance runners: a\n                    systematic review","volume":"43","author":"Y Campos","year":"2022","journal-title":"Int J Sports Med"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1123\/ijspp.2017-0327","article-title":"The effect of periodization and training intensity distribution on middle- and\n                    long-distance running performance: a systematic review","volume":"13","author":"M Kenneally","year":"2018","journal-title":"Int J Sports Physiol Perform"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"5564","DOI":"10.1038\/s41467-021-25801-2","article-title":"Next generation reservoir computing","volume":"12","author":"D J Gauthier","year":"2021","journal-title":"Nat Commun"},{"key":"ref32","first-page":"73","article-title":"Dilated convolutions neural networks for time series forecasting","volume":"22","author":"A Borovykh","year":"2019","journal-title":"J Comput Finance"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","article-title":"A survey on long short-term memory networks for time series prediction","volume":"99","author":"B Lindemann","year":"2021","journal-title":"Procedia CIRP"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"106303","DOI":"10.1016\/j.engappai.2023.106303","article-title":"Time consideration in machine learning models for train comfort prediction using\n                    LSTM networks","volume":"123","author":"P G Mart\u0131nez-Llop","year":"2023","journal-title":"Eng Appl of Artif Intell"}],"container-title":["Sports Medicine International Open"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2401-6234.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T11:13:59Z","timestamp":1730805239000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2401-6234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"references-count":30,"journal-issue":{"issue":"CP","published-online":{"date-parts":[[2024,1,8]]},"published-print":{"date-parts":[[2024]]}},"alternative-id":["a24016234"],"URL":"https:\/\/doi.org\/10.1055\/a-2401-6234","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["2367-1890"],"issn-type":[{"type":"electronic","value":"2367-1890"}],"subject":[],"published":{"date-parts":[[2024,10,9]]}}}