Abstract
When training for endurance activities, such as the marathon, the risk of injury is ever-present, especially for first-time or inexperienced athletes. And because injuries depend on various factors, there is an opportunity to provide athletes with greater levels of support and guidance when it comes to the risks associated with their training. Hence, in this work we propose a case-based reasoning approach to predict injury risk for marathoners and provide actionable explanations so that runners can understand this risk and potentially reduce it. We do this using the type of activity data collected by common training apps, with extended training breaks used as a proxy for injury (in the absence of explicit injury data), and we show how future breaks can be predicted based on the training patterns of similar runners. Furthermore, we demonstrate how counterfactual explanations can be used to highlight those features that are unique to injured runners (those suffering from training breaks) to emphasise training behaviours that may be responsible for higher levels of injury risk for the target runner. We evaluate our work with a dataset of real-world training data by more than 5,000 real marathon runners.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Mann, R.: Biomechanics of running. Running Injuries, pp. 1–20 (1989)
Kluitenberg, B., van Middelkoop, M., Diercks, R., van der Worp, H.: What are the differences in injury proportions between different populations of runners? A systematic review and meta-analysis. Sports Med. 45(8), 1143–1161 (2015). https://doi.org/10.1007/s40279-015-0331-x
Napier, C., MacLean, C.L., Maurer, J., Taunton, J.E., Hunt, M.A.: Kinetic risk factors of running-related injuries in female recreational runners. Scand. J. Med. Sci. Sports 28, 2164–2172 (2018)
Vannatta, C.N., Heinert, B.L., Kernozek, T.W.: Biomechanical risk factors for running-related injury differ by sample population: a systematic review and meta-analysis. Clin. Biomech. 75, 10499 (2020)
Nielsen, R.O., Buist, I., Sørensen, H., Lind, M., Rasmussen, S.: Training errors and running related injuries: a systematic review. Int. J. Sports Phys. Ther. 7, 58–75 (2012)
Baltich, J., Emery, C., Whittaker, J., Nigg, B.: Running injuries in novice runners enrolled in different training interventions: a pilot randomized controlled trial. Scand. J. Med. Sci. Sports 27, 08 (2016)
Damsted, C., Parner, E.T., Sørensen, H., Malisoux, L., Nielsen, R.O.: ProjectRun21: do running experience and running pace influence the risk of running injury-A 14-week prospective cohort study. J. Sci. Med. Sport 22, 281–287 (2019)
Kemler, E., Blokland, D., Backx, F., Huisstede, B.: Differences in injury risk and characteristics of injuries between novice and experienced runners over a 4-year period. Phys. Sportsmed. 46, 485–491 (2018)
Agresta, C.E., Peacock, J., Housner, J., Zernicke, R.F., Zendler, J.D.: Experience does not influence injury-related joint kinematics and kinetics in distance runners. Gait Posture 61, 13–18 (2018)
Fokkema, T.: Prognosis and prevention of injuries in recreational runners. Ph.D. thesis, University of Rotterdam (2020)
Fokkema, T., et al.: Online multifactorial prevention programme has no effect on the number of running-related injuries: a randomised controlled trial. Br. J. Sports Med. 53, 1479–1485 (2019)
Fokkema, T., Vos, R.-J., Bierma-Zeinstra, S., Middelkoop, M.: Opinions, barriers, and facilitators of injury prevention in recreational runners. J. Orthop. Sports Phys. Ther. 49, 1–22 (2019)
Fields, K.B., Delaney, M., Hinkle, J.S.: A prospective study of type A behavior and running injuries. J. Fam. Pract. 30, 425–429 (1990)
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(1), 2325967113487316 (2013)
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, 819–824 (2017)
Malisoux, L., Nielsen, R.O., Urhausen, A., Theisen, D.: A step towards understanding the mechanisms of running-related injuries. J. Sci. Med. Sport 18, 523–528 (2015)
Lazarus, B.H., et al.: Proposal of a global training load measure predicting match performance in an elite team sport. Front. Physiol. 8, 930 (2017)
Barros, E.S., et al.: Acute and chronic effects of endurance running on inflammatory markers: a systematic review. Front. Physiol. 8, 779 (2017)
Bowen, L., Gross, A.S., Gimpel, M., Bruce-Low, S., Li, F.-X.: Spikes in acute: chronic workload ratio (ACWR) associated with a 5–7 times greater injury rate in English Premier League football players: a comprehensive 3-year study. Br. J. Sports Med. (2019). https://doi.org/10.1136/bjsports-2018-099422
Bornn, L., Ward, P., Norman, D.: Training schedule confounds the relationship between acute: chronic workload ratio and injury, Sloansportsconference Com (2019)
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F., Fernández, J., Medina, D.: Effective injury forecasting in soccer with GPS training data and machine learning. PLOS One 13, e0201264 (2018)
Gabbett, T.J.: The training—injury prevention paradox: should athletes be training smarter and harder? Br. J. Sports Med. 50(5), 273–280 (2016)
López-Valenciano, A., et al.: A preventive model for muscle injuries: a novel approach based on learning algorithms. Med. Sci. Sports Exerc. 50, 915–927 (2018)
Claudino, J.G., Capanema, D.O., de Souza, T.V., Serrão, J.C., Machado Pereira, A.C., Nassis, G.P.: Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports Med. Open 5(1), 1–12 (2019). https://doi.org/10.1186/s40798-019-0202-3
Carey, D.L., Ong, K.-L., Whiteley, R., Crossley, K.M., Crow, J., Morris, M.E.: Predictive modelling of training loads and injury in Australian football, arXiv preprint arXiv:1706.04336 (2017)
Kampakis, S.: Predictive modelling of football injuries, arXiv preprint arXiv:1609.07480 (2016)
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F.M., Fernàndez, J., Medina, D.: Effective injury forecasting in soccer with GPS training data and machine learning. PloS One 13(7), e0201264 (2018)
Kampakis, S.: Comparison of machine learning methods for predicting the recovery time of professional football players after an undiagnosed injury. In: MLSA@PKDD/ECML (2013)
Rajšp, A., Fister, I.: A systematic literature review of intelligent data analysis methods for smart sport training. Appl. Sci. 10(9), 3013 (2020)
Berndsen, J., Lawlor, A., Smyth, B.: Running with recommendation. In: HealthRecSys@ RecSys, pp. 18–21 (2017)
Berndsen, J., Smyth, B., Lawlor, A.: Pace my race: recommendations for marathon running. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 246–250. ACM (2019)
Smyth, B., Cunningham, P.: Running with cases: a CBR approach to running your best marathon. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 360–374. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61030-6_25
Smyth, B., Cunningham, P.: An analysis of case representations for marathon race prediction and planning. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 369–384. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_25
Feely, C., Caulfield, B., Lawlor, A., Smyth, B.: Using case-based reasoning to predict marathon performance and recommend tailored training plans. In: Watson, I., Weber, R. (eds.) ICCBR 2020. LNCS (LNAI), vol. 12311, pp. 67–81. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58342-2_5
Feely, C., Caulfield, B., Lawlor, A., Smyth, B.: Providing explainable race-time predictions and training plan recommendations to marathon runners. In: Fourteenth ACM Conference on Recommender Systems, RecSys 2020, New York, NY, USA, pp. 539-544. Association for Computing Machinery (2020)
Smyth, B., Lawlor, A., Bernsden, J., Feely, C.: Recommendations for marathon runners, User Modeling and User Adapted Interaction (Unpublished)
Keane, M.T., Smyth, B.: Good counterfactuals and where to find them: a case-based technique for generating counterfactuals for explainable AI (XAI). In: Watson, I., Weber, R. (eds.) ICCBR 2020. LNCS (LNAI), vol. 12311, pp. 163–178. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58342-2_11
Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)
Acknowledgments
Supported by Science Foundation Ireland through the Insight Centre for Data Analytics (12/RC/2289_P2) and the SFI Centre for Research Training in Machine Learning (18/CRT/6183).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Feely, C., Caulfield, B., Lawlor, A., Smyth, B. (2021). A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-86957-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86956-4
Online ISBN: 978-3-030-86957-1
eBook Packages: Computer ScienceComputer Science (R0)