Article

An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon Runners

Authors:

Brian Caulfield,

Aonghus Lawlor,

Barry SmythAuthors Info & Claims

Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12–15, 2022, Proceedings

Pages 335 - 349

https://doi.org/10.1007/978-3-031-14923-8_22

Published: 12 September 2022 Publication History

Abstract

As running has become an increasingly popular method of personal exercise, more and more recreational runners have been testing themselves by participating in endurance events such as marathons. Even though elite endurance runners have been the subject of considerable research, the training habits and performance potential of recreational runners are not as well-understood. Consequently, recreational runners often have to rely on one-size-fits-all training programmes and race prediction models. As a result, recreational runners frequently suffer from a lack of expert feedback during training and if their race-time prediction is inaccurate this can significantly disrupt their race planning and lead to a sub-optimal race-time after months of hard work. The main contribution of this work is to describe an extended case-based reasoning system for predicting the race-times of recreational runners which, for the first time, uses a combination of training history and past race-times in order to improve prediction accuracy. The work is evaluated using real-world data from more than 150,000 marathon training programmes.

References

[1]

Feely C, Caulfield B, Lawlor A, and Smyth B Watson I and Weber R Using case-based reasoning to predict marathon performance and recommend tailored training plans Case-Based Reasoning Research and Development 2020 Cham Springer 67-81

[2]

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)

[3]

Smyth B and Cunningham P Aha DW and Lieber J Running with cases: a CBR approach to running your best marathon Case-Based Reasoning Research and Development 2017 Cham Springer 360-374

[4]

Smyth B and Cunningham P Cox MT, Funk P, and Begum S An analysis of case representations for marathon race prediction and planning Case-Based Reasoning Research and Development 2018 Cham Springer 369-384

[5]

Smyth, B., Cunningham, P.: Marathon race planning: a case-based reasoning approach. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, 13–19 July 2018, Stockholm, Sweden, pp. 5364–5368 (2018)

[6]

Smyth, B., Cunningham, P.: A novel recommender system for helping marathoners to achieve a new personal-best. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, 27–31 August 2017, pp. 116–120 (2017)

[7]

Keogh A, Smyth B, Caulfield B, Lawlor A, Berndsen J, and Doherty C Prediction equations for marathon performance: a systematic review Int. J. Sports Physiol. Perform. 2019 14 9 1159-1169

[8]

Costill D Physiology of marathon running JAMA 1972 221 1024-1029

[9]

Faude O, Kindermann W, and Meyer T Lactate threshold concepts Sports Med. 2009 39 6 469-490

[10]

Doherty C, Keogh A, Davenport J, Lawlor A, Smyth B, and Caulfield B An evaluation of the training determinants of marathon performance: a meta-analysis with meta-regression J. Sci. Med. Sport 2020 23 2 182-188

[11]

Riegel PS Athletic records and human endurance: a time-vs.-distance equation describing world-record performances may be used to compare the relative endurance capabilities of various groups of people Am. Sci. 1981 69 3 285-290

[12]

Slovic P Empirical study of training and performance in the marathon Res. Q. Am. Alliance Health Phys. Educ. Recreat. 1977 48 4 769-777

[13]

Gasparetti, F., Aiello, L.M., Quercia, D.: Evaluating the efficacy of traditional fitness tracker recommendations. In: Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion, IUI 2019, New York, NY, USA, pp. 15–16. ACM (2019)

[14]

Schneider, O.S., MacLean, K.E., Altun, K., Karuei, I., Wu, M.M.: Real-time gait classification for persuasive smartphone apps: structuring the literature and pushing the limits. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, IUI 2013, New York, NY, USA, pp. 161–172. ACM (2013)

[15]

Mulas, F., Pilloni, P., Manca, M., Boratto, L., Carta, S.: Using new communication technologies and social media interaction to improve the motivation of users to exercise,. In: Second International Conference on Future Generation Communication Technologies FGCT 2013, London, United Kingdom, 12–14 November 2013, pp. 87–92 (2013)

[16]

Berndsen, J., Lawlor, A., Smyth, B.: Running with recommendation. In: HealthRecSys@ RecSys, pp. 18–21 (2017)

[17]

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)

[18]

Boratto L, Carta S, Fenu G, Manca M, Mulas F, and Pilloni P The role of social interaction on users motivation to exercise: a persuasive web framework to enhance the self-management of a healthy lifestyle Pervasive Mob. Comput. 2017 36 98-114

[19]

Pilloni P, Piras L, Carta S, Fenu G, Mulas F, and Boratto L Recommender system lets coaches identify and help athletes who begin losing motivation IEEE Comput. 2018 51 3 36-42

[20]

Buttussi, F., Chittaro, L., Nadalutti, D.: Bringing mobile guides and fitness activities together: a solution based on an embodied virtual trainer. In: Proceedings of the 8th Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2006, New York, NY, USA, pp. 29–36. ACM (2006)

[21]

Hosseinpour M and Terlutter R Your personal motivator is with you: a systematic review of mobile phone applications aiming at increasing physical activity Sports Med. 2019 49 1425-1447

[22]

Mulas, F., Carta, S., Pilloni, P., Manca, M.: Everywhere run: a virtual personal trainer for supporting people in their running activity. In: Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, ACE 2011, Lisbon, Portugal, 8–11 November 2011, p. 70 (2011)

[23]

Feely C, Caulfield B, Lawlor A, and Smyth B Sánchez-Ruiz AA and Floyd MW A case-based reasoning approach to predicting and explaining running related injuries Case-Based Reasoning Research and Development 2021 Cham Springer 79-93

[24]

Kurbalija, V.: Time series analysis and prediction using case based reasoning technology. Ph.D. thesis, University of Novi Sad, Serbia (2009)

[25]

Smyth B Fast starters and slow finishers: a large-scale data analysis of pacing at the beginning and end of the marathon for recreational runners J. Sports Anal. 2018 4 229-242

[26]

Smyth, B., Lawlor, A.: Longer disciplined tapers improve marathon performance for recreational runners. Front. Sports Act. Living 275 (2021)

Cited By

Feely CCaulfield BLawlor ASmyth B(2024)Recommending Personalised Targeted Training Adjustments for Marathon RunnersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688192(1051-1056)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688192
Feely CCaulfield BLawlor ASmyth B(2024)Using Pseudo Cases and Stratified Case-Based Reasoning to Generate and Evaluate Training Adjustments for Marathon RunnersArtificial Intelligence XLI10.1007/978-3-031-77918-3_7(88-101)Online publication date: 17-Dec-2024
https://dl.acm.org/doi/10.1007/978-3-031-77918-3_7
Cui DGuo JLiu PZhang X(2024)Using Case-Based Causal Reasoning to Provide Explainable Counterfactual Diagnosis in Personalized Sprint TrainingCase-Based Reasoning Research and Development10.1007/978-3-031-63646-2_27(418-429)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-63646-2_27
Show More Cited By

Index Terms

An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon Runners

Index terms have been assigned to the content through auto-classification.

Recommendations

Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations
UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

These days we have all become increasingly aware of the role that exercise plays in a healthy lifestyle. Activities such as cycling, triathlons, and running have become popular ways for people to keep fit and test their abilities. For recreational ...
Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners
RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Millions of people participate in marathon events every year, typically devoting at least 12-16 weeks to building their endurance and fitness so that they can safely complete these gruelling 42.2km races. Most runners follow a training plan that is ...
A Case-Based Reasoning Approach to Post-injury Training Recommendations for Marathon Runners
Case-Based Reasoning Research and Development
Abstract
Recreational running is a popular way for people to exercise, but it attracts a high rate of injury. The widespread adoption of wearable sensors has led to a large volume of real-world data about how people train and recover. While such data has ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12–15, 2022, Proceedings

Sep 2022

419 pages

ISBN:978-3-031-14922-1

DOI:10.1007/978-3-031-14923-8

Editors:
Mark T. Keane
University College Dublin, Dublin, Ireland
,
Nirmalie Wiratunga
The Robert Gordon University, Aberdeen, UK

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 12 September 2022

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Feely CCaulfield BLawlor ASmyth B(2024)Recommending Personalised Targeted Training Adjustments for Marathon RunnersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688192(1051-1056)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688192
Feely CCaulfield BLawlor ASmyth B(2024)Using Pseudo Cases and Stratified Case-Based Reasoning to Generate and Evaluate Training Adjustments for Marathon RunnersArtificial Intelligence XLI10.1007/978-3-031-77918-3_7(88-101)Online publication date: 17-Dec-2024
https://dl.acm.org/doi/10.1007/978-3-031-77918-3_7
Cui DGuo JLiu PZhang X(2024)Using Case-Based Causal Reasoning to Provide Explainable Counterfactual Diagnosis in Personalized Sprint TrainingCase-Based Reasoning Research and Development10.1007/978-3-031-63646-2_27(418-429)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-63646-2_27
Feely CCaulfield BLawlor ASmyth B(2024)A Case-Based Reasoning Approach to Post-injury Training Recommendations for Marathon RunnersCase-Based Reasoning Research and Development10.1007/978-3-031-63646-2_22(338-353)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-63646-2_22
Feely CCaulfield BLawlor ASmyth B(2023)Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training RecommendationsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592952(183-193)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592952
Ivanova IWald M(2023)How can we model climbers’ future visits from their past records?Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597408(60-65)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597408

View Options

View options

Figures

Tables

Media

View Table of Conten