%0 Conference Paper %1 Feely_2023 %A Feely, Ciara %A Caulfield, Brian %A Lawlor, Aonghus %A Smyth, Barry %B Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization %D 2023 %I ACM %K health recommender umap2023 %P 183-193 %R 10.1145/3565472.3592952 %T Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations %U https://doi.org/10.1145%2F3565472.3592952 %X 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 athletes there is no shortage of training advice or programmes to follow, yet most offer only one-size-fits-all, or minimally tailored guidance, which often leaves novices under-supported on their fitness journeys. In this work, we describe a case-based reasoning system to generate personalised training recommendations for marathon runners, based on their training histories and the training histories of similar runners with comparable race goals. The system harnesses the type of activity data that is routinely collected by smartwatches and apps like Strava. It uses prefactual explanations to suggest to runners how they may wish to adjust their training as their fitness goals evolve. We evaluate the approach using a large-scale dataset of more than 300,000 real-world runners and we show that it is feasible to generate tailored, personalised recommendations for up to 80% of these runners. Additionally, we show that the recommendations produced are realistic and reasonable for a runner to implement, as part of their training programme. These suggestions typically include a small number (3-5) of incremental training adaptations, such as a change in weekly distance, long-run distance, or mean training pace. We argue that by engaging runners in this type of dialog about their training progress and race goals, we can better support novice runners, as their training unfolds, which may help to keep runners motivated on their long journey to race day.

@inproceedings{Feely_2023, abstract = {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 athletes there is no shortage of training advice or programmes to follow, yet most offer only one-size-fits-all, or minimally tailored guidance, which often leaves novices under-supported on their fitness journeys. In this work, we describe a case-based reasoning system to generate personalised training recommendations for marathon runners, based on their training histories and the training histories of similar runners with comparable race goals. The system harnesses the type of activity data that is routinely collected by smartwatches and apps like Strava. It uses prefactual explanations to suggest to runners how they may wish to adjust their training as their fitness goals evolve. We evaluate the approach using a large-scale dataset of more than 300,000 real-world runners and we show that it is feasible to generate tailored, personalised recommendations for up to 80% of these runners. Additionally, we show that the recommendations produced are realistic and reasonable for a runner to implement, as part of their training programme. These suggestions typically include a small number (3-5) of incremental training adaptations, such as a change in weekly distance, long-run distance, or mean training pace. We argue that by engaging runners in this type of dialog about their training progress and race goals, we can better support novice runners, as their training unfolds, which may help to keep runners motivated on their long journey to race day.}, added-at = {2023-06-29T15:22:08.000+0200}, author = {Feely, Ciara and Caulfield, Brian and Lawlor, Aonghus and Smyth, Barry}, biburl = {https://www.bibsonomy.org/bibtex/27af6258b6462764fd9f884d38c080edd/brusilovsky}, booktitle = {Proceedings of the 31st {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations | Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3565472.3592952}, interhash = {a9e602a01394bb5ada92a824f466f397}, intrahash = {7af6258b6462764fd9f884d38c080edd}, keywords = {health recommender umap2023}, month = jun, pages = {183-193}, publisher = {{ACM}}, timestamp = {2023-06-29T15:22:08.000+0200}, title = {Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations}, url = {https://doi.org/10.1145%2F3565472.3592952}, year = 2023 }