ArticleUser modeling and user-adapted interaction2022
Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners.
Article in User modeling and user-adapted interaction, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Machine learning-based personalized training models for optimizing marathon performance through pyramidal and polarized training intensity distributions.Scientific reports · 2025Trial
- High-intensity interval training versus plyometric training on performance measures among recreational runners: a randomized controlled trial.BMC sports science, medicine & rehabilitation · 2026Article
- Wearable technology for athletes: material innovations, performance monitoring, and emerging paradigms.Mikrochimica acta · 2026Review
- Autoimmune Thyroid Diseases and Physical Activity and Sports-More Unknowns than Facts.Biomedicines · 2025Review
- Enhancing Marathon Enthusiast Engagement Through AI: A Quantitative Study on the Role of Social Media in Sports Communication.Brain and behavior · 2025Article
- Early marathon running metrics from inertial measurement units predict significant pace reduction.Frontiers in sports and active living · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of training for a marathon, combined with the availability of real-time activity data, provide a unique and worthwhile opportunity for machine learning and for recommender systems techniques to support runners as they train, race, and recover. We present a number of case studies-a mix of original research plus some recent results-to highlight what can be achieved using the type of activity data that is routinely collected by the current generation of mobile fitness apps, smart watches, and wearable sensors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.