Yesterday, a paper I co-authored with colleagues from CYENS was presented at CHI Greece 2025. The paper is entitled “A Longitudinal Evaluation of Heart Rate Efficiency for Amateur Runners”. It presents a web-based system that uses large language models (LLMs) to automatically generate structured short-form video (i.e., reels) from lecture long-form videos while preserving instructor-authored material. It first presents Fitplotter, which is a client-side web application designed for the visualization and analysis of data associated with fitness and activity tracking devices. Next, we revisited and formalized Heart Rate Efficiency (HRE), defined as the product of pace and heart rate, as a practical and explainable metric to track aerobic fitness in everyday running.
Drawing on more than a decade of training data from one athlete, and supplemented by publicly available logs from twelve runners, we showed that HRE provides more stable and meaningful feedback on aerobic development than heart rate or pace alone. We showed that HRE correlates with training volume, reflects seasonal progress, and remains stable during long runs in well-trained individuals. We also discuss how HRE can support everyday training decisions, improve the user experience in fitness tracking, and serve as an explainable metric to proprietary ones of commercial platforms. Our findings have implications for designing user-centered fitness tools that empower amateur athletes to understand and manage their own performance data.
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