Research
More than 150 peer-reviewed papers, cited over 25,700 times
My research is about how athletes respond to training: high-intensity interval training, heart rate variability, pacing, fatigue and heat. More recently it has turned to how AI can turn that science into daily training decisions, which is the work behind Athletica. Below are the papers I'm proudest of, each with a one-line summary, then my recent papers on AI in coaching. The full list is on Google Scholar and ORCID.
Key papers
Sports Medicine, 2013, with Martin Buchheit. How to set interval intensity, duration and recovery to maximise time spent near VO2max.
The scientific basis for high-intensity interval training
Sports Medicine, 2002, with David Jenkins. Why already well-trained endurance athletes need high-intensity work, not just more volume, to keep improving.
Interval training program optimization in highly trained endurance cyclists
Medicine and Science in Sports and Exercise, 2002, with David Jenkins and colleagues. My PhD project: two interval sessions a week improved 40-km time-trial performance by 4 to 6% in cyclists who were already highly trained.
Sports Medicine, 2013, with Martin Buchheit. The anaerobic and neuromuscular cost of different interval formats, and how to fit them into a training week.
Describing and understanding pacing strategies during athletic competition
Sports Medicine, 2008, with Chris Abbiss. A classification of the pacing strategies athletes use and what drives them.
Sports Medicine, 2013, with Daniel Plews and colleagues. Why HRV should be read as a rolling average and its day-to-day variation, not as single-day values.
Training for intense exercise performance: high-intensity or high-volume training?
Scandinavian Journal of Medicine and Science in Sports, 2010. How high-intensity and high-volume training work through different signalling pathways, and how to combine them.
Models to explain fatigue during prolonged endurance cycling
Sports Medicine, 2005, with Chris Abbiss. Fatigue in long events is best explained by several physiological systems interacting, not by any single model.
International Journal of Sports Physiology and Performance, 2017, with Daniel Plews and colleagues. A smartphone camera can measure resting HRV in agreement with a chest strap and ECG.
European Journal of Applied Physiology, 2012, with Daniel Plews and colleagues. In two elite triathletes, day-to-day HRV variation flagged the one heading toward non-functional overreaching.
AI in coaching
Sports Science 3.0: integrating technology and AI with foundational knowledge
Sport Performance and Science Reports, with Martin Buchheit. The case for combining foundational training science with AI and continuous athlete data.
AI-assisted HRV monitoring: enhancing training load response and decision-making
Sport Performance and Science Reports, with Andrea Zignoli, Daniel Plews and Martin Buchheit. How AI can help read HRV alongside training load to support daily decisions.
The computational paths of knowledge in AI coaching
Sport Performance and Science Reports, with Andrea Zignoli. How an AI coach can reason from physiological models and athlete data to a training recommendation.