Effects of Different Cardiorespiratory Training Program on Endurance Performance

Conventional training methods are typically administered in a fixed progressive manner, which can lead to sub-optimal responses and injuries. Artificial intelligence (i.e. CURATE.AI) can be harnessed to personalise physical training strategies. Using a single participant training profile, a parabolic/quadratic response to the intervention can be generated to identify the training intensity needed to optimise training outcomes. Previous studies showed CURATE.AI could dynamically modulate drug dosing in oncology. Extending the utility of results to human performance, this study will adapt CURATE.AI with the goal of optimising endurance performance through individualised training regimes. Up to 20 participants will be recruited and randomised into two groups to undergo a calibration phase, which involves performing 3 sessions of exercise sessions per week over 2 weeks per intensity (low, moderate and high) in a crossover study design. Exercise sessions will be interspersed with a 2.4 km time trial, a VO2peak test and 2 weeks of wash out period. The utility phase will divide participants into two groups to undergo 3 exercise sessions per week, totalling to 12 exercise sessions. Either an AI-led training or a conventional training programme will be performed to compare the differences in training outcomes. Blood plasma will be obtained at selected time points in both phases to evaluate the effects of training on blood lipid profiles. Findings from this study can potentially optimise efficacy and efficiency of endurance performance through personalised training with AI.