Case example – Triathlete, female

Athlete profile: Emma, a dedicated female athlete training for a triathlon.

Scenario: Emma’s training load is tracked from her anabolic hormone levels and heart rate, with her testosterone to cortisol ratio closely following the measured training load. The testosterone to cortisol ratio consistently reflects Emma’s individual response to training load, making it valuable her training load management. Her HRV remains largely unchanged and doesn’t display a consistent relationship with her training load.

Findings: While HRV is a valuable tool for assessing overall physiological stress, its response can be influenced by factors like hydration, sleep quality, and non-training-related stressors, making it less reliable as a universal marker of training load for Emma. For Emma, anabolic hormone monitoring leads to more effective training load and schedule management – and better performance outcomes in her triathlon training. Her case highlights the importance of selecting the right monitoring method and tool for tracking individual training loads.

With the example individual, testosterone to cortisol ratio followed the measured training load while HRV remained largely blind. Hormonal markers exhibit more consistent individual pattern in response to training stress.

HRV is a measure of the variation in time between successive heartbeats. While HRV can be a valuable tool for monitoring overall physiological stress, its relationship with training load can be influenced by various factors, including individual differences, hydration, sleep, and other non-training-related stressors. Therefore HRV does not consistently reflect the impact of training load, making it non-consistent as a universal marker of training load.