What is the most effective way to structure a 12-week training plan using RGT Cyclings workout planning tools to optimize performance for a highly competitive rider, and is it better to focus on periodized training with specific phases or adopt a more dynamic approach that continuously adapts to the riders progress and goals.
Some argue that traditional periodized training is outdated and that riders should instead focus on continuous progression, while others claim that a structured approach is still the best way to achieve peak performance.
How do you balance the need for consistency and progression with the need for recovery and adaptation, and what metrics or data points should be used to inform training decisions and adjustments.
Is it better to prioritize specific training zones, such as threshold or VO2max, or focus on more holistic measures of fitness, such as Training Stress Score or Chronic Training Load.
What role do you think artificial intelligence and machine learning can play in optimizing training plans, and are there any potential drawbacks or limitations to relying on data-driven approaches.
Can a well-structured training plan using RGT Cyclings tools alone be enough to propel a rider to the top of their competitive category, or are there other factors, such as nutrition, recovery, and mental preparation, that play a more critical role in determining success.
Some argue that traditional periodized training is outdated and that riders should instead focus on continuous progression, while others claim that a structured approach is still the best way to achieve peak performance.
How do you balance the need for consistency and progression with the need for recovery and adaptation, and what metrics or data points should be used to inform training decisions and adjustments.
Is it better to prioritize specific training zones, such as threshold or VO2max, or focus on more holistic measures of fitness, such as Training Stress Score or Chronic Training Load.
What role do you think artificial intelligence and machine learning can play in optimizing training plans, and are there any potential drawbacks or limitations to relying on data-driven approaches.
Can a well-structured training plan using RGT Cyclings tools alone be enough to propel a rider to the top of their competitive category, or are there other factors, such as nutrition, recovery, and mental preparation, that play a more critical role in determining success.