Kann maschinelles Lernen die Erholung nach dem Training vorhersagen?

Kann maschinelles Lernen die Erholung nach dem Training vorhersagen?

Endurance athletes often struggle to find a balance between pushing themselves in training and allowing for adequate recovery. Researchers at the University of Auckland’s Sports Performance Research Institute New Zealand conducted a study with 43 endurance athletes to determine factors that predict workout recovery. The athletes tracked their workouts, sleep, heart-rate variability, well-being, and diet. The study found that variables such as soreness, sleep quality, resting heart rate, and protein intake contributed to predicting recovery scores in the morning.

The study also aimed to identify which training and lifestyle variables influence changes in heart-rate variability (HRV), a marker often used to assess recovery and readiness to train. The analysis revealed that the most significant factor in predicting HRV changes was how much it had changed the previous day. Resting heart rate and several other variables had less impact on HRV changes. The study highlighted limitations in predicting HRV response using only actionable variables, suggesting that future research should consider additional factors like alcohol intake, illness, and menstrual cycle.

Overall, the research emphasized that the factors influencing workout recovery and HRV are unique to each individual. While there are broad patterns that apply to everyone, some people may be affected by specific factors such as intense training, poor sleep, or inadequate nutrition. Understanding these individual factors is essential for endurance athletes in optimizing their training and avoiding burnout. By identifying personal recovery patterns, athletes can adjust their training load and lifestyle to achieve long-term performance improvements without risking overtraining.