Is the Relationship between Acute and Chronic Workload a Valid Predictive Injury Tool? A Bayesian Analysis.

Area of Science:

• Sports Medicine
• Exercise Physiology
• Injury Prevention

Background:

• Acute load (AL) and acute chronic workload ratio (ACWR) are commonly used metrics to monitor training stress in athletes.
• Understanding the relationship between training load and injury risk is crucial for optimizing performance and athlete well-being.

Purpose of the Study:

• To evaluate the predictive relationship between injury risk, acute load (AL), and the acute chronic workload ratio (ACWR) in professional soccer players.
• To compare the efficacy of a standard ACWR with a novel, randomly generated ACWR in predicting injury probability.

Main Methods:

• A retrospective cohort study design was employed, analyzing data from 35 Argentine first-division soccer players during 2018.
• Player data, including training load metrics, were collected over 6-month and 1-year periods for different subsets of the cohort.
• Statistical analysis focused on comparing ACWR and AL between groups, noting significant differences and identical effect sizes.

Main Results:

• The probability of injury was found to be similar across different quantiles when conditioned by either the standard ACWR or a randomly generated ACWR.
• No statistically significant differences were observed between the injury prediction capabilities of the standard ACWR and the random ACWR.
• Statistically significant differences were found in mean differences for ACWR and AL between groups, with identical effect sizes.

Conclusions:

• The standard ACWR, derived from internal load monitoring, offers no superior predictive value for injury probability compared to a synthetic ACWR using a random denominator.
• ACWR should not be utilized as a sole indicator for analyzing the causal link between training load and injury occurrence.
• Further research is needed to develop more robust methods for load monitoring and injury risk assessment in team sports.