Machine Learning-Guided Training for Elite Athletes (MLGT)

This study evaluated the efficacy of an adaptive, machine learning-driven training protocol compared to traditional athletic preparation over a full 9-month competitive sports season. The primary objective was to determine if a dynamic, technology-led approach to training load management could minimize time-loss injuries while concurrently optimizing athletic performance markers.

Participants were elite athletes randomly allocated into two parallel groups:

• The Experimental Group, which underwent training regimens dynamically adjusted using a machine learning algorithm that analyzed individual biomechanical data and historical workload parameters to optimize training volume and intensity.
• The Control Group, which followed standard, predetermined high-performance athletic training protocols typical for competitive season preparation.

Throughout the 9-month intervention period, daily tracking was maintained by technical and coaching staff. Data collection focused on the incidence, severity, and duration of all time-loss sports injuries. Concurrently, sport-specific performance parameters were periodically assessed to evaluate physical conditioning and competitive readiness. Statistical analyses were subsequently conducted to compare cumulative injury rates, total days lost to injury, and net performance adaptations between the two cohorts.

Inclusion criteria

• Must be a competitive, elite-level or sub-elite track and field athlete specializing in short-to-mid distance running events.
• Aged between 18 and 35 years old.
• Actively participating in structured athletic training programs for at least 2 years prior to enrollment.
• Free from any acute musculoskeletal injuries or medical conditions that prevent full participation in high-intensity training protocols.
• Capable and willing to provide written informed consent to participate in the study.

Exclusion criteria

1. Current or recent (within the past 3 months) major lower-limb injury or surgery that restricts maximal sprint or aerobic performance.

• Concurrent use of performance-enhancing drugs or medications that influence metabolic or cardiovascular responses.

• Inability to maintain consistent participation in the designated training protocols due to scheduling conflicts or travel.

• Any underlying cardiovascular, respiratory, or systemic condition that creates a health risk during exhaustive exercise testing.