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Exercise and Cardiac Output

Exercise and Cardiac Output

Regular physical activity is essential for maintaining cardiovascular health, with aerobic exercises being particularly effective. According to the American Heart Association, 150 minutes of moderate to intense aerobic exercise per week is recommended for a healthy heart. Aerobic activities may include brisk walking, running, bicycling, cross-country skiing, and swimming, ideally performed three to five times per week.
Sustained exercise increases the muscles' oxygen demand, which can be...

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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

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Elite Soccer Players' Weekly Workload Assessment Through a New Training Load and Performance Score.

Guglielmo Pillitteri¹, Alessio Rossi², Tindaro Bongiovanni³

¹University of Palermo.

Research Quarterly for Exercise and Sport

|July 9, 2024

Summary

This summary is machine-generated.

Monitoring training load in professional U19 soccer players using wearable sensors and perceived exertion revealed key external load indicators. This approach helps optimize performance and reduce injury risk by analyzing workload and physical performance scores.

Keywords:

Football performance training load training microcycle

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Area of Science:

Sports Science
Biomechanics
Exercise Physiology

Background:

Effective monitoring of training load is crucial for enhancing athlete performance and minimizing injury risk in professional soccer.
Identifying key external load indicators in young elite athletes is essential for tailored training prescription.

Purpose of the Study:

To identify the primary external load indicators in professional U19 soccer players.
To develop and validate an objective approach for assessing training load and physical performance.

Main Methods:

Utilized wearable inertial sensors and the CR-10 Rating of Perceived Exertion (RPE) scale to monitor 24 professional Italian U19 soccer players.
Employed Principal Component Analysis (PCA) to identify three key load components: metabolic volume, mechanical intensity, and metabolic/mechanical intensity.
Computed Performance (PERF) and total workload (WORK) scores to analyze the weekly microcycle.

Main Results:

PERF and WORK scores demonstrated low correlation (r = -0.20), indicating their independence.
A weekly microcycle pattern was detectable across all assessed load scores.
Statistical differences were observed between match day and playing position for PCA components and the PERF score.

Conclusions:

An innovative machine learning approach was proposed to objectively assess players' physical performance and training load by reducing complex datasets.
This method provides practitioners with valuable insights to optimize training prescription within the weekly microcycle based on distinct PERF and WORK scores.