The Effects of CrossFit ® Practice on Physical Fitness and Overall Quality of Life

Manoel Rios (), David B. Pyne and Ricardo J. Fernandes
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Manoel Rios: Superior School of Sport and Education, Jean Piaget Polytechnic Institute of the North, 4405-678 Vila Nova de Gaia, Portugal
David B. Pyne: Research Institute for Sport & Exercise, University of Canberra, Canberra 2617, Australia
Ricardo J. Fernandes: Centre of Research, Education, Innovation and Intervention in Sport and Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal

IJERPH, 2024, vol. 22, issue 1, 1-11

Abstract: We have examined the impact of CrossFit ® workout sessions on physical fitness, comparing the obtained outcomes with the recommendations of the American College of Sports Medicine. In addition, we provide suggestions to improve training monitoring, as well as practical applications for researchers, coaches and practitioners. CrossFit ® imposes high cardiorespiratory and metabolic demands, promoting improvements in circulatory capacity, oxidative metabolism and muscular endurance. Sustained elevations in heart rate contribute to cardiovascular conditioning, while a post-exercise hypotensive effect may help to reduce cardiovascular risks. Structured CrossFit ® programs have led to improvements in maximal strength and muscular endurance, with substantial increases in squat performance observed in both untrained and recreationally active individuals. In addition, CrossFit ® improves mental health through its motivating community. However, the high metabolic demands, increased creatine kinase levels and reduced performance in the countermovement jump reveal that muscle damage and neuromuscular fatigue can persist for up to 48 h. Balancing these intense sessions with adequate recovery is crucial, as improper management may lead to overtraining and compromise fitness gains. Future research should explore long-term cardiovascular adaptations, differences in gains and recovery between males and females and the application of real-time biomarker and artificial intelligence technologies to improve the training efficiency and safety. Machine learning algorithms could further personalize feedback, adapting to each individual’s biomechanics and physiological responses over time.

Keywords: CrossFit ®; health; physical fitness; exercise; benchmark; workouts (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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