Alberto Canzone ¹ Giacomo Belmonte ¹ Antonino Patti ^1* Domenico Savio Salvatore Vicari ^1,2 Fabio Rapisarda ¹ Valerio Giustino ¹ Patrik Drid ³ Antonino Bianco ¹

• ¹ Department of Psychological, Pedagogical and Educational Sciences, University of Palermo, Palermo, Italy
• ² Section of Neurology, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Veneto, Italy
• ³ Faculty of Sport and Physical Education, University of Novi Sad, Novi Sad, Vojvodina, Serbia

Background: Artificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. Machine learning, a subset of artificial intelligence, concentrates on empowering computers to learn and enhance from data autonomously; this narrative review seeks to elucidate the utilization of artificial intelligence in fostering physical activity, training, exercise, and health outcomes, addressing a significant gap in the comprehension of practical applications. Methods: Only Randomized Controlled Trials (RCTs) published in English were included. Inclusion criteria: all RCTs that use artificial intelligence to program, supervise, manage, or assist physical activity, training, exercise, or health programs. Only studies published from January 1, 2014, were considered. Exclusion criteria: all the studies that used robot-assisted, robot-supported, or robotic training were excluded. Results: A total of 1772 studies were identified. After the first stage, where the duplicates were removed, 1004 articles were screened by title and abstract. A total of 24 studies were identified, and finally, after a full-text review, 15 studies were identified as meeting all eligibility criteria for inclusion. The findings suggest that artificial intelligence holds promise in promoting physical activity across diverse populations, including children, adolescents, adults, the elderly, and individuals with disabilities. Conclusion: Our research found that artificial intelligence, machine learning and deep learning techniques were used: a) as part of applications to generate automatic messages and be able to communicate with users; b) as a predictive approach and for gesture and posture recognition; c) as a control system; d) as data collector; and e) as a guided trainer.