The multiple uses of artificial intelligence in exercise programs: a narrative review
BackgroundArtificial 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 comput...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1510801/full |
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| author | Alberto Canzone Alberto Canzone Giacomo Belmonte Antonino Patti Domenico Savio Salvatore Vicari Domenico Savio Salvatore Vicari Fabio Rapisarda Valerio Giustino Patrik Drid Antonino Bianco |
| author_facet | Alberto Canzone Alberto Canzone Giacomo Belmonte Antonino Patti Domenico Savio Salvatore Vicari Domenico Savio Salvatore Vicari Fabio Rapisarda Valerio Giustino Patrik Drid Antonino Bianco |
| author_sort | Alberto Canzone |
| collection | DOAJ |
| description | BackgroundArtificial 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.MethodsOnly 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.ResultsA total of 1772 studies were identified. After the first stage, where the duplicates were removed, 1,004 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, older adult, and individuals with disabilities.ConclusionOur 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. |
| format | Article |
| id | doaj-art-dabdf3728463447a8f753518e03f5d80 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-dabdf3728463447a8f753518e03f5d802025-01-31T06:40:14ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011310.3389/fpubh.2025.15108011510801The multiple uses of artificial intelligence in exercise programs: a narrative reviewAlberto Canzone0Alberto Canzone1Giacomo Belmonte2Antonino Patti3Domenico Savio Salvatore Vicari4Domenico Savio Salvatore Vicari5Fabio Rapisarda6Valerio Giustino7Patrik Drid8Antonino Bianco9Sport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, ItalyDepartment of Biomedical and Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, ItalySport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, ItalySport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, ItalySport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, ItalyDepartment of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, ItalySport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, ItalySport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, ItalyFaculty of Sport and Physical Education, University of Novi Sad, Novi Sad, SerbiaSport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, ItalyBackgroundArtificial 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.MethodsOnly 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.ResultsA total of 1772 studies were identified. After the first stage, where the duplicates were removed, 1,004 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, older adult, and individuals with disabilities.ConclusionOur 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1510801/fullhealthwellbeingwellnessmachine-learningdeep-learning“artificial intelligence and movement” |
| spellingShingle | Alberto Canzone Alberto Canzone Giacomo Belmonte Antonino Patti Domenico Savio Salvatore Vicari Domenico Savio Salvatore Vicari Fabio Rapisarda Valerio Giustino Patrik Drid Antonino Bianco The multiple uses of artificial intelligence in exercise programs: a narrative review Frontiers in Public Health health wellbeing wellness machine-learning deep-learning “artificial intelligence and movement” |
| title | The multiple uses of artificial intelligence in exercise programs: a narrative review |
| title_full | The multiple uses of artificial intelligence in exercise programs: a narrative review |
| title_fullStr | The multiple uses of artificial intelligence in exercise programs: a narrative review |
| title_full_unstemmed | The multiple uses of artificial intelligence in exercise programs: a narrative review |
| title_short | The multiple uses of artificial intelligence in exercise programs: a narrative review |
| title_sort | multiple uses of artificial intelligence in exercise programs a narrative review |
| topic | health wellbeing wellness machine-learning deep-learning “artificial intelligence and movement” |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1510801/full |
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