{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T13:19:09Z","timestamp":1793711949207,"version":"4.6.1"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1510801","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T01:43:16Z","timestamp":1738287796000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["The multiple uses of artificial intelligence in exercise programs: a narrative review"],"prefix":"10.3389","volume":"13","author":[{"given":"Alberto","family":"Canzone","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Belmonte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonino","family":"Patti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Domenico Savio Salvatore","family":"Vicari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Rapisarda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valerio","family":"Giustino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrik","family":"Drid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonino","family":"Bianco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1590\/S1807-59322005000100012","article-title":"Physical activity and mental health: the association between exercise and mood","volume":"60","author":"Peluso","year":"2005","journal-title":"Clinics (Sao Paulo)"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1503\/cmaj.051351","article-title":"Health benefits of physical activity: the evidence","volume":"174","author":"Warburton","year":"2006","journal-title":"CMAJ"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"E121","DOI":"10.5888\/pcd17.200105","article-title":"Association between physical activity and odds of chronic conditions among Workers in Spain","volume":"17","author":"L\u00f3pez-Bueno","year":"2020","journal-title":"Prev Chronic Dis"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"849093","DOI":"10.3389\/fpubh.2022.849093","article-title":"Enhancing mental health, well-being and active lifestyles of university students by means of physical activity and exercise research programs","volume":"10","author":"Herbert","year":"2022","journal-title":"Front Public Health"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"e0259499","DOI":"10.1371\/journal.pone.0259499","article-title":"Artificial intelligence applications in social media for depression screening: a systematic review protocol for content validity processes","volume":"16","author":"Owusu","year":"2021","journal-title":"PLoS One"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"15345","DOI":"10.3934\/mbe.2023686","article-title":"Artificial intelligence for video game visualization, advancements, benefits and challenges","volume":"20","author":"Wu","year":"2023","journal-title":"Math Biosci Eng"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.14309\/ajg.0000000000001723","article-title":"A smartphone application using artificial intelligence is superior to subject self-reporting when assessing stool form","volume":"117","author":"Pimentel","year":"2022","journal-title":"Am J Gastroenterol"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1159\/000520474","article-title":"Validation of a market-approved artificial intelligence Mobile health app for skin Cancer screening: a prospective multicenter diagnostic accuracy study","volume":"238","author":"Sangers","year":"2022","journal-title":"Dermatology"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1432876","DOI":"10.3389\/fcvm.2024.1432876","article-title":"Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives","volume":"11","author":"Papalamprakopoulou","year":"2024","journal-title":"Front Cardiovasc Med"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"100165","DOI":"10.1016\/j.xagr.2023.100165","article-title":"Feasibility and impact of a mental health chatbot on postpartum mental health: a randomized controlled trial","volume":"3","author":"Suharwardy","year":"2023","journal-title":"AJOG Glob Rep"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1146\/annurev-biodatasci-122220-110053","article-title":"Developing and implementing predictive models in a learning healthcare system: traditional and artificial intelligence approaches in the veterans health administration","volume":"5","author":"Atkins","year":"2022","journal-title":"Annu Rev Biomed Data Sci"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1002\/lary.30698","article-title":"Machine learning analysis of physical activity data to classify postural dysfunction","volume":"133","author":"Vanstrum","year":"2023","journal-title":"Laryngoscope"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.jinf.2023.07.006","article-title":"Artificial intelligence, machine learning and deep learning: potential resources for the infection clinician","volume":"87","author":"Theodosiou","year":"2023","journal-title":"J Inf Secur"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.3390\/nu16071073","article-title":"Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review","volume":"16","author":"Theodore Armand","year":"2024","journal-title":"Nutrients"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e27850","DOI":"10.2196\/27850","article-title":"Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review","volume":"7","author":"Xu","year":"2021","journal-title":"JMIR Cancer"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"eDoctor: machine learning and the future of medicine","volume":"284","author":"Handelman","year":"2018","journal-title":"J Intern Med"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.cmi.2019.09.009","article-title":"Machine learning for clinical decision support in infectious diseases: a narrative review of current applications","volume":"26","author":"Peiffer-Smadja","year":"2020","journal-title":"Clin Microbiol Infect"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"885599","DOI":"10.3389\/fsurg.2022.885599","article-title":"The application of artificial intelligence in spine surgery","volume":"9","author":"Zhou","year":"2022","journal-title":"Front Surg"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav Ther"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s10522-017-9683-y","article-title":"A review of supervised machine learning applied to ageing research","volume":"18","author":"Fabris","year":"2017","journal-title":"Biogerontology"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1186\/s12911-019-1004-8","article-title":"Comparing different supervised machine learning algorithms for disease prediction","volume":"19","author":"Uddin","year":"2019","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.tig.2017.12.005","article-title":"Supervised machine learning for population genetics: a new paradigm","volume":"34","author":"Schrider","year":"2018","journal-title":"Trends Genet"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"5710","DOI":"10.1109\/JBHI.2023.3318205","article-title":"Supervised learning of physical activity features from functional accelerometer data","volume":"27","author":"Banker","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1002\/mas.21602","article-title":"Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry","volume":"39","author":"Verbeeck","year":"2020","journal-title":"Mass Spectrom Rev"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"e234198","DOI":"10.1001\/jamanetworkopen.2023.4198","article-title":"An unsupervised machine learning approach to evaluating the Association of Symptom Clusters with Adverse Outcomes among Older Adults with Advanced Cancer: a secondary analysis of a randomized clinical trial","volume":"6","author":"Xu","year":"2023","journal-title":"JAMA Netw Open"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"6347","DOI":"10.3390\/s21196347","article-title":"Unsupervised machine learning applied to seismic interpretation: towards an unsupervised automated interpretation tool","volume":"21","author":"Celecia","year":"2021","journal-title":"Sensors (Basel)"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"103364","DOI":"10.1016\/j.jbi.2019.103364","article-title":"Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records","volume":"102","author":"Wang","year":"2020","journal-title":"J Biomed Inform"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1038\/s41746-023-00974-w","article-title":"Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart counts app and smart devices","volume":"6","author":"Gupta","year":"2023","journal-title":"NPJ Digit Med"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.1021\/acs.jcim.2c00373","article-title":"Exploring potential energy surfaces using reinforcement machine learning","volume":"62","author":"Mills","year":"2022","journal-title":"J Chem Inf Model"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"5528","DOI":"10.3390\/s20195528","article-title":"Reinforcement learning based fast self-recalibrating decoder for Intracortical brain-machine Interface","volume":"20","author":"Zhang","year":"2020","journal-title":"Sensors (Basel)"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"e338","DOI":"10.2196\/jmir.7994","article-title":"Encouraging physical activity in patients with diabetes: intervention using a reinforcement learning system","volume":"19","author":"Yom-Tov","year":"2017","journal-title":"J Med Internet Res"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1","article-title":"Deep learning in medical image analysis","volume":"1213","author":"Chan","year":"2020","journal-title":"Adv Exp Med Biol"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"62","DOI":"10.3390\/s23010062","article-title":"Deep learning in diverse intelligent sensor based systems","volume":"23","author":"Zhu","year":"2022","journal-title":"Sensors (Basel)"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1080\/17461391.2020.1789749","article-title":"Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data","volume":"21","author":"Hamid","year":"2021","journal-title":"Eur J Sport Sci"},{"key":"ref36","year":"2022"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"e27535","DOI":"10.2196\/27535","article-title":"Effects of an artificial intelligence-assisted health program on workers with neck\/shoulder pain\/stiffness and low Back pain: randomized controlled trial","volume":"9","author":"Anan","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1249\/MSS.0000000000003114","article-title":"Identifying predictors of adherence to the physical activity goal: a secondary analysis of the SMARTER weight loss trial","volume":"55","author":"Bizhanova","year":"2023","journal-title":"Med Sci Sports Exerc"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"e0287433","DOI":"10.1371\/journal.pone.0287433","article-title":"Football players' strength training method using image processing based on machine learning","volume":"18","author":"Cao","year":"2023","journal-title":"PLoS One"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s12984-021-00831-5","article-title":"User training for machine learning controlled upper limb prostheses: a serious game approach","volume":"18","author":"Kristoffersen","year":"2021","journal-title":"J Neuroeng Rehabil"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.amepre.2017.05.001","article-title":"Bicycle trains, cycling, and physical activity: a pilot cluster RCT","volume":"53","author":"Mendoza","year":"2017","journal-title":"Am J Prev Med"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.3390\/nu14214608","article-title":"A smartphone healthcare application, CALO mama plus, to promote weight loss: a randomized controlled trial","volume":"14","author":"Nakata","year":"2022","journal-title":"Nutrients"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"e40422","DOI":"10.2196\/40422","article-title":"The role of pain duration and pain intensity on the effectiveness of app-delivered self-Management for low Back Pain (selfBACK): secondary analysis of a randomized controlled trial","volume":"11","author":"Nordstoga","year":"2023","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s12916-022-02237-z","article-title":"Multimorbidity and co-occurring musculoskeletal pain do not modify the effect of the SELFBACK app on low back pain-related disability","volume":"20","author":"\u00d8ver\u00e5s","year":"2022","journal-title":"BMC Med"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"7352","DOI":"10.3390\/s22197352","article-title":"Comparative effectiveness of artificial intelligence-based interactive home exercise applications in adolescents with obesity","volume":"22","author":"Oh","year":"2022","journal-title":"Sensors (Basel)"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"105317","DOI":"10.1016\/j.archger.2023.105317","article-title":"Self-determined sequence exercise program for elderly with sarcopenia: a randomized controlled trial with clinical assistance from explainable artificial intelligence","volume":"119","author":"He","year":"2024","journal-title":"Arch Gerontol Geriatr"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3390\/medicina57010034","article-title":"The combined effects of sports smart bracelet and multi-component exercise program on exercise motivation among the elderly in Macau","volume":"57","author":"Lao","year":"2021","journal-title":"Medicina (Kaunas)"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"e2320400","DOI":"10.1001\/jamanetworkopen.2023.20400","article-title":"Effect of an artificial intelligence-based self-management app on musculoskeletal health in patients with neck and\/or low Back pain referred to specialist care: a randomized clinical trial","volume":"6","author":"Marcuzzi","year":"2023","journal-title":"JAMA Netw Open"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"9952","DOI":"10.3390\/ijerph19169952","article-title":"Hybrid exercise program for sarcopenia in older adults: the effectiveness of explainable artificial intelligence-based clinical assistance in assessing skeletal muscle area","volume":"19","author":"Wei","year":"2022","journal-title":"Int J Environ Res Public Health"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1007\/s12603-023-1991-0","article-title":"Hybrid exercise program enhances physical fitness and reverses frailty in older adults: insights and predictions from machine learning","volume":"27","author":"Wei","year":"2023","journal-title":"J Nutr Health Aging"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"105902","DOI":"10.1016\/j.clinbiomech.2023.105902","article-title":"Physical clinical care and artificial-intelligence-guided core resistance training improve endurance and patient-reported outcomes in subjects with lower back pain","volume":"103","author":"Bates","year":"2023","journal-title":"Clin Biomech (Bristol, Avon)"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1158\/2159-8290.CD-21-0090","article-title":"Artificial intelligence in Cancer research and precision medicine","volume":"11","author":"Bhinder","year":"2021","journal-title":"Cancer Discov"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1007\/s11596-021-2474-3","article-title":"Application of artificial intelligence in medicine: An overview","volume":"41","author":"Liu","year":"2021","journal-title":"Curr Med Sci"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1111\/cas.14377","article-title":"Artificial intelligence in oncology","volume":"111","author":"Shimizu","year":"2020","journal-title":"Cancer Sci"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.tcm.2020.11.007","article-title":"Artificial intelligence in cardiology","volume":"32","author":"Itchhaporia","year":"2022","journal-title":"Trends Cardiovasc Med"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1056\/NEJMra2302038","article-title":"Artificial intelligence and machine learning in clinical medicine, 2023","volume":"388","author":"Haug","year":"2023","journal-title":"N Engl J Med"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1510801\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T01:43:19Z","timestamp":1738287799000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1510801\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":57,"alternative-id":["10.3389\/fpubh.2025.1510801"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2025.1510801","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,31]]},"article-number":"1510801"}}