AI-driven personalized exercise prescription: effects on university students’ fitness adaptation and exercise adherence
DOI:
https://doi.org/10.55040/qxgr9967Keywords:
artificial intelligence, personalized exercise prescription, fitness adaptation, exercise adherenceAbstract
Artificial intelligence has increasingly been used to support personalized health and fitness interventions, particularly in designing exercise programs that respond to individual fitness levels and training needs. This study examined the effects of AI-driven personalized exercise prescription on fitness adaptation and exercise adherence among university students. A quasi-experimental pretest-posttest control group design was employed, involving 120 undergraduate students randomly assigned to an experimental group receiving AI-driven personalized exercise prescriptions or a control group following a standard physical education exercise program. The intervention lasted eight weeks, with three exercise sessions per week. Results showed that both groups significantly improved in health-related fitness after the intervention; however, the experimental group demonstrated greater improvements in cardiovascular endurance, muscular endurance, muscular strength, and flexibility compared with the control group. Posttest comparisons significantly favored the experimental group in cardiovascular endurance, muscular endurance, muscular strength, and flexibility, while exercise adherence was also higher in the experimental group than in the control group, with adherence rates of 90.0% and 75.4%, respectively. These findings indicate that AI-driven personalized exercise prescription can improve students’ fitness adaptation and exercise adherence more effectively than a standard physical education exercise program. The integration of AI-based personalized exercise technologies may help strengthen university physical education by promoting health-related fitness and sustained participation in physical activity.
References
An, D., Pan, J., Ran, F., Bai, D., & Zhang, J. (2024). Effects of Physical Exercise Input on the Exercise Adherence of College Students: The Chain Mediating Role of Sports Emotional Intelligence and Exercise Self-Efficacy. Journal of Intelligence. https://doi.org/10.3390/jintelligence12100094
Arufe-Giráldez, V., Pereira Loureiro, J., Groba González, M. B., Nieto Riveiro, L., Canosa Domínguez, N. M., Miranda-Duro, M. d. C., Concheiro Moscoso, P., Rodríguez-Padín, R., Roibal Pravio, J., Lagos Rodríguez, M., & Ramos-Álvarez, O. (2024). Multi-Context Strategies and Opportunities for Increasing Levels of Physical Activity in Children and Young People: A Literature Review. Children, 11(12), 1475. https://doi.org/10.3390/children11121475
Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020). Human Digital Twin for Fitness Management. IEEE Access, 8, 26637–26664. https://doi.org/10.1109/ACCESS.2020.2971576
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Dergaa, I., Ben Saad, H., El Omri, A., Glenn, J., Clark, C., Washif, J. A., Guelmami, N., Hammouda, O., Al-Horani, R., Reynoso-Sánchez, L., Romdhani, M., Paineiras-Domingos, L. L., Vancini, R., Taheri, M., Mataruna-Dos-Santos, L. J., Trabelsi, K., Chtourou, H., Zghibi, M., Eken, Ö., … Chamari, K. (2024). Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model. Biology of Sport. https://doi.org/10.5114/biolsport.2024.133661
Doherty, C., Lambe, R., O’Grady, B., O’Reilly-Morgan, D., Smyth, B., Lawlor, A., Hurley, N., & Τράγος, Η. (2024). An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial. Jmir Mhealth and Uhealth, 12, e49443. https://doi.org/10.2196/49443
Düking, P., Zinner, C., Reed, J. L., Holmberg, H.-C., Holmberg, H.-C., & Sperlich, B. (2020). Predefined vs data-guided training prescription based on autonomic nervous system variation: A systematic review. Scandinavian Journal of Medicine & Science in Sports, 30(12), 2291–2304. https://doi.org/10.1111/SMS.13802
Eynon, M. J., Foad, J., Downey, J., Bowmer, Y., & Mills, H. (2019). Assessing the psychosocial factors associated with adherence to exercise referral schemes: A systematic review. Scandinavian Journal of Medicine & Science in Sports, 29(5), 638–650. https://doi.org/10.1111/SMS.13403
Fang, Y., Peng, S., Khairani, A. Z., & Liang, J. (2024). A Systematic Review and Meta-Analysis of the Efficacy of Physical Activity Interventions among University Students. Sustainability, 16(4), 1369. https://doi.org/10.3390/su16041369
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Gao, Z., Chee, C. S., Dev Omar Dev, R., Li, F., Li, R., Gao, J., & Liu, Y. (2024). Exploring the role of social capital in enhancing physical activity among college and university students: A systematic review. PLOS ONE, 19(11), e0314610. https://doi.org/10.1371/journal.pone.0314610
Gula, L. P. (2024). Health and skill-related physical fitness tests: An assessment in determining the students' physical fitness level. Retos: Nuevas Tendencias en Educación Física, Deporte y Recreación, (58), 1014–1021. https://dialnet.unirioja.es/servlet/articulo?codigo=9670093
Huang, Z., & Luo, J. (2024). Comparative analysis of the effects of different physical education elective courses on college students’ physical fitness indices. Applied Mathematics and Nonlinear Sciences. https://doi.org/10.2478/amns-2024-2875
Klain, I. P., de Matos, D. G., Leitão, J. C., Cid, L., & Moutão, J. (2015). Self-Determination and Physical Exercise Adherence in the Contexts of Fitness Academies and Personal Training. Journal of Human Kinetics, 46(1), 241–249. https://doi.org/10.1515/HUKIN-2015-0052
Li, Y., Xu, J., Zhang, X., & Chen, G. (2024). The relationship between exercise commitment and college students’ exercise adherence: The chained mediating role of exercise atmosphere and exercise self-efficacy. Acta Psychologica, 246, 104253. https://doi.org/10.1016/j.actpsy.2024.104253
Lin, C.-C., Lin, Y.-S., Yeh, C. H., Huang, C.-C., Kuo, L.-C., & Su, F.-C. (2023). An Exergame-Integrated IoT-Based Ergometer System Delivers Personalized Training Programs for Older Adults and Enhances Physical Fitness: A Pilot Randomized Controlled Trial. Gerontology, 69(6), 768–782. https://doi.org/10.1159/000526951
Martin, B. J. M. (2026). Autonomous motivation as a mechanism of change in a gamified digital physical activity intervention: A randomized controlled trial. The Physical Educator, 83, 291–321. https://doi.org/10.18666/TPE-2026-V83-I3-13523
McMillian, D. J., Cobb-Ozanne, R., & Withers, K. (2023). Motivational Characteristics And Fulfillment Of Psychological Needs Among Physically Active Undergraduate Students. Medicine and Science in Sports and Exercise, 55(9S), 152. https://doi.org/10.1249/01.mss.0000981116.84040.56
Mohammed, M. H. H. (2016). P-6 Effect of eight weeks basketball and swimming on health-related fitness of male university students. British Journal of Sports Medicine, 50. https://doi.org/10.1136/BJSPORTS-2016-097120.59
Murphy, B. A., Chaabane, S., Weill Cornell Medicine, Q., Obrenovic, B., Shava, B. K., Kupenga-Maposa, T., Musingwini, T., Samudzi, T., Muchemwa, S., Chibanda, D., & Dambi, J. M. (2024). Perceived benefits and barriers to exercise and associated factors among Zimbabwean undergraduate students: a cross-sectional study. Frontiers in Sports and Active Living. https://doi.org/10.3389/fspor.2024.1205914
Nuuttila, O.-P., Nummela, A., Korhonen, E., Häkkinen, K., & Kyröläinen, H. (2022). Individualized Endurance Training Based on Recovery and Training Status in Recreational Runners. Medicine and Science in Sports and Exercise, 54, 1690–1701. https://doi.org/10.1249/MSS.0000000000002968
Parker, K., Uddin, R., Ridgers, N. D., Brown, H., Veitch, J., Salmon, J., Timperio, A., Sahlqvist, S., Cassar, S., Toffoletti, K., Maddison, R., & Arundell, L. (2021). The Use of Digital Platforms for Adults’ and Adolescents’ Physical Activity During the COVID-19 Pandemic (Our Life at Home): Survey Study. Journal of Medical Internet Research, 23(2), 1–10. https://doi.org/10.2196/23389
Peng, S., Othman, A. T., Khairani, A. Z., Zeng, G., & Xiaogang, Z. (2023). Effectiveness of pedometer- and accelerometer-based interventions in improving physical activity and health-related outcomes among college students: A systematic review and meta-analysis. Digital Health. https://doi.org/10.1177/20552076231188213
Pineda, H. S., Andal, E. Z., & Hermosa, J. P. (2024). Exploring the faculty and students’ readiness level for the effective implementation of physical activity toward health and fitness courses (PATHFIT). International Journal of Multidisciplinary: Applied Business and Education Research, 5(1), 161–168. https://doi.org/10.11594/ijmaber.05.01.16
Rojo-Ramos, J., Gómez Paniagua, S., García-Guillen, M. J., & Galán-Arroyo, C. (2024). Influence of sex on the motives for physical activity among university students in Extremadura. Sportis. Scientific Technical Journal of School Sport, Physical Education and Psychomotricity, 11(1), 1-20. https://doi.org/10.17979/sportis.2025.11.1.11027
Santiago, R. G., & Ibarra, V. C. (2023). Health, physical fitness, and skills of first year college students: Evidence from a Philippine college. Malaysian Mental Health Journal, 2(1), 7–12. https://doi.org/10.26480/mmhj.01.2023.07.12
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
Shannon, S., Carlin, A., Woods, C., Nevill, A. M., Murphy, N., & Murphy, M. H. (2022). Adherence to aerobic and muscle-strengthening components of the physical activity guidelines and mental health. Health Promotion International, 37 5(5). https://doi.org/10.1093/heapro/daac083
Silva, R. M. F., Mendonça, C. R., Azevedo, V. D., Memon, A. R., Noll, P. R. e S., & Noll, M. (2022). Barriers to high school and university students’ physical activity: A systematic review. PLOS ONE, 17(4), e0265913. https://doi.org/10.1371/journal.pone.0265913
Sun, T., Xu, Y., Xie, H., Ma, Z., & Wang, Y. (2021). Intelligent Personalized Exercise Prescription Based on an eHealth Promotion System to Improve Health Outcomes of Middle-Aged and Older Adult Community Dwellers: Pretest-Posttest Study. Journal of Medical Internet Research, 23(5). https://doi.org/10.2196/28221
Tan, M., Xiao, Y., Jing, F., Xie, Y., Lu, S., Xiang, M., & Ren, H. (2024). Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health: a randomized controlled trial protocol. Frontiers in Psychiatry. https://doi.org/10.3389/fpsyt.2024.1352420
Teixeira, D., Andrade, T., Brandāo Faria, J. A., Marques, P., Bastos, V. R., Rodrigues, F., Sousa, A., & Pereira, H. V. (2024). Exercise intensity agreement, need satisfaction, and exercise behavior: A sex‐moderated mediation model. European Journal of Sport Science. https://doi.org/10.1002/ejsc.12173
Weatherwax, R., Nelson, M. C., & Dalleck, L. C. (2024). The Impact of Personalized versus Standardized Cardiorespiratory and Muscular Training on Health-Related Outcomes and Rate of Responders. Journal of Sports Science and Medicine. https://doi.org/10.52082/jssm.2024.209
Zeng, N., Liao, N., Han, C., Liu, W., & Gao, Z. (2020). Leveraging Fitness Tracker and Personalized Exercise Prescription to Promote Breast Cancer Survivors’ Health Outcomes: A Feasibility Study. Journal of Clinical Medicine, 9(6), 1775. https://doi.org/10.3390/JCM9061775
Zhang, T.-T., Zhao, J., & Yu, L. (2023). The Effect of Fitness Apps Usage Intensity on Exercise Adherence Among Chinese College Students: Testing a Moderated Mediation Model. Psychology Research and Behavior Management, 16, 1485–1494. https://doi.org/10.2147/PRBM.S408276
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Arlene B. Ucol (Autor/a)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The works published in this journal are subject to the following terms:
The author retains the economic rights (copyright) of the published works and grants the journal the right of first publication.
The works are published in the electronic edition of the journal under a Creative Commons Attribution–NonCommercial–ShareAlike license. They may be copied, used, distributed, transmitted, and publicly displayed, provided that: (i) authorship and the original source of publication (journal, publisher, and URL of the work) are cited; (ii) they are not used for commercial purposes; (iii) the existence and specifications of this license are indicated.
Self-archiving conditions. Authors are permitted and encouraged to disseminate electronically the post-print versions (publisher’s version), as this facilitates earlier circulation and dissemination, potentially increasing citation and reach within the academic community. Authors may also deposit their articles in repositories and other platforms that support the dissemination of information, provided that the version used is the post-print (publisher’s version). RoMEO color: Blue.
