Precision Exercise Programming in Optimizing Cardiometabolic Health and Physical Performance through Artificial Intelligence Assisted Training

Irmantara Subagio; Sulton Nur Arifin; Egy Raafi Alafian; Egy Raafi Alafian; Ary Nindya Kuswara

doi:10.55927/fjas.v5i8.98

Authors

Irmantara Subagio Universitas Negeri Surabaya
Sulton Nur Arifin Universitas Negeri Surabaya
Egy Raafi Alafian Universitas Negeri Surabaya
Egy Raafi Alafian Universitas Negeri Surabaya
Ary Nindya Kuswara Universitas Negeri Surabaya

DOI:

https://doi.org/10.55927/fjas.v5i8.98

Keywords:

Artificial Intelligence, Precision Exercise, Cardiometabolic Health, Physical Performance, Personalized Training

Abstract

The development of artificial intelligence allows for the development of more precise exercise programs based on individual physiological responses, but its effectiveness on cardiometabolic health and physical performance still requires empirical testing. This study aims to analyze the effect of artificial intelligence-assisted training on cardiometabolic indicators and physical performance. The study used a 12-week randomized controlled trial of 120 participants allocated to an AI-based exercise group and conventional exercises. Data were collected through measurements of VO₂max, blood pressure, resting heart rate, body composition, blood glucose, as well as strength and endurance tests, and then analyzed using the mixed-ANOVA model. The results showed the AI-based group experienced significant improvements in VO₂max, strength, and endurance as well as improvements in cardiometabolic indicators. These findings confirm the potential of AI in optimizing exercise personalization and the effectiveness of exercise interventions.

References

Apriyanto, K. D., Kushartanti, B. M. W., Ambardini, R. L., & Páez, L. C. (2024). Hubungan antara fleksibilitas dan komposisi tubuh dengan kemampuan daya tahan jantung paru pada mahasiswa non keolahragaan. MEDIKORA, 23(1), 43–53. https://doi.org/10.21831/medikora.v23i1.72203 Akses artikel.

Ariani, L. P. T. A., Sudiana, I. K., & Kusuma, K. C. A. (2022). Continuous and competitive circuit training: Methods to increase VO₂max on young badminton player. Journal Sport Area, 7(2), 236–245. https://doi.org/10.25299/sportarea.2022.vol7(2).9423 Akses artikel.

Artanayasa, I. W., Kusuma, K. C. A., & Ariawan, K. U. (2023). Push-Up Counter (PUC) as an instrument of arm muscle strength: Validity and reliability. Journal Sport Area, 8(3), 350–359. https://doi.org/10.25299/sportarea.2023.vol8(3).13245 Akses artikel.

Chen, H.-K., Chen, F.-H., & Lin, S.-F. (2021). An AI-based exercise prescription recommendation system. Applied Sciences, 11(6), 2661. https://doi.org/10.3390/app11062661 Akses artikel.

Dergaa, I., Ben Saad, H., El Omri, A., Glenn, J. M., Clark, C. C. T., Washif, J. A., Guelmami, N., Hammouda, O., Al-Horani, R. A., Reynoso-Sánchez, L. F., Romdhani, M., Paineiras-Domingos, L. L., Vancini, R. L., 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, 41(2), 221–241. https://doi.org/10.5114/biolsport.2024.133661 Akses artikel.

Ellyas, I. S., Sunardi, S., Mariyanto, M., Lelono, S., Margono, A., & Kristiyanto, A. (2023). Effect of circuit weight training on aerobic fitness, body fat percentage, muscle mass percentage, and hand grip strength in the elderly. Jurnal Keolahragaan, 11(2), 248–255. https://doi.org/10.21831/jk.v11i2.65453 Akses artikel.

Guan, R., Li, H., Jiao, Y., & Yu, H. (2022). A nonrandomized controlled trial of individualized exercise prescription combined with remote exercise management in patients who are overweight or obese. BMC Sports Science, Medicine and Rehabilitation, 14, 99. https://doi.org/10.1186/s13102-022-00479-1 Akses artikel.

Haryanto, S., Wibisana, M. I. N., & Prastiwi, B. K. (2024). Efektifitas latihan fartlek dan latihan circuit terhadap peningkatan VO₂max pada atlet handball. Jurnal Segar, 12(2), 99–107. https://doi.org/10.21009/segar/1202.04 Akses artikel.

Jubair, H., & Mehenaz, M. (2025). Utilizing machine learning algorithms for personalized workout recommendations and monitoring: A systematic review on smartwatch-assisted exercise prescription. Digital Health, 11, 20552076251355365. https://doi.org/10.1177/20552076251355365 Akses artikel.

Kamarudin, K., Sasmarianto, S., & Rahmalia, W. (2024). Differences in the effect of circuit training and fartlex training on increasing VO₂max of pencak silat athletes. Indonesian Journal of Physical Education and Sport Science, 4(2), 140–147. https://doi.org/10.52188/ijpess.v4i2.705 Akses artikel.

Kim, K.-B., Choe, H., & Sung, H. (2024). Effects of individualized exercise on risk factors of metabolic syndrome: A scoping review. Journal of Obesity & Metabolic Syndrome, 33(1), 20–26. https://doi.org/10.7570/jomes23020 Akses artikel.

Kirton, M. J., Burnley, M. T., Ramos, J. S., Weatherwax, R., & Dalleck, L. C. (2022). The effects of standardised versus individualised aerobic exercise prescription on fitness-fatness index in sedentary adults: A randomised controlled trial. Journal of Sports Science and Medicine, 21(3), 347–355. https://doi.org/10.52082/jssm.2022.347 Akses artikel.

Lehtonen, E., Gagnon, D., Eklund, D., Kaseva, K., & Peltonen, J. E. (2022). Hierarchical framework to improve individualised exercise prescription in adults: A critical review. BMJ Open Sport & Exercise Medicine, 8(2), e001339. https://doi.org/10.1136/bmjsem-2022-001339 Akses artikel.

Li, B., & He, W. (2025). Research the role of artificial intelligence in developing personalized training plans to maximize the potential of basketball players. Journal of Computational Methods in Sciences and Engineering, 25(5), 4833–4848. https://doi.org/10.1177/14727978251337959 Akses artikel.

Munoz-Macho, A. A., Domínguez-Morales, M. J., & Sevillano-Ramos, J. L. (2024). Performance and healthcare analysis in elite sports teams using artificial intelligence: A scoping review. Frontiers in Sports and Active Living, 6, 1383723. https://doi.org/10.3389/fspor.2024.1383723 Akses artikel.

Noone, J., Mucinski, J. M., DeLany, J. P., Sparks, L. M., & Goodpaster, B. H. (2024). Understanding the variation in exercise responses to guide personalized physical activity prescriptions. Cell Metabolism, 36(4), 702–724. https://doi.org/10.1016/j.cmet.2023.12.025 Akses artikel.

Ramos, J. S., Dalleck, L. C., Fennell, M., Martini, A., Welmans, T., Stennett, R., Keating, S. E., Fassett, R. G., & Coombes, J. S. (2021). Exercise training intensity and the fitness-fatness index in adults with metabolic syndrome: A randomized trial. Sports Medicine - Open, 7, 100. https://doi.org/10.1186/s40798-021-00395-7 Akses artikel.

Seçkin, A. Ç., Ateş, B., & Seçkin, M. (2023). Review on wearable technology in sports: Concepts, challenges and opportunities. Applied Sciences, 13(18), 10399. https://doi.org/10.3390/app131810399 Akses artikel.

Septiawan, B. D. A., Susilawati, D., & Lengkana, A. S. (2025). Pengaruh metode latihan lari fartlek dan circuit training terhadap peningkatan VO₂max. Jurnal Porkes, 8(1), 480–489. https://doi.org/10.29408/porkes.v8i1.29933 Akses artikel.

Strain, T., Flaxman, S., Guthold, R., Semenova, E., Cowan, M., Riley, L. M., Bull, F. C., Stevens, G. A., Country Data Author Group, & Garcia, L. (2024). National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: A pooled analysis of 507 population-based surveys with 5.7 million participants. The Lancet Global Health, 12(8), e1232–e1243. https://doi.org/10.1016/S2214-109X(24)00150-5 Akses artikel open access.

Vec, V., Tomažič, S., Kos, A., & Umek, A. (2024). Trends in real-time artificial intelligence methods in sports: A systematic review. Journal of Big Data, 11, 148. https://doi.org/10.1186/s40537-024-01026-0 Akses artikel open access.

Zahedani, A. D., McLaughlin, T., Veluvali, A., Aghaeepour, N., Hosseinian, A., Agarwal, S., Ruan, J., Tripathi, S., Woodward, M., Hashemi, N., & Snyder, M. (2023). Digital health application integrating wearable data and behavioral patterns improves metabolic health. npj Digital Medicine, 6, 216. https://doi.org/10.1038/s41746-023-00956-y Akses artikel open access.