Volume 11, Issue 4 (3-2026)
J Sport Biomech 2026, 11(4): 392-409 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mukta F T J, Rickta J F, Arafat M Y. AI-Guided vs. Traditional Training in Adolescent Soccer Players: Effects on Performance and Injury Risk. J Sport Biomech 2026; 11 (4) :392-409
URL: http://biomechanics.iauh.ac.ir/article-1-423-en.html
URL: http://biomechanics.iauh.ac.ir/article-1-423-en.html
1- Department of Physical Education & Sports Science, Jessore University of Science and Technology, Bangladesh.
2- Physical Education Office; Chittagong University of Engineering & Technology, Bangladesh.
2- Physical Education Office; Chittagong University of Engineering & Technology, Bangladesh.
Abstract: (34 Views)
Objective Artificial intelligence (AI)-guided training methods provide a personalized approach, leveraging real-time physiological and biomechanical data to optimize performance and reduce injury risk. The present research compared a 12-week AI-guided personalized training program with traditional coach-led training on performance metrics and injury incidence in adolescent football players.
Methods A randomized controlled trial (RCT) was conducted with 60 adolescent athletes (ages 14–17 years) recruited from a football academy. Pre- and post-intervention performance was assessed using the Functional Movement Screen (FMS), 20 m sprint, T-test (agility), and countermovement jump (CMJ), while injury incidence was monitored by a certified physiotherapist.
Results The AI-guided group demonstrated significantly greater improvements than the control group in FMS scores (+20%), sprint time (−4.93%), agility (−6.48%), and CMJ height (+11.86%), with large effect sizes (d = 0.88–1.42). Injury incidence was significantly lower in the AI group (10%) compared with the control group (36.7%) (p = .034; risk ratio = 3.67; 95% Confidence Interval).
Conclusion These findings highlight the efficacy of AI-driven training in enhancing athletic performance and reducing injury risk among adolescent athletes, emphasizing the value of personalized, data-informed approaches over traditional methods. Further research with larger cohorts and extended follow-ups is recommended to validate these results across diverse sports populations.
Methods A randomized controlled trial (RCT) was conducted with 60 adolescent athletes (ages 14–17 years) recruited from a football academy. Pre- and post-intervention performance was assessed using the Functional Movement Screen (FMS), 20 m sprint, T-test (agility), and countermovement jump (CMJ), while injury incidence was monitored by a certified physiotherapist.
Results The AI-guided group demonstrated significantly greater improvements than the control group in FMS scores (+20%), sprint time (−4.93%), agility (−6.48%), and CMJ height (+11.86%), with large effect sizes (d = 0.88–1.42). Injury incidence was significantly lower in the AI group (10%) compared with the control group (36.7%) (p = .034; risk ratio = 3.67; 95% Confidence Interval).
Conclusion These findings highlight the efficacy of AI-driven training in enhancing athletic performance and reducing injury risk among adolescent athletes, emphasizing the value of personalized, data-informed approaches over traditional methods. Further research with larger cohorts and extended follow-ups is recommended to validate these results across diverse sports populations.
Keywords: Artificial intelligence, Traditional methods, Adolescent athletes, Performance metrics, Injury risk
Type of Study: Research |
Subject:
General
Received: 2025/08/12 | Accepted: 2025/09/19 | Published: 2025/09/21
Received: 2025/08/12 | Accepted: 2025/09/19 | Published: 2025/09/21
References
1. Rogers DL, Tanaka MJ, Cosgarea AJ, Ginsburg RD, Dreher GM. How mental health affects injury risk and outcomes in athletes. Sports Health. 2024;16(2):222-229. [DOI:10.1177/19417381231179678] [PMID]
2. Drew MK, Raysmith BP, Charlton PC. Injuries impair the chance of successful performance by sportspeople: a systematic review. British Journal of Sports Medicine. 2017;51(16):1209-1214. [DOI:10.1136/bjsports-2016-096731] [PMID]
3. Forelli F, Moiroux-Sahraoui A, Nielsen-Le Roux M, Miraglia N, Gaspar M, Stergiou M, Bjerregaard A, Mazeas J, Douryang M, MOIROUX A, Bjerregaard Sr A. Stay in the Game: Comprehensive Approaches to Decrease the Risk of Sports Injuries. Cureus. 2024;16(12):1-11. [DOI:10.7759/cureus.76461] [PMID]
4. Kelley S, Martin K, Perlmuter M, Sofla M. Psychological Injury Rehabilitation: The Link Between Body and Mind. Imagine: A Promise Scholars & McNair Scholars Journal. 2024;2(1):1-22.
5. Aicale R, Tarantino D, Maffulli N. Overuse injuries in sport: a comprehensive overview. Journal of Orthopaedic Surgery and Research. 2018;13(1):1-11. [DOI:10.1186/s13018-018-1017-5] [PMID]
6. Paterno MV, et al. Prevention of overuse sports injuries in the young athlete. Orthopedic Clinics of North America. 2023;44(4):545-552. [DOI:10.1016/j.ocl.2013.06.009] [PMID]
7. Hamoongard M, Hadadnezhad M, Mohammadi Orangi B. A Narrative Review on the Effect of Variability-Based Motor Learning Approaches on Kinetic and Kinematic Factors Related to Anterior Cruciate Ligament Injury in Athletes. Journal of Sport Biomechanics. 2025;10(4):276-93. [DOI:10.61186/JSportBiomech.10.4.276]
8. Faude O, Rößler R, Junge A. Football injuries in children and adolescent players: are there clues for prevention? Sports Medicine. 2013;43(9):819-37. [DOI:10.1007/s40279-013-0061-x] [PMID]
9. Myer GD, Faigenbaum AD, Ford KR, Best TM, Bergeron MF, Hewett TE. When to initiate integrative neuromuscular training to reduce sports-related injuries and enhance health in youth? Current Sports Medicine Reports. 2011;10(3):155-66. [DOI:10.1249/JSR.0b013e31821b1442] [PMID]
10. Oliver JL, Lloyd RS, Read PJ, Myer GD,. Developing the foundations of movement competency in youth. Strength and Conditioning Journal. 2020:42(6):15-23.
11. Zemková E, Hamar D. Sport-specific assessment of the effectiveness of neuromuscular training in young athletes. Frontiers in Pphysiology. 2018;9:264. [DOI:10.3389/fphys.2018.00264] [PMID]
12. Bishop C, Read P, Chavda S, Turner A. Inter-limb asymmetries: Understanding how to calculate them and influence training prescription. Strength and Conditioning Journal. 2018;40(4),1-6. [DOI:10.1519/SSC.0000000000000371]
13. Zhou D, Keogh JW, Ma Y, Tong RK, Khan AR, Jennings NR. Artificial intelligence in sport: A narrative review of applications, challenges and future trends. Journal of Sports Sciences. 2025;16:1-6. [DOI:10.1080/02640414.2025.2518694] [PMID]
14. Du T, Bi N. Application of Artificial Intelligence Advances in Athletics Industry: A Review. Concurrency and Computation: Practice and Experience. 2025;37(3):e8372. [DOI:10.1002/cpe.8372]
15. Claudino JG, Capanema DD, de Souza TV, Serrão JC, Machado Pereira AC, Nassis GP. Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports Medicine-Open. 2019;5(1):1-12. [DOI:10.1186/s40798-019-0202-3] [PMID]
16. Liu H, Gómez MA, Gonçalves B, Sampaio J. Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences. 2016;34(6):509-18. [DOI:10.1080/02640414.2015.1117121] [PMID]
17. Owen R, Owen JA, Evans SL. Artificial intelligence for sport injury prediction. InArtificial intelligence in sports, movement, and health. 2024;69-79. Cham: Springer Nature Switzerland. [DOI:10.1007/978-3-031-67256-9_5]
18. Williams, C. A., & Armstrong, N. The influence of growth and maturation on physical performance. In N. Armstrong & W. van Mechelen (Eds.), Oxford textbook of children's sport and exercise medicine.2019;(3rd ed.,pp. 35-50). Oxford University Press.
19. Rickta JF, Arafat MY, Mukta FT. A Study on Correlation among Physique, Motor fitness and Performance of Soccer Player. International Journal of Physical Education Sports Management and Yogic Sciences. 2021;11(1):28-33. [DOI:10.5958/2278-795X.2021.00004.7]
20. Alexe DI, Čaušević D, Čović N, Rani B, Tohănean DI, Abazović E, Setiawan E, Alexe CI. The relationship between functional movement quality and speed, agility, and jump performance in elite female youth football players. Sports. 2024;12(8):214. [DOI:10.3390/sports12080214] [PMID]
21. McCunn R, aus der Fünten K, Fullagar HH, McKeown I, Meyer T. Reliability and association with injury of movement screens: a critical review. Sports Medicine. 2016;46(6):763-781. [DOI:10.1007/s40279-015-0453-1] [PMID]
22. Mukta FT, Rickta JF, Arafat MY. Monitoring of athletes condition: Male handball players body part pain in handball performance. Journal of Sports Research. 2025;12(1):24-32. [DOI:10.18488/90.v12i1.4334]
23. Mukta FT, Rickta JF, Islam MZ, Arafat MY. Correlation between Functional Movement Patterns and Performance Metrics in National level Female Handball players. International Journal of Kinesiology and Sport Science. 2025;13(3):81-86. [DOI:10.7575/aiac.ijkss.v.13n.3p.81]
24. Kadhim JH, Hamzah FM, Hussain LM. A Review of the Use of Artificial Intelligence Algorithms for Predicting Injuries and Performance in Football Players. Mustansiriyah Journal of Sports Science. 2025;7(2):148-161. [DOI:10.62540/mjss.2025.2.7.12]
25. Bianchi F, Soligard T, Eirale C, Zwiers R, Bahr R. Injury surveillance and workload monitoring in youth sports: The role of emerging technologies. Sports Medicine. 2021;51(4):639-652.
26. Qeysari S F, Emamrezaii S, Eslamizad A, Qeysari S K. Comparison of External Focus Instructions Based on Mechanics and Performance in the Vertical jump: Examining the constrained action hypothesis. Journal of Sport Biomechanics. 2023;9(3):178-191 [DOI:10.61186/JSportBiomech.9.3.178]
27. Huang Z, Wang W, Jia Z, Wang Z. Exploring the Integration of Artificial Intelligence in Sports Coaching: Enhancing Training Efficiency, Injury Prevention, and Overcoming Implementation Barriers. Journal of Computer and Communications. 2024;12(12):201-217. [DOI:10.4236/jcc.2024.1212012]
28. Nazari F, Fatahi A. Football Biomechanics and Performance Enhancement: A Systematic Review. Journal of Sport Biomechanics. 2023;9(3):252-270 [DOI:10.61186/JSportBiomech.9.3.252]
29. Mateus N, Abade E, Coutinho D, Gómez MÁ, Peñas CL, Sampaio J. Empowering the sports scientist with artificial intelligence in training, performance, and health management. Sensors. 2024;25(1):1-12. [DOI:10.3390/s25010139] [PMID]
30. Chen Z, Dai X. Utilizing AI and IoT technologies for identifying risk factors in sports. Heliyon. 2024;10(11):1-15. [DOI:10.1016/j.heliyon.2024.e32477] [PMID]
31. Ghorbani M, Varmaziar M, Heydarian M. A Review of Training Protocols for Preventing Anterior Cruciate Ligament Injuries in Soccer Players. Journal of Sport Biomechanics. 2025;11(1):46-62 [DOI:10.61186/JSportBiomech.11.1.46]
32. Silva A, Ferraz R, Branquinho L, Dias T, Teixeira JE, Marinho DA. Effects of applying a multivariate training program on physical fitness and tactical performance in a team sport taught during physical education classes. Frontiers in Sports and Active Living. 2023;5:1291342. [DOI:10.3389/fspor.2023.1291342] [PMID]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |