Estudo comparativo dos métodos de reabilitação tradicionais versus métodos de reabilitação assistida por IA para lesões dos membros inferiores em jogadores de basquetebol: seguimento semi-experimental de 12 meses

Autores

DOI:

https://doi.org/10.47197/retos.v74.118127

Palavras-chave:

Reabilitação assistida por IA, jogadores de basquetebol, lesões nos membros inferiores, métodos de reabilitação

Resumo

Introdução: A prevenção e a reabilitação de lesões são componentes fundamentais da ciência desportiva moderna e da gestão de atletas. As lesões desportivas impactam negativamente o desempenho atlético e a longevidade da carreira.

Método: Este estudo tem como objetivo comparar a eficácia dos protocolos de reabilitação tradicionais versus programas de reabilitação modernos assistidos por técnicas de Inteligência Artificial (IA) em jogadores de basquetebol que sofreram lesões nos membros inferiores (joelho, tornozelo ou musculatura).

Resultado: Os resultados primários incluem o tempo de retorno ao jogo (RTP), as alterações nos indicadores de desempenho físico (força muscular, potência explosiva, equilíbrio, precisão de lançamento) e as taxas de relesão ao longo de um período de seguimento de 12 meses. Os desfechos secundários examinam a satisfação do atleta e do profissional de saúde com cada protocolo.

Conclusão: O estudo utiliza medidas basais, avaliações pós-intervenção aos 3 e 6 meses e um seguimento de 12 meses para verificar a ocorrência de relesão. A hipótese é que o grupo assistido por IA apresentará um tempo de retorno ao jogo mais curto, maiores ganhos nas medidas de desempenho e uma menor incidência de relesão em comparação ao grupo tradicional. Estas descobertas podem orientar a prática de reabilitação no desporto e apoiar a adoção baseada em evidências de ferramentas de IA em programas de recuperação de atletas.

Biografias do Autor

  • Yazan, S, Haddad, Yarmouk University

    Assistant Professor, Lecturer at  Physical Education department, Assistant Professor, Yarmouk University, Jordan

  • Ruba, F Kharashqah, jadara university

    Assistant Professor, Faculty of Physical Education - Department of Physical Education, University of Jadara, Jordan

  • Aysheh, Y, Ababaneh, jadara university

    Assistant Professor, Full-time lecturer, Faculty of physical Educational, Family Guidance and sport Department, Jadara University, Jordan

  • Ra'ed, R, Bataineh, jadara university

    Assistant Professor, Faculty of Physical Educational, Family Guidance and Sports Department, Jadara University, Jordan

  • Tajuddin, A, Alwedyan, petra university

    Assistant Professor, Sport management, Educational Sciences Department, University of Petra, Jordan

  • Mohammad, F, Alzu’bi, jadara university

    Faculty of Physical Educational, Family Guidance and Sports Department, Jadara University, Jordan

  • Hassan, F, Kulaep, alpha Company

    Full-time lecturer in physical education and handball, Jordan, 

  • Laith, K, Al-Sababha, petra university

    Aassistant Professor, Physical Education, Patra University, Jordan

Referências

Adetiba, E., Iweanya, V. C., Popoola, S. I., Adetiba, J. N., & Menon, C. J. C. E. (2017). Automated detection of heart defects in athletes based on electrocardiography and artificial neural. network. 4(1), 1411220. https://doi.org/10.1080/23311916.2017.1411220.

Amendolara, A., Pfister, D., Settelmayer, M., Shah, M., Wu, V., Donnelly, S., . . . Bills, K. (2023). An Over-view of Machine Learning Applications in Sports Injury Prediction. Cureus, 15(9), e46170. https://doi.org/10.7759/cureus.46170.

Andriollo, L., Picchi, A., Sangaletti, R., Perticarini, L., Rossi, S. M. P., Logroscino, G., & Benazzo, F. (2024). The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives. Healthcare (Basel), 12(3). https://doi.org/10.3390/healthcare12030300.

Arzehgar, A., Seyedhasani, S. N., Ahmadi, F. B., Bagheri Baravati, F., Sadeghi Hesar, A., Kachooei, A. R., & Aalaei, S. (2025). Sensor-based technologies for motion analysis in sports injuries: a scoping re-view. BMC Sports Sci Med Rehabil, 17(1), 15. https://doi.org/10.1186/s13102-025-01063-z.

Babu, A., Thuau, D., & Mandal, D. (2023). AI-enabled wearable sensor for real-time monitored personal-ized training of sportsperson. MRS Communications, 13(6), 1071-1075. https://doi.org/10.1557/s43579- 023-00464-w.

Boltaboyeva, A., Baigarayeva, Z., Imanbek, B., Ozhikenov, K., Getahun, A. J., Aidarova, T., & Karymsa-kova, N. (2025). A Review of Innovative Medical Rehabilitation Systems with Scalable AI-Assisted Platforms for Sensor-Based Recovery Monitoring. Applied Sciences, 15(12), 6840. https://doi.org/10.3390/app15126840

Calderón-Díaz, M., Silvestre Aguirre, R., Vásconez, J. P., Yáñez, R., Roby, M., Querales, M., & Salas, R. (2024). Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis. 24(1), 119. https://doi.org/10.3390/s24010119.

Cancino-Jiménez, J., Moya-Jofre, C., Araya-Ibacache, M., Retamal-Espinoza, M., Arriagada-Tarifeño, D., Cifuentes-Silva, E., Miarka, B., & Aedo Muñoz, E. (2025). Two different techniques for the recon-struction of the anterior cruciate ligament. Which is better concerning postural control? . Retos, 72, 643-653. https://doi.org/10.47197/retos.v72.114375

Cejudo-Alba, R., Borràs Boix, X., & Martínez Gramage, J. (2025). Methodological protocol of running on a treadmill using IMU in healthy people. Scoping review. Retos, 73, 269-287. https://doi.org/10.47197/retos.v73.116214

Corban, J., Lorange, J. P., Laverdiere, C., Khoury, J., Rachevsky, G., Burman, M., & Martineau, P. A. (2021). Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries. Orthop J Sports Med, 9(7),23259671211014206. https://doi.org/10.1177/23259671211014206

Desai, V. (2024). The Future of Artificial Intelligence in Sports Medicine and Return to Play. Semin Mus-culoskelet Radiol, 28(2), 203-212. https://doi.org/10.1055/s-0043-1778019.

Ekambaram, D., & Ponnusamy, V. (2023). AI-assisted Physical Therapy for Post-injury Rehabilitation: Current State of the Art. IEIE Transactions on Smart Processing & Computing, 12(3), 234-242. https://doi.org/10.5573/IEIESPC.2023.12.3.234

Gai, X. (2025). Application of flexible sensor multimodal data fusion system based on artificial synapse and machine learning in athletic injury prevention and health monitoring. Discover Artificial In-telligence, 5(1), 31. https://doi.org/10.1007/s44163-025-00254-4.

Gu, C., Lin, W., He, X., Zhang, L., & Zhang, M. (2023). IMU-based motion capture system for rehabilitation applications: A systematic review. Biomimetic Intelligence and Robotics, 3(2), 100097. https://doi.org/10.1016/j.birob.2023.100097.

Guelmami, N., Fekih-Romdhane, F., Mechraoui, O., & Bragazzi, N. L. (2023). Injury prevention, opti-mized training and rehabilitation: how is AI reshaping the field of sports medicine. New Asian Journal of Medicine, 1(1), 30-34. doi: 10.61186/najm.1.1.30.

Guo, X., Liu, P., & Li, T. (2024). Sports Injury Prediction and Prevention: Analysis Methods Based on Big Data and Artificial Intelligence. Paper presented at the 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). https://doi.org/10.1109/ICBASE63199.2024.10762288.

Hernández Oñate, G., Campo, M. Ángel, Calero-Saa, P., & Tierradentro Gómez, L. M. (2025). Functional characteristics in soccer players with and without knee injury history: a cross-sectional study. Retos, 74, 395-405. https://doi.org/10.47197/retos.v74.110951

Hliš, T., Fister, I., & Fister Jr, I. (2024). Digital twins in sport: Concepts, taxonomies, challenges and prac-tical potentials. Expert Systems with Applications, 258, 125104. https://doi.org/10.1016/j.eswa.2024.125104.

Huang, Y., Huang, S., Wang, Y., Li, Y., Gui, Y., & Huang, C. (2022). A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning. Front Physiol, 13, 937546. https://doi.org/10.3389/fphys.2022.937546

LaBoone, P. A., & Marques, O. (2024). Overview of the future impact of wearables and artificial intelli-gence in healthcare workflows and technology. International Journal of Information Manage-ment Data Insights, 4(2), 100294. https://doi.org/10.1016/j.jjimei.2024.100294.

Lanotte, F., O'Brien, M. K., & Jayaraman, A. (2023). AI in Rehabilitation Medicine: Opportunities and Challenges. Ann Rehabil Med, 47(6), 444-458. https://doi.org/10.5535/arm.23131.

Leong, W. Y., Leong, Y. Z., & Leong, W. S. (2024). Sports Medicine Protocols: A Comprehensive Guide to Injury Management and Rehabilitation. Paper presented at the 2024 IEEE 6th Eurasia Confer-ence on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). https://doi.org/10.1109/ECBIOS61468.2024.10885452.

Letsholo, T. C., Dawood, M. A., & Boby, F. A. (2025). Incidence and prevalence of injury in adolescent female cricketers. Retos, 74, 383-394. https://doi.org/10.47197/retos.v74.117537

Li, A., & Huang, W. (2024). A comprehensive survey of artificial intelligence and cloud computing appli-cations in the sports industry. Wireless Networks, 30(8), 6973-6984. https://doi.org/10.1007/s11276-023- 03567-3.

Li, W. (2024). A Big Data Approach to Forecast Injuries in Professional Sports Using Support Vector Machine. Mobile Networks and Applications. https://doi.org/10.1007/s11036-024-02377-x.

Luo, Z., Wang, Y., Zhang, T., & Wang, J. (2025). Effectiveness of AI-assisted rehabilitation for musculo-skeletal disorders: a network meta-analysis of pain, range of motion, and functional out-comes. Frontiers in Bioengineering and Biotechnology, 13, 1660524. https://doi.org/10.3389/fbioe.2025.1660524

Mishra, N., Habal, B. G. M., Garcia, P. S., & Garcia, M. B. (2024). Harnessing an AI-Driven Analytics Model to Optimize Training and Treatment in Physical Education for Sports Injury Prevention. Paper presented at the Proceedings of the 2024 8th International Conference on Education and Mul-timedia Technology, Tokyo, Japan. https://doi.org/10.1145/3678726.3678740.

Nurfaiza, M. T., Purnama, S. K., Syaifullah, R., Azizah, A. N., Perdana, S. S., Hazar, F., Saputra, D., & Purwo-to, S. P. (2025). Occurance of injury during ASEAN Paragames Cambodia 2023: a study in Indo-nesia para-athletics team. Retos, 70, 367-374. https://doi.org/10.47197/retos.v70.115164

Qazi, A., & Iqbal, A. (2024). ExerAide: AI-assisted multimodal diagnosis for enhanced sports perfor-mance and personalised rehabilitation. In Proceedings of the IEEE/CVF Conference on Comput-er Vision and Pattern Recognition (pp. 3430-3438). https://openaccess.thecvf.com/content/CVPR2024W/CVsports

Wang, P., Wang, A., & Wang, S. (2025). Integrating multimodal AI technologies for sports injury predic-tion and rehabilitation: Systematic review. Journal of Human Sport and Exercise , 21(1), 22-37. https://doi.org/10.55860/w6j5wc21

Wang, P., Wang, A., & Wang, S. (2026). Integrating multimodal AI technologies for sports injury predic-tion and rehabilitation: Systematic review. Journal of Human Sport and Exercise, 21(1), 22-37. https://doi.org/10.55860/w6j5wc21

Zhang, X., Rong, X., & Luo, H. (2024). Optimizing lower limb rehabilitation: The intersection of machine learning and rehabilitative robotics. Frontiers in rehabilitation sciences, 5, 1246773.https://doi.org/10.3389/fresc.2024.1246773

Downloads

Publicado

01-01-2026

Edição

Secção

Artigos de caráter científico: trabalhos de pesquisas básicas e/ou aplicadas.

Como Citar

Haddad, Y. S., Kharashqah, R. F., Ababaneh, A. Y., Bataineh, R. R., Alwedyan, T. A., Alzu’bi, M. F., Bani Hani, S., Kulaep, H. F., & Al-Sababha, L. K. (2026). Estudo comparativo dos métodos de reabilitação tradicionais versus métodos de reabilitação assistida por IA para lesões dos membros inferiores em jogadores de basquetebol: seguimento semi-experimental de 12 meses. Retos, 74, 833-844. https://doi.org/10.47197/retos.v74.118127