Exploring Periodization and Macrocycle Planning: Athlete and Coach Insights
Keywords:
Periodization, macrocycle planning, athletic training, individualization, flexibility, wearable technologyAbstract
This systematic review examines the principles, challenges, and emerging trends in periodization and macrocycle planning in athletic training. Drawing on a comprehensive analysis of existing literature, the study reveals a diverse array of periodization approaches, ranging from traditional linear and undulating models to innovative strategies such as block periodization and hybrid models. Coaches face various challenges in macrocycle planning, including fluctuating player availability, external scheduling constraints, and limited resources, necessitating adaptability and communication to optimize training outcomes. An emerging trend highlighted in the review is the integration of wearable technology and data analytics, enabling real-time physiological monitoring and individualized training prescription. Moreover, the study emphasizes the critical importance of individualization and flexibility in periodization and macrocycle planning, recognizing athletes as unique individuals with distinct physiological and psychological characteristics. By addressing challenges, embracing technological advancements, and prioritizing individualized programming, coaches can optimize training effectiveness and enhance performance outcomes in athletic training.
References
Andrin, G., Kilag, O. K., Groenewald, E., Benitez, J., Dagala, F., & Ubay, R. (2024). Borderless Learning Environments: Impacts on Educational Management Strategies. International Multidisciplinary Journal of Research for Innovation, Sustainability, and Excellence (IMJRISE), 1(2), 43-49.
Bompa, T. O., & Haff, G. G. (2009). Periodization. Theory and methodology of training, 5.
Bugtai, G., Batilaran, J., & Kilag, O. K. (2024). Enhancing Science Education in Middle Schools: A Systematic Review. International Multidisciplinary Journal of Research for Innovation, Sustainability, and Excellence (IMJRISE), 1(2), 7-13.
Churchill, T. (2014). Modelling athletic training and performance: a hybrid artificial neural network ensemble approach (Doctoral dissertation, University of Canberra).
Dobiasch, M., Oppl, S., Stöckl, M., & Baca, A. (2022). Direct mobile coaching as a paradigm for the creation of mobile feedback systems. Applied Sciences, 12(11), 5558.
Duncan, M., Noon, M., & Seabra, A. (2023). Science and Practice of Youth Soccer. Routledge, Taylor & Francis Group.
Issurin, V. B. (2010). New horizons for the methodology and physiology of training periodization. Sports medicine, 40, 189-206.
Kilag, O. K., Obaner, E., Vidal, E., Castañares, J., Dumdum, J. N., & Hermosa, T. J. (2023). Optimizing Education: Building Blended Learning Curricula with LMS. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(4), 238-250.
Kornaros, G. (2022). Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective. IEEE Access, 10, 58603-58622.
Manire, E., Kilag, O. K., Habig, M., Satin, R., Genovania, M. R., & Tan, S. J. (2023). A Technological Approach to Early Childhood Education: Unveiling the SEEDS Pedagogy. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(5), 333-344.
Monteiro-Guerra, F., Rivera-Romero, O., Fernandez-Luque, L., & Caulfield, B. (2019). Personalization in real-time physical activity coaching using mobile applications: a scoping review. IEEE journal of biomedical and health informatics, 24(6), 1738-1751.
Mujika, I., & Padilla, S. (2000). Detraining: loss of training-induced physiological and performance adaptations. Part I: short term insufficient training stimulus. Sports medicine, 30, 79-87.
Peake, J. M., Kerr, G., & Sullivan, J. P. (2018). A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Frontiers in physiology, 9, 329783.
Rabie, S., Laurenzi, C. A., Field, S., Skeen, S., & Honikman, S. (2022). A mixed-methods feasibility study of Nyamekela4Care: an intervention to support improved quality of care among service providers in low-resource settings. SSM-Mental Health, 2, 100154.
Redublado, H. J., Velez, L., Serano, A., & Kilag, O. K. (2024). Enhancing Physical Activity and Movement Skills in Youth: A Systematic Review of School-Based Interventions. International Multidisciplinary Journal of Research for Innovation, Sustainability, and Excellence (IMJRISE), 1(3), 73-78.
Saadati, S. A. (2023). Recent Innovations in Sports Physiology: Shaping the Future of Athletic Performance. Health Nexus, 1(2), 15-27.
Smith, A., Ntoumanis, N., & Duda, J. (2010). An investigation of coach behaviors, goal motives, and implementation intentions as predictors of well-being in sport. Journal of Applied Sport Psychology, 22(1), 17-33.
Stone, J. (2023). Longitudinal Sport Science Implementation in American Collegiate Men’s Basketball.
Stone, M. H., Stone, M., & Sands, W. A. (2007). Principles and practice of resistance training. Human Kinetics.
Teikari, P., & Pietrusz, A. (2021). Precision strength training: Data-driven artificial intelligence approach to strength and conditioning.
Uy, F., Abendan, C. F., Kilag, O. K., Villegas, M. A., Ymas, S., & Pahayahay, D. (2023). Enhancing School Safety: Integrating William Glasser's Choice Theory in Crisis Preparedness and Response. Excellencia: International Multi-disciplinary Journal of Education, 1(5), 148-158.
Verkhoshansky, Y., & Siff, M. C. (2009). Supertraining (pp. 393-420). Rome: Verkhoshansky SSTM.
