Uncovering the Science Behind Effective Training Zones

Achieving peak performance in sports isn't just about hard work, dedication, and efficient technique—it's also about mastering the body's energy systems and metabolic pathways. By understanding these complex processes, athletes and coaches can optimize training regimens and significantly enhance performance. In this article, we demystify these essential concepts and explain how they influence training planning and zone design.

In our previous article, "Swimming Training Zones: Advancing Intensity Prescription – The Need for Better Tools," we highlighted the importance of personalized intensity prescription. While new technologies like AI offer great potential, they cannot solve all the problems in sports training by themselves. Simply feeding AI with scientific papers and data is not enough. AI cannot evaluate and integrate all the nuanced sports theories effectively, yet. Therefore, it's crucial to first refine our conceptual models, such as training zones, to provide a solid foundation upon which AI can build more precise and effective training strategies.

The Need for Revising Training Zones

Training zones are specific ranges of exercise intensity designed to guide and optimize athletic training. Defined by physiological markers such as heart rate (HR), lactate concentration, perceived exertion, and percentages of VO2 max, each zone targets specific physiological adaptations and corresponds to different levels of effort. These zones are based on exercise physiology research, highlighting how the body responds to varying exercise intensities. Over time, the concept of training zones has evolved, influenced by sports science, medicine, and coaching. Key physiological markers like lactate threshold, VO2 max, and heart rate variability have been instrumental in defining these zones, as they elicit distinct physiological responses and adaptations at different exercise intensities.

While training zones are fundamental for structuring and evaluating effective training programs, many existing systems do not address the unique needs of swimmers. Generic training zones, particularly those with five or fewer zones or those based solely on heart rate data, often lack the precision required for optimal performance enhancement. Training zones are crucial for several reasons:

• Specificity: They enable athletes to target specific energy systems and muscle fibres, leading to more effective training adaptations.

• Optimization: Training at the appropriate intensity helps athletes optimize performance and avoid overtraining or undertraining.

• Monitoring: Training zones provide a framework for monitoring and adjusting training intensity, ensuring athletes train at the right level to achieve their goals.

• Recovery: They aid in planning recovery sessions, crucial for preventing injuries and promoting long-term athletic development.

• Individualization: Training zones can be tailored to individual athletes based on their unique physiological responses, making training more personalized and effective.

Comprehensive training zone systems can significantly enhance the development and implementation of AI tools for sports training in the following ways:

• Data-Driven Insights: AI tools can analyze large amounts of data from training sessions, providing insights into how athletes respond to different training zones. This helps fine-tune training programs for optimal performance.

• Personalization: AI can use data from comprehensive training zone systems to create personalized training plans that cater to the unique physiological responses of individual athletes.

• Monitoring and Feedback: AI tools can continuously monitor training intensity and volume, providing real-time feedback to athletes and coaches. This ensures athletes train at the right intensity and make necessary adjustments.

• Injury Prevention: By analyzing data on training load and recovery, AI tools can identify patterns that may lead to overtraining and injuries, allowing for proactive adjustments to training programs.

• Performance Optimization: AI can use data from comprehensive training zone systems to identify the most effective training strategies for improving performance, including optimizing the balance between different training zones to achieve specific goals.

• Adaptability: AI tools can quickly adapt to changes in an athlete's condition or performance, providing dynamic adjustments to training programs to ensure training remains effective and relevant.

By revising and expanding training zone systems, we can leverage AI tools to create more precise, individualized, and effective training programs that enhance athletic performance and promote long-term development.

Training Zones Foundations

Understanding the interaction of energy systems is crucial for developing effective sports training and fitness programs. Traditionally, the resynthesis of ATP—the primary energy currency in muscles—has been attributed to three integrated systems: the ATP-PCr system, anaerobic glycolysis, and the aerobic system. However, recent research highlights the complexity and overlap of these systems during exercise, challenging this simplified view.

The ATP-PCr system provides immediate energy for short, high-intensity efforts but is quickly depleted. As exercise continues, anaerobic glycolysis becomes the dominant source of ATP, leading to lactate accumulation. Contrary to the outdated notion that the aerobic system only becomes relevant during prolonged exercise, it begins contributing to energy production much earlier and significantly more than previously thought. This early engagement of the aerobic system helps sustain high-intensity efforts and delays fatigue.

Research by Swanwick and Matthews (2018) and Gastin (2001) emphasizes that all physical activities activate each energy system to varying degrees based on the intensity and duration of the exercise. This interaction ensures a continuous supply of ATP and highlights the importance of training all energy systems to optimize performance. For example, during high-intensity exercise lasting 60-120 seconds, there is substantial involvement of both anaerobic and aerobic pathways, demonstrating that peak oxygen uptake (VO2max) can be achieved even in traditionally anaerobic activities.

By acknowledging the dynamic interplay of energy systems, coaches and athletes can design training programs that target specific metabolic pathways, leading to more effective adaptations and enhanced performance. This comprehensive understanding underscores the limitations of the traditional 5-zone heart rate model, which oversimplifies energy contributions and lacks the specificity needed for competitive training. Adopting a more nuanced approach, such as a detailed multi-zone system, can better address the unique energy demands of different sports and optimize athletic development.

Percentage contribution of each energy system to the total energy supply during all-out exercise, based on the data from Swanwick & Matthews (2018).

Why Not Use Existing Training Zones?

Existing training zone systems often lack the specificity and adaptability required for comprehensive training. Most of them are designed with general fitness in mind and do not account for the distinct physiological demands of specific sports training. Generic zones can lead to inadequate training stimuli, wasted effort, and increased risk of injury, and are not fit to support the development and implementation of AI tools for personalised sports training.

Disadvantages of 5-Zone or Fewer Training Systems:

• Predominant Use of Intensity: Most training zone systems, especially those that reference only heart rate, do not consider other crucial variables like duration, rest, training methods, and density. These variables are essential for prescribing exercise effectively. Variations or omissions of any of these variables leave the training load effects unknown. Comprehensive systems integrate these variables to provide a more complete and effective training regimen.

• Limited Specificity in Training Adaptations: Simplified systems may not provide the specificity needed to target different muscle fibre types and metabolic pathways effectively. Comprehensive systems like the 9-zone model allow for more precise training adaptations by targeting specific energy systems and muscle fibres.

• Inadequate Development of Aerobic and Anaerobic Capacities: A simplified system may not adequately develop both aerobic and anaerobic capacities. Comprehensive systems can better address the specific needs of athletes by providing targeted training for both aerobic and anaerobic energy systems.

• Reduced Ability to Optimize Performance: Comprehensive systems allow for more precise control over training intensity and volume, leading to better optimization of performance. A simplified system may lack the granularity needed to fine-tune training for peak performance.

• Potential for Overtraining or Undertraining: Without the detailed structure of a comprehensive system, athletes may be at a higher risk of overtraining or undertraining. Detailed systems provide clear guidelines for recovery and intensity, reducing the risk of training errors.

• Lack of Detailed Monitoring and Feedback: Simplified systems may not provide the detailed monitoring and feedback needed to track progress and make necessary adjustments. Comprehensive systems offer more precise metrics for evaluating training effectiveness.

• Inability to Address Individual Differences: Athletes have unique physiological responses to training. A comprehensive system can better accommodate individual differences by providing a wider range of training intensities and recovery protocols.

• Missed Opportunities for Specific Adaptations: Comprehensive systems can target specific adaptations such as lactate threshold improvement, VO2max enhancement, and anaerobic power development. Simplified systems may miss these specific adaptations due to broader categorization.

• Reduced Flexibility in Training Design: Simplified systems may limit the flexibility in designing training programs that address the varied demands of different swimming events and individual athlete needs. Comprehensive systems offer more flexibility in tailoring training programs.

To address these issues, Wise Racer developed a comprehensive training zone system that integrates a deeper understanding of energy systems and metabolic pathways. By revising traditional training zones, we aim to provide more precise and individualized training support to coaches, athletes, and fitness enthusiasts. Stay tuned for the next article, where we will delve into the key metabolic pathways that drive swimming performance and how they can be optimized through targeted training.

Summary

Understanding and mastering the body's energy systems and metabolic pathways are crucial for optimizing athletic performance. Traditional training zones, while foundational, often lack the specificity required for sports training. Revising these zones to include more precise markers enables more targeted and effective training. The integration of AI in training offers significant benefits, including personalized plans and real-time feedback, but relies on well-defined training models. Recognizing the complexity of energy systems highlights the need for comprehensive training approaches. Simplified systems can lead to suboptimal outcomes, underscoring the advantages of a more nuanced, detailed training zone system, like the one developed by Wise Racer, which aims to enhance individual performance and reduce training-related risks.

We Want to Hear From You!

We'd love to hear your thoughts on the concepts discussed in this article. How do you incorporate an understanding of energy systems into your training or coaching practices? Have you experimented with different training zone systems, and what results have you seen?

References

• Alghannam, A. F., Ghaith, M. M., & Alhussain, M. H. (2021). Regulation of Energy Substrate Metabolism in Endurance Exercise. International Journal of Environmental Research and Public Health, 18(9), 4963. https://doi.org/10.3390/ijerph18094963. Retrieved from NCBI.
• Baker, J., (McCormick) G. M. C., & Robergs, R. (2010). Interaction among Skeletal Muscle Metabolic Energy Systems During Intense Exercise. Journal of Nutrition and Metabolism, 2010, 905612. https://doi.org/10.1155/2010/905612. Retrieved from ResearchGate.
• Barclay, C. J. (2017). Energy demand and supply in human skeletal muscle. Journal of Muscle Research and Cell Motility, 38(2), 143-155. https://doi.org/10.1007/s10974-017-9467-7. Retrieved from PubMed.
• Brooks, G. A. (2018). The Science and Translation of Lactate Shuttle Theory. Cell Metabolism, 27(4), 757-785. https://doi.org/10.1016/j.cmet.2018.03.008. Retrieved from PubMed.
• Garmin. (n.d.). Forerunner 245/245 Music - Heart Rate Features. Retrieved from Garmin.
• Gastin, P. B. (2001). Energy system interaction and relative contribution during maximal exercise. Sports Medicine, 31(10), 725-741. https://doi.org/10.2165/00007256-200131100-00003. Retrieved from PubMed.
• Ghosh, A. K. (2004). Anaerobic threshold: its concept and role in endurance sport. The Malaysian Journal of Medical Sciences: MJMS, 11(1), 24-36. Retrieved from NCBI.
• Hargreaves, M., & Spriet, L. L. (2020). Skeletal muscle energy metabolism during exercise. Nature Metabolism, 2(9), 817-828. https://doi.org/10.1038/s42255-020-0251-4. Retrieved from PubMed.
• Hearris, M. A., Hammond, K. M., Fell, J. M., & Morton, J. P. (2018). Regulation of Muscle Glycogen Metabolism during Exercise: Implications for Endurance Performance and Training Adaptations. Nutrients, 10(3), 298. https://doi.org/10.3390/nu10030298. Retrieved from PubMed.
• Maglischo, E. W. (1997). Swim Training Theory. Kinesiology, Volume 2, No. 1, pp. 4-8, 1997. Retrieved from ResearchGate.
• Olbrecht, J. (2011). Lactate Production and Metabolism in Swimming. World Book of Swimming: From Science to Performance, 255-275. Retrieved from ResearchGate.
• Olbrecht, J., & Mader, A. (2005). Individualisation of training based on metabolic measures. Retrieved from ResearchGate.
• Parolin, M. L., Chesley, A., Matsos, M. P., Spriet, L. L., Jones, N. L., & Heigenhauser, G. J. (1999). Regulation of skeletal muscle glycogen phosphorylase and PDH during maximal intermittent exercise. American Journal of Physiology, 277(5), E890-900. https://doi.org/10.1152/ajpendo.1999.277.5.E890. Retrieved from PubMed.
• Polar. (n.d.). Running Heart Rate Zones: Basics. Retrieved from Polar.
• Seifert, L., Chollet, D., & Mujika, I. (Eds.). (2011). World Book of Swimming: From Science to Performance. Rodriguez, M. Energy Systems in Swimming. Retrieved from ResearchGate.
• Swanwick, E., & Matthews, M. (2018). Energy Systems: A New Look at Aerobic Metabolism in Stressful Exercise. MOJ Sports Medicine, 2. https://doi.org/10.15406/mojsm.2017.02.00039. Retrieved from ResearchGate.
• Tanner, R., & Bourdon, P. (2006). Standardisation of Physiology Nomenclature. Retrieved from ResearchGate.
• van der Zwaard, S., Brocherie, F., & Jaspers, R. T. (2021). Under the Hood: Skeletal Muscle Determinants of Endurance Performance. Frontiers in Sports and Active Living, 3, 719434. https://doi.org/10.3389/fspor.2021.719434. Retrieved from NCBI.
• Vorontsov, A. (1997). Development of Basic and Special Endurance in Age-Group Swimmers: A Russian Perspective. Swimming Science Bulletin. Retrieved from ResearchGate.