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Updated: Jun 2, 2025

Determining the Contribution of the Energy Systems During Exercise
Published on: March 20, 2012
Educational strategies for teaching metabolic profiles across three endurance training zones
Denise V Macedo1, Bernardo N Ide1
1Department of Biochemistry and Tissue Biology, Institute of BiologyState University of Campinas, Campinas, Brazil.
Abstract:
This article explores an innovative educational approach using a metabolic board designed to enhance understanding of muscle metabolism across three endurance training zones: Z1 (light intensity), Z2 (moderate intensity), and Z3 (intense/severe intensity). The aerobic threshold marks the transition from light to moderate domains and the anaerobic threshold separates moderate from intense domains, with both thresholds adapting to training. Exercises within each training zone elicit specific adaptive responses through distinct signaling pathways, but the metabolic profile induced remains relatively constant across these intensity domains. The assembly of the metabolic board is guided by interpretative questions derived from recent incremental exercise studies. By interacting with the board, students gain clear insights into the rationale for choosing between continuous and interval exercises. This interactive tool simplifies complex physiological processes into understandable components, clarifying the relationships among motor unit types and their metabolism and principal energy sources at each intensity level. By assembling the board, students demystify muscle metabolism as a continuum of responses crucial for sustaining various exercise intensities. This integration of theoretical knowledge with practical application empowers students and professionals to make informed decisions about training prescriptions, a critical element of training periodization.NEW & NOTEWORTHY Our pedagogical tool offers a unique and enriching learning experience. By active construction of a metabolic board using real data, focused on a predominance of a specific muscle fiber type and its metabolic characteristics across three ranges of exercise intensity domains, the tool promotes deep learning for sports science professionals.
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