Continuous Glucose Monitoring (CGM) as a Tool for Training Quality Optimization: A Systematic Review of Metabolic Management in Non-Diabetic Athletes
DOI:
https://doi.org/10.12775/QS.2026.67.74262Keywords
Continuous Glucose Monitoring, Endurance Athletes, Metabolic Flexibility, Glycemic Variability, Training Load Monitoring, Precision Sports Nutrition, Physiological AdaptationAbstract
Introduction. Continuous Glucose Monitoring (CGM) has transitioned from a clinical diabetes management tool to a prominent technology in sports science. While glucose availability is critical for endurance performance and recovery, the application of CGM metrics in non-diabetic athletes remains debated. There is a pressing need to bridge the gap between clinical glycemic monitoring and its potential for optimizing training quality and physiological adaptation.
Research objective. This review synthesizes current knowledge on glycemic regulation in endurance athletes, the diagnostic challenges of interpreting CGM metrics, and the integration of AI into multi-sensor physiological monitoring. The study aims to delineate the distinction between physiological metabolic adaptation and clinical dysglycemia, providing evidence-based insights for metabolic management in athletic cohorts.
Methodology. The review is based on a structured search of peer-reviewed literature through July 2026. Keywords included: “continuous glucose monitoring”, “endurance athletes”, “metabolic flexibility”, “glycemic variability”, and “training load”. Priority was given to randomized controlled trials, systematic reviews, and international consensus documents. Articles were analyzed for their relevance in distinguishing acute biochemical responses from long-term exercise-induced adaptations.
Conclusions. CGM is a valuable descriptive tool for assessing metabolic load. However, its role as an autonomous decision-making system is limited by the absence of sport-specific reference ranges and inherent technical constraints during high-intensity exertion. Future metabolic management necessitates a contextualized, integrative approach that combines CGM data with multi-sensor feedback and clinical expertise to enhance training quality while avoiding the pitfalls of data overinterpretation.
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Copyright (c) 2026 Emilia Browarska , Katarzyna Superson, Katarzyna Miemczyk, Nicole Gajewska, Igor Mszyca, Anna Aksamit, Filip Tadeusz Wołek, Karolina Natalia Mularczyk, Kacper Kucharski, Łukasz Michalski
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