Continuous Glucose Monitoring (CGM) in Long-Distance Runners: A Narrative Review of Performance Optimization and Hypoglycemia Prevention. - Dołęga-Mostowska, Dominika; Danieluk, Julia; Adamaszek, Natalia; Gliński, Michał; Kacprzak, Urszula; Stochaj, Laura; Sochowska, Julia; Buczowska, Agnieszka; Flejszman, Michalina; Wójcik, Justyna (Quality in Sport, 2026)
Background. Continuous Glucose Monitoring (CGM) is increasingly used by ultra-endurance athletes to refine complex fueling strategies. However, transitioning this clinical tool into a performance monitoring remains difficult, largely due to the interstitial-to-blood lag time that can distort real-time data interpretation. Aim. This study aims to synthesize current literature to evaluate the application of CGM data during extreme exercise, examining how exercise intensity, dietary protocols, and technical artifacts influence metabolic data interpretation and fueling efficacy. Material and Methods. A review of literature was performed with a primary focus on literature from 2020 to 2026, while including foundational technical research. The analysis examined the impact of a broad range of physical and technical factors on the practical application and limitations of CGM. Results. CGM accuracy, measured by MARD, often drops during high-intensity exercise and dehydration. There are clear differences between interstitial readings and blood glucose levels that athletes must account for. Data integrity is affected by factors like thermal stress, caffeine intake, and localized tissue compression. Findings also show that metabolic responses to CGM vary depending on sex and dietary status, such as LCHF. While the technology helps track carbohydrate absorption during 120 g/h protocols, it can also mask a systemic energy deficit if the athlete relies only on stable readings. Sensor lag remains a critical issue, where glucose levels on the screen do not always reflect actual glycogen availability or energy status. Conclusions. To improve performance, athletes should focus on trend arrows rather than static numbers. CGM is a monitoring tool, not an absolute source of physiological truth. Using a structured protocol helps manage sensor lag and prevents metabolic crashes. This approach makes it easier to stay on track with fueling and pacing during extreme effort.