lessons learned from Para cyclists

Continuous Glucose Monitoring (CGM) is gaining attention as a tool for optimising dietary counselling in athletes. By providing real-time glucose data, CGMs offer the potential to enhance our understanding of how athletes’ bodies respond to training, recovery, and nutrition. In our study, published in the European Journal of Sport Science, we investigated the use of CGMs in Para athletes to explore their potential applications and limitations.

In our study (1), we followed 13 elite Para cyclists, including both hand bikers and conventional cyclists, for two weeks. Participants wore CGMs on the back of the upper arm and logged meals and training sessions. We compared CGM data with glucose readings taken from finger-prick blood samples to evaluate the accuracy of CGMs during rest and exercise.

Our aim was to uncover glucose profiles in Para cyclists and determine the accuracy of CGMs under different conditions. We focused on a number of key questions:

1. To what extent fluctuate glucose concentrations throughout the day and night, and are there any signs of disturbed glucose regulation?

2. Is CGM as accurate during exercise as it is at rest?

3. And what practical considerations should practitioners keep in mind when using CGMs with (Para) athletes?

We found that Para cyclists spent 91% of their time in the healthy glucose range (3.9–7.8 mmol/L). Mild hyperglycemia (≥7.8 mmol/L) was commonly observed after meals, particularly following lunch. Severe hyperglycemia (≥10 mmol/L), as frequently seen in individuals with diabetes, was nearly absent in this population. These findings suggest that Para cyclists generally maintain well-regulated glucose levels. Athletes with spinal cord injuries (SCI) seemed to have a higher risk of nocturnal hypoglycemia (<3.9 mmol/L). Notably, one SCI athlete spent an average of 36% of the night in a hypoglycemic state. Nocturnal hypoglycemia has the potential to impair sleep quality (2) and has been shown to cause next-day fatigue in individuals with diabetes (3). While these effects were not examined in the current study, they present an intriguing area for future research, particularly regarding their impact on recovery and performance in SCI athletes.

Accuracy is assessed by calculating the difference between the CGM and the finger prick blood glucose values and converting it into a percentage. A difference of 10% is considered accurate and between 10-20% is considered acceptable. CGM accuracy was acceptable at rest (12%) but declined considerably during exercise (34%). The discrepancy was larger for hand bikers (41%) compared with conventional cyclists (24%), likely because the sensors were placed on the upper arm, which is near active arm muscles in hand bikers. This reduction in accuracy mainly reflected an overestimation of CGM-derived glucose concentrations compared with capillary blood glucose.

During exercise, several physiological factors can impair the accuracy of continuous glucose monitoring. CGM sensors measure glucose in the interstitial fluid but are calibrated to reflect blood glucose concentrations. Under resting conditions, this calibration is reliable because interstitial and blood glucose levels are closely correlated. However, during exercise, increased blood flow and glucose uptake by active muscles can disrupt this relationship (4), leading to less accurate CGM readings. Furthermore, exercise-induced changes in body temperature and pH may alter the enzymatic activity of the glucose sensor, further amplifying measurement errors. These effects are likely more pronounced near active muscles, which could explain the greater measurement errors observed in hand bikers compared to conventional cyclists (see blog Are continuous glucose monitors (CGMs) accurate?).

CGMs provide valuable insights into glucose profiles, potentially supporting advanced feeding strategies around training. However, glucose fluctuations, such as mild post-meal hyperglycemia, are a normal physiological response to diet, exercise, and stress. In athletes, these fluctuations may be more pronounced due to higher carbohydrate and energy intake needed to support training demands. Such variations should not be misinterpreted as impaired glucose regulation or intolerance. While CGMs can aid in refining nutrition strategies, overinterpretation of data with unnecessary dietary changes should be avoided (see blogs CGM in sport and How can CGM be used?).

The higher risk of nocturnal hypoglycemia in SCI athletes warrants attention. Research should explore its impact on sleep and performance, while dietary adjustments, such as evening meal composition, may help mitigate these episodes.

Practitioners using CGMs with athletes should be aware of their limitations during exercise. CGMs tend to overestimate glucose concentrations compared to capillary blood glucose, particularly when the sensor is placed near active muscles. Careful consideration of sensor placement site and cautious interpretation of exercise data are essential to avoid misinformed decisions.

Our recently published study provides valuable insights into the application of CGM in Para cyclists, who generally demonstrate well-regulated glucose concentrations without evidence of impaired glucose tolerance. However, the elevated risk of nocturnal hypoglycaemia in athletes with spinal cord injuries warrants attention, particularly regarding its potential effects on sleep quality, fatigue, and long-term health.

While CGMs offer significant potential for analysing glucose dynamics in response to food intake and exercise, their practical use in athletic populations requires careful consideration due to factors such as exercise-induced inaccuracies, overinterpretation of data, and the need to balance insights with established nutritional guidelines. This study underscores the importance of critically evaluating emerging tools like CGMs to ensure they are applied effectively to optimize health and performance.

1. Weijer, V., Van Der Werf, R., Van Der Haijden, M., Jeukendrup, A., Van Loon, L. J. C. and Dijk, J. V. (2024) ‘Continuous glucose monitoring in para cyclists: An observational study’, Eur J Sport Sci, 24(12): 1809-1819.

2. Banarer, S. and Cryer, P. E. (2003) ‘Sleep-related hypoglycemia-associated autonomic failure in type 1 diabetes: reduced awakening from sleep during hypoglycemia’, Diabetes, 52(5): 1195-1203.

3. King, P., Kong, M.-F., Parkin, H., Macdonald, I. A. and Tattersall, R. B. (1998) ‘Well-Being, Cerebral Function, and Physical Fatigue After Nocturnal Hypoglycemia in IDDM’, Diabetes Care, 21(3): 341-345.

4. Moser, O., Yardley, J. E. and Bracken, R. M. (2018) ‘Interstitial Glucose and Physical Exercise in Type 1 Diabetes: Integrative Physiology, Technology, and the Gap In-Between’, Nutrients, 10(1): 93.