Along with other wearable tech, Continuous Glucose Monitors (CGMs) have become increasingly popular with endurance athletes. CGMs monitor interstitial glucose concentrations in real time and offer various applications such as logging meals, and activity. We know how important carbohydrates are for performance, and we also know that individuals have highly variable pre- and during-exercise fueling practices, so how might we be able to use these devices to our advantage?
Fuel oxidation during endurance exercise is somewhat complex. Fat and carbohydrate are the main fuel sources with carbohydrate being limited in capacity (stored as liver glycogen and skeletal muscle glycogen), necessitating the intake of exogenous carbohydrate to support performance. Blood glucose is maintained via homeostatic regulation so even if we aren’t taking in exogenous carbohydrates, the liver will release glucose by breaking down glycogen and amino acids as necessary to maintain blood glucose. During exercise, it’s not unusual to see blood glucose drop to near hypoglycemic levels ~70 mg/dl or slightly below. It’s also not unusual to see it increase at the start of exercise and during intense exercise. But, we don’t know exactly how much glycogen we have in our liver and muscle at a given time and blood glucose concentration doesn’t completely reflect rates of glucose appearance from the liver or GI tract and disappearance or uptake into tissues. Also, we don’t understand if; and, the degree to which blood glucose itself could influence an athlete’s RPE, pace, power output, or performance. Because fatigue is so complex and multifactorial, simply maintaining high blood glucose does not mean an athlete won’t experience fatigue due to other factor(s). Furthermore, CGMs measure subcutaneous interstitial fluid glucose rather than actual blood glucose (typically measured via capillary or venous plasma). It has been suggested that the CGM reading has a 5 to 10 min delay behind the actual blood glucose concentration. Recent evidence indicates that CGM sensors overestimate glucose compared to venous measures but they may still be reliable at predicting a blood glucose pattern. The location of the device can also induce some variability in the measure during exercise.
Given these complexities, could CGMs still help athletes to narrow down best fueling practices? If you have the opportunity to use one or you can’t resist investing in the latest tech, CGMs might provide some insight into your fueling needs and strategy. For example, CGM data may help you in choosing types and quantities of carbohydrates and timing carbohydrate intake during a pre-event carbohydrate loading phase. The data may also help you optimize your pre-exercise fueling strategy for various types of efforts and events. However, because glucose levels do not necessarily translate into performance outcomes, it’s still questionable whether a CGM could actually help with your during-race fueling strategy. Additionally, highly trained endurance athletes are really good at burning fat so a CGM might not provide meaningful information for an athlete competing in long distance, sub-threshold types of events.
What about the use of CGMs for Diabetes Management? Self-monitoring of glucose is key for managing diabetes.CGMs can help athletes identify periods of hypo or hyperglycemia through more frequent tracking of glucose concentrations, and provide an opportunity to respond with changes to insulin or diet. In fact, research has suggested that individuals using a CGM report significant decreases in 24-hr glycemic variability.
As CGMs become more widely available and used in training/competition, we can learn more about their application in sport. If you’re considering using one, ask yourself this: “Does the information create more stress than simply fueling with healthy foods according to recommendations that are fine-tuned for you as an individual athlete?” While having access to data is cool, remember to not underestimate the value of using RPE, mood, perceived energy/fatigue and hunger/appetite, as well.
PMID: 36572039, 30940441, 23112916, 33740420, 34931427
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