With the increasing popularity of continuous glucose monitors (CGMs) for people without diabetes and their increasing use in different parts of society there can be the potential for misuse and misunderstanding. There are many common misunderstandings and misconceptions in CGM use, including what constitutes “normal” or “healthy” glucose dynamics as well as how its values are best interpreted. Like many things, the simple falsehood or half truth is more attractive than the complicated truth. This article attempts to aid in better understanding of the complex truth.

1) Not all Continuous Glucose Monitors are the same

A common criticism of CGM use in populations without diabetes stems from some of the existing research in the space. Whilst this makes some logical sense, as currently this is the best available evidence, this research may not be strictly applicable to newer generations of CGM.

Historically not all CGMs (and thus used in research) had live glucose via bluetooth. They may have had datapoints as infrequently as every 15 mins and many have required repeated calibration. Additionally both materials and lifespans have changed.

The previous relative infrequent datapoint acquisition is not a bad thing, and during times of relative physiological equilibrium, it is completely appropriate. Once physiological perturbations are introduced into the system a higher resolution of data gives a significantly different picture though. This may come in the form of food or movement; situations where historically ‘lag’ between CGMs and glucometer (finger prick, capillary glucose) have been reported in the literature.

A crude analogy to use in these situations may be heart rate. In times of physiological equilibrium such as sitting quietly, one data point every 15 mins or even a 15 min average heart rate is appropriate and insightful. However, once you start exercising, a higher level of resolution is required as the picture is not accurately depicted by 15 min average data. This is similar with respect to interstitial glucose.

2) Not Understanding Glucose Values are a Concentration

As with all tools, it is imperative to understand means in which they work to understand factors that may influence them. An example that may be more familiar to readers is optical heart rate, where changes in light are read and used to report heart rate. In certain cases this may yield values more reflective of cadence than heart rate for runners.

When it comes to CGM, understanding what is measured and how is crucial. Most people are probably aware that certain substances like Vitamin C (ascorbic acid) interact with glucose oxidase (the enzyme used in CGMs to measure glucose) and such can give artificially high glucose levels. What people generally don’t think about, or perhaps appreciate, is that the measurement of glucose is a concentration. It is reported in mg/dL (or at times mmol) which means that changes in this value may reflect changes in the total amount of glucose in the interstitial fluid or changes in the amount of interstitial fluid itself. Thus a high value could reflect increases in glucose or reductions in interstitial fluid.

It does bear noting that fluid shifts are magnitudes slower, less common and less dynamic than changes in glucose. They definitely occur, but for changes in interstitial fluid to occur fluid needs to shift into or out of it from another fluid compartment (the blood/plasma or the intracellular fluid) which is a slightly longer process.

3) Believing Blood Glucose is More Accurate

Another very common criticism of CGM use in people without diabetes is that there is a significant “lag” between values of blood glucose (usually via glucometer aka fingerstick glucose) and interstitial glucose (as measured by a CGM).

There are a few assumptions this criticism makes: that capillary glucose is in itself accurate, that the glucometer is itself accurate and that blood is more useful than interstitial fluid.

Some of these are beyond the scope of this blog article, but suffice to say there are sources of error that can be introduced to capillary glucose measures. Similarly, capillary glucose is itself different to glucose from different arterial or venous sources. This is because there are a number of metabolic processes which occur between ingestion, digestion, absorption and eventual utilization of carbohydrates.

As with CGMs, glucometers have an acceptable percentage error (known as MARD, short for Mean Average Relative Difference). MARD, in its simplest explanation, is the percentage difference between the reference value and reported value. There is an acceptable MARD for glucometers and CGMs, thus using either to evaluate the other doesn’t make a great deal of sense given the error could be in either device (or both). Ironically, MARD was seen to decrease during times of exercise in a recent study utilizing Supersapiens (which speaks to the importance of testing newer generation, higher resolution sensors as mentioned above).

Finally, and most crucially, suggesting that blood is of more relevance to working muscle may be erroneous. This is likely at least in part an artifact of the historical ability to measure glucose being blood not interstitial fluid as discussed in this blog and on this podcast.

4) Not Contextualizing Glucose Data

Glucose data without context is largely meaningless. It can be difficult to interpret in a helpful way without this context. This is why creating events, marking rushes and utilizing the notes section of your events is key. Similarly, the industry leading Supersapiens dashboard, is exceptionally helpful, particularly when looking at exercise data.

Similarly, as time progresses and a user gains experience they tend to start to appreciate the longer term timeline of impact of glucose. That is, as experience increases, users tend to expand their understanding of glucose into longer windows and understand how yesterday’s glucose impacts today’s glucose.

Figure 1: Supersapiens Dashboard with and without events

5) Being Overly Focussed on the Glucose Value Rather than the Glucose Trend

Many CGM users become overly focused on specific glucose values rather than the trend of glucose. There are very few true thresholds in human physiology and glucose physiology is no different. Nothing major changes across a 5 or 10 mg/dL range.

The dynamics and trends of glucose are of much more significance and relevance than the actual glucose value for people without diabetes, and perhaps those with diabetes too.

This is further underscored by the aforementioned MARD; meaning that any one value could be both higher or lower.

6) Not Understanding the Basics of Glucose, Glycemia and Food

Understanding the basics of glycemia (glucose in the blood) and what aspects of food impact it is crucial to understanding CGM dynamics and data. Without this understanding, it can be easy to misattribute glucose dynamics to factors that are unlikely driving them. This may not sound significant, but the myriad of factors impacting glucose can make this process hard enough without a misunderstanding. The practice of repeatedly running experiments in a real world setting, to try and see some potential signal through what is otherwise quite noisy information is key and an appropriate understanding of what MAY impact glucose dynamics aids in this.

One classically misattributed factor in changing glucose is coffee, which in itself has little physiological basis to change glucose. That said, some milks can, mostly milks which are more processed, with higher carbohydrate loads, less protein and less fat. Classically this describes oat milk, something many users find is quite stark in its ability to cause glucose rushes.

The processing of food often involves removal of factors which aid in glucose stability, be it that the food becomes more easily digestible as it is in a simpler form, or the removal of other factors like fiber. All of this works to increase the glycemic index of foods.

Glycemic index (GI) is a crude but helpful tool when it comes to understanding glycemia (and the name is hidden in it). Higher GI foods will cause quicker changes in glucose and vice versa. That said, the GI of any one food is only particularly relevant if that food is consumed in isolation, once a mixed meal is consumed the GI of the meal is altered by the other components of the meal. This is in large part why food order is effective in limiting glucose rushes.

A concept that evolved from GI is that of Glycemic Load (GL) which reflects the combination of the GI and amount of carbohydrate in the meal. This probably better reflects the impact foods have on glucose levels when consumed as a mixed meal. It emphasizes the role of carbohydrates and specifically the amount of carbohydrates in changes in glucose. Other macronutrients such as fat and protein have negligible roles in increasing glucose. This is why, as you will read below, glucose should be used as another metric and data point (and an important one) but not a single measure of health or diet quality.

7) Breaking Goodhart’s Law

Goodhart’s law simplified states that “when a measure becomes the target, it ceases to be a good measure”. Glucose is a metric which can convey a significant amount of information and provide a valuable means to improve aspects of performance or health. That said, in itself, the goal is rarely better glucose itself. Goals will vary for different CGM users, but they will usually be to perform or feel better, which often encompasses health too.

To give an example that may contextualize this; eating nothing but bacon would usually yield a lower and more stable glucose, metrics often associated with ‘healthier’ or ‘better’ glucose. This, however, is not what most people would consider a healthy diet or healthier outcome.

8) Worrying about every Glucose Rush (aka Spike)

Similarly to the previous point, glucose should be kept in context of the broader picture. Beyond this, not all glucose rushes are bad and there may be times where they serve as useful. Additionally, it should be noted that not all rushes are the same. Some of these are much more helpful and healthy, for example those induced by high intensity exercise.

The final thing to note on this thought is that stress itself can induce increases in glucose, as well as a milieu of other potentially negative health consequences. As such, being overly concerned about a glucose rush likely exacerbates it and thus is probably counter productive.

9) Believing Glucose is stable and within a narrow range in people without diabetes

This belief is part of what underpins some of the viewpoint that CGMs do not have value for people without diabetes. This may also be a manifestation of use of older generation CGMs, as mentioned above, and use in relatively stable physiological conditions rather than exercise.

Supersapiens’ dataset and the athletes in it has taught us an enormous amount about glucose. The ability for athletes without diabetes to have a glucose level well in excess of 140mg/dL for hours has been one example of a huge learning. Hopefully this further helps the world of diabetes given a change in our understanding as discussed on a recent podcast episode.

Glucose levels Supersapiens
Figure 2: Example Exercise data from Supersapiens App

10) Misunderstanding the Correlation Between Glucose, Energy and Glycogen

Athletes often ask and hope that glucose can be used as a surrogate for glycogen and even energy more broadly. Glucose can unfortunately not be used as a true surrogate for energy or glycogen. It is however impacted in part by glycogen stores, and can itself impact your feeling of energy. These two examples are perfect displays of correlation, NOT causation. That is, the factors are related but not solely, directly causative.

Whilst inconsistent (thus further proving the lack of causality), many athletes notice an increase in glucose of around 10mg/dL and subsequently increased number of rushes when carbohydrate loading, which as most readers would know, is the practice of systematically increasing glycogen levels.

Further to this, it should be noted that glucose sensed by CGMs can come from a few sources; ingested glucose (in whatever complexity of form, be it simple such as sugars or complex such as starches) or liver glycogen, NOT muscle glycogen.

Like many aspects of health and performance, the simple falsehood is more attractive than the complex truth. That is not to say the simplicity does not serve someone initially, but the key is to move beyond this, a level deeper to gain a better understanding.

The journey into CGM use is a fascinating and rewarding one which continues to deliver learnings as time progresses.


  1. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013 Aug;46(1):81-95. doi: 10.1007/s12160-013-9486-6. PMID: 23512568.