Health

Continuous glucose monitors for people without diabetes: what they actually show

Over-the-counter CGMs are now legal, marketed to wellness consumers, and being worn by tens of thousands of non-diabetic adults. The evidence base for what those readings mean in healthy people is much thinner than the marketing implies.

By Aaron Lindholm

Published February 11, 2026 · 7 min read

The bottom line

Two over-the-counter CGM devices have been approved for use in non-diabetic adults in the United States. The technology is real and useful in diabetes management; the case for routine use in healthy adults is much weaker. The headline glucose 'spikes' that consumer apps flag as actionable are within the normal physiological range and do not predict cardiometabolic outcomes in non-diabetic people.

The continuous glucose monitor began as a clinical tool for people with type 1 diabetes — a small sensor worn on the back of the upper arm or abdomen, sampling interstitial glucose every few minutes for two weeks at a time. For diabetes management, the technology has been transformative; the published evidence on improvements in glycemic control, hypoglycemia avoidance, and quality of life in people with insulin-treated diabetes is unambiguous.

In 2024, two manufacturers received FDA clearance to sell over-the-counter CGMs aimed at non-diabetic consumers. Stelo, from Dexcom, and Lingo, from Abbott, are now available without a prescription. Both have been heavily marketed to a wellness audience: people who do not have diabetes, who want to track their glucose response to meals, and who have been given the impression that this data will tell them something useful about their metabolic health.

The technology in those devices is the same technology that has worked well in diabetes care. The interpretive framework around the readings — what the wellness apps tell users to do with the data — is much shakier than the marketing suggests.

What the CGM actually measures

A CGM samples glucose in the interstitial fluid, the watery layer between cells just under the skin. Interstitial glucose tracks blood glucose with a delay of about 5 to 15 minutes and with somewhat larger lag during rapid changes — right after a meal, during exercise, when blood glucose is changing fastest. The accuracy of modern CGMs against a venous blood reference is in the range of 9 to 12 percent mean absolute relative difference, with somewhat larger errors at the lower end of the glucose range.

For diabetes management, this accuracy is sufficient because the clinical decisions being made — insulin dosing, hypoglycemia avoidance — operate at scales where a 10 percent measurement error matters less than the trend information the continuous sampling provides. For non-diabetic users, the relevant decisions are usually about food choices, and the question is whether the data justify the decision being made.

What the data look like in non-diabetic people

A non-diabetic adult’s glucose, recorded continuously, does not look like a flat line. It rises by 30 to 60 mg/dL after most meals — more after a high-carbohydrate meal, less after a high-protein meal, almost not at all after a meal of pure fat. The rise typically peaks 30 to 90 minutes after eating and returns to baseline within two to three hours. Glucose also varies during exercise, during stress, during sleep, and across the menstrual cycle.

This is normal physiology. The headline graphs that consumer apps generate, with peaks marked as “spikes” and troughs marked as “crashes,” are presenting normal physiological excursions in language imported from diabetes care. In a non-diabetic person, a post-meal rise to 140 mg/dL is not a spike; it is the body doing what it is designed to do.

The published research on whether smaller post-meal glucose excursions, in non-diabetic people, predict better cardiometabolic outcomes, is genuinely thin. A few cohort studies have suggested associations between postprandial glucose excursions and various cardiovascular markers, but these studies typically include people with prediabetes and diabetes alongside the non-diabetic subjects, and the associations within the non-diabetic subgroup alone are weaker. There is no randomized trial showing that interventions to flatten normal post-meal glucose excursions in healthy people produce health benefits.

What the wellness apps tell users

The apps that pair with consumer CGMs typically present an interpretation that goes well beyond what the data support. A typical user logs a meal, sees a 50 mg/dL rise, and is told that the meal “spiked their glucose” and should be modified. The logical conclusion the app encourages is that meals which produce smaller glucose rises are better for the user’s health.

Several problems with that interpretation are worth flagging. First, individual glucose responses to the same meal vary substantially day to day; the same person eating the same food at the same time can have a noticeably different response on different days, depending on prior sleep, prior activity, recent meals, stress, and the menstrual cycle. The single-meal response a CGM app shows the user is not a stable measurement of “this food’s effect on me.”

Second, optimizing meals for the smallest possible glucose response in a non-diabetic person can produce dietary patterns that are unfavorable in other ways — for example, replacing carbohydrates from fruits and whole grains with greater proportions of saturated fat, or producing meals that are insufficient in fiber. The wellness apps do not generally model these tradeoffs.

Third, the optimization target itself is not well established. There is no evidence-based threshold below which post-meal glucose rises in non-diabetic people produce health benefits.

When a CGM might genuinely be useful

In non-diabetic adults, a CGM can be genuinely useful in a few specific situations. People who have screening results suggestive of prediabetes — elevated fasting glucose, an HbA1c in the 5.7 to 6.4 percent range — can sometimes use a short course of CGM to identify dietary patterns that are pushing glucose higher than a more moderate alternative would. People with reactive hypoglycemia or unexplained post-meal symptoms can occasionally use a CGM to characterize what is happening. Some athletes, particularly endurance athletes, use CGMs to inform fueling strategies during long workouts. None of these are the primary marketing pitch for the over-the-counter devices.

For someone with normal screening labs, no symptoms, and no specific clinical question, the CGM is more likely to generate worry about normal physiology than to identify any actionable problem. The fact that the marketing has been effective enough to convince hundreds of thousands of such users to start wearing the devices does not change that.

What the clinical research community thinks

The American Diabetes Association’s professional guidelines, as of this writing, do not recommend routine CGM use for non-diabetic individuals. The Endocrine Society has been similarly cautious. The dietary-research community — the people who study how food affects metabolic health in trial settings — has been broadly skeptical of the popular interpretation that post-meal glucose excursions in healthy people are a useful target for intervention.

This is not the same as saying the technology has no role outside diabetes; it has plausible roles in early detection of prediabetes and in research settings. It is to say that the dominant consumer-marketing claim — that monitoring your glucose continuously will tell you something useful about how to eat — outruns what the evidence actually shows.

What we’d say to a friend

If you have diabetes or prediabetes and your physician has talked with you about monitoring, the over-the-counter CGM is a real tool and worth considering. If you don’t, and you are interested for general wellness or curiosity reasons, the device is unlikely to give you actionable information that you couldn’t get more cheaply from an annual fasting glucose and HbA1c. The interesting biology you might think you are seeing is mostly normal physiology, presented through an app interface that imports diabetes-management framing into a context where it does not apply.

Frequently asked questions

Are over-the-counter CGMs accurate?

The recently approved over-the-counter CGM devices have similar accuracy to the prescription versions of the same hardware — typically within about 10 percent of a venous blood glucose reference, with somewhat larger errors in the lower glucose range. Accuracy is not the main issue with their use in non-diabetic populations; interpretation is.

Should I get a CGM if I'm worried about prediabetes?

If you have screening blood-test results suggestive of prediabetes (fasting glucose, HbA1c, or oral glucose tolerance test) and your physician has discussed monitoring with you, a short course of CGM use can occasionally inform clinical decisions. For someone with no abnormal screening results who simply wants to 'check' their metabolic health, the device is much less likely to provide actionable information.

Are post-meal glucose spikes harmful?

In non-diabetic people, postprandial glucose typically rises by 30 to 60 mg/dL after a meal, with peaks occurring 30 to 90 minutes after eating, and returns to baseline within two to three hours. This is normal physiology. The current evidence does not support the position that 'flattening' these normal post-meal rises in non-diabetic people improves health outcomes.

Why does my CGM show different numbers than a fingerstick?

CGMs measure glucose in the interstitial fluid, not in the blood directly. There is a delay of approximately 5 to 15 minutes between blood glucose changes and interstitial glucose changes, especially during rapid changes (right after a meal, during exercise). The two readings are correlated but not identical.

What about the data from the wellness apps?

The wellness apps that pair with CGMs typically interpret normal physiological glucose excursions as 'spikes' that the user should optimize away by changing food choices. The science behind those interpretations is much weaker than the apps imply. Many of the dietary recommendations these apps generate are based on individual glucose responses that are not stable across days, are not strongly correlated with cardiometabolic health in non-diabetic people, and would not be made by a clinician working with the same data.

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