Does CGM Tell the Whole Story? A Second Look at Lingo for Metabolic Monitoring
I recently revisited Lingo, Abbott’s direct-to-consumer continuous glucose monitoring (CGM) system, after a new wave of clients began seeking guidance on how to use it to support metabolic health, including diabetes prevention, insulin resistance, and weight loss.
Last fall, I tested and reviewed the device with a focus on its hardware, technical features, usability, and accuracy—you can read that review here. This time, however, I’m evaluating Lingo from a different angle: its potential role in health coaching and behavior change. What can continuous glucose monitoring really tell us about metabolic health—or overall well-being? Does it provide the full picture? More importantly, how does it actually influence behavior?
Let’s take another look at Lingo, this time through a physiological and lifestyle-oriented lens.
What is Lingo CGM?
Lingo, Abbott’s direct-to-consumer CGM, closely resembles the Libre 2 but is a distinct device—only compatible with the Lingo app and lacking features like alarms or caregiver integration. It tracks real-time glucose and provides personalized coaching to support metabolic health by analyzing responses to food, activity, and stress. Key features include a glucose graph, spike tracking (Lingo Count), food/activity logging, tailored tips, and behavior-focused challenges.
The target audience is similar, though not exactly the same, as Dexcom’s new direct-to-consumer CGM, Stelo— you can read my full review of Stelo here.
The Promise of CGM for Metabolic Health
Intensive glucose monitoring for metabolic health isn’t new. Programs like Nutrisense, Levels, and Signos have been offering direct-to-consumer CGM products, prescribed by their in-house physicians, for quite some time. Here are several reasons someone might use CGM outside of diabetes care:
Personalized Nutrition Feedback
CGM can help identify which foods trigger significant glucose “spikes”—typically defined as rapid or steep increases in glucose levels after meals—and “crashes,” which occur when a surge of insulin causes glucose to drop quickly, often by more than 2–3 mg/dL per minute. While “spikes” and “crashes” aren’t clinical terms, they offer a vivid and relatable way to describe the body’s glycemic responses.
Weight Management & Cravings
Programs like Levels and Signos argue that by promoting better glucose stability, CGM can help reduce hunger swings and support sustainable weight loss efforts. For some, it’s a tool to validate or fine-tune strategies like low-glycemic or time-restricted eating.
Personally, I do notice an uptick in hunger 2–3 hours after having a sweet treat—like my Friday morning chocolate croissant—but that may have more to do with a lack of protein than a post-sugar glucose spike.
Early Identification of Insulin Resistance
CGM can reveal subtle patterns, such as consistently elevated fasting glucose, that may indicate early metabolic dysfunction, even in individuals with normal A1c levels. It can also identify trends in how a person responds to food, activity, stress, and sleep.
These responses often correlate with changes in body composition, such as increased fat mass and decreased muscle mass, which are associated with a progressive decline in glucose sensitivity.
Health Optimization and Biohacking
Some individuals use CGM to “optimize” performance or longevity—aiming for tighter glucose control to reduce inflammation, oxidative stress, or other theorized contributors to aging and chronic disease.
And while there’s nothing inherently wrong with taking a proactive approach to health and wellness, I sometimes wonder if CGM is becoming a solution in search of a problem.
Behavioral Opportunities with Lingo
My time with Lingo did provide some interesting insights into my glucose data and trends. That said, I try to keep in mind that the readings come with a margin of error—sometimes up to ±20%. I also remind myself that, as someone without diabetes, I don’t need to hold myself to diabetes-specific glucose targets.
For example, it’s completely normal for me to see glucose values in the 60s—typically 3 to 4 hours after a higher-carb meal (similar to reactive hypoglycemia), during exercise, or after alcohol. These dips don’t require treatment; they’re just part of my personal pattern.
I also noticed which foods helped me stay more stable—like apples, nuts, and cottage cheese—and which so-called “healthy” foods triggered more rapid spikes, such as oatmeal, tomato sauce, non-alcoholic beer, and homemade pizza. Interestingly, I also saw that my post-meal glucose tends to run higher in the evening, especially at dinner. That gave me pause.
Do I tolerate carbs better in the morning? Maybe my evening meals should focus more on protein and produce. It’s something I’m still exploring.
Behavioral Risks with Lingo
I’d be lying if I said I didn’t have real concerns about the behavioral risks associated with using CGM for metabolic health. I see these patterns emerging in some of my clients—and, to be honest, I started noticing them in myself as well.
I’m not someone who currently tracks calories, macros, or weight (anymore), but there was a time when it felt all-consuming. I worry that excessive monitoring, especially without clinical context, can feed into risky or simply evidence-lacking behaviors.
Low-Carb or Bust
I couldn’t help but get the sense that all the coaching metrics and feedback were nudging me toward a low-carb eating pattern, aimed at flattening my glucose curve and minimizing any spikes. The weekly trends report praised me when my Lingo Count was low—even during a week when my meals included bacon, butter, and BBQ (the three B’s!). Ironically, that was also the week I experienced significant acid reflux.
I didn’t feel like the app was really “listening” when I logged my meals. There was no positive feedback when I chose whole grains over refined ones. And what about when I opted for a non-alcoholic beer? Honestly, my glucose probably would’ve been even lower with a regular alcoholic drink, given what we know about alcohol’s effect on hepatic gluconeogenesis.
It left me wondering: Is the underlying message really that low carb = flat glucose = good metabolic health?
Glucose Perfectionism
If there’s such a thing as overanalyzing CGM data, leave it to a registered dietitian to find it. My personal mantra has always been: Anything worth doing is worth overdoing.
But that doesn’t really apply to glucose management—because perfection doesn’t exist. Avoiding all glucose spikes is both impossible and, frankly, irrelevant for someone without diabetes. Once again, I found myself putting energy into solving a problem that doesn’t really exist.
When I started using Lingo, my Lingo Count was around 60, but it quickly dropped to 30 as I began meeting the app’s targets. My average afternoon lingo count was just 14—well below the threshold. But because my morning average was only 7, the app kept nudging me to “improve” my eating pattern by increasing my calorie intake in the morning to prevent overeating later in the day.
If the app had been paying attention to my food logs, it would’ve seen that I actually eat a fairly substantial breakfast—usually 20–30 grams of protein, 10-15 grams of fat, and 30–45 grams of carbs—because I’m up early and need fuel for the day. In fact, my higher afternoon readings are likely a residual effect from that meal. My lunch and dinner tend to be lighter. So why is it telling me to eat more for breakfast?
Lack of Clinical Oversight
The disconnect between my glucose trends and actual eating patterns started to frustrate me after a while. Here I am, a highly educated nutrition professional, in the best shape of my life (20% body fat, A1c of 5.4, 77% lean body mass), and yet the app seemed to be offering overly “sciencey” recommendations that didn’t reflect my real-life context. All the while, it was gamifying my data.
My Lingo Count started at 60 and dropped to 30—great, but… where’s the rationale? What’s the goal? At this point, it’s starting to feel more like the Limbo than Lingo. Where’s the clinical oversight?
The Role of Education and Interpretation
If it’s not already abundantly clear, CGM data requires professional interpretation and guidance to drive meaningful ( and safe) behavior change. Unfortunately, there’s still far too much data and far too little support for the general public.
One of the biggest issues is the lack of clarity around what constitutes a “normal” glucose response outside the context of diabetes. Without clear, evidence-based benchmarks, it becomes easy for users to misinterpret fluctuations and make unnecessary or even harmful changes to their diet and lifestyle.
This ambiguity gives me real pause. If we’re going to expand the use of CGM beyond diabetes care, we need to ensure users have access to qualified support—whether that’s through registered dietitians, diabetes care and education specialists, or well-trained health coaches.
Final Thoughts on Responsible CGM Use
Direct-to-consumer continuous glucose monitoring systems—like Lingo and Stelo—can be valuable tools for supporting metabolic health when paired with personalized education, professional support, and a nuanced understanding of glucose physiology. I would love to see more collaboration between app developers, clinicians, and diabetes care and education specialists.
In fact, I strongly believe that diabetes care and education specialists are uniquely positioned to lead the future of biowearables and metabolic health technology.
For individuals considering or currently using CGM, I encourage critical thinking and personalized interpretation of glucose data. Understanding what these values mean in the broader context of your health and wellness is key to making CGM a meaningful and empowering tool.
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