What can a CGM show about exercise, food, and glucose? A Libre test

A two-week test of the Abbott FreeStyle Libre continuous glucose monitor, paired with the Supersapiens app, showed how exercise, nutrition, and daily habits related to glucose readings. Fueling well before training gave more consistent energy, walking around meals improved stability, and feeling tired did not always match low glucose. WHOOP and MyFitnessPal data added training, stress, and meal context.

Continuous data is changing how health recommendations are personalized. Tracking is becoming more accessible, but the biggest challenge remains making the data actionable for users.

As the technology improves, it will be essential to get this data to companies that can act on it. Understanding what data is available and how it can support wellness interventions is the next step toward Medicine 3.0 and a preventive care model.

My recent foray into continuous glucose monitoring, using the Abbott FreeStyle Libre paired with the Supersapiens app, gave an illuminating glimpse into the relationship between exercise, nutrition, and glucose levels. Most of the data confirmed my own anecdotal experience, but it was pretty cool to see it play out as it happened. During this time I also used MyFitnessPal to track my macros and completed both total-body strength sessions and monostructural cardio sessions tracked via WHOOP.

I had a few goals when I began my tracking:

• See the impact of different pre-, intra-, and post-workout fueling strategies for strength and cardio training
• Observe the impact of different foods on my glucose (the fun one!)
• Test how habits such as standing, walking, and the order of eating affected the rise and stability of glucose
• Check whether low energy correlates with low glucose
• See how eating glucose affected sleep

Here is a breakdown of the setup and a sample day with Supersapiens, WHOOP, and MyFitnessPal data from July 19, 2023.

The Abbott FreeStyle Libre is a compact, ergonomic sensor. I barely noticed it on my arm, and application didn't hurt. I was also relieved that a tiny wire (like a fishing line), not a needle, stayed in for two weeks. The patch Supersapiens provided to keep the sensor in place gave some reassurance for all the bumping into walls I do 🙂

The Supersapiens interface is user-friendly, with a live graph that makes glucose trends easy to read. The app alerted me when a “rush” was detected and let me log the reason (movement, food, mood, other). I could add notes and tags and layer my meals and workout data.

Pairing the FreeStyle Libre with Supersapiens was easy and straightforward. The sensor took an hour to calibrate after setup.

The Supersapiens app educates users about glucose and its impact on overall health, not just displays data. Differentiating between glucose rushes, rises, and stability periods helped me understand how foods or activities influence levels. The educational resources were fantastic, but I would like to see them applied to the live data to create learning moments. Much of this is basic knowledge for athletes but not for the general population. I look forward to trying other platforms to compare the experience (Levels, Nutrisense, January).

The Supersapiens scoring system shows minimum, maximum, and peak glucose levels, so users can gauge how nutrition and exercise affect their readings. I didn't notice a correlation between my performance and recovery metrics and the overall impact on my training or energy.

Meal data from MyFitnessPal (via Apple Health) gave a general view of how specific foods related to glucose fluctuations, which made it convenient to spot foods that provided sustained energy versus quick spikes. I logged and transcribed the first few days by hand, and that was a huge pain: a reminder that more passive data collection significantly improves the user experience ⭐

Stress and activity data from WHOOP painted a fuller picture of my training, sleep, recovery, and stress, and showed how closely stress, activity, and glucose interact.

1. Being adequately fueled well before a training session led to more consistent energy than fueling just before or during the activity. I ate an intentionally very high-fat, high-protein breakfast to see how it would affect levels.
2. Walking before and after meals improved glucose stability.
3. When adequately fueled, I didn't need intra-workout carbs for sessions under 90 minutes. If you don't need the calories, stick with water and LMNT.

Glucose monitoring isn't just for athletes. Athletes can test performance strategies, and anyone interested in their health can see how foods or activities relate to their glucose readings. I'm looking for ways to bring this into a “beginner” nutrition program that gives clients feedback and teaches them to make decisions throughout the day. 🍪 Watching your glucose shoot over 200 mg/dL after one bite of a Crumbl cookie when fasted just hits differently.

Feeling tired did not reliably mean low glucose. Rate of Perceived Exertion (RPE) during workouts often aligned with glucose data, but not always: there were times I felt energetic while the data showed dipping glucose. I had assumed that tiredness or brain fog meant low glucose, but low-energy moments didn't always align with low interstitial glucose, a reminder not to rely only on subjective feelings.

This experiment reinforced the value of objective, continuous data for understanding how the body responds to food and training, and for making more informed decisions about nutrition, exercise, and well-being. These are personal observations from one person, not medical guidance. ROOK moves and standardizes the data; it does not diagnose or replace clinical judgment.

For how CGM data reaches health apps, see integrating Dexcom glucose data through ROOK.