Matt Lavin
Matt is a technical director at LifeOmic. When he’s not working, Matt enjoys programming for fun, meditation, anything adventurous outdoors, and over-analyzing his diet and exercise.

Matt Lavin
Matt is a technical director at LifeOmic. When he’s not working, Matt enjoys programming for fun, meditation, anything adventurous outdoors, and over-analyzing his diet and exercise.


I’ve always enjoyed self-experimentation, especially in the areas of health and wellness. I was inspired by my colleague Matt Ferguson’s article on what he learned from wearing a continuous glucose monitor (CGM). This gave me the idea to use a CGM to do a bit of controlled analysis of my carb intake and resulting glucose spikes. 

Like Matt, I am also not diabetic, but I do have access to a CGM sensor. I tend to be pretty analytical and curious, so I decided to run an experiment. Because I like cardio and strength training, I want to be sure to eat carbs, but I understand that glucose spikes aren’t ideal. I wanted to discover how the different types of carbs that I commonly eat affect my glucose. My ultimate goal is to eat a relatively high amount of carbs in a day without huge spikes.

To provide some context for what is normal for me, I commonly eat 80-90g of carbs in a regular meal. During these meals, I eat a fairly even split of carbs, fats, and proteins. When I wear a CGM, I can see that my glucose rises to 140 mg/dl (my postprandial spike target) rather consistently after a mixed meal. Armed with this knowledge, I proceeded with my experiment. 

What I did

I decided to test four different carbs individually: black beans, brown rice, barley, and pasta. I wasn’t trying to mimic an oral glucose tolerance test, which is 75g of glucola. I didn’t care about comparing myself to others or to a standard. My goal was to compare each carb with the others. I guessed that 50g of net carbs per ‘meal’ would be large enough to see a spike but not be excessively large. 

I decided to test four different carbs individually: black beans, brown rice, barley, and pasta. My goal was to compare how each individual carb affected my blood glucose levels.

I set up the experiment so that my first meal of the day would be only a pre-measured amount of that carb alone for four different days in a row. I would eat at exactly 8:40am after having fasted for about 14 hours. My only allowed morning beverage would be black coffee. I didn’t do any exercise before eating or in the two hours after, and I monitored my glucose numbers during this entire period. I was careful to move around in my usual manner (mostly sitting at my desk). Avoiding exercise was important because it is known to trigger glucose uptake into the muscles and would skew my results. 

To prepare, I pre-cooked each carb separately. I ran into some trouble trying to figure out exactly how much of each ingredient would add up to 50 grams of net carbs. I quickly discovered that the printed nutritional information on the packaging can vary wildly from online sources. It’s amazing how much variation exists online for the nutritional info of black beans. I tried my best to do the math as accurately as possible, making good use of my food scale. My dry weight totals for 50g of net carbs for each ingredient came out to 67g of pasta, 70g of brown rice, and 81g of barley. For the 50g of net carbs from beans, I rinsed and drained 326g of pre-cooked black beans. 

How did eating this way feel?

The subjective experience of eating these ingredients was different each day. Eating 50g of net carbs of pasta felt like nothing. The volume was small and my stomach felt light. Both the brown rice and the barley were satisfying, and I felt pleasantly satiated. However, the black beans sat heavily in my stomach, leaving me feeling uncomfortable. I love black beans, but 50g of net carbs from black beans was the worst meal to eat.  

Now, let’s talk data. What did the CGM show each day?

Throughout the four days of the experiment, my fasting glucose was almost always perfectly flat at 90mg/dl right before eating. That’s a perfect baseline to easily make comparisons.

Peak Glucose Values

Black beans: 167 mg/dl at 80 minutes after eating

Pasta: 155 mg/dl at 73 minutes after eating

Brown rice: 180 mg/dl at 55 minutes after eating

Barley: 165 mg/dl at 70 minutes after eating

The glucose response from pasta was a big surprise for me. I expected that spike to more closely mirror that of brown rice rather than black beans or barley. These results combined with the fact that the portion size felt small might mean that my math could be off somehow. As for the other three carbs, the shifting shape of the graph moving from tall/early to flat/later matches my expectations for how fiber content would affect the response. Since black beans and barley have more fiber than brown rice, the spike was delayed and less pronounced.

What is my body’s ideal carb?

One interesting takeaway for me is that I will try to cook barley more often than brown rice in the future. I like having some rice in the fridge to add to meals if I’m hungry, and barley seems like a nice replacement. It is just as filling as rice without feeling as voluminous as black beans, but barley results in a slower, more gradual glucose spike. 

I also learned firsthand how hard it is to accurately measure net carbs. Why is all the available information on these ingredients so inconsistent? It feels like this should be easier and is also knowable. If you are tracking your food for calorie counting, don’t take the calculated calorie value too seriously. Just aim for a range and be happy with +/- 10% accuracy.

What should I do next?

I have one sensor left – another 10 days for readings – and I’d like to run another experiment. Do you think it would be fun to see the glucose readings after consuming more types of carbs, such as pretzels/fruits/vegetables? Or would it be more interesting to control for something like barley and then add different amounts of olive oil to see how the added fat slows down digestion? Another idea could be to control for barley and then vary the amounts. Please comment below on what my next experiment on myself should be!


Matt Lavin

Matt Lavin is a technical director at LifeOmic. He’s been obsessed with programming for as long as he can remember and has recently been applying his skills to the health domain. Matt earned a BA in Computer Science from NCSU. When he’s not working, Matt enjoys more programming for fun, meditation, anything adventurous outdoors, and over-analyzing his diet and exercise.

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