Please Critique my Experiment Design: Measuring the Effect of Low-carb Ingredients on Blood Sugar

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For my next set of experiments, I want to measure the effect of different foods on blood sugar. I’m particularly interested in the effect of:

low-carb flour and sugar replacements (e.g. oat-fiber, lupin flour, allulose, etc.)
combinations of ingredients (e.g. how much does indigestible fiber, fat, or protein slow carb absorption

When I tried this before, I added ingredients to my normal meals measured the change in my normal BG trends (see Next Experiments). This proved too noisy and I couldn’t get a clean measure of the effect of even pure glucose in a reasonable number of measurements (see Next Experiments).

This time, I have a continuous glucose monitor (Freestyle Libre, post coming soon on accuracy vs. fingerstick and attempts to calibrate it) and am going to try to more carefully isolate the effects of the ingredient being tested.

This is going to be a lot of work and take many weeks, so I was hoping to get some feedback on my experimental design before I start. If you’re interested, please take a look and leave your feedback/critique in the comments.

It’d really improve the experiment to have more people participating. Let me know in the comments or by e-mail if you want to join in (see sidebar).

Proposed Experiment

Note: I put some specific questions at the end

Goals:
- Determine effect of individual ingredients on the blood sugar of person with Type 2 diabetes
- Determine effect of combining ingredients on same.
- Develop model to predict the effect on blood sugar of meals that’s more accurate than standard carb+protein counting
Approach:
1. Calibrate Instruments: Over several days, measure blood sugar by both CGM (Freestyle Libre) and BGM (Freestyle Lite). Develop a calibration curve to increase accuracy of CGM data
  - Note: I’m already doing this and initial indication is that ~75% of the discrepancy between the two meters can be accounted for by a simple linear gain + offset error
2. Establish Baseline: Monitor blood sugar while skipping breakfast & lunch (both food & insulin) to identify a period of time where my blood sugar is stable for a long enough (need at least 2-4 hours).
  - Based on previous experiments, I’ll need to wait until after lunch.
  - Will collect data on at least 3 days in which I’m not exercising in the morning (M, W, F)
  - To reduce potential noise, need to be careful not to overeat or eat late the night before.
3. Measure Food Effects: For each ingredient or combination of interest, follow the same procedure as in the baseline, but at the selected time, consume a fixed, measured quantity of the ingredient and monitor blood sugar by CGM and BGM (every 30 min.) for 2 hours or until my blood sugar is stable for at least 1 h.
  - Initial quantity will be selected based on my previous experience of what will raise my blood sugar by ~20 mg/dL.
  - Based on the initial results, I will test different quantities of the ingredients until I have a dose-response curve with BG increases from 0 to 40 mg/dL or the quantity exceeds what I would reasonably consume in a sitting, whichever is smaller.
  - Number experiments will be at least 3 per ingredient or combination.
Initial Ingredients to Test:
- Glucose tablet – baseline to which everything else will be compared
- Dissolved glucose – effect of dissolving an ingredient
- Whey protein – effect of protein
- Casein protein – effect of protein type
- Allulose – my favorite “indigestible” sweetener for baking & ice-cream
- Oat-fiber – low-calorie, low-carb flour replacement I use for muffins and cookies
- Inulin – used in a lot of low-carb foods

Questions

Current design tests one ingredient at a time. This is a lot simpler and lets me get results for the first ingredients sooner, but does introduce a systematic variation between ingredients (the week). My thought was to mitigate this by re-testing glucose at some frequency to measure week-to-week variation. Do you think this is sufficient or is there a better design?
I’m not planning to repeat quantities of a given ingredient multiple times, but instead vary the quantity. Since the end result of interest is change in BG as a function of quantity, I figured this would be more experimentally efficient. Are there any problems with this approach?
Since experiments will be done on M, W, F, there will be a 1-2 day washout period between ingredients. Is this sufficient or do I need to separate ingredients by week to ensure a two day washout?
Are there any other ingredients you’d like to see me test?
Are you interested in joining the experiment?

– QD

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Experiment #4 – Tracking blood sugar during a 24 hour fast

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**Figure 1.** Measured blood glucose concentration vs. time. Reference bands and lines show my personal target range and high/low thresholds.

**Table 1.** Summary statistics of blood glucose concentration during the fast.

I tried again this week to measure the effect of different food ingredients on my blood sugar. I started with plain glucose, but quickly ran into a problem. The first two times I ate 3g of glucose, which normally would raise my blood sugar ~15 mg/dL, my blood sugar actually dropped. I did these experiments at 2pm, 8.5 h after eat breakfast, so I shouldn’t have had any effects from either the food or medication.

Based on these results, I’m not going to be able to get clean measurements of the effect of food unless I better understand my baseline blood glucose, so I decided to monitor my blood sugar during a day of fasting.

As with my two-day tracking experiment a couple weeks ago, this was quite informative. Here’s a most important things I learned, my new questions, and ideas for next experiments:

Key Learnings:

Full data and summary statistics in Figure 1 and Table 1

Checking blood sugar during a fast is a useful control experiment and helps determine if the phenomena I’m observing are due to specific interventions vs. natural or time of day-based variation.
Even with my morning insulin, I’m seeing an ~10 mg/dL increase that persists for ~5 h. I should try increasing the dose by ~0.25u.
There’s a measurable drop in my BG when I’m driving to/from work. I saw this in 3/4 of the drives during my previous glucose tracking experiment, but I didn’t pick up on it because my commutes occur right before mealtimes. Need to investigate further to see if this is real & consistent.
I saw an ~15 mg/dL drop starting at 1p that persisted until 5:30p.
- This is the time period when I was trying to do the food effect tests and may be why I was seeing the weird drop in BG.
- This occurred 9 hours after my last dose of insulin (0.5u each of Novolog and Tresiba), so must be the result of something my body is doing. Is this drop from fasting (e.g. running out of glycogen) or something that occurs normally?
- My BG stabilized at 65-75 mg/dL, which indicates that that range is something that can occur naturally and not due to medication. Given this, should I correct lows in this range or let them be?
- I always get tired around 2-3p, lasting until about 5-6p. I’ve always chalked this up to the end of the work day and then getting re-energized by dinner/being home, but maybe there’s more going on. Need to test interventions to eliminate this afternoon fatigue.

Questions:

How consistent are the effects I observed? Which are due to fasting vs. effects that occur during a normal day?
Is the driving effect real? If so, are there ways to mitigate it? Even if it’s only a short-term effect, it could be causing fatigue or other reduced mental capacity while driving.
How can I mitigate the 10 mg/dL increase in the morning?
Is the afternoon drop connected with feeling tired and less mentally capable? If so, how can I mitigate the effect?

Next Experiments:

I’m always interested in ideas for new experiments, so please leave a comment if there’s something you’d like me to try.

Repeat this fasting experiment a couple more times to see if the observations are reproducible.
- Also try fasting for shorter durations (single meals) to check if effects are from the duration of the fast vs. ones that would occur normally.
Measure fatigue and/or mental acuity see how it correlates with time of day and BG.
Test an increase in morning insulin to reduce the effect of the dawn phenomenon.

Details

Purpose

To better understand trends in my blood glucose over the course of a day fasting and determine if there are trends or events that I should investigate further.

Design/Methods

General. Blood glucose was measured approximately every 15 min. using a FreeStyle Freedom Lite glucose meter and FreeStyle lancets & test strips. No special precautions were taken to clean the lancing site before measurement. To take a sample, the lancing devices was used to pierce the skin at an ~45 deg. angle from the finger. Blood was then squeezed out by running the thumb and pointer finger of the opposite hand from the first knuckle to the lancing site of the finger. Blood was then wicked into a test strip that had been inserted into the meter and the glucose reading was recorded.

For medication, I took my normal morning and evening medication, but did not dose for meals.

Data

Link

Results & Discussion

See key learnings & questions above.

Conclusion & Next Experiments

See summary above.

– QD

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Experiment #3: Exercise Effect

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From my two days of “continuous” tracking, I saw sharp drops in blood sugar during cardio exercise, followed by a return to baseline over 30-45 min. During these drops, I was going extremely low, almost 40 mg/dL. I didn’t have any physical symptoms that I could tell, but this worries me enough that I want to understand the phenomenon better and see if there’s a way to mitigate it.

Towards this end, over the last week, whenever I exercised, I measured my blood glucose before and at 15 min. intervals after exercise until my BG had recovered to baseline or I did something that would change my BG (ate, took medicine, etc.).

Key Learnings:

The drop after exercise is large and significant, averaging 25 mg/dL for a 200 kCal bike ride and 10 mg/dL for MMA and strength training.
With one exception, whenever I could measure, my BG recovered to baseline within 45 min. and usually started to come back up within 15 min.
- This indicates I’m not in any danger with my current exercise regime, but should reassess if I significantly change the intensity, duration or type of exercise.
There are no clear trends in the magnitude of the drop of recovery time with anything I measured, but there are hints of effects with intensity, time of day, and type of exercise. I need to collect more data.

Next Experiments:

I’m always interested in ideas for new experiments, so please leave a comment if there’s something you’d like me to try.

Exercise studies: Continue to collect data, varying intensity and type of exercise to try to figure out trends.
Long peak from breakfast & lunch: Measure blood glucose at 15 min. intervals a few more times after breakfast & lunch. Try varying protein and fiber content to see if those are the causes. Try normal-acting insulin to mitigate.
Food & medication studies: It’s proving difficult to disentangle the numerous effects going on at any given meal by adding or subtracting particular foods (data too noisy, too many measurements required to observe a statistically significant effect). Instead:
- Skip meals and medication to measure background trends.
- Consume individual ingredients (glucose, protein, fiber, etc.) or take individual medications to measure their direct effects.
- Measure combinations of ingredients and medications to measure interaction effects.
- This will require more experiments, but I think in the end it will require less time & effort

Details

Purpose

Determine the short term effect of exercise on my blood sugar and if I am in danger of a severe hypoglycemic event.

Design/Methods

General. Blood glucose was measured using a FreeStyle Freedom Lite glucose meter and FreeStyle lancets & test strips. No special precautions were taken to clean the lancing site before measurement. To take a sample, the lancing devices was used to pierce the skin at an ~45 deg. angle from the finger. Blood was then squeezed out by running the thumb and pointer finger of the opposite hand from the first knuckle to the lancing site of the finger. Blood was then wicked into a test strip that had been inserted into the meter and the glucose reading was recorded.

Measurements were taken before exercise and at 15 minute intervals after exercise until my blood sugar had returned to the baseline value or I did something that would change my BG (ate, took medicine, etc.).

Recovery time was calculated as the time until blood glucose returned to within 5 mg/dL of the value before the start of exercise.

For bicycling, I used a stationary bike and recorded the reported calories burned. This is almost certainly inaccurate, but appears to be a consistent function of the pedaling speed and resistance setting. As such, I used it as a quantitative measure of the intensity of the exercise.

Data

Link

Results & Discussion

**Figure 1.** Blood glucose vs. time before and after biking

**Figure 2.** Blood glucose vs. time before and after strength training

**Figure 3.** Blood glucose vs. time before and after MMA

Overall. The full set of BG vs. time traces are shown in Figures 1, 2, and 3. The data roughly corresponds to the previously observed trend, a drop in BG during exercise followed by recovery over 15-60 minutes. To get a better understanding of what’s going on, I calculated the change in blood glucose from the initial measured value and bucketed readings in 15 minute intervals from the end of exercise (i.e. 15, 30, 45, and 60 min. post-exercise).

**Figure 4.** Initial BG drop vs. type of exercise. Reference lines show average value.

Type of Exercise. Unsurprisingly, as shown in Figure 4, the type of exercise had the largest effect on the magnitude of my drop in BG, with biking showing an average drop of 25 mg/dL and MMA & strength training showing average drops of ~10 mg/dL.

Unfortunately, it’s difficult to draw any conclusions from this, as I do different types of workouts at different times of day (e.g. MMA on weekday mornings, biking & strength training on weekend mornings or weekday evenings).

**Figure 5.** Initial BG drop and recovery time vs. exercise intensity.

**Figure 6.** Initial BG drop and recovery time vs. initial BG.

**Figure 7.** Initial BG drop and recovery time vs. time of day.

**Figure 8.** Recovery time vs. initial BG drop.

Exploratory Analysis of Trends. I next attempted to see if there were any trends in the BG effect with intensity of exercise, starting BG, or time of day. As shown in Figures 5, 6, 7, and 8, the amount of data I have so far is not sufficient to determine any clear trends. However, a there are hints of a few possibilities:

Figure 5: Initial BG drop may be positively correlated with exercise intensity. This conclusion relies on a single data point from a 400 kCal bike ride and so is highly suspect.
- Need to get more data at different intensities. May also need a better measure of intensity. Maybe look at both kCal and duration of ride?
Figure 6: Initial BG drop and recovery time show a maximum and minimum, respectively, when initial BG is close to “normal” (80-95 mg/dL). Again, not nearly enough data to have any confidence in this conclusion, but worth looking at again when I have more data.
Figure 8: With one outlier, recovery time increases as the BG drop gets smaller. This is a counterintuitive to me and also worth keeping an eye on.

Conclusion

See summary above. All-in-all, this initial data is promising and allays my worry about a dangerous hypoglycemic event during exercise, but I need to get more data in order to get anything interesting out of it. I’ll keep monitoring and post again when I’ve learned more.

– QD

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Recruiting for Community Self-Experiment: How do Hot Showers Affect Blood Glucose?

cross-posted to r/diabetes, r/diabetes_t1, r/diabetes_t2, r/QuantifiedSelf, and the Quantified Self Forum to recruit as many participants as possible. I’m also posting it here in case there’s anyone who reads the blog who’s not in any of those forums (not likely, but might as well).

A few days ago, u/NeutyBooty posted on how hot showers caused their blood glucose to rise. Lot’s of commenters confirmed the general observation, but for some it appeared to be a CGM artifact, for some it matched their finger-stick meter, and others they see a BG drop.

I’ve been interested in self-tracking and experimentation for a while and this seems like a perfect opportunity for a communal self-experiment.

We currently have 7 Redditors participating from the original thread, but I’m hoping we can get even more people signed up so we can get a really great data set. Anyone’s who’s interested in participating, please comment or PM me.

The basic idea is to agree on a simple experimental protocol, each of us run the experiment, combine and analyze the data, and see if we can figure out 1) Is the shower effect real or a CGM artifact and 2) how does it vary from person-to-person?

The 7 of us organized and worked out the protocol using group chat and and a new subreddit, r/QuantifiedDiabetes. We’re starting the experiments and looking for more participants.

Here’s the details:

Background:
- In u/NeutyBooty‘s post on hot showers causing blood glucose to rise, lot’s of commenters confirmed the general observation, but for some it appeared to be a CGM artifact, for some it matches BGM, and for others they see a BG drop.
- From my PMs, some of us have CGM’s, some have regular BGM’s, and some have both.
Questions to answer:
- Is the “hot shower effect” a real change in blood glucose or an artifact of CGM sensors getting warm (or some other environmental change)?
- What is the person-to-person variation in the magnitude and direction of the “hot shower effect?”
Protocol:
- Pick a time when your blood glucose is relatively stable (no recent meals, medication, exercise, etc.)
- Turn on the shower to the hottest temperature you’re comfortable with and let the temperature stabilize. If possible, measure the temperature (e.g. with an instant read thermometer).
- Measure your blood glucose with both a CGM and regular finger-stick meter and record the data.
  - If you don’t have both types of meters, use whichever you do have (data will still be useful for the second goal)
- Take a 20 minute shower.
- As soon as you finish the shower, measure your blood glucose again with both a CGM and regular finger-stick meter and record the data.
- Monitor your blood sugar for one hour (measure every 15 min. for finger-stick meter)
- Record anything that might have affected blood glucose during the experiment.
- Repeat the experiment multiple times (preferably ≥3, but any data is better than nothing) to assess within-person variability.
- Post your data in a comment or PM to u/sskaye. I’ll compile it and make available to everyone to analyze
  - If you want your data to be anonymous, just let me know and I’ll remove all identifying info.
- Optional variations:
  - Vary the time or temperature of the shower
  - Try a bath, hot tub, or sauna instead of a shower.
Data to collect:
- For each glucose measurement: time, blood glucose, any important observations
- General: whatever demographic info you’re comfortable sharing (e.g. male/female, T1/T2/LADA, age)

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My Routine: Diet, Medication, & Excercise

Last updated 1/21/20

Since most of my experiments will be about the effect of modifying my diet, medication, or exercise, I thought it’d be useful to document my baseline routine.

Diet:
- Strategy:
  - Ultra low-carb (<15 g/day) for easier glucose management
  - High protein (120 g/day, 1.6 g/kg bodyweight)
  - Same meal every day for breakfast and lunch (makes medication tuning easier)
  - Consistent meat and vegetable quantities for dinner (couldn’t tolerate 3 identical meals/day, too boring)
- Breakfast:
  - 1 serving Ketochow with 2 tbsp. butter.
  - 3 oat-fiber protein muffins
  - 406 calories, 44 g protein, 5.7 g net carb.
- Lunch:
  - 1 serving Ketochow with 2 tbsp. butter.
  - 3 oat-fiber protein muffins
  - 406 calories, 44 g protein, 5.7 g net carb.
- Dinner:
  - 300 g meat
  - 150 g low-carb vegetable (spinach, okra, broccoli, cauliflower, green beans)
  - Note: this is the meal I “cheat” on most often. I have a few favorite recipes for which I’ve calibrated insulin doses and will go out to eat once in a while.
- Snacks:
  - I try to limit this as much as possible (1-2 times/wk) and to offset with additional exercise.
  - Homemade low-carb ice-cream, cookies, biscuits, etc.
  - Pork rinds
  - High cocoa chocolate (≥85%)
Medication:
- Strategy:
  - Keep blood sugar as steady as possible (80-90 mg/dL) with a combination of fast- and regular-acting insulin plus a small amount of slow-acting basal insulin to reduce strain on my remaining beta cells.
  - Oral:
    - Metformin (Glucophage brand), extended release, 2×1000 mg (upon waking up and before bed)
  - Insulin:
    - All dilutions are 3:1 (25% insulin)
    - Waking up: 0.5 units Tresiba (slow-acting), 1.5 units diluted Novolog (fast-acting, to off-set the dawn phenomenon)
    - Breakfast: 7 units diluted Novolog
    - Lunch: 4 units diluted Novolog
    - Dinner: 8 units diluted Humalog
    - Before bed: 0.5 units Tresiba (long-acting)
    - Adjustments: 0.5 unit diluted Humalog for each additional gram of carbohydrate or 28 grams of protein above my normal meal.
Exercise:
- Total time:
  - 7 h/wk, but the martial-arts classes are fun.
- Strength-training:
  - 2 days/wk, 30 min. bodyweight upper-body and core
  - 2 days/wk, 30 min. dumbbell upper-body and core
  - 4 days/wk 15 min. stationary bike set at high resistance
- Cardio:
  - 2 days/wk, 1 h mixed-martial arts (high intensity)
  - 2 days/wk, 1 h kung-fu (low intensity)
  - 4 days/wk 15 min. stationary bike set at high resistance (same as under strength-training)

– QD

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