To figure out what's really going on, we decided to do a communal self-experiment. Over the past two weeks, 8 Redditor with diabetes have been measuring their blood glucose before and after showering. So far, we have 22 measurements, so I thought it would be useful to post an initial exploratory analysis of the data to see if the wider community had an insights or suggestions.
In the comments, please chime in with any thoughts, additional analyses, or questions. If there's any graph, calculation, etc. you'd like to see, let me know and I'll add it. We also need more experimenters, so if your interested, let me know.
Highlights:
Initial indications are that we are seeing a real and consistent increase in BG from hot showers, not a sensor artifact.
So far, we are not seeing a clear person-to-person variation in the effect (more data needed).
There's some very tentative but interesting trends in the data:
In order to get a clear answer on person-to-person variation and to better pull out any correlations, we need more data, especially repeat data from more people. If you're interested in joining the experiment, let me know.
DETAILS:
Method: Protocol here. All data was converted into consistent units and put into an excel spreadsheet. From the raw data, I calculated change in BG from start of shower, as well as the largest relative change, and the time until largest relative change (see spreadsheet for calculation details). Visualization was done using
Tableau.
Analysis:
First, let's look at the big question: are we seeing an effect? For this question, I plotted largest observed change over the 1 hour monitoring period for each shower as measured by both BGM and CGM.
Max ĪBGM & ĪCGM for each shower, colored by experimenter.
Looking at the graphs you can see the following:
We are seeing a measurable rise in blood sugar from a hot shower.
The effect is approximately the same size when measured by BGM vs. CGM, suggesting it's not a sensor artifact
We're not (yet) seeing a clear person-to-person variation. For both BGM and CGM, with the exception of 1 outlier in each case, there's a pretty consistent increase in BG after a shower.
Interestingly, while we consistently see an increase in BG after showering, the timing of that increase is much more variable. If instead of looking at Max ĪBG over the monitoring period, you look at ĪBG 15 minutes after the shower, you get:
ĪBGM & ĪCGM@15 min. for each shower, colored by experimenter.
While we still see the effect, it's a a lot more variable, especially in the BGM measurements.
Next, even though there's not enough data for solid conclusions, I thought it'd be interesting to see if there was any interesting patterns/correlations in the data. I looked at:
ĪCGM@15 min. vs. ĪBGM@15 min. - only three data points, so can't really say anything
Max ĪCGM vs. Max ĪBGM - two data points, can't say anything
Max ĪBGM vs. hour of the day - no trend across the whole data set, but within Experimenter H's, there's an indication of a greater rise later in the day (R2 = 0.40, p = 0.08)
Max ĪCGM vs. hour of the day - no clear trend across the whole data set, nor within experimenters
Max ĪBGM vs. starting BGM - no trend across the whole data set, but within Experimenter H's data, there's an indication of a strong negative correlation (R2 = 0.57, p = 0.03).
Max ĪCGM vs. starting CGM - no clear trend across the whole data set, nor within experimenters.
Max ĪBGM vs. hour of the day, colored by experimenter. Data from Experimenter H highlighted, showing a clearing increasing trend (R2 = 0.4, p = 0.08)
Max ĪBGM vs. initial BGM, colored by experimenter. Data from Experimenter H highlighted, showing a clearing decreasing trend (R2 = 0.57, p = 0.03)
