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It’s great to have a new sensor and a new set of data to play with. Simply having a curious mind might be enough to justify an investment in Continuous Glucose Monitoring tech. However both you and I are probably interested in the Supersapiens Biosensor from the perspective of if and how it can help our training and performance.
Off-Hours vs Glucose Performance Zones & Low-Intensity Zones
The Supersapiens app gives a continuous 24×7 reading of your Glucose levels.
The meaning of any one particular reading depends on context. That context can be whether you are exercising or not. It can be whether you are intensely exercising or lightly exercising. And the context of whether or not the value is rising or falling in relation to the workout average is important too. Even more complexity and nuance come in when you consider glucose averages from before and after your workout.
I’m guessing that for a non-sports person, the job of understanding your Glucose is easier and you probably just need to know how your body reacts to certain nutritional stimuli. Mostly carbs and any insulin response that follows.
From a lifestyle point of view, that information is important for an athlete too who simply wants to better understand and regulate aspects of their body. For this purpose, Supersapiens has an Off-Hours range and the highs and lows of this typical range are continuously shown on the app. Relatively obvious things happen like your reading going above the upper limit of 30-minutes, or so, after having a piece of cake. I was expecting less obvious things to be shown as well, For example, I’d assumed that my late afternoon nap would be explained by low blood sugar levels but instead, the reality seems to be that I’m just getting older!
Turning to in-workout data, there is much more complexity to comprehend.
One of the initial things I’ve tried to understand over the last week is the degree to which I am properly fuelling my workouts. That involved performing similar workouts in differently fuelled states ie when not fuelled; when I think I’m fuelled; and then if and when I’ve made any corrections to my fuelling. I also might have to do those same tests for low-intensity supposedly fat-burning workouts and harder ones as well as for maximal efforts. That’s a lot of tests and I certainly need to do more.
Supersapiens determine a whole spectrum of Glucose levels but the two main types are low-intensity zones and higher-intensity Performance Zones.
A Non-Fuelled, Low-Intensity Workout
My understanding is that endurance workouts should be mostly fuelled by fat-burning and that a relatively stable and low glucose reading should be expected. Indeed that’s what I found in this easy run of 70 minutes or so.
In this case, Supersapiens automatically uploaded my run from the Apple Watch 6 and then performed its Glucose analysis over the duration of the workout.
My glucose reading dropped during the run but the drop would be ‘normal’ from the point of view of a regular day. However viewed in the context of the workout (second image) the glucose levels were low and they weren’t low because I was using all the glucose up, I simply didn’t need much of it. So the third image contextualises this in Supersapiens Low-Intensity Zones and shows other info like, for example, the low variability of the reading. Fine.
A thought: Perhaps straying into your true Zone 3 (of 5 zones) would be revealed by an uptick in glucose levels?
Take Out: From these kinds of tests I am fairly happy that they probably are the fat burning workouts I intended them to be. If there was more variability or if the glucose dropped really low then I might have something to look closer at but these seem cool to me. There may well be an argument that I could still have kept the glucose levels higher with some fuelling and that might have aided recovery.
The same workout sync functionality has just gone live for Training Peaks too.
A Fasted, Non-Fuelled 150-Minute Ride With Hard Efforts
This ride is one that you know you should fuel. For testing purposes, I didn’t fuel it at all.
The previous day I only had very light exercise and so I wasn’t at all fatigued for this ride and felt good in that sense and I did some decent performances over a few 3-7 minutes long Strava segments.
Take Out: I almost certainly would have performed even better if I fuelled this workout. Considering I performed fairly well, I was surprised that I was able to do that with low levels of fuelling.
I would expect glucose levels to rise during my Tempo efforts but for the VO2max efforts, I would expect Glucose supply to fall behind consumption and for the curve to fall, which it looks like it did.
The whole Glucose chart could have been shifted upwards by pre-fuelling and mid-ride fuelling. That would probably have helped me during the VO2 efforts and probably also helped me make the ride home easier than it was.
A Fuelled 40-minute effort
This was a hard tempo/threshold effort for the first half of the workout with the last half recorded as a recovery ride home.
The first image shows the fuelling events before and during the ride and then the same glucose curve is given context in the second image where the Glucose Performance Zone is shown. As you can see the fuelling stands me in good order at the start of the hard effort. I’m assuming that the glucose levels start to fall sharply toward the end of the hard effort rather before recovering as I rode home.
I probably ate something as soon as I arrived home as well but that’s not shown as an event on the first image.
The third and fourth images show my glucose levels in the workout as sub-zones and also in the context of average and of pre-event levels.
Take Out: The broad positive effect of fuelling is clear when this workout is compared to the previous one. Glucose levels are notably higher and always in the performance zone. Possibly, I had my second gel slightly too early? It might also show that my post-ride recovery nutrition kept the Glucose levels too high?
We all know that we need to fuel to perform and to recover, yet perhaps some of us might be more guilty than others at sometimes taking fuelling less seriously over shorter rides. Having this data gives clarity to your fuelling status and performance. There is simply no avoiding the reality of the data. Perhaps the glucose performance zones are less useful when they include significant recovery periods like this ride did.
Had I followed standard advice I’m guessing I would have fuelled this correctly. For me, it’s probably the 2-5 hour rides where I’m not sure of my nutritional needs.
More Info: supersapiens.com
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