Understanding the Difference Between Open Source and DIY in Diabetes

There’s been a lot of excitement (yay!) about the results of the CREATE trial being published in NEJM, followed by the presentation of the continuation results at EASD. This has generated a lot of blog posts, news articles, and discussion about what was studied and what the implications are.

One area that I’ve noticed is frequently misunderstood is how “open source” and “DIY” are different.

Open source means that the source code is openly available to view. There are different licenses with open source; most allow you to also take and reuse and modify the code however you like. Some “copy-left” licenses commercial entities to open-source any software they build using such code. Most companies can and do use open source code, too, although in healthcare most algorithms and other code related to FDA-regulated activity is proprietary. Most open source licenses allow free individual use.

For example, OpenAPS is open source. You can find the core code of the algorithm here, hosted on Github, and read every line of code. You can take it, copy it, use it as-is or modify it however you like, because the MIT license we put on the code says you can!

As an individual, you can choose to use the open source code to “DIY” (do-it-yourself) an automated insulin delivery system. You’re DIY-ing, meaning you’re building it yourself rather than buying it or a service from a company.

In other words, you can DIY with open source. But open source and DIY are not the same thing!

Open source can and is usually is used commercially in most industries. In healthcare and in diabetes specifically, there are only a few examples of this. For OpenAPS, as you can read in our plain language reference design, we wanted companies to use our code as well as individuals (who would DIY with it). There’s at least one commercial company now using ideas from the OpenAPS codebase and our safety design as a safety layer against their ML algorithm, to make sure that the insulin dosing decisions are checked against our safety design. How cool!

However, they’re a company, and they have wrapped up their combination of proprietary software and the open source software they have implemented, gotten a CE mark (European equivalent of FDA approval), and commercialized and sold their AID product to people with diabetes in Europe. So, those customers/users/people with diabetes are benefitting from open source, although they are not DIY-ing their AID.

Outside of healthcare, open source is used far more pervasively. Have you ever used Zoom? Zoom uses open source; you then use Zoom, although not in a DIY way. Same with Firefox, the browser. Ever heard of Adobe? They use open source. Facebook. Google. IBM. Intel. LinkedIn. Microsoft. Netflix. Oracle. Samsung. Twitter. Nearly every product or service you use is built with, depends on, or contains open source components. Often times open source is more commonly used by companies to then provide products to users – but not always.

So, to more easily understand how to talk about open source vs DIY:

  • The CREATE trial used a version of open source software and algorithm (the OpenAPS algorithm inside a modified version of the AndroidAPS application) in the study.
  • The study was NOT on “DIY” automated insulin delivery; the AID system was handed/provided to participants in the study. There was no DIY component in the study, although the same software is used both in the study and in the real world community by those who do DIY it. Instead, the point of the trial was to study the safety and efficacy of this version of open source AID.
  • Open source is not the same as DIY.
  • OpenAPS is open source and can be used by anyone – companies that want to commercialize, or individuals who want to DIY. For more information about our vision for this, check out the OpenAPS plain language reference design.
Venn diagram showing a small overlap between a bigger open source circle and a smaller DIY circle. An arrow points to the overlapping section, along with text of "OpenAPS". Below it text reads: "OpenAPS is open source and can be used DIY. DIY in diabetes often uses open source, but not always. Not all open source is used DIY."

Continuation Results On 48 Weeks of Use Of Open Source Automated Insulin Delivery From the CREATE Trial: Safety And Efficacy Data

In addition to the primary endpoint results from the CREATE trial, which you can read more about in detail here or as published in the New England Journal of Medicine, there was also a continuation phase study of the CREATE trial. This meant that all participants from the CREATE trial, including those who were randomized to the automated insulin delivery (AID) arm and those who were randomized to sensor-augmented insulin pump therapy (SAPT, which means just a pump and CGM, no algorithm), had the option to continue for another 24 weeks using the open source AID system.

These results were presented by Dr. Mercedes J. Burnside at #EASD2022, and I’ve summarized her presentation and the results below on behalf of the CREATE study team.

What is the “continuation phase”?

The CREATE trial was a multi-site, open-labeled, randomized, parallel-group, 24-week superiority trial evaluating the efficacy and safety of an open-source AID system using the OpenAPS algorithm in a modified version of AndroidAPS. Our study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14 percentage points higher among those who used the open-source AID system (95% confidence interval [CI], 9.2 to 18.8; P<0.001) compared to those who used sensor augmented pump therapy; a difference that corresponds to 3 hours 21 minutes more time spent in target range per day. The system did not contribute to any additional hypoglycemia. Glycemic improvements were evident within the first week and were maintained over the 24-week trial. This illustrates that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID. This initial study concluded that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS, a widely used open-source AID solution, is efficacious and safe. These results were from the first 24-week phase when the two groups were randomized into SAPT and AID, accordingly.

The second 24-week phase is known as the “continuation phase” of the study.

There were 52 participants who were randomized into the SAPT group that chose to continue in the study and used AID for the 24 week continuation phase. We refer to those as the “SAPT-AID” group. There were 42 participants initially randomized into AID who continued to use AID for another 24 weeks (the AID-AID group).

One slight change to the continuation phase was that those in the SAPT-AID used a different insulin pump than the one used in the primary phase of the study (and 18/42 AID-AID participants also switched to this different pump during the continuation phase), but it was a similar Bluetooth-enabled pump that was interoperable with the AID system (app/algorithm) and CGM used in the primary outcome phase.

All 42 participants in AID-AID completed the continuation phase; 6 participants (out of 52) in the SAPT-AID group withdrew. One withdrew from infusion site issues; three with pump issues; and two who preferred SAPT.

What are the results from the continuation phase?

In the continuation phase, those in the SAPT-AID group saw a change in time in range (TIR) from 55±16% to 69±11% during the continuation phase when they used AID. In the SAPT-AID group, the percentage of participants who were able to achieve the target goals of TIR > 70% and time below range (TBR) <4% increased from 11% of participants during SAPT use to 49% during the 24 week AID use in the continuation phase. Like in the primary phase for AID-AID participants; the SAPT-AID participants saw the greatest treatment effect overnight with a TIR difference of 20.37% (95% CI, 17.68 to 23.07; p <0.001), and 9.21% during the day (95% CI, 7.44 to 10.98; p <0.001) during the continuation phase with open source AID.

Those in the AID-AID group, meaning those who continued for a second 24 week period using AID, saw similar TIR outcomes. Prior to AID use at the start of the study, TIR for that group was 61±14% and increased to 71±12% at the end of the primary outcome phase; after the next 6 months of the continuation phase, TIR was maintained at 70±12%. In this AID-AID group, the percentage of participants achieving target goals of TIR >70% and TBR <4% was 52% of participants in the first 6 months of AID use and 45% during the continuation phase. Similarly to the primary outcomes phase, in the continuation phase there was also no treatment effect by age interaction (p=0.39).

The TIR outcomes between both groups (SAPT-AID and AID-AID) were very similar after each group had used AID for 24 weeks (SAPT-AID group using AID for 24 weeks during the continuation phase and AID-AID using AID for 24 weeks during the initial RCT phase).. The adjusted difference in TIR between these groups was 1% (95% CI, -4 to 6; p=-0.67). There were no glycemic outcome differences between those using the two different study pumps (n=69, which was the SAPT-AID user group and 18 AID-AID participants who switched for continuation; and n=25, from the AID-AID group who elected to continue on the pump they used in the primary outcomes phase).

In the initial primary results (first 24 weeks of trial comparing the AID group to the SAPT group), there was a 14 percentage point difference between the groups. In the continuation phase, all used AID and the adjusted mean difference in TIR between AID and the initial SAPT results was a similar 12.10 percentage points (95% CI, p<0.001, SD 8.40).

Similar to the primary phase, there was no DKA or severe hypoglycemia. Long-term use (over 48 weeks, representing 69 person-years) did not detect any rare severe adverse events.

CREATE results from the full 48 weeks on open source AID with both SAPT (control) and AID (intervention) groups plotted on the graph.

Conclusion of the continuation study from the CREATE trial

In conclusion, the continuation study from the CREATE trial found that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS is efficacious and safe with various hardware (pumps), and demonstrates sustained glycaemic improvements without additional safety concerns.

Key points to takeaway:

  • Over 48 weeks total of the study (6 months or 24 weeks in the primary phase; 6 months/24 weeks in the continuation phase), there were 64 person-years of use of open source AID in the study, compared to 59 person-years of use of sensor-augmented pump therapy.
  • A variety of pump hardware options were used in the primary phase of the study among the SAPT group, due to hardware (pump) availability limitations. Different pumps were also used in the SAPT-AID group during the AID continuation phase, compared to the pumps available in the AID-AID group throughout both phases of trial. (Also, 18/42 of AID-AID participants chose to switch to the other pump type during the continuation phase).
  • The similar TIR results (14 percentage points difference in primary and 12 percentage points difference in continuation phase between AID and SAPT groups) shows durability of the open source AID and algorithm used, regardless of pump hardware.
  • The SAPT-AID group achieved similar TIR results at the end of their first 6 months of use of AID when compared to the AID-AID group at both their initial 6 months use and their total 12 months/48 weeks of use at the end of the continuation phase.
  • The safety data showed no DKA or severe hypoglycemia in either the primary phase or the continuation phases.
  • Glycemic improvements from this version of open source AID (the OpenAPS algorithm in a modified version of AndroidAPS) are not only immediate but also sustained, and do not increase safety concerns.
CREATE Trial Continuation Results were presented at #EASD2022 on 48 weeks of use of open source AID

Wondering about the “how” rather than the “why” of autoimmune conditions

I’ve been thinking a lot about stigma, per a previous post of mine, and how I generally react to, learn about, and figure out how to deal with new chronic diseases.

I’ve observed a pattern in my experiences. When I suspect an issue, I begin with research. I read medical literature to find out the basics of what is known. I read a high volume of material, over a range of years, to see what is known and the general “ground truth” about what has stayed consistent over the years and where things might have changed. This is true for looking into causal mechanisms as well as diagnosis and then more importantly to me, management/treatment.

I went down a new rabbit hole of research and most articles were publicly accessible

A lot of times with autoimmune related diseases…the causal mechanism is unknown. There are correlations, there are known risk factors, but there’s not always a clear answer of why things happen.

I realize that I am lucky that my first “thing” (type 1 diabetes) was known to be an autoimmune condition, and that probably has framed my response to celiac disease (6 years later); exocrine pancreatic insufficiency (19+ years after diabetes); and now Graves’ disease (19+ years after diabetes). Why do I think that is lucky? Because when I’m diagnosed with an autoimmune condition, it’s not a surprise that it IS an autoimmune condition. When you have a nicely overactive immune system, it interferes with how your body is managing things. In type 1 diabetes, it eventually makes it so the beta cells in your pancreas no longer produce insulin. In celiac, it makes it so the body has an immune reaction to gluten, and the villi in your small intestine freak out at the microscopic, crumb-level presence of gluten (and if you keep eating gluten, can cause all sorts of damage). In exocrine pancreatic insufficiency, there is possibly either atrophy as a result of the pancreas not producing insulin or other immune-related responses – or similar theories related to EPI and celiac in terms of immune responses. It’s not clear ‘why’ or which mechanism (celiac, T1D, or autoimmune in general) caused my EPI, and not knowing that doesn’t bother me, because it’s clearly linked to autoimmune shenanigans. Now with Graves’ disease, I also know that low TSH and increased thyroid antibodies are causing subclinical hyperthyroidism symptoms (such as occasional minor tremor, increased resting HR, among others) and Graves’ ophthalmology symptoms as a result of the thyroid antibodies. The low TSH and increased thyroid antibodies are a result of my immune system deciding to poke at my thyroid.

All this to say…I typically wonder less about “why” I have gotten these things, in part because the “why” doesn’t change “what” to do; I simply keep gathering new data points that I have an overactive immune system that gives me autoimmune stuff to deal with.

I have contrasted this with a lot of posts I observe in some of the online EPI groups I am a part of. Many people get diagnosed with EPI as a result of ongoing GI issues, which may or may not be related to other conditions (like IBS, which is often a catch-all for GI issues). But there’s a lot of posts wondering “why” they’ve gotten it, seemingly out of the blue.

When I do my initial research/learning on a new autoimmune thing, as I mentioned I do look for causal mechanisms to see what is known or not known. But that’s primarily, I think, to rule out if there’s anything else “new” going on in my body that this mechanism would inform me about. But 3/3 times (following type 1 diabetes, where I first learned about autoimmune conditions), it’s primarily confirmed that I have autoimmune things due to a kick-ass overactive immune system.

What I’ve realized that I often focus on, and most others do not, is what comes AFTER diagnosis. It’s the management (or treatment) of, and living with, these conditions that I want to know more about.

And sadly, especially in the latest two experiences (exocrine pancreatic insufficiency and Graves’ disease), there is not enough known about management and optimization of dealing with these conditions.

I’ve previously documented and written quite a bit (see a summary of all my posts here) about EPI, including my frustrations about “titrating” or getting the dose right for the enzymes I need to take every single time I eat something. This is part of the “management” gap I find in research and medical knowledge. It seems like clinicians and researchers spend a lot of time on the “why” and the diagnosis/starting point of telling someone they have a condition. But there is way less research about “how” to live and optimally manage these things.

My fellow patients (people with lived experiences) are probably saying “yeah, duh, and that’s the power of social media and patient advocacy groups to share knowledge”. I agree. I say that a lot, too. But one of the reasons these online social media groups are so powerful in sharing knowledge is because of the black hole or vacuum or utter absence of research in this space.

And it’s frustrating! Social media can be super powerful because you can learn about many n=1 experiences. If you’re like me, you analyze the patterns to see what might be reproducible and what is worth experimenting in my own n=1. But often, this knowledge stays in the real world. It is not routinely funded, studied, operationalized, and translated in systematic ways back to healthcare providers. When patients are diagnosed, they’re often told the “what” and occasionally the “why” (if it exists), but left to sometimes fall through the cracks in the “how” of optimally managing the new condition.

(I know, I know. I’m working on that, in diabetes and EPI, and I know dozens of friends, both people with lived experiences and researchers who ARE working on this, from diabetes to brain tumors to Parkinson’s and Alzheimer’s and beyond. And while we are moving the needles here, and making a difference, I’m wanting to highlight the bigger issue to those who haven’t previously been exposed to the issues that cause the gaps we are trying to fill!)

In my newest case of Graves’ disease, it presented with subclinical hyperthyroidism. As I wrote here, that for me means the lower TSH and higher thyroid antibodies but in range T3 and T4. In discussion with my physician, we decided to try an antithyroid drug, to try to lower the antibody levels, because the antibody levels are what cause the related eye symptoms (and they’re quite bothersome). The other primary symptom I have is higher resting HR, which is also really annoying, so I’m also hoping it helps with that, too. But the game plan was to start taking this medication every day; and get follow-up labs in about 2 months, because it takes ~6 weeks to see the change in thyroid levels.

Let me tell you, that’s a long time. I get that the medication works not on stored thyroid levels; thus, it impacts the new production only, and that’s why it takes 6 weeks to see it in the labs because that’s how long it takes to cycle through the stored thyroid stuff in your body.

My hope was that within 2-3 weeks I would see a change in my resting HR levels. I wasn’t sure what else to expect, and whether I’d see any other changes.

But I did.

It was in the course of DAYS, not weeks. It was really surprising! I immediately started to see a change in my resting HR (across two different wearable devices; a ring and a watch). Within a week, my phone’s health flagged it as a “trend”, too, and pinpointed the day (which it didn’t know) that I had started the new medication based on the change in the trending HR values.

Additionally, some of my eye symptoms went away. Prior to commencing the new medication, I would wake up and my eyes would hurt. Lubricating them (with eye drops throughout the day and gel before bed) helped some, but didn’t really fix the problem. I also had pretty significant red, patchy spots around the outside corner of one of my eyes, and eyelid swelling that would push on my eyeball. 4 days into the new medication, I had my first morning where I woke up without my eyes hurting. The next day it returned, and then I had two days without eye pain. Then I had 3-4 days with the painful eyes. Then….now I’m going on 2 weeks without the eye pain?! Meanwhile, I’m also tracking the eye swelling. It went down to match the eye pain going away. But it comes back periodically. Recently, I commented to Scott that I was starting to observe the pattern that the red/patchy skin at the corner and under my right eye would appear; then the next day the swelling of and above the eyelid would return. After 1-2 days of swelling, it would disappear. Because I’ve been tracking various symptoms, I looked at my data the other day and saw that it’s almost a 6-7 day pattern.

Interesting!

Again, the eye stuff is a result of antibody levels. So now I am curious about the production of antibodies and their timeline, and how that differs from TSH and thyroid hormones, and how they’re impacted with this drug.

None of that is information that is easy to get, so I’m deep in the medical literature trying again to find out what is known, whether this type of pattern is known; if it’s common; or if this level of data, like my within-days impact to resting HR change is new information.

Most of the research, sadly, seems to be on pre-diagnosis or what happens if you diagnose someone but not give them medication in hyperthyroid. For example, I found this systematic review on HRV and hyperthyroid and got excited, expecting to learn things that I could use, but found they explicitly removed the 3 studies that involved treating hyperthyroidism and are only studying what happens when you don’t treat it.

Sigh.

This is the type of gap that is so frustrating, as a patient or person who’s living with this. It’s the gap I see in EPI, where little is known on optimal titration and people don’t get prescribed enough enzymes and aren’t taught how to match their dosing to what they are eating, the way we are taught in diabetes to match our insulin dosing to what we’re eating.

And it matters! I’m working on writing up data from a community survey of people with EPI, many of whom shared that they don’t feel like they have their enzyme dosing well matched to what they are eating, in some cases 5+ years after their diagnosis. That’s appalling, to me. Many people with EPI and other conditions like this fall through the cracks with their doctors because there’s no plan or discussion on what managing optimally looks like; what to change if it’s not optimal for a person; and what to do or who to talk to if they need help managing.

Thankfully in diabetes, most people are supported and taught that it’s not “just” a shot of insulin, but there are more variables that need tracking and managing in order to optimize wellbeing and glucose levels when living with diabetes. But it took decades to get there in diabetes, I think.

What would it be like if more chronic diseases, like EPI and Graves’ disease (or any other hyper/hypothyroid-related diseases), also had this type of understanding across the majority of healthcare providers who treated and supported managing these conditions?

How much better would and could people feel? How much more energy would they have to live their lives, work, play with their families and friends? How much more would they thrive, instead of just surviving?

That’s what I wonder.

Wondering "how" rather than "why" of autimmune conditions, by @DanaMLewis from DIYPS.org

What is in my running pack for running ultramarathons or training for a marathon

After three years of using a multi-purpose activity backpack as my running pack, the strap connector broke, and I had to find and re-stock a new running pack. I use a running pack for when I’m doing long runs for marathon or ultramarathon training.  I ended up pulling everything out of my old backpack and evaluating whether I still wanted to carry it on every long run. For the most part, everything got moved over to the new pack. There were a few cases where I had excessive duplicates (more on that below and why) where I ended up reducing the quantity. But everything else made the list for what I carry with me on long runs every single time.

  1. Hydration – via a camelbak or other bladder with a hose (example). I prefer straight water in my hydration pack and to separately manage electrolytes and fuel separately. The bonus of just having water is it’s easier to clean the hydration pack after each run!Tips: put ice cubes in your bladder and fill it with cold water. Cold water is awesome for long, hot runs in the sun. Also, my old hydration pack had an insulated compartment that kept the ice water cold for hours. My new running vest does not, and in fact has holes in the back for air flow that also means the heat from my back melts my ice pretty fast. To work around this in the new vest is to slide the filled hydration bladder into a padded mailing envelope that’s open at the top. It’s not quite as insulated as true insulation, but it protects the bladder from some of the heat coming off of your back and it probably stays cool 60% instead of 20% as long as before, which is a huge improvement.Extra tip: use a Qtip or similar to clean out the mouthpiece of your hose every few runs!
  2. Diabetes backups  – this means things like a backup insulin pump site. On long unsupported runs, it can also mean my blood glucose meter. (I wear a CGM so I don’t always take a meter along on runs unless it’s in an unsupported area where I don’t have easy crew access or support within a few miles). I’ve had several runs where my pump site has stopped working or ripped out, so having a backup pump site is just as necessary as having bandaids.The other source of backups is extra low carbs, e.g. sugar in case my blood sugar goes low. I usually keep a stash of carbs in my shorts pocket, but I also keep extra in my backpack in case I run through everything in my pocket. This is in addition to regular food/fuel for ultrafueling, it has to be faster-acting glucose/sugar that can more quickly fix a dropping or already-low blood sugar level.(This is one of the places I mentioned where I had excessive duplicates. I have continued to add extra to my backup stashes, and ended up with well over 100+ grams of “backup” carbs just in case. I ended up cutting down the total amount of carbs to closer to ~50 grams instead.)

    Emergency backup carbs maybe don't need to be 100g worth

    You can read some more about my strategy for running with diabetes here.

  3. Baggie with extra socks – I always carry a pair of extra socks, although I’ve never needed them in a normal training long run, I did end up using them in my 50k that involved crossing a river up to my knees five times.
  4. Bandaids – Just like hiking, but I carry bandaids in case of bleeding cuts or scratches or worse, blisters on my heels, feet, or toes. I carry some that are blister-style and some regular style, smaller ones and larger ones, all the way up to large multi-inch squares that can cover the backs of my heels if I don’t already have them covered.More recently, I also started carrying small squares and strips of kinesiology tape for the same purpose. I originally did kinesio tape strips in case my knee needed some extra support, but I’ve found the kinesio tape also works well to cover my toes or backs of my heels in lieu of bandaids for blister prevention. For fixing blisters, I have to dry my feet really well or the kinesio tape doesn’t stay well or easily rubs off; so I tend to cover the toes that blister frequently as well as my heels prior to my runs so they’re less likely to generate blisters and require fixing mid-runs. I get a large roll of kinesiology tape (example) and cut it into smaller pieces as needed for all of these uses cases.I also keep at least one mini individual packet of antibiotic ointment (example) in the baggie as well.
  5. Lubrication – I carry a lubrication stick (Squirrel Nut Butter, because it works for me and is easy to reapply) to making sure between my thighs and other areas don’t chafe. When I sweat a lot, I often have to reapply every few hours to my thighs. While this can also be accomplished by carrying dabs of vaseline or your preferred lubrication in a baggie, the SNB stick is lightweight and I don’t mind carrying it so it’s easy to reapply and the hassle doesn’t prevent me from wanting to prevent chafing.
  6. Stuff to fix GI problems – it’s common to have GI issues when running, but I also had a two-year stretch of known GI issues that ultimately turned out to be undiscovered exocrine pancreatic insufficiency. During this time, I always carried individual Immodium and GasX in case I needed them.
  7. Electrolyte pills – I prefer to measure and track electrolytes separate from my hydration, so I use electrolyte pills (example) that I swallow on a scheduled basis to keep my electrolyte levels topped off. I’ve tried chew kinds (but they make me burp), so I stick with a baggie full of electrolyte pills. I bring extra just in case I drop some, but I generally eyeball and count out to make sure I have enough for each super long run.
  8. Any medication you need during the run – For me, that includes enzymes for fuel because I have exocrine pancreatic insufficiency and I need enzymes to help me digest any of my fuel. I have expensive, larger dose prescription pills that I usually use for meals, but it would make running even more expensive if I had to use a $9 pill every 30 minutes for a fuel snack. Luckily, there are over the counter versions of enzyme pills (more about that here) that are single-enzyme or multi-enzyme, that are more in the ballpark of $0.35 per pill, and I have a baggie of both kinds that I use to cover each snack.
  9. Fuel or snacks – A lot of ultra runners use gels, but I have been experimenting with ‘real’ foods. Basically, anything that’s around ~20g of carbs and less than ~10g of fat and 5-10g of protein that I like to eat. So far, that list includes chili Cheese Fritos, yogurt covered pretzels, peanut butter pretzel nuggets, beef sticks, Honey Stinger Stroopwaffles (the gluten free kinds – beware that only some of their flavors are GF!), mini date or fruit bars, fruit snacks, sweet potato tots, ¼ of a ham and cheese quesadilla, ¼ of a PBJ sandwich, a waffle, mini PayDay bars…. Noting that all of these are gluten free versions or are naturally gluten free, because I have celiac disease. I do a lot of work in advance to test these snacks carefully on training runs before I commit to using them repeatedly throughout longer runs so I know my body likes them during runs as well as other times. I only take the fresh/hot snacks (sweet potato tots, quesadilla etc) and eat those at the start or when my husband re-fills my pack for me mid-run, so I don’t have to worry about them spoiling. Everything else is shelf stable so when I pack a few more than I need per run and leave some in my pack, they’re not an issue to sit there for weeks until I manage to eat them in my rotation of snacks on a future run.
  10. Miscellaneous other supplies – car keys, house keys, hand sanitizer, a mask for going into trail bathrooms, and a battery and cord for charging my phone.

Phew. That’s a lot of stuff. And yes, it does end up being more supplies and more weight than most people carry. But…I use pretty much everything in my pack every few runs. Stuff happens: pump sites fall out, blisters happen, chafing happens, GI stuff happens..and I’ve found that training and running with a little extra weight in my pack is worth having the proper supplies when I need them, rather than having to end runs early due to lack of preparation or minor supplies that would enable me to keep running.

Every time I go out for a run, I add the requisite amount of snacks, enzymes, electrolyte pills, and hydration for the run. Any time I come back from a run and I have depleted a supply off of the above list – such as using my backup pump site – I immediately go and refill that supply so I don’t have to remember to refill it prior to the next run. Keeping the above supplies topped off and ready to go always in my backpack means they’re always there when I need them, and the peace of mind of knowing how I can handle and that I can handle these situations while running is priceless.

Note: previously I was using a backpack, because it was $30 and for my running it was good enough. However, when the strap broke, I looked to buy the same backpack again and it was $60. It was fine for $30 but if I was going to double the cost, I decided to research alternative running packs and vests. Vests seem to be more common in ultrarunners, so I looked for those, although they’re a lot more expensive (often $125-200). I was disappointed with how small of a volume some of them held, or they were just ugly. I liked the look of a purple one I found that came with a 1.5L bladder….but ugh. I fit a 3L bladder in my previous backpack and typically fill it 2-2.5L full as a baseline, and all the way up for a longer (6h+) unsupported run. I decided to risk getting this vest even though it was smaller and try putting my larger 3L capacity bladder in the new vest. (Luckily it was on sale for $90 at the time  which made it a little less annoying to buy compared to a $150 one.) The bladder does fit, but it sticks out the top and hits the back of my neck if it’s all the way full (3L). So for the most part, I’m filling the 3L capacity bladder about 2L full (and as noted in this post earlier, putting it inside an insulated envelope to help retain the cold for longer), and that works for me.

One thing I do like a lot from my new running vest is the front pockets. My old backpack I had to partially take off and twist around me in order to get snacks out. With two large front pockets, I can fit several hours of fuel in there so there is no twisting involved to get my fuel out, which is helping with my goal to fuel every 30 minutes. I do wish there was a separate smaller pouch – my old backpack had a small old school flip phone size “cell phone” pocket that I used to keep my baggies of enzymes and electrolytes in. Right now, I just have those baggies floating around the top of those pockets and it’s fairly easily to grab and pull out the right baggie, but I’m toying with adding some kind of small strap-on holster/pouch to the shoulder just for enzymes so I don’t have to worry as much about them jostling out when my pockets are completely full of snacks. But otherwise, these front pockets are overall a nice improvement.

A purple running vest on the left; supplies described in blog post in the middle laid out on the ground, and my old purple backpack used for running on the right.
A cat in mid air jumping over the purple runing vest in the left of the picture; another cat sitting to the right of the old purple backpack used for running.
Outtake! Mint jumping over my new running vest and running supplies while Mo looks on from the right next to my old running backpack.
A cat sitting on and sniffing the new smells of a new, purple running vest
Mint helpfully inspected my new running vest as soon as I set it on the ground.

New Research on Glycemic Variability Assessment In Exocrine Pancreatic Insufficiency (EPI) and Type 1 Diabetes

I am very excited to share that a new article I wrote was just published, looking at glycemic variability in data from before and after pancreatic enzyme replacement therapy (PERT) was started in someone with type 1 diabetes with newly discovered exocrine pancreatic insufficiency (EPI or PEI).

If you’re not aware of exocrine pancreatic insufficiency, it occurs when the pancreas no longer produces the amount of enzymes necessary to fully digest food. If that occurs, people need supplementary enzymes, known as pancreatic enzyme replacement therapy (PERT), to help them digest their food. (You can read more about EPI here, and I have also written other posts about EPI that you can find at DIYPS.org/EPI.)

But, like MANY medications, when someone with type 1 diabetes or other types of insulin-requiring diabetes starts taking them, there is little to no guidance about whether these medications will change their insulin sensitivity or otherwise impact their blood glucose levels. No guidance, because there are no studies! In part, this may be because of the limited tools available at the time these medications were tested and approved for their current usage. Also this is likely in part because people with diabetes make up a small fraction of the study participants that most of these medications are tested on. If there are any specific studies on the medications in people with diabetes, these studies likely were done before CGM, so little data is available that is actionable.

As a result, the opportunity came up to review someone’s data who happened to have blood glucose data from a continuous glucose monitor (CGM) as well as a log of what was eaten (carbohydrate entries) prior to commencing pancreatic enzyme replacement therapy. The tracking continued after commencing PERT and was expanded to also include fat and protein entries. As a result, there was a useful dataset to compare the impacts of pancreatic enzyme replacement therapy on blood glucose outcomes and specifically, looking at glycemic variability changes!

(You can read an author copy here of the full paper and also see the supplementary material here, and the DOI for the paper is https://doi.org/10.1177/19322968221108414 . Otherwise, below is my summary of what we did and the results!)

In addition to the above background, it’s worth noting that Type 1 diabetes is known to be associated with EPI. In upwards of 40% of people with Type 1 diabetes, elastase levels are lowered, which in other cases is correlated with EPI. However, in T1D, there is some confusion as to whether this is always the case or not. Based on recent discussions with endocrinologists who treat patients with T1D and EPI (and have patients with lowered elastase that they think don’t have EPI), I don’t think there have been enough studies looking at the right things to assess whether people with T1D and lowered elastase levels would benefit from PERT and thus have EPI. More on this in the future!

Because we now have technology such as AID (automated insulin delivery) and CGM, it’s possible to evaluate things beyond simple metrics of “average blood sugar” or “A1c” in response to taking new medications. In this paper, we looked at some basic metrics like average blood sugar and percent time in range (TIR), but we also did quite a few calculations of variables that tell us more about the level of variability in glucose levels, especially in the time frames after meals.

Methods

This person had tracked carb entries through an open source AID system, and so carb entries and BG data were available from before they started PERT. We call this “pre-PERT”, and selected 4 weeks worth of data to exclude major holidays (as diet is known to vary quite a bit during those times). We then compared this to “post-PERT”, the first 4 weeks after the person started PERT. The post-PERT data not only included BGs and carb entries, but also had fat and protein entries as well as PERT data. Each time frame included 13,975 BG data points.

We used a series of open source tools to get the data (Nightscout -> Nightscout Data Transfer Tool -> Open Humans) and process the data (my favorite Unzip-Zip-CSVify-OpenHumans-data.sh script).

All of our code for this paper is open source, too! Check it out here. We analyzed time in range, TIR 70-180, time out of range, TOR >180, time below range, TBR <70 and <54, the number of hyperglycemic excursions >180. We also calculated total daily dose of insulin, average carbohydrate intake, and average carbohydrate entries per day. Then we calculated a series of variability related metrics such as Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), Coefficient of Variation (CV), Standard Deviation (SD), and J_index (which stresses both the importance of the mean level and variability of glycemic levels).

Results

This person already had an above-goal TIR. Standard of care goal for TIR is >70%; before PERT they had 92.12% TIR and after PERT it was 93.70%. Remember, this person is using an open source AID! TBR <54 did not change significantly, TBR <70 decreased slightly, and TOR >180 also decreased slightly.

More noticeably, the total number of unique excursions above 180 dropped from 40 (in the 4 weeks without PERT) to 26 (in 4 weeks when using PERT).

The paper itself has a few more details about average fat, protein, and carb intake and any changes. Total daily insulin was relatively similar, carb intake decreased slightly post-PERT but was trending back upward by the end of the 4 weeks. This is likely an artifact of being careful to adjust to PERT and dose effectively for PERT. The number of meals decreased but the average carb entry per meal increased, too.

What I find really interesting is the assessment we did on variability, overall and looking at specific meal times. The breakfast meal was identical during both time periods, and this is where you can really SEE visible changes pre- and post-PERT. Figure 2 (displayed below), shows the difference in the rate of change frequency. There’s less of the higher rate of changes (red) post-PERT than there is from pre-PERT (blue).

Figure 2 from GV analysis on EPI, showing lower frequency of high rate of change post-PERT

Similarly, figure 3 from the paper shows all glucose data pre- and post-PERT, and you can see the fewer excursions >180 (blue dotted line) in the post-PERT glucose data.

Figure 3 from GV analysis paper on EPI showing lower number of excursions above 180 mg/dL

Table 1 in the paper has all the raw data, and Figure 1 plots the most relevant graphs side by side so you can see pre- and post-PERT before and after after all meals on the left, versus pre and post-PERT before and after breakfast only. Look at TOR >180 and the reduction in post-breakfast levels after PERT! Similarly, HBGI post-PERT after-breakfast is noticeably different than HBGI pre-PERT after-breakfast.

Here’s a look at the HBGI for breakfast only, I’ve highlighted in purple the comparison after breakfast for pre- and post-PERT:

High Blood Glucose Index (HBGI) pre- and post-PERT for breakfast only, showing reduction in post-PERT after breakfast

Discussion

This is a paper looking at n=1 data, but it’s not really about the n=1 here. (See the awesome limitation section for more detail, where I point out it’s n=1, it’s not a clinical study, the person has ‘moderate’ EPI, there wasn’t fat/protein data from pre-PERT, it may not be representative of all people with diabetes with EPI or EPI in general.)

What this paper is about is illustrating the types of analyses that are possible, if only we would capture and analyze the data. There are gaping holes in the scientific knowledge base: unanswered and even unasked questions about what happens to blood glucose with various medications, and this data can help answer them! This data shows minimal changes to TIR but visible and significant changes to post-meal glycemic variability (especially after breakfast!). Someone who had a lower TIR or wasn’t using an open source AID may have more obvious changes in TIR following PERT commencement.

This paper shows several ways we can more easily detect efficacy of new-onset medications, whether it is enzymes for PERT or other commonly used medications for people with diabetes.

For example, we could do a similar study with metformin, looking at early changes in glycemic variability in people newly prescribed metformin. Wouldn’t it be great, as a person with diabetes, to be able to more quickly resolve the uncertainty of “is this even working?!” and not have to suffer through potential side effects for 3-6 months or longer waiting for an A1c lab test to verify whether the metformin is having the intended effects?

Specifically with regards to EPI, it can be hard for some people to tell if PERT “is working”, because they’re asymptomatic, they are relying on lab data for changes in fat soluble vitamin levels (which may take time to change following PERT commencement), etc. It can also be hard to get the dosing “right”, and there is little guidance around titrating in general, and no studies have looked at titration based on macronutrient intake, which is something else that I’m working on. So, having a method such as these types of GV analysis even for a person without diabetes who has newly discovered EPI might be beneficial: GV changes could be an earlier indicator of PERT efficacy and serve as encouragement for individuals with EPI to continue PERT titration and arrive at optimal dosing.

Conclusion

As I wrote in the paper:

It is possible to use glycemic variability to assess changes in glycemic outcomes in response to new-onset medications, such as pancreatic enzyme replacement therapy (PERT) in people with exocrine pancreatic insufficiency (EPI) and insulin-requiring diabetes. More studies should use AID and CGM data to assess changes in glycemic outcomes and variability to add to the knowledge base of how medications affect glucose levels for people with diabetes. Specifically, this n=1 data analysis demonstrates that glycemic variability can be useful for assessing post-PERT response in someone with suspected or newly diagnosed EPI and provide additional data points regarding the efficacy of PERT titration over time.

I’m super excited to continue this work and use all available datasets to help answer more questions about PERT titration and efficacy, changes to glycemic variability, and anything else we can learn. For this study, I collaborated with the phenomenal Arsalan Shahid, who serves as technology solutions lead at CeADAR (Ireland’s Centre for Applied AI at University College Dublin), who helped make this study and paper possible. We’re looking for additional collaborators, though, so feel free to reach out if you are interested in working on similar efforts or any other research studies related to EPI!

A DIY Fuel Enzyme Macronutrient Tracker for Running Ultras (Ultramarathons)

It takes a lot of energy to run ultramarathons (ultras).

To ensure they have enough fuel to complete the run, people usually want to eat X-Y calories per hour, or A-B carbs per hour, while running ultramarathons. It can be hard to know if you’re staying on top of fueling, especially as the hours drag on and your brain gets tired; plus, you can be throwing away your trash as you go so you may not have a pile of wrappers to tell you what you ate.

During training, it may be useful to have a written record of what you did for each run, so you can establish a baseline and work on improving your fueling if that’s something you want to focus on.

For me specifically, I also find it helpful to record what enzyme dosing I am taking, as I have EPI (exocrine pancreatic insufficiency, which you can read more about here) and if I have symptoms it can help me identify where my dosing might have been off from the previous day. It’s not only the amount of enzymes but also the timing that matters, alongside the timing of carbs and insulin, because I have type 1 diabetes, celiac, and EPI to juggle during runs.

Previously, I’ve relied on carb entries to Nightscout (an open source CGM remote monitoring platform which I use for visualizing diabetes data including OpenAPS data) as a record of what I ate, because I know 15g of carbs tracks to a single serving of chili cheese Fritos that are 10g of fat and 2g of protein, and I take one lipase-only and one pancrelipase (multi-enzyme) pill for that; and 21g of carbs is a Honey Stinger Gluten Free Stroopwaffle that is 6g of fat and 1g of protein, and I typically take one lipase-only. You can see from my most recent ultra (a 50k) where I manually took those carb entries and mapped them on to my blood sugar (CGM) graph to visualize what happened in terms of fuel and blood sugar over the course of my ultra.

However, that was “just” a 50k and I’m working toward bigger runs: a 50 mile, maybe a 100k (62 miles), and/or a 100 mile, which means instead of running for 7-8 hours I’ll be running for 12-14 and 24-30(ish) hours! That’s a lot of fuel to need to eat, and to keep track of, and I know from experience my brain starts to get tired of thinking about and eating food around 7 hours. So, I’ll need something better to help me keep track of fuel, enzymes, and electrolytes over the course of longer runs.

I also am planning on being well supported by my “crew” – my husband Scott, who will e-bike around the course of my ultra or my DIY ultra loops and refill my pack with water and fuel. In some cases, with a DIY ultra, he’ll be bringing food from home that I pre-made and he warms up in the microwave.

One of the strategies I want to test is for him to actually hand me the enzymes for the food he’s bringing me. For example, hand me a baggie of mashed potatoes and also hand me the one multi-enzyme (pancrelipase, OTC) pill I need to go with it. That reduces mental effort for me to look up or remember what enzyme amount I take for mashed potatoes; saves me from digging out my baggie of enzymes and having to get the pill out and swallow it, put the baggie away without dropping it, all while juggling the snack in my hands.

He doesn’t necessarily know the counts of enzymes for each fuel (although he could reproduce it, it’s better if I pre-make a spreadsheet library of my fuel options and that helps me both just pick it off a drop down and have an easy reference for him to glance at. He won’t be running 50-100 miles, but he will be waking up every 2-3 hours overnight and that does a number on his brain, too, so it’s easier all around if he can just reference the math I’ve already done!

So, for my purposes: 1) easy tracking of fuel counts for real-time “am I eating according to plan” and 2) retrospective “how did I do overall and should I do something next time” and 3) for EPI and BG analysis (“what should I do differently if I didn’t get the ideal outcome?”), it’s ideal to have a tracking spreadsheet to log my fuel intake.

Here’s what I did to build my ultimate fuel self-tracking self-populating spreadsheet:

First, I created a tab in my spreadsheet as a “Fuel Library”, where I listed out all of my fuel. This ranges from snacks (chili cheese Fritos; Honey Stinger Gluten Free Stroopwaffle; yogurt-covered pretzels, etc.); to fast-acting carbs (e.g. Airhead Minis, Circus Peanuts) that I take for fixing blood sugars; to other snack/treats like chocolate candy bars or cookies; as well as small meals and warm food, such as tomato soup or part of a ham and cheese quesadilla. (All gluten free, since I have celiac. Everything I ever write about is always gluten free!)

After I input the list of snacks, I made columns to input the sodium, calories, fat, protein, and carb counts. I don’t usually care about calories but a lot of recommendations for ultras are calories/hr and carbs/hr. I tend to be lower on the carb side in my regular daily consumption and higher on fat than most people without T1D, so I’m using the calories for ultrarunning comparison to see overall where I’m landing nutrient-wise without fixating on carbs, since I have T1D and what I personally prefer for BG management is likely different than those without T1D.

I also input the goal amount of enzymes. I have three different types of pills: a prescription pancrelipase (I call PERT, which stands for pancreatic enzyme replacement therapy, and when I say PERT I’m referring to the expensive, prescription pancrelipase that’s been tested and studied for safety and efficacy in EPI); an over-the-counter (OTC) lipase-only pill; and an OTC multi-enzyme pancrelipase pill that contains much smaller amounts of all three enzymes (lipase, protease, amylase) than my PERT but hasn’t been tested for safety and efficacy for EPI. So, I have three enzyme columns: Lipase, OTC Pancrelipase, and PERT. For each fuel I calculate which I need (usually one lipase, or a lipase plus a OTC pancrelipase, because these single servings are usually fairly low fat and protein; but for bigger meal-type foods with more protein I may ‘round up’ and choose to take a full PERT, especially if I eat more of it), and input the number in the appropriate column.

Then, I opened another tab on my spreadsheet. I created a row of headers for what I ate (the fuel); time; and then all the macronutrients again. I moved this down to row 3, because I also want to include at the top of the spreadsheet a total of everything for the day.

Example-DIY-Fuel-Enzyme-Tracker-ByDanaMLewis

In Column A, I selected the first cell (A4) for me, then went to Data > Data Validation and clicked on it. It opens this screen, which I input the following – A4 is the cell I’m in for “cell range”, the criteria is “list from a range”, and then I popped over to the tab with my ‘fuel library’ and highlighted the relevant data that I wanted to be in the menu: Food. So that was C2-C52 for my list of food. Make sure “show dropdown list in cell” is marked, because that’s what creates the dropdown in the cell. Click save.

Use the data validation section to choose to show a dropbox in each cell

You’ll want to drag that down to apply the drop-down to all the cells you want. Mine now looked like this, and you can see clicking the dropdown shows the menu to tap on.

Clicking a dropbox in the cell brings up the "menu" of food options from my Fuel Library tab

After I selected from my menu, I wanted column B to automatically put in the time. This gets obnoxious: google sheets has NOW() to put in the current time, but DO NOT USE THIS as the formula updates with the latest time any time you touch the spreadsheet.

I ended up having to use a google script (go to “Extensions” > Apps Script, here’s a tutorial with more detail) to create a function called onEdit() that I could reference in my spreadsheet. I use the old style legacy script editor in my screenshot below.

Older style app script editor for adding scripts to spreadsheet, showing the onEdit() function (see text below in post for what the script is)

Code I used, if you need to copy/paste:

function onEdit(e) {

var rr = e.range;

var ss = e.range.getSheet();

var headerRows = 2;  // # header rows to ignore

if (rr.getRow() <= headerRows) return;

var row = e.range.getRow();

var col = e.range.getColumn();

if(col == 1){

e.source.getActiveSheet().getRange(row,2).setValue(new Date());

}

}

After saving that script (File > Save), I went back to my spreadsheet and put this formula into the B column cells: =IFERROR(onEdit(),””). It fills in the current date/time (because onEdit tells it to if the A cell has been updated), and otherwise sits with a blank until it’s been changed.

Note: if you test your sheet, you’ll have to go back and paste in the formula to overwrite the date/time that gets updated by the script. I keep the formula without the “=” in a cell in the top right of my spreadsheet so I can copy/paste it when I’m testing and updating my sheet. You can also find it in a cell below and copy/paste from there as well.

Next, I wanted to populate my macronutrients on the tracker spreadsheet. For each cell in row 4, I used a VLOOKUP with the fuel name from A4 to look at the sheet with my library, and then use the column number from the fuel library sheet to reference which data element I want. I actually have things in a different order in my fuel library and my tracking sheet; so if you use my template later on or are recreating your own, pay attention to matching the headers from your tracker sheet and what’s in your library. The formula for this cell ended up being “=IFERROR(VLOOKUP(A4,’Fuel Library’!C:K,4, FALSE),””)”, again designed to leave the column blank if column A didn’t have a value, but if it does have a value, to prefill the number from Column 4 matching the fuel entry into this cell. Columns C-J on my tracker spreadsheet all use that formula, with the updated values to pull from the correctly matching column, to pre-populate my counts in the tracker spreadsheet.

Finally, the last thing I wanted was to track easily when I last ate. I could look at column B, but with a tired brain I want something more obvious that tracks how long it’s been. This also is again to maybe help Scott, who will be tasked with helping me stay on top of things, be able to check if I’m eating regularly and encourage me gently or less gently to be eating more as the hours wear on in my ultras.

I ended up creating a cell in the header that would track the last entry from column B. To do this, the formula I found is “INDEX(B4:B,MATCH(143^143,B4:B))”, which checks for the last number in column B starting in B4 and onward. It correctly pulls in the latest timestamp on the list.

Then, in another cell, I created “=NOW()-B2”, which is a good use for the NOW() formula I warned about, because it’s constantly updating every time the sheet gets touched, so that any time I go to update it’ll tell me how long it’s been since between now and the last time I ate.

But, that only updates every time I update the sheet, so if I want to glance at the sheet, it will be only from the last time I updated it… which is not what I want. To fix it, I need to change the autorefresh calculation settings. Go to File > Settings. Click “Calculations” tab, and the first drop down you want to change to be “On change and every minute”.

Under File > Settings there is a "Calculate" tab with a dropdown menu to choose to update on change plus every minute

Now it does what I want, updating that cell that uses the NOW() formula every minute, so this calculation is up to date even when the sheet hasn’t been changed!

However, I also decided I want to log electrolytes in my same spreadsheet, but not include it in my top “when did I last eat” calculator. So, I created column K and inserted the formula IF(A4=”Electrolytes”,””,B4), which checks to see if the Dropdown menu selection was Electrolytes. If so, it doesn’t do anything. If it’s not electrolytes, it repeats the B4 value, which is my formula to put the date and time. Then, I changed B2 to index and match on column K instead of B. My B2 formula now is INDEX(K4:K,MATCH(143^143,K4:K)), because K now has the food-only list of date and time stamps that I want to be tracking in my “when did I last eat” tracker. (If you don’t log electrolytes or don’t have anything else to exclude, you can keep B2 as indexing and matching on column B. But if you want to exclude anything, you can follow my example of using an additional column (my K) to check for things you do want to include and exclude the ones you don’t want. Also, you can hide columns if you don’t want to see them, so column K (or your ‘check for exclusions’ column wherever it ends up) could be hidden from view so it doesn’t distract your brain.

I also added conditional formatting to my tracker. Anytime A2, the time since eaten cell, is between 0-30 minutes, it’s green: indicating I’m on top of my fueling. 30-45 minutes it turns yellow as a warning that it’s time to eat. After 45 minutes, it’ll turn light red as a strong reminder that I’m off schedule.

I kept adding features, such as totaling my sodium consumption per hour, too, so I could track electrolytes+fuel sodium totals. Column L gets the formula =IF(((ABS((NOW()-B4))*1440)<60),F4,””) to check for the difference between the current time and the fuel entry, multiplying it by 1440 to convert to minutes and checking to see that it’s less than 60 minutes. If it is, then it prints the sodium value, and otherwise leaves it blank. (You could skip the ABS part as I was testing current, past, and future values and wanted it to stop throwing errors for future times that were calculated as negatives in the first argument). I then in C2 calculate the sum of those values for the total sodium for that hour, using =SUM(L4:L)

(I thought about tracking the past sodium per hour values to average and see how I did throughout the run on an hourly basis…but so far on my 3 long runs where I’ve used the spreadsheet, the very fact that I am using the tracker and glancing at the hourly total has kept me well on top of sodium and so I haven’t need that yet. However, if I eventually start to have long enough runs where this is an issue, I’ll probably go back and have it calculate the absolute hour sodium totals for retrospective analysis.)

This works great in the Google Sheets app on my phone, which is how I’ll be updating it during my ultras, although Scott can have it open on a browser tab when he’s at home working at his laptop. Every time I go for a long training run, I duplicate the template tab and label it with the date of the run and use it for logging my fueling.

(PS – if you didn’t know, you can rearrange the order of tabs in your sheet, so you can drag the one you want to be actively using to the left. This is useful in case the app closes on your phone and you’re re-opening the sheet fresh, so you don’t have to scroll to re-find the correct tab you want to be using for that run. In a browser, you can either drag and drop the tabs, or click the arrow next to the tab name and select “move left” or “move right”.)

Clicking the arrow to the right of a tab name in google sheets brings up a menu that includes the option to move the tab left or right

Click here to make a copy of my spreadsheet.

If you click to make a copy of a google spreadsheet, it pops up a link confirming you want to make a copy, and also might bring the app script functionality with it. If so, you can click the button to view the script (earlier in the blog post). If it doesn't include the warning about the script, remember to add the script yourself after you make a copy.

Take a look at my spreadsheet after you make a copy (click here to generate a copy if you didn’t use the previous mentioned link), and you’ll note in the README tab a few reminders to modify the fuel library and make sure you follow the steps to ensure that the script is associated with the sheet and validation is updated.

Obviously, you may not need lipase/pancrelipase/PERT and enzyme counts; if you do, your counts of enzymes needed and types of enzyme and quantity of enzymes will need updating; you may not need or want all of these macronutrients; and you’ll definitely be eating different fuel than I am, so you can update it however you like with what you’re eating and what you want to track.

This spreadsheet and the methods for building it can also be used for other purposes, such as tracking wait times or how long it took you to do something, etc.

(If you do find this blog post and use this spreadsheet concept, let me know – I’d love to hear if this is useful for you!)

2022 Strawberry Fields Forever Ultramarathon Race Report Recap

I recently ran my second-ever 50k ultramarathon. This is my attempt to provide a race recap or “race report”, which in part is to help people in the future considering this race and this course. (I couldn’t find a lot of race reports investigating this race!)

It’s also an effort to provide an example of how I executed fueling, enzyme dosing (because I have exocrine pancreatic insufficiency, known as EPI), and blood sugar management (because I have type 1 diabetes), because there’s also not a lot of practical guidance or examples of how people do this. A lot of it is individual, and what works for me won’t necessarily work for anyone, but if anything hopefully it will help other people feel not alone as they work to figure out what works for them!

Context of my running and training in preparation

I wrote quite a bit in this previous post about my training last year for a marathon and my first 50k. Basically, I’m slow, and I also choose to run/walk for my training and racing. This year I’ve been doing 30:60 intervals, meaning I run 30 seconds and walk 60 seconds.

Due to a combination of improved training (and having a year of training last year), as well as now having recognized I was not getting sufficient pancreatic enzymes so that I was not digesting and using the food I was eating effectively, this year has been going really well. I ended up training as far as a practice 50k about 5 weeks out from my race. I did several more mid- to high-20 mile runs as well. I also did a next-day run following my long runs, starting around 3-4 miles and eventually increasing to 8 miles the day after my 50k. The goal of these next-day runs was to practice running on tired legs.

Overall, I think this training was very effective for me. My training runs were easy paced, and I always felt like I could run more after I was done. I recovered well, and the next-day runs weren’t painful and I did not have to truncate or skip any of those planned runs. (Previous years, running always felt hard and I didn’t know what it was like to recover “well” until this year.) My paces also increased to about a minute/mile faster than last year’s easy pace. Again, that’s probably a combination of increased running overall and better digestion and recovery.

Last year I chose to run a marathon and then do a 50k while I was “trained up” for my marathon. This year, I wanted to do a 50k as a fitness assessment on the path to a 50 mile race this fall. I looked for local-ish 50k options that did not have much elevation, and found the Strawberry Fields Forever Ultra.

Why I chose this race, and the basics about this race

The Strawberry Fields Forever Ultra met most of my goal criteria, including that it was around the time that I wanted to run a 50k, so that I had almost 6 months to train and also before it got to be too hot and risked being during wildfire smoke season. (Sadly, that’s a season that now overlaps significantly with the summers here.) It’s local-ish, meaning we could drive to it, although we did spend the night before the race in the area just to save some stress the morning of the race. The race nicely started at 9am, and we drove home in the evening after the race.

The race is on a 10k (6.2 miles) looped course in North Bonneville, Washington, and hosted a 10k event (1 lap), a 50k event (5 laps), and also had 100k (10 laps) or (almost) 100 miles (16 laps). It does have a little bit of elevation – or “little” by ultramarathon standards. The site and all reports describe one hill and net 200 feet of elevation gain and loss. I didn’t love the idea of a 200 foot hill, but thought I could make do. It also describes the course as “grass and dirt” trails. You’ll see a map later where I’ve described some key points on the course, and it’s also worth noting that this course is very “crew-able”. Most people hang out at the start/finish, since it’s “just” a 10k loop and people are looping through pretty frequently. However, if you want to, either for moral or practical support, crew could walk over to various points, or my husband brought his e-bike and biked around between points on the course very easily using a mix of the other trails and actual roads nearby.

The course is well marked. Any turn had a white sign with a black arrow on it and also white arrows drawn on the ground, and there were dozens of little red/pink fluorescent flags marking the course. Any time there was a fork in the path, these flags (usually 2-3 for emphasis, which was excellent for tired brains) would guide you to the correct direction.

The nice thing about this race is it includes the 100 mile option and that has a course limit of 30 hours, which means all the other distances also have this course limit of 30 hours. That’s fantastic when a lot of 50k or 50 mile (or 100k, which is 62 miles) courses might have 12 hour or similar tighter course limits. If you wanted to have a nice long opportunity to cover the distance, with the ability to stop and rest (or nap/sleep), this is a great option for that.

With the 50k, I was aiming to match or ideally beat my time from my first 50k, recognizing that this course is harder given the terrain and hill. However, I think my fitness is higher, so beating that time even with the elevation gain seemed reasonable.

Special conditions and challenges of the 2022 Strawberry Fields Forever Ultramarathon

It’s worth noting that in 2021 there was a record abnormal heat wave due to a “heat dome” that made it 100+ degrees (F) during the race. Yikes. I read about that and I am not willing to run a race when I have not trained for that type of heat (or any heat), so I actually waited until the week before the race to officially sign up after I saw the forecast for the race. The forecast originally was 80 F, then bounced around mid 60s to mid 70s, all of which seemed doable. I wouldn’t mind some rain during the race, either, as rainy 50s and 60s is what I’ve been training in for months.

But just to make things interesting, for the 2022 event the Pacific Northwest got an “atmospheric river” that dumped inches of rain on Thursday..and Friday. Gulp. Scott and I drove down to spend the night Friday night before the race, and it was dumping hard rain. I began to worry about the mud that would be on the course before we even started the race. However, the rain finished overnight and we woke up to everything being wet, but not actively raining. It was actually fairly warm (60s), so even if it drizzled during the race it wouldn’t be chilly.

During the start of the race, the race director said we would get wet and joked (I thought) about practicing our backstroke. Then the race started, and we took off.

My race recap / race report the 2022 Strawberry Fields Forever Ultramarathon

I’ve included a picture below that I was sent a month or so before the race when I asked for a course map, and a second picture because I also asked for the elevation profile. I’ve marked with letters (A-I) points on the course that I’ll describe below for reference, and we ran counterclockwise this year so the elevation map I’ve marked with matching letters where “A” is on the right and “I” is on the left, matching how I experienced the course.

The course is slightly different in the start/finish area, but otherwise is 95% matching what we actually ran, so I didn’t bother grabbing my actual course map from my run since this one was handy and a lot cleaner than my Runkeeper-derived map of the race.

Annotated course map with points A-I
StrawberryFieldsForever-Ultra-Elevation-Profile

My Runkeeper elevation profile of the 50k (5 repeated laps) looked like this:
Runkeeper elevation profile of 5 loops on the Strawberry Fields Forever 50k course

I’ll describe my first experience through the course (Lap 1) in more detail, then a couple of thoughts about the experiences of the subsequent laps, in part to describe fueling and other choices I made.

Lap 1:

We left the start by running across the soccer field and getting on a paved path that hooked around the ballfield and then headed out a gate and up The Hill. This was the one hill I thought was on the course. I ran a little bit and passed a few people who walked on a shallower slope, then I also converted to a walk for the rest of the hill. It was the most crowded race start I’ve done, because there were so many people (150 across the 10k, 50k, 100k, and 100 miler) and such a short distance between the start and this hill. The Hill, as I thought of it, is point A on the course map.

Luckily, heading up the hill there are gorgeous purple wildflowers along the path and mountain views. At the top of the hill there are some benches at the point where we took a left turn and headed down the hill, going down the same elevation in about half a mile so it was longer than the uphill section. This downhill slope (B) was very runnable and gravel covered, whereas going up the hill was more dirt and mud.

At the bottom of the hill, there was a hairpin turn and we turned and headed back up the hill, although not all the way up, and more along a plateau in the side of the hill. The “plateau” is point C on the map. I thought it would be runnable once I got back up the initial hill, but it was mud pit after mud pit, and I would have two steps of running in between mud pits to carefully walk through. It was really frustrating. I ended up texting to my parents and Scott that it was about 1.7 miles of mud (from the uphill, and the plateau) before I got to some gravel that was more easily runnable. Woohoo for gravel! This was a nice, short downhill slope (D) before we flattened out and switched back to dirt and more mud pits.

This was the E area, although it did feel more runnable than the plateau because there were longer stretches between muddy sections.

Eventually, we saw the river and came out from the trail into a parking lot and then jogged over onto the trail that parallels the river for a while. This trail that I thought of as “River Road” (starting around point F) is just mowed grass and is between a sharp bluff drop with opening where people would be down at the river fishing, and in some cases we were running *underneath* fishing lines from the parking spots down to the river! There were a few people who would be walking back and forth from cars to the river, but in general they were all very courteous and there was no obstruction of the trail. Despite the mowed grass aspect of the trail, this stretch physically and psychologically felt easier because there were no mud pits for 90% of it. Near the end there were a few muddy areas right about the point we hopped back over into the road to connect up a gravel road for a short spurt.

This year, the race actually put a bonus aid station out here. I didn’t partake, but they had a tent up with two volunteers who were cheerful and kind to passing runners, and it looked like they had giant things of gatorade or water, bottled water, and some sugared soda. They probably had other stuff, but that’s just what I saw when passing.

After that short gravel road bit, we turned back onto a dirt trail that led us to the river. Not the big river we had been running next to, but the place where the Columbia River overflowed the trail and we had to cross it. This is what the race director meant by practicing our backstroke.

You can see a video in this tweet of how deep and far across you had to get in this river crossing (around point G, but hopefully in future years this isn’t a point of interest on the map!!)

Showing a text on my watch of my BIL warning me about a river crossing

Coming out of the river, my feet were like blocks of ice. I cheered up at the thought that I had finished the wet feet portion of the course and I’d dry off before I looped back around and hit the muddy hill and plateau again. But, sadly, just around the next curve, came a mud POND. Not a pit, a pond.

Showing how bad the mud was

Again, ankle deep water and mud, not just once but in three different ponds all within 30 seconds or so of each other. It was really frustrating, and obviously you can’t run through them, so it slowed you down.

Then finally after the river crossing and the mud ponds, we hooked a right into a nice, forest trail that we spent about a mile and a half in (point H). It had a few muddy spots like you would normally expect to get muddy on a trail, but it wasn’t ankle deep or water filled or anything else. It was a nice relief!

Then we turned out of the forest and crossed a road and headed up one more (tiny, but it felt annoying despite how small it looks on the elevation profile) hill (point I), ran down the other side of that slope, stepped across another mud pond onto a pleasingly gravel path, and took the gravel path about .3 miles back all the way to complete the first full lap.

Phew.

I actually made pretty good time the first loop despite not knowing about all the mud or river crossing challenges. I was pleased with my time which was on track with my plan. Scott took my pack about .1 miles before I entered the start/finish area and brought it back to me refilled as I exited the start/finish area.

Lap 2:

The second lap was pretty similar. The Hill (A) felt remarkably harder after having experienced the first loop. I did try to run more of the downhill (B) as I recognized I’d make up some time from the walking climb as well as knowing I couldn’t run up the plateau or some of the mud pits along the plateau (C) as well as I had expected. I also decided running in the mud pits didn’t work, and went with the safer approach of stepping through them and then running 2 steps in between. I was a little slower this time, but still a reasonable pace for my goals.

The rest of the loop was roughly the same as the first, the mud was obnoxious, the river crossing freezing, the mud obnoxious again, and relief at running through the forest.

Scott met me at the end of the river road and biked along the short gravel section with me and went ahead so he could park his bike and take video of my second river crossing, which is the video above. I was thrilled to have video of that, because the static pictures of the river crossing didn’t feel like it did the depth and breadth of the water justice!

At the end of lap 2, Scott grabbed my pack again at the end of the loop and said he’d figured out where to meet me to give it back to me after the hill…if I wanted that. Yes, please! The bottom of the hill where you hairpin turn to go back up the plateau is the 1 mile marker point, so that means I ran the first mile of the third lap without my pack, and not having the weight of my full pack (almost 3L of water and lots of snacks and supplies: more on that pack below) was really helpful for my third time up the hill. He met me as planned at the bottom of the downhill (B) and I took my pack back which made a much nicer start to lap 3.

Lap 3:

Lap 3 for some reason I came out of the river crossing and the mud ponds feeling like I got extra mud in my right shoe. It felt gritty around the right side of my right food, and I was worried about having been running for so many hours with soaked feet. I decided to stop at a bench in the forest section and swap for dry socks. In retrospect, I wish I had stopped somewhere else, because I got swarmed by these moth/gnat/mosquito things that looked gross (dozens on my leg within a minute of sitting there) that I couldn’t brush off effectively while I was trying to remove my gaiters, untie my shoes, take my shoes off, peel my socks and bandaids and lambs wool off, put lubrication back on my toes, put more lambs wool on my toes, put the socks and shoes back on, and re-do my gaiters. Sadly, it took me 6 minutes despite me moving as fast as I could to do all of those things (this was a high/weirdly designed bench in a shack that looked like a bus stop in the middle of the woods, so it wasn’t the best way to sit, but I thought it was better than sitting on the ground).

(The bugs didn’t hurt me at the time, but two days later my dozens of bites all over my leg are red and swollen, though thankfully they only itch when they have something chafing against them.)

Anyway, I stood up and took off again and was frustrated knowing that it had taken 6 minutes and basically eaten the margin of time I had against my previous 50k time. I saw Scott about a quarter of a mile later, and I saw him right as I realized I had also somewhere lost my baggie of electrolyte pills. Argh! I didn’t have back up for those (although I had given Scott backups of everything else), so that spiked my stress levels as I was due for some electrolytes and wasn’t sure how I’d do with 3 or so more hours without them.

I gave Scott my pack and tasked him with checking my brother-in-law’s setup to see if he had spare electrolytes, while he was refilling my pack to give me in lap 4.

Lap 4:

I was pretty grumpy given the sock timing and the electrolyte mishap as I headed into lap 4. The hill still sucked, but I told myself “only one more hill after this!” and that thought cheered me up.

Scott had found two electrolyte options from my brother-in-law and brought those to me at the end of mile 1 (again, bottom of B slope) with my pack. He found two chewable and two swallow pills, so I had options for electrolytes. I chewed the first electrolyte tab as I headed up the plateau, and again talked myself through the mud pits with “only one more time through the mud pits after this!”.

I also tried overall to bounce back from the last of mile 4 where I let myself get frustrated, and try to take more advantage of the runnable parts of the course. I ran downhill (B) more than the previous laps, mostly ignoring the audio cues of my 30:60 intervals and probably running more like 45:30 or so. Similarly, the downhill gravel after the mud pits (D) I ran most of without paying attention to the audio run cues.

Scott this time also met me at the start of the river road section, and I gave him my pack again and asked him to take some things out that he had put in. He put in a bag with two pairs of replacement socks instead of just one pair of socks, and also put in an extra beef stick even though I didn’t ask for it. I asked him to remove it, and he did, but explained he had put it in just in case he didn’t find the electrolytes because it had 375g of sodium. (Sodium is primarily the electrolyte I am sensitive to and care most about). So this was actually a smart thing, although because I haven’t practiced eating larger amounts of protein and experienced enzyme dosing for it on the run, I would be pretty nervous about eating it in a race, so that made me a bit unnecessarily grumpy. Overall though, it was great to see him extra times on the course at this point, and I don’t know if he noticed how grumpy I was, but if he did he ignored it and I cheered up again knowing I only had “one more” of everything after this lap!

The other thing that helped was he biked my pack down the road to just before the river crossing, so I ran the river road section like I did lap 3 and 4 on the hill, without a pack. This gave me more energy and I found myself adding 5-10 seconds to the start of my run intervals to extend them.

The 4th river crossing was no less obnoxious and cold, but this time it and the mud ponds didn’t seem to embed grit inside my shoes, so I knew I would finish with the same pair of socks and not need another change to finish the race.

Lap 5:

I was so glad I was only running the 50k so that I only had 5 laps to do!

For the last lap, I was determined to finish strong. I thought I had a chance of making up a tiny bit of the sock change time that I had lost. I walked up the hill, but again ran more than my scheduled intervals downhill, grabbed my bag from Scott, picked my way across the mud pits for the final time (woohoo!), ran the downhill and ran a little long and more efficiently on the single track to the river road.

Scott took my pack again at the river road, and I swapped my intervals to be 30:45, since I was already running closer to that and I knew I only had 3.5 or so miles to go. I took my pack back at the end of river road and did my last-ever ice cold river crossing and mud pond extravaganza. After I left the last mud pond and turned into the forest, I switched my intervals to 30:30. I managed to keep my 30:30 intervals and stayed pretty quick – my last mile and a half was the fastest of the entire race!

I came into the finish line strong, as I had hoped to finish. Woohoo!

Overall strengths and positives from the race

Overall, running-wise I performed fairly well. I had a strong first lap and decent second lap, and I got more efficient on the laps as I went, staying focused and taking advantage of the more runnable parts of the course. I finished strong, with 30:45 intervals for over a mile and 30:30 intervals for over a mile to the finish.

Also, I didn’t quit after experiencing the river crossing and the mud ponds and the mud pits of the first lap. This wasn’t an “A” race for me or my first time at the distance, so it would’ve been really easy to quit. I probably didn’t in part because we did pay to spend the night before and drove all that way, and I didn’t want to have “wasted” Scott’s time by quitting, when I was very capable of continuing and wasn’t injured. But I’m proud of mostly the way I handled the challenges of the course, and for how I readjusted from the mental low and frustration after realizing how long my sock change took in lap 3. I’m also pleased that I didn’t get injured, given the terrain (mud, river crossing, and uneven grass to run on for most of the course). I’m also pleased and amazed I didn’t hurt my feet, cause major blisters, or have anything really happen to them after hours of wet, muddy, never-drying-off feet.

The huge positive was my fueling, electrolytes, and blood glucose management.

I started taking my electrolyte pills that have 200+mg of sodium at about 45 minutes into the race, on schedule. My snack choices also have 100-150mg of sodium, which allowed me to not take electrolyte pills as often as I would otherwise need to (or on a hotter day with more sweat – it was a damp, mid-60s day but I didn’t sweat as much as I usually do). But even with losing my electrolytes, I used two chewable 100mg sodium electrolytes instead and otherwise ended up with sufficient electrolytes. Even with ideal electrolyte supplementation, I’m very sensitive to sodium losses and am a salty sweater, and I have a distinct feeling when my electrolytes are insufficient, so not having that feeling during after the race was a big positive for me.

So was my fueling overall. The race started at 9am, and I woke up at 6am to eat my usual pre-race breakfast (a handful of pecans, plus my enzyme supplementation) so that it would both digest effectively and also be done hitting my blood sugar by the time the race started. My BGs were flat 120s or 130s when I started, which is how I like them. I took my first snack about an hour and 10 minutes into the race, which is about 15g carb (10g fat, 2g protein) of chili cheese flavored Fritos. For this, I didn’t dose any insulin as I was in range, and I took one lipase-only enzyme (which covers about 8g of fat for me) and one multi-enzyme (that covers about 6g of fat and probably over a dozen grams of protein). My second snack was an hour later, when I had a gluten free salted caramel Honey Stinger stroopwaffle (21g carb, 6 fat, 1 protein). For the stroopwaffle I ended up only taking a lipase-only pill to cover the fat, even though there’s 1g of protein. For me, I seem to be ok (or have no symptoms) from 2-3g of uncovered fat and 1-2g of uncovered protein. Anything more than that I like to dose enzymes for, although it depends on the situation. Throughout the day, I always did 1 lipase-only and 1 multi-enzyme for the Fritos, and 1 lipase-only for the stroopwaffle, and that seemed to work fine for me. I think I did a 0.3u (less than a third of the total insulin I would normally need) bolus for my stroopwaffle because I was around 150 mg/dL at the time, having risen following my un-covered Frito snack, and I thought I would need a tiny bit of insulin. This was perfect, and I came back down and flattened out. An hour and 20 minutes after that, I did another round of Fritos. An hour or so after that, a second stroopwaffle – but this time I didn’t dose any insulin for it as my BG was on a downward slope. An hour later, more Fritos. A little bit after that, I did my one single sugar-only correction (an 8g carb Airhead mini) as I was still sliding down toward 90 mg/dL, and while that’s nowhere near low, I thought my Fritos might hit a little late and I wanted to be sure I didn’t experience the feeling of a low. This was during the latter half of loop 4 when I was starting to increase my intensity, so I also knew I’d likely burn a little more glucose and it would balance out – and it did! I did one last round of Fritos during lap 5.
CGM graph during 50k ultramarathon

This all worked perfectly. I had 100% time in range between 90 and 150 mg/dL, even with 102g of “real food” carbs (15g x 4 servings of Fritos, 21g x 2 waffles), and one 8g Airhead mini, so in total I had 110g grams of carbs across ~7+ hours. This perfectly matched my needs with my run/walk moderate efforts.

BG and carb intake plotted along CGM graph during 50k ultramarathon

I also nailed the enzymes, as during the race I didn’t have any GI-related symptoms and after the race and the next day (which is the ultimate verdict for me with EPI), no symptoms.

So it seems like my practice and testing with low carbs, Fritos, and waffles worked out well! I had a few other snacks in my pack (yogurt-covered pretzels, peanut butter pretzel nuggets), but I never thought of wanting them or wanting something different. I did plan to try to do 2 snacks per hour, but I ended up doing about 1 per hour. I probably could have tolerated more, but I wasn’t hungry, my BGs were great, and so although it wasn’t quite according to my original plan I think this was ideal for me and my effort level on race day.

The final thing I think went well was deciding on the fly after loop 2 to have Scott take my pack until after the hill (so I ran the up/downhill mile without it), and then for additional stretches along river road in laps 4 and 5. I had my pocket of my shorts packed with dozens of Airheads and mints, so I was fine in terms of blood sugar management and definitely didn’t need things for a mile at a time. I’m usually concerned about staying hydrated and having water whenever I want to sip, plus for swallowing electrolytes and enzyme pills to go with my snacks, but I think on this course with the number of points Scott could meet me (after B, at F all through G, and from I to the finish), I could have gotten away with not having my pack the whole time; having WAY less water in the pack (I definitely didn’t need to haul 3L the whole time, that was for when I might not see Scott every 2-3 laps) and only one of each snack at a time.

Areas for improvement from my race

I trained primarily on gravel or paved trails and roads, but despite the “easy” elevation profile and terrain, this was essentially my first trail ultra. I coped really well with the terrain, but the cognitive burden of all the challenges (Mud pits! River crossing! Mud ponds!) added up. I’d probably do a little more trail running and hills (although I did some) in the final weeks before the race to help condition my brain a little more.

I’ll also continue to practice fueling so I can eat more regularly than every hour to an hour and a half, even though this was the most I’ve ever eaten during a run, I did well with the quantities, and my enzyme and BG management were also A+. But I didn’t eat as much as I planned for, and I think that might’ve helped with the cognitive fatigue, too, by at least 5-10%.

I also now have the experience of a “stop” during a race, in this case to swap my socks. I’ve only run one ultra and never stopped before to do gear changes, so that experience probably was sufficient prep for future stops, although I do want to be mentally stronger/less frustrated by unanticipated problem solving stops.

Specific to this course, as mentioned above, I could’ve gotten away with less supplies – food and water – in my pack. I actually ran a Ragnar relay race with a group of fellow T1s a few years back where I finished my run segment and…no one was there to meet me. They went for Starbucks and took too long to get there, so I had to stand in the finishing chute waiting for 10-15 minutes until someone showed up to start the next run leg. Oh, and that happened in two of the three legs I ran that day. Ooof. Standing there tired, hot, with nothing to eat or drink, likely added to my already life-with-type-1-diabetes-driven-experiences of always carrying more than enough stuff. But I could’ve gotten away very comfortably with carrying 1L of water and one set of each type of snacks at a time, given that Scott could meet me at 1 mile (end of B), start (F) and end of river road (before G), and at the finish, so I would never have been more than 2-2.5 miles without a refill, and honestly he could’ve gotten to every spot on the trail barring the river crossing bit to meet me if I was really in need of something. Less weight would’ve made it easier to push a little harder along the way. Basically, I carried gear like I was running a solo 30 mile effort at a time, which was safe but not necessary given the course. If I re-ran this race, I’d feel a lot more comfortable with minimal supplies.

Surprises from my race

I crossed the finish line, stopped to get my medal, then was waiting for my brother-in-law to finish another lap (he ran the 100k: 62 miles) before Scott and I left. I sat down for 30 minutes and then walked to the car, but despite sitting for a while, I was not as stiff and sore as I expected. And getting home after a 3.5 hour car ride…again I was shocked at how minimally stiff I was walking into the house. The next morning? More surprises at how little stiff and sore I was. By day 3, I felt like I had run a normal week the week prior. So in general, I think this is reinforcement that I trained really well for the distance and my long runs up to 50k and the short to medium next day runs also likely helped. I physically recovered well, which is again part training but also probably better fueling during the race, and of course now digesting everything that I ate during and after the race with enzyme supplementation for EPI!

However, the interesting (almost negative, but mostly interesting) thing for me has been what I perceived to be adrenal-type fatigue or stress hormone fatigue. I think it’s because I was unused to focusing on challenging trail conditions for so many hours, compared to running the same length of hours on “easy” paved or gravel trails. I actually didn’t listen to an audiobook, music, or podcast for about half of the race, because I was so stimulated by the course itself. What I feel is adrenal fatigue isn’t just being physically or mentally tired but something different that I haven’t experienced before. I’m listening to my body and resting a lot, and I waited until day 4 to do my first easy, slow run with much longer walk intervals (30s run, 90s walk instead of my usual 30:60). Day 1 and 2 had a lot of fatigue and I didn’t feel like doing much, Day 3 had notable improvement on fatigue and my legs and body physically felt back to normal for me. Day 4 I ran slowly, Day 5 I stuck with walking and felt more fatigue but no physical issues, Day 6 again I chose to walk because I didn’t feel like my energy had fully returned. I’ll probably stick with easy, longer walk interval runs for the next week or two with fewer days running until I feel like my fatigue is gone.

General thoughts about ultramarathon training and effective ultra race preparation

I think preparation makes a difference in ultramarathon running. Or maybe that’s just my personality? But a lot of my goal for this race was to learn what I could about the course and the race setup, imagine and plan for the experience I wanted, plan for problem solving (blisters, fuel, enzymes, BGs, etc), and be ready and able to adapt while being aware that I’d likely be tired and mentally fatigued. Generally, any preparation I could do in terms of deciding and making plans, preparing supplies, etc would be beneficial.

Some of the preparation included making lists in the weeks prior about the supplies I’d need in my pack, what Scott should have to refill my pack, what I’d need the night and morning before since we would not be at home, and after-race supplies for the 3.5h drive home.

From the lists, the week before the race I began grouping things. I had my running pack filled and ready to go. I packed my race outfit in a gallon bag, a full set of backup clothes in another gallon bag and labeled them, along with a separate post-run outfit and flip flops for the drive home. I also included a washcloth for wiping sweat or mud off after the run, and I certainly ended up needing that! I packed an extra pair of shoes and about 4 extra pairs of socks. I also had separate baggies with bandaids of different sizes, pre-cut strips of kinesio tape for my leg and smaller patches for blisters, extra squirrel nut butter sticks for anti-chafing purposes, as well as extra lambs wool (that I lay across the top of my toes to prevent socks from rubbing when they get wet from sweat or…river crossings, plus I can use it for padding between my toes or other blister-developing spots). I had sunscreen, bug spray, sungless, rain hat, and my sunny-weather running visor that wicks away sweat. I had low BG carbs for me to put in my pockets, a backup bag for Scott to refill, and a backup to the backup. The same for my fuel stash: my backpack was packed, I packed a small baggie for Scott as well as a larger bag with 5-7 of everything I thought I might want, and also an emergency backup baggie of enzymes.

*The only thing I didn’t have was a backup baggie of electrolyte pills. Next time, I’ll add this to my list and treat them like enzymes to make sure I have a separate backup stash.

I even made a list and gave it to Scott that mapped out where key things were for during and after the race. I don’t think he had to use it, because he was only digging through the snack bag for waffles and Fritos, but I did that so I didn’t have to remember where I had put my extra socks or my spare bandaids, etc. He basically had a map of what was in each larger bag. All of this was to reduce the decision and communication because I knew I’d have decision fatigue.

This also went for post-race planning. I told Scott to encourage me to change clothes, and it was worth the energy to change so I didn’t sit in cold, wet clothes for the long drive home. I pre-made a gluten free ham and cheese quesadilla (take two tortillas, fill with shredded cheese and slices of ham, microwave, cut into quarters, stick in baggies, mark with fat/protein/carb counts, and refrigerate) so we could warm that up in the car (this is what I use) so I had something to eat on the way home that wasn’t more Fritos or waffles. I didn’t end up wanting it, but I also brought a can of beef stew with carrots and potatoes, that I generally like as a post-race or post-run meal, and a plastic container and a spoon so I could warm up the stew if I wanted it. Again, all of this pre-planned and put on the list weeks prior to the race so I didn’t forget things like the container or the spoon.

The other thing I think about a lot is practicing everything I want to do for a race during a training run. People talk about eating the same foods, wearing the same clothes, etc. I think for those of us with type 1 diabetes (or celiac, EPI, or anything else), it’s even more important. With T1D, it’s so helpful to have the experience adjusting to changing BG levels and knowing what to do when you’re dropping or low and having a snack, vs in range and having a fueling snack, or high and having a fueling snack. I had 100% TIR during this run, but I didn’t have that during all of my training runs. Sometimes I’d plateau around 180 mg/dL and be over-cautious and not bring my BGs down effectively; other times I’d overshoot and cause a drop that required extra carbs to prevent or minimize a low. Lots of practice went into making this 100% TIR day happen, and some of it was probably a bit of luck mixed in with all the practice!

But generally, practice makes it a lot easier to know what to do on the fly during a race when you’re tired, stressed, and maybe crossing an icy cold river that wasn’t supposed to be part of your course experience. All that helps you make the best possible decisions in the weirdest of situations. That’s the best you can hope for with ultrarunning!

Findings from the world’s first RCT on open source AID (the CREATE trial) presented at #ADA2022

September 7, 2022 UPDATEI’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or view an author copy here. You can also see a Twitter thread here, if you are interested in sharing the study with your networks.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NEJMoa2203913


(You can also see a previous Twitter thread here summarizing the study results, if you are interested in sharing the study with your networks.)

TLDR: The CREATE Trial was a multi-site, open-labeled, randomized, parallel-group, 24-week superiority trial evaluating the efficacy and safety of an open-source AID system using the OpenAPS algorithm in a modified version of AndroidAPS. Our study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14 percentage points higher among those who used the open-source AID system (95% confidence interval [CI], 9.2 to 18.8; P<0.001) compared to those who used sensor augmented pump therapy; a difference that corresponds to 3 hours 21 minutes more time spent in target range per day. The system did not contribute to any additional hypoglycemia. Glycemic improvements were evident within the first week and were maintained over the 24-week trial. This illustrates that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID. This study concluded that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS, a widely used open-source AID solution, is efficacious and safe.

The backstory on this study

We developed the first open source AID in late 2014 and shared it with the world as OpenAPS in February 2015. It went from n=1 to (n=1)*2 and up from there. Over time, there were requests for data to help answer the question “how do you know it works (for anybody else)?”. This led to the first survey in the OpenAPS community (published here), followed by additional retrospective studies such as this one analyzing data donated by the community,  prospective studies, and even an in silico study of the algorithm. Thousands of users chose open source AID, first because there was no commercial AID, and later because open source AID such as the OpenAPS algorithm was more advanced or had interoperability features or other benefits such as quality of life improvements that they could not find in commercial AID (or because they were still restricted from being able to access or afford commercial AID options). The pile of evidence kept growing, and each study has shown safety and efficacy matching or surpassing commercial AID systems (such as in this study), yet still, there was always the “but there’s no RCT showing safety!” response.

After Martin de Bock saw me present about OpenAPS and open source AID at ADA Scientific Sessions in 2018, we literally spent an evening at the dinner table drawing the OpenAPS algorithm on a napkin at the table to illustrate how OpenAPS works in fine grained detail (as much as one can do on napkin drawings!) and dreamed up the idea of an RCT in New Zealand to study the open source AID system so many were using. We sought and were granted funding by New Zealand’s Health Research Council, published our protocol, and commenced the study.

This is my high level summary of the study and some significant aspects of it.

Study Design:

This study was a 24-week, multi-centre randomized controlled trial in children (7–15 years) and adults (16–70 years) with type 1 diabetes comparing open-source AID (using the OpenAPS algorithm within a version of AndroidAPS implemented in a smartphone with the DANA-i™ insulin pump and Dexcom G6® CGM), to sensor augmented pump therapy. The primary outcome was change in the percent of time in target sensor glucose range (3.9-10mmol/L [70-180mg/dL]) from run-in to the last two weeks of the randomized controlled trial.

  • This is a LONG study, designed to look for rare adverse events.
  • This study used the OpenAPS algorithm within a modified version of AndroidAPS, meaning the learning objectives were adapted for the purpose of the study. Participants spent at least 72 hours in “predictive low glucose suspend mode” (known as PLGM), which corrects for hypoglycemia but not hyperglycemia, before proceeding to the next stage of closed loop which also then corrected for hyperglycemia.
  • The full feature set of OpenAPS and AndroidAPS, including “supermicroboluses” (SMB) were able to be used by participants throughout the study.

Results:

Ninety-seven participants (48 children and 49 adults) were randomized.

Among adults, mean time in range (±SD) at study end was 74.5±11.9% using AID (Δ+ 9.6±11.8% from run-in; P<0.001) with 68% achieving a time in range of >70%.

Among children, mean time in range at study end was 67.5±11.5% (Δ+ 9.9±14.9% from run-in; P<0.001) with 50% achieving a time in range of >70%.

Mean time in range at study end for the control arm was 56.5±14.2% and 52.5±17.5% for adults and children respectively, with no improvement from run-in. No severe hypoglycemic or DKA events occurred in either arm. Two participants (one adult and one child) withdrew from AID due to frustrations with hardware issues.

  • The pump used in the study initially had an issue with the battery, and there were lots of pumps that needed refurbishment at the start of the study.
  • Aside from these pump issues, and standard pump site/cannula issues throughout the study (that are not unique to AID), there were no adverse events reported related to the algorithm or automated insulin delivery.
  • Only two participants withdrew from AID, due to frustration with pump hardware.
  • No severe hypoglycemia or DKA events occurred in either study arm!
  • In fact, use of open source AID improved time in range without causing additional hypoglycemia, which has long been a concern of critics of open source (and all types of) AID.
  • Time spent in ‘level 1’ and ‘level 2’ hyperglycemia was significantly lower in the AID group as well compared to the control group.

In the primary analysis, the mean (±SD) percentage of time that the glucose level was in the target range (3.9 – 10mmol/L [70-180mg/dL]) increased from 61.2±12.3% during run-in to 71.2±12.1% during the final 2-weeks of the trial in the AID group and decreased from 57.7±14.3% to 54±16% in the control group, with a mean adjusted difference (AID minus control at end of study) of 14.0 percentage points (95% confidence interval [CI], 9.2 to 18.8; P<0.001). No age interaction was detected, which suggests that adults and children benefited from AID similarly.

  • The CREATE study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy.
  • This difference reflects 3 hours 21 minutes more time spent in target range per day!
  • For children AID users, they spent 3 hours 1 minute more time in target range daily (95% CI, 1h 22m to 4h 41m).
  • For adult AID users, they spent 3 hours 41 minutes more time in target range daily (95% CI, 2h 4m to 5h 18m).
  • Glycemic improvements were evident within the first week and were maintained over the 24-week trial. Meaning: things got better quickly and stayed so through the entire 24-week time period of the trial!
  • AID was most effective at night.
Difference between control and AID arms overall, and during day and night separately, of TIR for overall, adults, and kids

One thing I think is worth making note of is that one criticism of previous studies with open source AID is regarding the self-selection effect. There is the theory that people do better with open source AID because of self-selection and self-motivation. However, the CREATE study recruited a diverse cohort of participants, and the study findings (as described above) match all previous reports of safety and efficacy outcomes from previous studies. The CREATE study also found that the greatest improvements in TIR were seen in participants with lowest TIR at baseline. This means one major finding of the CREATE study is that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID.

This therefore means there should be NO gatekeeping by healthcare providers or the healthcare system to restrict AID technology from people with insulin-requiring diabetes, regardless of their outcomes or experiences with previous diabetes treatment modalities.

There is also no age effect observed in the trail, meaning that the results of the CREATE Trial demonstrated that open-source AID is safe and effective in children and adults with type 1 diabetes. If someone wants to use open source AID, they would likely benefit, regardless of age or past diabetes experiences. If they don’t want to use open source AID or commercial AID…they don’t have to! But the choice should 100% be theirs.

In summary:

  • The CREATE trial was the first RCT to look at open source AID, after years of interest in such a study to complement the dozens of other studies evaluating open source AID.
  • The conclusion of the CREATE trial is that open-source AID using the OpenAPS algorithm within a version of AndroidAPS, a widely used open-source AID solution, appears safe and effective.
  • The CREATE trial found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy; a difference that reflects 3 hours 21 minutes more time spent in target range per day.
  • The study recruited a diverse cohort, yet still produced glycemic outcomes consistent with existing open-source AID literature, and that compare favorably to commercially available AID systems. Therefore, the CREATE Trial indicates that a range of people with type 1 diabetes might benefit from open-source AID solutions.

Huge thanks to each and every participant and their families for their contributions to this study! And ditto, big thanks to the amazing, multidisciplinary CREATE study team for their work on this study.


September 7, 2022 UPDATE – I’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or like all of the research I contribute to, access an author copy on my research paper.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NE/Moa2203913

Note that the continuation phase study results are slated to be presented this fall at another conference!

Findings from the RCT on open source AID, the CREATE Trial, presented at #ADA2022

Why it feels harder to dose pancreatic enzyme replacement therapy (PERT) than insulin

In 2002 when I was diagnosed with Type 1 diabetes, I struggled with being handed a vial of insulin and told vaguely to eat X amount of food and take Y amount of insulin. There was no ability to eat more and adjust the dose accordingly. It was frustrating. The only tool I had was a huge (imagine three iPhone 13 or equivalently large smartphones sitting on top of each other) blood glucose meter that took a lot of blood and a long time (a minute or more) to return a single blood glucose data point. The feedback loop wasn’t very useful, even when I tested my blood sugar manually 10-14 times per day.

Thankfully, in the last two decades, diabetes tools have evolved. Meters got smaller, faster, and take less blood. There has also been the devlopment of continuous glucose monitors (CGM) which I can wear and get near real-time readings of glucose data and can see what’s happened in the past. And, paired with an algorithm that also knows about the history of any insulin dosing on my insulin pump, and it can automatically adjust my insulin delivery in real time to predict, prevent, and reduce hypo- and hyperglycemia. (AID is awesome and if you haven’t heard about it, there’s a 4-minute free animated video here that explains it.) Diabetes no longer is quite the headache it was twenty – or even ten – years ago.

But realizing that I have exocrine pancreatic insufficiency (known as EPI or PEI) and learning how to take pancreatic enzyme replacement therapy (known as PERT) is a similar headache to diabetes in 2002.

With insulin, taking too much can cause hypoglycemia (low blood sugar). Taking too little can cause hyperglycemia (high blood sugar). Yet, with diabetes, you can measure blood glucose and see the response to insulin within a minutes-to-hours time frame. You can also use an insulin pump and an automated insulin delivery system to titrate and adjust insulin in real time.

However, for EPI, you need to take enzymes (that your pancreas doesn’t produce enough of) to help you digest your food. Your pancreas makes three types of enzymes: lipase, to help fat digest; protease, to help protein digest; and amylase, to help starches and carbohydrates digest. These are taken by mouth as a pill that you swallow. Together in one pill, it’s called “pancrelipase”, and it’s for pancreatic enzyme replacement therapy (PERT). (I’m personally bad about using pancrelilpase/PERT interchangeably, because PERT is faster to say and type, but it is possible to use standalone enzymes in PERT).

Because they are pills that you have to swallow when you eat, it’s hard to dose. Taking too little means you may have GI-related symptoms in the hours following the meal and feeling bad until the next day or so. Taking too much is expensive, although unlike insulin it’s rare to take “too much” and cause bad side effects (although possible at super high doses). There’s also the “pill burden”, because swallowing a bunch of pills is annoying and sometimes hard, both physically to swallow and to remember to take them throughout your meal.

You also can’t take more hours later if you forgot to take them or realize you didn’t dose enough for that meal. If you underdosed, you underdosed and just get to experience the symptoms that come with it. Sometimes, it’s not clear why you are having symptoms. Because there are three enzymes being replaced, it’s possible that the dosing was off for any one of the three enzymes. But again, there’s no measurement or feedback loop, or a sign that appears saying “you underdosed protease, take more next time”. The best you can do is try different sized meals over time with different doses of PERT, trying to reverse engineer your lipase:fat and protease:protein and amylase:carb ratios and continuously update them as you have new data.

It’s a lot of work, the feedback loop is slow, getting it “wrong” is painful physically and psychologically, and there are no vacations from it. Everything I eat, now that I have EPI, needs enzymes, and given the fact that I have automated insulin delivery to help manage insulin dosing, I am finding PERT to be a lot harder and more annoying (currently).

A comparison of dosing insulin and dosing enzymes. Insulin can cause hypo- or hyperlgycemia but there are tools (CGM and BG meters) and a feedback loop in diabetes. With enzymes, there is no fast feedback loop and underdosing is common. There is no ability to correct an underdose and there are multiple variables that can influence the outcome.

There’s no happy ending to this post, but this is one of the reasons why I am so interested in partnering with researchers to do research on EPI. There are a LOT of improvements that can be made, ranging from improving titration guidance of PERT to testing the efficacy of different over the counter enzymes to finding new technology that might begin to provide a feedback loop into EPI (either for short-term assessment or longer-term use for those who prefer it). If you’re someone interested in this type of research, please don’t hesitate to reach out (Dana@OpenAPS.org).

(PS, if you didn’t see them, I have other posts about EPI at DIYPS.org/EPI – including one about PS –  PERT Pilot, the first iOS app for Exocrine Pancreatic Insufficiency! It’s an iOS app that allows you to record as many meals as you want, the PERT dosing and outcomes, to help you visualize and review more of your PERT dosing data! It’s one of the things I decided to build to help address the challenges I know those of us with EPI face every day.)


You can also contribute to a research study and help us learn more about EPI/PEI – take this anonymous survey to share your experiences with EPI-related symptoms!

Everything I did wrong (but did anyway) training for a marathon after a broken ankle and marathon running with type 1 diabetes

This is another one of those posts for a niche audience. If you found this post, you’re likely looking for information about:

  • Running after a broken ankle (or are coming from my “tips for returning to weight bearing” and looking for an update from me, two years after my trimalleolar ankle fracture)
  • Running with the “Galloway method”, also known as run-walk or run/walk methods for marathon or similar long distances – but with information about run-walking for slow runners.
  • Running a marathon with type 1 diabetes, or running an ultra with type 1 diabetes
  • Running a marathon and training for a marathon and going without fuel or less fuel
    *Update: also running an ultramarathon with the same methods (less fuel than typical)!

There’s a bit of all of this in the post! (But TLDR – I ran my marathon (finally), successfully, despite having a previously broken ankle. And despite running it with type 1 diabetes, I had no issues managing my blood sugars during even the longest training runs, even with significantly less fuel than is typically used by marathon runners. I also ran a 50k ultra using the same methods!)

running a marathon after a broken ankle and with type 1 diabetes

First up, some context that explains why I chose run-walking as my method of running a marathon (as that also influences fueling choices) and what it is like to be a slow marathon runner (6 hour marathon ish). I broke my ankle in January 2019 and began running very tiny amounts (literally down the block to start) in summer 2019. I progressed, doing a short run interval followed by a walk interval, increasing the total numbers of intervals, and then slowly progressing to extend the length (distance and/or time) of the running intervals. In early fall 2019, I was attempting a couch-to-5k type program where I would extend my running intervals even longer, but I still had subsequent injuries (a very stubborn big toe joint, then intermetatarsal bursitis in TWO spots (argh)) that made this not work well. Eventually, I went back to running 30 seconds and walking 30 seconds, then keeping those “short” intervals and extending my run. I focused on time at first: going from 5 to 10 to 15 to 20 etc minutes, rather than focusing on distance. Once I built up to about 30 minutes of run-walking (30:30, meaning running 30 seconds and walking 30 seconds), I switched to adding a quarter or half mile each time depending on how I was feeling. But doing 30:30 seemed to work really well for me in terms of the physical impact to my feet, even with long miles, and also mentally, so I stuck with it. (You can go read about the Galloway run-walk-run method for more about run-walking; most people build up to running more, say 5 minutes or 8 minutes followed by a minute of walking, or maybe run 1 mile and then walk for a minute, or walk through the aid stations, but I found that 30:30 is what I liked and stuck with it or 60:30 as my longest intervals.)

This worked so well for me that I did not think about my right ankle a single time during or after my marathon! It took days to even remember that I had previously broken my ankle and it could’ve been problematic or weaker than my other ankle during my marathon. It took a long time to get to this point – I never thought I’d be forgetting even for a few days about my broken ankle! But two years later, I did.)

When COVID-19 struck, and as someone who paid attention early (beginning late January 2020), I knew my marathon would not be taking place in July 2020 and would be postponed until 2021. So I focused on keeping my feet healthy and building up a running “base” of trying to stay healthy feet-wise running twice a week into fall 2020, which worked fairly well. At the start of 2021, I bumped up to three runs a week consistently, and eventually began making one run every other a week longer. My schedule looked something like this:

Monday – 3 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 4 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 5 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 6 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – (back to) 3 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 8 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – (back to) 3  miles  Wednesday – 5 miles   Friday – 4 miles

Monday – 10 miles  Wednesday – 3 miles   Friday – 3 miles

Note that these runs I refer to were all technically run-walks, where I ran 30 seconds and walked 30 seconds (aka 30:30) until I covered the miles. I was running slow and easy, focusing on keeping my heart rate below its maximum and not worrying about speed, so between that and run-walking I was often doing 15m30s miles. Yes, I’m slow. This all enabled me to build up to safely be able to run 3 runs weekly at first, and then eventually progressed to adding a fourth run. When I added a fourth run, I was very conservative and started with only 1 mile for two weeks in a row, then 2 miles, then up to 3 miles. Eventually, later in my training, I had some of my other runs in the week be a bit longer (4-5 miles) in addition to my “long” run.

But, because I’m so slow, this means it takes a lot of time to run my long runs. If you estimate a 15-minute mile for easy math, that means an 8 mile “long” run would take at least 2 hours. With marathon training (and my goal to train up to multiple 22-24 mile runs before the marathon), that took A LOT of time. And, because of my broken ankle and intermetatarsal experiences from 2019, I was very cautious and conservative about taking care of my feet during training. So instead of following the usual progression of long runs increasing 2-3 weeks in a row, followed by a “cutback” long week, after I hit two hours of long running (essentially 8 miles, for me), I started doing long runs every other week. The other week was a “cutback” long run, which was usually 8 miles, 10 miles (for several weeks), up to eventually 12-14. In terms of “time on feet”, this meant 2-3 hours “cutback” long runs, which according to many people is the max you should be running for marathon training. That doesn’t quite work for slow runners such as myself where you might be doing a 6-hour marathon or 7-hour marathon or thereabouts. (The standard advice also maybe doesn’t apply when you are doing run-walking for your marathon training.)

I had ~6 months to build up to my marathon (from January to the end of July), so I had time to do this, which gave me a buffer in my overall training schedule in case of scheduling conflicts (which happened twice) and in case of injury (which thankfully didn’t happen). I ended up scheduling training long runs all the way to full marathon distance (26ish miles), because I wanted to practice my fueling (especially important for type 1 diabetes marathon runners, which I’ll talk about next) as well as get my feet used to that many hours of run-walking. I did my long runs without care for speed, so some of them were closer to 16-minute mile averages, some were around 15-minute mile averages for the entire run, and the day I ran the full marathon course for training I ended up doing 16+ minute miles and felt fabulous at the end.

I ended up doing a few “faster” “shorter” long runs (on my cutback weeks), where I would do a half marathon-ish distance on the actual marathon course (a public trail), and try to go faster than my super slow long run pace. I had several successful runs where I was at or near marathon pace (which for me would be around 13m30s). So yes, you can train slow and run fast for a marathon, even without much speed work, and even if you are doing a run-walk method, and even if you’re as slow as I am. Running ~15-minute miles took forever but kept my feet and body healthy and happy through marathon training, and I was still able to achieve my sub-6 hour marathon goal (running 13:41 average pace for 26.2+ miles) on race day.

Now let’s talk about fueling, and in particular fueling for people with type 1 diabetes and for people wondering if the internet is right about what fueling requirements are for marathon runners.

I previously wrote (for a T1D audience) about running when fasted, because then you don’t have to deal with insulin on board at the start of a run. That’s one approach, and another approach is to have a smaller meal or snack with fewer carbs before the run, and time your run so that you don’t need to bolus or inject for that meal before you start your run. That’s what I chose for most of my marathon training, especially for longer runs.

On a typical non-running day, I would eat breakfast (½ cup pecans, ¼ cup cranberries, and a few sticks of cheese), my OpenAPS rig would take care of insulin dosing (or I could bolus for it myself), and my BGs would be well managed. However, that would mean I had a lot of insulin on board (IOB) if I tried to run within an hour of that. So instead, during marathon training, I ended up experimenting with eating a smaller amount of pecans (¼ cup) and no cranberries, not bolusing or letting OpenAPS bolus, and running an hour later. I had a small BG rise from the protein (e.g. would go from 100 mg/dL flat overnight to 120-130 mg/dL), and then running would balance out the rest of it.

I generally would choose to target my blood sugar to 130 mg/dL at the start of long runs, because I prefer to have a little bit of buffer for if/when my blood sugar began to drop. I also figured out that if I wasn’t having IOB from breakfast, I did not need to reduce my insulin much in advance of the run, but do it during the duration of the run. Therefore, I would set a higher temporary target in my OpenAPS rig, and if I was doing things manually, I would set a temporary basal rate on my insulin pump to about ⅓ of my usual hourly rate for the duration of the run. That worked well because by the time the beginning of my run (30-45 minutes) brought my BG down a little bit from the start with the protein breakfast bump (up to 130 mg/dL or so), that’d also be when the reduced insulin effect would be noticeable, and I would generally stay flat instead of having a drop at the beginning or first hour of my run.

After my first hour or so, I just kept an eye periodically on my blood sugars. My rule of thumb was that if my BG drifted down below 120 mg/dL, I would eat a small amount of carbs. My carb of choice was an individually wrapped peppermint (I stuffed a bunch in my pocket for the run) that was 3-4g of carb. If I kept drifting down or hadn’t come back up to 120 mg/dL 10-15 minutes later, I would do another. Obviously, if I was dropping fast I would do more, but 75% of the time I only needed one peppermint (3-4g of carb) to pause a drift down. If you have a lot of insulin on board, it would take more carbs, but my method of not having IOB at the start of long runs worked well for me. Sometimes, I would run my entire long run with no carbs and no fuel (other than water, and eventually electrolyte pills). Part of this is likely due to the fact that I was run-walking at such low intensity (remember 15-ish minute miles), but part of this is also due to figuring out the right amount of insulin I needed for endurance running and making sure I didn’t have excess insulin on board. On my faster runs (my half marathon distance fast training runs, that were 2+ minutes/mile faster than my slow long runs) and my marathon itself, I ended up needing more carbs than a super slow run – but it ended up being about 30 grams of carbohydrate TOTAL.

Why am I emphasizing this?

Well, the internet says (and most coaches, training plans, etc) that you need 30g of carbs PER HOUR. And that you need to train your stomach to tolerate that many carbs, because your muscles and brain need it. And without that much fuel, you will ‘hit the wall’.

My hypothesis, which may be nuanced by having type 1 diabetes and wearing a CGM and being able to track my data closely and manage it not only by carbs but also titrating insulin levels (which someone without diabetes obviously can’t do), is that you don’t necessarily need that many carbs, even for endurance running or marathon running.

I’m wondering if there’s a correlation between people who max out their long runs around 16-20 miles and who then “hit the wall” around mile 20 of a marathon. Perhaps some of it is muscle fatigue because they haven’t trained for the distance and some of it is psychological of feeling the brain fatigue.

During my marathon, in which I ran 2+ min/mi faster than most of my training runs, I did not ever experience hypoglycemia, and I did not “hit the wall”. Everything hurt, but I didn’t “hit the wall” as most people talk about. Those might be related, or it might be influenced by the fact that I had done a 20, 22, 24, 26, and another 21 mile run as part of my training, so my legs were “used” to the 20+ mile distance?

So again – some of my decreased fueling needs may be because I was already reducing my insulin and balancing my blood sugars (really well), and if my blood sugar was low (hypoglycemia), I would’ve needed more carbs. Or you can argue my lower fueling needs are because I’m so slow (15-16 minute mile training runs, or a 13m40s marathon pace). But in any case, I wanted to point out that if the fueling advice you’re getting or reading online seems like it’s “too much” per hour, there are people who are successful in hitting their time goals and don’t hit the wall on lower fueling amounts, too. You don’t necessarily have to fuel for the sake of fueling.

Note that I am not doing “low carb” or “keto” or anything particular diet-wise (other than eating gluten-free, because I also have celiac disease) outside of my running fuel choices. This was a successful strategy for me, and I eat what might be considered a moderate carb diet outside of running fuel choices.

Ps – if you don’t fuel (carbs or other nutrients) during your runs, don’t forget about your electrolytes. I decided to keep drinking water out of a Camelbak in a running pack, rather than filling it with Gatorade or a similar electrolyte drink, but I’m pretty electrolyte sensitive so I needed to do something to replace them. I got electrolyte pills and would take them every 30 minutes or so on long training runs when it was hotter. Play around with timing on those: if you don’t sweat a lot or aren’t a salty sweater, you may not need as many as often. I ended up doing the bulk of my long runs on hot days, and I sweat a lot, so every 30 minutes was about right for me. On cooler runs, one per hour was sufficient for me. (I tried these chewable tabs in lemon-lime but didn’t like the salt feeling directly in my mouth; I ended up buying these to swallow instead: I didn’t have any digestion issues or side effects from them, and they successfully kept my electrolytes to manageable levels. The package says not to take more than 10 within a 24 hour period, but I ended up taking 12 for my longest training run and the marathon itself and suffered no ill effects. It’s probably set to max 10 because of the amount of salt compared to the typical daily amount needed..but obviously, if you’re doing endurance running you need more than the daily amount of salt you would need on a regular day. But I’m not a doctor and this isn’t medical advice, of course – I’m just telling you what I chose to do).

In terms of training, here’s everything the internet told me to do for marathon training and everything I did “wrong” according to the typical advice:

  • Your long run should be 20-30% of your overall weekly mileageWhat I did: Sometimes my long runs got up to 70% of my weekly mileage, because I was only running 3 and then 4 days a week, and not doing very long mid-week runs.
  • Have longer mid-week runs, and build those up in addition to your true long runWhat I did: I did build up to a few 5-6 mile mid-week runs, but I chose consistency of my 4 runs per week rather than overdoing it with mid-week medium runs
  • Run 5-6 days a weekWhat I did: Only run 4 times a week, because I wanted a rest day after each run, and wanted a rest day prior to my longest run. I ran Monday, Wednesday, Friday, then added Saturday short runs. Monday was my long run (because I have the benefit of a flexible schedule for work).
  • Get high mileage (start from a base of 30-40 miles a week and build up to 50-60 miles!)What I did: I started with a “base” of 10 miles a week with two runs that I was very proud of. I went to three runs a week, and then 4. My biggest running week during training was 40.55 miles, although they were all 20+ mile weeks (long or cutback weeks) after the first two months of training.
  • Do progressively longer long runs for two or three weeks in a row and then do one cutback week, then continue the progressionWhat I did: Because of the time on my feet cost of being a slower runner, I did an every-other-week long-run progression alternating with a shorter cutback week.
  • Long run, tempo run, speed work, etc. plus easy runs! All the things each week!What I did: a long run per week, then the rest of my runs were usually easy runs. I tried a handful of times to do some “speed” work, but I often time was trying to keep my feet from being injured and it felt like running faster caused my feet to be sore or have other niggles in my legs, so I didn’t do much of that, other than doing some “cutback” long runs (around half marathon distance, as well as my last 21-mile run) at close to marathon pace to get a feel for how it felt to run at that pace for longer.

TLDR, again:

I signed up for a marathon in fall 2018 planning to run it in July 2019 but was thwarted by a broken ankle in January 2019 and COVID-19(/20) for 2020, so I ultimately trained for and ran it in July 2021. I am a slow runner, and I was able to achieve my sub-6 hour marathon goal using run-walk and without causing additional injury to my feet. And, because of my “slow” or less intense running, I needed a lot less fuel than is typically recommended for marathoners, and still managed my blood glucose levels within my ideal target ranges despite 5, 6, and even 7 hours run on my feet. Yes, you can run marathons with type 1 diabetes. And yes, you can run any length endurance runs with type 1 diabetes! I also ran a 50k ultramarathon using the same methods.