Functional Self-Tracking is The Only Self-Tracking I Do

“I could never do that,” you say.

And I’ve heard it before.

Eating gluten free for the rest of your life, because you were diagnosed with celiac disease? Heard that response (I could never do that) for going on 14 years.

Inject yourself with insulin or fingerstick test your blood glucose 14 times a day? Wear an insulin pump on your body 24/7/365? Wear a CGM on your body 24/7/365?

Yeah, I’ve heard you can’t do that, either. (For 20 years and counting.) Which means I and the other people living with the situations that necessitate these behaviors are…doing this for fun?

We’re not.

More recently, I’ve heard this type of comment come up about tracking what I’m eating, and in particular, tracking what I’m eating when I’m running. I definitely don’t do that for fun.

I have a 20+ year strong history of hating tracking things, actually. When I was diagnosed with type 1 diabetes, I was given a physical log book and asked to write down my blood glucose numbers.

“Why?” I asked. They’re stored in the meter.

The answer was because supposedly the medical team was going to review them.

And they did.

And it was useless.

“Why were you high on February 22, 2003?”

Whether we were asking this question in March of 2003 or January of 2023 (almost 20 years later), the answer would be the same: I have no idea.

BG data, by itself, is like a single data point for a pilot. It’s useless without the contextual stream of data as well as other metrics (in the diabetes case, things like what was eaten, what activity happened, what my schedule was before this point, and all insulin dosed potentially in the last 12-24h).

So you wouldn’t be surprised to find out that I stopped tracking. I didn’t stop testing my blood glucose levels – in fact, I tested upwards of 14 times a day when I was in high school, because the real-time information was helpful. Retrospectively? Nope.

I didn’t start “tracking” things again (for diabetes) until late 2013, when we realized that I could get my CGM data off the device and into the laptop beside my bed, dragging the CGM data into a CSV file in Dropbox and sending it to the cloud so an app called “Pushover” would make a louder and different alarm on my phone to wake me up to overnight hypoglycemia. The only reason I added any manual “tracking” to this system was because we realized we could create an algorithm to USE the information I gave it (about what I was eating and the insulin I was taking) combined with the real-time CGM data to usefully predict glucose levels in the future. Predictions meant we could make *predictive* alarms, instead of solely having *reactive* alarms, which is what the status quo in diabetes has been for decades.

So sure, I started tracking what I was eating and dosing, but not really. I was hitting buttons to enter this information into the system because it was useful, again, in real time. I didn’t bother doing much with the data retrospectively. I did occasional do things like reflect on my changes in sensitivity after I got the norovirus, for example, but again this was mostly looking in awe at how the real-time functionality of autosensitivity, an algorithm feature we designed to adjust to real-time changes in sensitivity to insulin, dealt throughout the course of being sick.

At the beginning of 2020, my life changed. Not because of the pandemic (although also because of that), but because I began to have serious, very bothersome GI symptoms that dragged on throughout 2020 and 2021. I’ve written here about my experiences in eventually self-diagnosing (and confirming) that I have exocrine pancreatic insufficiency, and began taking pancreatic enzyme replacement therapy in January 2022.

What I haven’t yet done, though, is explain all my failed attempts at tracking things in 2020 and 2021. Or, not failed attempts, but where I started and stopped and why those tracking attempts weren’t useful.

Once I realized I had GI symptoms that weren’t going away, I tried writing down everything I ate. I tried writing in a list on my phone in spring of 2020. I couldn’t see any patterns. So I stopped.

A few months later, in summer of 2020, I tried again, this time using a digital spreadsheet so I could enter data from my phone or my computer. Again, after a few days, I still couldn’t see any patterns. So I stopped.

I made a third attempt to try to look at ingredients, rather than categories of food or individual food items. I came up with a short list of potential contenders, but repeated testing of consuming those ingredients didn’t do me any good. I stopped, again.

When I first went to the GI doctor in fall of 2020, one of the questions he asked was whether there was any pattern between my symptoms and what I was eating. “No,” I breathed out in a frustrated sigh. “I can’t find any patterns in what I’m eating and the symptoms.”

So we didn’t go down that rabbit hole.

At the start of 2021, though, I was sick and tired (of being sick and tired with GI symptoms for going on a year) and tried again. I decided that some of my “worst” symptoms happened after I consumed onions, so I tried removing obvious sources of onion from my diet. That evolved to onion and garlic, but I realized almost everything I ate also had onion powder or garlic powder, so I tried avoiding those. It helped, some. That then led me to research more, learn about the categorization of FODMAPs, and try a low-FODMAP diet in mid/fall 2021. That helped some.

Then I found out I actually had exocrine pancreatic insufficiency and it all made sense: what my symptoms were, why they were happening, and why the numerous previous tracking attempts were not successful.

You wouldn’t think I’d start tracking again, but I did. Although this time, finally, was different.

When I realized I had EPI, I learned that my body was no longer producing enough digestive enzymes to help my body digest fat, protein, and carbs. Because I’m a person with type 1 diabetes and have been correlating my insulin doses to my carbohydrate consumption for 20+ years, it seemed logical to me to track the amount of fat and protein in what I was eating, track my enzyme (PERT) dosing, and see if there were any correlations that indicated my doses needed to be more or less.

My spreadsheet involved recording the outcome of the previous day’s symptoms, and I had a section for entering multiple things that I ate throughout the day and the number of enzymes. I wrote a short description of my meal (“butter chicken” or “frozen pizza” or “chicken nuggets and veggies”), the estimate of fat and protein counts for the meal, and the number of enzymes I took for that meal. I had columns on the left that added up the total amount of fat and protein for the day, and the total number of enzymes.

It became very apparent to me – within two days – that the dose of the enzymes relative to the quantity of fat and protein I was eating mattered. I used this information to titrate (adjust) my enzyme dose and better match the enzymes to the amount of fat or protein I was eating. It was successful.

I kept writing down what I was eating, though.

In part, because it became a quick reference library to find the “counts” of a previous meal that I was duplicating, without having to re-do the burdensome math of adding up all the ingredients and counting them out for a typical portion size.

It also helped me see that within the first month, I was definitely improving, but not all the way – in terms of fully reducing and eliminating all of my symptoms. So I continued to use it to titrate my enzyme doses.

Then it helped me carefully work my way through re-adding food items and ingredients that I had been avoiding (like onions, apples, and pears) and proving to my brain that those were the result of enzyme insufficiency, not food intolerances. Once I had a working system for determining how to dose enzymes, it became a lot easier to see when I had slight symptoms from slightly getting my dosing wrong or majorly mis-estimating the fat and protein in what I was eating.

It provided me with a feedback loop that doesn’t really exist in EPI and GI conditions, and it was a daily, informative, real-time feedback loop.

As I reached the end of my first year of dosing with PERT, though, I was still using my spreadsheet. It surprised me, actually. Did I need to be using it? Not all the time. But the biggest reason I kept using it relates to how I often eat. I often look at an ‘entree’ for protein and then ‘build’ the rest of my meal around that, to help make sure I’m getting enough protein to fuel my ultrarunning endeavors. So I pick my entree/main thing I’m eating and put it in my spreadsheet under the fat and protein columns (=17 g of fat, =20 g of protein), for example, then decide what I’m going to eat to go with it. Say I add a bag of cheddar popcorn, so that becomes (=17+9 g of fat) and (=20+2 g of protein), and when I hit enter, those cells now tell me it’s 26 g of fat and 22 g of protein for the meal, which tells my brain (and I also tell the spreadsheet) that I’ll take 1 PERT pill for that. So I use the spreadsheet functionally to “build” what I’m eating and calculate the total grams of protein and fat; which helps me ‘calculate’ how much PERT to take (based on my previous titration efforts I know I can do up to 30g of fat and protein each in one PERT pill of the size of my prescription)

Example in my spreadsheet showing a meal and the in-progress data entry of entering the formula to add up two meal items' worth of fat and protein

Essentially, this has become a real-time calculator to add up the numbers every time I eat. Sure, I could do this in my head, but I’m usually multitasking and deciding what I want to eat and writing it down, doing something else, doing yet something else, then going to make my food and eat it. This helps me remember, between the time I decided – sometimes minutes, sometimes hours in advance of when I start eating and need to actually take the enzymes – what the counts are and what the PERT dosing needs to be.

I have done some neat retrospective analysis, of course – last year I had estimated that I took thousands of PERT pills (more on that here). I was able to do that not because it’s “fun” to track every pill that I swallow, but because I had, as a result of functional self-tracking of what I was eating to determine my PERT dosing for everything I ate, had a record of 99% of the enzyme pills that I took last year.

I do have some things that I’m no longer entering in my spreadsheet, which is why it’s only 99% of what I eat. There are some things like a quick snack where I grab it and the OTC enzymes to match without thought, and swallow the pills and eat the snack and don’t write it down. That maybe happens once a week. Generally, though, if I’m eating multiple things (like for a meal), then it’s incredibly useful in that moment to use my spreadsheet to add up all the counts to get my dosing right. If I don’t do that, my dosing is often off, and even a little bit “off” can cause uncomfortable and annoying symptoms the rest of the day, overnight, and into the next morning.

So, I have quite the incentive to use this spreadsheet to make sure that I get my dosing right. It’s functional: not for the perceived “fun” of writing things down.

It’s the same thing that happens when I run long runs. I need to fuel my runs, and fuel (food) means enzymes. Figuring out how many enzymes to dose as I’m running 6, 9, or 25 hours into a run gets increasingly harder. I found that what works for me is having a pre-built list of the fuel options; and a spreadsheet where I quickly on my phone open it and tap a drop down list to mark what I’m eating, and it pulls in the counts from the library and tells me how many enzymes to take for that fuel (which I’ve already pre-calculated).

It’s useful in real-time for helping me dose the right amount of enzymes for the fuel that I need and am taking every 30 minutes throughout my run. It’s also useful for helping me stay on top of my goal amounts of calories and sodium to make sure I’m fueling enough of the right things (for running in general), which is something that can be hard to do the longer I run. (More about this method and a template for anyone who wants to track similarly here.)

The TL;DR point of this is: I don’t track things for fun. I track things if and when they’re functionally useful, and primarily that is in real-time medical decision making.

These methods may not make sense to you, and don’t have to.

It may not be a method that works for you, or you may not have the situation that I’m in (T1D, Graves, celiac, and EPI – fun!) that necessitates these, or you may not have the goals that I have (ultrarunning). That’s ok!

But don’t say that you “couldn’t” do something. You ‘couldn’t’ track what you consumed when you ran or you ‘couldn’t’ write down what you were eating or you ‘couldn’t’ take that many pills or you ‘couldn’t’ inject insulin or…

You could, if you needed to, and if you decided it was the way that you could and would be able to achieve your goals.

Two New Children’s Books – And How I Illustrated Them Without Being An Illustrator

I wrote two new books! You can find “Cooper’s Crutches” and “Chloe’s Cookies” on Amazon in paperback and Kindle formats.

Two children's books lay on the carpet: Cooper's Crutches and Chloe's Cookies, both written by Dana M. Lewis

One of these books I wrote years ago, about a month or so after I broke my ankle, inspired by the initial reactions from one of my nephews about me being on crutches. This new book is called Cooper’s Crutches.

I let it sit for several years, though, because I didn’t have illustrations for it. I’ve used a different illustrator or artist for each of my books so far.

A few weeks ago, though, I started thinking about experimenting with AI-driven illustrations for various projects, including wondering whether I could illustrate a children’s book or other projects with it.

The answer is: not yet. It’s hard to create a character who persists throughout image generation for enough scenes that can fit a two-dozen page storyline, although it would probably work for one or two images! (Especially if you managed to AI-illustrate a character that you could then place in various AI-illustrated scenes. The challenge is also having different poses for the same character, to illustrate a story.)

It then occurred to me to search around and I stumbled across a library of free, open source illustrations. Woohoo! Maybe those would work. Actually, I couldn’t even download that one due to a bug in their site, so I started searching (now that I knew to look for it) and found several other sets of illustrations. I even found a site called Blush that had a series of illustrations by various artists, and a web interface (GUI) that allowed you to modify images slightly then download them.

It’s like paper dolls, but digital – you can adjust the coloring of the hair, hair style, accessories, etc to modify the illustrated character.

I gave it a try, building some illustrations and downloading them. I then did some DIY-ing again in PowerPoint to modify them to help illustrate the full story in my children’s book. I printed a proof copy, but the versions I had downloaded for free were too low resolution and were fuzzy. However, the idea as a whole had worked great! I signed up for a free trial of the “Pro” version of Blush which enabled me to download both high-resolution PNG (image) files as well as SVG files.

Having SVG files theoretically would enable me to further modify and customize these, but as a non-illustrator even though I could load them in Figma and modify them, I still struggled to export them as high-enough resolution to work for printing in a book. I gave up and went back to DIY-ing the modifications in PowerPoint. They’re not perfect, but for the use case of my books (for a very small, niche audience), I doubt they care that they’re not perfect.

Here’s a selection of a few of the pages (not in order) in Cooper’s Crutches:

Excerpt images from Cooper's Crutches by Dana M. Lewis

At the same time that I started playing with these illustrations, I wondered whether I had any more ideas for books that I could illustrate at the same time with the same methods. I had had Cooper’s book written and waiting to illustrate; I now had a method to illustrate, but I wasn’t sure what story to illustrate.

But like all of my children’s books, inspiration again struck based on a situation and conversation I had with one of my nieces. She’s newly lactose intolerant and is taking lactase any time she has milk, like with milk and cookies for a bedtime snack. Lactase is an enzyme…and I’ve been taking enzymes of another sort this year, for exocrine pancreatic insufficiency.

Thus the next book, Chloe’s Cookies, was created!

Here’s a selection of a few of the pages (not in order) in the book:

Excerpt images from Chloe's Cookies, by Dana M. Lewis

Both Cooper’s Crutches and Chloe’s Cookies are illustrated with illustrations from a variety of artists who make their work available on Blush, including: Veronica Iezzi; Susana Salas; Pau Barbaro; Ivan Mesaroš; Mariana Gonzalez Vega; Deivid Saenz; and Cezar Berje.

The neat thing about Blush is their license: you can use the illustrations in any way, including commercial products, and you can modify or combine it with other works (like I did, modifying the images and combining illustrations from various artists) however you like.

I think I’ve likely maximized my use of Blush between these two books; unless other collections get uploaded in the future. But if you need a handful of illustrations that you can customize, definitely check it out!

And if you have ideas for other cool illustration libraries that I could use for future books, please let me know! (Or if you’re an artist who would like to contribute to one of my future books. :) )

TLDR:

I have two new children’s books, and you can find “Cooper’s Crutches” and “Chloe’s Cookies” on Amazon in paperback and Kindle formats.

Illustrating Children's Books without being an illustrator, plus introducting two new children's books by Dana M. Lewis

Looking Back Through 2022 (What You May Have Missed)

I ended up writing a post last year recapping 2021, in part because I felt like I did hardly anything – which wasn’t true. In part, that was based on my body having a number of things going on that I didn’t know at the time. I figured those out in 2022 which made 2022 hard and also provided me with a sense of accomplishment as I tackled some of these new challenges.

For 2022, I have a very different feeling looking back on the entire year, which makes me so happy because it was night and day (different) compared to this time last year.

One major example? Exocrine Pancreatic Insufficiency.

I started taking enzymes (pancreatic enzyme replacement therapy, known as PERT) in early January. And they clearly worked, hooray!

I quickly realized that like insulin, PERT dosing needed to be based on the contents of my meals. I figured out how to effectively titrate for each meal and within a month or two was reliably dosing effectively with everything I was eating and drinking. And, I was writing and sharing my knowledge with others – you can see many of the posts I wrote collected at DIYPS.org/EPI.

I also designed and built an open source web calculator to help others figure out their ratios of lipase and fat and protease and protein to help them improve their dosing.

I even published a peer-reviewed journal article about EPI – submitted within 4 months of confirming that I had it! You can read that paper here with an analysis of glucose data from both before and after starting PERT. It’s a really neat example that I hope will pave the way for answering many questions we all have about how particular medications possibly affect glucose levels (instead of simply being warned that they “may cause hypoglycemia or hyperglycemia” which is vague and unhelpful.)

I also had my eyes opened to having another chronic disease that has very, very expensive medication with no generic medication option available (and OTCs may or may not work well). Here’s some of the math I did on the cost of living with EPI and diabetes (and celiac and Graves) for a year, in case you missed it.

Another other challenge+success was running (again), but with a 6 week forced break (ha) because I massively broke a toe in July 2022.

That was physically painful and frustrating for delaying my ultramarathon training.

I had been successfully figuring out how to run and fuel with enzymes for EPI; I even built a DIY macronutrient tracker and shared a template so others can use it. I ran a 50k with a river crossing in early June and was on track to target my 100 mile run in early fall.

However with the broken toe, I took the time off needed and carefully built back up, put a lot of planning into it, and made my attempt in late October instead.

I succeeded in running ~82 miles in ~25 hours, all in one go!

I am immensely proud of that run for so many reasons, some of which are general pride at the accomplishment and others are specific, including:

  • Doing something I didn’t think I could do which is running all day and all night without stopping
  • Doing this as a solo or “DIY” self-organized ultra
  • Eating every 30 minutes like clockwork, consuming enzymes (more than 92 pills!), which means 50 snacks consumed. No GI issues, either, which is remarkable even for an ultrarunner without EPI!
  • Generally figuring out all the plans and logistics needed to be able to handle such a run, especially when dealing with type 1 diabetes, celiac, EPI, and Graves
  • Not causing any injuries, and in fact recovering remarkably fast which shows how effective my training and ‘race’ strategy were.

On top of this all, I achieved my biggest-ever running year, with more than 1,333 miles run this year. This is 300+ more than my previous best from last year which was the first time I crossed 1,000 miles in a year.

Professionally, I did quite a lot of miscellaneous writing, research, and other activities.

I spent a lot of time doing research. I also peer reviewed more than 24 papers for academic journals. I was asked to join an editorial board for a journal. I served on 2 grant review committees/programs.

I also wrote a lot.

*by ton, I mean way more than the past couple of years combined. Some of that has been due to getting some energy back once I’ve fixed missing enzyme and mis-adjusted hormone levels in my body! I’m up to 40+ blog posts this year.

And personally, the punches felt like they kept coming, because this year we also found out that I have Graves’ disease, taking my chronic disease count up to 4. Argh. (T1D, celiac, EPI, and now Graves’, for those curious about my list.)

My experience with Graves’ has included symptoms of subclinical hyperthyroidism (although my T3 and T4 are in range), and I have chosen to try thyroid medication in order to manage the really bothersome Graves’-related eye symptoms. That’s been an ongoing process and the symptoms of this have been up and down a number of times as I went on medication, reduced medication levels, etc.

What I’ve learned from my experience with both EPI and Graves’ in the same year is that there are some huge gaps in medical knowledge around how these things actually work and how to use real-world data (whether patient-recorded data or wearable-tracked data) to help with diagnosis, treatment (including medication titration), etc. So the upside to this is I have quite a few new projects and articles coming to fruition to help tackle some of the gaps that I fell into or spotted this year.

And that’s why I’m feeling optimistic, and like I accomplished quite a bit more in 2022 than in 2021. Some of it is the satisfaction of knowing the core two reasons why the previous year felt so physically bad; hopefully no more unsolved mysteries or additional chronic diseases will pop up in the next few years. Yet some of it is also the satisfaction of solving problems and creating solutions that I’m uniquely poised, due to my past experiences and skillsets, to solve. That feels good, and it feels good as always to get to channel my experiences and expertise to try to create solutions with words or code or research to help other people.

More Tools To Help Diabetes Researchers and Other Researchers

A few years ago I made a big deal about a tool I had created, converting someone’s web tool into a command line tool to be able to take complex json data and convert it to csv. Years later, I (and thousands of others, it’s been downloaded 1600+ times!) am still using this tool because there’s nothing better that I’ve found when you have data that you don’t know the data structure for or the data structure varies across files.

I ended up creating a repository on Github to store it with details on running it, and have expanded it over the last (almost) six years as I and others have added additional tools. For example, it’s where Arsalan, one of my frequent collaborators, and I store open source code from some of our recent papers.

Recently, I added two more small scripts. This was motivated to help researchers who have been successfully using the OpenAPS Data Commons and want to update their dataset with a later version of the data. Chances are, they have cleaned and worked with a previous version of the dataset, and instead of having to re-clean all of the data all over again, this set of scripts should help narrow down what the “new” data is that needs to be pulled out, cleaned, and appended to a previously cleaned dataset.

You can check out the full tool repository here (it has several other scripts in addition to the ones mentioned above). The latest are two python scripts that checks the content of an existing folder and lists out the memberID and filenames for each. This is useful to run on an existing, already-cleaned dataset to see what you currently have. It can also be run on the latest/newest/bigger dataset available. Then, the second script can be run to compare the memberIDs and file names in the newer/biggest/larger dataset against the previously cleaned/smaller/older dataset. Those that “match” already exist in the version of the dataset they have; they don’t need to be pulled again. The others don’t exist in the current dataset, and can be popped into a script to pull out just those data files to then be cleaned and appended to the existing dataset.

As a heads up specifically for those working with the OpenAPS Data Commons, it is best practice to name/describe the version of the dataset via the size. For example, you might be working with the n=88 or n=122 version of the dataset. If you used the above method, you would then describe it along the lines of taking and cleaning the n=122 version; selecting new files available from the n=183 version and appending them to the n=122 version; and the resulting dataset is n=(122+number of new files used).

Folks who access the n=183 version of the dataset and haven’t previously used a smaller version of the dataset can reference using the n=183 and clarifying how many files they ended up using, e.g. describing that they followed X method to clean the data starting from the n=183 version and their resulting dataset is n=166, for example.

It is important to clarify which version and size of the dataset is being used.

PS – this method works on other data file types, too! You’d change the variable/column header names in the script to update this for other cases.

We Have Changed the Standards of Care for People With Diabetes

We’ve helped change the standard of care for people with diabetes, with open source automated insulin delivery.

I get citation alerts sometimes when my previous research papers or articles are cited. For the last few years, I get notifications when new consensus guidelines or research comes out that reference or include mention of open source automated insulin delivery (AID). At this time of year, the ADA Standards of Care is released for the following year, and I find out usually via these citation alerts.

Why?

This year, in 2023, there’s a section on open source automated insulin delivery!

A screenshot of the 2023 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

But did you know, that’s not really new? Here’s what the 2022 version said:

A screenshot of the 2022 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

And 2021 also included…

A screenshot of the 2021 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

And 2020? Yup, it was there, too.

A screenshot of the 2020 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

All the way back to 2019!

A screenshot of the 2019 ADA Standards of Care under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

If you read them in chronological order, you can see quite a shift.

In 2019, it was a single sentence noting their existence under a sub-heading of “Future Systems” under AID. In 2020, the content graduated to a full paragraph at the end of the AID section (that year just called “sensor-augmented pumps”). In 2021, it was the same paragraph under the AID section heading. 2022 was the year it graduated to having its own heading calling it out, with a specific evidence based recommendation! 2023 is basically the same as 2022.

So what does it say?

It points out patients are using open source AID (which they highlight as do-it-yourself closed loop systems). It sort of incorrectly suggests healthcare professionals can’t prescribe these systems (they can, actually – providers can prescribe all kinds of things that are off-label – there’s just not much point of a “prescription” unless it’s needed for a person’s elementary school (or similar) who has a policy to only support “prescribed” devices).

And then, most importantly, it points out that regardless, healthcare providers should assist in diabetes management and support patient choice to ensure the safety of people with diabetes. YAY!

“…it is crucial to keep people with diabetes safe if they are using these methods for automated insulin delivery. Part of this entails ensuring people have a backup plan in case of pump failure. Additionally, in most DIY systems, insulin doses are adjusted based on the pump settings for basal rates, carbohydrate ratios, correction doses, and insulin activity. Therefore, these settings can be evaluated and modified based on the individual’s insulin requirements.”

You’ll notice they call out having a backup plan in case of pump failure.

Well, yeah.

That should be true of *any* AID system or standalone insulin pump. This highlights that the needs of people using open source AID in terms of healthcare support are not that different from people choosing other types of diabetes therapies and technologies.

It is really meaningful that they are specifically calling out supporting people living with diabetes. Regardless of technology choices, people with diabetes should be supported by their healthcare providers. Full stop. This is highlighted and increasingly emphasized, thanks to the movement of individuals using open source automated insulin delivery. But the benefits of this is not limited to those of us using open source automated insulin delivery; this spills over and expands to people using different types of BG meters, CGM, insulin pumps, insulin pens, syringes, etc.

No matter their choice of tools or technologies, people with diabetes SHOULD be supported in THEIR choices. Not choices limited by healthcare providers, who might only suggest specific tools that they (healthcare providers) have been trained on or are familiar with – but the choices of the patient.

In future years, I expect the ADA Standard of Care for 2024 and beyond to evolve, in respect to the section on open source automated insulin delivery.

The evidence grading should increase from “E” (which stands for “Expert consensus or clinical experience”), because there is now a full randomized control trial in the New England Journal of Medicine on open source automated insulin delivery, in addition to the continuation results (24 weeks following the RCT; 48 full weeks of data) accepted for publication (presented at EASD 2022), and a myriad of other studies ranging from retrospective to prospective trials. The evidence is out there, so I expect that this evidence grading and the text of the recommendation text will evolve accordingly to catch up to the evidence that exists. (The standards of care are based on literature available up to the middle of the previous year; much of the things I’ve cited above came out in later 2022, so it matches the methodology to not be included until the following year; these newest articles should be scooped up by searches up to July 2023 for the 2024 edition.)

In the meantime, I wish more people with diabetes were aware of the Standards of Care and could use them in discussion with providers who may not be as happy with their choices. (That’s part of the reason I wrote this post!)

I also wish we patients didn’t have to be aware of this and don’t have to argue our cases for support of our choices from healthcare providers.

But hopefully over time, this paradigm of supporting patient choice will continue to grow in the culture of healthcare providers and truly become the standard of care for everyone, without any personal advocacy required.

Did you know? We helped change the standards of care for people living with diabetes. By Dana M. Lewis from DIYPS.org

Dealing With And Avoiding Chronic Disease Management Burnout

I’ve been thinking about juggling lately, especially as this year I’ve had to add a series of new habits and behaviors and medications to manage not one but two new chronic diseases. Getting one new chronic disease is hard; getting another is hard; and the challenges aren’t necessarily linear or exponential, and they’re not necessarily obvious up front.

But sometimes the challenges do compound over time.

In January when I started taking pancreatic enzyme replacement therapy (PERT) for exocrine pancreatic insufficiency (EPI or PEI), I had to teach myself to remember to take enzymes at every meal. Not just some time around the meal, but 100% every time before (by only a few minutes) or right at the start of the meal. With PERT, the timing matters for efficacy. I have a fast/short feedback loop – if I mis-time my enzymes or don’t take them, I get varying symptoms within a few hours that then bother me for the rest of the day, overnight, and into the next morning. So I’m very incentivized to take the enzymes and time them effectively when I eat. However, as I started to travel (my first trip out of the country since the pandemic started), I was nervous about trying to adapt to travel and being out of my routine at home where I’ve placed enzymes in visible eye sight of every location where I might consume food. Thankfully, that all went well and I managed not to forget taking enzymes when I ate and all was well. But I know I’m still building the habit of taking enzymes and eating, and that involves both always having enzymes with me and remembering to get them out and take them. It sounds like a trivial amount of things to remember, but this is added on top of everything else I’m doing for managing my health and well-being.

This includes other “simple” things like taking my allergy medications – because I’m allergic to cats (and we have them!), trees, dust, etc. And vitamins (I’m vitamin D deficient when I don’t take vitamin D).

And brushing my teeth and flossing.

You do that too, right? Or maybe you’re one of those people who struggle to remember to floss. It’s normal.

The list of well-being management gets kind of long when you think about all the every day activities and habits you have to help you stay at your best possible health.

Eat healthy! (You do that, right? 😉 )

Hydrate!

Exercise!

Etc.

I’ve also got the background habits of 20 years of living with diabetes: keeping my pump sites on my body; refilling the reservoir and changing the pump site every few days; making sure the insulin doesn’t get too hot or cold; making sure my CGM data isn’t too noisy; changing my CGM sensor when needed; estimating ballpark carbs and entering them and/or temporary targets to indicate exercise into my open source AID; keeping my AID powered; keeping my pump powered; keeping my phone – which has my CGM visibility on it – powered and nearby. Ordering supplies – batteries and pump sites and reservoirs and CGM transmitters and CGM sensors and insulin and glucagon.

Some of these are daily or every few days tasks; others are once or twice a month or every three months.

Those stack up sometimes where I need to refill a reservoir and oops, get another bottle of insulin out of the fridge which reminds me to make a note to check on my shipment of insulin which hasn’t arrived yet. I also need to change my pump site and my CGM sensor is expiring at bedtime so I need to also go ahead and change it so the CGM warmup period will be done by the time I go to sleep. I want to refill my reservoir and change the pump site after dinner since the dinner insulin is more effective on the existing site; I think of this as I pull my enzymes out to swallow as I start eating. I’ll do the CGM insertion when I do my pump site change. But the CGM warmup period is then in the after-dinner timeframe so I then have to keep an eye on things manually because my AID can’t function without CGM data so 2 hours (or more) of warmup means extra manual diabetes attention. While I’m doing that, I also need to remember to take my allergy medication and vitamin D, plus remembering to take my new thyroid medication at bedtime.

Any given day, that set of overlapping scenarios may be totally fine, and I don’t think anything of them.

On other days, where I might be stressed or overwhelmed by something else – even if it’s not health-related – that can make the above scenario feel overwhelmingly difficult.

One of the strategies I discussed in a previous post relative to planning travel or busy periods like holidays is trying to separate tasks in advance (like pre-filling a reservoir), so the action tasks (inserting a pump site and hooking it up to a new reservoir) don’t take as long. That works well, if you know the busy period is coming.

But sometimes you don’t have awareness of a forthcoming busy period and life happens. Or it’s not necessarily busy, per se, but you start to get overwhelmed and stressed and that leaks over into the necessary care and feeding of medical stuff, like managing pump sites and reservoirs and sensors and medication.

You might start negotiating with yourself: “do I really need to change that pump site today? It can wait until tomorrow”. Or you might wait until your reservoir actually hits the ‘0’ level (which isn’t fully 0; there’s a few units plus or minus some bubbles left) to refill it. Or other things like that, whether it’s not entering carbs into your pump or AID or not bolusing. Depending on your system/setup, those things may not be a big deal. And for a day or two, they’re likely not a big deal overall.

But falling into the rut of these becoming the new normal is not optimal – that’s burnout, and I try to avoid getting there.

When I start to have some of those thought patterns and recognize that I have begun negotiating with myself, I try to voice how I’m feeling to myself and my spouse or family or friends. I tell them I’m starting to feel “crispy” (around the edges) – indicating I’m not fully burnt out, but I could get all the way to burnout if I don’t temporarily change some things. (Or permanently, but often for me temporary shifts are effective.)

One of the first things I do is think through what is the bare minimum necessary care I need to take. I go above and beyond and optimize a LOT of things to get above-target outcomes in most areas. While I like to do those things, they’re not necessary. So I think through the list of necessary things, like: keeping a working pump site on my body; keeping insulin in a reservoir attached to my pump; keeping my CGM sensor working; and keeping my AID powered and nearby.

That then leaves a pile of tasks to consider:

  1. Not doing at all for ___ period of time
  2. Not doing myself but asking someone else to do for ____ period of time

And then I either ask or accept the offers of help I get to do some of those things.

When I was in high school and college, I would have weekends where I would ask my parents to help. They would take on the task of carb counting (or estimating) so I didn’t have to. (They also did HEAPS of work for years while I was on their insurance to order and keep supplies in the house and wrangle with insurance so I didn’t have to – that was huge background help that I greatly appreciated.)

Nowadays, there are still things I can and do get other people to help with. Sometimes it’s listening to me vent (with a clear warning that I’m just venting and don’t need suggestions); my parents often still fill that role for me! Since I’m now married and no longer living alone, Scott offers a lot of support especially during those times. Sometimes he fills reservoirs for me, or more often will bring me supplies from the cabinet or fridge to wherever I’m sitting (or even in bed so I don’t have to get up to go change my site). Or he’ll help evaluate and determine that something can wait until a later time to do (e.g. change pump site at another time). Sometimes I get him to open boxes for me and we re-organize how my supplies are to make them easier to grab and go.

Those are diabetes-specific examples, but I’ve also written about how helpful additional help can be sometimes for EPI too, especially with weighing and estimating macronutrient counts so I can figure out my PERT dosing. Or making food once I’ve decided what I want to eat, again so I can separate deciding what to eat and what the counts/dosing is from the action tasks of preparing or cooking the food.

For celiac, one of the biggest changes that has helped was Scott asking family members to load the “Find Me Gluten Free” app on their phone. That way, if we were going out to eat or finding a takeout option, instead of everyone ALWAYS turning to me and saying “what are the gluten free options?”, they could occasionally also skim the app to see what some of the obvious choices were, so I wasn’t always having to drive the family decision making on where to eat.

If you don’t have a chronic illness (or multiple chronic illnesses), these might not sound like a big deal. If you do (even if you have a different set of chronic disease(s)), maybe you recognize some of this.

There are estimates that people with diabetes make hundreds of decisions and actions a day for managing living with diabetes. Multiply that times 20 years. Ditto for celiac, for identifying and preparing and guarding against cross-contamination of said gluten-free food – multiply that work every day times 14 years. And now a year’s worth of *every* time I consider eating anything to estimate (with reading nutrition labels or calculating combinations based on food labels or weighing and googling and estimating compared to other nutrition labels) how much enzymes to take and remembering to swallow the right number of pills at the optimal times. Plus the moral and financial weight of deciding how to balance efficacy with cost of these enzymes. Plus several months now of an additional life-critical medication.

It’s so much work.

It’s easy to get outright burnt out, and common to start to feel a little “crispy” around the edges at times.

If you find yourself in this position, know that it’s normal.

You’re doing a lot, and you’re doing a great job to keep yourself alive.

You can’t do 110% all the time, though, so it is ok to figure out what is the bare minimum and some days throughout the year, just do that, so you can go back to 110%-ing it (or 100%-ing) the other days.

With practice, you will increasingly be able to spot patterns of scenarios or times of the year when you typically get crispy, and maybe you can eventually figure out strategies to adapt in advance (see me over here pre-filling reservoirs ahead of Thanksgiving last week and planning when I’d change my pump site and planning exactly what I would eat for 3 days).

TLDR:

  • Living with chronic disease is hard. And the more diseases you have, the harder it can be.
  • If you live with or love someone with chronic disease(s), ask them if you can help. If they’re venting, ask if they want you to listen (valuable!) or to let you know if at any point they want help brainstorming or for you to provide suggestions (helpful *if* desired and requested).
  • If you’re the one living with chronic disease(s), consider asking for help, even with small things. Don’t let your own judgment (“I should be able to do this!”) get in your way of asking for help. Try it for a day or for a weekend.
Dealing with and avoiding chronic disease burnout by Dana M. Lewis

Costs, Price and Calculations for Living With Diabetes and Exocrine Pancreatic Insufficiency and Celiac and Graves

Living with diabetes is expensive. However, the cost and price goes beyond the cost of insulin, which you may have heard about lately. In addition to insulin, you need tools and supplies to inject the insulin (e.g. syringes, insulin pens, or an insulin pump). Depending on those methods, you need additional supplies (e.g. pen needles for insulin pens, reservoirs and infusion sets for insulin pumps). You also need blood glucose monitoring supplies, whether that is meter and up to a dozen glucose test strips a day and/or a continuous glucose monitor which is made up of a disposable sensor and a reusable transmitter.

All those costs add up on a daily basis for people living with diabetes, even if you have health insurance.

Understanding the costs of living with chronic illness with health insurance in the US

Every year in the US we have “open enrollment” time when we opt-in or enroll into our choice of health insurance plan for the following year. I am lucky and have access to insurance through my husband’s employer, who covers part of the cost for him and me (as a spouse). We have a high-deductible (HSA-qualified) health plan, so our deductible (the amount we must pay before insurance begins to pay for a portion of the costs) is usually around $1,500-$2,500 USD for me. After that, I might pay either a fixed copay ($10 or $25 or similar) for a doctor’s visit, or a percentage (10% or 20%) while the insurance covers the rest of the cost. Then there is a fixed “out of pocket (OOP) max” cost for the year, which might be something like $3,000 USD total. Sometimes the OOP max is pretty close to the deductible, because we typically choose the ‘high deductible’ plan (with no monthly cost for the insurance plan) over a plan where we have a lower deductible but pay a monthly premium for the insurance.

That’s a very rough summary of how I see my health insurance. Everyone has different health insurers (the company providing the insurance) and different plans (the costs will be different based on whether it’s through a different employer or if it’s an individual plan).

So the costs to people with diabetes can vary quite a bit in the US, depending on whether you have insurance: there is variation in the monthly cost of the plan, the amount of the deductible, and the amount of the out of pocket max.

In order to choose my plan for the following year, I look at the total cost for the year of my health supplies and health care, then look at the plans. Usually, the high deductible plan “feels” more expensive because I might have to reach $2,500 before insurance kicks in; however, the out of pocket cap may only be $500 beyond that, so that I’m going to pay a maximum of $3,000 for the year in insurance-covered costs*. There are other types of plans that are lower deductible, such as insurance kicking in after a $250 deductible. That sounds better, right? Well, those plans come with a monthly cost (premium) of $250. So you need to factor that in ($250×12=$3,000) alongside the deductible and any costs up to the out of pocket max ($2,500). From this, you’d pay the $3,000 total yearly premium plus up to $2,500 OOP, or $5,500. Thus, even though it has a lower deductible and OOP, you’re in total paying much more ($5,500 vs $3,000) if you’re someone like me.

Why? Because I have >$3,000 of health supply costs every year.

This is why every few years (mostly after I forget what I learned the last time), I do the math on how much my supply costs to see if I’m still making the most cost-effective choices for me with my insurance plans.

I wanted to share this math methodology below, also because this year I have new variables, which are two new chronic diseases (exocrine pancreatic insufficiency and Graves) that add additional costs and healthcare needs and require me to want to re-check my math.

* Clarifying that previously and most years I pay out of pocket for minor, relatively low-cost health supplies like vitamins or tape to cover my CGM that I buy and do not get through insurance coverage, so my total costs are usually over that OOP max, but likely not by more than a few hundred dollars.

Note: Do not attempt to use this as an absolute cost of diabetes for anyone else. These numbers are based on my use cases in terms of volume of insulin, insurance coverage, etc. Ditto for trying to use the costs for EPI. Where relevant below, I provide rough estimates of my methodology so that another individual with diabetes or EPI/PEI could use similar methods to calculate their own rough costs, if they wished. However, this cannot be used to determine any average cost to people with diabetes more broadly, so don’t excerpt or cite this in those ways. This is purely n=1 math with conclusions that are unique to this n=1 (aka me) but with methods that can be extended for others.

I’ll cover my estimates for costs of diabetes, celiac, exocrine pancreatic insufficiency (EPI or PEI), and Graves’ disease below. This doesn’t account for visits (e.g. doctor’s appointments), lab tests, or other health costs such as x-rays for breaking bones, because those vary quite a bit year to year and aren’t guaranteed fixed costs. But the supplies I need for diabetes, EPI, etc are fixed costs, which I use to anchor my math. Given that they end up well above my OOP max, the then-variable amount of other costs (doctor’s appointments, lab work, etc) is minor in comparison and irrelevant regardless of how much it varies year to year.

The costs (for me) of daily living with diabetes

(You read the caveat note above, right? This is my math based on my volume of insulin, food intake, personal insulin sensitivity, etc. Lots of variables, all unique to me.)

To calculate the yearly costs of living with diabetes, I make a list of my diabetes supplies.

Primarily for me, those are:

  • Insulin
  • CGM sensors
  • CGM transmitter
  • Pump sites
  • Reservoirs

(Not included: meter/test strips or the cost of a pump or the cost of any hardware I’m using for my open source automated insulin delivery. I’ve not bought a new in-warranty pump in years, and that alone takes care of the OOP max on my insurance plan if I were to buy a pump that year. Anyway, the above list is really my recurring regular costs, but if you were purchasing a pump or on a subscription plan for a pump, you’d calculate that in as well).

First, I calculate the daily cost of insulin. I take the cost of a vial of my insulin and divide it by 1,000, because that’s how many units a vial of insulin has. Then I multiply that by the average number of units I use per day to get the cost per day of insulin, which for me is $4.36. (The yearly cost of insulin would be $1,592.)

Then, I calculate my CGM sensors. I take the total cost for a 3 month order of sensors and divide by the number of sensors; then divide by 10 days (because a sensor lasts about 10 days) to get the cost per day of a CGM sensor: about $11 per day. But, you also have to add in the cost of the re-usable transmitter. Again, factor the cost of a transmitter over the number of days it covers; for me it’s about $2 per day. In total, the cost per day of CGM is about $13 and the yearly cost of CGM is roughly $4,765.

Next is pump sites and reservoirs. You need both to go with your insulin pump: the pump site is the catheter site into your body and the tubing (this cumulatively gets replaced every few days), and the reservoir is disposable and is filled with insulin. The cost per day of pump sites and reservoirs is about $6 ($4.67 for a pump site and $1.17 for a reservoir) and the yearly cost of pump sites and reservoirs is $2,129.

If you add up these supplies (pump sites and reservoirs, CGM sensor and transmitter, insulin), the daily cost of diabetes for me is about $23. The yearly cost of diabetes for me is $8,486.

Give that $8,486 is well over the out of pocket max cost of $3,000, you can see why that for diabetes alone there is reason to pick the high deductible plan and pay a max of $3,000 for these supplies out of pocket.

The daily and yearly costs of living with celiac disease

But I don’t just have type 1 diabetes, so the above are not my only health supply costs.

I also have celiac disease. The treatment is a 100% gluten free diet, and eating gluten free is notoriously more expensive than the standard cost of food, whether that is groceries or eating out.

However, the cost of gluten free food isn’t covered by health insurance, so that doesn’t go in my cost calculation toward pricing the best insurance plan. Yet, it does go into my “how much does it cost every day from my health conditions” mental calculation.

I recently looked at a blog post that summarized the cost of gluten free groceries by state compared to low/medium/high grocery costs for the average person. By extrapolating my state’s numbers from a high-cost grocery budget, plus adding $5 each for eating out twice a week (typically gluten free food has at least a $2-3 surcharge in addition to being at higher cost restaurants, plus the fact that I can’t go eat at most drive-throughs, which is why I use $5/meal to offset the combined cost of the actual surcharge plus my actual options being more expensive).

I ended up estimating about a $3 daily average higher cost of being gluten free, or $1,100 per year cost of eating gluten free for celiac.

That’s probably an underestimate for me, but to give a ballpark, that’s another $1,000 or more I’m paying out of pocket in addition to healthcare costs through insurance.

The daily and yearly cost of living with exocrine pancreatic insufficiency and the daily and yearly cost of pancreatic enzyme replacement therapy

I spent a pleasant (so to speak) dozen or so years when “all” I had to pay for was diabetes supplies and gluten free food. However, in 2022, I was diagnosed with exocrine pancreatic insufficiency (and more recently also Graves’ disease, more on that cost below) and because I have spent ~20 years paying for diabetes, I wasn’t super surprised at the costs of EPI/PEI. However, most people get extreme sticker shock (so to speak) when they learn about the costs of pancreatic enzyme replacement therapy (PERT).

In summary, since most people don’t know about it: exocrine pancreatic insufficiency occurs for a variety of reasons, but is highly correlated with all types of diabetes, celiac, and other pancreatic conditions. When you have EPI, you need to take enzymes every time you eat food to help your body digest fat, protein, and carbohydrates, because in EPI your pancreas is not producing enough enzymes to successfully break down the food on its own. (Read a lot more about EPI here.)

Like diabetes, where different people may use very different amounts of insulin, in EPI people may need very different amounts of enzymes. This, like insulin, can be influenced by their body’s makeup, and also by the composition of what they are eating.

I use PERT (pancreatic enzyme replacement therapy) to also describe the prescription enzyme pills used for EPI. There are 6 different brands approved by the FDA in the US. They also come in different sizes; e.g. Brand A has 3,000, 6,000, 12,000, 24,000, 36,000 size pills. Those size refer to the units of lipase. Brand B has 3,000, 5,000, 10,000, 15,000, 20,000, 25,000, 40,000. Brands C, D, E and F have similar variety of sizes. The point is that when people compare amounts of enzymes you need to take into account 1) how many pills are they taking and 2) how much lipase (and protease and amylase) each of those pills are.

There is no generic for PERT. PERT is made from ground up pig pancreas. It’s expensive.

There are over the counter (OTC) enzymes made from alternative (plant etc) sources. However, there are ZERO studies looking at safety and efficacy of them. They typically contain much less lipase per pill; for example, one OTC brand pill contains 4,000 units of lipase per pill, or another contains 17,500 units of lipase per pill.

You also need to factor in the reliability of these non-approved pills. The quality of production can vary drastically. I had one bottle of OTC pills that was fine; then the next bottle of OTC pills I started to find empty capsules and eventually dumped them all out of the bottle and actually used a colander to filter out all of the enzyme powder from the broken capsules. There were more than 30 dud pill capsules that I found in that batch; in a bottle of 250 that means around 12% of them were unusable. That makes the reliability of the other ones suspect as well.

A pile of powder in the sink next to a colander where a bunch of pills sit. The colander was used to filter out the loose powder. On the right of the image is a baggie with empty pill capsules, illustrating where this loose powder came from. This shows the unreliability of over the counter (OTC) enzymes.

If the reliability of these pills even making it to you without breaking can be sketchy, then you need to assume that the counts of how much lipase (and protease and amylase) may not be precisely what the label is reporting. Again, there have been no tests for efficacy of these pills, so anyone with EPI or PEI needs to use these carefully and be aware of these limitations.

This unreliability isn’t necessarily true of all brands, however, or all types of OTC enzymes. That was a common brand of pancrelipase (aka contains lipase, protease, and amylase). I’ve had more success with the reliability of a lipase-only pill that contains about 6,000 units of lipase. However, it’s more expensive per pill (and doesn’t contain any of the other enzymes). I’ve used it to “top off” a meal with my prescription PERT when my meal contains a little bit more fat than what one PERT pill would “cover” on its own.

This combination of OTC and prescription PERT is where the math starts to get complicated for determining the daily cost and yearly cost of pancreatic enzyme replacement therapy.

Let’s say that I take 6-8 prescription PERT pills every day to cover what I eat. It varies because I don’t always eat the same type or amount of food; I adjust based on what I am eating.

The cost with my insurance and a 90 day supply is $8.34 for one PERT pill.

Depending on whether I am eating less fat and protein on a particular day and only need 6 PERT, the cost per day of enzymes for EPI might be $50.04, whereas if I eat a little more and need 8 PERT, the cost per day of enzymes for EPI could be up to $66.72.

The costs per year of PERT for EPI then would range from $18,000 (~6 per day) to $24,000 (~8 per day).

Please let that sink in.

Eighteen to twenty four thousand dollars to be able to successfully digest my food for a single year, not taking into account the cost of food itself or anything else.

(See why people new to EPI get sticker shock?!)

Even though I’m used to ‘high’ healthcare costs (see above estimates of $8,000 or more per year of diabetes costs), this is a lot of money. Knowing every time that I eat it “costs” at least one $8.34 pill is stressful. Eating a bigger portion of food and needing two or three pills? It really takes a mental toll in addition to a financial cost to think about your meal costing $25.02 (for 3 pills) on top of the cost of the food itself.

This is why OTC pills are interesting, because they are drastically differently priced. The 4,000 unit of lipase multi-enzyme pill that I described costs $0.09 per pill, which is about $0.02 per 1000 units of lipase. Compared to my prescription PERT which is $0.33 per 1000 units of lipase, it’s a lot cheaper.

But again, check out those pictures above of the 4,000 units of lipase OTC pills. Can you rely on those?

Not in the same way you can with the prescription PERT.

In the course of taking 1,254 prescription PERT pills this year (so far), I have not had a single issue with one of those pills. So in part the high cost is to ensure the safety and efficacy. Compare that to 12% (or more) of the OTC pills being complete duds (empty pill capsules that have emptied their powder into the bottle) and some % of unreliability even with a not-broken capsule.

Therefore it’s not feasible to me to completely replace prescription PERT with OTC pills, although it’s tempting purely on price.

I previously wrote at a high level about the cost calculations of PERT, but given my desire to look at the annual cost for estimating my insurance plan (plus many more months of data), I went deeper into the math.

I need to take anywhere from 2-6 OTC pills (depending on the brand and size) to “match” the size of one PERT. I found a new type (to me) of OTC pills that are more units of lipase (so I need 2 to match one PERT) instead of the two other kinds (which took either 4 or 6 to match one PERT), which would enable me to cut down on the number of pills swallowed.

The number of pills swallowed matters.

So far (as of mid-November, after starting PERT in early January), I have swallowed at least 1,254 prescription PERT enzyme pills. I don’t have as much precision of numbers on my OTC pills because I don’t always log them (there’s probably a few dozen I haven’t written down, but I probably have logged 95% of them in my enzyme tracking spreadsheet that I use to help calculate the amount needed for each meal/snack and also to look at trends.), but it’s about 2,100 OTC enzyme pills swallowed.

This means cumulatively this year (which is not over), I have swallowed over 3,300 enzyme pills. That’s about 10 enzyme pills swallowed every day!

That’s a lot of swallowing.

That’s why switching to a brand that is more units of lipase per pill, where 2 of these new OTC kind matches one PERT instead of 4-6, is also significant. While it is also slightly cheaper than the combination of the two I was using previously (a lipase-only and a multi-enzyme version), it is fewer pills to achieve the same amount.

If I had taken prescription PERT instead of the OTCs, it would have saved me over 1,600 pills to swallow so far this year.

You might be thinking: take the prescription PERT! Don’t worry about the OTC pills! OMG that’s a lot of pills.

(OMG, it *is* a lot of pills: I think that as well now that I’m adding up all of these numbers.)

Thankfully, so far I am not having issues with swallowing these pills. As I get older, that might change and be a bigger factor in determining my strategy for how I dose enzymes; but right now, that’s not the biggest factor. Instead, I’m looking at efficacy (getting the right amount of enzymes to match my food), the cost (in terms of price), and then optimizing and reducing the total number of pills if I can. But the price is such a big variable that it is playing the largest role in determining my strategy.

How should we collectively pay for this?

You see, I don’t have EPI in a vacuum.

As I described at the top of the post, I already have $8,000+ of yearly diabetes costs. The $18,000 (or $24,000 or more) yearly enzyme costs are a lot. Cumulatively, just these two alone mean my supply costs are $26-32,000 (or more), excluding other healthcare costs. Thankfully, I do have insurance to cover costs after I hit my out of pocket max, but the bigger question is: who should be paying for this?

If my insurer pays more, then the employer pays more, which means employees get worse coverage on our pooled insurance plan. Premiums go up and/or the plans cover less, and the out of pocket costs to everyone goes up.

So while it is tempting to try to “stuff” all of my supply needs into insurance-covered supplies, in order to reduce my personal out of pocket costs in the short run, that raises costs for everyone in the long run.

This year, for all of those (remember I estimated 2,100 OTC pills swallowed to date) OTC pills I bought, it cost me $515. Out of pocket. Not billed through insurance; they know nothing about it.

It feels like a lot of money. However, if you calculate how many PERT it replaced and the cost per PERT pill, I saved $4,036 by swallowing 1,652 extra pills.

Is paying $500 to save everyone else $4000 worth it?

I think so.

Again, the “price” question gets interesting.

The raw costs of yearly supplies I don’t pay completely; remember with health insurance I am capped at $3,000 out of pocket for supplies I get through insurance. However, again, it’s worth considering that additional costs do not cost me but they cost the insurer, and therefore the employer and our pool of people in this insurance plan and influences future costs for everyone on insurance. So if I can afford (although I don’t like it) $500-ish out of pocket and save everyone $4,000 – that’s worth doing.

Although, I think I can improve on that math for next year.

I was taking the two OTC kinds that I had mentioned: one that was lipase-only and very reliable, but $0.28/pill or $0.04 per 1000 units of lipase (and contains ~6000 units of lipase). The less reliable multi-enzyme pill was cheaper ($.09) per pill but only contains 4000 units of lipase, and was $.02 per 1000 units of lipase. That doesn’t factor in the duds and the way I had to increase the number of pills to account for the lack of faith I had in the 4000 units of lipase always being 4000 units of lipase.

The new OTC pill I mentioned above is $0.39 per pill, which is fairly equivalent price to a combined lipase-only and multi-enzyme pill. In fact, I often would take 1+1 for snacks that had a few grams of protein and more than a few grams of lipase. So one new pill will cover 17,000 units of lipase (instead of 10,000, made up of 6000+4000) at a similar cost: $0.39 instead of $0.36 (for the two combined). And, it also has a LOT more protease per pill, too. It has >2x the amount of protease as the multi-enzyme OTC pill, and is very similar to the amount of protease in my prescription PERT! I’ve mostly discussed the math by units of lipase, but I also dose based on how much protein I’m eating (thus, protease to cover protein the way lipase covers fat digestion), so this is also a benefit. As a result, two of the new OTC pill now more than match 1 PERT on lipase, double the protease to 1 PERT, and is only two swallows instead of the 4-6 swallows needed with the previous combination of OTCs.

I have only tested for a few days, but so far this new OTC is working fairly well as a substitute for my previous two OTC kinds.

Given the unreliability of OTCs, even with different brands that are more reliable than the above picture, I still want to consume one prescription PERT to “anchor” my main meals. I can then “top off” with some of the new OTC pills, which is lower price than more PERT but has the tradeoff cost of slightly less reliability compared to PERT.

So with 3 main meals, that means at least 3 PERT per day ($8.34 per pill) at $25.02 per day in prescription PERT costs and $9,132 per year in prescription PERT costs. Then to cover the additional 3-5 PERT pills I would otherwise need, assuming 2 of the new OTC covers 1 PERT pills, that is 6-10 OTC pills.

Combined, 3 PERT + 6 OTC pills or 3 PERT + 10 OTC pills would be $27.36 or $28.92 per day, or $9,986 or $10,556 per year.

Still quite a bit of money, but compared to 6-8 PERT per day (yearly cost $18,264 to $24,352), it saves somewhere between $7,708 per year (comparing 6 PERT to 3 PERT + 6 OTC pills per day) all the way up to $14,366 per year (comparing 8 PERT to 3 PERT +10 OTC pills per day).

And coming back to number of pills swallowed, 6 PERT per day would be 2,190 swallows per year; 8 PERT pills per day is 2,920 swallows per year; 3 PERT + 6 OTC is 9 pills per day which is 3,285 swallows per year; and 3 PERT + 10 OTC is 13 swallows per day which is 4,745 swallows per year.

That is 1,095 more swallows per year (3PERT+6 OTC vs 6 PERT) or 1,825 more swallows per year (3 PERT + 10 OTC vs 8 PERT).

Given that I estimated I swallowed ~10 enzyme pills per day this year so far, the estimated range of 9-13 swallows with the combination of PERT and OTC pills (either 3 PERT + (6 or 10) OTC) for next year seems reasonable.

Again, in future this might change if I begin to have issues swallowing for whatever reason, but in my current state it seems doable.

The daily and annual costs of thyroid treatment for Graves’ Disease

No, we’re still not done yet with annual health cost math. I also developed Graves’ disease with subclinical hyperthyroidism this year, putting me to a grand total of 4 chronic health conditions.

Luckily, though, the 4th time was the charm and I finally have a cheap(er) one!

My thyroid med DOES have a generic. It’s cheap: $11.75 for 3 months of a once-daily pill! Woohoo! That means $0.13 per day cost of thyroid treatment and $48 per year cost of thyroid treatment.

(Isn’t it nice to have cheap, easy math about at least one of 4 things? I think so!)

Adding up all the costs of diabetes, celiac disease, exocrine pancreatic insufficiency and Graves’ Disease

High five if you’ve read this entire post; and no problem if you skimmed the sections you didn’t care about.

Adding it all up, my personal costs are:

  • Diabetes: $23.25 per day; $8,486 per year
  • Celiac: $3 per day; $1,100 per year (all out of pocket)
  • Exocrine Pancreatic Insufficiency:
    • Anywhere from $50.04 up to $66.72 per day with just prescription PERT pills; $18,265 (6 per day) to $24,353 (8 per day) per year
    • With a mix of prescription and OTC pills, $27.36 to $28.92 per day; $9,986 to $10,556 per year.
    • Of this, the out of pocket cost for me would be $2.34 to $3.90 per day; or $854 up to $1,424 per year.
  • Thyroid/Graves: $0.13 per day; $48 per year

Total yearly cost:

  • $27,893 (where EPI costs are 6 prescription PERT per day); 2,190 swallows
  • $33,982 (where EPI costs are 8 prescription PERT per day); 2,920 swallows
  • $19,615 (where EPI costs are 3 prescription PERT and 6 OTC per day); 3,285 swallows
  • $20,185 (where EPI costs are 3 prescription PERT and 9 OTC per day); 4,745 swallows

* My out of pocket costs per year are $854-$1424 for EPI when using OTCs to supplement prescription PERT and an estimated $1,100 for celiac-related gluten free food costs. 

** Daily cost-wise, that means $76.42, $93.10, $53.74, or $55.30 daily costs respectively.

*** The swallow “cost” is 1,095-1,825 more swallows per year to get the lower price cost of enzymes by combining prescription and OTC.

Combining these out of pocket costs with my $3,000 out of pocket max on my insurance plan, I can expect that I will therefore pay around $4,900 to $5,600 next year in health supply costs, plus another few hundred for things like tape or vitamins etc. that aren’t major expenses.

TLDR: 

  • Diabetes is expensive, and it’s not just insulin.
    • Insulin is roughly 19% of my daily cost of diabetes supplies. CGM is currently 56% of my diabetes supply costs.
  • EPI is super expensive.
    • OTC pills can supplement prescription PERT but have reliability issues.
    • However, combined with prescription PERT it can help drastically cut the price of EPI.
    • The cost of this price reduction is significantly more pills to swallow on a daily basis, and adds an additional out of pocket cost that insurance doesn’t cover.
    • However in my case; I am privileged enough to afford this cost and choose this over increasing everyone in my insurance plan’s costs.
  • Celiac is expensive and mostly an out of pocket cost.
  • Thyroid is not as expensive to manage with daily medication. Yay for one of four being reasonably priced!

REMEMBER to not use these numbers or math out of context and apply them to any other person; this is based on my usage of insulin, enzymes, etc as well as my insurance plan’s costs.

Yearly costs, prices, and calculations of living with 4 chronic diseases (type 1 diabetes, celiac, Graves, and exocrine pancreatic insufficiency)

Regulatory Approval Is A Red Herring

One of the most common questions I have been asked over the last 8 years is whether or not we are submitting OpenAPS to the FDA for regulatory approval.

This question is a big red herring.

Regulatory approval is often seen and discussed as the one path for authenticating and validating safety and efficacy.

It’s not the only way.

It’s only one way.

As background, you need to understand what OpenAPS is. We took an already-approved insulin pump that I already had, a continuous glucose monitor (CGM) that I already had, and found a way to read data from those devices and also to use the already-built commands in the pump to send back instructions to automate insulin delivery via the decision-making algorithm that we created. The OpenAPS algorithm was the core innovation, along with the realization that this already-approved pump had those capabilities built in. We used various off the shelf hardware (mini-computers and radio communication boards) to interoperate with my already approved medical devices. There was novelty in how we put all the pieces together, though the innovation was the algorithm itself.

The caveat, though, is that although the pump I was using was regulatory-approved and on the market, which is how I already had it, it had later been recalled after researchers, the manufacturer, and the FDA realized that you could use the already-built commands in the pump’s infrastructure. So these pumps, while not causing harm to anyone and no cases of harm have ever been recorded, were no longer being sold. It wasn’t a big deal to the company; it was a voluntary recall, and people like me often chose to keep our pumps if we were not concerned about this potential risk.

We had figured out how to interoperate with these other devices. We could have taken our system to the FDA. But because we were using already-off-the-market pumps, there was no way the FDA would approve it. And at the time (circa 2014), there was no vision or pathway for interoperable devices, so they didn’t have the infrastructure to approve “just” an automated insulin delivery algorithm. (That changed many years later and they now have infrastructure for reviewing interoperable pumps, CGM, and algorithms which they call controllers).

The other relevant fact is that the FDA has jurisdiction based on the commerce clause in the US Constitution: Congress used its authority to authorize the FDA to regulate interstate commerce in food, drugs, and medical devices. So if you’re intending to be a commercial entity and sell products, you must submit for regulatory approval.

But if you’re not going to sell products…

This is the other aspect that many people don’t seem to understand. All roads do not lead to regulatory approval because not everyone wants to create a company and spend 5+ years dedicating all their time to it. That’s what we would have had to do in order to have a company to try to pursue regulatory approval.

And the key point is: given such a strict regulatory environment, we (speaking for Dana and Scott) did not want to commercialize anything. Therefore there was no point in submitting for regulatory approval. Regardless of whether or not the FDA was likely to approve given the situation at the time, we did not want to create a company, spend years of our life dealing with regulatory and compliance issues full time, and maybe eventually get permission to sell a thing (that we didn’t care about selling).

The aspect of regulatory approval is a red herring in the story of the understanding of OpenAPS and the impact it is having and could have.

Yes, we could have created a company. But then we would not have been able to spend the thousands of hours that we spent improving the system we made open source and helping thousands of individuals who were able to use the algorithm and subsequent systems with a variety of pumps, CGMs, and mobile devices as an open source automated insulin delivery system. We intentionally chose this path to not commercialize and thus not to pursue regulatory approval.

As a result of our work (and others from the community), the ecosystem has now changed.

Time has also passed: it’s been 8 years since I first automated insulin delivery for myself!

The commercial players have brought multiple commercial AIDs to market now, too.

We created OpenAPS when there was NO commercial option at the time. Now there are a few commercial options.

But it is also an important note that I, and many thousands of other people, are still choosing to use open source AID systems.

Why?

This is another aspect of the red herring of regulatory approval.

Just because something is approved does not mean it’s available to order.

If it’s available to order (and not all countries have approved AID systems!), it doesn’t mean it’s accessible or affordable.

Insurance companies are still fighting against covering pumps and CGMs as standalone devices. New commercial AID systems are even more expensive, and the insurance companies are fighting against coverage for them, too. So just because someone wants an AID and has one approved in their country doesn’t mean that they will be able to access and/or afford it. Many people with diabetes struggle with the cost of insulin, or the cost of CGM and/or their insulin pump.

Sometimes providers refuse to prescribe devices, based on preconceived notions (and biases) about who might do “well” with new therapies based on past outcomes with different therapies.

For some, open source AID is still the most accessible and affordable option.

And in some places, it is still the ONLY option available to automate insulin delivery.

(And in most places, open source AID is still the most advanced, flexible, and customizable option.)

Understanding the many reasons why someone might choose to use open source automated insulin delivery folds back into the understanding of how someone chooses to use open source automated insulin delivery.

It is tied to the understanding that manual insulin delivery – where someone makes all the decisions themselves and injects or presses buttons manually to deliver insulin – is inherently risky.

Automated insulin delivery reduces risk compared to manual insulin delivery. While some new risk is introduced (as is true of any additional devices), the net risk reduction overall is significantly large compared to manual insulin delivery.

This net risk reduction is important to contextualize.

Without automated insulin delivery, people overdose or underdose on insulin multiple times a day, causing adverse effects and bad outcomes and decreasing their quality of life. Even when they’re doing everything right, this is inevitable because the timing of insulin is so challenging to manage alongside dozens of other variables that at every decision point play a role in influencing the glucose outcomes.

With open source automated insulin delivery, it is not a single point-in-time decision to use the system.

Every moment, every day, people are actively choosing to use their open source automated insulin delivery system because it is better than the alternative of managing diabetes manually without automated insulin delivery.

It is a conscious choice that people make every single day. They could otherwise choose to not use the automated components and “fall back” to manual diabetes care at any moment of the day or night if they so choose. But most don’t, because it is safer and the outcomes are better with automated insulin delivery.

Each individual’s actions to use open source AID on an ongoing basis are data points on the increased safety and efficacy.

However, this paradigm of patient-generated data and patient choice as data contributing toward safety and efficacy is new. There are not many, if any, other examples of patient-developed technology that does not go down the commercial path, so there are not a lot of comparisons for open source AID systems.

As a result, when there were questions about the safety and efficacy of the system (e.g., “how do you know it works for someone else other than you, Dana?”), we began to research as a community to address the questions. We published data at the world’s biggest scientific conference and were peer-reviewed by scientists and accepted to present a poster. We did so. We were cited in a piece in Nature as a result. We then were invited to submit a letter to the editor of a traditional diabetes journal to summarize our findings; we did so and were published.

I then waited for the rest of the research community to pick up this lead and build on the work…but they didn’t. I picked it up again and began facilitating research directly with the community, coordinating efforts to make anonymized pools of data for individuals with open source AID to submit their data to and for years have facilitated access to dozens of researchers to use this data for additional research. This has led to dozens of publications further documenting the efficacy of these solutions.

Yet still, there was concern around safety because the healthcare world didn’t know how to assess these patient-generated data points of choice to use this system because it was better than the alternative every single day.

So finally, as a direct result of presenting this community-based research again at the world’s largest diabetes scientific conference, we were able to collaborate and design a grant proposal that received grant funding from New Zealand’s Health Research Council (the equivalent of the NIH in the US) for a randomized control trial of the OpenAPS algorithm in an open source AID system.

An RCT is often seen as the gold standard in science, so the fact that we received funding for such a study alone was a big milestone.

And this year, in 2022, the RCT was completed and our findings were published in one of the world’s largest medical journals, the New England Journal of Medicine, establishing that the use of the OpenAPS algorithm in an open source AID was found to be safe and effective in children and adults.

No surprises here, though. I’ve been using this system for more than 8 years, and seeing thousands of others choose the OpenAPS algorithm on an ongoing, daily basis for similar reasons.

So today, it is possible that someone could take an open source AID system using the OpenAPS algorithm to the FDA for regulatory approval. It won’t likely be me, though.

Why not? The same reasons apply from 8 years ago: I am not a company, I don’t want to create a company to be able to sell things to end users. The path to regulatory approval primarily matters for those who want to sell commercial products to end users.

Also, regulatory approval (if someone got the OpenAPS algorithm in an open source AID or a different algorithm in an open source AID) does not mean it will be commercially available, even if it will be approved.

It requires a company that has pumps and CGMs it can sell alongside the AID system OR commercial partnerships ready to go that are able to sell all of the interoperable, approved components to interoperate with the AID system.

So regulatory approval of an AID system (algorithm/mobile controller design) without a commercial partnership plan ready to go is not very meaningful to people with diabetes in and of itself. It sounds cool, but will it actually do anything? In and of itself, no.

Thus, the red herring.

Might it be meaningful eventually? Yes, possibly, especially if we collectively have insurers to get over themselves and provide coverage for AID systems given that AID systems all massively improve short-term and long-term outcomes for people with diabetes.

But as I said earlier, regulatory approval does necessitate access nor affordability, so an approved system that’s not available and affordable to people is not a system that can be used by many.

We have a long way to go before commercial AID systems are widely accessible and affordable, let alone available in every single country for people with diabetes worldwide.

Therefore, regulatory approval is only one piece of this puzzle.

And it is not the only way to assess safety and efficacy.

The bigger picture this has shown me over the years is that while systems are created to reduce harm toward people – and this is valid and good – there have been tendencies to convert to the assumption that therefore the systems are the only way to achieve the goal of harm reduction or to assess safety and efficacy.

They aren’t the only way.

As explained above, FDA approval is one method of creating a rubber stamp as a shorthand for “is this considered to be safe and effective”.

That’s also legally necessary for companies to use if they want to sell products. For situations that aren’t selling products, it’s not the only way to assess safety and efficacy, which we have shown with OpenAPS.

With open source automated insulin delivery systems, individuals have access to every line of code and can test and choose for themselves, not just once, but every single day, whether they consider it to be safer and more effective for them than manual insulin dosing. Instead of blindly trusting a company, they get the choice to evaluate what they’re using in a different way – if they so choose.

So any questions around seeking regulatory approval are red herrings.

A different question might be: What’s the future of the OpenAPS algorithm?

The answer is written in our OpenAPS plain language reference design that we posted in February of 2015. We detailed our vision for individuals like us, researchers, and companies to be able to use it in the future.

And that’s how it’s being used today, by 1) people like me; and 2)  in research, to improve what we can learn about diabetes itself and improve AID; and 3) by companies, one of whom has already incorporated parts of our safety design as part of a safety layer in their ML-based AID system and has CE mark approval and is being sold and used by thousands of people in Europe.

It’s possible that someone will take it for regulatory approval; but that’s not necessary for the thousands of people already using it. That may or may not make it more available for thousands more (see earlier caveats about needing commercial partnerships to be able to interoperate with pumps and CGMs).

And regardless, it is still being used to change the world for thousands of people and help us learn and understand new things about the physiology of diabetes because of the way it was designed.

That’s how it’s been used and that’s the future of how it will continue to be used.

No rubber stamps required.

Regulatory Approval: A Red Herring

What Do You See When You See (Or Think Of) Diabetes?

What do you see when you see (or think of) diabetes?

In my house, I see small piles of low treatments (for hypoglycemia) in every place that I hang out. On my desk next to my computer. In my bedside table. On the counter next to the door where I grab them before heading out for a run or a walk. On the edge of the bathtub in my shower, because low blood sugars happen everywhere.

Sometimes, one of my nephews spots them in a translucent pocket on my shorts. His brain sees candy at first, not a medical treatment. Which is fine – he’s young. He’s learning that for Aunt Dana, they’re not “candy” or a “treat” – they’re a medical treatment.

All of the nieces and nephews have learned or are learning that Aunt Dana has “robot parts”, which is how they see my pump clipped to my pocket or waist band or the hard lump (CGM sensor) they feel or see on my arm.

What I hope people see, though, is that diabetes is not a death sentence. Thanks to improvements in insulin, insulin delivery, and blood glucose measuring, it’s no longer visibly tied to possible complications of diabetes, like amputations, kidney dialysis, or loss of vision. That is what I saw when I was diagnosed with diabetes in 2002, and what was presented to me.

I hope instead that people see people with diabetes like me living our lives, running 82 mile ultramarathons (for those of us who wish to do that), experiencing pregnancy (for those who wish to do that), achieving our career goals, living life in whatever ways we want to live our lives. Just like everyone else.

It’s worth noting that when typing this, autocorrect in my first sentence suggested “treat” instead of “treatment”.

That’s how computers “see” diabetes, too, with sugar and carbs equivalent with diabetes. Despite the fact that medical research shows that diabetes is a complicated combination of genetics, immune system shenanigans (my words), and numerous other factors not in a person’s control, humans haven’t gotten that message. People are still stigmatized and joked about.

So computers learn that. And that’s what they see.

When I was testing Stable Diffusion (an open source AI tool for generating images) recently, I learned about a site “Lexica” that shows you what other people have generated with similar key words. I thought it would be interesting to get ideas for better images to visualize concepts in posts about diabetes, so I searched diabetes.

A screenshot of search results in Lexica for the term "diabetes". Primarily it is images of people portrayed as very overweight and many images of a lot of food.

I should’ve known better. Humans say and think “diabetes” in response to seeing pictures of carbohydrates, so that’s what computers learn.

AI doesn’t know any better because humans haven’t taught themselves any better.

Sadly, “insulin pump” as a key word is disheartening in a different way.

A screenshot of image results from Lexica for the term "insulin pump", which mostly shows a mix of devices that look like blood glucose meters or pulse oximeters.

There are so few existing visuals and images of people with insulin pumps that the visual images generated by AI are a mix of weird hybrid old school computer components and blood glucose monitors or other medical devices.

“Hypoglycemia” mostly generates cartoons in foreign languages or made up languages that I’m guessing are jokes by people without diabetes about having low blood sugar and using it as an excuse for various things. “Hyperglycemia” brings a mix of the hypoglycemia-style cartoons and the diabetes-style images of carbs and how the AI thinks people with diabetes all look.

I’ve noticed this with AI-writing tools, too. AI is good at completing your sentence or writing a few sentences based on well known concepts and topics that already exist today. It’s not yet good at helping you write content about new concepts or building on existing content.

It’s trained on the content of today and the past, which means all of the biases, stereotypes, and stigmatizing content that aren’t good today are also extrapolated into our future with AI.

I don’t have all the answers or solutions (I wish I did), but I want to flag this as a problem. We can’t expect AI to do better trained on what we have and do today, because what we do today (stigmatize, stereotype, and harm people living with chronic diseases) is not ok and not good enough.

We need to change today and train AI with different inputs in order to get different outputs.

That starts with us changing our behavior today. As I wrote a few days ago, please speak up when you see chronic diseases being used as a “joke” and when we see people being stereotyped or when we see racism occurring.

It’s hard, it’s uncomfortable – both to speak up, and to be corrected.

I’ve been corrected before, on verbal patterns and phrases I learned from society that I didn’t realize were harmful and stigmatizing to other people.

I’m working on learning to say “I’m sorry, you’re right, and let me learn from this” and trying to do better in the future, living up to my statement that I’m going to learn from that moment.

It can absolutely be done. It desperately needs to be done, by all of us.

We can course-correct, whether it’s in a one on one conversation, something we see in a small social network in social media, or even in a large room at a conference.

I still remember and appreciate greatly when I flagged that a diabetes joke was made at a conference on stage over four years ago. Upon hearing the joke, I noted that half the room laughed; and that it wasn’t ok. So I spoke up on Twitter, because I was live tweeting from the conference. I didn’t think much would come from it. But it did. Amazingly, it did.

John Wilbanks saw my tweet, realized it wasn’t ok, and instead of tweeting support or agreement (which also would have been great), took an amazing, colossally huge and unexpected step. He literally got up from his seat, went to the microphone, and interrupted the panel that had moved on to other topics. He called out the fact that diabetes was used as a joke a few minutes prior and that it wasn’t ok.

He put on a master class for how to speak up and how to use his power to intervene.

It was incredibly powerful because although the “joke” had gone over most people’s heads and they didn’t think it was a big deal, he brought attention to the fact that it had happened, was hurtful and harmful, and created a moment for reflection for the entire room of hundreds of people.

We need more of this.

When someone flags that they are being stereotyped, stigmatized, being discriminated against – we need to speak up. We need to support them.

It matters not just for today (although it matters incredibly much for today, too) but also for the future.

AI (artificial intelligence) learns from what we teach it, much like our children learn from what we teach and show them. I don’t have kids, but I know what I do and how I behave matters to my nieces and nephews and how they see the future.

We need to understand that AI is learning from what we are doing today, and what we do today matters. It should be enough to want to not be racist, discriminating, stereotyping, and harmful to other people today. But it’s not enough.

The loudest voices are often the ones establishing “normal” for our culture, our children, and the AI systems that may be running much of the world before our children graduate college. We need to speak up to help shape the conversation today, because  what we are doing today is teaching our children, our technology, and is what we’ll get in the future, ten-fold.

And I want the future to look different and be better, for all of us.

What do you see when you think of diabetes? And what are we teaching our children and our technology?

What It Feels Like To Run 100 Miles Or Similar Long Ultramarathons

Sometime in the last year, I decided I wanted to run 100 miles. In part, because I wanted to tackle the complex challenge and problem-solving that is even figuring out how to do it.

My situation as an ultrarunner is slightly atypical: I have type 1 diabetes and need to closely manage insulin levels and glucose levels while running; I have celiac disease so I can only eat 100% gluten free things; and I have exocrine pancreatic insufficiency (EPI) so I need to swallow enzymes with everything that I eat, including when I run. It’s a logistical cornucopia of challenges…which is in part why I wanted to do it. It wouldn’t be half as rewarding if it were easy? Or something like that.

But mainly, I wanted to prove to myself that I can do hard things, even things that most people think I can’t do. No, I can’t produce my own insulin, but I can locomote for 100 miles at one time despite this and the other challenges I have to deal with along the way.

Plus, there’s the “normal” ultrarunning challenges of fueling, hydrating, managing electrolytes, keeping your feet from becoming a ball of blisters, etc.

Ultrarunning is a sport where it generally doesn’t matter how fast you go, and the farther the distance the more of an equalizer it is. I’m a slow runner, and I had trained at an easy slow pace that I planned to run during my race (self-organized). Not having the pressure of time cutoffs would help. I was also curious whether running so slow at the start would possibly help me maintain a more even pace split across the entire run, and whether I could ultimately achieve a reasonable time by keeping consistent slow paces, compared to many I’ve read about who go a bit too fast at the start and end up with wildly different paces at the end. Everyone hurts running an ultra no matter how much you run or walk or both and no matter how fast or slow you go, but I was hoping that more consistent pacing and effort would minimize how terrible everything felt if I could pull that off.

Background

I trained, ran a 50k in June, and resumed training and worked back up to 24 mile long runs and all was going well, until I massively broke a toe and had 6 weeks off. Then I resumed training and re-built back up to running 29 miles, ending around midnight for night-run training. At that point, I had one more long run scheduled (32-ish miles), but decided I would rather skip the last long run and push my 100 mile run up a few weeks to try to beat the impending rainy season that Seattle would eventually get.

The joke was on me. We had 6+ weeks of terrible air quality, which peaked into a two-day stretch of downright “hazardous” (ugh) air quality the two days before my run. Air quality was finally improving overnight before and the morning of my run, thanks in part to the most rain we had gotten in 128 days. Woohoo! So I got to add some wet and cold running challenges to my list of problem-solving that I’d tackle during my run.

Overall, though, my training had gone well, and I had spent enough time planning and prepping that I felt relatively confident. Mostly, confident that no matter how well or long I trained, it was going to hurt. All over. For what felt like forever, and then I still wouldn’t be anywhere near done. And confident that I had planned and prepped to the best of my ability, and that I could figure out how to tackle whatever situations I faced as they came.

How I felt before the race

Aside from having cabin fever from being inside (AQI was too hazardous to go out even with a mask), I felt fairly good in terms of running fitness. I had been tapering, my legs felt fresh, I was fueling and hydrating and everything felt fine. Unfortunately, though, while I managed to escape many taper niggles, I experienced a round of ovulation pain that I don’t get every month but was lucky enough to get this month, for the 3 days prior to my race. (I’m not sure why, but in the last few years after never experiencing ovulation pain, I have started to get ovulation pain similar to period pain and cramps and general icky feelings. My doctor isn’t concerned about it, but it’s unfun, and in this case poorly timed.) So I was a bit grumpy about going into my race in a less-than-perfect state, even though “perfect” state is an ideal and usually there is something wrong, whether it’s a taper niggle or something else.

The thing I was most pleased about was my feet. My broken toe had healed well and hadn’t been giving me any issues. However, after I broke my toe it changed my foot strike or how my feet move in my shoes in a way that caused epic blisters and then I kept getting blisters on top of blisters for several runs. I finally figured out that I needed to try something different, stopped causing new blisters, and the existing blisters healed, peeled off, and went away. So my feet were in great shape, and despite being nervous about the effect of the rain on my feet during my 100 miles, I at least was starting from a “clean slate” with healthy, non-blistered feet.

The start

I set my alarm and woke up and checked air quality. The winds and the start of the rain had blown it absolutely clear, so I was able to head out without a mask for the first time in weeks! (Last time I ran with it for all 8 hours of my long run, which is annoying when you need to fuel every 30 min.)

I wasn’t even a mile in when I had my first problem. I started with a long sleeve shirt and my rain jacket, knowing I’d warm up and want to take it off soon after I started. As I removed my arms from my rain jacket (keeping it zipped around my waist) and shuffled my arms in and out of my running vest, I suddenly felt water hit my feet and looked down. Water was gushing out of my hydration hose! I grabbed it and stuck my finger over the end: the bite valve had flown off somehow while I was getting out of my jacket. Ugh.

Luckily, though, this is where all of my planning and reading of others’ experiences had come in handy. While this had never happened to me, I had read in someone’s blog that this had happened and it took them 20 minutes to find the valve. I had a bright waistlamp and it was getting increasingly lighter outside as the sun rose, so I hoped mine would be easier to spot. I figured it was stuck in my rain jacket sleeve so I worked to check my sleeve and vest for the valve. No go. I looked around and didn’t see it. I turned and walked back a bit, looking for it on and off the trail. No luck. I finally pulled out my phone and called Scott, while still holding my finger over the hydration hose to keep it from leaking out 3 liters of water. While I talked to him and told him I probably needed him to get dressed and bike out a replacement valved to me, I turned around and walked forward again one more time. Aha! Found it. It had flown way to the left side of the trail. I replaced it and breathed a sigh of relief. It had added only 4 minutes to my first mile time.

Well, I thought: that’s one way to keep my early paces slow! I hung up with Scott, and carried on.

The first lap I was very focused on making sure my socks and shoes were in good shape. I am pretty good at gutting it out if I have blisters or foot issues, but that’s not a good strategy when you’re going to cover 99 more miles. So 6 miles into my first lap, I stopped at a bench, took my socks off, and re-lubricated my feet. Later on the way back (this first lap was an out-and-back), I stopped at mile 16 and similarly sat on a rock to re-lubricate and add lamb’s wool to reduce rubbing on the side of my foot.

A picture of Dana Lewis running down the rainy paved trail, with resupply gear (dry shoes, water, fuel) in the foreground of the picture. She's wearing shorts, a rain jacket, and a rain hat. She is smiling and around 12 miles into her eventual 82 mile run).

Yet overall, lap 1 went well. It started raining after about 20 minutes so I ran with my rain hat and rain jacket on (I put it on after my bite valve escapades at mile 1), and intermittently put my hood over my hat and took it off when the rain picked up or lessened, respectively. But it pretty much rained the whole time. Scott met me as planned after my turnaround spot (about 12 miles in) and refilled my hydration pack and I re-packed my vest with snacks, enzymes, and electrolytes and carried on.

At the end of lap 1 (almost 24 miles), I physically felt pretty decent. I had been working to focus on the lap I was in and what I needed to do for the next lap. Nothing else. No thoughts of how many miles I would run or hours it would take. My watch had stopped itself in the rain and canceled the run (argh), so I wasn’t going to have a running total of time throughout the entire run like I wanted. But this might have been a feature, as it kept me from using my watch for that and I set a new lap/run each time I headed out so I could keep an eye on the segment pace, even though I had no idea what the overall pace time really was.

A paved trail picture taken from on the trail. Trees and a river are to the left; more trees line the trail to the right. It is very cloudy, the trail is visibly wet.I went slow the first lap (part of why I was feeling so strong), and I took my time in between laps. I pulled off my socks and shoes. I used hand sanitizer on them to draw some of the water out, then re-lubricated and added Desitin (to continue to help draw water out of my feet and aid in preventing blisters). Then I put on a fresh pair of toe socks and added more lamb’s wool in between key toes that typically are blister-prone. At this stage I had no blisters, and other than wet soggy feet was in good shape! Sitting for 10 minutes for my sock and foot care change chilled me, though, and I was happy to start moving again and warm back up.

The middle

I headed out on lap 2, which similarly went well. This was my “triangle” shaped loop/lap. The only issue I had this lap was that it was the only section of my route where the trail crossed 3 small intersections. Two had lights but one did not. At the intersection without a light, there were no cars so I continued running across the pedestrian crossing. As I stepped out I saw a car whipping around the corner with their head turned looking for cars in the opposite direction. Not sure if they would turn in time to see me, I slammed on my physical brakes. They did turn and see me and stopped in plenty of time, so I continued across the crossing and on the trail. However, that had tweaked my right ankle and it felt sore and weak. Argh. It felt better after a few more minutes, but it intermittently (once every hour or so) would feel weak and sore throughout the rest of my run as a result.

After lap 2, I again sat to remove my socks and shoes, dry my feet, put hand sanitizer on them, re-lubricate, etc. My feet were definitely wet and wrinkly, so I added even more Desitin to my feet. It wasn’t raining super hard but it was a constant hard drizzle that was soaking through to my socks and feet even though there weren’t many puddles (yet). This time, though, I used some reusable hot packs while I sat to change shoes, so I wasn’t as chilled when I left.

Lap 3 (back to an out-and-back route) also went well, and I was starting to realize that I was in surprisingly good physical shape. My feet were intermittently a little bit sore from pounding the ground for hours, but they weren’t constantly annoying like I’ve had on some training runs. I had long surpassed my longest running distance (previously 32 miles; at the end of lap 3 I would reach 52 miles) and longest ever running time. I did develop one or two small blisters, but they didn’t bother me. Usually, I build up huge blisters and they’re a constant annoyance. During my race, maybe thanks to the Desitin etc, I only noticed the blisters (which were fairly tiny) when they popped themselves. I had one on each foot pop and sting for a minute and then not bother me again, which was pleasant! Lap 3 was also when it got dark, so I’d headed out with my double waist lamp. I have two sets of two waist lamps that we strapped to each other; I turn one on and run it out (somewhere around ~3 hours) and then turn the belt around and turn the other lamp on. This lasts me the longest laps I have, even if I was going at walking speed. It’s plenty of light for the paved trail even on the darkest nights, but because it was raining it was cloudy and the city’s light pollution reflected off the clouds so that trail itself was easy to see! So while I only saw a few stars at the end of the night in between patches of cloud, for most of the night the night-running aspects were pretty easy. Dana sits on a bench at a picnic table in a public park. It is dark. She is wearing long rain pants, a rain jacket, and a rain hat and is bent over her bare feet, applying lubrication. It is dark and nighttime, so she has an extra waistlamp on the table illuminating her feet. Other ultrarunning supplies are strewn across the table.

Interestingly regarding my feet, after lap 3 they were still white and wrinkly a bit, but they were definitely drying out. They were much drier than they had been after lap 2, so the combination of hand sanitizer and Desitin was working. I was pleased, and again slathered with more lubricant and Desitin before putting on fresh socks and heading back out for lap 4, which would be a repeat of my “triangle” lap.

Physically, I was mostly ok. My feet weren’t hurting. I had expected my IT bands to get tight and bother my right knee and for my hips and back to start getting sore: my left knee did intermittently hurt some, but it was like a 3/10 annoyance and came and went. Stretching my hip flexors didn’t change the tightness of my IT band, but it was also the least amount of knee pain I’ve ever had when things got tight, so it was very manageable and I didn’t stress about it. It was hard to believe that with the completion of this lap (lap 4) that I’d have finished a 100k (62 miles) and added a few miles to it!

It seemed like the triangle loop wanted to keep things interesting, though. On Lap 4, after I had turned off into the section that has the intersections and the “triangle” part of the loop, my hydration hose made a gurgling noise. I felt the back of my hydration pack, which was rock solid with ice…but no water left. Oops, I thought. I was at mile 6 out of 13. If I kept going forward on my route, it would take me an estimated 4+ miles to get back to the next water fountain. Or I could call and wake up Scott, who had just fallen asleep for his first 2 hour nap overnight (it was around 1am by now), to bring me water, but that would take him 20-30 minutes before reaching me.

It mattered that I didn’t have water. Not just in terms of thirst and hydration, but I also needed water to be able to swallow my electrolyte pills (every 45 minutes) and my fuel (every 30 minutes when I ate a snack) and the digestive enzymes I absolutely require to digest my food since I have EPI. I definitely needed water so that my hydration, fueling, electrolytes, etc. wouldn’t suffer.

I could go back, but I hated to backtrack. It would be a mile back to the previous water fountain, although I wasn’t even sure it would be turned on and working. Mentally, though, I groaned at the thought of “turning around” and finishing the loop in reverse and trying to figure out how many miles I would cut off that loop and how many I’d have to added to my very last loop to make up for it.

Luckily, I realized a better idea. Because I was on the section of the triangle running alongside a road (hence the annoying intersection crossings), the intersections are where the road turned off into various parking lots. Across the road at one of the two intersections with lights was a gas station! I could see it glowing from a quarter of a mile away. I crossed my fingers hoping it would still be open, because I could go inside and buy a bottle of water to hold me over. It was open! I crossed the intersection and went in, grabbed a liter of water, bought it, went outside, and refilled my hydration bladder under the bright lights of the gas station.

A 1-liter wattle bottle held in a hand covered with a blue nitrile gloves.
I’m wearing nitrile gloves to help keep my hands drier and warmer given the cold, endless rain.

I was pretty proud of that solution, especially because it was ~1am and I had been running for 17 hours and was able to troubleshoot and solve that problem on the fly! Without sending it, I also drafted a text to send to Scott near the end of that loop when he’d be awake, to list out which foods and gear I wanted at the next refuel, and to specify what happened and how I solved it and request that I get more water and less ice for the next loop.

(Running out of water was on my list of things I planned for in all of my preparation, so while I had low expectations of my mental capacity as the miles piled up, that likely helped because I had mentally listed out where all the available water fountains were, so I could run my loop mentally forward and backward to figure out where the closest one was. In this case it was a mile behind me; going forward it would have been 4+ miles or more than an hour away. The gas station ended up being 15 minutes from where I realized I was out of water).

Finishing lap 4 was exciting, because I only had 3 laps left to go! I had one more out and back loop, and my father-in-law was driving down in the wee hours of the morning to run part of it with me to keep me company. We hadn’t planned on that all along, but he and Scott had been texting and working it out, so Scott just told me that was the plan and I was thrilled. I was a little bit tired overall, but more energetic than I thought.

The sock change before lap 5 was disappointing, though. After lap 3, my feet had been drying out a little bit. Now after lap 4 they were wet and soft again, like they were after lap 2. The rain had been more constant. I took the time (15-20 min) I needed to dry and treat them with hand sanitizer, lubricant, Desitin, replace fresh toe socks and lambs wool and dry shoes. They weren’t hurting, so I was hoping the light rain would taper off and my feet would dry out again.

The (beginning of the) end

I headed out on to lap 5, buoyed by the thought that I only had ~4 miles ‘til I had company. The rain picked up again (argh) and as my father-in-law met me on the trail with his headlamp and rain gear, he asked if it had been raining this much the whole time. No, I said, and pointed out that it had only been raining hard in 10-20 minute chunks and this one had been going since before I met him so it should lighten up soon. He commented on how energetic and chatty I was. “You’re pretty chatty,” he said, “for 5am!” (I am well-known in both our families for NOT being a morning person). I joked about how impressive it was for me being this chatty not only at 5am but also for it being 22 hours into my run!

Unfortunately, 3 miles into the section he ran with me, it went from annoying hard drizzle to an epic mega downpour. My shoes went from damp from constant hard drizzle to super soaked from top all the way down to the insoles squishing with every step. I was frustrated, because this much rain was also making it hard to use my phone. My phone had an alarm going off every 30 minutes to remind me to fuel; I needed to pull out my phone each time and turn off the very loud alarm (it was effective!) and then open up my spreadsheet and enter what I ate and what electrolytes I took. Then I also had to pull the baggie out of my vest pocket, select out the number of enzyme pills I needed with wet and cold gloved fingers, re-seal the baggie and put it back in my vest, and get out the fuel from the other pocket of my vest and eat it. Even tired, I was managing to fuel successfully and stay on top of my schedule. I was increasingly proud of this.

But the rain and the inability to use my phone when I wanted to was starting to irritate me, in part likely because I was trying not to stress about what the volume of water was doing to my feet. They weren’t actively hurting, but I knew this much water for this long of time could be dangerous and I needed to be careful. It was still downpouring when we reached the turnaround and headed back to his car. I dropped him off at his car and carried on. I was tired, soaked, cold, but physically in great shape otherwise in terms of legs, knees, hips, back etc all holding up and not feeling like i had run ~78+ miles at that point!

I had just eaten another snack and went to press buttons on my pump to give myself some insulin for the snack. It didn’t seem to work. I have a vibration pattern so I can use the pump without seeing it; but the “enter” button was not working. I had been concerned about the volume of water my pump was going to be exposed to and mentally prepared for that, but it was SO disheartening to suddenly feel the pattern of 6+ vibrations followed by an audio beep indicating an error state had been reached on the pump. I cursed to myself, out in the rain after 24 hours of running, knowing what I would find when I pulled my pump out from under my jacket. Sure enough, “button error”, because water had gotten under the buttons and to protect itself, the pump went into a “I won’t do anything” state. That meant that the insulin I needed for my latest snack wasn’t going to happen and any future insulin wasn’t going to happen.

I pulled out my phone and started a text to Scott, explaining that I had a button error and needed him to pull out my backup pump. I told him where it was, told him to put in a new battery and program it with the basal rate that I wanted. I then sent a text saying it was raining a lot and it would be easier if he called me if he needed to talk, because it was so hard to use my phone in the rain. He read the text so I knew he was awake, so I called him and talked to him while I trudged on and he was getting dressed and packing up my replacement pump and the gear I needed for lap 6. Then we hung up and I carried on, grumbling along the way and starting to feel the physical effects of not having enough insulin for the past hour or so.

A picture of a glucose graph from a CGM. The dots are flat in the first hour of the screenshot, then slowly and almost exactly lineary head up and to the right.

My blood glucose levels were rising, but I wasn’t worried about that. I knew once I had replacement insulin my blood sugars would come down nicely. I had prepared for this; there was a “high BG” baggie with supplies ready to go! But the combination of the 25+ hours of rain, the extra hard rain and cold temps from the last several hours, my feet starting to be bothered from the wet soaking, and then on top of it all the chemical feeling of not having insulin going in my body: it was a lot. I really focused on the physical state I was in, evaluating what I wanted to do. I knew that I could fix the cold state (switch to dry clothes; use hot packs) and my blood sugars (new replacement pump, take some inhalable insulin for a faster fix while the new pump insulin would be kicking in within an hour and fixed from there). But my feet were starting to bother me in a way that I wasn’t sure could be fixed with a 20 minute sock change.

Scott biked up to me right as I passed my favorite trail bathroom, the stalwart of my ultra, and had me turn around and head in there to be out of the rain. It was clean, big, had toilet paper, and was well lit and had the door open (wasn’t locked) all night long. I stepped inside the bathroom while Scott parked his bike by the building and whipped out the baggie with the replacement pump. I checked that no one else was in the women’s bathroom and he stepped inside, and impressively (to me) pulled out the baggie that held a garbage bag. I had packed it so I could more easily change clothes in public bathrooms by standing on it and placing my clothes on it so they wouldn’t be on the ground. He instead laid the garbage bag on top of the garbage can lid and set out my dry clothes, helped me out of my wet soaked rain jacket, hat, and shirts, and handed me my dry shirts followed by some hot packs. He gave me a giant one and told me to stuff it down my shirt, which I did. I took some inhalable insulin (which hits in about 15 minutes), then held the smaller hot packs in my hands while he was pulling out the bag with my replacement pump. I rewound and primed the pump with my existing reservoir and tubing, then reconnected it to my pump site and primed it. That problem (lack of insulin) was now solved, and I knew that my blood glucose would come back down to target over the next hour.

Next up, I could walk/run (or walk) the remaining 1.5 miles back to my normal turn around point, which was a table under a park awning that was relatively dry. I knew that I needed to be warmer and stay dry, and although I had dry clothes on now, I wasn’t sure that sitting outside even with hot packs while I tried to address my feet would warm me up. I told Scott that I wanted to go back to the house (thinking I’d walk the ~1.5 miles to the house). Then I could dry out my feet, get warm, and go back out if I wanted to continue. But I had a hunch I didn’t want to continue. My feet were feeling like they were getting to be in a not-good state from the level of water they had retained after 25 hours, despite all the excellent foot care.

I thought about it and realized that I was satisfied with running 82 miles. I was in otherwise decent physical shape and energy, I had been nailing my electrolytes and fueling and blood sugars the entire run. I had successfully run overnight; more than 24 hours; and by far (2.6x) the longest distance I had ever run. I could keep running to 100 miles (about 18 more miles), but no one cared if I did. I didn’t have to prove anything to anyone, including myself. I had planned, strategized, and executed above and beyond what I had thought was possible, both in terms of physical and mental performance. I had no major injuries, and I wanted to keep it that way. I knew I had the willpower and persistence to keep going; I was stubborn enough to do it; but as the last bit of icing on top of my ultramarathon cake, I wanted to have the mental strength to decide to stop where I was so I wouldn’t create a long-lasting injury in the last 18 miles from sheer stubbornness.

So I stopped. I told Scott I would decide for sure after I got home and dried off and warmed up, but that I was pretty sure this would be a stop and not just a pause. Rather than let me walk home in the rain, he insisted I stay in the warm dry bathroom while he biked home and got the car and brought it to the nearest trail entrance, which was about a quarter of a mile away (more good planning on my part!). Once he had gotten in the car and called me, I slowly walked out to meet him at the parking lot, reaching it right as he pulled in. The walk on my feet confirmed to me that they were done. They weren’t exceptionally blistered or injured, but I knew the cumulative water effect and soggy skin would likely lead to some damage if I continued on them. We headed home. I sat down and took off my socks and shoes and sure enough, my feet were wet, white, and very wrinkly and starting to crease. I took a hot shower then dried off, put hand sanitizer on my feet to help dry them out, and laid down with them sticking out of the covers to help them air out. Within a few hours, they had dried out, and showed me some blisters on the bottom of my right foot that were not really bad, but if I had kept going on them, the wet wrinkly tissue would’ve been very prone to more extreme damage. I reflected on the choice to stop and was still happy with my decision.

The 24 hours after I ran 82 miles

After my shower and laying down, I realized that I was (still) in great physical shape. Some parts of me were starting to stiffen up now that I had stopped, but they hadn’t bothered me at all during running. That was my hips that now hurt if I tried to lay on my side but not on my front or my back; and my thighs felt sore when I straightened and bent my legs. I had never even been tempted during my run to take pain meds because I was never overly sore and didn’t have any injuries.

(Note: you shouldn’t take NSAIDs during extreme events due to the risks of overworked kidneys having problems. I had packed Tylenol, which is acetaminophen, in case I needed it for pain management, but specifically did not pack any oral NSAIDs and warned Scott about offering me any. I did pack topical NSAID *gel* which is an extremely low quantity of NSAID compared to even one oral NSAID pill, and I used that once on my shoulder blades during the run. After my run, I waited several hours and made sure my kidneys were fine via hydration before I took any NSAID.)

It is very surprising to me that despite my longest training runs being almost a third of the distance I did, that I ended up in better physical shape at the end than I did during some training runs! This is probably in part due to going even slower (as planned) during my ultra, but I was really pleased. It might have also been due to the fact that I mentally trained for it to hurt really bad and to continue anyway. Again, lots of mental training and prep.

I ended up napping 2 hours after I got home and showered, and then was awake a few more hours and took another one hour nap. I ate several small meals throughout the day and stayed in bed to rest and not stress my feet further, then went to sleep at a normal bedtime and managed to sleep 9.5 hours through the night. Woohoo! I really wasn’t expecting that. I did wake up many times and find myself bending and flexing my knees or my ankles to help me roll over and could feel them being sore, but it wasn’t painful enough to fully wake me up or keep me from falling back to asleep within seconds, so it felt like a fully rested un-broken night of sleep.

The bottoms of my feet felt weird as they dried out, but progressively felt better and felt close to normal (normal meaning as normal as you are with a routine blister on the bottom of your forefoot) by the time I woke up the next morning (24 hours after ending my run). Everything that stiffened up in the first few hours after I stop has been gradually loosening up, so other than my forefeet still being sensitive with blisters, I’m walking around normally again.

The good, the bad, the ugly, and what I wish I had done differently

I had prepared for so much to go wrong, both those things in my control and things out of my control. And I think that’s why it actually didn’t hurt as much or go as wrong as it could have, despite all the variables in play. I nailed my pacing plan, energy levels, hydration levels, fueling intake, electrolyte intake, and enzyme intake.

I had estimated that I would need to take up to ~160 enzymes to cover my fueling. Remember that I stopped at ~25 hours (82 miles) instead of ~32 hours (100 miles) so I took less than that, but still a lot.

I consumed 50 (fifty!!!) snacks, one every 30 minutes, and swallowed multiple enzyme pills each time. I consumed at least 98 enzyme pills (!!!) in this 25 hour time period. I was concerned that my body wouldn’t be able to digest the pills or have some other issue with them, because I have never taken anywhere near this number of pills in a single day. But, it worked, and flawlessly: I had ZERO EPI-related issues and ZERO other gastrointestinal (GI) symptoms. GI symptoms are super common in ultras, even for people without things like EPI, so I’m incredibly thrilled with how well my planning and practicing paid off so I could execute my fueling plan and not have any issues.

My goal had been to take in ~250 calories per hour and ~500 mg of sodium per hour (from both the snacks every 30 min and electrolyte pills every 45 min). I use calories as my rolling metric because while most ultrarunners prioritize carbs, I’m running slower and likely more fat adapted than most people, and also need digestive enzymes no matter what I’m eating so taking small amounts of fat and protein are fine for me. Plus it makes for more interesting running snacks. So using calories as the global running metric of consumption rather than just carbs or fat etc. works for me. I nailed it, and across all 25 hours of my run I averaged 671 mg of sodium per hour and 279 calories per hour. I did have one hour where I somehow dropped low on sodium and felt it, and took an extra electrolyte pill to help catch up. It fixed the “low on sodium” feeling and I didn’t have any issues again. I had slightly more variability toward the end of the run, but that’s just due to the timing of when I logged it into my spreadsheet (due to the wet-phone issues I described earlier) and the auto-calculation on which hour it falls into; overall I still was maintaining the goal levels every hour.

A graph of calorie consumption, sodium consumption, and carb consumption per hour for all 25 hours of the 82 mile run.

(The purple dotted line is carbs, because I was curious about how that level fluctuated given that I didn’t prioritize my run snacks based on carbs at all. I generally seek <20 grams of carbs per snack but have a few that are closer to 30 grams; otherwise <10 or so grams of fat and however many grams of protein I don’t care).

How do I have all this data? I used my macronutrient spreadsheet as I went, selecting the snack I was going to eat from the drop-down list that then pre-populated the rest of the data in the sheet and updated a pivot table that summarized my rolling totals per hour. It was getting increasingly hard to use my phone in the mega downpour rain in the last few hours, which is why the timing of logging them was a little variable and the numbers look a little more bouncy each hour toward the end, but my consumption was still on time thanks to my every 30 minute phone alarms and so the logging was the only thing that varied and I was still above-goal overall although trending downward slightly.

This spreadsheet means I can also summarize my total consumption across 25 hours: I consumed an eye-popping 817 grams of carbs; 365 grams of fat; 136 grams of protein; 16,775 mg of sodium; and 6,979 total calories. That matched the 98+ enzyme pills (and 33 electrolyte pills, which are 210 mg of sodium each and reflected in the overall sodium counts), so I also swallowed >131 pills in the 25 hour time period running. Wow.

It’s common to end up in a calorie deficit due to the hours and miles that an ultra demand of your body, but my watch estimates I burned around 8,000+ calories (maybe an undercount since it stopped itself a few times), so I didn’t have as big of a deficit as I had originally predicted.

There were so many (50!) opportunities to mess up my digestion, and I didn’t mess up once. I’m really proud of that! I also had such a variety of snack types and textures that even though I was never really hungry, I ate my snacks like clock work and didn’t get major palate fatigue or get to the point that I wanted to stop chewing and needed to switch to my backup list of liquid fuel. The only time I slightly felt off was when I did a Snickers for one snack at the end of my lap and then my next snack was hot mashed potatoes – combined, that was 390 calories (one of my top two hours of calorie consumption) and felt like a little too much food, either because of the calories or the volume of mashed potatoes. It was only a minor annoyance, though, and the feeling passed within another 15 minutes and I didn’t have issues with any other combination of snacks. I did get tired of peanut butter pretzel nuggets, because they’re drier than many of my other snacks and took a lot of water to swallow. So I stopped choosing those in lieu of my other snacks and left those as emergency backups.

Looking back, I wish I could have done something differently about my feet, but I don’t think there’s anything else I could have done. I changed socks and into dry shoes at every single lap. I dried them and tried to draw out water with hand sanitizer and Desitin. I lubricated with Squirrel Nut Butter and Desitin, and overall came out with very few blisters compared to my typical shorter long runs (e.g. 25-30 miles). But we did get 0.72 inches of rain in that 24 hour period, and a lot of it was dumped onto my feet in the 4-7am time period. If I’d had a way of knowing 24 hours in advance exactly when the rain was going to let up with enough confidence to delay the run for a day, it turns out it would’ve been drier, but the forecast before I started running was for similar chances of rain all weekend. The laws of feet physics and the timing was just not good, and that was out of my control. I’ll keep researching other strategies for wet feet management, but I think I had done everything I could, did it well, and it just was what it was.

Overall, I can’t think of anything else I would have changed (other than my training, it would have been swell not to have broken my toe and been not weight bearing for 6 weeks!). Fueling, electrolytes, enzymes, blood sugars, pacing, mental game: flawless. I was even picking up the pace and still running and walking 30:90 second intervals, and I think I would have continued to pick up the pace and pushed it to the finish, estimating that I would have come in under 32 hours overall for 100 miles (around a 19 min/mi average pace overall, or a bit under that).

But I chose to stop at 82 miles, and being willing to do that was a huge mental PR, too.

So I’m pleased, proud, and thrilled to have run an 82 mile ultramarathon, and physically and mentally feel better than I would have predicted would be possible after 24 hours.

What it feels like to run (almost) a 100 mile ultramarathon, by Dana M. Lewis on DIYPS.org