Introducing PERT Pilot, the first iOS app designed for people with exocrine pancreatic insufficiency (EPI / PEI) and the only iOS app for specifically recording pancreatic enzyme replacement therapy (PERT) dosing!
PERT Pilot is designed to help people living with Exocrine Pancreatic Insufficiency (EPI or PEI) more easily deal with pancreatic enzyme replacement therapy (PERT). Aka, “taking enzymes”.
The PERT Pilot calculator enables you log the PERT that you are taking along with a meal, how many pills you take for it, and whether this dosing seems to work for you or not.
PERT Pilot then shows you the relationship between how much PERT you have been taking and what you are eating, supporting you as you fine-tune your enzyme intake.
PERT Pilot also enables you to share what’s working – and what might not be working – with your healthcare provider. PERT Pilot not only lists every meal you’ve entered, but also has a visual graph so you can see each meal and how much fat and protein from each meal were dosed by one pill – and it’s color coded by the outcome you assigned that meal! Green means you said that meal’s dosing “worked”; orange means you were “unsure”, and red matches the meals you said “didn’t work” for that level of dosing.
You can press on any meal and edit it, and you can swipe to delete a meal.
PERT Pilot also has is an education section so you can learn more about EPI and why you need PERT, and how this approach to ratios may help you more effectively dose your PERT in the future.
Why use PERT Pilot if you have EPI or PEI or PI?
PERT Pilot is the first and only specific app for those of us living with EPI (PEI or PI). People who use the approach in PERT Pilot of adapting their PERT dosing to what they are eating for each meal or snack often report fewer symptoms. PERT Pilot was designed and built by someone with exocrine pancreatic insufficiency, just like you!
With PERT Pilot you can:
Log your meals and PERT dosing. No other app specifically is designed for PERT dosing.
Edit or adjust your meal entry at any time – including if you wake up the next morning and realize your last dose from the day before ‘didn’t work’.
Review your dosing and see all of your meals, dosing, and outcomes – including a visual graph that shows you, for each meal, what one pill ‘covered’ so you can see where there are clusters of dosing that worked and if there are any clear patterns in what didn’t work for you.
You can also export your data, as a PDF list of all meals or a CSV file (which you can open in tools like Excel or other spreadsheet tools) if you want to analyze your data elsewhere!
Note: this app was not funded by nor has any relationship to any pharmaceutical or medical-related companies. It’s simply built by a person with EPI for other people with EPI.
Here is a quick demonstration of PERT Pilot in action:
You can share your feedback about PERT Pilot:
Feel free to email me (Dana+PERTPilot@OpenAPS.org) any time.
I’d love to hear what works or is helpful, but also if something in the app isn’t yet working as expected.
Or, if you use another approved brand of PERT that’s not currently listed, let me know and I can add it in.
And, you can share your feature requests! I’m planning to build more features soon (see below).
What’s coming next for PERT Pilot:
I’m not done improving the functionality! I plan to add an AI meal estimation feature (UPDATE: now available!), so if you don’t know what’s in what you’re eating at a restaurant or someone else’s home cooked meal you can simply enter a description of the meal and have macronutrient estimates generated for you to use or modify.
Download PERT Pilot today! It’s free to download, so go ahead and download it and check it out! If you find it useful, please also leave a rating or review on the App Store to help other people find it in the future. You can also share it via social media, and give people a link to download it: https://bit.ly/PERT-Pilot-iOS
So what am I using these AI tools for? And how might YOU use some of these AI tools? And what are the limitations? This is what I’ve learned:
The most frequent way I use these AI tools is for getting started on a project, especially those related to writing.
You know the feeling of staring at a blank page and not knowing where to start? Maybe it’s the blank page of a cold email; the blank page of an essay or paper you need to write; the blank page of the outline for a presentation. Starting is hard!
Even for this blog post, I had a list of bulleted notes of things I wanted to remember to include. But I wasn’t sure how I wanted to start the blog post or incorporate them. I stuck the notes in ChatGPT and asked it to expand the notes.
What did it do? It wrote a few paragraph summary. Which isn’t what I wanted, so I asked it again to use the notes and this time “expand each bullet into a few sentences, rather than summarizing”. With these clear directions, it did, and I was able to look at this content and decide what I wanted to edit, include, or remove.
Sometimes I’m stuck on a particular writing task, and I use ChatGPT to break it down. In addition to kick-starting any type of writing overall, I’ve asked it to:
Take an outline of notes and summarize them into an introduction; limitations section; discussion section; conclusion; one paragraph summary; etc.
Take a bullet point list of notes and write full, complete sentences.
Take a long list of notes I’ve written about data I’ve extracted from a systematic review I was working on, and ask it about recurring themes or outlier concepts. Especially when I had 20 pages (!) of hand-written notes in bullets with some loose organization by section, I could feed in chunks of content and get help getting the big picture from that 20 pages of content I had created. It can highlight themes in the data based on the written narratives around the data.
A lot of times, the best thing it does is it prompts my brain to say “that’s not correct! It should be talking about…” and I’m able to more easily write the content that was in the back of my brain all along. I probably use 5% of what it’s written, and more frequently use it as a springboard for my writing. That might be unique to how I’m using it, though, and other simple use cases such as writing an email to someone or other simplistic content tasks may mean you can keep 90% or more of the content to use.
2. It can also help analyze data (caution alert!) if you understand how the tools work.
Huge learning moment here: these tools are called LLMs (large language models). They are trained on large amounts of language. They’re essentially designed so that, based on all of those words (language) it’s taken in previously, to predict content that “sounds” like what would come after a given prompt. So if you ask it to write a song or a haiku, it “knows” what a song or a haiku “looks” like, and can generate words to match those patterns.
It’s essentially a PATTERN MATCHER on WORDS. Yeah, I’m yelling in all caps here because this is the biggest confusion I see. ChatGPT or most of these LLMs don’t have access to the internet; they’re not looking up in a search engine for an answer. If you ask it a question about a person, it’s going to give you an answer (because it knows what this type of answer “sounds” like), but depending on the amount of information it “remembers”, some may be accurate and some may be 100% made up.
Why am I explaining this? Remember the above section where I highlighted how it can start to sense themes in the data? It’s not answering solely based on the raw data; it’s not doing analysis of the data, but mostly of the words surrounding the data. For example, you can paste in data (from a spreadsheet) and ask it questions. I did that once, pasting in some data from a pivot table and asking it the same question I had asked myself in analyzing the data. It gave me the same sense of the data that I had based on my own analysis, then pointed out it was only qualitative analysis and that I should also do quantitative statistical analysis. So I asked it if it could do quantitative statistical analysis. It said yes, it could, and spit out some numbers and described the methods of quantitative statistical analysis.
But here’s the thing: those numbers were completely made up!
It can’t actually use (in its current design) the methods it was describing verbally, and instead made up numbers that ‘sounded’ right.
So I asked it to describe how to do that statistical method in Google Sheets. It provided the formula and instructions; I did that analysis myself; and confirmed that the numbers it had given me were 100% made up.
The takeaway here is: it outright said it could do a thing (quantitative statistical analysis) that it can’t do. It’s like a human in some regards: some humans will lie or fudge and make stuff up when you talk to them. It’s helpful to be aware and query whether someone has relevant expertise, what their motivations are, etc. in determining whether or not to use their advice/input on something. The same should go for these AI tools! Knowing this is an LLM and it’s going to pattern match on language helps you pinpoint when it’s going to be prone to making stuff up. Humans are especially likely to make something up that sounds plausible in situations where they’re “expected” to know the answer. LLMs are in that situation all the time: sometimes they actually do know an answer, sometimes they have a good guess, and sometimes they’re just pattern matching and coming up with something that sounds plausible.
LLM’s can expand general concepts and write language about what is generally well known based on its training data.
Try to ask it a particular fact, though, and it’s probably going to make stuff up, whether that’s about a person or a concept – you need to fact check it elsewhere.
It can’t do math!
But what it can do is teach you or show you how to do the math, the coding, or whatever thing you wish it would do for you. And this gets into one of my favorite use cases for it.
3. You can get an LLM to teach you how to use new tools, solve problems, and lower the barrier to entry (and friction) on using new tools, languages, and software.
It’s now so easy to generate code for scripts, regardless of which language you have previous experience with. I used to write all of my code as bash scripts, because that’s the format I was most familiar with. But ChatGPT likes to do things as Python scripts, so I asked it simple questions like “how do I call a python script from the command line” after I asked it to write a script and it generated a python script. Sure, you could search in a search engine or Stack Overflow for similar questions and get the same information. But one nice thing is that if you have it generate a script and then ask it step by step how to run a script, it gives you step by step instructions in context of what you were doing. So instead of saying “to run a script, type `python script.py’”, using placeholder names, it’ll say “to run the script, use ‘python actual-name-of-the-script-it-built-you.py’ “ and you can click the button to copy that, paste it in, and hit enter. It saves a lot of time for figuring out how to take placeholder information (which you would get from a traditional search engine result or Stack Overflow, where people are fond of things like saying FOOBAR and you have no idea if that means something or is meant to be a placeholder). Careful observers will notice that the latest scripts I’ve added to my Open Humans Data Tools repository (which is packed with a bunch of scripts to help work with big datasets!) are now in Python rather than bash; such as when I was adding new scripts for fellow researchers looking to check for updates in big datasets (such as the OpenAPS Data Commons). This is because I used GPT to help with those scripts!
It’s really easy now to go from an idea to a script. If you’re able to describe it logically, you can ask it to write a script, tell you how to run it, and help you debug it. Sometimes you can start by asking it a question, such as “Is it possible to do Y?” and it describes a method. You need to test the method or check for it elsewhere, but things like uploading a list of DOIs to Mendeley to save me hundreds of clicks? I didn’t realize Mendeley had an API or that I could write a script that would do that! ChatGPT helped me write the script, figure out how to create a developer account and app access information for Mendeley, and debug along the way so I ended up within an hour and a half of having a tool that easily saved me 3 hours on the very first project that I used it with.
I’m gushing about this because there’s probably a lot of ideas you have that you immediately throw out as being too hard, or you don’t know how to do it. It takes time, but I’m learning to remember to think “I should ask the LLM this” and ask it questions such as:
Is it possible to do X?
Write a script to do X.
I have X data. Pretend I am someone who doesn’t know how to use Y software and explain how I should do Z.
Another thing I’ve done frequently is ask it to help me quickly write a complex formula to use in a spreadsheet. Such as “write a formula that can be used in Google Sheets to take an average of the values in M3:M84 if they are greater than zero”.
It gives me the formula, and also describes it, and in some cases, gives alternative options.
Other things I’ve done with spreadsheets include:
Ask it to write a conditional formatting custom formula, then give me instructions for expanding the conditional formatting to apply to a certain cell range.
Asking it to check if a cell is filled with a particular value and then repeating the value in the new cell, in order to create new data series to use in particular charts and graphs I wanted to create from my data.
Help me transform my data so I could generate a box and whisker plot.
Ask it for other visuals that might be effective ways to illustrate and visualize the same dataset.
Explain the difference between two similar formulas (e.g. COUNT and COUNTA or when to use IF and IFS).
It’s also helped me get past the friction of setting up new tools. Case in point, Jupyter notebooks. I’ve used them in the web browser version before, but often had issues running the notebooks people gave me. I debugged and did all kinds of troubleshooting, but have not for years been able to get it successfully installed locally on (multiple of) my computers. I had finally given up on effectively using notebooks and definitely given up on running it locally on my machine.
However, I decided to see if I could get ChatGPT to coax me through the install process.
I told it:
“I have this table with data. Pretend I am someone who has never used R before. Tell me, step by step, how to use a Jupyter notebook to generate a box and whisker plot using this data”
(and I pasted my data that I had copied from a spreadsheet, then hit enter).
It outlined exactly what I needed to do, saying to install Jupyter Notebook locally if I hadn’t, gave me code to do that, installing the R kernel, told me how to do that, then how to start a notebook all the way down to what code to put in the notebook, the data transformed that I could copy/paste, and all the code that generated the plot.
However, remember I have never been able to successfully get Jupyter Notebooks running! For years! I was stuck on step 2, installing R. I said:
“Step 2, explain to me how I enter those commands in R? Do I do this in Terminal?”
It said “Oh apologies, no, you run those commands elsewhere, preferably in Rstudio. Here is how to download RStudio and run the commands”.
So, like humans often do, it glossed over a crucial step. But it went back and explained it to me and kept giving more detailed instructions and helping me debug various errors. After 5-6 more troubleshooting steps, it worked! And I was able to open Jupyter Notebooks locally and get it working!
All along, most of the tutorials I had been reading had skipped or glossed over that I needed to do something with R, and where that was. Probably because most people writing the tutorials are already data scientists who have worked with R and RStudio etc, so they didn’t know those dependencies were baked in! Using ChatGPT helped me be able to put in every error message or every place I got stuck, and it coached me through each spot (with no judgment or impatience). It was great!
I was then able to continue with the other steps of getting my data transformed, into the notebook, running the code, and generating my first ever box and whisker plot with R!
This is where I really saw the power of these tools, reducing the friction of trying something new (a tool, a piece of software, a new method, a new language, etc.) and helping you troubleshoot patiently step by step.
Does it sometimes skip steps or give you solutions that don’t work? Yes. But it’s still a LOT faster than manually debugging, trying to find someone to help, or spending hours in a search engine or Stack Overflow trying to translate generic code/advice/solutions into something that works on your setup. The beauty of these tools is you can simply paste in the error message and it goes “oh, sorry, try this to solve that error”.
Because the barrier to entry is so low (compared to before), I’ve also asked it to help me with other project ideas where I previously didn’t want to spend the time needed to learn new software and languages and all the nuances of getting from start to end of a project.
Such as, building an iOS app by myself.
I have a ton of projects where I want to temporarily track certain types of data for a short period of time. My fall back is usually a spreadsheet on my phone, but it’s not always easy to quickly enter data on a spreadsheet on your phone, even if you set up a template with a drop down menu like I’ve done in the past (for my DIY macronutrient tool, for example). For example, I want to see if there’s a correlation in my blood pressure at different times and patterns of inflammation in my eyelid and heart rate symptoms (which are symptoms, for me, of thyroid antibodies being out of range, due to Graves’ disease). That means I need to track my symptom data, but also now some blood pressure data. I want to be able to put these datasets together easily, which I can, but the hardest part (so to speak) is finding a way that I am willing to record my blood pressure data. I don’t want to use an existing BP tracking app, and I don’t want a connected BP monitor, and I don’t want to use Apple Health. (Yes, I’m picky!)
I decided to ask ChatGPT to help me accomplish this. I told it:
“You’re an AI programming assistant. Help me write a basic iOS app using Swift UI. The goal is a simple blood pressure tracking app. I want the user interface to default to the data entry screen where there should be three boxes to take the systolic, diastolic blood pressure numbers and also the pulse. There should also be selection boxes to indicate whether the BP was taken sitting up or laying down. Also, enable the selection of a section of symptom check boxes that include “HR feeling” and “Eyes”. Once entered on this screen, the data should save to a google spreadsheet.”
This is a completely custom, DIY, n of 1 app. I don’t care about it working for anyone else, I simply want to be able to enter my blood pressure, pulse, whether I’m sitting or laying down, and the two specific, unique to me symptoms I’m trying to analyze alongside the BP data.
And it helped me build this! It taught me how to set up a new SwiftUI project in XCode, gave me code for the user interface, how to set up an API with Google Sheets, write code to save the data to Sheets, and get the app to run.
(I am still debugging the connection to Google Sheets, so in the interim I changed my mind and had it create another screen to display the stored data then enable it to email me a CSV file, because it’s so easy to write scripts or formulas to take data from two sources and append it together!)
Is it fancy? No. Am I going to try to distribute it? No. It’s meeting a custom need to enable me to collect specific data super easily over a short period of time in a way that my previous tools did not enable.
Here’s a preview of my custom app running in a simulator phone:
I did this in a few hours, rather than taking days or weeks. And now, the barrier to entry to creating more custom iOS is reduced, because now I’m more comfortable working with XCode and the file structures and what it takes to build and deploy an app! Sure, again, I could have learned to do this in other ways, but the learning curve is drastically shortened and it takes away most of the ‘getting started’ friction.
That’s the theme across all of these projects:
Barriers to entry are lower and it’s easier to get started
It’s easier to try things, even if they flop
There’s a quicker learning curve on new tools, technologies and languages
You get customized support and troubleshooting without having to translate through as many generic placeholders
PS – speaking of iOS apps, based on building this one simple app I had the confidence to try building a really complex, novel app that has never existed in the world before! It’s for people with exocrine pancreatic insufficiency like me who want to log pancreatic enzyme replacement therapy (PERT) dosing and improve their outcomes – check out PERT Pilot and how I built it here.
4. Notes about what these tools cost
I found ChatGPT useful for writing projects in terms of getting started, even though the content wasn’t that great (on GPT-3.5, too). Then they came out with GPT-4 and made a ChatGPT Pro option for $20/month. I didn’t think it was worth it and resisted it. Then I finally decided to try it, because some of the more sophisticated use cases I wanted to use it for required a longer context window, and in addition to a better model it also gave you a longer context window. I paid the first $20 assuming I’d want to cancel it by the end of the month.
The $20 has been worth it on every single project that I’ve used it for. I’ve easily saved 5x that on most projects in terms of reducing the energy needed to start a project, whether it was writing or developing code. It has saved 10x that in time cost recouped from debugging new code and tools.
GPT-4 does have caps, though, so even with the $20/month, you can only do 25 messages every 3 hours. I try to be cognizant of which projects I default to using GPT-3.5 on (unlimited) versus saving the more sophisticated projects for my GPT-4 quota.
For example, I saw a new tool someone had built called “AutoResearcher”, downloaded it, and tried to use it. I ran into a bug and pasted the error into GPT-3.5 and got help figuring out where the problem was. Then I decided I wanted to add a feature to output to a text file, and it helped me quickly edit the code to do that, and I PR’ed it back in and it was accepted (woohoo) and now everyone using that tool can use that feature. That was pretty simple and I was able to use GPT-3.5 for that. But sometimes, when I need a larger context window for a more sophisticated or content-heavy project, I start with GPT-4. When I run into the cap, it tells me when my next window opens up (3 hours after I started using it), and I usually have an hour or two until then. I can open a new chat on GPT-3.5 (without the same context) and try to do things there; switch to another project; or come back at the time it says to continue using GPT-4 on that context/setup.
Why the limit? Because it’s a more expensive model. So you have a tradeoff between paying more and having a limit on how much you can use it, because of the cost to the company.
Most important note: LLMs don’t “think” or “know” things the way humans do. They output language they predict you want to see, based on its training and the inputs you give it. It’s like the autocomplete of a sentence in your email, but more words on a wider range of topics!
Also, the LLM can’t do math. But they can write code. Including code to do math.
(Some, but not all, LLMs have access to the internet to look up or incorporate facts; make sure you know which LLM you are using and whether it has this feature or not.)
Ways to get started:
The most frequent way I use these AI tools is for getting started on a project, especially those related to writing.
Ask it to help you expand on notes; write summaries of existing content; or write sections of content based on instructions you give it
It can also help analyze data (caution alert!) if you understand the limitations of the LLM.
The most effective way to work with data is to have it tell you how to run things in analytical software, whether that’s how to use R or a spreadsheet or other software for data analysis. Remember the LLM can’t do math, but it can write code so you can then do the math!
You can get an LLM to teach you how to use new tools, solve problems, and lower the barrier to entry (and friction) on using new tools, languages, and software.
Build a new habit of asking it “Can I do X” or “Is it possible to do Y” and when it says it’s possible, give it a try! Tell it to give you step-by-step instructions. Tell it where you get stuck. Give it your error messages or where you get lost and have it coach you through the process.
What’s been your favorite way to use an LLM? I’d love to know other ways I should be using them, so please drop a comment with your favorite projects/ways of using them!
I’ve been training for a big goal of mine: running a 100k in a specific amount of time. Yes, I’ve run farther than that before: last year I ran ~82 miles. However, I had someone in my family network who ran 100k last year, and I realized their time made a reasonable goal for me. I’m competitive, so the extra motivation of striving for a certain time is helpful for channeling my “racing”, even if I’m “racing” someone virtually (who ran a year ago!).
Like last year, I decided I would run my 100k (which is 62+ miles) as a solo or DIY ultramarathon. I originally plotted five laps of various lengths, then figured out I could slightly alter my longest route by almost a mile, making it so I would do 2 laps of the same length, a third lap of my original longest length, and then a fourth lap of a shorter length that’s also one of my preferred running routes. Only four laps would be mentally easier than doing five laps, even though it would end up being exactly the same distance. Like last year, I leveraged extensive planning (most of it done last year) to plan my electrolytes, enzymes, and fueling in advance. I had a lot less work to do this year, because I simply refreshed the list of gear and prep work from last year, shortened of course to match the length of my expected race (less than 18 hours vs ~24+ hours). The main thing I changed in terms of preparation is that while I set out a few “just in case” supplies, most of them I left in their places, figuring they’d be easy enough to find in the house by Scott (my husband) if I needed to ask him to bring out anything in particular. The few things I laid out were emergency medical supplies like inhaled insulin, inhaled glucagon, a backup pump site, etc. And my usual piles of supplies – clothes, fuel to refill my vest, etc – for each lap.
One thing that was different for my 100k was my training. Last year, I was coming back from a broken toe and focused on rebuilding my feet. I found that I needed to stick with three runs per week. This year, I was back up to 4-5 runs per week and building up my long runs beginning in January, but in early February I felt like my left shin was getting niggle-y and I backed down to 3 runs a week. Plus, I was also more active on the weekends, including most weekends where we were cross-country skiing twice, often covering 10-15 miles between two days of skiing, so I was getting 3+ extra hours of “time on legs”, albeit differently than running. Instead of just keeping one longer run, a medium run, and two shorter runs (my original plan), I shifted to one long run, one medium long run (originally 8 and then jumping to 13 miles because it matched my favorite route), and the big difference was making my third run about 8 miles, too. This meant that I carried my vest and fueled for all three runs, rather than just one or two runs per week. I think the extra time training with the weight of my vest paid off, and the miles I didn’t do or the days I didn’t run didn’t seem to make a difference in regard to recovering during the weeks of training or for the big run itself. Plus, I practiced fueling every week for every run.
I also tapered differently. Once I switched to three runs a week, my shin felt a lot better. However, in addition to cross country skiing, Scott and I also have access now to an outdoor rock climbing wall (so fun!) and have been doing that. It’s a different type of workout and also helps with full body and upper body strength, while being fun and not feeling like a workout. I bring it up mostly because three weeks ago, I think I hurt the inside of my hip socket somehow by pressing off a foothold at a weird angle, and my hip started to be painful. It was mostly ok running, but I backed off my running schedule and did fewer miles for a week. The following week I was supposed to do my last longest long run – but I felt like it wouldn’t be ideal to do with my hip still feeling intermittently sore. Sometimes it felt uncomfortable running, other times it didn’t, but it didn’t feel fully back to normal. I decided to skip the last long run and stick with a week of my medium run length (I did 13, 13, and 8). That felt mostly good, and it occurred to me that two shorter weeks in a row were essentially a taper. If I didn’t feel like one more super long run (originally somewhere just under a 50k) was necessary to prepare, then I might as well consider moving my ‘race’ up. This is a big benefit of DIY’ing it, being able to adjust to injury or schedule – or the weather! The weather was also forecasted to be REALLY nice – no rain, high 50s F, and so I tentatively aimed to do a few short runs the following week with my 100k on the best weather day of the weekend. Or if the weather didn’t work out, I could push it out another week and stick with my original plan.
My taper continued to evolve, with me running 4 easy miles on Monday (without my vest) to see how my hip felt. Mostly better, but it still occasionally niggled when walking or running, which made me nervous. I discussed this endlessly with Scott, who as usual thought I was overcomplicating it and that I didn’t need to run more that week before my 100k. I didn’t like the idea of running Monday, then not running again until (Friday-Sunday, whenever it ended up being), but a friend unexpectedly was in town and free on Wednesday morning, so I went for a walk outside with her and that made it easy to choose not to run! It was going to be what it was going to be, and my hip would either let me run 100k or it would let me know to make it a regular long run day and I could stop at any time.
So – my training wasn’t ideal (shifting down to 3 runs a week) and my taper was very unexpected and evolved differently than it usually does, but listening to my body avoided major injury and I woke up feeling excited and with a good weather forecast for Friday morning, so I set off at 6am for my 100k.
(Why 6am start, if I was DIYing? My goal was to finish by 11:45pm, to beat the goal time of 11:46pm, which would have been 17 hours and 46 minutes. I could start later but that would involve more hours of running at night and keeping Scott awake longer, so I traded for an hour of running before it got light and finishing around midnight for a closer to normal bedtime for us both.)
*One other major thing I did to prep was that as soon as I identified that I wanted to shift my race up a week, I went in and started scheduling my bedtimes, beginning with the night before the race. If I raced at 6 from home, I would wake up at 5 to get ready, so I wanted to be sleeping by 9pm at the latest in order to get close to a normal night of sleep. Ideally it would be closer to 8-8:30. I set my bed time and each night prior, marked the bedtime 15 minutes later, so that when I started I was trying to push my bedtime from ~11pm to 10:45 pm then the next night 10:30pm etc. It wasn’t always super precise – I’ve done a better job achieving the goal bedtimes previously, but given that I did an early morning cross country ski race on the morning of daylight saving time the week before (ouch), it went pretty ok, and I woke up at 5am on race morning feeling rested and better than I usually do on race days. 7 hours and 45 minutes of sleep is an hour to an hour and a half less than usual, but it’s a LOT better than the 4-5 hours of sleep I might have otherwise gotten without shifting my schedule.
THE START (MILES 0-17)
I set out at 6am, It was 33 degrees (F), so I wore shorts and a short sleeve shirt, with a pair of fleece lined pants over my shorts and a long sleeve shirt, rain jacket, ear cover, and gloves on my hand. It was dry, which helped. I was the only one out on the trail in the dark, and I had a really bright waist lamp and was running on a paved trail, so I didn’t have issues seeing or running. I felt a bit chilly but within 3 minutes could tell I would be fine temperature wise. As I got on the trail, I glanced up and grinned – the stars were out! That meant I could “check” something off my experience list at the very start. (I make a list of positive and less great experiences to ‘check off’ mentally, everything from seeing the stars or seeing bunnies or other wildlife to things like blisters, chafing, or being cold or tired or having out of whack glucose levels – to help me process and “check them off” my list and move on after problem solving, rather than dwelling on them and getting myself into a negative mood). The other thing I chuckled about at the start was passing the point where, about a half mile in to my 82 miles, I had popped the bite valve off of my hydration hose and gotten water everywhere and couldn’t find the bite valve for 3 minutes. That didn’t happen this time, phew! So this run was already off to a great start, just by nothing wild like that happening within the first few minutes. I peeled off my ear cover at 0.75 miles and my gloves at a mile. My jacket then peeled off to tie around my waist by the second mile, and I was surprised when my alarm went off at 6:30am reminding me to take in my first fuel. My plan calls for fuel every 30 minutes, which is why I like starting at the top of the hour (e.g. 6:00am) so I can use the alarm function on my phone to have alarms pre-set for the clock times when I need to fuel. As I continued my run/walk, just like I do in all my training runs, I pulled my enzymes out of my left pocket, swallowed them, put them away, grabbed my fuel out of my right pocket (starting with chili cheese Fritos), then also entered it into my fuel tracking spreadsheet so I could keep an eye on rolling calorie and sodium consumption throughout my run. (Plus, Scott can also see it and keep an eye on it as an extra data point that I’m doing well and following all planned activities, as well as having live GPS tracking and glucose tracking capabilities). I carried on, and as the sky began to lighten, I could see frost covering the ground beside the trail – brrr! It actually felt a little bit colder as the sun rose, and I could see wafts of fog rolling along the river. I started to see more people out for early morning runs, and I checked my usual irritation at people who were likely only out for (3? 5? 10? Psh!) short morning runs while I was just beginning an all day slog.
I was running well and a little ahead of my expected pace, closer to my usual long run/walk paces (which have been around 14:30-14:50 min/mi lately). I was concerned it was too fast and I would burn out as so many people do, but I did have wiggle room in my paces and had planned for an eventual slow down regardless. I made it to the first turnaround, used the trail bathroom there, and continued on, noting that even with the bathroom stop factored in, I was still on or ahead of schedule. I texted Scott to let him know to check my paces earlier than he might otherwise, and also stopped in my tracks to take a picture of a quail-like bird (which Scott thinks was a pheasant) that I’d never seen before. Lap 1 continued well, and I was feeling good and maintaining an overall sub-15 pace while I had been planning for a 15:10/ish average pace, so although Scott told me he didn’t need me to warn him about being particular miles away for aid station stops, I saw he was still at home by the time I was less than a mile out, and texted him. He was finishing a work call and had to rush to finish packing and come meet me. It wouldn’t have been a big deal if he had “missed” me at the expected turnaround spot, because there’s other benches and places where we could have met after that, but I think he was still stressed out (sorry!) about it, although I wasn’t. However, he biked up to me right at the turnaround spot, grabbed my vest and headed back to our normal table for refueling, while I used the bathroom and then headed out to meet him.
The other thing that might have stressed him out a little – and did stress me out a little bit – was my glucose levels. They were running normal levels for me during a run, around ~150mg/dL in the first 2-3 hours of my run. This is higher than I normally like to be for non-running times but is reasonable for long runs. I usually run a bit higher at the start and then settle in around 120-130mg/dL, because the risk of having too much insulin at the start from breakfast is prone to causing lows in the first hour; therefore I let myself reduce insulin prior to the run so that the first hour or so runs higher. However, instead of coming down as usual from the start of my run, I started a steady rise from 150 to 180. That was weird, but maybe it was a physiological response to the stress? I issued a correction, but I kept rising. I crossed 200 when I should have been beginning to flatten, and it kept going. What on earth? I idly passed my hand over my abdomen to check my pump site, and couldn’t feel my pump site. It had come unclipped!!! This was super frustrating, because it means I didn’t know how much insulin was in my body or when it had come unclipped. (Noteworthy that in 20+ years of using an insulin pump, this has NEVER happened before until this month, and it has now happened twice, so I need to record the batch/lot numbers and report it – this batch of sites is easily coming unclipped with a tug on the tubing, which is clearly dangerous because you can’t feel it come unclipped and don’t know until you see rising glucose levels.) “Luckily” though, this was when I was within 30 minutes or so of being back to Scott, so I texted him and told him to grab the inhaled insulin baggie I had set out, and I would use that at the aid station to more quickly get my body back into a good state (both in terms of feeling the insulin action as well as normalizing glucose levels more quickly. For those who don’t know, injected/pump insulin takes ~45 minutes to peak activity in the body, whereas inhaled insulin is much faster in the ballpark of ~15-20 minutes peak action, so in situations like this I prefer to, when possible, use inhaled insulin to normalize how my body is feeling while also resuming/fixing the pump site for normal insulin from then on).
As planned, at every aid station stop he brought water and ice to refill my camelback, which he did while I was at the bathroom. When I came up to the table where he was, I quickly did some inhaled insulin. Then I sat down and took off my socks and shoes and inspected my feet. My right foot felt like it had been rubbing on the outside slightly, so I added a piece of kinesiology tape to the outer edge of my foot. I already had pieces on the bottom of my feet to help prevent blisters like I got during my 82, and those seemed to be working, and it was quick and easy to add a straight piece of tape, re-stick pieces of lamb’s wool next to each big toe (to prevent blisters there), put fresh socks on, and put a fresh pair of shoes on. I also changed my shirts. It was now 44 F and it was supposed to warm up to 61 F by the end of this next lap. I stood up to put my pack on again and realized I had forgotten to peel off my pants! Argh. I had to unlace my shoes again, which was the most annoying part of my stop. I peeled off the pants (still wearing my shorts under), put my shoes back on and laced them again, then put my vest back on. I removed the remaining trash from my vest pockets, pulled out the old enzyme and electrolyte baggies, and began to put the new fuel supply and enzyme and electrolyte supply in the front vest pockets. Last time for my 82, I had Scott do the refilling of my vest, but this time I just had him set out my gallon bag that contained all of these, so that I could place the snacks how I like best and also have an idea of what I had for that lap. I would need to double check that I had enzymes and electrolytes, anyway, so it ended up being easier for me to do this and I think I’ll keep doing this moving forward. Oh, and at each aid station stop we popped my (non-ultra) Apple Watch on a watch charger to top off the charge, too. I also swapped in a new mini battery to my pack to help keep my phone battery up, and then took off. All this, including the bathroom time, took about 15 minutes! I had budgeted 20 minutes for each stop, and I was pleased that this first stop was ahead of schedule in addition to my running slightly ahead of schedule, because that gave me extra buffer if I slowed down later.
LAP 2 (MILES 18-34)
The next lap was the same route as the first, and felt like a normal long run day. It was mid 40s and gradually warmed up to 63 F and actually felt hot for the second half! It hadn’t been 60+ degrees in Seattle since October (!) so my body wasn’t used to the “heat”. I was still feeling good physically and running well – in fact, I was running only ~10s slower than my average pace from lap 1! If I kept this up and didn’t fall off the pace much in the second lap, I would have a very nice buffer for the end of the race. I focused on this lap and only thought about these 16-17 miles. I did begin to squirt water from my camelback on to the ‘cooling’ visor I have, which evaporates and helps your head feel cooler – especially since I wasn’t used to the heat and was sweating more, that felt good. The end of the second lap, I started to feel like I was slightly under my ideal sodium levels. I’m pretty sensitive to sodium; I also drink a lot (I was carrying 3-3.5L for every 17 mile lap!); and I’m a salty sweater. Add increased heat, and even though I was right on track with my goal of about ~500mg/hour of sodium intake between my fuel and additional electrolyte pills, I felt a bit under, and so the next while I added an extra electrolyte pill to increase my sodium intake, and the feeling went away as expected.
(My glucose levels had come back down nicely within the first few miles of this lap, dipped down but as I was fueling every 30 minutes, came nicely into range and stayed 100% in range with no issues for the next ~12 hours of the run!)
This time, Scott was aware that I was ahead of expected paces and had been mapping my paces. He told me that if I stayed at that pace for the lap, I would be able to slow down to a 16 min/mi pace for lap 3 (16 miles) and down further to a 17 min/mi pace for the last (almost 13 miles) lap and still beat my goal time. That sounded good to me! He ended up biking out early to meet me so he could start charging my watch a few minutes early, and I ended up taking one of my next snacks – a warmed up frozen waffle – for my ‘last’ snack of the lap because it was time for a snack and there was no reason to wait even though it was part of the ‘next’ lap’s fuel plan. So I got to eat a warm waffle, which was nice!
Once we got almost there, Scott took my vest and biked ahead to begin the camelback process. I hit the turnaround, made another quick bathroom stop, and ran over to the table. This time, since it was 60s and I would finish my next lap while it was still above 50 degrees and light, I left my clothing layers as-is, other than a quick shirt switch to get rid of my sweaty shirt. I decided not to undo my shoes and check my feet for blisters; they felt fine and good. Because I didn’t need a shoe change or have anything going on to troubleshoot, I was in and out in 5 minutes! Hooray, that gave me another 10 minute buffer (in addition to 5 before, plus all my running ahead of schedule). I took off for lap 3, but warned Scott I would probably be slowing down.
LAP 3 (MILES 35-50)
The third lap was almost the same route, but shorter by a little less than a mile. I was originally concerned, depending on how much I had slowed down, that I would finish either right around sunset or after sunset, so that Scott might need to bring me out a long sleeve shirt and my waist lamp. However, I was ahead of schedule, so I didn’t worry about it, and again set out trying to not fall off my paces too much. I slowed down only a tiny bit on the way out, and was surprised at the turnaround point that I was now only slightly above a 15 min/mi pace! The last few miles I felt like slowing down more, but I was motivated by two thoughts: one was that I would finish this lap and essentially be at 50 miles. This meant, given my excellent pacing, that I would be “PR”ing my 50 mile pace. I’ve not run a standalone 50 miles before, just as part of my 82 mile when I wasn’t paying attention to pace at all (and ran 2-3 min/mi slower as a result), so I was focused on holding my effort level to be close to the same. Plus, after this lap, I “only” had a ~13 mile single lap left. That was my usual route, so it would be mentally easier, and it’s my last lap, so I knew I would get a mental boost from that. Psychologically, having the 50 mile mark to PR here really helped me hold my pace! I ended up only slowing down ~13s average pace compared to the ~10s deterioration between laps 1 and 2. I was pretty pleased with that, especially with hitting 50 miles then!
At this aid station stop, I was pretty cheerful even though I kept telling Scott I would be slowing down. I took ~10 minutes at this stop because I had to put my jacket back on around my waist and put my double headlamp on (which I wear around my waist) for when it got dark, plus do the normal refueling. I changed my short sleeve shirt again so I had a dry shirt, and debated but went ahead and put my fresh long sleeve shirt on and rolled up the sleeves. I figured I’d be putting it on as soon as it got dark, and I didn’t want to have to hassle with getting my vest on and off (while moving) in order to get the shirt on, especially because I’d also have to do that with my jacket later, so I went with the long sleeve shirt on and rolled up the sleeves for now. I had originally planned to put my long pants back on over my shorts, but it was still 63 degrees and the forecast was only going to get down to 45 degrees by midnight, and I seemed ahead of schedule and should finish by then. If I did get really cold, Scott could always bike out early and bring me more layers, but even 45 degrees in the dark with long sleeves, jacket, ear cover, and two pairs of gloves should be fine, so I went without the pants.
Speaking of ahead of schedule, I was! I had 5 minutes from the first aid station, 15 minutes from the second aid station, 5 minutes from this last aid station…plus another ~15 minutes ahead of what I thought my running time would have been at this point. Woohoo!
LAP 4 (MILES 51-63)
However, as soon as I walked off with my restocked vest, I immediately felt incredibly sore thighs. Ouch! My feet also started complaining suddenly. I did an extra walk interval and resumed my run/walking and my first mile out of the aid station stop was possibly my slowest mile (barring any with a bathroom stop) for the entire race, which is funny, because it was only about a 16:30 pace. But I figured it would be downhill from there and I’d be lucky to hold a sub 17 pace for these last 13 miles, especially because most of them would be in the dark and I naturally move a bit slower in the dark. Luckily, I was so far ahead that I knew that even a 17 min/mi average pace (or even slower) would be fine. However, I had joked to Scott coming into the end of lap 3 that I was tempted to just walk lap 4 (because I was finally starting to be tired) but then I’d have to eat more snacks, because I’d be out there longer. Sounds funny, but it was true – I was eating ok but occasionally I was having trouble swallowing my enzyme pills. Which is completely reasonable, I had been swallowing dozens of those (and electrolyte pills) all day and putting food down my throat for ~12+ hours consistently. It wasn’t the action of swallowing that was a problem, but I seemed to be occasionally mistiming how I would get the pills washed to the back of my mouth at the top of my throat to be able to swallow them down. Once or twice I had to take in some extra water, so it really wasn’t a big deal, but it was a slight concern that if I stopped being able to enzyme, I couldn’t fuel (because I have EPI) and I’d either have to tough it out without fueling (bad idea) or stop (not a fun idea). So I had that little extra motivation to try to keep run/walking!
Luckily, that first mile of the last lap was the worst. My thighs were still sore but less so and my feet stopped yelling at me and were back to normal. I resumed a reasonable run/walk pace, albeit at closer to a 15:30+ pace, which was a bigger jump from my previous lap average pace. I didn’t let it stress me out, but I was wishing I felt like fighting harder. But I didn’t, and focused on holding that effort level. I texted Scott, telling him I was averaging sub-16 pace (barely) at miles 4 and 5, then asking him to check my assumption that if I didn’t completely walk it in, I could maybe be an hour ahead of schedule? He confirmed that I “only” needed 16:53 average pace for the lap to come in at 10:30pm (75 minutes ahead of goal) and that if I kept sub-16 I could come in around 10:19pm. Hmmm, that was nice to hear! I didn’t think I would keep sub-16 because it was getting dark and I was tired, ~55 miles into the run, but I was pretty sure I’d be able to be sub 17 and likely sub 16:53! I carried on, turning my light on as it got dark. I was happily distracted by checking happy experiences off my mental list, mostly seeing bunnies beside and darting across the trail in the dark!
I hit the almost-halfway mileage point of the last lap, but even though it wasn’t halfway in mileage it felt like the last big milestone – it was the last mini-hill I had to climb to cross a bridge to loop around back to finish the lap. Hooray! I texted Scott and told him I coudn’t believe that, with ~7 miles left, I would be done in <2 hours. It was starting to sink in that I’d probably beat my goal of 11:45 and not doubt that it was real, and that I’d beat it by more than a few minutes. I then couldn’t resist – and was also worried Scott wouldn’t realize how well I was moving and be prone to coming out too late – and texted him again when I was <5 miles out and then 4 miles out. But by the time I was at 3 miles, he replied to ask if I needed anything else other than the bag I had planned for him to bring to the finish. Nope, I said.
At that point, I was back on my home turf, as I think about the last 2-3 miles that I run or walk on most days of the week. And I had run these miles 3 times already (in each direction, too), but it was pretty joyful getting to the point where I know not only every half mile marker but every tenth of a mile. And when I came up under the last bridge and saw a bright light biking toward me, it was Scott! He made it out to the 1.75 mile mark and rode in with me, which was fun. I was still holding just under sub-16 pace, too. I naturally pick up the pace when he’s biking with me – even when I’ve run 60+ miles! – and I was thinking that I’d be close but a few minutes under an hour and a half of schedule. It didn’t really matter exactly, but I like even numbers, yet I didn’t feel like I had tons of energy to push hard to the end – I was pleased enough to still be moving at a reasonable speed at this point!
Finally, about a half mile out, Scott biked ahead to set up the finish for me. (Purple painter’s tape and a sign I had made!) I glanced at my watch as I rounded the last corner, about .1 mile away, and though “oh, I was so close to beating the goal by over an hour and a half, too bad I didn’t push harder a few minutes ago so I could come in by 10:16 and be an hour and a half ahead”. I ran a tiny bit more but didn’t have much speed, walked a few last steps, then ran the rest of the way so Scott could video me coming into the finish. I could see the light from his bike’s light glowing on the trail, and as I turned the corner to the finish I was almost blinded by his waist light and his head lamp. I ran through the finish tape and grinned. I did it! He stopped videoing and told me to stop my trackers. I did but told him it didn’t matter, because I was somewhere under an hour and a half. We took a still picture, then picked up my tape and got ready to head home. I had done it! I had run 100k, beat my goal time…and it turns out I DID beat it by over an hour and a half! We checked the timestamp on the video Scott took of the finish and it has me crossing at 10:16pm, so that makes it a 16 hour and 16 minute finish – woohoo!
My last lap ended up being ~37 seconds average pace slower, so I had :10, :13, and :37 differences between the laps. Not too bad for that distance! I think I could’ve pushed a little harder, but I honestly didn’t feel like it psychologically, since I was already exceeding all of my goals, and I was enjoying focusing on the process meta-goals of trying to keep steady efforts and paces. Overall, my average pace was 15:36 min/mi which included ~30 min of aid station stops; and my average moving pace (excluding those 30 minutes of aid station time but did include probably another ~8-10 min of bathroom stops) was 15:17 min/mi. I’m pleased with that!
One of the things I do for all training runs but also races is input my fueling as I go, because it helps me make sure I’m actually fueling and spot any problems as they start to develop. As I mentioned, at one point I felt a tiny bit low on sodium and sure enough, I had dipped slightly below 500mg/hr in the two hottest hours of the day when I had also been sweating more and drinking more than I had been previously. Plus, it means I have cool post-run data to see how much I consumed and figure out if I want to adjust my strategy. This time, though? I wouldn’t change a thing. I nailed it! I averaged 585 mg/hour of sodium across all ~16 hours of my run. I also averaged ~264 calories/hour, which is above my ~250/hr goal. I did skip – intentionally – the very last snack at the top of the 16th hour, and it still meant that I was above goal in all my metrics. I don’t set goals for carb intake, but in case you were wondering, I ended up averaging 29.9 grams of carbs/hour (min 12, max 50, and the average snack is 15.4 carbs), but that’s totally coincidental. Overall, I consumed 3,663 calories, which was 419 carbs, 195 g of fat, and 69 grams of protein.
With EPI, as I mentioned that means I have to swallow enzyme pills with every snack, which was every 30 minutes. I swallowed 71 OTC enzyme pills (!) to match all that fuel, plus 26 electrolyte pills…meaning I swallowed 97 pills in 16 hours. You can see why I get tired of swallowing!
Here’s a visual where you can see my consumption of calories, sodium, (and carbs) over the course of my race. The dip at the end is because I intentionally skipped the second snack of the hour 16 because I was almost done. Up to 15 hours (excluding the last hour), I had a slightly rolling increase in sodium/hr and a very slight decrease in calories/hr, with carbs/hr slightly increasing. Including the 16th hour (with a skipped snack intentionally), this changed the trends to slight rolling decrease in sodium/hr; the slight decrease trend in calories/hr continued; but it flattened the carbs/hr trend line to be neutral.
In contrast to my 82 mile where I had more significant fluctuations in sodium (and really felt it), I’m glad I was able to keep my sodium consumption at goal levels and also more easily respond when the conditions changed (hotter weather causing more sweat and more water intake than previous hours) so I could keep myself from getting into a hole sodium-wise. Overall, I feel like I get an A+ for executing my fueling and sodium strategy as planned. GI-wise, I get an A+++ because I had ZERO GI symptoms during and after the run! That’s really rare for any ultrarunners, let alone those of us with GI conditions (in my case, exocrine pancreatic insufficiency). Plus, despite the unclipped pump site and BG rise that resulted, I resumed back to typical running glucose levels for me and achieved 100% TIR 70-180 after that and I think likely 100% TIR for a more narrow range like 70-140, too, although I haven’t bothered to run those stats because I don’t care exactly what the numbers are. More importantly, I never went low, I never had any big drops or rises, and other than the brief 30 minutes of annoyance due to an unclipped pump site, diabetes did not factor any more into my thinking than blister management or EPI pill swallowing or sodium did – which is great!
Here’s a view of what I had leftover after my run. I had intentionally planned for an extra snack for every lap, plus I ran faster so I needed fewer overall. I also had packed extra enzymes and electrolytes for every lap, hoping I would never need to stress about running out on any individual lap – and I didn’t, so those amounts worked well.
As soon as I stopped running and took a picture at the finish line, we got ready to head home. My muscles froze up as soon as I stopped, just like always, so I moved like a tin person for a few steps before I loosened back up and was able to walk normally. I got home, and was able to climb into the shower (and out!) without too much hardship. I climbed into bed, hydrated, and was able to go to sleep pretty normally for about 5 hours. I woke up at 5am pretty awake, which possibly was also due to the fact that I had been sleep shifting my sleep schedule, but I also felt really stiff and used the opportunity to point and flex my ankles. I slept every 20-30 minutes off and on for another few hours before I finally got up at 8am and THEN felt really sore and stiff! My right lower shin was sore and had felt sore just a tiny bit in the last few miles of my run, so it wasn’t surprising that it was sore. My right hip, which is the one I had been watching prior to the race, was sore again. I hobbled around the house and started to loosen up, enough that I decided that I would put shoes on and try to go for a short easy walk. Usually, I can’t psychologically fathom putting shoes on my feet after an ultra, but my feet felt really decent! I had some blisters, sure, but I hadn’t even noticed them running and they didn’t hurt to walk on. My hip and ankle were more noticeable. I didn’t try to take the stairs and used the elevator, then began hobbling down the sidewalk. Ouch. My hip was hurting so much that I stopped at the first bench and laid down on it to stretch my hip out. Then I walked .3 miles to the next bench and again stretched my hip. A little better, so we went out a bit farther with the plan to turn around, but my hip finally loosened up after a half mile where I could mostly walk normally! Hooray. In total, I managed 1.5 miles or so of a walk, which is pretty big for me the day after an ultra run.
Meaningfully, overnight, I still had 100% time in range (ideal glucose levels). I did not have to do any extra work, thanks to OpenAPS and autosensitivity which adjusts automatically to any increases and later return to normal insulin sensitivity from so much activity!
The next night, I slept even better, and didn’t notice any in-bed stiffness, although again on the second morning I felt stiff getting out of bed, but was able to do my full 5k+ walk route with my hip loosening up completely by a mile so that I didn’t even think about it!
On day 3, I feel 90% back to normal physically. I’m mostly fatigued,which Scott keeps reminding me is “as one should be” after runnning 100k! The nice change is that with previous ultras or long runs, I’ve felt brain fog for days or sometimes weeks – likely due to not fueling enough. But with my A+ fueling, my brain feels great – and good enough that it’s annoyed with my body still being a little bit tired. Interestingly, my body is both tired but also itching for more activity and new adventures. My friend compared it to “sea legs” where the brain has learned that the body should always be in motion, which is a decent analogy.
WHAT I HAVE LEARNED
I wouldn’t change anything in terms of my race pacing, execution, aid station stops, fueling, etc. for this run.
What I want to make sure I do next time includes continuing to adapt my training to listen to my body, rather than sticking to my pre-decided plan of how much to run. I feel like I can do that both because I now have 3000+ miles on my body of lifetime running (that I didn’t have for my first ultra); and I now have two ultras (last year’s 82 miles post-broken toe and this year’s 100k with minor hiccups like a sore shin and a hip at different times) where I was forced to or chose to adapt training, and it turned out just as good as I would have expected. For my 100k, I think the adaptation to 3 runs per week, all with my vest, ended up working well. This is the first run where I didn’t have noticeable shoulder soreness from my pack!
Same goes for taper: I don’t think, at my speed/skill level, that exact taper strategy makes a difference, and this experience confirmed it, doing DIY ultras and being able to flex a week forward or back based on how I’m physically feeling and when the best weather will be is now my preferred strategy for sure.
If you’re new to ultras and haven’t read any of my other posts, consider reading some of the following, which I’ve alluded to in my post and directly contribute to the above situation being so positive:
Feel free to leave questions if you have any, either about slow ultra running in general or any other aspects of ultra running! I’m a places-from-last kind of ultra runner, but I’m happy to share my thinking process if it helps anyone else plan their own adventures.
I was honored last year to be asked to write an article about the basics of continuous glucose monitoring (CGM) for primary care providers by the BMJ, which was released today online.
This, like most of my academic literature article writing, was an unpaid gig. So why did I do it?
Well, most people with diabetes are treated primarily by primary care providers (“GPs” or “PCPs” or “family doctors”, etc). It’s somewhat rare for most people with diabetes to see an endocrinologist! It also varies regionally, even within the same country. And, primary care providers obviously treat a lot of widely varying conditions, from acute to chronic, so they may not have time or energy to stay up to date on all treatment options for all conditions.
This therefore felt like a great opportunity to contribute some information about CGM, an incredibly useful piece of technology for anyone with diabetes who wants it, specifically written and targeted for primary care providers who may not have the exposure to CGM technology that endocrinology providers have had over the years. And, like most things, the technology (thankfully) has changed quite a bit. Accuracy, ease of use, cost, and many other factors have changed dramatically in the last almost two decades since CGMs were introduced on the market!
I sought out two fellow experts in CGM and diabetes technology to co-author the article with me. I asked Ben Wheeler, an excellent pediatric endocrinologist who has done quite a bit of research on “intermittently scanned” CGMs (isCGM); and Tamara Oser, who is the director of the Primary Care Diabetes Lab (and a parent and a spouse of people living with diabetes) and worked to facilitate uptake of CGM in primary care settings.
I’m also appreciative that a parent and teen with newly diagnosed diabetes and new experiences with CGM both reviewed this article when it was drafted and shared their perspective to it; as well as appreciative of valuable input from a friend with many years of experience with diabetes who has used 8 (!) different CGM systems.
We are starting to see a shift in adoption and coverage of CGM, thankfully. Historically, people with diabetes haven’t always had insurance cover CGM. Even if insurance does cover CGM, sometimes we have to fight an uphill battle every year to re-prove that we (still) have diabetes and that we still need CGM. Sometimes good outcomes from using CGM disqualifies us from the next year’s coverage of CGM (in which case we have to appeal our cases for coverage). It’s frustrating! That’s why it’s so nice to see increasing guidelines, recommendations, and even country-specific guidelines encouraging funding and coverage of CGM for people with all types of diabetes. The biggest latest news – as of yesterday (March 2, 2023) – was that in the U.S., Medicare will now be covering CGM for people with type 2 diabetes on insulin. This is a huge group of people who previously didn’t have CGM coverage before!
So here it is, just out today online (March 3, 2023), and projected to be in the March 25, 2023 print edition of the BMJ: an article on continuous glucose monitoring (CGM) for primary providers. I’m hoping it helps pave the way for more providers to feel comfortable prescribing CGM for more people with diabetes; increased their knowledge in working with people with diabetes who have CGM prescribed from other providers; and also reduce unconscious and conscious bias against people with diabetes being offered this important, life-changing and life-saving technology.
P.S. – if you can’t access the article from the link above, as a reminder I always store an accessible author copy of my research articles at DIYPS.org/research!
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?
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.
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 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)
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.
Once you have a chart with your data, you can go into the “Customize” tab on the right and scroll down. Under “Series”, you can select which series you want, then scroll down and click “Trendline” to make the trendline appear. The customize menu then expands with trendline options.
I had never noticed this before, but “Label” is set to default to “Custom”. This creates a label that defaults to “Trendline __YourSeriesName___”. In the example I’m showing here, I have series A labeled as “Var A”, so if I turn the Trendline on, it defaults to adding the “Custom” label of Trendline Var A.
But you can change this!
Click the dropdown where it says “Custom” and select “Use Equation”.
Now it will show the label as the y=mx+b equation, so you can find out the slope (m) of your trendline.
In my example this means the slope of the Var A green line is 0.267.
You can modify this name, though, and get the best of both worlds. Click on the equation in the legend, and you will get an editable text box. I like to put the series name (e.g., Var A) in front of the equation so I can more easily see at a glance which series trend line it is explaining:
In my particular case, I want a quick glance of the slope, so I modify mine to read (Var A) 0.267 and (Var B) 0.061.
The only downside to this is the custom names will not automatically update. So if your brain can handle seeing the full mx+B equation, it might be better to leave it with the default equation as the trendline label name without modifying it at all, so it hopefully updates if you update the data on your graph. Otherwise, you’ll want to make a mental note to come back and update this manually by re-toggling the variable to equation and then editing it again to show the updated slope.
In 365 days of pancreatic enzyme replacement therapy, I have consumed (at least) 3,277 pills.
That’s an average of 8.98 pills per day!
As I previously wrote, the number of pills is in part because I’m trying to lower the total costs (to everyone involved in paying for it) of my PERT by taking a mix of prescription PERT and OTC enzymes to try to balance effective dosing, cost, and the number of pills I swallow. I take one pill with my standard breakfast, so the remaining ~8 average pills are usually split between lunch, dinner, and/or a snack if I have one. (This is also influenced by my ultrarunning where I typically take ~2 pills every 30 minutes with my snacks/fuel for running, so long training days of 4 hours would involve 8 or more pills just for running fuel; obviously longer runs would involve even more, which drives the pills/day average higher.) If I wanted to reduce the total number of pills, I could by driving up the cost by using bigger, prescription PERT pills in lieu of some of the OTC options. However, most of the time, 3-4 pills per meal mixed between prescription and OTC is doable for me. I typically would choose to round up more PERT and reduce OTC pill count when I’m less certain about the macronutrient content of the meal or I want more confidence in better outcomes.
Speaking of better outcomes – is PERT effective?
For me, yes!
Overall, I feel so much better. Most of the time, I hardly ever have ANY symptoms (such as gas, bloating, or feeling icky) let alone my more extreme symptoms of “disrupting” my GI system. In the year of taking PERT, 78% of the time I had no disruption or any noticeable symptoms.
The average length of time between days with noticeable symptoms was 5.37 days.
And, if you look at the second half of the year, this increased quite a bit: 88% of the time I had no noticeable symptoms and the streak length of days between symptom days increased to 6.81 average days! The max streak is now 28 days (and counting)!
That’s approaching a full month without any GI symptoms (woohoo) of any kind, and means less than 1 or 2 instances of symptoms per month for me in the last several months. That’s probably better than average for most people, even people without known GI conditions, and getting a lot closer back to my personal level of “normal”.
And obviously, this is continuing to increase over time as I improve my PERT dosing strategy.
This is pretty meaningful to think about.
PERT made a difference overall straight away, but I was also starting with very small portions of food and a very restricted diet. (This is because before I realized I had EPI I had done all kinds of behavioral gymnastics to try to eliminate foods like onion, garlic, and other foods that seemed to cause issues). So first I figured out PERT successfully for what I was eating; then carefully expanded my portion sizes back to typical quantities of food; then slowly expanded my diet to cover all the foods I used to eat before I started having all my GI problems.
It very much felt like I had three phases this year:
Phase 1: Use PERT to cover small quantities of small varieties of food. Figure out what foods I could eat that could “fit” into one PERT pill.
Phase 2: Start to figure out what quantities of food I wanted to eat, and get the PERT to match the food.
Phase 3: Finish expanding out my food choices to cover everything I was eating before and tackling all my “firsts” with PERT.
You can see this evolution in my diet, too, when you look at the relative changes in the amount of fat and protein I have eaten over the course of the year. (The one big obvious outlier on the graph in October is my 82 mile ultramarathon where I ate every 30 minutes for 25 hours!) There’s been a slight increase in my fat consumption over the course of the year, and protein consumption has stayed relatively flat as I’ve been making a very conscious effort to eat enough protein to fuel my ultrarunning endeavors throughout the year.
You can then see the relationship with increased number of pills (albeit pills with different amounts of lipase) over the course of the year, relative to the fat and protein consumed.
(Note that the pills per day is using a hidden right axis, whereas the fat and protein share the same left axis numbers, also not shown)
For anyone who is new (just diagnosed or recently diagnosed within a few weeks or months) to EPI, here’s what I would hope you take away:
PERT works, but it needs to match what you are eating. Come up with a strategy (here’s mine – you can use it!) to adjust your dosing to match what you are eating. What you eat changes, and so should your PERT dosing.
Things will improve over time, and you will get more effective at matching your dosing to what you are eating. You should be able to have more and more “streaks” of days without symptoms, or with reduced symptoms. However, this may take a few months, because you’ll likely also be – at the same time – re-expanding your variety of foods that you’re eating. The combination of eating more and different foods AND tweaking your dosing can make it take a little bit longer to figure it all out.
If you’re not seeing success, talk with your doctor. There are different sizes of PERT pills – if you’re struggling to take X number of pills, you may be able to take fewer pills of a bigger size. There are different brands of PERT – so if one isn’t working for you (after you match your dosing to how much fat and protein is in each meal), you can switch and try another brand. There are also OTC options, which you can use to “top off” your prescription PERT or substitute, but you need to have an effective strategy for adjusting your dose that you can translate to your OTCs to be sure that they’re working.
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.
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.
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 had 13 papers published (with several more in the queue already written and submitted but will be published next year if accepted). You can see links to all of my published research at DIYPS.org/research
*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.
I’ve had exocrine pancreatic insufficiency (known as EPI or PEI) for a year now. I have had type 1 diabetes for 20+ years and am experienced in adjusting my medication (previously insulin) in response to everything that I eat or drink.
With EPI, though, most people are given a static prescription, such as one saying “take 3 pills with each meal”.
Well, what if every meal is not the same size?
Let’s think about a couple of hypothetical meals.
Meal A: Baked chicken, sweet potato, and broccoli. This meal likely results in ~31 grams of carbohydrates; 7 grams of fat; and ~30 grams of protein.
How would you dose for this meal? Most people do what they are told and dose based on the fat content of the meal. If they typically take 3 pills, they may take all 3 pills or take fewer pills if this is less fat than their typical meal.
Many people post in EPI social media groups post about restaurant dinners that sound like this complaining about side effects they experience with this type of meal. The commonly mentioned theory is that maybe the chicken is cooked in oil. However, the entire meal is so low in fat compared to other meals that it is unlikely to be the fat content causing symptoms if the typical meal dose of PERT is used, even if the chicken is cooked in oil.
Let’s discuss another meal.
Meal B: A bowl of chili topped with cheddar cheese and a piece of cornbread.
This meal results in ~45 grams of carbs; ~30 grams of fat; and ~42 grams of protein.
The fat content between these two meals is quite a bit different (7 grams of fat versus 30 grams of fat). Yet, again, most people are told simply to dose by the amount of fat, so someone might take a lower dose for the chicken meal because it has so little fat relative to other meals.
This could result in symptoms, though. The pancreas actually produces THREE kinds of enzymes. That’s why pancreatic enzyme replacement therapy medicine, called pancrelipase as a common name, has THREE types of enzymes: lipase, to help digest fat; protease, to help digest protein; and amylase, to help digest carbohydrates. A typical PERT pill has different amounts of these three enzymes, although it is usually described by the size/quantity of lipase it has – yet the other enzymes still play an important role in digestion.
I’ve observed that it’s pretty common for people to completely ignore the protein in what they’re eating. But as I mentioned, that seems to be the most obvious thing to try dosing for if “low fat” meals are causing issues. (It could also be sensitivity to carbohydrates, but the above example meal is fairly low carbohydrate.) My personal experience has also been that I am sensitive to fat and protein, and dose my meals based on these macronutrients, but other than eating fruit on an empty stomach (when I would add PERT/enzyme, despite the zero fat and protein in it), I don’t need to dose based on carbohydrates.
But I do need to dose for BOTH fat AND protein in what I’m eating. And I have a theory that a lot of other people with EPI do, too.
So how do you do this?
How do you dose for meals of different sizes, and take into account both fat and protein for these varying meals?
First, you need to figure out what dosing “works” for you and begin to estimate some “ratios” that you can use.
Most people begin experimenting and find a quantity of food that they can eat with the dose that they typically take. This meal size is going to vary person to person; it’ll also vary based on what it is in the meal they’re eating (such as chicken vs chili, from the above examples).
Once you find a dose that “works” and try it out a few times on the same meal, you can use this to determine what your ratios/dosing should be.
Let’s use two examples with different dose sizes and types of PERT.
(PS – did you know there are 6 FDA-approved PERT brands in the US? Sometimes one works for someone where a different brand does not. If you’re struggling with the first type of PERT you’ve been prescribed, and you’ve already ruled out that you’re dosing correctly (see below), make sure to talk to your doctor and ask about trying a different brand.)
First, let’s calculate the ratios of lipase needed per gram of fat.
Let’s say the meal that “works” with your typical dose is 30 grams of fat. If 30 grams of fat is fine on your current dose, I would eat another meal with a slightly higher amount of fat (such as 35 or 40 grams of fat). When you get to an amount that “doesn’t work” – meaning you get symptoms – then you go back to the dose that does “work” to use in the math.
If the meal that “worked” was 30 grams of fat I would do the following math for each of these two examples:
Example A: You need 1 pill of Zenpep 25,000 to cover this meal
Example B: You need 3 pills of Creon 36,000 to cover this meal
Example A: 1 pill of Zenpep 25,000 is 1 multiplied by 25,000, or 25,000 units of lipase. Take that (25,000) and divide it by the grams of fat in the meal that works (30 grams). This would be 25,000/30 = 833. This means you need 833 units of lipase to “cover” 1 gram of fat. You can round up to ~1000 units of lipase to make it easier; your ratio would be 1000 units of lipase for every 1 gram of fat.
Example B: 3 pills of Creon 36,000 is 3 multiplied by 36,000, which is 108,000 units of lipase. Take that number (108,000) and divide it by the grams of fat in the meal that works (30 grams). This would be 108,000/30 = 3,600. This means you need 3,600 units of lipase to “cover” 1 gram of fat.
The next time you wanted to eat a meal, you would look at the grams of fat in a meal.
Let’s say you’re going to eat two bowls of chili and two pieces of cornbread. Let’s assume that is about 64 grams of fat. (Two bowls of chili and two cornbread is 30×2=60, plus a bit of butter for the cornbread so we’re calling it 64 grams of fat).
Example A: Take the meal and multiply it by your ratio. 64 (grams of fat) x 1,000 (how many units of lipase you need to cover 1 grant of fat) = 64,000. A Zenpep 25,000 has 25,000 lipase. Since you need 64,000 (units of lipase needed to cover the meal), you would divide it by your pill/dose size of 25,000. 64,000 divided by 25,000 is 2.56. That means for these ratios and a prescription of Zenpep 25,000 pill size, you need *3* Zenpep 25,000 to cover a meal of 64g of fat. (Remember, you can’t cut a PERT, so you have to round up to the next pill size.)
Example B: Take the meal and multiply it by your ratio. 64 (grams of fat) times 3,600 (how many units of lipase you need to cover 1 grant of fat) = 230,400. A Creon 36,000 has 36,000 lipase. Since you need 230,400 units of lipase to cover the meal, you would divide it by your pill/dose size of 36,000. 230,400 divided by 36,000 is 6.4. This means you need *7* Creon 36,000 to cover a meal of 64g of fat. (Again, you can’t cut a PERT, so you have to round up to 7 from 6.4.)
Another way to think about this and make it easier in the future is to determine how much one pill “covers”.
Example A: A Zenpep 25,000 “covers” 25 grams of fat if my ratio is 1000 units of lipase for every gram of fat (25,000/1000=25).
So if a meal is under 25g of fat? 1 pill. A meal under 50g (25×2)? 2 pills. 75g (25×3)? 3 pills. And so on. Once you know what a pill “covers”, it’s a little easier; you can simply assess whether a meal is above/below your pill size of 1 (25g), 2 (50g), 3 (75g) etc.
Example B: A Creon 36,000 “covers” 10 grams of fat if my ratio is 3,600 units of lipase for every gram of fat (36,000/3600=10).
So if a meal is under 10 grams of fat? 1 pill. 20 grams of fat is 2 pills (10×2); 30 grams of fat is 3 pills (10×3); etc.
When people with EPI share experiences online, they often describe their dose size (such as 1 x 25,000 or 3 x 36,000 like examples A and B above) for most meals, but the meal size and composition is rarely discussed.
Personally, I can eat pretty widely varying amounts of fat in each meal on a day to day basis.
That’s why, instead of a flat dosing that works for everything (because I would be taking a LOT of pills at every meal if I was trying to take enough to cover my highest fat meals every time), I have found it to be more effective to estimate each meal to determine my meal dosing.
Remember that meal estimates aren’t very precise. If you use a nutrition panel on a box serving, the serving size can vary a bit. Restaurants (especially chains) have nutrition information, but the serving size can vary. So recognize that if you are calculating or estimating 59 grams of fat and that means either 2 vs 3 pills or 6 vs 7 pills, that you should use your judgment on whether you want to round up to the next pill number – or not.
Let’s put the hypothetical meals side by side and compare dosing with examples A and B from above:
Using the previous meal examples with either 7 or 30 grams of fat:
With Example A (ratio of 25g of fat for every 1 pill, or 1000 units of lipase to cover 1 gram of fat), we would need 1 pill for the chicken meal and 2 for the chili meal. Why? The chili is >25 grams of fat which means we need to round up to 2 pills.
With Example B (ratio of 10 grams of fat for every 1 pill or 3600 units of lipase to cover 1 gram of fat), we would need 1 pill to cover the chicken (because it’s less than 10 grams of fat) and 3 – or more – pills for the chili. Why “or more”? Well, something like chili is likely to be imprecisely counted – and if you’re like me, you’d want a bit of extra cheese, so chances are I would round up to a 4th pill here to take in the imprecision of the measurements of the ingredients.
PERT Dosing for Protein
Wait, didn’t you say something about protein?
Yes, I did. Fat isn’t the only determinant in this math!
I do the same type of math with grams of protein and units of protease. (Remember, PERT has all 3 types of enzymes, even though it is labeled by the amount of lipase. You can look online or on the bottle label to see how much protease is in your PERT.)
For our examples, Zenpep 25,000 contains 85,000 units of protease. Creon 36,000 contains 114,000 units of protease.
For the meal that ‘worked’ of 30 grams of fat, we also want to know the protein that worked. For easy math, let’s also say 30 grams of protein is in this meal.
Following the same math as before:
Example A (Zenpep 25,000): 30 grams of protein divided by 1×85,000 units of protease is ~2,833 units of protease to every 1 gram of protein. Again, I like to think about how much 1 pill “covers” protein-wise. In this case, 1 Zenpep 25,000 “covers” 30 grams of protein.
Example B (Creon 36,000): 30 grams of protein divided into 3 x 114,000 units of protease is 11,400 units of protease per gram of protein. Again, I like to think about how much 1 pill “covers” protein-wise as well. In this case, 1 Creon 36,000 “covers” 10 grams of protein.
Here’s how many pills are needed for protein:
With Example A (ratio of 30g of protein for every 1 pill), we would need 1 pill for the chicken meal and 2 for the chili meal. Why? The chili is 42, which is greater than (30×1) grams of protein which means we need to round up to 2 pills.
With Example B (ratio of 10 grams of protein for every 1 pill), we would need 3 or more pills to cover the chicken. Why 3 or more? Again, it’s on the top edge of what 3 pills would cover, so I’d be likely to round up to 4 pills here. For the chili, 5 pills are needed (42 is more than 4 x 10 and is less than 5 x 10).
So how do you decide the number of pills to take for these meals? Let’s go back to our two example meals and compare the amount needed, pill-wise, for both fat and protein for each meal and each example.
When the pill numbers MATCH (e.g. the same number needed for fat and protein), which is the case for both examples with Zenpep 25,000, it’s easy: take that number of pills total! For Zenpep 25,000, I would take 1 pill for the Chicken (1 fat | 1 protein); and I would take 2 pills for the Chili (2 fat | 2 protein). Remember that PERT pills contain all three enzymes, so the fat and protein are sufficiently *each* covered by the quantities of lipase and protease in this pill type.
When the pill numbers are DIFFERENT between your fat and protein estimates, you use the LARGER number of pills. For Creon 36,000, with the chicken meal the protein quantity is much larger than the fat quantity; I would in this case dose 4 total pills (1 fat | 4 protein), which would then cover the protein in this meal and would also sufficiently cover the amount of fat in this meal. For the chili meal, it is closer: I estimated needing 4 pills for fat and 5 for protein; in this case, I would take 5 total pills which would then successfully cover the protein and the fat in the meal.
If you find the math challenging to do, don’t worry: once you determine your ratios and figure out how much one pill “covers”, it gets a lot easier.
(The calculator is for entering one meal at a time, and doesn’t save them, but if you’d like AI to estimate what is in your meal and help you log and save multiple meals, check out PERT Pilot if you have an iPhone.)
Here’s what it looks like using the two examples above:
You can input your meal that “works”, what your dose is that “works” (the number of pills and pill type), and it will share what your ratios are and what one pill “covers”.
You can also use the second part of the calculator to estimate the amount you need for a future meal! Say it’s coming up on a holiday and you’re going to eat a much larger meal than you normally do.
You can input into the calculator that you’ll be eating 90 grams of fat and 75 grams of protein.
Here’s the example with our dose from Example A (Zenpep 25,000):
Here’s the example large meal with our dose from Example B (Creon 36,000):
You can also hit the button to expand the calculations to see the math it is doing, and how it compares between the fat and protein pill estimates to see what “drives” the total number of pills needed.
You can even download a PDF with this math to have on hand. Here’s what the PDF download looks like for Example B (Creon 36,000):
Switching dose sizes or PERT brand types
This calculator can also be useful if you were originally prescribed a smaller quantity of PERT (e.g. Creon 3000 or Zenpep 3000) and you find yourself taking many numbers of these pills (6 or more) to cover a small meal for you, let alone more pills for a larger meal.
You can input this into the calculator and get your ratios; then in the second part, identify a different pill size, to see how many numbers of pills you’d take on a different dose.
You can also use it to help you understand how much you might need if you are switching between brands. Let’s say you were prescribed Zenpep 25,000 and you need to try Creon, either because you don’t think Zenpep works well for you or your insurance is more willing to cover the Creon brand.
You would use the top part of the calculator with your current brand and size (e.g. Zenpep 25,000 of which you take 6 for a standard meal of 30 grams of fat and 30 grams of protein) and then input the new brand and size and the same size meal (e.g. Creon 36,000 and another 30 grams of fat and 30 grams of protein meal) to see that you’d likely need 5 Creon 36,000 to match the 6 Zenpep 25,000 you were taking for a standard size (30 gram of fat and 30 gram of protein) meal.
Note: I’m not suggesting 30 grams of fat and protein at each meal is “standard” or the “right” size of the meal – I picked arbitrary numbers here to illustrate these examples, so make sure to include the meal that your PERT dosing successfully covers for YOU!
As a reminder, I’m not a doctor – I’m a person living with EPI. None of this is medical advice. I use this math and this calculator for my own personal use and share it in case it’s helpful to others. If you have questions, please do talk to your doctor. If you’re still experiencing symptoms with your enzyme dosing, you definitely should talk with your doctor. Your prescription size might need updating compared to what you were originally prescribed.
Also, please note that the calculator is open source; you can find the code here, and I welcome contributions (pull requests) and suggestions! You can leave feedback on Github or share feedback in this form. For example, if you’re using a different type of enzyme not listed in the calculator (currently 2/6 of the US FDA-approved versions are listed), please let me know and I can work to add the relevant list.
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.
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.