OpenAPS

How I Use LLMs like ChatGPT And Tips For Getting Started

by Dana Lewis

You’ve probably heard about new AI (artificial intelligence) tools like ChatGPT, Bard, Midjourney, DALL-E and others. But, what are they good for?

Last fall I started experimenting with them. I looked at AI art tools and found them to be challenging, at the time, for one of my purposes, which was creating characters and illustrating a storyline with consistent characters for some of my children’s books. I also tested GPT-3 (meaning version 3.0 of GPT). It wasn’t that great, to be honest. But later, GPT-3.5 was released, along with the ChatGPT chat interface to it, which WAS a big improvement for a lot of my use cases. (And now, GPT-4 is out and is an even bigger improvement, although it costs more to use. More on the cost differences below)

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:

  1. 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.

In short:

  • 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.

One of the first things I did was ask ChatGPT to help me write a script. In fact, that’s what I did to expedite the process of finding tweets where I had used an image in order to get a screenshot to embed on my blog, rather than embedding the tweet.

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).

This has been incredibly helpful especially with some of my self-tracked datasets (particularly around thyroid-related symptom data) where I’m still trying to figure out the relationship between thyroid levels, thyroid antibody levels, and symptom data (and things like menstrual cycle timing). I’ve used it for creating the formulas and solutions I’ve talked about in projects such as the one where I created a “today” line that dynamically updates in a chart.

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!

A box and whisker plot, illustrated simply to show that I used R and Jupyter finally successfully!

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:

Simulator iphone with a basic iOS app that intakes BP, pulse, buttons for indicating whether BP was taken sitting or laying down; and toggles for key symptoms (in my case HR feeling or eyes), and a purple save button.

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.

Nope.

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.

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TLDR:

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:

    1. 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
    2.  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!
    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.
      • 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. 

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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!

Personally, the latest project that I built with an LLM has been PERT Pilot!

How I use LLMs (like ChatGPT) and tips for getting started

How I PRed My 100k Time

by Dana Lewis

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.

My 100k run supplies set out on the floor. I have a bag of OTC enzymes (for exocrine pancreatic insufficiency), 8-10 individually packaged snacks ranging from Fritos to yogurt pretzels to sandwich cookies, cashews, and beef sticks, a bag of electrolyte pills, and eye drops and disposable tooth brushes. Each lap (4 total) has a set of each of these.

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)

My ultra running experience checklist, to highlight the good and the less good as I run. This shows that I saw stars, bunnies, and a loon and a pheasant, but did not see my usual eagles, heron, or heard any ducks splashing in the river at night.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. Morning-sunrise-during-100kAs 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.

PheasantI 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.

A 24 hour view of my CGM graph to show my glucose levels before (overnight), during the run including marks where my pump site likely unclipped, where I reclipped it, and how my glucose was in range for the remainder of the run.
A 24 hour view of my CGM graph to show my glucose levels before (overnight), during the run including marks where my pump site likely unclipped, where I reclipped it, and how my glucose was in range for the remainder of the run.

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!

A picture at night in the dark with me running, light at my waist, toward the purple painter tape stretched out as my finish line.

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!

FUN STATS

A pivot table with conditional formatting showing when my sodium, calories, and carbs per hour met my hourly goal amounts.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!

A graph showing the rates of sodium/hr for each 16 hours of the run (averaging above 500mg/hr); calories per hour (averaging above 250/hour), and carbs per hour.

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.

A view of the enzymes and electrolyte baggies after my run, with a few left in each baggie as I planned for extras. I also had some snacks I didn't eat, both because I planned one extra per lap but I also ran faster than I expected, so I needed fewer overall

POST-RUN RECOVERY

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!

A 12 hour view of glucose levels after my 100k. This was 100% TIR between 70-180 and probably a tighter range, although I did not bother to calculate what the tighter range is.

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.

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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.

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

by Dana Lewis

“I could never do that,” you say.

And I’ve heard it before.

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

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

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

We’re not.

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

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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.

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

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

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

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

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

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

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

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

So we didn’t go down that rabbit hole.

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

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

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

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

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

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

I kept writing down what I was eating, though.

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

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

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

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

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

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

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

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

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

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

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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.)

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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.

Looking Back Through 2022 (What You May Have Missed)

by Dana Lewis

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

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

One major example? Exocrine Pancreatic Insufficiency.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I also wrote a lot.

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

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

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

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

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

More Tools To Help Diabetes Researchers and Other Researchers

by Dana Lewis

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

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

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

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

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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.

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

We Have Changed the Standards of Care for People With Diabetes

by Dana Lewis

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

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

Why?

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

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

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

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

And 2021 also included…

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

And 2020? Yup, it was there, too.

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

All the way back to 2019!

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

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

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

So what does it say?

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

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

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

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

Well, yeah.

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

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

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

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

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

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

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

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

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

New Chapter: Personalizing Research: Involving, Inviting, and Engaging Patient Researchers

by Dana Lewis

TLDR: A new chapter I wrote, invited for a book on Personal Health Informatics, is out! You can read a summary below describing my chapter. You can also find a link to a full pre-print (a copy of my submitted, unedited version) of the article (as well as author copies of all of my articles) on my research page.

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In November 2020 I was invited to submit a proposal for a chapter for a pending book on personal health informatics. Like journal articles, you can be invited to submit for a book chapter as part of a larger book topic.

Knowing that book chapters take a long time to come out, I carefully thought about the topic of my article and whether I could write something that would be relevant approximately a year after I wrote it.

The context of the book was:

“high-quality scholarly work that seeks to provide clarity, consistency, and reproducibility, with a shared view of the status-quo of consumer and pervasive health informatics and its relevance to precision medicine and healthcare applications and system design. The book will offer a snapshot of this emerging field, supported by the methodological, practical, and ethical perspectives from researchers and practitioners in the field. In addition to being a research reader, this book will provide pragmatic insights for practitioners in designing, implementing, and evaluating personal health informatics in the healthcare settings.”

They also wanted to include patient perspectives, which is part of the reason I was invited to submit a proposal for a chapter, and asked if I could write about citizen science from the patient perspective.

I decided to write more broadly about patient perspectives in research, and since the audience of this book is likely to be academic researchers and practitioners already in the field, seek to provide some ideas and input as to how they could think about practically inviting and engaging patient partners in research, as well as supporting the burgeoning field of patient researchers who lead their own research.

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I submitted my draft article in April 2021; received feedback and submitted the revision in August 2021; and the book was due to be published in “spring 2022”.

::crickets::

The book is now out in November 2022, hooray! It is called Personal Health Informatics and you can find it online here.

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Abstract from my chapter:

There are many benefits to engaging and involving patients in traditional, researcher-led research, ranging from improved recruitment and increased enrollment to accelerating and facilitating the implementation of research outcomes. Researchers, however, may not be aware of when and where they can involve patients (people with lived healthcare experience) in research or what the benefits may be of improving patient engagement in the research process or of expanding patient involvement to other research stages. This chapter seeks to highlight the benefits and opportunities of engaging patients in traditional research and provide practical suggestions for inviting or recruiting patients for participation in research, whether or not there is an established patient and public involvement (PPI) program. This includes tips for developing a productive working relationship and culture between researchers and the patients involved in research. There are also many patients themselves conducting research, and often without the benefits, resources, and opportunities made available to traditional researchers. Traditional researchers should identify and recognize researchers who have emerged from non-traditional paths who are driving and engaging in their own research, and provide support and resources where appropriate to foster further patient-driven research. This investment can lead to collaboration opportunities for additional highly relevant and effective research studies with traditional researchers in the future. This chapter provides examples of patient researchers and offers tools to support traditional researchers who want to support patient-led research efforts and improve their ability to successfully engage patient stakeholders in their own research.

Here are some of the highlights and recommendations from my chapter:

  • Invite patients to participate in research, and do it early.
  • Ask patients how they’d like to be involved in research.
  • Relationship building and culture setting is important. Address the power dynamics within your project and team.
  • Set expectations for everyone involved on the team.
  • Consider training and skill-building opportunities for patients who are partnering in research.
  • If you’re looking to support a patient who is already initiating or performing research, first ask: “How can I help?”. This article includes a list of suggestions of how you can help them.

This article also highlights many exceptional researchers who are patients and their work, including:

Note the chapter discusses explicitly how not everyone has a PhD or an MD; this is not a requisite to doing high-quality research!

The chapter concludes with “clinical pearls’’, which are four suggested tips to use in daily practice, and includes some suggested resources like the Opening Pathways Readiness Quiz. It also includes a suggestion of making a “To Don’t” list in collaboration with patient research partners.

The chapter also contains two review questions:

  1. Imagine that you have a research project where you would like to apply for funding, and the funder mandates that you have a patient involved in your research project. At what stage do you involve a patient in your project, and how do you do so?
  2. You are at a scientific conference and observe a patient giving a presentation about their own research or project. They’re not a traditional researcher – they don’t have a PhD or have a day job as a researcher. You want to approach them and offer your help with their research. What do you offer when you approach them?

To see the answers to these review questions, check out the article in full! :)

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TLDR: A new chapter I wrote, invited for a book on Personal Health Informatics, is out! You can find a link to a full pre-print (a copy of my submitted, unedited version) of the article (as well as author copies of all of my articles) on my research page.

If you’d like to cite this in one of your articles, note that the DOI for the article is https://doi.org/10.1007/978-3-031-07696-1_17 and an example citation is:

Lewis, D. (2022). Personalizing Research: Involving, Inviting, and Engaging Patient Researchers. In: Hsueh, PY.S., Wetter, T., Zhu, X. (eds) Personal Health Informatics. Cognitive Informatics in Biomedicine and Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-07696-1_17

Excerpted tips from the book chapter "Personalizing Research: Involving, Inviting, and Engaging Patient Researchers" by Dana Lewis

Regulatory Approval Is A Red Herring

by Dana Lewis

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

This question is a big red herring.

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

It’s not the only way.

It’s only one way.

—

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

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

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

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

But if you’re not going to sell products…

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

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

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

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

—

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

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

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

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

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

Why?

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

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

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

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

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

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

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

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

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

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

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

This net risk reduction is important to contextualize.

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

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

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

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

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

—

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

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

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

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

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

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

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

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

—

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

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

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

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

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

Thus, the red herring.

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

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

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

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

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

—

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

They aren’t the only way.

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

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

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

—

So any questions around seeking regulatory approval are red herrings.

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

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

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

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

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

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

No rubber stamps required.

Regulatory Approval: A Red Herring

Understanding the Difference Between Open Source and DIY in Diabetes

by Dana Lewis

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

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

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

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

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

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

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

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

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

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

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

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

by Dana Lewis

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

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

What is the “continuation phase”?

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

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

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

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

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

What are the results from the continuation phase?

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

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

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

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

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

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

Conclusion of the continuation study from the CREATE trial

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

Key points to takeaway:

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