Accepted, Rejected, and Conflict of Interest in Gastroenterology (And Why This Is A Symptom Of A Bigger Problem)

Recently, someone published a new clinical practice update on exocrine pancreatic insufficiency (known as EPI or PEI) in the journal called Gastroenterology, from the American Gastroenterology Association (AGA). Those of you who’ve read any of my blog posts in the last year know how much I’ve been working to raise awareness of EPI, which is very under-researched and under-treated clinically despite the prevalence rates in the general population and key sub-populations such as PWD. So when there was a new clinical practice update and another publication on EPI in general, I was jazzed and set out to read it immediately. Then frowned. Because, like so many articles about EPI, it’s not *quite* right about many things and it perpetuates a lot of the existing problems in the literature. So I did what I could, which was to check out the journal requirements for writing a letter to the editor (LTE) in response to this article and drafting and submitting a LTE article about it. To my delight, on October 17, 2023, I got an email indicating that my LTE was accepted.

You can find my LTE as a pre-print here.

See below why this pre-print version is important, and why you should read it, plus what it reminds us about what journal articles can or cannot tell us in healthcare.

Here’s an image of my acceptance email. I’ll call out a key part of the email:

A print of the acceptance email I received on October 17, 2023, indicating my letter would be sent to authors of the original articles for a chance to choose to respond (or not). Then my LTE would be published.

Letters to the Editor are sent to the authors of the original articles discussed in the letter so that they might have a chance to respond. Letters are not sent to the original article authors until the window of submission for letters responding to that article is closed (the last day of the issue month in which the article is published). Should the authors choose to respond to your letter, their response will appear alongside your letter in the journal.

Given the timeline described, I knew I wouldn’t hear more from the journal until the end of November. The article went online ahead of print in September, meaning likely officially published in October, so the letters wouldn’t be sent to authors until the end of October.

And then I did indeed hear back from the journal. On December 4, 2023, I got the following email:

A print of the email I received saying the LTE was now rejected
TLDR: just kidding, the committee – members of which published the article you’re responding to – and the editors have decided not to publish your article. 

I was surprised – and confused. The committee members, or at least 3 of them, wrote the article. They should have a chance to decide whether or not to write a response letter, which is standard. But telling the editors not to publish my LTE? That seems odd and in contrast to the initial acceptance email. What was going on?

I decided to write back and ask. “Hi (name redacted), this is very surprising. Could you please provide more detail on the decision making process for rescinding the already accepted LTE?”

The response?

Another email explaining that possible commercial affiliations influenced their choice to reject the article after accpeting it originally
In terms of this decision, possible commercial affiliations, as well as other judgments of priority and relevance among other submissions, dampened enthusiasm for this particular manuscript. Ultimately, it was not judged to be competitive for acceptance in the journal.

Huh? I don’t have any commercial affiliations. So I asked again, “Can you clarify what commercial affiliations were perceived? I have none (nor any financial conflict of interest; nor any funding related to my time spent on the article) and I wonder if there was a misunderstanding when reviewing this letter to the editor.”

The response was “There were concerns with the affiliation with OpenAPS; with the use of the term “guidelines,” which are distinct from this Clinical Practice Update; and with the overall focus being more fit for a cystic fibrosis or research audience rather than a GI audience.”

A final email saying the concern with my affiliation of OpenAPS, which is not a commercial organization nor related to the field of gastroenterology and EPI

Aha, I thought, there WAS a misunderstanding. (And the latter makes no sense in the context of my LTE – the point of it is that most research and clinical literature is a too-narrow focus, cystic fibrosis as one example – the very point is that a broad gastroenterology audience should pay attention to EPI).

I wrote back and explained how I, as a patient/independent researcher, struggle to submit articles to manuscript systems without a Ringgold-verified organization. (You can also listen to me describe the problem in a podcast, here, and I also talked about it in a peer-reviewed journal article about citizen science and health-related journal publishing here). So I use OpenAPS as an “affiliation” even though OpenAPS isn’t an organization. Let alone a commercial organization. I have no financial conflict of interest related to OpenAPS, and zero financial conflict of interest or commercial or any type of funding in gastroenterology at all, related to EPI or not. I actually go to such extremes to describe even perceived conflicts of interest, even non-financial ones, as you can see this in my disclosure statement publicly available from the New England Journal of Medicine here on our CREATE trial article (scroll to Supplemental Information and click on Disclosure Forms) where I articulate that I have no financial conflicts of interest but acknowledge openly that I created the algorithm used in the study. Yet, there’s no commercial or financial conflict of interest.

A screenshot from the publicly available disclosure form on NEJM's site, where I am so careful to indicate possible conflicts of interest that are not commercial or financial, such as the fact that I developed the algorithm that was used in that study. Again, that's a diabetes study and a diabetes example, the paper we are discussing here is on exocrine pancreatic insufficiency (EPI) and gastroenterology, which is unrelated. I have no COI in gastroenterology.

I sent this information back to the journal, explaining this, and asking if the editors would reconsider the situation, given that the authors (committee members?) have misconstrued my affiliation, and given that the LTE was originally accepted.

Sadly, there was no change. They are still declining to publish this article. And there is no change in my level of disappointment.

Interestingly, here is the article in which my LTE is in reply to, and the conflict of interest statement by the authors (committee members?) who possibly raised a flag about supposed concern about my (this is not true) commercial affiliation:

The conflict of interest statement for authors from the article "AGA Clinical Practice Update on the Epidemiology, Evaluation, and Management of Exocrine Pancreatic Insufficiency 2023"

The authors disclose the following: David C. Whitcomb: consultant for AbbVie, Nestlé, Regeneron; cofounder, consultant, board member, chief scientific officer, and equity holder for Ariel Precision Medicine. Anna M. Buchner: consultant for Olympus Corporation of America. Chris E. Forsmark: grant support from AbbVie; consultant for Nestlé; chair, National Pancreas Foundation Board of Directors.

As a side note, one of the companies with consulting and/or grant funding to two of the three authors is the biggest manufacturer of pancreatic enzyme replacement therapy (PERT), which is the treatment for EPI. I don’t think this conflict of interest makes these clinicians ineligible to write their article; nor do I think commercial interests should preclude anyone from publishing – but in my case, it is irrelevant, because I have none. But, it does seem weird given the stated COI for my (actually not a) COI then to be a reason to reject a LTE, of all things.

Here’s the point, though.

It’s not really about the fact that I had an accepted article rejected (although that is weird, to say the least…).

The point is that the presence of information in medical and research journals does not mean that they are correct. (See this post describing the incorrect facts presented about prevalence of EPI, for example.)

And similarly, the lack of presence of material in medical and research journals does not mean that something is not true or is not fact! 

There is a lot of gatekeeping in scientific and medical research. You can see it illustrated here in this accepted-rejected dance because of supposed COI (when there are zero commercial ties, let alone COI) and alluded to in terms of the priority of what gets published.

I see this often.

There is good research that goes unpublished because editors decide not to prioritize it (aka do not allow it to get published). There are many such factors in play affecting what gets published.

There are also systemic barriers.

  • Many journals require fees (called article processing charges or “APC”s) if your article is accepted for publication. If you don’t have funding, that means you can’t publish there unless you want to pay $2500 (or more) out of pocket. Some journals even have submission fees of hundreds of dollars, just to submit! (At least APCs are usually only levied if your article is accepted, but you won’t submit to these journals if you know you can’t pay the APC). That means the few journals in your field that don’t require APCs or fees are harder to get published in, because many more articles are submitted (thus, influencing the “prioritization” problem at the editor level) to the “free” journals.
  • Journals often require, as previously described, your organization to be part of a verified list (maintained by a third party org) in order for your article to be moved through the queue once submitted. Instead of n/a, I started listing “OpenAPS” as my affiliation and proactively writing to admin teams to let them know that my affiliation won’t be Ringgold-verified, explaining that it’s not an org/I’m not at any institution, and then my article can (usually) get moved through the queue ok. But as I wrote in this peer-reviewed article with a lot of other details about barriers to publishing citizen science and other patient-driven work, it’s one of many barriers involved in the publication process. It’s a little hard, every journal and submission system is a little different, and it’s a lot harder for us than it is for people who have staff/support to help them get articles published in journals.

I’ve seen grant funders say no to funding researchers who haven’t published yet; but editors also won’t prioritize them to publish on a topic in a field where they haven’t been funded yet or aren’t well known. Or they aren’t at a prestigious organization. Or they don’t have the “right” credentials. (Ahem, ahem, ahem). It can be a vicious cycle for even traditional (aka day job) researchers and clinicians. Now imagine that for people who are not inside those systems of academia or medical organizations.

Yet, think about where much of knowledge is captured, created, translated, studied – it’s not solely in these organizations.

Thus, the mismatch. What’s in journals isn’t always right, and the process of peer review can’t catch everything. It’s not a perfect system. But what I want you to take away, if you didn’t already have this context, is an understanding that what’s NOT in a journal is not because the information is not fact or does not exist. It may have not been studied yet; or it may have been blocked from publication by the systemic forces in play.

As I said at the end of my LTE:

It is also critical to update the knowledge base of EPI beyond the sub-populations of cystic fibrosis and chronic pancreatitis that are currently over-represented in the EPI-related literature. Building upon this updated research base will enable future guidelines, including those like the AGA Clinical Practice Update on EPI, to be clearer, more evidence-based, and truly patient-centric ensuring that every individual living with exocrine pancreatic insufficiency receives optimal care.

PS – want to read my LTE that was accepted then rejected, meaning it won’t be present in the journal? Here it is on a preprint server with a DOI, which means it’s still easily citable! Here’s an example citation:

Lewis, D. Navigating Ambiguities in Exocrine Pancreatic Insufficiency. OSF Preprints. 2023. DOI: 10.31219/osf.io/xcnf6

New Survey For Everyone (Including You – Yes, You!) To Help Us Learn More About Exocrine Pancreatic Insufficiency

If you’ve ever wanted to help with some of my research, this is for you. Yes, you! I am asking people in the general public to take a survey (https://bit.ly/GI-Symptom-Survey-All) and share their experiences.

Why?

Many people have stomach or digestion problems occasionally. For some people, these symptoms happen more often. In some cases, the symptoms are related to exocrine pancreatic insufficiency (known as EPI or PEI). But to date, there have been few studies looking at the frequency of symptoms – or the level of their self-rated severity – in people with EPI or what symptoms may distinguish EPI from other GI-related conditions.

That’s where this survey comes in! We want to compare the experiences of people with EPI to people without EPI (like you!).

Will you help by taking this survey?

Your anonymous participation in this survey will help us understand the unique experiences individuals have with GI symptoms, including those with conditions like exocrine pancreatic insufficiency (EPI). In particular, data contributed by people without EPI will help us understand how the EPI experience is different (or not).

A note on privacy:

  • The survey is completely anonymous; no identifying information will be collected.
  • You can stop the survey at any point.

Who designed this survey:

Dana Lewis, an independent researcher, developed the survey and will manage the survey data. This survey design and the choice to run this survey is not influenced by funding from or affiliations with any organizations.

What happens to the data collected in this survey:

The aggregated data will be analyzed for patterns and shared through blog posts and academic publications. No individual data will be shared. This will help fill some of the documented gaps in the EPI-related medical knowledge and may influence the design of targeted research studies in the future.

Have Questions?
Feel free to reach out to Dana+GISymptomSurvey@OpenAPS.org.

How else can you help?
Remember, ANYONE can take this survey. So, feel free to share the link with your family and friends – they can take it, too!

Here’s a link to the survey that you can share (after taking it yourself, of course!): https://bit.ly/GI-Symptom-Survey-All

You (yes you!) can help us learn about exocrine pancreatic insufficiency by taking the survey linked on this page.

MacrosOnTheRun: an iOS app for tracking activity fuel consumption

Last year, I built a spreadsheet template (and shared it here) to use while training and running ultramarathons to track my fuel consumption. It was helpful for me, as a person with exocrine pancreatic insufficiency, to see and decide based on macronutrient counts for each snack how many enzyme pills I needed to take each time I fueled, which is every 30 minutes.

This year, I got tired of messing with the spreadsheet while running. I don’t mind the data entry, but because of the iterative calculations updating with the hourly and overall totals of carbs, sodium, calories per hour etc, the Google Sheet would get bogged down over time, especially when I was running for 16 hours (like during my 100k in March). That would cause the Google Sheets app to crash and reload, or kick me out of the sheet and require me to click back in, wait for it to catch up, before entering my fuel item. It only took a couple of seconds, but it was annoying to have that delay while I was running.

I thought about not logging my fueling while running, especially because I had switched to a slightly more expensive but also larger over-the-counter (OTC) enzyme pill that basically covers every single snack I take with one single pill. That requires less mid-run decision making about how many to take, so it’s less important during the run to see each snack’s composition: I simply swallow a pill each time I do fuel.

Yet, after 1-2 runs of 2-3 hours where I didn’t log my intake, I still found myself missing the data from the run. Although the primary use case of in-run decision making wasn’t there for enzyme dosing, the secondary use case of making sure I was consuming enough sodium per hour and calories per hour relative to my goals was still there. I still wanted to offload that hourly tracking so I didn’t have to remember how much I had had in the last hour. Plus, the post-run data summary was nice, because it helped me evaluate my fueling overall in the grand scheme of my daily nutritional intake, which is particularly helpful for me in making sure I’m consuming enough protein to match my ultra-running activities.

And, I had figured out last year how to develop iOS apps (check out PERT Pilot if you have EPI, and Carb Pilot if you’re someone who’d like to simply use AI to generate estimates of how many carbs or macronutrients are in what you’re eating) with the help of an LLM. So I decided to try to build a custom, just for me app to mimic my spreadsheet in order to easily track my fueling on the run.

Tada! I made MacrosOnTheRun.Macros on the run logo showing "on the run" below the word Macros, stylized to look like 'on the run' is a drop down menu, reminiscent of the fuel list drop down in the app

It’s pretty simple: I open the app, hit ‘start run’, and then click the drop down and tap the fuel item (or electrolyte) that I’m consuming. I hit “add fuel”, and the items drops into the list on the screen and is added to the hourly and overall estimates shown above the drop down.

Screenshot of MacrosOnTheRun showing a pre-populated fuel list to select from and on the right, a screenshot at the end of a 9 hour run with fuel totals and individual fuel items entered
An example during a long run where after the run I open the app to export my in-run data. This is after the run, so you’ll see it’s been 97 minutes since the last fuel when I took that screenshot, and thus the sodium per hour and calories per hour calculation shows 0 given that it’s been >60 minutes since the last fuel. Below that is the total run stats, including enzymes and electrolytes counts. Given that I fuel like clockwork every 30 minutes, you can infer this was a 9 hour run since I took 18 enzymes!

When I’m done with the run, I tap the “stop and export” button at the bottom, which opens the iOS share sheet and enables me to email the CSV file to myself, so I can copy/paste the data back into the same spreadsheet template I was using before. It’s useful because I have all my runs stored as individual tabs in the sheet, and the template (same one I was using last year) autopopulates the pivot table with hourly summaries so I can see across each hour whether I was meeting my sodium and fueling goals. (Check out the 27 hour summary table in my 100 mile recap if you’re curious to see an example!)

Right now, I haven’t bothered to add a feature to edit in-app what the fuel list is – mine is programmed in via the code of the app itself, since I’m the only one using it – and I haven’t published it to the iOS App Store because I didn’t think anyone else would want to use it.

But, if I’m wrong, and this is something you’d like to use – let me know by commenting here or emailing me (Dana+MacrosOnTheRun@OpenAPS.org) and letting me know. If there’s interest, I can modify the app to allow in-app fuel list entry and modifications of the fuel list and then share it via TestFlight or in the App Store for other people to download and use.

Running a Multi-Day Ultramarathon (Aiming for 200 Miles)

I used to make a lot of statements about things I thought I couldn’t do. I thought I couldn’t run overnight, so I couldn’t attempt to run 100 miles. I could never run 200 mile races the way other people did. Etc. Yet last year I found myself training for and attempting 100 miles (I chose to stop at 82, but successfully ran overnight and for 25 hours) and this year I found myself working through the excessive mental logistics and puzzle of determining that I could train for and attempt to run 200 miles, or as many miles as I could across 3-4 days.

Like my 100 mile attempt, I found some useful blog recaps and race reports of people’s official races they did for 200-ish mile races. However, like the 100 attempts, I found myself wanting more information for the mental training and logistical preparation people put into it. While my 200 mile training and prep anchored heavily on what I did before, this post describes more detail on how my training, prep, and ‘race’ experience for a multi-day or 200 mile ultra attempt.

DIY-ing a 200

For context, I have a previous post describing the myriad reasons of why I often choose to run DIY ultras, meaning I’m not signing up for an official race. Most of those reasons hold true for why I chose to DIY my 200. Like my 100 (82) miles, I mapped a route that was based on my home paved trail that takes me out and around the trails I’m familiar with. It has its downsides, but also the upsides: really good trail bathrooms and I feel safe running them. Plus, it’s easy and convenient for my husband to crew me. Since I expected this adventure to take 3-4 days (more on that below), that’s a heavy ask of my husband’s time and energy, so sticking with the easy routes that work for him is optimal, too. So while I also sought to run 200 miles just like any other 200-mile ultra runner, my course happens to have minimal elevation. Not all 200 mile ultramarathon races have a ton of elevation – some like the Cowboy 200 are pretty flat – so my experience is closer to that than the experience of those running mountain based ultras with 30,000 feet (or more) of elevation gain. And I’m ok with that!

Sleep

One of the puzzles I had to figure out to decide I could even attempt a 200 miler is sleep. With a 100 mile race, most people don’t sleep at all (nor did I) and we just run through the night. With 200 miles, that’s impossible, because it takes 3, 4, 5 days to finish and biologically you need sleep. Plus, I need more sleep than the average person. I’m a champion sleeper; I typically sleep much longer than everyone else; and I know I couldn’t function with an hour here or there like many people do at traditional races. So I actually designed my 200 mile ultra with this in mind: how could I cover 200 miles AND get sleep? Because I’m running to/from home, I have access to my kitchen, shower, and bed, so I decided that I would set up my run to run each day and come home and eat dinner, shower, and sleep each night for a short night in my bed.

I then decided that instead of winging it and running until I dropped before eating, showering, and sleeping, I would aim for running 50 miles each day. Then I’d come in, eat, shower, and sleep and get up the next morning and go again. 4 days, 3 nights, 50 miles each day: that would have me finishing around 87-90ish hours total (with the clock running from my initial start), including ~25 hours or more of total downtime between the eating/showering/sleeping/getting ready. That breakdown of 3.67 days is well within the typical finish times of many 200 mile ultras (yes, comparing to those with elevation gain), so it felt like it was both a stretch for me but also doable and in a sensible way that works for me and my needs. I mapped it all out in my spreadsheet, with the number of laps and my routes and pacing to finish 50 miles per day; the two times per day I would need my husband to come out and crew me at ‘aid station stops’ in between laps, and what time I would finish each night. I then factored in time to eat and shower and get ready for bed, sleep, and time to get up in the morning. Given the fact that I expected to run slower each day, the sleep windows go from 8 hours down to less than 6 hours by night 3. That being said, if I managed to sleep 5 hours per night and 15 hours total, that’s probably almost twice as much as most people get during traditional races!

Like sleep, I was also very cognizant of the fact that a 200 probably comes down to mental fortitude and will power to keep going; meticulous fueling; and excellent foot care. Plus reasonable training, of course.

Meticulous fueling

I have previously written about building and using a spreadsheet to track my fuel intake during ultras. This method works really well for me because after each training run I can see how much I consumed and any trends. I started to spot that as I got tired, I would tend to choose certain snacks that happened to be slightly lower calorie. Not by much, but the snack selections went from those that are 150-180 calories to 120-140 calories, in part because I perceived them to be both ‘smaller’ (less volume) and ‘easier to swallow’ when I was tired. Doubled up in the same hour, this meant that I started to have hours of 240 calories instead of more than 250. That doesn’t sound like much, but I need every calorie I can get.

I mapped out my estimated energy expenditure based on the 50 miles per day, and even consuming 250 calories per hour, I would end up with several thousand calories of deficit each day! I spent a lot of time testing food that I think I can eat for dinner on the 3 nights to ensure that I get a good 1000 calories or more in before going to bed, to help address and reduce the growing energy deficit. But I also ended up optimizing my race fuel, too. Because I ran so many long runs in training where I fueled every 30 minutes, and because I had been mapping out my snack list for each lap for 50 miles a day for 4 days, I’ve been aware for months that I would probably get food fatigue if I didn’t expand my fuel list. I worked really hard to test a bunch of new snacks and add them to the rotation. That really helped even in training, across all 12 laps (3 laps a day to get 50 miles, times 4 days), I carefully made sure I wouldn’t have too many repeats and get sick of one food or one group of things I planned to eat. I also recently realized that some of the smaller items (e.g. 120 calorie servings) could be increased. I’m already portioning out servings from a big bag into small baggies; in some cases adding one more pretzel or one more piece of candy (or more) would drive up the calories by 10-20 per serving. Those small tweaks I made to 5 of my ~18 possible snacks means that I added about 200 calories on top of what was already represented in those snacks. If I happen to choose those 5 snacks as part of my list for any one lap, that means I have a bonus 200 calories I’ve convinced myself to consume without it being a big deal, because it’s simply one more pretzel or one more piece of candy in the snack that I’m already use to consuming. (Again, because I’m DIYing my race and have specific needs relative to running with celiac, diabetes, and exocrine pancreatic insufficiency, for me, pre-planning my fuel and having it laid out in advance for every run, or in the race every single lap, is what works for me personally.)

Here’s a view of how I laid out my fuel. I had worked on a list of what I wanted for each lap, checking against repeats across the same day and making sure I wasn’t too heavily relying on any one snack throughout all the days. I then bagged up all snacks individually, then followed my list to lay them out by each lap and day accordingly. I also have a bag per day each for enzymes and electrolytes, which you’ll see on the left. Previously, I’ve done one bag per lap, but to reduce the number of things I’m pulling in and out of my vest each time, I decided I could do one big bag each per day (and that did end up working out well).

Two pictures side by side, with papers on the floor showing left to right laps 1-3 on the top and along the left side days 1-4, to create a grid to lay out my snacks. On the left picture, I have my enzymes, electrolytes per day and then a pile of snacks grouped for each lap. On the right, all the snacks and enzymes and electrolytes have been put into gallon bags, one for each lap.

Contingency planning

Like I did for my 100, I was (clearly) planning for as many possibilities as I could. I knew that during the run – and each evening after the run – I would have limited excess mental capacity for new ideas and brainstorming solutions when problems come up. The more I prepared for things that I knew were likely to happen – fatigue, sore body, blisters, chafing, dropping things, getting tired of eating, etc – the more likely that they would be small things and not big things that can contribute to ending a race attempt. This includes learning from my past 100 attempt and how I dealt with the rain. First of all, I planned to move my race if it looks like we’ll get 6 months of rain in a single 24 hour period! But also, I scheduled my race so that if I do have a few hours of really hard rain, I could choose to take a break and come in and eat/shower/change/rest and go back out later, or extend and finish a lap on the last day or the day after that. I was not running a race that would yank me from the course, but I did have a hard limit after day 5 based on a pre-planned doctor’s appointment that would be a hassle to reschedule, so I needed to finish by the night after day 5. But this gave me the flexibility to take breaks (that I wasn’t really planning to take but was prepared to if I needed to due to weather conditions).

Training for a 200 mile ultramarathon

Like training plans for marathons and 100 milers, the training plans I’ve read about for 200 mile ultramarathons intimidate me. So much mileage! So much time for a slow run/walker like me. I did try to look at sample 200 mile ultra plans and get a sense for what they’re trying to achieve – e.g. when do they peak their mileage before the race, how many back to back runs of what general length in terms of time etc – and then loosely keep that in mind.

But basically, I trained for this 200 mile ultra just like I trained for my marathon, 50k, 100k, and 82 miler. I like to end up doing long runs (which for me are run/walks of 30 seconds run, 60 seconds walk, just like I do shorter runs) of up to around 50k distance. This time, I did two total training runs that were each around 29 miles, just based on the length of the trail I had to run. I could have run longer, but mentally had the confidence that another ~45 minutes per run wasn’t going to change my ability to attempt 50 miles a day for 4 days. If I didn’t have 3 years of this training style under my personal belt, I might feel different about it. That’s longer than many people run, but I find the experience of 7-8 hours of time on my feet fueling, run/walking, and problem solving (including building up my willpower to spend that much time moving) to be what works for me.

The main difference for my 200 is probably also that it’s my 3rd year of ultrarunning. I was able to increase my long runs a little bit more of a time, when historically I used to add 2 miles a time to a long run. I jumped up 4 miles at a time – again, run/walking so very easy on my legs – when building up my long runs, so I was able to end up with 2 different 29 mile runs, two weeks apart, even though I really kicked off training specifically for this 8 weeks prior (10 weeks including taper) to the run. In between I also did a weekend of back to back to back runs (meaning 3 days in a row) where I ran 16 miles, another 16 miles, and 13 miles to practice getting up and running on tired legs. In past cycles I had done a lot more back to back (2-day) with a long and a medium run, but this time I did less of the 2-day and did the one big 3-day since I was targeting a 4-day experience. In future, if I were to do this again, given how well my body held up with all this training, I might have done more back to back, but I took things very cautiously and wanted to not overtrain and cause injury from ramping up too quickly.

As part of that (trying not to over do it), instead of doing several little runs throughout the week I focused on more medium-long runs with my vest and fueling, so I would do something like a long run (starting at 10 miles building up to 29 miles), a medium-long run (8 miles up to 13 miles or 16 miles) and another medium-ish run (usually 8 miles). Three runs a week, and that was it. Earlier in the 8 weeks, I was still doing a lot of hiking off the season, so I had plenty of other time-on-feet experiences. Later in the season I sometimes squeezed in a 4th short run of the week if we wouldn’t be hiking, and ran without my vest and tried to do some ‘speed work’ (aka run a little faster than my easy long run pace). Nothing fancy. Again, this is based on my slow running style (that’s actually a fixed interval of short run and short walk, usually 30 seconds run and 60 seconds walk), my schedule, my personality, and more. If you read this, don’t think my mileage or training style is the answer. But I did want to share what I did and that it generally worked for me.

I did struggle with wondering if I was training “enough”. But I never train “enough” compared to others’ marathon, 50k, 100k, 100 mile plans, either. I’m a low mileage-ish trainer overall, even though I do throw in a few longer runs than most people do. My peak training for marathon, 50k, and 100k is usually around low 50s (miles per week). Surprisingly, this 200 cycle did get me to some mid 60 mile weeks! One thing that also helped me mentally was adding in a rolling 7 day calculation of the miles, not just looking at miles per calendar week. That helped when I shifted some runs around due to scheduling, because I could see that I was still keeping a reasonable 55-low60s mileage over 7 days even though the calendar week total dropped to low 40s because of the way the runs happened to land in the calendar weeks.

Generally, though, looking back at how my training was more than I had accomplished for previous races; I feel better than ever (good fueling really helps!); I didn’t have any accidents or overtraining injuries or niggles; I decided a few weeks before peak that I was training enough and it was the right amount for me.

Another factor that was slightly different was how much hiking I had done this year. I ran my 100k in March then took some time off, promising my husband that we would hike “more” this year. That also coincided with me not really bouncing back from my 100k recovery period: I didn’t feel like doing much running, so we kept planning hiking adventures. Eventually I realized (because I was diagnosed with Graves’ disease last year, I’m having my thyroid and antibody and other related blood work done every 3 months while we work on getting everything into range) that this coincided with my TSH going too high for my body’s happiness; and my disinterest in long runs was actually a symptom (for me) of slightly too-high TSH. I changed my thyroid medication and within two weeks felt HUGELY more interested in long running, which is what coincided with reinvigorating my interest in a fall ultra, training, and ultimately deciding to go for the 200. But in the meantime, we kept hiking a lot – to the tune of over 225 miles hiked and over 53,000 feet of elevation gain! I never tracked elevation gain for hiking before (last year, not sure I retrospectively tracked it all but it was closer to 100 miles – so definitely likely 2x increase), but I can imagine this is definitely >2x above what I’ve done on my previous biggest hiking year, just given the sheer number of hikes that we went out on. So overall, the strengthening of my muscles from hiking helped, as did the time on feet. Before I kicked off my 8 week cycle, we were easily spending 3-4 hours a hike and usually at least two hikes a weekend, so I had a lot of time on feet almost every hike equivalent to 12 or more miles of running at that point. That really helped when I reintroduced long runs and aided my ability to jump my long run in distance by 4 miles at a time instead of more gently progressing it by 2 miles a week as I had done in the past.

How my 200 mile attempt actually went

Spoiler alert: I DNF (did not finish) 200 miles. Instead, I stopped – happily – at 100 miles. But it wasn’t for a lack of training.

Day 1 – 51 miles – All as planned

I set out on lap 1 on Day 1 as planned and on time, starting in the dark with a waist lamp at 6am. It was dark and just faintly cool, but warm enough (51F) that I didn’t bother with long sleeves because I knew I would warm up. (Instead, for all days, I was happy in shorts and a short sleeve shirt when the temps would range from 49F to 76F and back down again.) I only had to run for about an hour in the dark and the sky gradually brightened. It ended up being a cloudy, overcast and nice weather day so it didn’t get super bright first thing, but because it wasn’t wet and cold, it wasn’t annoying at all. I tried to start and stay at an easy pace, and was running slow enough (about ~30s/mile slower than my training paces) that I didn’t have to alter my planned intervals to slow me down any more. All was fairly well and as planned in the first lap. I stopped to use the bathroom at mile 3.5 and as planned at my 8 mile turnaround point, and also stopped to stuff a little more wool in a spot in my shoe a mile later. That added 2 minutes of time, but I didn’t let it bother me and still managed to finish lap 1 at about a 15:08 min/mi average pace, which was definitely faster than I had predicted. I used the bathroom again at the turnaround while my husband re-filled my hydration pack, then I stuffed the next round of snacks in my vest and took off. The bathroom and re-fueling “aid station” stop only took 5 minutes. Not bad! And on I went.

A background-less shot of me in my ultrarunning gear. I'm wearing a grey moisture-wicking visor; sunglasses; a purple ultrarunning vest packed with snacks in front and the blue tube of my hydration pack looped in front; a bright flourescent pink short sleeve shirt; grey shorts with pockets bulging on the side with my phone (left pocket) and skittles and headphones and keys (right pocket), and in this lap I was wearing bright pink shoes. Lap 2 was also pretty reasonable, although I was surprised by how often I wanted a bathroom. My period had started that morning (fun timing), and while I didn’t have a lot of flow, the signals my abdomen was giving my brain was telling me that I needed to go to the bathroom more often than I would have otherwise. That started to stress me out slightly, because I found myself wishing for a bathroom in the longest stretch without trail bathrooms and in a very populated area, the duration of which was about 5.5 miles long. I tried to drink less but was also aware of trying not to under hydrate or imbalance my electrolytes. I always get a little dehydrated during my period; and I was running a multi-day ultra where I needed a lot of hydration and more sodium than usual; this situation didn’t add up well! But I made it without any embarrassing moments on the trail. The second aid station again only took 5 minutes. (It really makes a world of difference to not have to dry off my feet, Desitin them up, and re-do socks and shoes every single aid station like I did last year!) I could have moved faster, but I was trying to not let small minutes of time frazzle me, and I was succeeding with being efficient but not rushed and continuing on my way. I had slowed down some during lap 2, however – dropping from a 15:08 to 15:20ish min/mi pace. Not much, but noticeable.

At sunset, with light blue sky fading to yellow at the horizon behind the row of tall, skinny bush like trees with gaps and a hot air balloon a hundred or so feet off the ground seen between the trees.Lap 3 I did feel more tired. I talked my husband into bringing me my headlamp toward the end of the last lap, instead of me having to carry it for 4+ hours before the sun went down. (Originally, I thought I would need it 2-3 hours into this last lap, but because I was moving so well it was now looking like 4 hours, and it would be a 2-3 mile e-bike ride for him to bring me the lamp when I wanted it. That was a mental win to not have to run with the lamp when I wasn’t using it!) I was still run/walking the same duration of intervals, but slowed down to about 16:01 pace for this lap. Overall, I would be at 15:40 average for the whole day, but the fatigue and my tired feet started to kick in on the third lap between miles 34-51. Plus, I stopped to take a LOT more pictures, because there was a hot air balloon growing in the distance as it was flying right toward me – and then by me next to the trail! It ended up landing next to the soccer fields a mile behind me after it passed me in this picture. I actually made it home right as the sun set and didn’t have to wear my lamp at all that evening.

Day 1 recovery was better and worse than I expected. I sat down and used my foot massager on my still-socked feet, which felt very good. I took a shower after I peeled my socks off and took a look at my feet for the first time. I had one blister that I didn’t know was growing at all pop about an hour before I finished, but it was under some of my pre-taped area. I decided to leave the tape and see how it looked and felt in the morning. I had 2-3 other tiny, not a big deal blisters that I would tape in the morning but didn’t need any attention that night.

I had planned to eat a reasonably sized dinner – preferably around 1000 calories – each night, to help me address my calorie deficit. And I had a big deficit: I had burned 5,447 calories and consumed 3,051 calories in my 13 hours and 13 minutes of running. But I could only eat ¼ of the pizza I planned for dinner, and that took a lot of work to force myself to eat. So I gave up, and went to bed with a 3,846 calorie deficit, which was bigger than I wanted.

And going to bed hurt. I was stiff, which I could deal with, but my feet that didn’t hurt much while running started SCREAMING at me. All over. They hurt so bad. Not blisters, just intense aches. Ouch! I started to doubt my ability to run the next day, but this is where my pre-planning kicked in (aided by my husband who had agreed to the rules we had decided upon): no matter what, I would get up in the morning, get dressed, and go out and start my first lap. If I decided to quit, I could, but I could not quit at night in bed or in the morning in the bed or in the house. I had to get up and go. So I went to sleep, less optimistic about my ability to finish 50 miles again on day 2, but willing to see what would happen.

Day 2: 34 instead of 50 miles, and walking my first ever lap

I actually woke up before my alarm went off on day 2. Because I had finished so efficiently the day before, I was able to again get a good night’s sleep, even with the early alarm and waking up again at 4:30am with plans to be going by 6am. The extra time was helpful, because I didn’t feel rushed as I got ready to go. I spent some extra time taping my new blisters. Because they hadn’t popped, I put small torn pieces of Kleenex against them and used cut strips of kinesio tape to protect the area. (Read “Fixing Your Feet” for other great ultra-related foot care tips; I learned about Kleenex from that book.) I also use lambs’ wool for areas that rub or might be getting hot spots, so I put wool back in my usual places (between big and second toes, and on the side of the foot) plus another toe that was rubbing but not blistered and could use some cushion. I also this year have been trying Tom’s blister powder in my socks, which seems to help since my feet are extra sweat prone, and I had pre-powdered a stack of socks so I could simply slip them on and get going once I had done the Kleenex/tape and wool setup. The one blister that had popped under my tape wasn’t hurting when I pressed on it, so I left it alone and just added loose wool for a little padding.

A pretty view of the trail with bright blue sky after the sun rose with green bushes (and the river out of sight) to the left, with the trail parallel to a high concrete wall of a road with cheery red and yellow leaved trees leaning over the trail.And off I went. I managed to run/walk from the start, and faster than I had projected on my spreadsheets originally and definitely faster than I thought was possible the night before or even before I started that morning. Sure, I was slower than the day before, but 15:40 min/mi pace was nothing to sneeze at, and I was feeling good. I was really surprised that my legs, hips and body did not hurt at all! My multi-day or back-to-back training seemed to pay off here. All was well for most of the first lap (17 miles again), but then the last 2 or so miles, my pace started dipping unexpectedly so I was doing 16+ min/mi without changing my easy effort. I was disappointed, and tired, when I came into my aid station turnaround. I again didn’t need foot care and spent less than 5 minutes here, but I told Scott as I left that I was going to walk for a while, because my feet had been hurting and they were getting worse. Not blisters: but the balls of my feet were feeling excruciating.

A close up of a yellow shelled snail against the paved trail that I saw while walking the world's slowest 17-mile lap on day 2.I headed out, and within a few minutes he had re-packed up and biked up to ride alongside me for a few minutes and chat. I told him I was probably going to need to walk this entire lap. We agreed this was fine and to be expected, and was in fact built into my schedule that I would slow down. I’ve never walked a full lap in an ultra before, so this would be novel to me. But then my feet got louder and louder and I told him I didn’t think I could even walk the full lap. We decided that I should take some Tylenol, because I wasn’t limping and this wouldn’t mask any pain that would be important cues for my body that I would be overriding, but simply muting the “ow this is a lot” screams that the bones in the balls of my feet were feeling. He biked home, grabbed some, and came back out. I took the Tylenol and sent him home again, walking on. Luckily, the Tylenol did kick in and it went from almost unbearable to manageable super-discomfort, so I continued walking. And walking. And walking. It took FOREVER, it felt like, having gone from 15-16 min/mi pace with 30 seconds of running, 60 seconds of walking, to doing 19-20 minute miles of pure walking. It was boring. I had podcasts, music, audiobooks galore, and I was still bored and uncomfortable and not loving this experience. I also was thinking about it on the way back about how I did not want to do a 3rd lap that day (to get me to my planned 50 miles) walking again.

Scott biked out early to meet me and bring me extra ice, because it was getting hot and I was an hour slower than the day before and risking running out of water that lap if he didn’t. After he refilled my hydration pack and brought it back to me while I walked on, I told him I wanted to be done for the day. He pointed out that when I finished this lap, I would be at 34 miles for the day, and combined with the day before (51), that put me at 85 miles, which would be a new distance PR for me since last year I had stopped at 82. That was true, and that would be a nice place to stop for the day. He reminded me of our ‘rules’ that I could go out the next day and do another lap to get me to 100, and decide during that lap what else I wanted to do. I was pretty sure I didn’t want to do more, but agreed I would decide the next day. So I walked home, completing lap 2 and 34 miles for the day, bringing me to 85 miles overall across 2 days.

Day 2 recovery went a little better, in part because I didn’t do 51 miles (only 34) and I had walked rather than ran the second lap, and also stopped earlier in the day (4pm instead of 7pm). I had more time to shower and bring myself to finally eat an entire 1000 calories before going to bed, again with my feet screaming at me. I had more blisters this time, mostly again on my right foot, but the balls of my feet and the bones of my feet ached in a way they never had before. This time, though, instead of setting my alarm to get up and go by 6am, I decided to sleep for longer, and go out a little later to start my first lap. This was a deviation from my plan, but another deviation I felt was the right one: I needed the sleep to help my body recover to be able to even attempt another lap.

Day 3: Only 16 miles, but hitting 100 for the first time ever

Instead of 6am, I set out on Day 3 around 8:30am. I would have taken even longer to go, but the forecast was for a warm day (we ended up hitting 81F) and I wanted to be done with the lap before the worst of the heat. I thought there was a 10% chance I’d keep going after this lap, but it was a pretty small chance. However, I set out for the planned 16 mile lap and was pleasantly surprised that I was run/walking at about a 15:40 pace! Again, better than I had projected (although yes, I had deviated from my mileage plan the day before), and it felt like a good affirmation that stopping the day before instead of slogging out another walking lap was the right thing to do.

After a first few miles, I toyed with the idea of continuing on. But I knew with the heat I probably wouldn’t stand more than one more lap, which would get me to 116. Even if I went out again the fourth day, and did 1-2 laps, that would MAYBE get me to 150, but I doubted I could do that without starting to cause some serious damage. And it honestly wasn’t feeling fun. I had enjoyed the first day, running in the dark, the fog, the daylight, and the twilight, seeing changing fall leaves and running through piles of them. The second day was also fun for the first lap, but the second lap walking was probably what a lot of ultra marathoners call the “death march” and just not fun. I didn’t want to keep going if it wasn’t fun, and I didn’t want to run myself into the ground (meaning to be so worn down that it would take weeks to months to recover) or into injury, especially when the specific milestones didn’t really mean anything. Sure, I wanted to be a 200 mile ultramarathoner, something that only a few thousand people have ever done – but I didn’t want to do it at the expense of my well-being. I spent a lot of time thinking about it, especially miles 4-8, and was thinking about the fact that the day before I had started, I had gone to a doctor’s appointment and had an official diagnosis confirming my fifth autoimmune disease, then proceeded to run (was running) 100 miles. Despite all the fun challenges of running with autoimmune conditions, I’m in really good health and fitness. My training this year went so well and I really enjoyed it. Most of this ultra had gone so well physically, and my legs and body weren’t hurting at all: the weakness was my feet. I didn’t think I could have trained any differently to address that, nor do I think I could change it moving forward. It’s honestly just hard to run that many hours or that many miles, as most ultramarathoners know, and your feet take a beating. Given that I was running on pavement for all of those hours, it can be even harder – or a different kind of hard – than kicking roots and rocks on a dirt trail. I figured I would metaphorically kick myself if I tried for 116 or 134 and injured myself in a way that would take 6-8 weeks to recover, whereas I felt pretty confident that if I stopped after this lap (at 100), I would have a relatively short and easy recovery, no major issues, and bounce back better than I ever have, despite it being my longest ever ultramarathon. Yes, I was doing it as a multi-day with sleep in between, but both in time on feet and in mileage, it was still the most I’d ever done in 2 or 3 days.

And, I was tired of eating. I was fueling SO well. Per my plans, I set out to do >500 mg of sodium per hour and >250 calories per hour. I had been nailing it every lap and every day! Day 1 I averaged 809 mg of sodium per hour and 290 calories per hour. Day 2 was even increased from that, averaging 934 mg of sodium per hour and 303 calories per hour! Given the decreased caloric burn of day 2 because I walked the second lap, my caloric deficit for day 2 was a mere ~882 calories (given that I also managed to eat a full dinner that night), even though I skipped the last hour as I finished the walking lap. Day 3 I was also fueling above my goals, but I was tired of it. Sooooo tired of it. Remember, I have to take a pill every time I eat, because I have exocrine pancreatic insufficiency (EPI or PEI). I was eating every 30 minutes as I ran or walked, so that meant swallowing at least one pill every 30 minutes. I had swallowed 57 pills on Day 1 and 48 pills on Day 2, between my enzymes and electrolyte pills. SO MANY PILLS. The idea of continuing to eat constantly every 30 minutes for another lap of ~5 or more hours was also not appealing. I knew if I didn’t eat, I couldn’t continue.

A chart with an hourly break down of sodium, calories, and carbs consumed per hour, plus totals of caloric consumption, burn, and calculated deficit across ~27 hours of move time to accomplish 100 miles run.

And so, I decided to stop after one more lap on day 3, even though I was holding up a respectable 15:41 min/mi pace throughout. I hit 100 miles and finished the lap at home, happy with my decision.

Two pictures of me leaning over after my run holding a sign (one reading 50 miles, one reading 100 miles) for each of my cats to sniff.(You can see from these two pictures that I smelled VERY interesting, sweaty and salty and exhausted at the end of day 1 and day 3, when I hit 50 miles and 100 miles, respectively. We have two twin kittens (now 3 years old) and one came out to sniff me first on the first day, and the other came out as I came home on the third day!)

Because I had only run one final lap (16 miles) on day 3, and had so many bonus hours in the rest of the day afterward when I was done and home, I was able to eat more and end up with only a 803 calorie deficit for the day. So overall, day 1 had the biggest deficit and probably influenced my fatigue and perception of pain on day 2, but because I had shortened day 2 and then day 3, my very high calorie intake every hour did a pretty good job matching my calorie expenditure, which is probably why I felt very little muscle fatigue in my body and had no significant sore areas other than the bottoms of my feet. I ended up averaging 821 mg/hr of sodium and 279 calories per hour (taking into account the fact that I skipped two final snacks at the end of day 2 when I was walking it out; ignoring that completely skipped hour would mean the average caloric intake on hours I ate anything at all was closer to 290 calories/hr!)

In total, I ended up consuming 124 pills in approximately 27 hours of move time across my 100 miles. (This doesn’t include enzyme pills for my breakfast or dinners each of those days, either – just the electrolyte and enzyme pills consumed while running!)

AFTERMATH

Recovery after day 3 was pretty similar to day 2, with me being able to eat more and limit my calorie deficit. I’ve had long ~30 mile training runs where I wasn’t very hungry afterward, but it surprised me that even two days after my ultra, I still haven’t really regained my appetite. I would have figured my almost 4000 calorie deficit from day 1 would drive a lot of hunger, so this surprised me.

So too has my physical state: 48 hours following the completion of my 100 miles, I am in *fantastic* shape compared to other multi-day back to back series of runs I’ve done, ultramarathons or not. The few blisters I got, mainly on my right foot, have already flattened themselves up and mostly vanished. I think I get more blisters on my right foot because of breaking my toe last year: my right foot now splays wider in my shoe, so it tends to get more blisters and cause more trouble than my left foot. I got only one blister on my left foot, which is still fluid filled but not painful and starting to visibly deflate now that I’m not rubbing it onto a shoe constantly any more. And my legs don’t feel like I ran at all, let alone running 51+34+16 miles!

I am tired, though. I don’t have brain fog, probably because of my excellent fueling, but I am fatigued in terms of overall energy and lack of motivation to get a lot done yesterday and today (other than writing this blog post!). So that’s probably pretty on par with my effort expended and matches what I expected, but it’s nice to be able to move around without hurting (other than my feet).

My feet in terms of general aches and ows are what came out the worst from my run. Day 2, what hurt was the bottom of the balls of my feet. Starting each night though, I was getting aches all over in all of the bones of my feet. After day 3, that night the foot aches were particularly strong, and I took some Tylenol to help with that. Yesterday evening and today though, the ache has settled down to very minor and only occasionally noticeable. The tendon from the top of my left foot up my ankle is sore and gets cranky when I wear my sneakers (although it didn’t bother me at all while running any of the days), so after tying and re-tying my shoelaces 18 times yesterday to try to find the perfect fit for my left foot, today I went on my recovery walk in flip flops and was much happier.

What I’m taking away from this 200 mile attempt that was only 100 miles:

I feel a little disappointed that I didn’t get anywhere near 200 miles, but obviously, I was not willing to hurt long enough or hard enough to get there. My husband called it a stretch goal. Rationally, I am very happy with my choices to stop at 100 and end up in the fantastic physical shape that I am in, and I recognize that I made a very rational choice and tradeoff between ending in good shape (and health) and the mainly ego-driven benefits of possibly achieving 200 miles (for me).

Would I do anything different? I can’t think of anything. If I somehow had an alternate do-over, I can’t think of anything I would think to change. I’d like to reduce my risk of blisters but I’m already doing all I can there, and dealing with changes in my right foot shape post-broken toe that I have no control over. And I’m not sure how to train more/better for reducing the bottom ball of foot pain that I got: I already trained multiple days, back to back, long hours of feet on pavement. It’s possible that having my doctor’s appointment the day before I started influenced my mental calculation of my future risk/benefit tradeoff of continuing more miles, and so not having had that then may have changed my calculations to do another lap or two, or go out on the 4th day (which I did not). But, I don’t have a do over, and I’ll never know, and I’m not too upset about that because I was able to control what I could control and am again pretty happy with the outcomes. 100 or 150 miles felt about the same to me, psychologically, in terms of satisfaction.

What I would tell other people about attempting multiple day ultramarathons or 200 mile ultramarathons:

Training back to back days is one option, as is long spurts of time on feet walking/hiking/running. I don’t think “just running” has to be the only way to train for these things. I’m also a big proponent of short intervals: If you hear people recommend taking walk breaks, it doesn’t have to be 1 minute every 10 minutes or every mile. It can be as short as every 30 seconds of running, take a walk break! There’s no wrong way to do it, whatever makes your body and brain happy. I get bored running longer (and don’t like it); other people get bored running the short intervals that I do – so find what works for you and what you’re actually willing to do.

Having plans for how you’ll rest X hours and go out and try to make it another lap or to the next aid station works really well, especially if you have crew/pacers/support (for me, my husband) who will stick to those rules and help you get back out there to try the next lap/section. Speaking of sleep/rest, laying down for a while helps as much as sleeping, so even if you can’t sleep, committing to the rest of X hours is also good for resting your feet and everything. I found that the hour laying down before I fell asleep helped my body process the noise of the “ouch” from my feet and it was a lot easier to sleep after that. Plan that you’ll have some down/up time before and after your sleep/rest time, and figure that into your time plans accordingly.

The cheesy “know your why” and “know what you want” recommendations do help. I didn’t want 200 miles badly enough to hurt more for longer and risk months of recovery (or the inability to recover). Maybe you’d be lucky enough to achieve 200 without hurting that bad, that long, or risking injury – or maybe you’ll have to make that choice, and you might make it differently than I did. (Maybe you’re lucky enough to not have 5 autoimmune things to juggle! I hope you don’t have to!) I kind of knew going in that I was only going to hit 200 if all went perfect.

Diabetes and this 200 mile ultramarathon that was a 100 mile ultra:

I just realized that I managed to write an ENTIRE race report without talking about diabetes and glucose management…because I had zero diabetes-related thoughts or issues during these several days of my run! Sweet! (Pun fully intended.)

Remember, I have type 1 diabetes and use an open source automated insulin delivery (AID) system (in my case, still using OpenAPS after alllllll these years), and I’ve talked previously about how I fuel while ultrarunning and juggling blood glucose management. Unlike previous ultras, I had zero pump site malfunctions (phew) and my glucose stayed nicely in range throughout. I think I had one small drift above range for 2 hours due to an hour of higher carb activity right when I shifted to walking the second lap on day 2, but otherwise was nicely in range all days and all nights without any extra thought or energy expended. I didn’t have to take a single “low carb”/hypoglycemia treatment! I think there was one snack I took a few minutes early when I saw I was drifting down slightly, but that was mostly a convenience thing and I probably would not have gone low (below target) even if I had waited for my planned fuel interval. But out of 46 snacks, only one 5-10 minutes early is impressive to me.

I had no issues after each day’s run, either: OpenAPS seamlessly adjusted to the increasing insulin sensitivity (using “autosensitivity” or “autosens”) so I didn’t have to do manual profile shifts or overrides or any manual interference. I did decide each night whether I wanted to let it SMB (supermicrobolus) as usual or stick to temp basal only to reduce the risk of hypoglycemia, but I had no post-dinner or overnight lows at all.

The most “work” I had to do was deciding to wear a second CGM sensor (staggered, 5 days after my other one started) so that I had a CGM sensor session going with good quality data that I could fall back to if my other sensor started to get jumpy, because the sensor session was supposed to end the night of day 4 of my planned run. I obviously didn’t run day 4, but even so I was glad to have another sensor going (worth the cost of overlapping my sensors) in order to have the reassurance of constant data if the first one died or fell out and I could seamlessly switch to an already-warmed up sensor with good data. I didn’t need it, but I was glad to have done that in prep.

(Because I didn’t talk about diabetes a lot in this post, because it was not very relevant to my experiences here, you might want to check out my previous race recaps and posts about utlrarunning like this one where I talk in more detail about balancing fueling, insulin, and glucose management while running for zillions of hours.)

TLDR: I ran 100 miles, and I did it my DIY way: my own course, my own (slow pace), with sleep breaks, a lot of fueling, and a lot of satisfaction of setting big goals and attempting to achieve them. I think for me, the process goals of figuring out how to even safely attempt ultramarathons are even more rewarding than the mileage milestones of ultrarunning.

Running a multi-day ultramarathon by Dana M. Lewis from DIYPS.org

What I’ve Learned From 5,000 Pills Of Pancreatic Enzyme Replacement Therapy (PERT) For Exocrine Pancreatic Insufficiency (EPI/PEI)

I recently reached a weird milestone that no one likely cares about, but that I find fascinating: in the first 534 days of exocrine pancreatic insufficiency (EPI / PEI), I’ve taken more than 5,000 pills of pancreatic enzyme replacement therapy (PERT).

That’s an average of 9.41 pills per day!

PERT (enzymes) helps my body successfully digest the food that I eat, because my pancreas is no longer producing enough enzymes. Like insulin treatment for diabetes, PERT will be a lifelong necessity for me: this number of pills consumed is one that only goes up from here.

Here’s a look at what the pills per day intake has looked like over this time:

  • Min: 2 (early days)
  • Max: 72 (hello, outlier of two ultramarathons! One was 62 miles, the other was 82 miles! Other 30-40+ pill days are likely also ultra 🏃🏼‍♀️ training days, e.g. around 50k of running, which is still 8-9 hours of running and fueling every 30 minutes)
  • Median: 8

Analyzing a graph of my daily PERT enzyme pills, there are noticeable spikes, particularly around my ultramarathon training days. Two distinct spikes at 72 pills per day correspond to my 100k (62 mile) and 82-mile ultra runs.

Here is a graph showing my PERT (enzyme) pills per day totals, there are a few noticeable spikes in the 20-40ish range that are likely ultra training days. The two spikes around 72/day are my 100k (62 mile) and 82 mile ultra runs.

Why so many pills?!

Not everyone with EPI takes as many pills as I do. The number is titrated (adjusted) based on what and how often I eat. A typical meal for me requires 2-3 prescription pills of PERT.

In my case, I sometimes use over-the-counter (OTC) enzymes to ‘top off’ a prescription pill.

For hikes and runs, which I do 4-5 times each week, I eat small amounts every 30 minutes if I’m out for more than 2 hours, which is 3+ times a week. For a run of 5 hours, where I consume 10 snacks, I’d use 10 pills if I went the prescription route. In contrast, I usually use 2-4 OTC pills per snack, which combined costs an average of $0.70. That means $7 in enzyme costs for 5 hours compared to $80 if I had taken prescription PERT! Multiply times several times a week, and you can see why I choose this strategy.

Balancing Cost ($) and Convenience (Fewer Pills)

The “cost” for using OTC pills, though, is 20-40 pills ($7) instead of 10 pills ($80). On a day-to-day basis, my choice depends on convenience, how confident I am in my counts/dosing (I’m very confident for hike/run pre-portioned snacks that I’ve tested rigorously), and other factors.

Increasingly, when I’m not pursuing physical activity, I’m more likely to choose fewer pills at the financial cost of prescription PERT. I’d like to choose fewer pills for physical activity, too, which is why I’ve recently shifted to a slightly more expensive OTC pill that has more enzymes in it, in order to take 1 pill for most snacks instead of 2-4. In a typical long run of 4 hours, for example, instead of 7 snacks resulting in 28 pills, those 7 snacks would instead result in 7 pills! (There’s also a challenge with finding these particular OTC pills, as prescription pill shortage has driven more people to try OTCs and now the OTC pills I prefer are regularly out of stock, too. If you’re curious about using OTC pills with EPI, or prior to a diagnosis of EPI, you may be interested in this post where I describe in more detail using over the counter (OTC) enzyme pills for this purpose.)

Long run days are outliers in my pill count per day numbers and graphs. However, even if I skipped those and only took 8 prescription PERT per day, I’d still have consumed over 4,200 enzyme pills at this point.

EPI or PEI leads to a lot of pill-swallowing, regardless of whether you’re using over the counter enzymes or prescription enzymes.

But they work! Oh, do they work. My GI symptoms used to be most days a week and caused me to feel miserable (read about my experience getting diagnosed with EPI here). Now, I rarely have any symptoms, and when they do occur (likely mistiming a dose compared to what I was eating or taking not quite enough to match what I was eating), they are significantly less bothersome. It’s awesome, and I feel back to “normal” for me well before all of my GI symptoms started years ago! So yes, I have to swallow many pills a day for EPI but my symptoms are completely and regularly managed as a result and my quality of life is back to being what it was before.

If you’re curious to read more about my experiences with EPI, or posts about adjusting enzymes to match what you’re eating, check out DIYPS.org/EPI for a list of other EPI related posts.

If you have EPI and have an iOS device, you also might be interested in checking out PERT Pilot, a free iOS app to track food intake and PERT dosing and outcomes.


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

You’d Be Surprised: Common Causes of Exocrine Pancreatic Insufficiency

Academic and medical literature often is like the game of “telephone”. You can find something commonly cited throughout the literature, but if you dig deep, you can watch the key points change throughout the literature going from a solid, evidence-backed statement to a weaker, more vague statement that is not factually correct but is widely propagated as “fact” as people cite and re-cite the new incorrect statements.

The most obvious one I have seen, after reading hundreds of papers on exocrine pancreatic insufficiency (known as EPI or PEI), is that “chronic pancreatitis is the most common cause of exocrine pancreatic insufficiency”. It’s stated here (“Although chronic pancreatitis is the most common cause of EPI“) and here (“The most frequent causes [of exocrine pancreatic insufficiency] are chronic pancreatitis in adults“) and here (“Besides cystic fibrosis and chronic pancreatitis, the most common etiologies of EPI“) and here (“Numerous conditions account for the etiology of EPI, with the most common being diseases of the pancreatic parenchyma including chronic pancreatitis, cystic fibrosis, and a history of extensive necrotizing acute pancreatitis“) and… you get the picture. I find this statement all over the place.

But guess what? This is not true.

First off, no one has done a study on the overall population of EPI and the breakdown of the most common co-conditions.

Secondly, I did research for my latest article on exocrine pancreatic insufficiency in Type 1 diabetes and Type 2 diabetes and was looking to contextualize the size of the populations. For example, I know overall that diabetes has a ~10% population prevalence, and this review found that there is a median prevalence of EPI of 33% in T1D and 29% in T2D. To put that in absolute numbers, this means that out of 100 people, it’s likely that 3 people have both diabetes and EPI.

How does this compare to the other “most common” causes of EPI?

First, let’s look at the prevalence of EPI in these other conditions:

  • In people with cystic fibrosis, 80-90% of people are estimated to also have EPI
  • In people with chronic pancreatitis, anywhere from 30-90% of people are estimated to also have EPI
  • In people with pancreatic cancer, anywhere from 20-60% of people are estimated to also have EPI

Now let’s look at how common these conditions are in the general population:

  • People with cystic fibrosis are estimated to be 0.04% of the general population.
    • This is 4 in every 10,000 people
  • People with chronic pancreatitis combined with all other types of pancreatitis are also estimated to be 0.04% of the general population, so another 4 out of 10,000.
  • People with pancreatic cancer are estimated to be 0.005% of the general population, or 1 in 20,000.

What happens if you add all of these up: cystic fibrosis, 0.04%, plus all types of pancreatitis, 0.04%, and pancreatic cancer, 0.005%? You get 0.085%, which is less than 1 in 1000 people.

This is quite a bit less than the 10% prevalence of diabetes (1 in 10 people!), or even the 3 in 100 people (3%) with both diabetes and EPI.

Let’s also look at the estimates for EPI prevalence in the general population:

  • General population prevalence of EPI is estimated to be 10-20%, and if we use 10%, that means that 1 in 10 people may have EPI.

Here’s a visual to illustrate the relative size of the populations of people with cystic fibrosis, chronic pancreatitis (visualized as all types of pancreatitis), and pancreatic cancer, relative to the sizes of the general population and the relative amount of people estimated to have EPI:

Gif showing the relative sizes of populations of people with cystic fibrosis, chronic pancreatitis, pancreatic cancer, and the % of those with EPI, contextualized against the prevalence of these in the general population and those with EPI. It's a small number of people because these conditions aren't common, therefore these conditions are not the most common cause of EPI!

What you should take away from this:

  • Yes, EPI is common within conditions such as cystic fibrosis, chronic pancreatitis (and other forms of pancreatitis), and pancreatic cancer
  • However, these conditions are not common: even combined, they add up to less than 1 in 1000!
  • Therefore, it is incorrect to conclude that any of these conditions, individually or even combined, are the most common causes of EPI.

You could say, as I do in this paper, that EPI is likely more common in people with diabetes than all of these conditions combined. You’ll notice that I don’t go so far as to say it’s the MOST common, because I haven’t seen studies to support such a statement, and as I started the post by pointing out, no one has done studies looking at huge populations of EPI and the breakdown of co-conditions at a population level; instead, studies tend to focus on the population of a co-condition and prevalence of EPI within, which is a very different thing than that co-condition’s EPI population as a percentage of the overall population of people with EPI. However, there are some great studies (and I have another systematic review accepted and forthcoming on this topic!) that support the overall prevalence estimates in the general population being in the ballpark of 10+%, so there might be other ‘more common’ causes of EPI that we are currently unaware of, or it may be that most cases of EPI are uncorrelated with any particular co-condition.

(Need a citation? This logic is found in the introduction paragraph of a systematic review found here, of which the DOI is 10.1089/dia.2023.0157. You can also access a full author copy of it and my other papers here.)


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

New Systematic Review of Exocrine Pancreatic Insufficiency (EPI) In Type 1 Diabetes and Type 2 Diabetes – Focusing on Prevalence and Treatment

I’m thrilled that the research I did evaluating the prevalence and treatment of EPI in both Type 1 diabetes and Type 2 diabetes (also presented as a poster at #ADA2023 – read a summary of the poster here) has now been published as a full systematic review in Diabetes Technology and Therapeutics.

Here is a pre-edited submitted version of my article that you can access if you don’t have journal access; and as a reminder, copies of ALL of my research articles are available on this page: DIYPS.org/research!

And if you don’t want to read the full paper, this is what I think you should take away from it as a person with diabetes or as a healthcare provider:

    1. What is EPI? 

      Exocrine pancreatic insufficiency (known as EPI in some places, and PEI or PI in other places) occurs when the pancreas no longer produces enough enzymes to digest food. People with EPI take pancreatic enzyme replacement therapy (PERT) whenever they eat (or drink anything with fat/protein).

    2. If I have diabetes, or treat people with diabetes, why should I be reading the rest of this about EPI?EPI often occurs in people with cystic fibrosis, pancreatitis, and pancreatic cancer. However, since these diseases are rare (think <0.1% of the general population even when these groups are added up all together), the total number of people with EPI from these causes is quite low. On the other hand, EPI is also common in people with diabetes, but this is less well-studied and understood. The research on other co-conditions is more frequent and often people confuse the prevalence WITHIN those groups with the % of those conditions occurring overall in the EPI community.This paper reviews every paper that includes data on EPI and people with type 1 diabetes or type 2 diabetes to help us better understand what % of people with diabetes are likely to face EPI in their lifetime.
    3. How many people with type 1 diabetes or type 2 diabetes (or diabetes overall) get EPI?TLDR of the paper: EPI prevalence in diabetes varies widely, reported between 5.4% and 77% when the type of diabetes isn’t specified. For Type 1 diabetes, the median EPI prevalence is 33% (range 14-77.5%), and for Type 2 diabetes, the median is 29% (range 16.8-49.2%). In contrast, in non-diabetes control groups, the EPI prevalence ranges from 4.4% to 18% (median 13%). The differences in ranges might be due to geographic variability and different exclusion criteria across studies.Diabetes itself is prevalent in about 10% of the general population. As such, I hypothesize that people with diabetes likely constitute one of the largest sub-groups of individuals with EPI, in contrast to what I described above might be more commonly assumed.
    4. Is pancreatic enzyme replacement therapy (PERT) safe for people with diabetes? 

      Yes. There have been safety and efficacy studies in people with diabetes with EPI, and PERT is effective just like in any other group of people with EPI.

    5. What is the effect of pancreatic enzyme replacement therapy (PERT) on glucose levels in people with diabetes?
      PERT itself does not affect glucose levels, but PERT *d0es* impact the digestion of food, which then changes glucose levels! So, most PERT labels warn to watch for hypoglycemia or hyperglycemia but the medicine itself doesn’t directly cause changes in glucose levels. You can read a previous study I did here using CGM data to show the effect of PERT actually causing improved glucose after meals in someone with Type 1 diabetes. But, in the systematic review, I found only 4 articles that even made note of glucose levels, and only 1 (the paper I linked above) actually included CGM data. Most of the studies are old, so there are no definitive conclusions on whether hypoglycemia or hyperglycemia is more common when a person with diabetes and EPI starts taking PERT. Instead, it’s likely very individual depending on what they’re eating, insulin dosing patterns before, and whether they’re taking enough PERT to match what they’re eating.TLDR here: more studies are needed because there’s no clear single directional effect on glucose levels from PERT in people with diabetes.Note: based on the n=1 study above, and subsequent conversations with other people with diabetes, I hypothesize that high variability and non-optimal post-meal glucose outcomes may be an early ‘symptom’ of EPI in people with diabetes. I’m hoping to eventually generate some studies to evaluate whether we could use this type of data as an input to help increase screening of EPI in people with diabetes.
    6. How common is EPI (PEI / PI) compared to celiac and gastroparesis in Type 1 diabetes and Type 2 diabetes? 

      As a person with (in my case, Type 1) diabetes, I feel like I hear celiac and gastroparesis talked about often in the diabetes community. I had NEVER heard of EPI prior to realizing I had it. Yet, EPI prevalence in Type 1 and Type 2 diabetes is much higher than that of celiac or gastroparesis!The prevalence of EPI is much higher in T1 and T2 than the prevalence of celiac and gastroparesis.Celiac disease is more common in people with diabetes (~5%) than in the general public (0.5-1%). Gastroparesis, when gastric emptying is delayed, is also more common in people with diabetes (5% in PWD).However, the  prevalence of EPI is 14-77.5% (median 33%) in Type 1 diabetes and 16.8-49.2% (median 29%) in Type 2 diabetes (and 5.4-77% prevalence when type of diabetes was not specified). This again is higher than general population prevalence of EPI.

      This data emphasizes that endocrinologists and other diabetes care providers should be more prone to initiate screening (using the non-invasive fecal elastase test) for individuals presenting with gastrointestinal symptoms, as the rates of EPI in diabetes are much higher in both Type 1 and Type 2 diabetes than the rates of celiac and gastroparesis.

    7. What should I do if I think I have EPI?
      Record your symptoms and talk to your doctor and ask for a fecal elastase (FE-1) screening test for EPI. It’s non-invasive. If your results are less than or equal to 200 (μg/g), this means you have EPI and should start on PERT. If you or your doctor feel that your sample may have influenced the results of your test, you can always re-do the test. But if you’re dealing with diarrhea, going on PERT may resolve or improve the diarrhea and improve the quality of the sample for the next test result. PERT doesn’t influence the test result, so you can start PERT and re-run the test any time.Symptoms of EPI can vary. Some people experience diarrhea, while others experience constipation. Steatorrhea or smelly, messy stools that stick to the side of the toilet are also common EPI symptoms, as is bloating, abdominal pain, and generally not feeling well after you eat.

      If you’ve been diagnosed with EPI, you may also want to check out some of my other posts (DIYPS.org/EPI) about my personal experiences with EPI and also this post about the amount of enzymes needed by most people with EPI. You may also want to check out PERT Pilot, a free iOS app, for recording and evaluating your PERT dosing.

If you want to read the full article, you can find copies of all of my research articles at DIYPS.org/research

If you’d like to cite this specific article in your future research, here’s an example citation:

Lewis, D. A Systematic Review of Exocrine Pancreatic Insufficiency Prevalence and Treatment in Type 1 and Type 2 Diabetes. Diabetes Technology & Therapeutics. http://doi.org/10.1089/dia.2023.0157

Why DIY AID in 2023? #ADA2023 Debate

I was asked to participate in a ‘debate’ about AID at #ADA2023 (ADA Scientific Sessions), representing the perspective that DIY systems should be an option for people living with diabetes.

I present this perspective as a person with type 1 diabetes who has been using DIY AID for almost a decade (and as a developer/contributor to the open source AID systems used in DIY) – please note my constant reminder that I am not a medical doctor.

Dr. Gregory P. Forlenza, an Associate Professor from Barbara Davis Center, presented a viewpoint as a medical doctor practicing in the US.

FYI: here are my disclosures and Dr. Forlenza’s disclosures:

On the left is my slide (Dana M. Lewis) showing I have no commercial support or conflicts of interest. My research in the last 3 years has previously been funded by the New Zealand Health Research Council (for the CREATE Trial); JDRF; and DiabetesMine. Dr. Forlenza lists research support from NIH, JDRF, NSF, Helmsley Charitable Trust, Medtronic, Dexcom, Abbott, Insulet, Tandem, Beta Bionics, and Lilly. He also lists Consulting/Speaking/AdBoard: Medtronic, Dexcom, Abbott, Insulet, Tandem, Beta Bionics, and Lilly.

I opened the debate with my initial presentation. I talk about the history of DIY in diabetes going back to the 1970s, when people with diabetes had to “DIY” with blood glucose meters because initially healthcare providers did not want people to fingerstick at home because they might do something with the information. Similarly, even insulin pumps and CGMs have been used in different “DIY” ways over the years – notably, people with diabetes began dosing insulin using CGM data for years prior to them being approved for that purpose. It’s therefore less of a surprise in that context to think about DIY being done for AID. (If you’re reading this you probably also know that DIY AID was done years before commercial AID was even available; and that there are multiple DIY systems with multiple pump and CGM options, algorithms, and phone options).

And, for people with diabetes, using DIY is very similar to how a lot of doctors recommend or prescribe doing things off label. Diabetes has a LOT of these types of recommendations, whether it’s different types of insulins used in pumps that weren’t approved for that type of insulin; medications for Type 2 being used for Type 1 (and vice versa); and other things that aren’t regulatory approved at all but often recommended anyway. For example, GLP-1’s that are approved for weight management and not glycemic control, but are often prescribed for glycemic control reasons. Or things like Vitamin D, which are widely prescribed or recommended as a supplement even though it is not regulatory-approved as a pharmaceutical agent.

I always like to emphasize that although open source AID is not necessarily regulated (but can be: one open source system has received regulatory clearance recently), that’s not a synonym for ‘no evidence’. There’s plenty of high quality scientific evidence on DIY use and non-DIY use of open source AID. There’s even a recent RCT in the New England Journal of Medicine, not to mention several other RCTs (see here and here, plus another pending publication forthcoming). In addition to those gold-standard RCTs, there are also reviews of large-scale big data datasets from people with diabetes using AID, such as this one where we reviewed 122 people’s glucose data representing 46,070 days’ worth of data; or another forthcoming publication where we analyzed the n=75 unique (distinct from the previous dataset) DIY AID users with 36,827 days’ of data (average of 491 days per participant) and also found above goal TIR outcomes (e.g. mean TIR 70-180 mg/dL of 82.08%).

Yet, people often choose to DIY with AID not just for the glucose outcomes. Yes, commercial AID systems (especially now second-generation) can similarly reach the goal of 70+% TIR on average. DIY helps provide more choices about the type and amount of work that people with diabetes have to put IN to these systems in order to get these above-goal OUTcomes. They can choose, overall or situationally, whether to bolus, count carbs precisely, announce meals at all, or only announce relative meal size while still achieving >80% TIR, no or little hypoglycemia, and less hyperglycemia. Many people using DIY AID for years have been doing no-bolus and/or no meal announcements at all, bringing this closer to a full closed loop, or at least, an AID system with very, very little user input required on a daily basis if they so choose. I presented data back in 2018(!) showing how this was being done in DIY AID, and it was recently confirmed in a randomized control trial (hello, gold standard!) showing that between traditional use (with meal announcements and meal boluses); meal announcement only (no boluses); and no announcement nor bolusing, that they all got similar outcomes in terms of TIR (all above-goal). There was also no difference in those modes of total daily insulin dose (TDD) or amount of carb intake. There was a small difference in time below range being slightly higher in the first mode (where people were counting carbs and bolusing) as compared to the other two modes – which suggests that MORE user input may actually be limiting the capabilities of the system!

The TLDR here is that people with diabetes can do less work/provide less input into AID and still achieve the same level of ideal, above-goal outcomes – and ongoing studies are showing the increased QOL and other patient-reported outcomes that also improve as a result.

Again, people may be predisposed to think that the main difference between commercial and DIY is whether or not it is regulatory approved (and therefore prescribable by doctors and able to be supported by a company under warranty); the bigger differences are instead around interoperability across devices, data access, and transparency of how the system works.

There’s even an international consensus statement on open source AID, created by an international group of 48 medical and legal experts, endorsed by 9 national and international diabetes organizations, supporting that open source AID used in DIY AID is a safe and effective treatment option, confirming that the scientific evidence exists and it has the potential to help people with diabetes and reduce the burden of diabetes. They emphasize that doctors should support patient (and caregiver) autonomy and choice of DIY AID, and state that doctors have a responsibility to learn about all options that exist including DIY. The consensus statement is focused on open source AID but also, in my opinion, applies to all AID: they say that AID systems should fully disclose how they operate to enable informed decisions and that all users should have real-time and open access to their own data. Yes, please! (This is true of DIY but not true of all commercial systems.)

The elephant in the room that I always bring up is cost, insurance coverage, and therefore access and accessibility of AID. Many places have government or insurance that won’t cover AID. For example, the proposed NICE guidelines in the UK wouldn’t provide AID to everyone who wants one. In other places, some people can get their pump covered but not CGM, or vice versa, and must pay out of pocket. Therefore in some cases, DIY has out of pocket costs (because it’s not covered by insurance), but is still cheaper than AID with insurance coverage (if it’s even covered).

I also want to remind everyone that choosing to DIY – or not – is not a once-in-a-lifetime decision. People who use DIY choose every day to use it and continue to use it; at any time, they could and some do choose to switch to a commercial system. Others try commercial, switch back to DIY, and switch back and forth over time for various reasons. It’s not a single or permanent decision to DIY!

The key point is: DIY AID provides safety and efficacy *and* user choice for people with diabetes.

Dr. Forlenza followed my presentation, talking about commercial AID systems and how they’ve moved through development more quickly recently. He points to the RCTs for each approved commercial system that exist, saying commercial AID systems work, and describing different feature sets and variety across commercial systems. He shared his thoughts on advantages of commercial systems including integration between components by the companies; regulatory approval meaning these systems can be prescribed by healthcare providers; company-provided warranties; and company provided training and support of healthcare providers and patients.

He makes a big point about a perceived reporting bias in social media, which is a valid point, and talks about people who cherry pick (my words) data to share online about their TIR.

He puts an observational study and the CREATE Trial RCT data up next to the commercial AID systems RCT data, showing how the second generation commercial AID reach similar TIR outcomes.

He then says “what are you #notwaiting for?”, pointing out in the US that there are 4 commercial systems FDA approved for type 1 diabetes. He says “Data from the DIY trials themselves demonstrate that DIY users, even with extreme selection bias, do not achieve better glycemic control than is seen with commercial systems.” He concludes that commercial AID has a wide variety of options; commercial systems achieve target-level outcomes; a perception that both glucose outcomes and QOL are being addressed by the commercial market, and that “we do not need Unapproved DIY solutions in this space”.

After Dr. Forlenza’s presentation, I began my rebuttal, starting with pointing out that he is incorrectly conflating perceived biases/self-reporting of social media posts with gold-standard, rigorously performed scientific trials evaluating DIY. Data from DIY AID trials do not suffer from ‘selection bias’ any more than commercial AID trials do. (In fact, all clinical trials have their own aspects of selection bias, although that isn’t the point here.) I reminded the audience of the not one but multiple RCTs available as well as dozens of other prospective and retrospective clinical trials. Plus, we have 82,000+ data points analyzed showing above-goal outcomes, and many studies that evaluate this data and adjust for starting outcomes still show that people with diabetes who use DIY AID benefit from doing so, regardless of their starting A1c/TIR or demographics. This isn’t cherry-picked social media anecdata.

When studies are done rigorously, as they have been done in DIY, we agree that now second-generation commercial AID systems reach (or exceed, depending on the system) ADA standard of care outcomes. For example, Dr. Forlenza cited the OP5 study with 73.9% TIR which is similar to the CREATE Trial 74.5% TIR.

My point is not that commercial systems don’t work; my point is that DIY systems *do* work and that the fact that commercial systems work doesn’t then override the fact that DIY systems have been shown to work, also! It’s a “yes, and”! Yes, commercial AID systems work; and yes, DIY AID systems work.

The bigger point, which Dr. Forlenza does not address, is that the person with diabetes should get to CHOOSE what is best for them, which is not ONLY about glucose outcomes. Yes, a commercial system- like DIY AID – may help someone get to goal TIR (or above goal), but DIY provides more choice in terms of the input behaviors required to achieve those outcomes! There’s also possible choice of systems with different pumps or CGMs, different (often lower) cost, increased data access and interoperability of data displays, different mobile device options, and more.

Also, supporting user choice of DIY is in fact A STANDARD OF CARE!

It’s in the ADA’s Standards of Care, in fact, as I wrote about here when observing that it’s in the 2023 Standards of Care…as well as in 2022, 2021, 2020, and 2019!

I wouldn’t be surprised if there are people attending the debate who think they don’t have any – or many – patients using DIY AID. For those who think that (or are reading this thinking the same), I ask a question: how many patients have you asked if they are using DIY AID?

There’s a bunch of reasons why it may not come up, if you haven’t asked:

  • They may use the same consumables (sites, reservoirs) with a different or previous pump in a DIY AID system.
  • Their prescribed pump (particularly in Europe and non-US places that have Bluetooth-enabled pumps) may be usable in a DIY AID.
  • They may not be getting their supplies through insurance, so their prescription doesn’t match what they are currently using.
  • Or, they have more urgent priorities to discuss at appointments, so it doesn’t come up.
  • Or, it’s also possible that it hasn’t come up because they don’t need any assistance or support from their healthcare provider.

Speaking of learning and support, it’s worth noting that in DIY AID, because it is open source and the documentation is freely available, users typically begin learning more about the system prior to initiating their start of closed loop (automated insulin delivery). As a result, the process of understanding and developing trust in the system begins prior to closed loop start as well. In contrast, much of the time there is limited available education prior to receiving the prescription for a commercial AID; it often aligns more closely with the timeline of starting the device. Additionally, because it is a “black box” with fewer available details about exactly how it works (and why), the process of developing trust can be a slower process that occurs only after a user begins to use a commercial device.

With DIY AID, because it is open source and the documentation is freely available, users typically begin learning more about the system prior to initiating their start of closed loop (automated insulin delivery). As a result, the process of understanding and developing trust in the system begins prior to closed loop start as well. In contrast, much of the time there is limited available education prior to receiving the prescription for a commercial AID; it often aligns more closely with the timeline of starting the device. Additionally, because it is a black box with less available details about exactly how it works (and why), the process of developing trust can be a slower process that occurs only after a user begins to use a commercial device. The learning & trust in AID timelines is something that needs more attention in commercial AID moving forward.

I closed my rebuttal section by asking a few questions out loud:

I wonder how healthcare providers feel when patients learn something before they do – which is often what happens with DIY AID. Does it make you uncomfortable, excited, curious, or some other feeling? Why?

I encouraged healthcare providers to consider when they are comfortable with off-label prescriptions (or recommending things that aren’t approved, such as Vitamin D), and reflect on how that differs from understanding patients’ choices to DIY.

I also prompted everyone to consider whether they’ve actually evaluated (all of) the safety and efficacy data, of which many studies exist. And to consider who benefits from each type of system, not only commercial/DIY but individual systems within those buckets. And to consider who gets offered/prescribed AID systems (of any sort) and whether subconscious biases around tech literacy, previous glucose outcomes, and other factors (race, gender, other demographic variables) result in particular groups of people being excluded from accessing AID. I also remind everyone to think about what financial incentives influence access and available of AID education, and where the education comes from.

Although Dr. Forlenza’s  rebuttal followed mine, I’ll summarize it here before finishing a recap of my rebuttal: he talks about individual selection bias/cherry picked data, acknowledging it can occur in anecdotes with commercial systems as well; talks about the distinction of regulatory approval vs. off label and unapproved; legal concerns for healthcare providers; and closes pointing out that many PWD see primary care providers, he doesn’t believe it is reasonable to expect PCPs to become familiar with DIY since there are no paid device representatives to support their learning, and that growth of AID requires industry support.

People probably wanted to walk out of this debate with a black and white, clear answer on what is the ‘right’ type of AID system: DIY or commercial. The answer to that question isn’t straightforward, because it depends.

It depends on whether a system is even AVAILABLE. Not all countries have regulatory-approved systems available, meaning commercial AID is not available everywhere. Some places and people are also limited by ACCESSIBILITY, because their healthcare providers won’t prescribe an AID system to them; or insurance won’t cover it. AFFORDABILITY, even with insurance coverage, also plays a role: commercial AID systems (and even pump and CGM components without AID) are expensive and not everyone can afford them. Finally, ADAPTABILITY matters for some people, and not all systems work well for everyone.

When these factors align – they are available, accessible, affordable, and adaptable – that means for some people in some places in some situations, there are commercial systems that meet those needs. But for other people in other places in other situations, DIY systems instead or also can meet that need.

The point is, though, that we need a bigger overlap of these criteria! We need MORE AID systems to be available, accessible, affordable, and adaptable. Those can either be commercial or DIY AID systems.

The point that Dr. Forlenza and I readily agree on is that we need MORE AID – not less.

This is why I support user choice for people with diabetes and for people who want – for any variety of reasons – to use a DIY system to be able to do so.

People probably want a black and white, clear answer on what is the ‘right’ type of AID system: DIY or commercial. It depends on whether a system is even AVAILABLE. Not all countries have regulatory-approved systems available, meaning commercial AID is not available everywhere. Some places and people are also limited by ACCESSIBILITY, because their healthcare providers won’t prescribe an AID system to them; or insurance won’t cover it. AFFORDABILITY, even if insurance coverage, also plays a role: commercial AID systems (and even pump and CGM components without AID) are expensive and not everyone can afford them. Finally, ADAPTABILITY matters for some people, and not all systems work well for everyone. The point is that we need a bigger overlap of these criteria! We need more alignment of these factors - more AID (DIY and commercial) available, accessible, affordable, and adaptable for people with diabetes. I support user choice for people with diabetes, which includes DIY AID systems

PS – I also presented a poster at #ADA2023 about the high prevalence rates of exocrine pancreatic insufficiency (EPI / PEI / PI) in Type 1 and Type 2 diabetes – you can find the poster and a summary of it here.

Exocrine Pancreatic Insufficiency (EPI/PEI) In Type 1 and Type 2 Diabetes – Poster at #ADA2023

When I was invited to contribute to a debate on AID at #ADA2023 (read my debate recap here), I decided to also submit an abstract related to some of my recent work in researching and understanding the prevalence and treatment of exocrine pancreatic insufficiency (known as EPI or PEI or PI) in people with diabetes.

I have a personal interest in this topic, for those who aren’t aware – I was diagnosed with EPI last year (read more about my experience here) and now take pancreatic enzyme replacement therapy (PERT) pills with everything that I eat.

I was surprised that it took personal advocacy to get a diagnosis, and despite having 2+ known risk factors for EPI (diabetes, celiac disease), that when I presented to a gastroenterologist with GI symptoms, EPI never came up as a possibility. I looked deeper into the research to try to understand what the correlation was in diabetes and EPI and perhaps understand why awareness is low compared to gastroparesis and celiac.

Here’s what I found, and what my poster (and a forthcoming full publication in a peer-reviewed journal!) is about (you can view my poster as a PDF here):

1304-P at #ADA2023, “Exocrine Pancreatic Insufficiency (EPI / PEI)  Likely Overlooked in Diabetes as Common Cause of Gastrointestinal-Related Symptoms”

Exocrine Pancreatic Insufficiency (EPI / PEI / PI) occurs when the pancreas no longer makes enough enzymes to support digestion, and is treated with pancreatic enzyme replacement therapy (PERT). Awareness among diabetes care providers of EPI does not seem to match the likely rates of prevalence and contributes to underscreening, underdiagnosis, and undertreatment of EPI among people with diabetes.

Methods:

I performed a broader systematic review on EPI, classifying all articles based on co-condition. I then did a second specific diabetes-specific EPI search, and de-duplicated and combined the results. (See PRISMA figure).

A PRISMA diagram showing that I performed two separate literature searches - one broadly on EPI before classifying and filtering for diabetes, and one just on EPI and diabetes. After filtering out irrelevant, animal, and off topic papers, I ended up with 41

I ended up with 41 articles specifically about EPI and diabetes, and screened them for diabetes type, prevalence rates (by type of diabetes, if it was segmented), and whether there were any analyses related to glycemic outcomes. I also performed an additional literature review on gastrointestinal conditions in diabetes.

Results:

From the broader systematic review on EPI in general, I found 9.6% of the articles on specific co-conditions to be about diabetes. Most of the articles on diabetes and EPI are simply about prevalence and/or diagnostic methods. Very few (4/41) specified any glycemic metrics or outcomes for people with diabetes and EPI. Only one recent paper (disclosure – I’m a co-author, and you can see the full paper here) evaluated glycemic variability and glycemic outcomes before and after PERT using CGM.

There is a LOT of work to be done in the future to do studies with properly recording type of diabetes; using CGM and modern insulin delivery therapies; and evaluating glycemic outcomes and variabilities to actually understand the impact of PERT on glucose levels in people with diabetes.

In terms of other gastrointestinal conditions, healthcare providers typically perceive the prevalence of celiac disease and gastroparesis to be high in people with diabetes. Reviewing the data, I found that celiac has around ~5% prevalence (range 3-16%) in people with type 1 diabetes and ~1.6% prevalence in Type 2 diabetes, in contrast to the general population prevalence of 0.5-1%. For gastroparesis, the rates in Type 1 diabetes were around ~5% and in Type 2 diabetes around 1.3%, in contrast to the general population prevalence of 0.2-0.9%.

Speaking of contrasts, let’s compare this to the prevalence of EPI in Type 1 and Type 2 diabetes.

  • The prevalence of EPI in Type 1 diabetes in the studies I reviewed had a median of 33% (range 14-77.5%).
  • The prevalence of EPI in Type 2 diabetes in the studies I reviewed had a median of 29% (16.8-49.2%).

You can see this relative prevalence difference in this chart I used on my poster:

The prevalence of EPI is much higher in T1 and T2 than the prevalence of celiac and gastroparesis.

Key Findings and Takeaways:

Gastroparesis and celiac are often top of mind for diabetes care providers, yet EPI may be up to 10 times more common among people with diabetes! EPI is likely significantly underdiagnosed in people with diabetes.

Healthcare providers who see people with diabetes should increase the screening of fecal elastase (FE-1/FEL-1) for people with diabetes who mention gastrointestinal symptoms.

With FE-1 testing, results <=200 μg/g are indicative of EPI and people with diabetes should be prescribed PERT. The quality-of-life burden and long-term clinical implications of undiagnosed EPI are significant enough, and the risks are low enough (aside from cost) that PERT should be initiated more frequently for people with diabetes who present with EPI-related symptoms.

EPI symptoms aren’t just diarrhea and/or weight loss: they can include painful bloating, excessive gas, changed stools (“messy”, “oily”, “sticking to the toilet bowl”), or increased bowel movements. People with diabetes may subconsciously adjust their food choices in response to symptoms for years prior to diagnosis.

Many people with diabetes and existing EPI diagnoses may be undertreated, even years after diagnosis. Diabetes providers should periodically discuss PERT dosing and encourage self-adjustment of dosing (similar to insulin, matching food intake) for people with diabetes and EPI who have ongoing GI symptoms. This also means aiding in updating prescriptions as needed. (PERT has been studied and found to be safe and effective for people with diabetes.)

Non-optimal PERT dosing may result in seemingly unpredictable post-meal glucose outcomes. Non-optimal postprandial glycemic excursions may be a ‘symptom’ of EPI because poor digestion of fat/protein may mean carbs are digested more quickly even in a ’mixed meal’ and result in larger post-meal glucose spikes.

As I mentioned, I have a full publication with this systematic review undergoing peer review and I’ll share it once it’s published. In the meantime, if you’re looking for more personal experiences about living with EPI, check out DIYPS.org/EPI, and also for people with EPI looking to improve their dosing with pancreatic enzyme replacement therapy – you may want to check out PERT Pilot (a free iOS app to record enzyme dosing).

Researchers, if you’re interested in collaborating on studies in EPI (in diabetes, or more broadly on EPI), please reach out! My email is Dana@OpenAPS.org

Air Quality, CO2 monitoring, and Situational Masking

I do a lot of things most people don’t want to do themselves – and I get that. (For example, recording macronutrients while running? Running for up to 16 or 25 hours? Let alone other choices like building DIY and making open source automated insulin delivery systems not only for myself but more widely available for other people.) I’ve also talked before about functional self-tracking and how I don’t track things for the sake of tracking, I track when the data/information is actionable either retrospectively or in real-time.

I’ve spent enough time now collecting real-time data on air quality (via a proxy of CO2 levels) that I think it would be useful to share for other people to consider the retrospective data for THEIR decision making.

You may not want (or be able to afford) a CO2 monitor, and you may not want to mask inside all the time, but the below outlines the general scenarios in which air quality tends to be better or worse and when you would get the most benefit from situational masking in response to those situations.

(Think about situational masking indoors like you think about situational masking for smoke and poor air quality outside. Most of the time, you likely don’t mask outside. But if you’re on the east coast right now or have lived through a previous west coast US summer with a “smoke season”, you’ve probably experienced multi-day air quality outside that was so poor that you considered or did wear a high-quality (N95/K95) mask outside or limit your time exposed to that outdoor air.)

Air quality assessment via CO2 monitoring

In the last few years, Scott and I acquired two different CO2 monitors. The first was cheap, required to be plugged into a battery pack to run it, and was simply viewable on the device display. It was useful to start to get a sense for what the CO2 levels were in indoor spaces as compared to outdoor air.

Later, we decided to invest in an Aranet CO2 monitor, which runs on two AA batteries and lasts months on a single pair of batteries. You can view the data on the device display AND see a retrospective and realtime graph of the data in your phone, because it connects via Bluetooth. You can see not only CO2 but also temperature, humidity, and air pressure.

We have found this useful because CO2 is something that we all produce when we breathe out. The more we breathe out, and the more people that are breathing out, the higher the CO2 levels. The more of that air that is replaced with low-CO2 outside air, the lower the CO2 levels. Measuring the CO2 then helps us understand the ventilation (how much air is flowing through the space and how often it is being cleared out) and the risks of being in that space. A higher CO2 level means more people and/or less air being cleared out of the space, meaning  you are more likely to be breathing in what someone else is breathing out.

How we evaluate CO2 levels

An outdoor CO2 level would be around ~450 ppm in urban areas, or as low as 400 ppm out in nature. Since a perfectly-ventilated space would be 100% outside air, we want to compare any indoor air CO2 reading to outdoor air.

For example, at home in our enclosed apartment with 2 people (and 2 cats), we typically run around 700 ppm, which means ~250 ppm above outdoor air levels. When we open our door or a window, it drops to ~500 ppm, or only ~50 ppm above outdoor air levels. Given that we have confirmed our air intake into our HVAC system for our apartment is outdoor air, this means the ~250 ppm we are sharing between the two of us is just our (and the cats) exhalations, rather than anyone outside our household. So those levels are acceptable to us, but our choice of interventions would change if we were sharing air with other people, especially random strangers. (Stranger danger is one way to think about air, further contextualized below with data.)

In a shared space with random strangers, your risk of COVID aerosol-based transmission is proportional to how elevated the CO2 level is above that of outside air, and the amount of time spent in that space. So a CO2 reading of 650 ppm, which is ~200 ppm over outside air, would be half as risky as a reading of 850 ppm, or ~400 ppm over outside air. And timing matters, so a 1 hour bus ride or the hour you spend boarding and waiting for takeoff on your plane when CO2 levels are highest and the air filtration (see below) is off will be of greater risk than short exposure to the same levels.

Now, we’ve also used our CO2 monitors in many other places, such as in airports and on planes and other public transportation, and other indoor shared spaces like grocery stores etc.

Here’s what we’ve learned about where CO2 levels trend (based on our repeated but n=1 testing).

Trains, buses, and rideshare (e.g. Uber, Lyft, etc) = BAD NEWS BEAR AIR

Public transportation, in every location and every country we have been in, has much higher CO2 levels.

What do I mean by much higher? Often 1000-1500 ppm easily (and sometimes >2000 ppm), which is anywhere from 500-1500 ppm above outdoor air quality.

Trains/metros/light rail where the doors are constantly opening and closing to outdoor air would seem like they would be better, but sometimes they still have (due to the density of riders) >1500 ppm.

Buses where you can’t open the window can be as high of CO2 levels as planes, without the benefit of air exchange or HEPA filtration of the air. Our recent 20 minute bus ride was up to >2500 ppm on a full bus.

Watch out for rideshares, too. Often times we get in a rideshare and the driver intentionally or accidentally has “recirc” or “recirculating air” on, meaning the air isn’t exchanged outside and the driver and riders are re-breathing each other’s air over and over and over and over again..yikes. Specifically looking at the console when you get in the car is useful: if you see the recirc button lit up, ask the driver to turn it off. If they don’t understand or refuse, or you don’t want to try to explain it, opening a window helps immensely to reduce the CO2 levels and the amount of re-breathing air. (The recirc icon usually looks like a car with a U-shaped arrow on it).

Planes (including airports, during boarding, in flight, and during landing/deplaning) = ALSO BAD NEWS BEAR AIR

Airports sometimes have better-ventilated spaces: you can often find less crowded corners of a terminal and see CO2 readings of <900 ppm. However, it’s still pretty common to be in the airport and see >1000 ppm, meaning that the CO2 is >500 ppm above outdoor air quality, and it is air from a whole assortment of random strangers coming and going, so it’s less safe than the air you’d be breathing in at home or in private spaces.

When boarding, both standing close in line with other people but also on the jet bridge and while you are on the plane, is usually even HIGHER CO2 levels than the airport. The typical air for a plane (that they tout with HEPA filters and high air exchange rates) is not turned on until you start to take off, and then it takes some time to exchange all of the air. This means there is a MUCH higher rate of re-breathing other people’s air while boarding and until you are in the air.

Now, we have measured CO2 levels during all of these times. If indoor airport air is around 900 ppm, it usually jumps to 1100-1300 on the jetbridge (if you’ve got a backed up line) and when you’re sitting on the plane watching other people board, it can go up to 2500+. And then it continues to go up as you have a full flight of people breathing in this enclosed space. During flight, we’ve seen CO2 levels hover between 1700-3000 ppm, and in some cases have gone up to ~4000 ppm. This is a lot of CO2! However, there are HEPA filters cleaning the ~half of the air that is recirculated instead of replaced. So, it’s harder to say when the airplane air systems ARE running (during most of the flight) whether the risk is as high (for infectious disease transmission) as it is in other environments that aren’t studiously exchanging and HEPA-filtering any recirculated air.

Note that when they spin down the engines after landing and all the way through taxiing, deplaning, and getting back into the airport – the CO2 level again tends to rise because they again change the air flow when they’re on the ground. So like standing in line to get on or waiting for other people to board, standing in line to get off/waiting for everyone to get off produces high CO2 levels *without the benefit of in-flight air exchange*, so it’s likely higher risk during those times than in the air during the middle of the flight, even if CO2 levels are equally high during flight.

Indoor spaces like grocery stores or conference rooms/meeting halls

Indoor spaces can vary quite a bit, and often by country or venue.

For example, most indoor spaces in the US we’ve found to often have a fairly high (e.g. 900+ ppm) indoor CO2 level, even without a huge density of people. For example, we quickly went into a grocery store the other day and the CO2 was high-800s without being around many people in the aisles, across the entire store. For not having people actively occupying the space, this is fairly high and less optimal.

In contrast, we recently were in Sweden for a conference and were honestly gobsmacked when we got off the plane and found the CO2 levels to be <600 ppm in the airport! And in the hotel lobby! And in the hotel elevator! And at the local grocery store!

(Seriously, it shocked us, because we’ve also recently been in the UK with our CO2 monitor and found US-like CO2 levels typically around 900-1000 ppm or higher, and also in Spain last year where we similarly found it to be >900 ppm even when not densely occupied. The exception to optimal air quality in Sweden was our ~20 minute bus ride where CO2 levels were >2500 ppm).

So, the CO2 levels may vary quite a bit and this is why measuring is helpful. Because you can’t assume that one country/one room means that all of the rooms in that country or even that venue will be the same.

Case in point? Conference rooms/halls or meeting rooms.

In Barcelona, Spain in April 2022, I spoke at a conference. The CO2 levels in the hallways and in the meeting room before the session started were around 800-900 ppm when not occupied. Again, a little high for not having people actively in the spaces. Then, when the conference started, Scott watched the CO2 monitor and saw it rise..and rise…and rise. Within 45 minutes, the CO2 levels were around 2000 ppm (>1500 ppm over outdoor air quality)! He went to the back of the room and opened the doors to try to get some air circulating in the room, although it didn’t make a big difference. That room did not have a high number of air exchanges per hour and was not successfully clearing out the air people were breathing out.

In Sweden (May 2023, where the CO2 was <600 in a lot of public indoor spaces), we found the same challenge in a high ceiling, large meeting hall. With 300 people, the start of the session had about 950 ppm (as opposed to the <600 ppm of less occupied hallways). Not too bad given 300 people in the space. However, by the end of the session, the CO2 level had risen to ~1350! And it continued to rise even as people had exited the room; we didn’t see a drop in CO2 levels until we went out in the hallway to continue talking to people, and it took another ~25 minutes before CO2 levels in the hallway were back down <600 ppm.

Again, we were surprised, because this venue (the hallways, lobby, elevator, etc) all had really great otherwise indoor air quality with CO2 <600 ppm!

But the challenge is the space (and the infrastructure for filtration and air exchanges); the number of people filling the space; and the amount of time, in terms of what happens to the CO2 levels.

The takeaway from this? Conference halls, meeting rooms, and anywhere where you are sitting with a group of people over a period of time is going to have a much higher CO2 level and it will increase in proportion to the time that you are occupying that space (e.g. a 30 minute or 1 hour session is going to have a much higher CO2 buildup than a 10 minute talk where the audience is turning over and leaving the room and it clears out some before the next session).

So what should you do about this information? Consider situational masking.

I really have found a CO2 monitor helpful, because even my best guesses about air quality (e.g. thinking Sweden’s conference hall would have good air quality given the size of the room and ceilings) aren’t always accurate. But if you don’t want to invest in a CO2 monitor, here’s where you can get the biggest bang for your buck with situational masking.

What do I mean by situational masking? Maybe you don’t think you’re at very high risk for COVID or other infectious illnesses, but you are interested in reducing the likelihood that you spread anything you get to other people (thanks!). But you don’t want to have to think about it, and maybe you’ve chosen previously to drop masking so you don’t have to think about it. Here’s a set of easy rules/situations in which, like learning to dump your liquids out before going through airport security, you can get into a habit of doing and not have to think about it much.

  • Public/shared transportation.

    Riding a bus, train, metro, or a car with a stranger and especially with multiple strangers – these have high CO2 levels.

  • Airports, boarding a plane and during takeoff, and during descent/landing/deboarding the plane.

    This is when the CO2 levels are highest and the air exchanges/HEPA filtration is not running.

    Think of it like the seatbelt sign. You board the plane and put your seatbelt on, then eventually once you’ve reached cruising altitude the seatbelt sign goes off. If you’re standing in a line of people (to board or deplane) OR if the seatbelt sign is ON, that’s a huge ROI for wearing a high-quality (N95 or KN95) mask. When the seatbelt sign first turns off during the flight (or you hear the 10k-feet chime) and you want to take and leave it off, or take it off a while to eat or drink – that’s less risky during those times due to the HEPA filtration and air exchanges during flight. But when the seatbelt sign goes on for the plane’s final descent? The air quality is going down, too, so putting your seatbelt AND your mask back on is a higher ROI thing to do.

    (You do you inside the airport, too, but see below about density of people and temperature as a guide for whether you might want to consider situational masking in airports when you’re not eating/drinking.)

 

  • Conferences or meetings where you are sitting for more than a few minutes and there are many people in the room.

    Even with super big rooms and super high ceilings, so far every conference space I’ve presented in during the last several years has high CO2 levels even before the talk starts, and is even higher (>500-1000 ppm added) by the end of the session). If you’re not presenting or eating and drinking and are just sitting there listening and engaging in the session…it’s a low hassle opportunity to pop a high-quality mask on so you’re not breathing so much of the air around you from everyone else. When you’re done with the session and head out and want to socialize? Like leaving the plane, you’ll be around fewer people, and the CO2 levels (and risk) goes down. But sitting there quietly is a great time to wear a good mask and reduce your intake of other people’s exhalations.

 

You might find yourself in situations where the room feels hot and stuffy, or in the case of conferences and meetings, the air feels FREEZING cold. It runs freezing cold because the room gets hot and stuffy with so many people, indicating this space is not well ventilated, so they pump the AC to change the temperature. But that is a compensation for a too-low rate of air exchanges, and pre-cooling doesn’t prevent CO2 and aerosol buildup, so a room that either gets freezing cold or hot and stuffy should be a signal that the air quality likely isn’t ideal.

So a good rule of thumb is, if you’re in a space that feels hot and stuffy OR freezing cold, that’s an indicator that the air quality might be non-optimal. Consider masking in those situations even if you don’t have a CO2 monitor to evaluate the air.

It would be great if we could get 10x people to consider situational masking like this. Avoid the worst of the bad-news-bear-air of public and shared transportation and indoor spaces, which would cut down on a lot of transmission, even if people otherwise are still socializing and eating in indoor spaces and doing whatever it is they want to do. The choice to situationally mask might occasionally protect them but would also protect everyone around them in those situations when their exhalations have the greatest risk of doing the most damage.

A good way to think about it is at a conference. You might be willing to go to bars and socialize, but someone who is higher risk may be choosing not to attend those indoor dining scenarios. That’s fine: you each get to make your own choices! But when you go and sit down next to that person in a conference session, your choices then influence that person by every breath you take in that conference session.

That’s why situational masking – knowing that a situation is low-hassle to wear a high-quality mask (sitting quietly in a session) but high-risk (due to the poor air quality) means you have a high ROI to pull a mask out of your pocket/bag and throw it on – can help the people around you very effectively with little hassle and thought on your part.

You can get in the habit of masking in the bad-news-bear-air situations/locations, and you don’t have to think much about it. You’ll make things a bit safer for yourself and for the people around you, for far less hassle than avoiding buying a drink before you go through airport security because you know you need to dump liquids out.

Data-driven situational masking based on indoor air quality