CGM

Why Open Humans is an essential part of my work to change the future of healthcare research

by Dana Lewis

I’ve written about Open Humans before; both in terms of how we’re creating Data Commons there for people using Nightscout and DIY closed loops like OpenAPS to donate data for research, as well as building tools to help other researchers on the Open Humans platform. Madeleine Ball asked me to share some more about the background of the community’s work and interactions with Open Humans, along with how it will play into the Opening Pathways grant work, so here it is! This is also posted on the OpenHumans blog. Thanks, Madeleine, and Open Humans!

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So, what do you like about Open Humans?

Health data is important to individuals, including myself, and I think it’s important that we as a society find ways to allow individuals to be able to chose when and how we share our data. Open Humans makes that very easy, and I love being able to work with the Open Humans team to create tools like the Nightscout Data Transfer uploader tool that further anonymizes data  uploads. As an individual, this makes it easy to upload my own diabetes data (continuous glucose monitoring data, insulin dosing data, food info, and other data) and share it with projects that I trust. As a researcher, and as a partner to other researchers, it makes it easy to build Data Commons projects on Open Humans to leverage data from the DIY artificial pancreas community to further healthcare research overall.

Wait, “artificial pancreas”? What’s that?

I helped build a DIY “artificial pancreas” that is really an “automated insulin delivery system”. That means a small computer & radio device that can get data from an insulin pump & continuous glucose monitor, process the data and decide what needs to be done, and send commands to adjust the insulin dosing that the insulin pump is doing. Read, write, read, rinse, repeat!

I got into this because, as a patient, I rely on my medical equipment. I want my equipment to be better, for me and everyone else. Medical equipment often isn’t perfect. “One size fits all” really doesn’t fit all. In 2013, I built a smarter alarm system for my continuous glucose monitor to make louder alarms. In 2014, with the partnership of others like Ben West who is also a passionate advocate for understanding medical devices, I “closed the loop” and built a hybrid closed loop artificial pancreas system for myself. In early 2015, we open sourced it, launching the OpenAPS movement to make this kind of technology more broadly accessible to those who wanted it.

You must be the only one who’s doing something like this

Actually, no. There are more than 400+ people worldwide using various types of DIY closed loop systems – and that’s a low estimate! It’s neat to live during a time when off the shelf hardware, existing medical devices, and open source software can be paired to improve our lives. There’s also half a dozen (or more) other DIY solutions in the diabetes community, and likely other examples (think 3D-printing prosthetics, etc.) in other types of communities, too. And there should be even more than there are – which is what I’m hoping to work on.

So what exactly is your project that’s being funded?

I created the OpenAPS Data Commons to address a few issues. First, to stop researchers from emailing and asking me for my individual data. I by no means represent all other DIY closed loopers or people with diabetes! Second, the Data Commons approach allows people to donate their data anonymously to research; since it’s anonymized, it is often IRB-exempt. It also makes this data available to people (patient researchers) who aren’t affiliated with an organization and don’t need IRB approval or anything fancy, and just need data to test new algorithm features or investigate theories.

But, not everyone implicitly knows how to do research. Many people learn research skills, but not everyone has the wherewithal and time to do so. Or maybe they don’t want to become a data science expert! For a variety of reasons, that’s why we decided to create an on-call data science and research team, that can provide support around forming research questions and working through the process of scientific discovery, as well as provide data science resources to expedite the research process. This portion of the project does focus on the diabetes community, since we have multiple Data Commons and communities of people donating data for research, as well as dozens of citizen scientists and researchers already in action (with more interested in getting involved).

What else does Open Humans have to do with it?

Since I’ve been administering the Nightscout and OpenAPS Data Commons, I’ve spent a lot of time on the Open Humans site as both a “participant” of research donating my data, as well as a “researcher” who is pulling down and using data for research (and working to get it to other researchers). I’ve been able to work closely with Madeleine and suggest the addition of a few features to make it easier to use for research and downloading large data sets from projects. I’ve also been documenting some tools I’ve created (like a complex json to csv converter; scripts to pull data from multiple OH download files and into a single file for analysis; plus writing up more details about how to work with data files coming from Nightscout into OH), also with the goal of facilitating more researchers to be able to dive in and do research without needing specific tool or technical experience.

It’s also great to work with a platform like Open Humans that allows us to share data or use data for multiple projects simultaneously. There’s no burdensome data collection or study procedures for individuals to be able to contribute to numerous research projects where their data is useful. People consent to share their data with the commons, fill out an optional survey (which will save them from having to repeat basic demographic-type information that every research project is interested in), and are done!

Are you *only* working with the diabetes community?

Not at all. The first part of our project does focus on learning best practices and lessons learned from the DIY diabetes communities, but with an eye toward creating open source toolkit and materials that will be of use to many other patient health communities. My goal is to help as many other patient health communities spark similar #WeAreNotWaiting projects in the areas that are of most use to them, based on their needs.

How can I find out more about this work?
Make sure to read our project announcement blog post if you haven’t already – it’s got some calls to action for people with diabetes; people interested in leading projects in other health communities; as well as other researchers interested in collaborating! Also, follow me on Twitter, for more posts about this work in progress!

Next generation #OpenAPS hardware work in progress – Pi HATs

by Dana Lewis

tl;dr – No, they’re not here yet, but this is coming soon! Yay for new & more hardware options! See here to pre-order an Explorer HAT, eta of April 2018

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Over the years, people have had a lot of awesome ideas on how

to improve the hardware that can be used with DIY closed looping. One such example, Oskar’s work with mmeowlink, led us to later work on smaller computer boards with built-in radio stick, aka the Edison/Explorer Board rig. We started working on that last fall; they were produced and available around November, and the community has been using those widely ever since.

However, like all things, the Edison/Explorer is not without it’s downsides. One of which is – there’s no screen. You historically have needed to plug in cables, or remote login to the rig, or have connectivity via your phone, to see what it’s doing. Sometimes this is more annoying than others.

Patrick Kelly, who has a daughter with T1D and began experimenting with OpenAPS, was one of the folks who wanted a screen on the rig. He suggested the idea, which Scott and I thought was awesome – but we don’t have the expertise to build that kind of hardware. Luckily, Patrick and his dad Jack Kelly, *do* have that expertise! They began exploring some of the options around creating a rig with a screen.

(This is one of my favorite parts of the OpenAPS community, where people bring in various types of expertise and we’re all able to collaborate to make everything from hardware and software and usability improvements!)

And at the same time…the rumors became reality, and we learned that Intel has decided to discontinue the Edison module. SAD PANDA. (Intel, if you’re reading this, please bring it back! We love the Edison!) That expedited the need to find the next generation hardware. Luckily, Patrick and Jack had been progressing on the screen, focusing on incorporating it into a “HAT” (board) for the Raspberry Pi. So after discussion with others in the community about pros/cons and availability about various other computing options other than the Pi, given the widespread availability of different types of Pi’s, we’ve decided to move forward with the Pi and a HAT (board) being the most usable option for the next round of hardware that we’ll be recommending to the community.

What exactly does a Pi HAT look like?

I’m so glad you asked 😉 Here is the Pi HAT with screen on a “Pi Zero W” (which I sometimes type as “Pi0” or “Pi 0”) and a “Pi 3” (pi three), compared to the Edison/Explorer Board. My trusty Chapstick is my unit of measurement, but given some of my international friends claim to not understand that yardstick, I threw in some Euro coins on the right as another measurement stick .;)

OpenAPS_hardware_development_Oct_2017_DanaMLewis
The Pi 0 is flipped on it’s back like a turtle – but the same Pi HAT can be used for the Pi 0 and the Pi 3. The HAT is bigger than the Pi so the radio stick doesn’t get blocked.

It’s the same radio as the Edison-based Explorer block, so same expected range.

What’s the point of the screen?

With a screen, you can easily see the logs of what the loop is doing: Pi_HAT_screen_OpenAPS_example_DanaMLewis

YOU CAN EASILY ADD AN OPEN WIFI NETWORK ON THE GO! (Yea, that all caps was intentional! :)). You can also see which wifi network it is on, check for IP address, etc.

Pi HAT adding wifi exampleWe’re still working on adding to the menus and playing around with what’s possible and what’s worthwhile for displaying on the menus by default.

You can do all kinds of fun stuff – which Scott found out after asking me one day, “what else should we add to the menu?” and I promptly said “a unicorn”. Scott said, “these don’t have emoji’s, though”.

Five minutes later, we have a DIY diabetes/OpenAPS unicorn built in ASCII, because why not? 😉

Pi_HAT_screen_unicorn_closeup_DanaMLewis

Ahem. Back to technical topics.

How is this board/HAT going to be made and when is it going to be available?

Like the Edison-based Explorer, the Pi’s Explorer HAT is an open source hardware design, and ERD (who sold the Explorer for the Edison) will also be doing the Pi HAT.

Timeline is not 100% nailed down yet, but it will probably be another month or so. (Which is about a year after the Edison Explorer was first ready…crazy how time flies in the open source community!) We’ll of course, as always, shout from the rooftops when it’s ready for ordering & experimenting with. We’ll also be updating the OpenAPS docs to reflect the new gear recommended to buy, the steps for getting it up and running, troubleshooting, etc.

What about Edison/Explorer boards? Will that rig type still be supported by OpenAPS? Should I get any more of those?

Yep. Edison/EB will still be supported & widely used. There are some still left.

  • But – if you already have an Edison/EB rig – I would make your next rig purchase a HAT for one of the Pi’s.
  • If you’re new to the OpenAPS community and supply still exists, I’d still consider grabbing the parts for an Edison/Explorer rig – they’re still great, and we’ll continue to use the ones we have for a long time, and will still be supported in documentation. But you’ll likely want a HAT for a Pi rig of some sort, too, to take advantage of the screen & all the features that go with that for ease of use.

What about battery life for the Pi0/Pi3? How fast does it run? AND YOU HAVEN’T ANSWERED ALL OF MY OTHER QUESTIONS?!?!

One of the downsides of our (Scott/my) approach of getting everything to the community as fast as possible – both hardware and software – means that sometimes (every time) we share things that are works in progress. (And we are testing a whole lot of stuff on software, too.) The new hardware is no different. We don’t have all the answers yet, and we’ll hope you’ll help us figure things out as we go! Here’s some of the pending questions we have:

  • Cost. (Pi’s are cheaper than Edison’s. Explorer HATs with screens are slightly more expensive. However, we’re expecting in sum that the HAT+screen rigs with Pi of choice will likely be cheaper than an Edison/Explorer.)
  • Battery life. We know the Pi0 itself is not as efficient as the Edison, so it’ll likely require a bigger battery for the same run time. (No idea exactly how much bigger because I’m not using these rigs in the real world 100% of time yet, because…)
  • Some Pi optimizations still need to be done. (The current code works just fine on a Pi3, but the Pi0 needs some optimization work done. The Pi 0, as you can see from the picture, is smaller, and will likely be the ‘mobile’ rig for many folks, while the Pi 3 might be a backpack/home rig.)
  • Other options for “HATs” that don’t have a screen. (Eric has also been prototyping another Pi HAT, that doesn’t have a screen, and it’ll be great to test and see how that works as a potential option, too. Hop into the openaps/hardware-dev channel to chat with him if you have questions about his approach. )

As we work on the optimizations (great place to dive in if you’re looking for a place to help out!) and updating the scripts and the docs to reflect the Pi suite of options, I’ll begin carrying this kind of rig and doing my usual break-everything-in-the-real-world-and-fix-all-the-things testing approach.

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I’m excited. It’s so great to have this kind of collaboration with expertise in so many areas, with everyone centered on the goal of making life with diabetes easier and safer! Shout out to the Kelly family & their colleagues for all the work on the screen & HATs; to Scott for a lot of development work on both hardware and software side; to Morgan & ERD for continuing to be a part of making great open hardware more widely available; and many other people who are working on bits and pieces to make everything possible!

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January 2018 update: rigs are still evolving! You can pre-order an Explorer HAT, eta of shipping is April 2018.

Showing the size of the Explorer "HAT" board next to chapstick for size comparison

See the openaps-menu software code here; and the Explorer HAT hardware repo is here.

How I change pump sites

by Dana Lewis

Last year, I wrote about how I “pre-soak” CGM sensors for better first-day BGs. That’s something I started doing years ago whenever possible.

Similarly, in the last few years, I’ve also changed how I change my pump sites with similar goals of improved outcomes, whenever possible.

What I used to do (i.e. for 12+ years):

  • Pull out pump site
  • Take shower
  • Put in new pump site
  • If the pump site didn’t work, spend all night high, or the next hours high while I debated whether it was just “slow” or if I needed a second new site. Ugh.

What I decided to start doing and have done ever since (unless a site gets pulled out by accident):

  • On day 3 when I decide to change my pump site, I do not take my “old” pump site out before my shower.
  • After my shower, I leave in the old pump site and put the new pump site on. Which means I am wearing TWO pump sites.
  • Put the tubing on the new site etc. as expected. But because I have the old site on, if I start to see BGs creep up, I can do one of two things:
    • 1) Swap tubing back to old site, give a bolus or a prime on the old site, then switch tubing back to new site. (I do this if I think the new site is working but “slow”)
    • 2) Swap tubing back to old site, ditch the new site, and then insert a second “new” site (or wait until the next morning to do so when I feel like it)
  •  Otherwise, if BGs are fine, I pull the “old” site out once I confirm the new site is good to go.

Is this method perfect? Nope. Does it usually help a lot when I have a new site that is kinked or otherwise a dud? Yup.

To me, it’s worth keeping the old site on for a few (or even ~12) hours. I know many people may not like the idea of “wearing two sites”. But it’s not wearing two sites for 3 days. And if you find yourself having a lot of kinked sites – that’s why and when I switched over to this approach.

YDMV, always. But hope this (post-soaking?) of pump sites, like the idea of pre-soaking CGM sensors, is helpful to someone else.

Not bolusing for meals (Fiasp, 0.6.0 algorithm in oref0 dev branch, and more)

by Dana Lewis

I tweeted last week+, “I just realized I’ve now gone about 3 weeks without meal bolusing.” That means just a meal announcement (i.e. carb entry estimate, a la 30 carbs or 60 carbs or whatever, based on my IFTTT buttons). No manual bolus.

Highlighting 3 weeks without meal bolusing, and just doing a carb announcement, with good outcomes thanks to OpenAPS

I kind of keep waiting for the other shoe to drop, because it sounds to good to be true. I’m sure you’re skeptical reading this.

I bet she’s doing SOME bolus.

Well, she must not be eating any carbs.

She must be having worse outcomes, bad post-meal BGs, etc.

Nope, nope, and nope.

  • While I started testing this new set of features with partial boluses and worked my way down (see more below on the testing topic), I’m now literally doing no manual meal bolus. I start eating, and press one button on my watch for a carb estimate entry (that via IFTTT goes to Nightscout and my rig).
  • I eat carbs. I’ve eaten 120 grams of carbs of gluten free biscuits and gravy; 60-90 grams of pasta; dinner followed by a few gluten free cookies, etc.
  • More nuanced details below, but:
    • My 70-180 time in range has stayed the same (93+%) compared to the versions I was testing before with manual meal boluses.
    • My 70-150 and 80-160 time in ranges have decreased slightly compared to manual meal boluses, but…
    • My average blood sugar has actually dropped down (as has my a1c to match).
    • (So this means I’m having a few more spikes above 160, usually topping off in 160-170 whereas before my manual meal boluses would have me top off around 150, when all was well.)

Also note – no eating soon required. No early bolus or pre-bolus. Just single button press as I stick food in my mouth.

Wow.

(See where I said, waiting for the other shoe to drop?)

That’s why I waited a while to even tweet about it. Maybe it’s a fluke. Maybe it won’t work for other people. Maybe, maybe, maybe. Who knows. It’s still fairly early to tell, but as other people are beginning to test the current dev branch of oref0 with 0.6.0-related features, other people are starting to see improvements as well. (And that could be some of the many other features we are adding to 0.6.0, ranging from exponential curves for insulin activity, to allowing SMBs to do more, to carb-ratio-tuned-autosensitivity, to huge autotune improvements, etc.) 

So while I don’t want to over-hype – and never do, what works for me will not work for everyone – I do want to share my cautious excitement over continuing to be able to push the envelope on algorithms and what might be possible outcome-wise for this kind of technology.

Suggesting no meal bolus means we can quit arguing about the name "artificial pancreas"

Here’s what is enabling me to be in the no-bolus zone for now well over a month, with still (to me) great outcomes worth the tradeoffs described above:

  1. Faster insulin. Thanks to our lovely looping friends in Germany/Austria, we came back from Europe with a few vials of Fiasp to try. I was HIGHLY skeptical about this. Some of our European friends saw great results right away, others didn’t. I didn’t get great results on it at first. Some of that may be due to natural changes between insulin types and not knowing exactly how to adjust my manual bolus strategy to the faster insulin action, but until we did some code changes to allow SMB‘s to do more and added some other features to what’s now 0.6.0, I wasn’t thrilled and in fact after about two weeks of it was about to switch off of it. So that brings me to #2.
  2. More improvements to the algorithm, which is now what will become the 0.6.0 release of oref0. There’s a whole lot of stuff packed in there. Exponential curves. Different carb absorption decay calculations. Allowing SMB to do more. Additional safety guards since we ramped SMB up.

How we started testing no-bolus approach:

  • I have always known that about 6u of insulin (thanks to testing dating back to my early DIYPS days, many many many moons ago) is about as much as I should bolus at any time. So, even if I ate 120 carbs, I usually did about a 6u bolus up front, and let the rig pick up the rest as needed over more hours. I started doing ~75% or something like that of boluses, based on wherever I felt like rounding to with my easy bolus buttons.
  • Whether I did 75% or 100%, I didn’t see a ton of difference at first…
  • ..so I took a leap and tried no-bolus with some SMB adjustments to allow it to ramp up faster with carb entry. Behaviorally, I find it a lot easier to do nothing 😀 vs. figure out the right amount of up front bolus. And outcomes wise (see above) it was very similar.

It definitely was an interesting approach to test. Between the Fiasp and the no-bolus up front, in some meals it matched really well and I had practically no rise. Due to incoming netIOB, food type, etc, sometimes I did have a rise – but while it spiked slightly higher (160-170 usually vs my earlier 150s with manual bolus), it was only up there for 2-3 data points and then came sharply down, leveling out smoothly in my preferred post-meal range. So an important lesson I learned was not to over-react to just the BG curve going up, without looking at the predictions to see where I was going to come just back down. (And as I had more than one meal where the spike and drop back to normal happened, it was very easy to adjust to the BG graph and not get that emotional tug to “do more” with a quick short rise like that).

Obviously, starting BG makes a difference. I’m usually starting <130 mg/dL when I see these spikes cap out at 170 or lower. I’ve started higher, and seen higher rises, too. They’re not all perfect: with occasional pump site issues, carb underestimates, unplanned carb stacking, and all the randomness of diabetes and a non-structured lifestyle (including live-testing bleeding edge algorithm changes), I’ve spent 12% of the last month >160 mg/dL, which is about the same as the 3 months before that. But in most cases (I’d say 95%), the no-bolus approach has actually yielded better outcomes than I expected AND has avoided post-meal lows better than I would have achieved with a manual bolus.

This is huge when you think about the QOL aspect of not having to do as much math at a meal; and when you think about all the complicating factors related to food – timing (do you bolus when you order, or when the food arrives, or earlier than that?), and the gluten factor. I have celiac disease, so if I’m eating out (which we do a lot, and especially since I travel frequently), bolusing prior to setting eyes on the food (knowing they didn’t plate it with bread, causing them to have to go back and start all over again) just isn’t smart. That’s why eating soon historically worked so well for me vs. traditional pre-boluses, because I could set the target entering the restaurant, bolus when I laid eyes on my hopefully safe food, and get reasonable (150 topping out) meal outcomes.

It also worked really well in the case where a restaurant cooked my gluten free pasta in the same pasta cooker and water as regular pasta, but didn’t inform me until after I found stray gluten noodles in the bottom of my pasta dish and started asking how that was possible since they (used to) do gluten free well. (Now, I pick up heaps of pasta, and sort pasta noodles one by one to make sure they all match before ever eating gluten free pasta. It makes waiters look at you very worriedly as you wave pasta around in the air, but better safe than glutened (again).) So, I was majorly glutened, and my digestion system was all out of sorts (isn’t that a nice polite way to describe getting glutened?) for many days, which of course impacted BG and insulin right then and for the days afterward. But because I had done carb entry and no-bolus, I was able to edit the carb entry down, and I didn’t have that much insulin stacked, and didn’t end up low after glutening, which is usually what happens.

Is that a super regular situation for most people? No. But it was super nice. And also helped me face pasta again last night, so I could put in a (very low in case of gluten) carb estimate, match my noodles, eat pasta, and let the SMBs ramp up to match absorption. It works very well for me.

Example BG graph from only announcing, not bolusing for, a meal with OpenAPS

Whether you have celiac or not, for many reasons (insert yours here), it’s nice to not to have to commit to the bolus up front. It’s closer to approaching what I think non-PWDs do at mealtimes: just eat.

(I haven’t done much testing (yet? TBD) for no-carb-entry and no-meal-bolus scenario, I expect I would have higher spikes but would be interesting to see if it would still come down reasonably fast. Probably wouldn’t be my go-to strategy because I don’t mind a general meal size estimate one button push, but would be nice to know what that curve shape would look like. If I test that, it’ll start with small snacks and ramp my way up.)

The questions I always get:

  1. Q: HOW DO I GET THIS?
    A: Caution: like all things OpenAPS but especially always true for the development branch, 0.6.0 is NOT released yet to master and is still highly experimental. I wouldn’t install dev unless you want to pay lots of close attention to it, and are willing to update multiple times over the course of the week, because Scott and I are merging features and tweaks almost daily to it.

    Got the disclaimers down? Ok. It’s in the dev branch of oref0. You should read this PR with notes on some more detail of what’s included, but you should also review the code diff to see all that’s changed, because it’s not all documented yet. Also, follow the instructions at the bottom to be able to install it without git. Hop into Gitter if you have questions about it!

    (Big huge thanks to folks like Tim and Matthias for early testing of 0.6.0; and to Tim for writing up about the initial rounds of 0.6.0-dev here (note that we’ve made further changes since this post), and others who’ve been testing & providing feedback and input into the dev branch!)

  2. Q: When will this get “released” to master?
    A: It depends. This is still a highly active dev branch, and we’re making a lot of changes and tweaking and testing things. The more people who test now and provide feedback will enable us to get to the final “prepare for release” testing stage. Lots and lots of testing, and things depend on how much existing needs tweaked, and what else we decide should go with this release. So, there’s never any specific release date.
  3. Q: What is Fiasp?
    A: Faster acting insulin that was only approved in Europe and Canada…until today. Convenient timing. I asked a PR person who messaged me about it, and they said it’s estimated to be available in U.S. pharmacies by late December/earlier Q1. As previously stated, available elsewhere in other parts of the world.

    Fiasp peaks sooner (say, ~45 minutes) with the same tail as everything else. It’s not instantaneous. For your million and one questions about whether it’s approved for your use in a tree, on a plane, at the zoo, and all other extrapolations – please ask Google/your doctor/the manufacturer, and not me. I don’t know. :)

  4. Q: Will any of this work for people NOT on Fiasp?
    A: Nothing is guaranteed (even for other people on Fiasp), but the folks who’ve started testing 0.6.0 even without Fiasp (on Humalog or Novolog/Novorapid, etc.) have been happier on it vs. earlier versions, too.

    I don’t expect Fiasp to work super well forever for me, given what I’ve heard from other people with months of experience on it…and given my first two weeks of Fiasp not being spectacular, I want people to not expect miracles. (Sorry, this blog post does not promise miracles, so sorry if you got super excited at the above. No miracles! This is not a cure! We still have diabetes!) Like all things artificial pancreas, I think it’s better to be cautiously hopeful with realistic expectations that things *might* be a little bit better than before, but as always, YDMV (your diabetes may/will always vary), your body will vary, and life happens, etc. so who knows.

Just 4 months ago, we published a blog post pointing out that the new features had allowed us to achieve 4 out of 5 of: no bolus; not counting carbs, medium/high carb meals, 80%+ time in range; and no hypoglycemia.  With Fiasp and  0.6.0 (currently what’s in the dev branch), we’ve now achieved all 5 simultaneously: I can eat large high-carb meals, enter very vague guesstimates of 60 or 90 carbs (no need for actual carb counting, just general size-based meal announcement), and still achieve 80%+ time in range 70-150 mg/dL without ever going <55 mg/dL.  Does that mean that OpenAPS with Fiasp finally meets the definition of a “real” Artificial Pancreas (step 5 on JDRF’s 6-step AP development pathway)?  We think it does.

So, tl;dr (because long post is long): with Fiasp and 0.6.0-dev branch, I’m able to not bolus for meals, and just enter a very generally sized meal estimate. It’s working well for me, and like all things, we’re working to make it available to other people via OpenAPS for others who want to try similar features/approaches. It may not work well for everyone. If it helps one other person, though, like everything else it’ll be worth it. Big thanks to Scott for LOTS of development in 0.6.0 and partnership in design of these features; too many people to name for testing and providing feedback and helping iterate on these features; and to the entire community for being awesome and helping us to continue to push the envelope on what might be possible for those of us with type 1 diabetes. :)

Why a non-academic (patient) publishes in academic journals

by Dana Lewis

Today I was able to share that my Letter to the Editor was published in the Journal of Diabetes Science and Technology. It’s on why we need to set expectations to help patients successfully adopt hybrid closed loop/artificial pancreas/automated insulin delivery system technology. (You can read it via image copies in the first link.)

JDST_screenshot_LTE_expectationsI’ve published a few times in academic journals. Last year, Scott and I published another Letter to the Editor in JDST with the OpenAPS outcomes study we had presented at the 2016 ADA Scientific Sessions conference.

But, I’m sure people are wondering why I choose to do so – especially as I am 1) a patient and 2) a non-academic. (Although in case you missed it – I’m now the Principal Investigator on a grant-funded study!)

While there are many healthcare providers, researchers, industry employees, FDA staff, etc. who read blogs like this and are up to speed on the bleeding edge of diabetes technology… there are easily 10x the number that do not.

And if they don’t know about the existence of this world, they won’t know about the valuable lessons we’re learning and won’t be able to share those lessons and knowledge with other healthcare providers and the patients that they treat.

So, in my pursuit to find more ways to share knowledge from our community with the rest of the diabetes community, this is why we submit abstracts for posters and presentations to conferences like ADA’s Scientific Sessions. Our abstracts are evaluated just like the abstracts from traditional healthcare providers (as far as they can tell, I’m just another academic, albeit one with fewer credentials ;)), and I’m proud that they’re evaluated and deemed worthy of poster presentations alongside mainstream researchers. Ditto for our written publications, whether they be letters to the editor or other types of articles submitted to journals and publications.

We need to find more ways to share and distribute knowledge with the “traditional” medical and academic research world. And I’d love to do more – so please share ideas if you have them. And if you’re someone who bridges the gap to the traditional world, I appreciate your help sharing these types of articles and conversations with your colleagues.

What you should know about closed looping (DIY like #OpenAPS or otherwise)

by Dana Lewis

I’ve been wearing a DIY closed loop for something like 979 days..which means something like ~20,000 hours with this technology. Additionally, I’m not the only one. At the time of writing this post (see the latest count here), there are (n=1)*369+ (and that’s an undercount just based on who’s told us they’re looping) other DIYers out there, so the community has an estimated 1,800,000+ hours of cumulative experience, too.

Suffice to say, we’ve all learned a lot about this technology and how hybrid closed loop makes a difference in life with diabetes.

I previously gave a talk almost two years ago to the Sports & Diabetes Group Northwest here in Seattle, talking about #DIYPS, how we closed the loop, and #OpenAPS. (And you can see a recent TEDX talk I gave on OpenAPS here.) That was a springboard for meeting some awesome individuals who became very early DIY loopers in the Seattle area. And one of them (who also wore a pancreas at HIS wedding :)) had suggested we do another talk for SDGNW to update on some of what we have learned since then. But unfortunately, he got called out of town for work and couldn’t join me for presenting, so I went solo (ish, because Scott also came and contributed). I used a new analogy, because I think there’s a lot to think about before choosing and using closed loop technology, whether it’s DIY or commercial, and wanted to write it up for sharing here.

what_to_know_about_looping_danamlewis

First, some reminders for those familiar and some context for those who are not close to this technology. We’re talking about a hybrid closed loop, which is what I’m referring to when I say “artificial pancreas” or “AP” here. This type of technology makes small adjustments every few minutes to provide more or less insulin with the goal of keeping blood glucose (BG) levels in range. It’s complicated by the fact that insulin often peaks at 60-90 minutes…but food hits in ~15 minutes. So there’s often “catch up” being done with insulin to deal with food eaten previously, and also with hormones and other things that impact BGs that aren’t measurable. (This is also why it’s called hybrid, because for best outcomes people will still be doing some kind of meal announcement/bolus to deal with insulin timing.) As a result, even with pumps and CGMs, diabetes is still hard. A closed loop can do the needed math every five minutes, doesn’t go to sleep, and is very precise. It can respond more quickly (because it’s paying attention) than a human will in most situations, because we’re out living our lives/working/sleeping and not paying attention ONLY to diabetes. It’s not a cure, but it helps make living with diabetes better than it used to be.

However, I equate it to being a pilot who has seen technology on planes evolve to include “autopilot”. Even with hybrid closed loop technology, we’re still flying the “plane”.

looping_is_like_flying_plane_danamlewis

Here’s what I mean. There are stages for picking out and deciding to use the technology; preparing to use it/getting in the mode where you CAN use it; using it successfully; getting ready for the times when you can’t use it; and smoothing the way for the next time you use it.

It’s not perfect 24/7, you see, because we’re still using pump sites and continuous glucose monitor (CGM) sensors. The CGM sensor may last for 7 days, but then you have to change it out (or cough restart it cough), and you have a gap in data, which means you can’t loop. So you have this type of cycle regularly, and here’s what you need to know about each of these stages, regardless of whether we’re talking about DIY (like OpenAPS) or a commercial closed loop solution.

Preparing for takeoff

prepare_for_looping_danamlewisWhen you’re getting into the plane, you have a flight plan. You know when you will and won’t use the technology on board. Same for diabetes & closed looping. Make sure to think about the following for your tech of choice:

When will your loop work? When does it not? What happens if it breaks? What are your back up tools? How do you operate it: what happens if your sensor loses data, or you don’t calibrate? How does the algorithm work? What will it target your BG to be? What behaviors will you have to do (meal bolus or announcement, etc.) and how can you alter those to optimize performance? Also, what are the warning signs of failure to let you know when you need to take additional action with corrective insulin or eating carbs?

Taking off and the new technology learning curve

taking_off_learning_curve_danamlewisJust like switching from MDI pump (or even iPhone to Android and vice versa), you have a learning curve. When you go into looping or automated insulin delivery mode, you have to figure things out. You need to be able to figure out what’s happening and why it’s doing what it’s doing, so if you’re not happy with what’s happening, you can make a change. Why are you running high? Why are you running low? Knowing why it’s doing what it’s doing is critical for adjusting – either tweaking the closed loop settings, if you can, or adjusting your own behavior. Especially in the first few cycles of new tech, you’ll have a lot of learning around “I used to do things like X, but now I need to do them like Y.”

Why you might not be taking off and able to loop

blocking_takeoff_danamlewisYou also need to know why you can’t loop. There are three major categories of things that will prevent you from looping:

  1. No sensor, no looping.
  2. In some systems, wonky or missing data, no looping
  3. Communication errors between pieces of a system.

Some of these are obvious fixes (put in a new sensor if one fell out, or decide to put in a new sensor if the old one is bad), but depending on the system may involve some troubleshooting to get things going again.

Also, some of the commercial systems will kick you out of looping for various reasons (including lack of calibration), in addition to preventing you from looping in the first place without them, so knowing what these basic things are required for looping is useful to make sure you CAN automate.

Flying high: maintenance when you’re actually looping

maintenance_when_looping_danamlewisThere are some critical behaviors required for looping. (After all, when flying, there’s always a pilot present in the cockpit..right?!)

Some of these are basic behaviors you’ll be used to if you’ve been wearing a pump and CGM previously: keeping pump sites changed so the insulin works, and changing and calibrating CGM sensors.

HOWEVER – many people who “stretch” their CGM sensors find that they don’t want to stretch their sensors as far, as the data degrades over time. You do you, but keep in mind this might change when you’re looping vs. not, because you’re relying on good data to operate the system.

That being said, in addition to good sensor life, calibration hygiene is critical. You don’t want to loop off of wonky data, but also some commercial systems will kick you out if your calibration is way off and/or if you miss a calibration. (Personal opinion on this is a big ugh, which is why no DIY system that I know of does this.)

But if you keep your sites and sensors in good condition, this is where life is good. You’re looping! It’s microadjusting and helping keep things in range. Yay! This means better sleep, more time in range, and feeling better all around.

However, you still have diabetes, you’re still in the plane, so you still need to keep an eye on things. Monitoring the system is important (to make sure you’re still in autopilot and don’t need to actually fly the plane manually), so make sure you know how you (and your loved ones) can monitor the system’s operation, and know what your backup alarms are in case of system failures.

Note: there are approximately eleventy bajillion ways to remote monitor in DIY systems, but even if you have a commercial system that comes pre-baked without remote monitoring… you can add a DIY solution for that. So don’t feel like if you have a commercial AP that you can never use anything DIY – you can totally mix and match!

Dealing with turbulence

turbulence_danamlewisWhat kind of airplane/flight analogy would this be without including turbulence? :)

Like the things that can prevent looping in the first place, there are things that can throw off your looping. I already mentioned wonky sensor data that may mean either a blip in your looping time, or may kick you off looping. Again, your sensor life and your calibration practices will likely change.

But the other big disturbance, so to speak, is around body sensitivity changes. You know all the ways it can happen: you’re getting sick, recovering from getting sick, getting ready for/or are on/or are right after your period, or have an adrenaline spike, or have hormones surging, or have a growth spurt, or just exercised, etc.

This is what makes diabetes oh so hard so often. But this is where different closed loop systems can help, so this is one area you should ask about when picking a system: how does it adjust and adapt to sensitivity changes, and on what time frame? (In the DIY world, we use a number of techniques with this, ranging from autosensitivity to adapt on a 24 hour rolling scale of sensitivity changes, as well as using autotune to track bigger picture trends and changes needed to underlying settings. Reminder – anyone can use autotune if they’re willing to log bolus & carb data in Nightscout, not just closed loopers, so check that out if you’re interested! All DIY closed loop systems also use dynamic carbohydrate absorption in their respective algorithms, so that if you have slowed digestion for ANY reason, ranging from gastroparesis to getting glutened if you have celiac to merely walking after a meal, the system takes that into account and adjusts accordingly.)

The other things that can help you tough out some turbulence? Setting different modes, like an activity mode for exercise. The two things to know about exercise are:

  1. You don’t want to go into exercise with a bucket of IOB, so set activity mode WELL BEFORE you go out for activity. Depending on how much netIOB you have, that time may vary, but planning ahead with an activity mode makes a big difference for not going low during activity – even with a closed loop.
  2. Your sensitivity may be impacted for hours afterward, into the next day. See above about having a system that can respond to sensitivity changes like that, but also think about having multiple targets you can use temporarily (if your system allows it) so you can give the system a bigger buffer while it sorts out your body’s sensitivity changes.

Preparing for landing and making time between loops more smooth

prepare_for_landing_danamlewisJust like you’ll want to plan to go on the closed loop, you’ll want to plan for how to cycle off and then back on again. Depending on your system, there may be things you can do to smooth things out. One of the things I do is pre-soak a CGM sensor to skip the first day jumpy numbers. That makes a big difference for the first hours back on a “new” looping session. The other thing I do is stagger receiver start times (where I have two receivers that I stop/start at different times, so I’m not stuck for two hours without BG data to loop on).

But even if you can’t do that, you can do some other general planning ahead – like making sure your looping session doesn’t end in the middle of a big meal that’s being digested, or overnight. Those are the times when you’ll want to be looping the most.

Landing and preparing for the next looping session

Landing_danamlewisJust like learning to fly where you take a lot of training flights and review how the flight went, you’ll want to think about how things went and what you might change behavior-wise for your next looping session. Some of the things that may change over time as you learn more about your tech of choice:

  • Timing of meal announcement or boluses
  • Precision (if needed, or otherwise lack thereof) around carb counting
  • Using things like “eating soon” mode to optimize meal-time insulin effectiveness and reduce post-meal spikes
  • Using different activity patterns and targets to get ideal outcomes around exercise
  • Tweaking underlying settings (if you can)

General thoughts on looping

general_looping_reminders_danamlewisSome last thoughts about closed looping in general, regardless of the tech you might choose now or in the future:

  1. Picking one kind of technology does NOT lock you into it forever. If you’re DIYing now, you can choose commercial later. If you start on a commercial system, you can still try a DIY system.
  2. Don’t compare the original iPhone with an iPhone 6. Let’s be blunt: the Dexcom 7plus is a different beast than the Dexcom G4/G5. Similarly, Medtronic’s original “harpoon” sensor is different than their newest sensor tech. The Abbott Navigator is different than their Libre. Don’t be held up by perceptions of the old tech – make sure to check out the new stuff with a somewhat open mind.
  3. (Similarly, hopefully, in the future we’ll get to say the same about first-generation devices and algorithms. These things in commercial systems should change over time in terms of algorithm capabilities, targets, features, and usability. They certainly have in DIY – we’ve gotten smaller pancreases, algorithm improvements, all kinds of interoperability integration, etc.)
  4. All systems (both DIY and commercial) have pros and cons. They also each will have their own learning curves. Some of that learning is generalized, and will translate between systems. But again, iPhone to Android or vice versa – your cheese gets moved and there will be learning to do if you switch systems.
  5. Remember, everyone learns differently – and everyone’s diabetes is different. Figure out what works well for you, and rock it!

 

What I wish CDEs (diabetes educators) and other HCPs knew about DIY and other diabetes tech (#OpenAPS or otherwise)

by Dana Lewis

I had the awesome opportunity to present at #AADE17, the annual education meeting for the American Association of Diabetes Educators, this past weekend. My topic was about OpenAPS and DIY diabetes… which really translates to some broader things I want all educators and HCPs to know about patients and technology, whether it’s DIY or just unknown to them. Unfortunately AADE didn’t record or livestream my session, so I wanted to write up a summary of the content here.

(If you’re new to this blog/me/OpenAPS, you can also watch this June 2017 TEDX talk where I share some of the story of how I ended up with a DIY artificial pancreas and how the OpenAPS community came to be; or this older talk from OSCON 2016 as well. As always, if you’re curious to learn more about OpenAPS or wondering how to build your own DIY artificial pancreas, OpenAPS.org is the first place to learn more!)

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Diabetes is hard. Even if you are privileged to have access to insulin, education, and technology – it can still be so incredibly hard to get it right. And even if you do everything “right”, the outcomes will still vary. And after all, the devices themselves are not perfect, and we still have diabetes.

The lack of varying alarms and the unchangeable volume is what led me to create DIYPS (my open loop and louder alarm system), and the same frustration with lack of data access and visualization led John Costik, Lane Desborough, Ben West, and so many others to explore creating other DIY tools, such as Nightscout. And thanks to social media, we all didn’t have to create in a vacuum: we can share code (this is what open source means) and insight through social media, and build upon each other’s work. As a result, these collaborations, sharing, and iterative development is how OpenAPS, the open source artificial pancreas system movement, was created.

I tweet and talk and share frequently about how great it is having #OpenAPS in my life. Norovirus? No problem. Changes in sensitivity due to exercise? Not the biggie it used to be.

Showing flat overnight CGM graph representing sleep uninterupted by hypoglycemia thanks to OpenAPS

However, this technology is by no means a cure. It still requires work on the part of the person with diabetes. We still have to:

  • Change pump sites
  • Change CGM sensors
  • Calibrate regularly
  • Deal with bonked pump sites and sensors that fall out

And also, given the speed of insulin, most people are still going to engage with the system for some kind of meal bolus or announcement. This is why it’s called “hybrid” closed loop technology. (However, depending on the sophistication of the technology, you start to get to be able to choose what you want to optimize for and the behaviors you want to choose to do less of, which is great.)

In some cases, we humans know more than the technology: such as when a meal is going to happen/is coming, and when exercise is going to happen. So it’s nice to be able to interoperate your devices and be able to use your phone, watch, computer, etc. to be able to tell the system what to do differently (i.e. set higher targets in the case of activity, or lower targets to achieve “eating soon” mode , or in the case of waking up).

But in a LOT of cases, it’s tiring for the human to have to think about all the things. Such as whether a pump site is slowly dying and causing apparent insulin resistant. Or such as when you’re more sensitive 12-24 hours after exercise. Or during menstrual cycles. Or when sick. Or during a growth spurt. Or during jet lag. Or during a trip where you can’t find anything to eat. Etc. It’s a lot for us PWD’s to track, and this is where computers come in handy. Things like autosensitivity in OpenAPS to automatically detect changes in sensitivity and adjust the variables for calculations automatically; and autotune, to track the data of what’s actually happening and make recommendations for changing your underlying pump settings (ISF, carb ratio, and basal rates).

And how has this technology been developed by patients? Iteratively, as we figure out what’s possible. It’s not about boiling the ocean; it’s about approaching problems bit by bit as we have new tools to solve them, or new people with energy to think about the problem in different ways. It’s like thinking about getting a car – you wouldn’t expect the manufacturer to sell bits and pieces of the car frame, and you don’t really expect medical device manufacturers to sell bits and pieces of a pump or other device. However, patients are closest to the REAL problems in living with diabetes. Instead of a “car”, they’re looking for solutions for getting from point A to point B. And so in the car analogy, that means starting with a skateboard, scooter, or bike – and ending up with a car is great, but the car is not the point.

So no, any piece of technology isn’t going to be a cure or solve all problems or work perfectly for everyone. But that is true whether it’s DIY or a commercial tool: one size certainly does not fit all. And patients are individuals with their own lives and their own challenges with diabetes, with different motivations around what aspects of life with diabetes feel like friction and what they feel equipped to tackle and solve.

So, here’s some of what’s on my list for what I’d like CDE’s and other HCP’s to know as a result of the proliferation of technology around diabetes:

  • Yes, DIY tech is often off label. But that’s ok – it just means it’s off label; it doesn’t prevent you from listening to why patients are using it, what we think it’s doing for us, and it doesn’t prevent you from asking questions, learning more, or still advising patients.
  • Don’t make us switch providers by refusing to discuss it or listen to it, just because it’s new/different/you don’t understand it. (By the way: we don’t expect you to understand all possible technology! You can’t be experts on everything, but that doesn’t mean shunning what you don’t know.)
  • You get to take advantage of the opportunity when someone brings something new into the office – it’s probably the first of many times you’ll see it, and the first patient is often on the bleeding edge and deeply engaged and understands what they’re using, and open to sharing what they’ve learned to help you, so you can also help other patients!
  • You also get to take advantage of the open source community. It’s open, not just for patients to use, but for companies, and for CDEs and other HCPs as well. There are dozens if not hundreds of active people on Twitter, Facebook, blogs, forums, and more who are happy to answer questions and help give perspective and insight into why/how/what things are.
  • Don’t forget – many of the DIY tools provide data and insight that currently don’t exist in any traditional and/or commercially and/or FDA-approved tool. Take autotune for example – there’s nothing else out there that we know of that will tune basal rates, ISF, and carb ratio for people with pumps. And the ability of tools like Nightscout reports to show data from a patient’s disparate devices is also incredibly helpful for healthcare providers and educators to use to help patients.

And one final point specific to hybrid closed loop technology: this technology is going to solve a lot of problems and frustrations. But, it may mean that patients will shift the prioritization of other quality of life factors like ease of use over older, traditionally learned diabetes behaviors. This means things like precise carb counting may go by the wayside for general meal size estimations, because the technology yields similar outcomes. Being aware of this will be important for when CDE’s are working with patients; knowing what the patterns of behaviors are and knowing where a patient has shifted their choices will be helpful for identifying what behaviors can be adapted to yield different outcomes.

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I think the increase in technology (especially various types of closed loops, DIY and commercial) will yield MORE work for CDE’s and HCP’s, rather than less. This means it’s even more important for them to get up to speed on current and evolving technology – because it’s by no means going away. And the first wave of DIY’ers have a lot we can share and teach not just other patients, but also CDE’s. So again, many thanks to AADE for the opportunity to share some of this perspective at #AADE17, and thanks to everyone for the engagement during and after the session!

Unexpected side-effect of closed looping: Body re-calibrations

by Dana Lewis

It’s fascinating how bodies adapt to changing situations.

For those of us with diabetes: do you remember the first time you took insulin after diagnosis? For me, I had been fasting for ~18 hours (because I felt so bad, and hadn’t eaten anything since dinner the night before) and drinking water, and my BG was still somehow 550+ at the endo’s office.

Water did nothing for my unquenchable thirst, but that first shot of insulin first sure did.

I still remember the vivid feeling of it being an internal liquid hydration for my body, and everything feeling SO different when it started kicking in.

In case the BG of 550+, the A1c of 14+ (don’t remember exact number), and me feeling terrible for weeks wasn’t enough, that’s one of the things that really reinforced that I have diabetes and insulin is something my body desperately needs but wasn’t getting.

Over the last ~14+ years, I’ve had a handful of times that reinforced the feeling of being dependent on this life-saving drug, and the drastic difference I feel with and without it. Usually, it’s been times where a pump site ripped out, or I was sick and high and highly resistant, and then finally stopped being as resistant and my blood sugar started responding to insulin finally after hours of being really high, and I started dropping.

But I’ve had different ways to experience this feeling lately, as a result of having live with a DIY closed loop (OpenAPS) for 2+ years – and it hasn’t involved anything drastic as a HIGH BG or equipment failure. It’s a result of my body re-calibrating to the new norm of my body being able to spend more and more time close to 100% in range, in a much tighter and lower range than I ever thought possible (especially now true with some of the flexibility and freedom oref1 now offers).

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I originally had a brief fleeting thought about how BGs in the low 200s used to feel like the 300s did. Then, I realized that 180 felt “high”. One day, it was 160.

Then one day, my CGM said flat in 120s and I felt “high”. (I calibrated, and turned out that it was really 140). I’ve had several other days where I’d hit 140s and feel like I used to do in the mid-200s (slightly high, and annoying, but no major high symptoms like 300-400 would cause – just enough to feel it and be annoyed).

That was odd enough as a fleeting thought, but it was really odd to wake up one morning and without even looking at my watch or CGM to see what my BGs had been all night, know that I had been running high.

I further classified “really odd” as “completely crazy” when that “running high” meant floating around the 130-140 range, instead of down in the 90-110 range, which is where I probably spend 95% of my nights nowadays.

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Last night is what triggered this blog post, plus a recurring observation that because I have a DIY closed loop that does so well at handling the small, unknown variances that cause disturbances in BG levels without me having to do much work, that as result it is MUCH easier to pinpoint major influences, like my liver dumping glucose (either because of a low or because it’s ‘full up’ and needs to get rid of the excess).

In last night’s case, it was a major liver dump of glucose.

Here’s what happened:

Scott and I went on a long walk, with the plan to stop for dinner on the way home. BG started dropping as I was about half a mile out from the restaurant, but I’m stubborn 😀 and didn’t want to eat a fruit strip when I was about to sit down an eat a burger. So, my BG was dropping low when I actually ate. I expected my BG to flatten on its own, given the pause in activity, so I bolused fairly normally for my burger, and we walked the last .5 miles home.

However, I ended up not rising from the burger like I usually do, and started dropping again. It was quite a drop, and I realize my burger digestion was different because of the previous low, so I ended up eating some fruit to handle the second low. My body was unhappy at two lows, and so my liver decided to save the day by dumping a bunch of glucose to help bring my blood sugar up. Double rebound effect, then, from the liver dump and the fruit I had eaten. Oh well, that’s what a closed loop is for!

Instead of rebounding into the high 300s (which I would have expected pre-closed loop), I maxed out at 220. The closed loop did a good job of bolusing on the way up. However, because of how much glucose my liver dumped, I stayed higher longer. (Again, this probably sounds crazy to anyone not looping, as it would have sounded to me before I began looping). I sat around 180 for the first three hours of the night, and then dropped down to ~160 for most of the rest of the night, and ended up waking up around 130.

And boy, did I know I had been high all night. I felt (and still feel, hours later) like I used to years ago when I would wake up in the 300s (or higher).

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Visuals

recalibration_3 hourHmm, 3 hours doesn’t look so bad despite feeling it.

recalibration_6 hour6 hour view shows why I feel it.

recalibration_12 hour12 hours. Sheesh.

recalibration_24 hour24 hours shows you the full view of the double low and why my liver decided I needed some help. Thanks, liver, for still being able to help if I really needed it!

recalibrating_pebble view of renormalizing Settling back to normal below 120, hours later.

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There are SO many amazing things about DIY closed looping. Better A1c, better average BG, better time in range, less effort, less work, less worrying, more sleep, more time living your life.

One of the benefits, though, is this bit of double-edged sword: your body also re-calibrates to the new “normal”, and that means the occasional extreme BG excursion (even if not that extreme!) may give you a different range of symptoms than you used to experience.

Different ways to make a difference

by Dana Lewis

tl,dr: There are many ways to make a difference, ranging from donating time/energy/ideas to financially supporting organizations who are making a difference.

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When I was first diagnosed with diabetes (at age 14, three months into high school – ugh), I was stunned. And I didn’t want anyone to know, because I didn’t want to be treated differently. So for the first few months, I learned how to take care of myself, and did that quietly and went about my life: school, color guard, etc. I was frustrated with the idea of having to do all this stuff for the rest of my life, and wanted as little as possible to have to talk/think about it beyond the bare minimum I had to do.

However, after I talked the Latin Club into making our fundraising dollars from the Rake-a-thon go toward the American Diabetes Association, and I saw the reaction of the local staff when I walked in and dropped a check on the desk and turned around and tried to walk out the door. (They didn’t let me just walk out!) I agreed to volunteer and do more, and it changed my life.

I don’t know what first thing I did, but I quickly came to realize that doing things for the broader population of people with diabetes – maybe they had type 2, maybe they lived somewhere else, didn’t matter – made me feel SO much better about my own life with type 1 diabetes. I wasn’t alone. And so my mantra became “Doing something for someone else is more important than anything you would do for yourself.” And it’s proved to be true for me for 14 years.

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Since I grew up in Alabama, that’s where I started getting involved first. Inspired by my parents’ volunteer efforts that I saw growing up, I would volunteer my time and energy for a variety of things:

  • Fundraising for the local walk
  • Actually helping out the day of the walk
  • Joining the planning committee for the walk and spending months helping figure everything out and doing both actual and metaphorical heavy lifting to help make the event happen

Because of my volunteer efforts, I was asked to speak at a fundraising breakfast in Birmingham. It was my first time ever giving a public talk, let alone publicly talking about living with diabetes. And because of the people I met that day, I began doing more volunteer things around the state – and it led me to applying and being selected as the National Youth Advocate for the American Diabetes Association, and later serving on national committees like the National Youth Strategies committee (where we developed and improved the “Wisdom” kits for newly diagnosed kids with diabetes, created a kid-focused section of the ADA website, etc.). And my involvement continued as I graduated college and moved to Seattle, still serving on national committees but also joining the Western Washington Leadership Board and doing the same type of local event volunteering, but now in Seattle. I also have done more around advocacy over the years, beyond my time as NYA. While in college, I was asked to testify before the Senate HELP committee, talking about the need to increase funding for disease research. I’ve also participated regularly in ADA’s Call to Congress, including this year, where Scott and I paid to fly to DC and talk with our Washington state representatives and senators about the critical need for funding NIH & CDC; maintaining critical diabetes programs; and the issues around insulin affordability.

But it was when I was asked to represent the US and attend the World Diabetes Congress in 2006 when my eyes were opened to the issues around insulin access and affordability.

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IDF first did a youth ambassador program in 2006, bringing around two dozen young adults with diabetes to the World Diabetes Congress to participate, train in advocacy activities, etc.

Having grown up in Alabama, where diabetes (particularly type 2) is highly prevalent, I knew that not everyone could afford pumps and CGMs (especially back then, when CGMs were brand new, way less accurate, and still super expensive, even with insurance coverage). I also knew that insulin & supplies were expensive, and some people struggled with gaining access to them. (And I always felt very fortunate that since diagnosis, my parents were able to afford my insulin & supplies.)

However, while in South Africa, I learned from my new friends from other parts of Africa and the rest of the world that this was the tippy top of the ice berg. I learned about:

  • Kids are walking alone on roads for miles and hours to get to a clinic to get a single, daily shot of insulin.
  • They may only test their BGs once a week, or month, or quarter.
  • It’s not just kids – adults would have to stop working and walk for hours, too, choosing to get insulin to stay alive to be able to work another few days to help their family survive.
  • Some people would only get insulin once a week, if that, or once a day – compared to me, where I might have several injections a day, as often as needed to keep my BGs in a safe range.

It was astonishing, saddening, maddening, and terrifying. And living so far away from this part of the world, I wasn’t sure how I could help, until I met Graham Ogle who created the “Life for a Child” program to help tackle the problem, with the vision that no child should die of diabetes. Life for a Child helps less resource-supported countries provide insulin, syringes, other supplies, and education (both for people living with diabetes and healthcare providers). And, they’re a very resource-efficient organization.

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When Scott and I first met, he knew nothing about diabetes (and actually thought my insulin pump was a pager – hah!). And while I volunteered a lot of my time and energy to help organizations, he is also dedicated to finding effective ways to safe lives, and as a result, is a longtime donor to Givewell.org and some of their top charities, like Against Malaria Foundation. Givewell is a nonprofit dedicated to finding giving opportunities and publishing the full details of their analysis to help donors decide where to give. And unlike charity evaluators that focus solely on financials, assessing administrative or fundraising costs, they conduct in-depth research aiming to determine how much good a given program accomplishes (in terms of lives saved, lives improved, etc.) per dollar spent.

Therefore, when Scott and I got married, we decided that in lieu of wedding-related gifts, we would ask people to support our charities of choice, to further increase the impact we would be able to have in addition to our own financial and other resource donations.

However, Life for a Child was not evaluated by Givewell. So Scott and I got on a Skype call with Graham Ogle to crunch through the numbers and try to come up with an idea for how effective Life for a Child is, similar to what Givewell has already done for other organizations.

For example, the Against Malaria Foundation, the recommended charity with the most transparent and straightforward impact on people’s lives, can buy and distribute an insecticide-treated bed net for about $5.  Distributing about 600-1000 such nets results in one child living who otherwise would have died, and prevents dozens of cases of malaria.  As such, donating 10% of a typical American household’s income to AMF will save the lives of 1-2 African kids *every year*.

Life for a Child seems like a fairly effective charity, spending about $200-$300/yr for each person they serve (thanks in part to in-kind donations from pharmaceutical firms). If we assume that providing insulin and other diabetes supplies to one individual (and hopefully keeping them alive) for 40 years is approximately the equivalent of preventing a death from malaria, that would mean that Life for a Child might be about half as effective as AMF, which is quite good compared to the far lower effectiveness of most charities, especially those that work in first world countries.

(And some of the other charities and organizations don’t have clear numbers that can be this clearly tracked to lives saved. It’s not to say they’re not doing good work and improving lives – they absolutely are, and we support them, too – but this is one of the most clear and measurable ways to donate money and have a known life-saving impact related to diabetes.)

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I am asked fairly frequently about what organization I would recommend donating to, in terms of diabetes research or furthering the type of work we’re doing with the OpenAPS community. It’s a bit of a complicated answer, because there is no organization around or backing the OpenAPS community’s work, and there are many ways to donate to diabetes research (i.e. through bigger organizations like ADA and JDRF or directly to research projects and labs if you know of a particular research effort you want to fund in particular).

And also, I think it comes down to seeing your donation make a difference. If you’d ask Scott, he would recommend AMF or other Givewell charities – but he’s seen enough people ask me about diabetes-related donation targets to know that people are often asking us because of wanting to make a difference in the lives of people with diabetes.

So, given all the ways I’ve talked about making a difference with different volunteer efforts (and the numerous organizations with which you could do so), and the options for making a financial donation: my recommendation for the biggest life-saving effort for your dollar will be to donate to Life for a Child, to help increase the number from the 18,000 children and 46 countries they’re currently helping in. (And, they now have a US arm, so if you are in the US your donation is tax-deductible).

You may have a different organization you decide to support – and that’s great. Thank you to everyone who donates money, time, and energy to organizations who are working to make our lives better, longer, and the world in general to be a better place for us all.

This. Matters. (Why I continue to work on #OpenAPS, for myself and for others)

by Dana Lewis

If you give a mouse a cookie or give a patient their data, great things will happen.

First, it was louder CGM alarms and predictive alerts (#DIYPS).

Next, it was a basic hybrid closed loop artificial pancreas that we open sourced so other people could build one if they wanted to (#OpenAPS, with the oref0 basic algorithm).

Then, it was all kinds of nifty lessons learned about timing insulin activity optimally (do eating soon mode around an hour before a meal) and how to use things like IFTTT integration to squash even the tiniest (like from 100mg/dL to 140mg/dL) predictable rises.

It was also things like displays, button, widgets on the devices of my choice – ranging from being able to “text” my pancreas, to a swipe and button tap on my phone, to a button press on my watch – not to mention tinier sized pancreases that fit in or clip easily to a pocket.

Then it was autosensitivity that enabled the system to adjust to my changing circumstances (like getting a norovirus), plus autotune to make sure my baseline pump settings were where they needed to be.

And now, it’s oref1 features that enable me to make different choices at every meal depending on the social situation and what I feel like doing, while still getting good outcomes. Actually, not good outcomes. GREAT outcomes.

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With oref0 and OpenAPS, I’d been getting good or really good outcomes for 2 years. But it wasn’t perfect – I wasn’t routinely getting 100% time in range with lower end of the range BG for a 24hour average. ~90% time in range was more common. (Note – this time in range is generally calculated with 80-160mg/dL. I could easily “get” higher time in range with an 80-180 mg/dL target, or a lot higher also with a 70-170mg/dL target, but 80-160mg/dL was what I was actually shooting for, so that’s what I calculate for me personally). I was fairly happy with my average BGs, but they could have been slightly better.

I wrote from a general perspective this week about being able to “choose one” thing to give up. And oref1 is a definite game changer for this.

  • It’s being able to put in a carb estimate and do a single, partial bolus, and see your BG go from 90 to peaking out at 130 mg/dL despite a large carb (and pure ballpark estimate) meal. And no later rise or drop, either.
  • It’s now seeing multiple days a week with 24 hour average BGs a full ~10 or so points lower than you’re used to regularly seeing – and multiple days in a week with full 100% time in range (for 80-160mg/dL), and otherwise being really darn close to 100% way more often than I’ve been before.

But I have to tell you – seeing is believing, even more than the numbers show.

I remember in the early days of #DIYPS and #OpenAPS, there were a lot of people saying “well, that’s you”. But it’s not just me. See Tim’s take on “changing the habits of a lifetime“. See Katie’s parent perspective on how much her interactions/interventions have lessened on a daily basis when testing SMB.

See this quote from Matthias, an early tester of oref1:

I was pretty happy with my 5.8% from a couple months of SMB, which has included the 2 worst months of eating habits in years.  It almost feels like a break from diabetes, even though I’m still checking hourly to make sure everything is connected and working etc and periodically glancing to see if I need to do anything.  So much of the burden of tight control has been lifted, and I can’t even do a decent job explaining the feeling to family.

And another note from Katie, who started testing SMB and oref1:

We used to battle 220s at this time of day (showing a picture flat at 109). Four basal rates in morning. Extra bolus while leaving house. Several text messages before second class of day would be over. Crazy amount of work [in the morning]. Now I just have to brush my teeth.

And this, too:

I don’t know if I’ve ever gone 24 hours without ANY mention of something that was because of diabetes to (my child).

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Ya’ll. This stuff matters. Diabetes is SO much more than the math – it’s the countless seconds that add up and subtract from our focus on school/work/life. And diabetes is taking away this time not just from a person with diabetes, but from our parents/spouses/siblings/children/loved ones. It’s a burden, it’s stressful…and everything we can do matters for improving quality of life. It brings me to tears every time someone posts about these types of transformative experiences, because it’s yet another reminder that this work makes a real difference in the real lives of real people. (And, it’s helpful for Scott to hear this type of feedback, too – since he doesn’t have diabetes himself, it’s powerful for him to see the impact of how his code contributions and the features we’re designing and building are making a difference not just to BG outcomes.)

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Thank you to everyone who keeps paying it forward to help others, and to all of you who share your stories and feedback to help and encourage us to keep making things better for everyone.