Please don’t shame or guilt patients (some do’s and don’ts for working with patients)

I have previously written about the cost of patient participation, reflecting on some of the asks I get to be involved in various projects, conferences, etc. Since that post, I’ve been pleased with some of the asks I’ve gotten. The good ones are good: they recognize that patient time is valuable, and proactively offer to cover not only costs of travel expenses but also a reasonable honorarium or payment for my time and expertise.

The bad ones are bad, though. In some cases, really bad. Most recently, I’ve encountered asks where it feels like they are shaming me for daring to ask if participants’ expertise is being valued by providing an honorarium for their time.

There’s a lot of social nuances around ‘asking for money’ and how we value people for their time (or not). As a PI of a grant-funded project myself, I get the tradeoffs in terms of paying people for their time to contribute vs. having that money to do other project work (speaking specifically of grant-funded projects, although the same logic applies to conference & other budgets). Personally, though, I believe the right thing to do, if you’re asking for expertise that’s not already on your team (and already being paid for), is to pay for it. You’d pay a legal expert to consult on your project if there wasn’t one on your team and you needed legal expertise. If you are having others consult or provide expertise to meet one of the goals of your project, you should pay for that, too. Otherwise, you’re asking people to do things and will be spending social cash in order to get people to do things for you. If you have a bank of relationship cash, great. But, that actually limits your ability to bring in the right (or best) experts, because it assumes you already have those relationships and they’re the best people. But thinking about filter bubbles in today’s world – chances are you do not have those relationships (yet). And if these people are experts, they should be paid for their contributions toward your project.

When some of your experts are patients and others are not, it gets even tricker. You may be able to use traditional quid pro quo relationship stuff to get someone whose “day job” it is to lend their expertise to projects like yours. But for many patients, their work isn’t their “day jobs” and they’re not getting paid. As I mentioned, they’re likely having to take vacation time or unpaid leave in order to do extracurricular things. But there are other costs, too, even in the relatively simple interaction of a patient being “asked” to do things. If you’re not a patient with a chronic condition, you may not be aware of this.

One of the ongoing struggles in being a patient with a chronic condition is not just the physical elements of the condition, and dealing with the condition itself, but also the psychosocial elements, including interacting with the world about it. Many patients are self-sufficient in managing their disease, but what about when it comes to dealing with other people who they must interface with about their needs? It’s hard. I speak from personal experience, from dealing with both type 1 diabetes and celiac. Even trying to pre-organize gluten free food at conferences, and then having to hunt down 15 humans at the conference when they “forget” and have to go see what’s gluten free…it’s a lot of work, and it’s stressful.

A friend recently shared her daughter’s experience with advocating for herself at school, based on her 504 plan. (A 504 plan is where the school and family agree up front on accommodations that the student may need, related to the medical condition. For Type 1 diabetes, that can include things like the ability to reschedule a test if the night before (or the day of), the student has exceptionally high blood sugar, because this can influence concentration and cognition, as well as making a person feel really sick.) In this instance, the daughter asked to reschedule a test and communicated her needs, but felt pressured by the teacher to take the test that day. The friend ended up having to communicate with the teacher, pointing out how her daughter did the right things, but that it’s hard for a child or teen to “stand up for themselves” to an adult – especially when meeting with resistance. This is especially true when they’re advocating for their needs based on a chronic condition.

And you know what? That’s still true even as an adult. It’s hard to always have to be different. It’s hard to have to fight 24/7/365 for your needs. Dealing with the resistance you encounter daily is *hard*. (This for me is where celiac is the more frequent pain and source of frustration. I am *so* tired of doing everything “right” in pre-requesting gluten free food due to a medical condition, and there being no gluten free food, or sub-optimal options that are not nutritious (lettuce is not a meal!).  The time and energy it takes from me to deal with this takes away from my ability to participate in an event. See this thread for an example. And it happens all the time. RAR.) Sometimes it feels easier to just come up with a workaround on your own rather than rely on other people. Or to just go with the flow, and deal with the (potentially negative) outcomes of the situation. But that’s not always possible. Either way, the drain and strain of this self-advocacy adds up, and becomes exhausting.

So, back to asking for patient participation in projects/conferences/things. Asking a patient to participate requires them to respond to you. And in many cases, it involves them having to ask for money or asking clarifying questions.  Patients often meet resistance to such requests, which is itself exhausting, especially if they get lots of asks. Based on some recent experiences, I wanted to suggest some do’s/don’ts to consider if you’re looking to ask a patient to help with something. This is by no means a comprehensive list or a “do exactly this and it’s good enough, you don’t have to think about doing the right thing anymore”. But to me, it’s a bare minimum for being able to start a conversation and to be taken seriously by patients:

DO:

  • Be specific about your ask and the time commitment involved.
    • Bonus: If you don’t have an existing relationship, be specific about why you think this person is a good fit and how you found them.
    • (Ideally this means they’re not just filling a check box for “any patient will do, we just need a patient involved.”)
  • Be upfront about what benefits and payment a patient will get.

DO NOT:

  • Shame or guilt-trip patients.
    • Yes, your project/conference/etc. is a worthwhile endeavor, and patient participation would add value.  But patients help other patients all the time. For free. And there’s a limit of how much people can do, especially when it involves taking time away from where they’ve already decided to spend their time helping people. Not to mention family time, work, etc.
  • Make a vague ask.
    • If you ask for time/expertise and do not offer payment to the person for their time or articulate any other benefits to them, you’re putting them into an awkward position: they either have to accept the ask without a clear idea of the benefit of doing so, respond back with a difficult and awkward ask for money, or say no / ignore a reasonable-seeming request.
  • Try to solely tug on the heart strings of “helping”.
    • This is shaming or guilt-tripping.
  • Confuse an honorarium with covering travel expenses to physically arrive at your event.
    • Honorarium is something that should cover time. If you offer “an honorarium to cover the flight”, that’s a travel expense reimbursement or per diem, not payment for the value of their time.
  • Ask for a coffee meeting or an introductory call based on social credibility referral from a friend/colleague, and then jump straight into “picking their expertise for free”.
    • An introductory encounter should be about getting to know each other and presenting an ask to a person. (Which hey, would save time if you did it by email and were clear per the above.) Don’t mask or skip over the ask.

Again, this is by no means of an exhaustive list (and I’d love for other patients to add their take to it). It’s not meant to blame or shame anyone, but to open what I think is a much-needed conversation about legitimizing and sustaining patient participant and engagement, because it IS valuable. I’d love fellow researchers who work with patients to share their ideas and best practices for outreaching and inviting patients to collaborate with them. We all need to talk about this to change some of the widespread behaviors that make it hard for patients to be able to participate on projects, even when we *do* want to help.

meta note: Hard conversations are hard. This blog post was hard to write and publish. It makes me feel uncomfortable in the same way I do pushing back on individual asks that don’t value my contributions.  As a patient, it’s hard to push back on “the way things are” – but I know it still needs to be done, even when it’s uncomfortable.

Vitamin D and insulin sensitivity

tl;dr – for me, Vitamin D hugely influences insulin sensitivity.

After the flu, I continued to be sick. We did the usual song and dance many people do around “hey, do you have pneumonia?”. Which, luckily, I didn’t, but I was still pretty sick and my after visit summary sheet said bronchitis. Also, my average BGs were going up, which was weird. After all, when I had the flu, I had spectacular BGs throughout. So I was pretty concerned when my time in range started dropping and my average BG started rising.

In diabetes, there are a lot of things that influence BGs. It can be a bad pump site; a bad bottle of insulin; stress; sickness; etc etc. that causes out of range BGs. Most of these are helped by having a DIY closed loop like OpenAPS. So, when your BGs start to rise above (your) normal and stay there, it’s indicative of something else going on. And because I was sick, that’s what I thought it was. But as I continued to gradually heal, I noticed something else: not only were my BG averages continuing to rise (not normal), but I also was needing a lot more insulin. Like, 20-30u more per day than usual. And that wasn’t just one day, it was 4 days of that much insulin being required. Yikes. That’s not normal, either.

So, I was thinking that I was hitting the Fiasp plateau, which made me really sad. I’ve been using Fiasp for many months now with good results. (For those of you who haven’t been tuned into the diabetes community online, while many people like Fiasp because it’s slightly faster, many people also have experienced issues with it, ranging from pump sites dying much faster than on other insulins; having issues with prolonged high BGs where “insulin acts like water”, etc.) But, I was prepared mentally to accept the plateau as the likely cause. I debated with Scott whether I should switch back to my other insulin for 2-3 sites and reservoirs to give my body a break, and try again. But I was still sick – so maybe I should wait until I was not clearing gunk out of my lungs. Or I was also pretty convinced that it was correlated with my absolute ZERO level of activity. (I had some rising BG averages briefly over Christmas where I was fearing the plateau, but turns out it was related to my inactivity, and getting more than zero steps a day resolved that.) I knew I would be moving around more the next week as I gradually felt better, so it should hopefully self-resolve. But making changes in diabetes sometimes feels like chicken and egg, with really complicated chickens and eggs – there’s a lot of variables and it’s hard to pin down a single variable that’s causing the root of the problem.

One other topic came up in our discussion – vitamin D. Scott asked me, “when was the last time you saw the sun?”. Which, because I’d been sick for weeks, and traveled for a week before that, AND because we live in Seattle and it’s winter, meant I couldn’t remember the last time I had seen the sun directly on my skin. (That sounds depressing, doesn’t it? Sheesh.)

So, I decided I would not switch back to the previous insulin I was using, and I would give it some time before I tried that, and I would focus on taking my vitamin D (because I hadn’t been taking it) and also trying to get at least SOME activity every day. I took vitamin D that night, went to bed, and….

…woke up with perfect BGs. But I didn’t hold my breath, because I was having ok nights but rough days that required the extra 30 units of insulin. But by the end of the day, I still had picture-perfect BGs (my “normal”), and I was back to using my typical average amount of insulin. PHEW. Day 2 also yielded great BG levels (for me, regardless of sickness) and around average level of insulin needed for the day time. Double phew. Day 3 is also going as expected BG and total insulin usage wise.

You might find yourself thinking, “how can it be as simple as Vitamin D? There’s probably something else going on.” I would think that – except for I have enough data to know that, when I’m vitamin D deficient, getting some vitamin D (either via pill or via natural form from sunlight) can pack a punch for insulin sensitivity. In 2014, Scott and I went out in February even when it was cold to sit in a park and get some sunshine. After about an hour of sitting and doing nothing, with no extra insulin on board, WHOOOSH. I went mega-low. I’ve had several other experiences where after being likely vitamin D deficient, and then spending an hour or so in sunlight, WHOOSH. And same for when there was no sunlight, but I took my vitamin D supplements after a while of not taking them. And no, they’re not mixed with cinnamon 😉 (That’s a diabetes joke, cinnamon does not cure diabetes. Nothing cures type 1 diabetes.)

So tl;dr – my insulin sensitivity is influenced by vitamin D, and I’ll be trying to do a better job to take my vitamin D regularly in the winters from now on!

Making changes in diabetes is hard by DanaMLewis

More than 3 years of DIY closed looping with #OpenAPS

I’ve been using a DIY closed loop (OpenAPS) for 1,152 days. That’s over three years (from December 2014) of looping. That’s 1,152 nights of being able to sleep without worrying about dying in my sleep. That’s 1,152 mornings of getting to text my mom because – and when – I want to, not because it’s the thing that keeps her from worrying that I’m dead. It’s immeasurable peace of mind, in addition to the best outcomes I’ve ever had in 15 years of diabetes. And it’s gotten better since the very beginning.

Here’s where we started, and where we’ve come since then:

Here’s what hasn’t changed:

  • It is 100% do it yourself (aka, DIY). There’s no company or entity who will hand you a fully functional DIY closed loop. You get to build it yourself, which is why (among other reasons) comparing the costs of a DIY system to the cost of a potential commercial system is like apples and horseradish. But it also means you have ultimate control over your system, your algorithm, how it works, and what settings it has. There’s ultimate transparency, not just in what you’re using, but in understanding the path any feature or tool took in development from initial idea, all the way to it being a piece of code that the community is actively using. And you get to choose which pieces you use.
  • It’s driven by the spirit of paying it forward. In code and in documentation, in the interactions among the community across numerous online channels, you see incredible gratitude and appreciate sharing between members of the community. Because we can remember what it’s like to not have this technology, and we see the difference it makes. You hear stories of people succeeding at all day soccer camps or in running marathons; at school; at work; people having healthy pregnancies; and all other number of beautiful stories framed in gratitude for having technology that helps make life more about life, and less about diabetes. As Cameron said last night, “I’ve gotten use[d] to the day to day normalcy of OpenAPS, but it’s the “this is gonna be bad” and then “oh. Maybe not” that get me now. :)”

I’ve been looping for 1,152 days and I’m still blown away with appreciation for everyone in this community who codes, collaborates, documents, shares, troubleshoots, and together help us all overcome some of the many challenges in living well with diabetes. Without this community, there wouldn’t now be >500+ people worldwide accessing DIY closed loop technology. And that’s worth more to me than my own closed looping. <3

3 years of closed looping with #OpenAPS by @DanaMLewis

Quantified sickness when you have #OpenAPS and the flu

Getting “real people sick*” is the worst. And it can be terrifying when you have type 1 diabetes, and know the sickness is both likely to send your blood sugars rocketing sky high, as well as leave you exhausted and weak and that much harder to deal with a plummeting low.

*(Scott hates this term because he doesn’t like the implication that PWD’s aren’t real. We’re real, all right. But I like the phrase because it differentiates between feeling bad from blood sugar-related reasons, and the kind of sickness that anyone can get.)

In February 2014, Scott got home from a conference on Friday, and on Saturday complained about being tired with a headache. By Sunday, I started feeling weary with a sore throat. By Monday morning, I had a raging fever, chills, and the bare minimum of energy required to drag myself into the employee health clinic and get diagnosed with the flu. And since they knew I was single and lived by myself, the conversation went from “here’s your prescription for Tamiflu” to “but you can’t be by yourself, maybe we should find a bed for you in the hospital” because of how sick I was. Luckily, I called Scott and asked him to come pick me up and let me stay at his place. And there I stayed in complete misery for several days, the sickest I’d ever been. I remember at one point on the second day, waking up from a fitful doze and seeing Scott standing across the room with his laptop on a dresser, using it as a standing desk because he was so worried about me that he didn’t want to leave the room at that point. It was that bad.

Luckily, I survived. (And good thing, right, given that we went on to build OpenAPS, yes? ;)) This year’s flu experience was different. This year I was real-people sick, but without the diabetes-related fear that I’d so often experienced in the past. My blood sugars were perfectly managed by OpenAPS. I didn’t go low. It didn’t matter if I didn’t eat, or did eat (potato soup, ice cream, and frozen fruit bars were the foods of choice). My BGs stayed almost entirely in range. And because they were so in range that it was odd, I started watching the sensitivity ratio that is calculated by autosensitivity to see how my insulin sensitivity was changing over the course of the sickness. And by day 5, I finally felt good enough to share some of that data (aka, tweet). Here’s what I found from this year’s flu experience:

  • Night 1 was terrible, because I got hardly any deep sleep (45 minutes, whereas 2+h is my usual average per night) and kept waking up coughing. I also was 40% insulin resistant all night long and into Day 2, meaning it took 40% more insulin than usual to keep my BGs at target.
  • Night 2 was even worse – ZERO deep sleep. Ahhhh! It was terrible. Resistance also nudged up to 50%.
  • Night 3 – hallelujah, deep sleep returned. I ended up getting 4h53m of deep sleep, and also was able to sleep for closer to 2 hour blocks at a time, with less coughing. Also, going into night 3 was pretty much the only “high” I had of being sick – up around 180 for a few hours. Then it fell off a cliff and whooshed down to the bottom of my target, marking the drastic end of insulin resistance. After that, insulin sensitivity was fairly normal.
  • Night 4 yielded more deep sleep (>5 hours), and a tad bit of insulin sensitivity (~10%), but it’s unclear whether that’s totally sickness related or more related to the fact that I wasn’t eating much in day 3 and day 4.
  • Night 5 felt like I was going backward – 1h36m of deep sleep, tons of coughing, and interestingly a tad bit of insulin resistance (~20%) again. Night 6 (last night) I supposedly got plenty of deep sleep again (>4h), but didn’t feel like it at all due to coughing. BGs are still perfectly in range, and insulin sensitivity back to usual.

This was all done still with no-bolus, and just carb announcement when I ate whatever it was I was eating. In several cases there was negative IOB on board, but I didn’t have the usual spikes that I would normally see from that. I had 120 carbs of gluten free biscuits and gravy yesterday, and I didn’t go higher than 130mg/dl.

It’s a weird feeling to have been this sick, and have perfectly normal blood sugars. But that’s why it’s so interesting to be able to look at other data beyond average, time in range, and A1c – we now have the tools and the data to be able to dive in and really understand more about what our bodies are doing in sick situations, whether it’s norovirus or the flu.

I’m thinking if everyone shared their data from when they had the flu, or norovirus, or strep throat, or whatever – we might be able to start to analyze and detect patterns of resistance and otherwise sensitivity changes over the course of typical illness. This way, when someone gets sick with diabetes, we’d know generally “expect around XX% resistance for Days 1-3, and then expect a drop off that looks like this on Day 4”, etc.

That would be way better than the traditional ways of just bracing yourself for sky-high highs and terrible lows with no understanding or ability to make things better during illness. The peace of mind I had during the flu this year was absolutely priceless. Some people will be able to get that with DIY closed loop technology; but as with so many other things we have learned and are learning from this community, I bet we can find ways to help translate these insights to be of benefit for all people with diabetes, regardless of which therapies they have access to or decide to use.

Want to help? Been sick? Consider donating your data to my diabetes sick-day analysis project. What you should do:

  1. If you’re using a closed loop, donate your data to the OpenAPS Data Commons. You can do all your data (yay!), or just the time frame you’ve been sick. Use the “message the project owner” feature to anonymously message and share what kind of illness you had, and the dates of sickness.
  2. Not using a closed loop, but have Nightscout? Donate your data to the Nightscout Data Commons, and do the same thing: Use the “message the project owner” feature to anonymously message and share what kind of illness you had, and the dates of sickness.

As we have more people who identify batches of sick-day data, I’ll look at what insights we can find around sensitivity changes before, during, and after sickness, plus other insights we can learn from the data.

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

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!

 

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

tl;dr – No, you can’t order one yet, this is coming soon! Yay for new & more hardware options!

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.

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!

January 2018 update: rigs are still evolving!

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

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.

Opening pathways for discovery, research, and innovation in health and healthcare

How can we get more patients and other communities to leverage the benefits of the #WeAreNotWaiting mindset for research, development, and innovation in health (and healthcare)?

That’s a question I’ve been asking myself for two years, after seeing the diverse efforts and valuable outpourings from the DIY diabetes community (ranging from amazing remote monitoring solutions for CGM to algorithms, hardware, and other software for automated insulin delivery systems).

But, how to scale? In diabetes, we’re perhaps uniquely positioned given our data-driven disease. However, I believe that the data and innovation approach we’ve taken in diabetes can help many other types of patient communities as well. I just didn’t know how to help scale it… until recently.

Last year when a group of us from the OpenAPS community participated in the Quantified Self Public Health Symposium in 2016, it prompted some follow up conversations with various academic researchers, including Eric Hekler from Arizona State University (ASU).

Eric started a conversation, and kept asking me: What could you do if you partnered with academic researchers? How can traditional researchers help the DIY community, OpenAPS or otherwise?

That also sparked a conversation with Paul Tarini, a senior program officer at the Robert Wood Johnson Foundation (RWJF), about potential funding for a project.

(Important to state here: OpenAPS itself is not a funded project. It has not been, and will not be. It is 100% DIY, non-commercial, and it has been built by a community of volunteers.)

What I wanted to talk to RWJF about was funding a collaboration with academic researchers for studying data and innovation coming out of the community; and to ultimately identify needs and build resources to help scale this type of community effort and empower other patient communities as well.

It took over a year, but we were able to work through initial project proposals and were then invited to submit a full proposal. And on Wednesday (September 6, 2017), I found out that we have been awarded the grant, and this project work will be funded by the Robert Wood Johnson Foundation. The project officially begins on September 15 and will run for 18 months.

So what exactly is this project?

Our project is titled “Learning to not wait: Opening pathways for discovery, research, and innovation in health and healthcare.”

It entails a number of things.

    1. We are creating an on-call data science team to support research in the DIY community. More details will be forthcoming, but essentially this team is there to help do research on the myriad of questions bubbling out of the community. For example – how does sensitivity change during growth spurts, during periods of inactivity, or when changing insulin types? What are some of the most successful mealtime insulin dosing strategies? Etc. People will be able to submit ideas, and get help formulating the idea into a researchable question, and get the research done.
    2. Studying the process of research when done by patients, and the barriers they/their research run into when spreading this scientific knowledge. I personally know there are a lot of barriers, but we need to document them and find solutions. (There are a lot of prejudice and perceived stigmas toward patient researchers doing this type of scientific work, around things like quality of research, methods of distributing knowledge, etc.)
    3. Convening a meeting with patients, traditional researchers, legal experts, and others in this innovative research space to discuss and address some of the known and being-found barriers for this type of research. I envision a white paper type publication to come out of this meeting to document the lay of the land as it is.
    4. Creating toolkit-type resources based on what we’ve learned and are learning in this project for helping patients new to DIY and this type of research take on various levels of research or innovation activity. Part of our project’s scope of work, in #WeAreNotWaiting spirit, includes beta testing with 2-3 other patient communities, so we can get feedback and iterate and roll these out as quickly as possible.

Our project has a couple of principles that I feel strongly about, and am also very proud of in approaching this body of work.

  • I am the scientific Principal Investigator of this project. This is unique in the world of grant-funded research, where a patient is driving the scientific discovery process. (I’m proud and very appreciative to have two amazing co-PI’s who are helping with some of the administrative work since the grant is being administered through Arizona State University Foundation, who is being an awesome partner given the uniqueness of this situation*.) My co-PI’s are Eric Hekler and Erik Johnston. The other members of the team include John Harlow, who’s a MacArthur Foundation Postdoctoral Fellow; Sayali Phatak, a PhD student at ASU; and Keren Hirsch from the ASU Decision Theater.
  • #WeAreNotWaiting is the mantra for this project and our entire team. We plan to be as efficient as possible in doing the project work, which includes being as timely as possible with sharing findings back with the community as soon as they’re ready (a given; there’s no reason to wait) as well as finding ways to publish that are faster than the very traditional academic publishing process, and being thoughtful about the right audiences outside the patient community for communicating about this project’s work.
  • Always asking why. As a brand new PI, I have a lot to learn. But as a non-traditional PI, I also am running into a lot of things that are done the way they’d be done if I was traditionally inside an organization. I plan to explore and challenge as many of these, and try to document the decisions I make in this project as I come to those forks in the road. In some cases, I choose the easier paths because for my project/work/focus, it does not matter. In other cases, based on principle, I choose the harder path-blazing approach.

* About the uniqueness of this project and the administrative details

Since I’m an individual patient researcher, not affiliated with the organization, we decided we would make the official grantee financial organization Arizona State University Foundation, since that’s where my co-PI’s were. But true to the nature of this project, I want to document the challenges and opportunities that come with that, so more to come about all the interesting lessons learned about the process of putting together the proposal and the grant approval process once we heard the grant would be awarded. That way, future patient researchers have a leg up on what is coming when taking on this type of project and are aware of what this approach entailed. The short version is I am a subcontractor to ASU for purpose of the grant; but am not employed or otherwise affiliated with ASU. Props to the many people at ASU who learned about me and this project in the approval process and rolled with it / helped make it happen.

So, what’s next? When do you start? What are you waiting on?!

Coming super soon – a project website with more details about this project.

For my fellow PWDs:

  • Stay tuned for the project website going live, which will also include more details about how individuals in the diabetes community can pitch ideas/get started working with the on-call data science team.

For patients reading this who are members of other patient disease communities:

  • Ping me if you’re SUPER excited and can’t wait to tell me :), or stay tuned for more info about the process for proposing that your patient community be one of the communities with whom we beta test some of the tools/resources developed toward the latter phases of this project.

If you’re someone else who’s interested in this work (such as a legal expert, other researcher, etc.):

  • Also ping me if you’re interested in hearing more about the meeting we plan to convene with a small multidisciplinary group to discuss and address barriers of patient-driven research. Even if we can’t get everyone interested to attend the in-person meeting, I would still love your input and collaboration for the white paper and/or other publications and intersections with this project.

For everyone else:

  • Please do let me know if there’s a particular aspect of this project that you’re curious to learn more about – whether it’s some of what I’m facing and documenting as a patient PI researcher, or otherwise. That’ll help me prioritize some of the blog posts and articles I’m writing about this process!

Thanks to everyone who managed to read this ginormous blog post.

I am incredibly excited about the project, and having resources to focus on how patients and non-traditional actors in healthcare can drive research, development, innovation, and knowledge sharing in non-traditional methods and from the ground up, plus prioritize and change the healthcare research agenda. Like my work in OpenAPS that stands on the shoulders of so many, I’m hoping this project is the first of many and gets to a place for others to leverage this work and take it beyond the scope of what we’ve all imagined is currently possible.

A huge thanks to the team partnering with me on this work; to ASU for being a great partner as an organization; to the Robert Wood Johnson Foundation for supporting this project (and in particular to our program manager, Paul Tarini, for his ongoing support throughout this entire process); and many extra thanks to Scott and all my family and friends for supporting me throughout the proposal process and being the recipients of some VERY excited and !!! filled texts when I found out we had officially been awarded the grant for this project.

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

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. 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!

 

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

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).

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.

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).

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.

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.