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!

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.