Presentations and poster content from @DanaMLewis at #2018ADA

DanaMLewis_ADA2018As I mentioned, I am honored to have two presentations and a co-authored poster being presented at #2018ADA. As per my usual, I plan to post all content and make it fully available online as the embargo lifts. There will be three sets of content:

  • Poster 79-LB in Category 12-A Detecting Insulin Sensitivity Changes for Individuals with Type 1 Diabetes using “Autosensitivity” from OpenAPS’ poster, co-authored by Dana Lewis, Tim Street, Scott Leibrand, and Sayali Phatak.
  • Content from my presentation Saturday, The Data behind DIY Diabetes—Opportunities for Collaboration and Ongoing Research’, which is part of the “The Diabetes Do-It-Yourself (DIY) Revolution” Symposium!
  • Content from my presentation Monday, Improvements in A1c and Time-in-Range in DIY Closed-Loop (OpenAPS) Users’, co-authored by Dana Lewis, Scott Swain, and Tom Donner.

First up: the autosensitivity poster!

Dana_Scott_ADA2018_autosens_posterYou can find the full write up and content of the autosensitivity poster in a post over on OpenAPS.org. There’s also a twitter thread if you’d like to share this poster with others on Twitter or elsewhere.

Summary: we ran autosensitivity retrospectively on the command line to assess patterns of sensitivity changes for 16 individuals who had donated data in the OpenAPS Data Commons. Many had normal distributions of sensitivity, but we found a few people who trended sensitive or resistant, indicating underlying pump settings could likely benefit from a change.
2018 ADA poster on Autosensitivity from OpenAPS by DanaMLewis

 

Presentation:
The Data behind DIY Diabetes—Opportunities for Collaboration and Ongoing Research’

This presentation was a big deal to me, as it was flanked by 3 other excellent presentations on the topic of DIY and diabetes. Jason Wittmer gave a great overview and context setting of DIY diabetes, ranging from DIY remote monitoring and CGM tools all the way to DIY closed loops like OpenAPS. Jason is a dad who created OpenAPS rigs for his son with T1D. Lorenzo Sandini spoke about the clinician’s perspective for when patients come into the office with DIY tools. He knows it from both sides – he’s using OpenAPS rigs, and also has patients who use OpenAPS. And after my presentation, Joyce Lee also spoke about the overarching landscape of diabetes and the role DIY plays in this emerging technology space.

Why did I present as part of this group today? One of the roles I’ve taken on in the last few years in the OpenAPS community (among others) is a collaborator and facilitator of research with and about the community. I put together the first outcomes study (see here in JDST or here in a blog post form on OpenAPS.org) in 2016. We presented a poster on Autotune last year at ADA (see here in a blog post form on OpenAPS.org). I’ve also worked to create and manage the OpenAPS Data Commons, as well as build tools for researchers to use this data, so individuals can easily and anonymously donate their DIY closed loop data for other research projects, lowering the friction and barriers for both patients and researchers. And, I’ve co-led or led several research projects with the community’s data as a result.

My presentation was therefore about setting the stage with background on OpenAPS & how we ended up creating the OpenAPS Data Commons; presenting a selection of research projects that have utilized data from the community; highlighting other research projects working with the OpenAPS community; announcing a new international collaboration (OPEN – more coming on that in the future!) for research with the DIY community; and hopefully encouraging other diabetes researchers to think about sharing their work, data, methods, tools, and insights as openly possible to help us all move forward with improving the lives of people with diabetes.

That is, of course, quite an abbreviated summary! I’ve shared a thread on Twitter that goes into detail on each of the key points as part of the presentation, or there’s a version of this Twitter/presentation content also written below.

If you’re someone who wants to do research with retrospective data from the OpenAPS Data Commons, you can find out more about it here (including instructions on how to request data). And if you’re interested in prospective research, please do reach out as well!

Full content for those who don’t want to read Twitter:

Patients are often seen as passive recipients of care, but many of us PWDs have discovered that problems are opportunities to change things. My journey to DIY began after I was frustrated by my inability to hear CGM alarms at night. 4 years ago, there was no way for me to access my own device data in real time OR retrospectively. Thanks to John Costik for sharing his code, I was able to get my CGM data & send it to the cloud and down to my phone, creating a louder alarm. Scott and I created an algorithm to push notifications to me to take action. This was an ‘open loop’ system we called #DIYPS. With Ben West’s help, we realized could combine our algorithm with small, off-the-shelf hardware & a radio stick to automate insulin delivery. #OpenAPS was thus created, open sourcing all components of DIY closed loop system so others could close the loop, too. An #OpenAPS rig consists of a small computer, radio chip, & battery. The hardware is constantly evolving. Many of us also use Nightscout to visualize our closed loop data, and share with loved ones.

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I closed the loop in December of 2015. As people learned about it, I got pushback: “It works for you, but how do you know it’s going to work for others?” I didn’t, and I said so. But that didn’t mean I shouldn’t share what was working for me.

Once we had dozens of users of #OpenAPS, we presented a research study at #2016ADA, with 18 individuals sharing outcomes data on A1c, TIR, and QOL improvements. (See that publication here: https://twitter.com/danamlewis/status/763782789070192640 ). I was often asked to share my data for people to analyze, but I’m not representative of entire #OpenAPS community. Plus, the community has kept growing: we estimate there are more than (n=1)*710+ (as of June 2018) people worldwide using different kinds of DIY APs. (Note: if you’d like to keep track of the growing #OpenAPS community, the count of loopers worldwide is updated periodically at  https://openaps.org/outcomes ).  I began to work with Open Humans to build the #OpenAPS Data Commons, enabling individuals to anonymously upload their data and consent to share it with the Data Commons.

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Criteria for using the #OpenAPS Data Commons:

  • 1) share insights back with the community, especially if you find something about an individual’s data set where we should notify them
  • 2) publish in an accessible (and preferably open) manner

I’ve learned that not many are prepared to take advantage of the rich (and complex) data available from #OpenAPS users; and many researchers have varying background and skillsets.  To aid researchers, I created a series of open source tools (described here: http://bit.ly/2l5ypxq, and tools available at https://github.com/danamlewis/OpenHumansDataTools ) to help researchers & patients working with data.

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We have a variety of research projects that have leveraged the anonymously donated, DIY closed loop data from the #OpenAPS Data Commons.

  • 2018ADA_Slide 112018ADA_Slide 12One research project, in collaboration with a Stanford team, evaluated published machine learning model predictions & #OpenAPS predictions. Some models (particularly linear regression) = accurate predictions in short term, but less so longer term when insulin peaks. This study is pending publication, but I’d like to note the challenge of more traditional research keeping pace with DIY innovation: the code (and data) studied was from January 2017. #OpenAPS prediction code has been updated 2x since then.
  • In response to the feedback from the #2016ADA #OpenAPS Outcomes study we presented, a follow up study on #OpenAPS outcomes was created in partnership with a team at Johns Hopkins. That study will be presented on Monday, 6-6:15pm (352-OR).
  • 2018ADA_Slide 13Many people share publicly online their outcomes with DIY closed loops. Sulka Haro has shared his script to evaluate the reduction in daily manual diabetes interventions after they began using #OpenAPS. Before: 4.5/day manual corrections; now they treat <1/day.
  • #OpenAPS features such as autosensitivity automatically detect sensitivity changes and insulin needs, improving outcomes. (See above at the top of this post for the full poster content).
  • If you missed it at #2017ADA (see here: http://bit.ly/2rMBFmn) , Autotune is a tool for assessing changes to basal rates, ISF, and carb ratio. Developed for #OpenAPS users but can also be used by traditional pumpers (and some MDI users also utilize it).

I’m also thrilled to share a new tool we’ve created: an #OpenAPS simulator to allow us to more easily back-test and compare settings changes & feature changes in #OpenAPS code.
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  • Screen Shot 2018-06-22 at 4.48.06 PM2018ADA_Slide 16  We pulled a recent week of data for n=1 adult PWD who does no-bolus, rough carb entry meal announcements, and ran the simulator to predict what the outcomes would be for no-bolus and no meal-announcement.

 

  • 2018ADA_Slide 172018ADA_Slide 18 We also ran the simulator on n=1 teen PWD who does no-bolus and no-meal-announcement in real life. The simulator tracked closely to his actual outcomes (validated this week with a lab-A1c of 6.1)

 

 

 

The new #OpenAPS simulator will allow us to better test future algorithm changes and features across a diverse data set donated by DIY closed loop users.

There are many other studies & collaborations ongoing with the DIY community.

  • Michelle Litchman, Perry Gee, Lesly Kelly, and myself have a paper pending review analyzing social-media-reported outcomes & themes from DIY community.
  • 2018ADA_Slide 19There are also multiple other posters about DIY outcomes here at #2018ADA:
  • 2018ADA_Slide 20 There are many topics of interest in DIY community we’d like to see studies on, and have data for. These include: “eating soon” (optimal insulin dosing for lesser post-prandial spikes); and variability in sensitivity for various ages, pregnancy, and menstrual cycle.
  • 2018ADA_Slide 21I’m also thrilled to announce funding will be awarded to OPEN (a new collaboration on Outcomes of Patients’ Evidence, with Novel, DIY-AP tech), a 36-month international collaboration assessing outcomes, QOL, further development, access of real-world AP tech, etc. (More to come on this soon!)

In summary: we don’t have a choice in living with diabetes. We *do* have a choice to DIY, and also to research to learn more and improve knowledge and availability of tools for us PWDs, more quickly. We would love to partner and collaborate with anyone interested in working with the DIY community, whether that is utilizing the #OpenAPS Data Commons for retrospective studies or designing prospective studies. If you take away one thing today: let it be the request for us to all openly share our tools, data, and insights so we can all make life with type 1 diabetes better, faster.

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A huge thank you as always to the community: those who have donated and shared data; those who have helped develop, test, troubleshoot, and otherwise help power the #OpenAPS and other DIY diabetes communities.

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Presentation:
Improvements in A1c and Time-in-Range in DIY Closed-Loop (OpenAPS) Users

(full tweet thread available here; or a description of this presentation below)

#OpenAPS is an open and transparent effort to make safe and effective Artificial Pancreas System (APS) technology widely available to reduce the burden of Type 1 diabetes. #OpenAPS evolved from my first DIY closed loop system and our desire to openly share what we’ve learned living with DIY closed loops. It takes a small, off-the-shelf computer; a radio; and a battery to communicate with existing insulin pumps and CGMs. As a PWD, I care a lot about safety: the safety reference design is the first thing in #OpenAPS that was shared, in order to help set expectations around what a DIY closed loop can (and cannot) do.

ADA2018_Slide 23ADA2018_Slide 24As I shared about my own DIY experience, people questioned whether it would work for others, or just me. At #2016ADA, we presented an outcomes study with data from 18 of the first 40 DIY closed loop users. Feedback on that study included requests to evaluate CGM data, given concerns around accuracy of self-reported outcomes.

This 2018 #OpenAPS outcomes study was the result. We performed a retrospective cross-over analysis of continuous BG readings recorded during 2-week segments 4-6 weeks before and after initiation of OpenAPS.

ADA2018_Slide 26For this study, n=20 based on the availability of data that met the stringent protocol requirements (and the limited number of people who had both recorded that data and donated it to the #OpenAPS Data Commons in early 2017).  Demographics show that, like the 2016 study, the people choosing to #OpenAPS typically have lower A1C than the average T1D population; have had diabetes for over a decade; and are long-time pump and CGM users. Like the 2016 study, this 2018 study found mean BG and TIR improved across all time categories (overall, day, and nighttime).

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Overall, mean BG (mg/dl) improved (135.7 to 128.3); mean estimated HbA1c improved (6.4 to 6.1%). TIR (70-180) increased from 75.8 to 82.2%. Overall, time spent high and low were all reduced, in addition to eAG and A1c reduction. Overnight (11pm-7am) had smaller improvement in all categories compared to daytime improvements in these categories.

Notably: although this study primarily focused on a 4-6 week time frame pre-looping vs. 4-6 weeks post-looping, the improvements in all categories are sustained over time by #OpenAPS users.

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ADA2018_Slide 35Conclusion: Even with tight initial control, persons with T1D saw meaningful improvements in estimated A1c, TIR, and a reduction in time spent high and low, during the day and at night, after initiating #OpenAPS. Although this study focused on BG data from CGM, do not overlook additional QOL benefits when analyzing benefits of hybrid closed loop therapy or designing future studies! See these examples shared from Sulka Haro and Jason Wittmer as example of quality of life impacts of #OpenAPS.

A huge thank you to the community: those who have donated and shared data; those who have helped develop, test, troubleshoot, and otherwise help power the #OpenAPS and other DIY diabetes communities.

And, special thank you to my co-authors, Scott Swain & Tom Donner, for the collaboration on this study. Lewis_Donner_Swain_ADA2018

Getting ready for #2018ADA (@DanaMLewis) & preparing to encourage photography

We’re a few weeks away from the 78th American Diabetes Scientific Sessions (aka, #2018ADA), and I’m getting excited. Partially because of the research I have the honor of presenting; but also because ADA has made strides to (finally) update their photography policy and allow individual presenters to authorize photography & sharing of their content. Yay!

As a result of preparing to encourage people to take pictures & share any and all content from my presentations, I started putting together my slides for each presentation, including the slide about allowing photography, which I’ll also verbally say at the start of the presentation. Interestingly to me, though, ADA only provided an icon for discouraging photography, saying that if staff notice that icon on any photos, that’s who will be asked to take down photos. I don’t want any confusion (in past years, despite explicit permission, people have been asked to take down photos of my work), so I wanted to include obvious ‘photography is approved’ icons.

And this is what I landed on for a photography encouraged slide, and the footer of all my other slides:

Encouraging photography in my slides Example encouraging use of photography in content slidesEncouraging photography in the footer of my slides

And, if anyone else plans to encourage (allow) photography and would like to use this slide design, you can find my example slide deck here that you are welcome to use: http://bit.ly/2018ADAexampleslides

I used camera and check mark icons which are licensed to be freely used; and I also licensed this slide deck and all content to be freely used by all! I hope it’s helpful.

Where you’ll find me at #2018ADA

And if you’re wondering where and what I’ll be presenting on with these slides…I’ll be sharing new content in a few different times and places!

On Saturday, I’m thrilled there is a full, 2-hour session on DIY-related content, and to get to share the stage with Jason Wittmer, Lorenzo Sandini, and Joyce Lee. That’s 1:45-3:45pm (Eastern), “The Diabetes Do-It-Yourself (DIY) Revolution”, in W415C (Valencia Ballroom). I’ll be discussing some of the data & research in DIY diabetes! A huge thanks to Joshua Miller for championing and moderating this session.

I’m also thrilled that a poster has been accepted on one of the projects from my RWJF grant work, in partnership with Tim Street (as well as Scott Leibrand, and Sayali Phatak who is heading our data science work for Opening Pathways). The embargo lifts on Saturday morning (content will be shared online then), and the poster will be displayed Saturday, Sunday, and Monday. Scott and I will also be present with the poster on Monday during the poster session from 12-1pm.

And last but not least, there is also an oral presentation on Monday evening with a new study on outcomes data from using OpenAPS. I’ll be presenting during the 4:30-6:30pm session (again in W415C (Valencia Ballroom)), likely during the 6-6:15pm slot. I’m thrilled that Scott Swain & Tom Donner, who partnered on this study & work, will also be there to help answer questions about this study!

As we have done in the past (see last year’s poster, for example), we plan to share all of this content online once the embargo lifts, in addition to the in-person presentations and poster discussions.

A huge thanks, as always, goes to the many dozens of people who have contributed to this DIY community in so many ways: development, testing, support, feedback, documentation, data donation, and more! <3

Hormones, CGM preferences, DIY, and why so many things are YDMV even when #WeAreNotWaiting

I posted one of my Nightscout graphs yesterday, showing a snapshot of my morning:

I hadn’t eaten, and my blood sugar still spiked up. I’ve noticed this happens in the mornings sometimes. When I have mentioned it over the years, people are quick to tell me my basal rates are wrong, and I should adjust them because dawn phenomenon. But actually, this isn’t dawn phenomenon. This happens after I physically get up and start moving for the day, whether that happens at 4am, or 6am, or 10am, or even waking up after noon. So, it’s not a basal thing, and modifying my basal rates doesn’t fix it. (And this is why I wanted to add wake-up mode to my suite of tools, to help address this.)

To me, this is a great example, (as I mentioned in my Twitter thread), of why diabetes is so hard: sooooo many things impact BG levels, and in many cases, we PWDs just have to roll with it and respond the best we can. In my case, #OpenAPS did a great job responding to the spike and bringing me back down within an hour or so.

One of the questions that popped up yesterday in response to that graph, though, was about the BG line: how did I have two BG lines?

The answer: I wear a G4 sensor, and usually have 2 receivers running off the same transmitter and sensor. One receiver is Share-d to my phone, and uploads to NS via the interwebz. The other receiver, although Share-capable, doesn’t (because the company only allows you to pair one receiver and upload via Share). I leave that CGM plugged into a rig to enable it to be a backup for offline looping. When online, this rig with the plugged in CGM uploads BGs from that receiver to NS.

Sometimes, because of different start/stop times and therefore differing calibration records, the receivers “drift” from each other, making it obvious on the graph when that happens.

Because if you give a mouse a cookie, other questions come up, someone had also asked me why I’m using G4, and why not G5. Someone else asked me in a different channel why I’m not using G5 and xDrip+ (a DIY option that doesn’t use Dexcom app or a Dexcom receiver for receiving the data or processing it), or another DIY tool to process my CGM data.

Now, as always, what I chose to use is my personal preference. It’s colored by my preference for what equipment I’m willing to carry; what phone I want to use; what data I want to have; my safety backup preferences; what my insurance covers and what I can afford; where I live; etc. So, just because I use this method, doesn’t mean I expect anyone else to want to do it. It’s just what I do. I don’t try to convince other people to use this method, and I also hope others can share info about what works for them without trying to hammer me over the head because what I’m doing is different. This is where YDMV (your diabetes may vary) comes in. It’s so true, and even within “people who DIY”, there’s a ton of variation – and that’s a good thing! I adore having options to find what works for me, and I want to have other people have options and choices to choose what works for them.

That being said, here’s the answer to how I run my CGMs and some of the things that have factored into my choice to not DIY CGM receivers/data processing most of the time:

  • With two G4 receivers, I can keep one in my pocket, paired to my phone and uploading via Share. When I’m out and about in the city or usually during the day, this is what I carry. When I run, I take the Share receiver.
  • But, I also like emergency back-ups. I like keeping a receiver plugged into an #OpenAPS rig so that if connectivity goes out/down, I can keep looping without a break in my stride. So, I could keep my Share receiver plugged into the rig, but that would involve me unplugging and replugging fairly frequently when I run errands or actually go for a short run, and meh. Hassle. So I keep “non-Share” receiver as the one that’s usually plugged into my ‘offline’ rig.
  • Having the G4 receiver plugged into the rig enables me to see raw data. Raw data is nice for a couple of things: assessing the health of my sensor (if it gets jumpy compared to the filtered data, I know the quality of the sensor is decreasing, and that helps me decide when to change it); giving me a clue to what’s going on when the filtered data goes to ??? or during the start up of a new sensor; and actually being able to run my rig and loop off some* of the raw data when I need to. (*With OpenAPS, you can choose to loop off of it within a certain range, and there’s an option to only set a certain amount of correction for a proportion of what otherwise would be proposed, with a higher level of raw data.)
  • With two receivers running, that also gives me more flexibility around sensor changes. Technically, the sensor is approved for 7 days. At the end of the 7 days, the receiver stops giving you data and forces you to “start” a new sensor session. That could be by inserting a new sensor; or it could be the same sensor on your body. But either way, theoretically it’s a 2 hour ‘warm up’ period from that session where you can’t see data. With 2 receivers, I can stagger the end and start of sensor sessions. I usually set a calendar alarm to restart one of the receivers on the night of the 6th day of the session, allowing me more flexibility on day 7 to choose when to restart or change my sensor.
  • This also means I can choose to “hot swap” when actually changing a sensor. I may choose to not hit ‘stop’ and ‘start’ on a sensor session on one of the receivers, but rather shut it off for about 30 minutes, and just do the stop/start on the other receiver (leaving it plugged into a rig to upload raw data to NS, and be able to see where the new sensor’s readings come in compared to the old one). When I power the non-restarted receiver back on about 30m after swapping the transmitter over to the new receiver (as soon as the raw readings have flattened out), it usually either goes to “no signal” for a few minutes, and then comes back with some data, an hour or more before the restarted sensor allows me to calibrate it and get data. There are downsides to this method: the data on the receiver that didn’t get restarted can be fairly inaccurate, as it’s still using the calibrations from the old sensor. So I don’t always do that, but when it’s more important to me to be able to see relative trend of where BG is (flat, or dropping or spiking), it’s nice to have that option. And since I often soak my new CGM sensors, the data from “day 1” of the sensor after a session “start” on the receiver is often better than if it was truly day 1 of the sensor being in my body.

Phew. Maybe that sounds like a lot of work, but the above setup works well for me for a variety of reasons, and also allows me the flexibility and choice for when I change sensors, when I am forced to be without data or potentially not loop, etc. Given that my schedule varies a lot, it helps since I’m not consistently in the same time zone and what works for starting or changing sensors one week in one part of the world doesn’t always align with convenience exactly 168 hours (7 days) later in another part of the world that I’m in, doing something differently.

Some of the reasons I haven’t switched to G5 include the fact that the transmitters only last for ~3 months instead of 6+ months; I’ve observed many people being frustrated by sensor not talking to the phone even when it’s right beside them; there’s no raw data on G5; you can’t have multiple receivers paired with your transmitter; etc.

Now, you might say, but that’s using Dexcom’s app, etc. With DIY solutions, those limitations don’t apply! And that’s true, to a degree – savvy folks in the community have figured out how to make it so you don’t *have* to use Dexcom’s app to display or process the data; you can replace the batteries on the transmitter; etc. But, just like my method above of using raw data isn’t necessarily going to work for everyone or might not be something someone else choose to do, the DIY options that go with G5 (or even G4 in some cases), aren’t something I believe is the right thing to do for me.

A lot of it comes down to safety. When we first started designing my DIY closed loop, we spent eons discussing how we could do this safely for me. And that evolved into further discussions about how other people could do this safely, too. A core of the OpenAPS Reference Design is that we are using already approved and vetted devices that exist on the market (e.g. existing pumps and CGMs). Those devices include approved and vetted methods for CGM data processing, too, which is even more important when the CGM data is being used to dose insulin as in OpenAPS. Now – this is not a requirement we can enforce: people can do what they want, and some people are even using non-CGMs (such as the Libre, a “Flash Glucose Monitoring” solution, plus a DIY NFC reader) as a CGM source for looping. But, whether it’s a DIY app or algorithm on CGM data, or a different glucose measuring device that’s not a CGM, that’s choice has some safety implications that I hope people are aware of.

First, the background for those who aren’t familiar: the CGM companies display a processed (“filtered”) version of the CGM data. That’s part of their proprietary stuff, but there’s reasons behind it: the raw data can be hectic and weird, and individual readings aren’t the point, anyway. The beauty of CGM is you can see the trends in addition to the estimated BG number.  In some scenarios, such as during sensor starts, during error messages that are displayed as ???, etc, the companies/FDA decided that the CGM should not show data, and instead show an error message/symbol, to help prevent anyone from making incorrect treatment decisions based off of confusing or misleading data.  That’s good enough most of the time.  As mentioned above, there are edge cases when seeing the raw is helpful, but most of the time, I’m happy with the filtered data.

But to me, there’s a difference between using raw or DIY-calibrated data for edge cases, vs. using them all the time. I’ve seen several cases in just the past few days with a newer “DIY CGM app”, which uses its own calibration algorithm for processing the unfiltered CGM readings.  These people have reported the app displaying normal BGs (say, 90 mg/dL), while they found themselves in the 40’s (rather low). It’s not clear whether that is due to the app’s calibration algorithm, something the user did in testing and calibrating, or if it’s just a bad sensor, and since most of them are not using the official receiver/app in parallel, that’s difficult to figure out.  But regardless, it’s happened enough times across numerous people for me to be concerned about a DIY CGM app being used as the primary source of CGM data. There are limitations to using company-built apps or physical devices for CGMs, but in the case where people can afford it, for safety I think it is important to at least use the approved and vetted receiver/app in parallel, to provide a backup and baseline level of alerting and alarming. The FDA & the companies have worked to create something that can be reliable for alarming when your BG is actually low (say <55 mg/dl) and alerting a human that something is going on. This is important regardless of whether people are looping or not, but it’s perhaps even more important when people are looping, since that data is driving insulin dosing decisions. Additionally, the company-created devices have been designed to deal with miscalibrations that aren’t in line with what the data from the receiver is showing, and have safety measures in place to “reject” calibrations and request new ones when necessary. Sure: there are times where that’s frustrating, but those features truly are “there for safety”, and are important for avoiding the rare but potentially serious outcomes that could be caused by incorrect CGM readings. Since safety is what we prioritize and design around in DIY closed looping, I hope people will consider that ,and prioritize safety first when choosing what to use as their primary data source.

Tl;dr – YDMV. I currently use G4 with two receivers, for the reasons described above. I think it’s important to prioritize safety over convenience most of the time, and understand the limitations of the solution that you choose (DIY or commercial). But everyone’s different, and their situation, preferences, etc. may drive different decision making. And did I mention YDMV?

Exploring other sensors that could be used with #OpenAPS and for diabetes in general

Nobody appeared to notice the other day when I tweeted about going through airport security with 13 pieces of adhesive on my body. Which is amusing to me, because normally I sport two: my insulin pump site, and my continuous glucose monitor (CGM) sensor. That particular day, I added another diabetes-related piece of adhesive (I was giving the Freestyle Libre, a flash aka not quite continuous glucose monitor, a try), and 10 pieces of adhesive not directly related to diabetes. Or maybe, it will be in the future – and that’s what I’m trying to figure out!

Last fall, my program officer from RWJF (for my role as PI on this RWJF-funded grant – read more about it here if you don’t know about my research work) made an introduction to a series of people who may know other people that I should speak to about our project’s work. One of these introductions was to a researcher at UCSD, Todd Coleman. I happened to be in San Diego for a meeting, so my co-PI Eric Hekler and I stopped by to meet Todd. He shared about his lab’s work to develop an ambulatory GI sensor to measure gastric (stomach) activity and my brain immediately started drooling over the idea of having a sensor to better help assess our methods in the DIY closed looping community for articulating dynamic carb absorption, aka how slow or fast carbs are absorbing and therefore impacting blood glucose levels. I took over part of the white board in his office, and started drawing him examples of the different data elements that we have #OpenAPS (my DIY hybrid closed loop “artificial pancreas”) calculate every 5 minutes, and how it would be fantastic to wear the GI sensor and graph the gastric activity data alongside this detailed level of diabetes data.

I immediately was envisioning a number of things:

  • Assessing basic digestion patterns and figuring out if the dynamic carb absorption models in OpenAPS were reasonable. (Right now, we’re going off of observations and tweaking the model based on BG data and manual carb entry data from humans. Finding ways to validate these models would be awesome.)
  • Seeing if we can quantify, or use the data to better predict, how post-meal activity like walking home after dinner impacts carb absorption. (I notice a lot of slowed digestion when walking home from dinner, which obviously impacts how insulin can and should be dosed if I know I’ll be walking home from dinner or not. But this is something I’ve learned from a lot of observation and trial and error, and I would love to have a more scientific assessment of this impact).
  • Seeing if this could be used as a tool to help people with T1D and gastroparesis, since slowed digestion impacts insulin dosing, and can be unpredictable and frustrating. (I knew gastroparesis was “common”, but have since learned that 40-50% of PWDs may experience gastroparesis or slowed digestion, and it’s flabbergasting how little is talked about in the diabetes community and how few resources are focused on coming up with new strategies and methods to help!)
  • Learning exactly what happens to digestion when you have celiac disease and get glutened.
  • Etc.

Fast forward a few months, where Todd and his post-doctoral fellow Armen Gharibans, got on a video call to discuss potentially letting me use one of their GI sensors. I still don’t know what I said to convince them to say yes, but I’m thrilled they did! Armen shipped me one of the devices, some electrodes, and a set of lipo batteries.

Here’s what the device looks like – it’s a 3D printed gray box that holds an open source circuit board with connectors to wearable electrodes. (With American chapstick and unicorn for scale, of course.)

DanaMLewis EGG for scale

And here’s what it looked like on me:

DanaMLewis wearing an ambulatory EGG

The device stores data on an SD card, so I had many flash backs to my first OpenAPS rig and how I managed to bork the SD cards pretty easily. Turns out, that’s not just a Pi thing, because I managed to bork one of my first EGG SD cards, too. Go figure!

And this device is why I went through airport security the other day with 10 electrodes on. (I disconnected the device, put it in my bag alongside my OpenAPS rigs, and they all went through the x-ray just fine, as always.)

Just like OpenAPS, this device is obviously not waterproof, and neither are the electrodes, so there are limitations to when I can wear it. Generally, I’ve been showering at night as usual, then applying a fresh set of electrodes and wearing the device after that, until the next evening when I take a shower. Right now, hard core activity (e.g. running or situps) generates too much noise in the stomach for the data to be usable during those times, so I’ve been wearing it on days when I’ve not been running and when I’ve not been traveling so Scott can help me apply and connect the right electrodes in the right places.

This device is straight from a lab, too, so like with #OpenAPS I’ve been an interesting guinea pig for the research team, and have found even low-level activity like bending over to put shoes on can trigger the device’s reset button. That means I’ve had to pay attention to “is the light still on and blinking” (which is hard since it’s on my abdomen under my shirt), so thankfully Armen just shipped me another version of the board with the reset button removed to see if that makes it less likely to reset. (Resetting is a problem because then it stops recording data, unless I notice it and hit the “start recording” button again, which drives me bonkers to have to keep looking at it periodically to see if it’s recording.) I just got the new board in the mail, so I’m excited to wear it and see if that resolves the reset problem!

Data-wise, it’s been fascinating to get a peek into my stomach activity and compare it to the data I have from OpenAPS around net insulin activity levels, dynamic carb absorption activity, expectations on what my BG *should* be doing, and what actually ended up happening BG-wise. I wore it one night after a 4 mile run followed by a big dinner, and I had ongoing digestion throughout the night, paired with increased sensitivity from the run so I needed less insulin overall despite still having plenty of digestion happening (and picture-perfect BGs that night, which I wasn’t expecting). I only have a few days worth of data, but I’m excited to wear it more and see if there are differences based on daily activity patterns, the influences of running, and the impact of different types of meals (size, makeup of meal, etc).

A huge thanks to Todd, Armen (who’s been phenomenal about getting me the translated GI data back in super fast turnaround time), and the rest of the group that developed the sensor. They just put out a press release about a publication with data from one of their GI studies, and this press release is a great read if you’re curious to learn more about the GI sensor, or this news piece. I’m excited to see what I can learn from it, and how we can potentially apply some of these learnings and maybe other non-diabetes sensors to help us potentially  improve daily diabetes management!

RIP, original looping pump

In 2014, a lightbulb went off. We could use Ben’s code to read and write to a particular version of an old pump. We had built an algorithm to take is input carb, BG, and insulin dosing data, and to output recommendations for action. If we hooked those together, we could close the loop. We could close the loop? Cool! We should try that. But wait – I didn’t have a compatible pump..

This is another place social media played a role in the story that is #OpenAPS. I posted on Facebook asking if anyone had one of these older model pumps sitting around unused, because we wanted to use it for research and try to close the loop. Would anyone dust off the dust bunnies on one and share? The answer turned out to be yes. A very kind person sent me the pump, we put it into play, and we closed the loop. I put the pump on, and the rest is history. Yay!

A year ago, I noticed a broken piece of blue plastic on the carpet of a hotel room. “Hey, that looks like my pump color…” “!!!! A piece of my pump broke off!”. Luckily, it was the upper edge of the reservoir; it did not impact usability of the pump; I taped it back on and continued to tape it over time. I thought eventually this, or other cracks or other physical broken pieces, would be what eventually would be the cause of failure of this beloved, well-used second-generation pump.

Some history for those who don’t know the backstory: we’re talking about using older generation pumps because it was discovered that someone (if in range, and with the right equipment) could remotely command the dosing of the pump. This was discovered by a security researcher, and the FDA had the manufacturer fix this in a future version. Thus, more modern pumps you couldn’t remotely set temp basals on, or remotely bolus. Turns out, that “hole” is what enabled us to close the loop: the ability to remotely set temporary basal rates. That is a risk. Some people don’t like that risk, and choose not to DIY closed loop because they don’t want to accept that risk. That’s fine. Other people decide that the reduction in the baseline risks of diabetes due to DIY closed looping outweigh any additional risk, and with appropriate safety guards like backup alarms, hardware and software dosing limits, etc. decide to use these pumps regardless. “YDMV” (your diabetes may vary) applies to what devices and systems you choose to help you with your diabetes, too. I personally choose to use these devices to close the loop, but acknowledge not everyone wants to – and that it shouldn’t have to be this choice that drives (for some people) whether or not to DIY loop. That is why I’ve had active conversations with every pump manufacturer for going on 4 years now about the need to have secure but documented communication protocols: I would love for people (including myself) to be able to have a secure, safe in-warranty pump with which to close the loop. Now: I’m only one person. I haven’t been able to move the needle on this myself. I’ve asked and encouraged (see this visual in the OpenAPS docs) members of the community to also take up and advocate for this. And people have. But I think it’s going to take the resources of something like the JDRF Open Protocol Initiative to really get companies to finally focus on this. And hopefully this will make the infrastructure changes needed to make it possible to achieve the vision of having a secure, in-warranty modern pump (and one that comes with the ability to choose your preferred CGM and preferred closed loop algorithm, too!).

I’ve continued to cut and apply sensor tape “pump bandaids” over the last year. But something changed about a month ago: suddenly, with normal AAA battery changes of the pump, the pump started losing the time settings with every battery change. (And battery changes happen more frequently with DIY closed looping because we communicate so frequently with the pump; mine go around ~6+ days). At first I thought I was just too slow in changing the battery. But even with a lightning quick battery change, the pump would lose the settings. No big deal…except that every time it required me to reset the clock, rewind, and reprime. Which meant drops of lost insulin (ugh), and a hassle overall.

I lasted about a month before I decided to give up. Not only was the change and reset process a pain, but because the internal battery that maintains settings when you change the AAA is apparently dying, it also means that some of the history gets wiped from the pump. Again – not a big deal because I’m uploading everything from the pump to multiple places every 5 minutes, but it is still annoying. It makes it hard to skim back through the last month on the pump to analyze how much insulin I’m using on average, when every 6 days two days get blotted off the pump’s record.

@DanaMLewis switches pumpsAnd so, I threw in the towel (sadly) last night on this beautiful, long-lasting work horse of a pump that I’ve been using for 3.5+ years. I have such an emotional tie to it because it’s what enabled me to close the loop. It’s what led us to be able to share DIY closed looping with the world. And because of the reality of using (mostly*) second hand pumps for DIY closed looping, throwing the towel on a partially busted but still-kind-of-usable loopable pump feels wrong when there are lots of people who are desperately looking for pumps so they can close the loop for themselves.

* I say mostly, because there are in fact other kinds of DIY-compatible looping pumps – they’re just not approved or available in the U.S. where I live. The DANA*R or DANA*RS pumps (made by SOOIL, they have nothing to do with me!) have bluetooth capabilities built in, so they can communicate with an Android phone. They’re very popular and usable in places like Europe & Asia, where they’re in-warranty and on the market. They can be used with AndroidAPS (which uses OpenAPS’s oref0 algorithms for looping). Because of the bluetooth comms, no extra device is needed, and the phone with AndroidAPS can communicate to the pump directly. Additionally, the AndroidAPS dev team has also been working hard on evaluating other pumps, and the Roche Combo was recently established as another pump that would be compatible with AndroidAPS; again due to built-in bluetooth capabilities.

Sadly, the DANA*R(S) is not FDA approved in the US and thus not available; and the Combo pump is no longer being actively distributed by Roche in the US (even though it’s approved) – so there are fewer pump options in the U.S. right now. But again, I’m hopeful for more change and more options in the future as the pump companies begin to leverage resources from the Open Protocol Initiative.

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

Women in open source make a difference

I was incredibly honored to find out that I had been nominated for the 2018 Women in Open Source award (and even more blown away to learn that I am one of the finalists). I wasn’t familiar with the award, and when I looked it up to learn more about it, the finalist list for the last few years gives me some serious imposter syndrome! Aside from that, there were a few things that caught my eye – and one of them was a citation from a study that found that only 11% of people contributing in open source are women.

To me, this number both makes sense, and doesn’t.

Why it makes sense (to me): open source can be hard on women.

I’ve been doing things in open source since 2014, falling into it because of DIYPS and because of getting to know people like Ben who are passionate open source advocates. Because I was helped by open source work, it was a key driver for my own passion behind making our work with OpenAPS open source, and is why I’m currently working on developing a series of open source tools to help researchers working with diabetes data. I don’t know that I would have done anything open source had I not found the perfect series of projects that led me there.

While there are many great people in the diabetes open source community, in the middle of 2014 I wrote this blog post about being female and being discounted. It was a hard post to write. But I felt it was important, because one of things both Scott and I noticed is a lot of the attitudes behind this seemed to be subconscious: directing technical questions about our project to Scott only; refusing to direct any substantial questions to me even after Scott specifically would redirect questions to me, etc. The only way I saw to (begin to) deal with the problem was to address it head on.

And, for me, things have improved with time. But it hasn’t gone away, and it still requires active addressing about once a month or so. And yes – these are (relatively) minor problems compared to what some women experience in open source, or in tech. But it’s some of the most common, frustrating friction that can easily drive women away when they get tired of experiencing stuff like that. And when they go away, it’s a loss for everyone.

Why this number doesn’t make sense (to me): women contribute incredible value to open source, and are high-volume contributors, especially if you look beyond the narrow definition applied to open source coding.

Every where I turn, I see women participating in open source. I see Kate Farnsworth and Christine Deltrap, two incredible individuals who have made watchfaces used by thousands of families to remote monitor their children’s blood sugars. I see Katie DiSimone, who has written hundreds of lines of documentation, and answers hundreds of technical troubleshooting questions across several channels. I see Mad Price Ball, who leads the Open Humans Foundation with open source work (*and* is an amazing mentor to women like me, who have non-traditional development backgrounds). I see Karen Sandler, a fierce advocate for making software open source, who herself is a finalist for the WOS award, too!

I also see a lot of my own contributions in open source, especially in the early days when Scott was the one doing most of the committing to Github for the tools we were building. Those were part of why I was told I was discounted in 2014, because my work didn’t “count”. Today when someone goes and looks at Github, if they look at the wrong toolkit (or just one, for example), it gets said that “Dana didn’t do anything on OpenAPS”. (Heh).

So I know there are also other women out there whose work is being overlooked when counting who’s doing open source. However, this type of work is absolutely crucial to open source projects, and these contributors drive an incredible amount of value. I’m glad the Red Hat Women in Open Source Awards site acknowledged this, and made this list:

  • Code and programming.
  • Quality assurance and bug triage.
  • Involvement in open hardware.
  • System administration and infrastructure.
  • Design, artwork, user experience, and marketing.
  • Documentation, tutorials, and other communications.
  • Translation and internationalization.
  • Open content.
  • Community advocacy and community management.
  • Intellectual property advocacy and legal reform.
  • Open source methodology.

The list was partially to help encourage people to nominate women; and also to help women to recognize all of the activities they do that’s open source. And it was helpful to me, too. Because of that list, instead of a handful of key examples of open source activity by women, I can instead name dozens of women. I bet you can, too. There’s so much incredible open source activity and value that happens in places outside of commit history, and if we want to recognize and acknowledge the work of everyone in open source communities, we should do a better job of acknowledging *all* of these types of activities and not just recognizing individuals (male or female) who have a traditional code-based commit history.

So if you’re reading this, it’s likely you’re a supporter of women in open source communities. Thank you for that! But I’d like to ask you to do two specific things.

1) Actively recognize the women working with you in open source. The internet can be a hard place to be, let alone work, when you are female. Help lift women up; recognize their work; and help them grow their skills.

2) Ok, this one is optional :) But if you’ve read all this way, you might consider clicking here and going to the Women in Open Source Awards site and voting for one the finalists in each category. It’s one vote per email address. Thanks!

Acknowledging all contributions in open source 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.