independent diabetes researchers

OpenAPS poster cited in Nature!

by Dana Lewis

I was thrilled to read a commentary by John Wilbanks and Eric Topol, out in Nature today, titled “Stop the privatization of health data“. (Click here to read a PDF version of the article.)

Tucked on the bottom of the second page of the (PDF version of the) article:

“For instance, in 2014, a woman with type 1 diabetes wired together a tiny processor, an insulin pump and a continuous glucose monitor to automate the regulation of her blood sugar levels. For a small community of patients, the collective use of such ‘home-made’ systems has resulted in improvements that are well ahead of those provided by devices and interventions emerging from conventional markets.1”

(The citation is to the poster that we presented on behalf of the #OpenAPS community at the American Diabetes Association Scientific Sessions meeting last month, with self-reported outcomes from 18 of the first 40 users and builders of DIY artificial pancreas systems)

OpenAPS (n=1)*98 as of July 19, 2016It’s worth noting that there are now (n=1)*98 users of #OpenAPS, so this “small community” is growing fast: doubling approximately every three months.

Wilbanks and Topol highlight some critical truths in their commentary, and call out another (frustrating) diabetes example to illustrate:

“Although patients can monitor their glucose levels at any instant, their aggregate records are not made accessible to them. And there is no mechanism by which patients or researchers outside the company can gain access to Medtronic’s tens of thousands of measurements.”

I’ve written about this specific example before, in fact: new ‘partnerships’ mean my personal health data is likely shared with IBM for Watson’s usage…but I don’t have access to this data or insights, and am in fact missing critical information and data visualization on my FDA-approved medical device that’s been on the market for years.

The call to action for device manufacturers, regulators, and the medical industry is simple: Give me, the patient, my data that I need so I can safely take care of myself and better manage my diabetes.

Wilbanks and Topol emphasize that this won’t happen “…unless each of us takes responsibility for our own health and disease, and for the information that we can generate about ourselves. When it comes to control over our own data, health data must be where we draw the line.”

This needs to happen everywhere, not just in diabetes. Will you join us in drawing the line?

What we heard and saw at #DData16 and #2016ADA

by Dana Lewis

As mentioned in the previous post, we had the privilege of coming to New Orleans this past weekend for two events – #DData16 and the American Diabetes Association Scientific Sessions (#2016ADA). A few things stuck out, which I wanted to highlight here.

At #DData16:

  • The focus was on artificial pancreas, and there was a great panel moderated by Howard Look with several of the AP makers. I was struck by how many of them referenced or made mention of #OpenAPS or the DIY/#WeAreNotWaiting movement, and the need for industry to collaborate with the DIY community (yes).
  • I was also floored when someone from Dexcom referenced having read one of my older blog posts that mentioned a question of why ??? was displayed to me instead of the information about what was actually going on with my sensor. It was a great reminder to me of how important it is for us to speak up and keep sharing our experiences and help device manufacturers know what we need for current and future products, the ones we use every day to help keep us alive.
  • Mark Wilson gave a PHENOMENAL presentation, using a great analogy about driving and accessing the dashboard to help people understand why people with diabetes might choose to DIY. He also talked about his experiences with #OpenAPS, and I highly recommend watching it. (Kudos to Wes for livestreaming it and making it broadly available to all – watch it here!) I’ve mentioned Mark & his DIY-ing here before, especially because one of his creations (the Urchin watchface) is one of my favorite ways to help me view my data, my way.
  • Howard DM’ed me in the middle of the day to ask if I minded going up as part of the patient panel of people with AP experiences. I wasn’t sure what the topic was, but the questions allowed us to talk about our experiences with AP (and in my case, I’ve been using a hybrid closed loop for something like 557 or so days at this point). I made several points about the need for a “plug n play” system, with modularity so I can choose the best pump, sensor, and algorithm for me – which may or may not be made all by the same company. (This is also FDA’s vision for the future, and Dr. Courtney Lias both gave a good presentation on this topic and was engaged in the event’s conversation all day!).

At #2016ADA:

  • There needs to be a patient research access program developed (not just by the American Diabetes Association for their future Scientific Sessions meetings, but at all scientific and academic conferences). Technology has enabled patients to make significant contributions to the medical and scientific fields, and cost and access are huge barriers to preventing this knowledge from scaling. At #2016ADA, “patient” is not even an option on the back of the registration form. Scott and I are privileged that we could potentially pay for this, but we don’t think we should have to pay ($410 for a day pass or $900 for a weekend pass) so much when we are not backed by industry or an academic organization of any sort. (As a side note, a big thank you to the many people who have a) engaged in discussion around this topic b) helped reach out to contacts at ADA to discuss this topic and c) asked about ways to contribute to the cost of us presenting this research this weekend.)
  • We presented research from 18 of the first 40 users of #OpenAPS. You can find the FULL CONTENT of our findings and the research poster content in this post on OpenAPS.org. We specifically posted our content online (and tweeted it out – see this thread) for a few reasons:
    • First, everything about #OpenAPS is open source. The content of our poster or any presentation is similarly open source.
    • Not everyone had time to come by the poster.
    • Not everyone has the privilege or funds to attend #2016ADA, and there’s no reason not to share this content online, especially when we will likely get more knowledge sharing as a result of doing so.
  • With the above in mind, we encouraged people stopping by to take whatever photos of our poster that they wanted, and told them about the content being posted online. (And in fact, in addition to the blog post about the poster, that information is now on the “Outcomes” page on OpenAPS.org.)
  • Frustratingly, some people were asked to take down posted photos of our poster. If anyone received such a note, please feel free to pass on my tweet that you have authorization by the authors to have taken/used the photo. This is another area (like the need to develop patient research access programs) that needs to be figured out by scientific/academic conferences – presenters/authors should be able to specifically allow sharing and dissemination of information that they are presenting.
  • Speaking of photos, I was surprised that around half a dozen clinicians (HCPs) stopped by and made mention of having used the picture of the #OpenAPS rig and the story of #OpenAPS in one of their presentations! I am thrilled this story is spreading, and being spread even by people we haven’t had direct contact with previously! (Feel free to use this photo in presentations, too, although I’d love to hear about your presentation and see a copy of it!)
  • We had many amazing conversations during the poster session on Sunday. It was scheduled for two hours (12-2pm), but we ended up being there around four hours and had hundreds of fantastic dialogues. Here were some of the most common themes of conversation:
    • Why are patients doing this?
      • Here’s my why: I originally needed louder alarms, built a smart alarm system that had predictive alerts and turned into an open loop system, and ultimately realized I could close the loop.
    • What can we learn from the people who are DIY-ing?
    • How can we further study the DIY closed loop community?
      • This is my second favorite topic, which touches on a few things – 1) the plan to do a follow up study of the larger cohort (since we now have (n=1)*84 loopers) with a full retrospective analysis of the data rather than just self-reported outcomes, as this study used; 2) ideas around doing a comparison study between one or more of the #OpenAPS algorithms and some of the commercial or academic algorithms; 3) ideas to use some of the #OpenAPS-developed tools (like a basal tuning tool that we are planning to build) in a clinical trial to help HCPs help patients adjust more quickly and easily to pump therapy.
    • What other pumps will work with this? How can there be more access to this type of DIY technology?
      • We utilize older pumps that allow us to send temp basal commands; we would love to use a more modern pump that’s able to be purchased on the market today, and had several conversations with device manufacturers about how that might be possible;  we’ll continue to have these conversations until it becomes a reality.
  • There is some great coverage coming of the poster & the #OpenAPS community, and I’ll post links here as I see them come out. For starters, Dave deBronkart did a 22 minute interview with Scott & I, which you can see here. DiabetesMine also included mention of the #OpenAPS poster in their conference roundup. And diaTribe wrote up the the poster as a “new now next”! Plus, WebMD wrote an article on #OpenAPS and the poster as well.
A picture of our #ADA2016 poster in the exhibit hall

Scott and I walked away from this weekend with energy for new collaborations (and new contacts for clinical trial and retrospective analysis partnerships) and several ideas for the next phase of studies that we want to plan in partnership with the #OpenAPS community. (We were blown away to discover that OpenAPS advanced meal assist algorithm is considered by some experts to be one of the most advanced and aggressive algorithms in existence for managing post-meal BG, and may be more advanced than anything that has yet been tested in clinical trials.) Stay tuned for more!

Research studies and usability thoughts

by Dana Lewis
It’s been a busy couple (ok, more than couple) of months since we last blogged here related to developments from #DIYPS and #OpenAPS. (For context, #DIYPS is Dana’s personal system that started as a louder alarms system and evolved into an open loop and then closed loop (background here). #OpenAPS is the open source reference design that enables anyone to build their own DIY closed loop artificial pancreas. See www.OpenAPS.org for more about that specifically.)

We’ve instead spent time spreading the word about OpenAPS in other channels (in the Wall Street Journal; on WNYC’s Only Human podcast; in a keynote at OSCON, and many other places like at the White House), further developing OpenAPS algorithms (incorporating “eating soon mode” and temporary targets in addition to building in auto-sensitivity and meal assist features), working our day jobs, traveling, and more of all of the above.

Some of the biggest improvements we’ve made to OpenAPS recently have been usability improvements. In February, someone kindly did the soldering of an Edison/Rileylink “rig” for me. This was just after I did a livestream Q&A with the TuDiabetes community, saying that I didn’t mind the size of my Raspberry Pi rig. I don’t. It works, it’s an artificial pancreas, the size doesn’t matter.

That being said… Wow! Having a small rig that clips to my pocket does wonders for being able to just run out the door and go to dinner, run an errand, go on an actual run, and more. I could do all those things before, but downsizing the rig makes it even easier, and it’s a fantastic addition to the already awesome experience of having a closed loop for the past 18 months (and >11,000 hours of looping). I’m so thankful for all of the people (Pete on Rileylink, Oscar on mmeowlink, Toby for soldering my first Edison rig for me, and many many others) who have been hard at work enabling more hardware options for OpenAPS, in addition to everyone who’s been contributing to algorithm improvements, assisting with improving the documentation, helping other people navigate the setup process, and more!

List of hardware for OpenAPS
That leads me to today. I just finished participating in a month-long usability study focused on OpenAPS users. (One of the cool parts was that several OpenAPS users contributed heavily to the design of the study, too!) We tracked every day (for up to 30 days) any time we interacted with the loop/system, and it was fascinating.

At one point, for a stretch of 3 days, we counted how many times we looked at our BGs. Between my watch, 3 phone apps/ways to view my data, the CGM receivers, Scott’s watch, the iPad by the bed, etc: dozens and dozens of glances. I wasn’t too surprised at how many times I glance/notice my BGs or what the loop is doing, but I bet other people are. Even with a closed loop, I still have diabetes and it still requires me to pay attention to it. I don’t *have* to pay attention as often as I would without a closed loop, and the outcomes are significantly better, but it’s still important to note that the human is still ultimately in control and responsible for keeping an eye on their system.

That’s one of the things I’ve been thinking about lately: the need to set expectations when a loop comes out on the commercial market and is more widely available. A closed loop is a tool, but it’s not a cure. Managing type 1 diabetes will still require a lot of work, even with a polished commercial APS: you’ll still need to deal with BG checks, CGM calibrations, site changes, dealing with sites and sensors that fall out or get ripped out…  And of course there will still be days where you’re sensitive or resistant and BGs are not perfect for whatever reason. In addition, it will take time to transition from the standard of care as we have it today (pump, CGM, but no algorithms and no connected devices) to open and/or closed loops.

This is one of the things among many that we are hoping to help the diabetes community with as a result of the many (80+ as of June 8, 2016!) users with #OpenAPS. We have learned a lot about trusting a closed loop system, about what it takes to transition, how to deal if the system you trust breaks, and how to use more data than you’re used to getting in order to improve diabetes care.

As a step to helping the healthcare provider community start thinking about some of these things, the #OpenAPS community submitted a poster that was accepted and will be presented this weekend at the 2016 American Diabetes Association Scientific Sessions meeting. This will be the first data published from the community, and it’s significant because it’s a study BY the community itself. We’re also working with other clinical research partners on various studies (in addition to the usability study, other studies to more thoroughly examine data from the community) for the future, but this study was a completely volunteer DIY effort, just like the entire OpenAPS movement has been.

Our hope is that clinicians walk away this weekend with insight into how engaged patients are and can be with their care, and a new way of having conversations with patients about the tools they are choosing to use and/or build. (And hopefully we’ll help many of them develop a deeper understanding of how artificial pancreas technology works: #OpenAPS is a great learning tool not only for patients, but also for all the physicians who have not had any patients on artificial pancreas systems yet.)

Stay tuned: the poster is embargoed until Saturday morning, but we’ll be sharing our results online beginning this weekend once the embargo lifts! (The hashtag for the conference is #2016ADA, and we’ll of course be posting via @OpenAPS and to #OpenAPS with the data and any insights coming out of the conference.)

Feedback on proposed FDA guidance on interoperable medical devices

by Dana Lewis

Our friend Anna McCollister-Slipp first alerted us to the proposed draft guidance recently released from the FDA, covering medical device interoperability. (You can read the draft guidance linked here.) We were subsequently among those asked by Amy Tenderich, and others, to share our initial thoughts and comments in response to the draft guidance. We wanted to publicly share our initial thoughts as a draft comment in response to the proposed guidelines (which we plan to officially submit as well), in hopes of encouraging subsequent discussion and additional commentary submitted in response to the draft guidance. We’d love to hear your thoughts after you read the linked guidance, as well as our comment below, and also encourage you to consider submitting a comment to the FDA regarding the guidance.

Draft comment response by Scott Leibrand & Dana Lewis:

The proposed FDA guidance on medical device interoperability is a gesture in the right direction, and is clearly intended to encourage medical devices to be designed with interoperability in mind. However, in the current draft form, the proposed guidance focuses too much on *discouraging* manufacturers from including the kinds of capabilities necessary to allow for continued innovation (particularly patient-led innovation as seen from the patient-driven #WeAreNotWaiting community).  Instead, much of the guidance assumes that manufacturers should only provide the bare minimum level of interoperability required for the intended use, and even goes so far as to suggest they “prevent access by other users” to any “interface only meant to be used by the manufacturer’s technicians for software updates or diagnostics”.  There is also much note of “authorized users”, which is language that is often currently leaned upon in the real world to exclude patients from accessing data on their own medical devices – so it would be worthwhile to further augment the guidance and/or more specifically review the implications of the guidance with an eye toward patients/users of medical devices.  The focus on including information on electronic data interfaces in product labeling is a good inclusion in the guidance, but it would be far more powerful (and less likely to be interpreted as a suggestion to cripple future products’ interoperability capabilities) if manufacturers were encouraged to properly include interface details for *all* their interfaces, not just those for which the manufacturer has already identified an intended use case.

Specific suggestions for improving the proposed guidance on medical device interoperability:
  • The guidance needs to more explicitly encourage manufacturers to design their products for *maximum* interoperability, including the ability for the device to safely interoperate with devices and for use cases that are not covered by the manufacturer’s intended uses.
  • Rather than designing device interoperability characteristics solely for intended uses, and withholding information related to non-intended uses, manufacturers should detail in product labeling the boundaries of the intended and tested use cases, and also provide information on all electronic data interfaces, even those with no manufacturer-intended use.  Labeling should be very clear on the interfaces’ design specifications, and should detail the boundaries of the uses the manufacturer intended, designed for, and tested.
  • The guidance should explicitly state that the FDA supports allowing third parties to access medical devices’ electronic data interfaces, according to the specifications published by the manufacturers, for uses other than those originally intended by the manufacturer.  They should make it clear that any off-label use by patients and health care professionals must be performed in a way that interoperates safely with the medical device per the manufacturer’s specifications, and it is the responsibility of the third party performing the off-label use, not the manufacturer, to ensure that they are making safe use of the medical device and its electronic data interface.  The guidance should make clear that the manufacturer is only responsible for ensuring that the medical device performs as specified, and that those specifications are complete and accurate.
With these kinds of changes, this guidance could be a powerful force for improving the pace of innovation in medical devices, allowing us to move beyond “proprietary” and “partnership” based solutions to solutions that harness the full power of third-party innovation by patients, health care professionals, clinical researchers and other investigators, and startup technology companies.  The FDA needs to set both clear rules that require manufacturers to document their devices capabilities as well as guidance that encourages manufacturers to provide electronic data interfaces that third parties can use to create new and innovative solutions (without introducing any new liability to the original manufacturer for having done so).  If the FDA does so, this will set the stage to allow innovation in medical devices to parallel the ever-increasing pace of technological innovation, while preserving and expanding patients rights to access their own data and control their own treatment.

The second year of #DIYPS (and my first full year with a closed loop)

by Dana Lewis

As we developed #DIYPS from a louder alarm system to a proactive alert system (details here about the original #DIYPS system before we closed the loop) to a closed loop that would auto-adjust my insulin pump basal rates as-needed overnight, I have been tracking the outcomes.

There were the first few nights of “wow! this works! I wake up at night when I’m high/low”. Then there were the first 100 nights that involved more iteration, testing, and improvements as we built it out more. And then suddenly it had been a year of using #DIYPS, and it was awesome to see how my average BG and a1c were down – and stayed down – while equally as important, my % time in range was up and stayed up. Not to mention, the quality of life improvements of having better nights of sleep were significant.

Year two has been along the same lines – more improvements on A1c/average BGs, time in range, and sleep – but heavily augmented by the fact that I now have a closed loop. If you follow me on Twitter or have checked out the hashtag, you might be tired of seeing me post CGM graphs. At this point, they all look very similar:

Looping for over a year and OpenAPS still successfully preventing overnight hypoglycemia Overnight safely looping with OpenAPS

(It’s worth noting that I still use #DIYPS, especially during the day to trigger “eating-soon” mode or basically get a snapshot glance at what my BGs are predicted to be, especially if I plan to go out without my loop in tow.)

In this past year, we also went from closing the loop with the #DIYPS algorithms (which required internet connectivity so I could tell the system when I was having carbs), to deciding we wanted to find a way to make it possible for more people to safely DIY a closed loop. (And, we feel very strongly that the DIY part of closing the loop is very important and deciding to do so is a very personal question.)

Thus, #OpenAPS was born in February 2015. Ben West spent a lot of time in 2015 building out the openaps toolkit to enable communication with diabetes devices to make things like closed loops possible. And so the first few months of #OpenAPS seemed slow, while we were busy working on the toolkit and finding ways to take what we learned with the #DIYPS closed loop and model a closed loop that didn’t require knowledge of carbs and could work without internet connectivity (see more about the #OpenAPS reference design here).

In July, we saw a tipping point – multiple other people began to close the loop, despite the fact that we didn’t have very much documented or available to guide them beyond the reference design. (These first couple of folks are incredible! Watch the #OpenAPS hashtag on Twitter to see them share some of their experiences.) With their help, the documentation has grown by leaps and bounds, as has the number of people who were subsequently able to close the loop.

As of 12/31/15 as I write this post, there are 22 people who have told me that they have a closed loop running that’s based on the OpenAPS reference design. I make a big deal about marking the date when I make a statement about the number of people running #OpenAPS (i.e. (n=1)*22), because every time I turn around, someone else seems to have done it!

It’s so exciting to see what’s happened in 2015, and what this type of #WeAreNotWaiting spirit has enabled. For Scott & me this year: we’ve climbed mountains around the world (from Machu Picchu to Switzerland), gotten married, changed jobs, and explored Europe together. Diabetes was there, but it wasn’t the focus.

There are dozens of other amazing stories like this in the #WeAreNotWaiting community. As we look to the new year, and I start to wonder about what might be next, I realize the speed of technology and the spirit of ingenuity in this community makes it impossible to predict exactly what we’ll see in 2016.

What I do know is this: we’ll see more people closing the loop, and we’ll see more ways to close the loop, using devices other than the Raspberry Pi, Carelink stick and Medtronic pump.  We’ll see more new ways to communicate with old & new diabetes devices and more ways to make the diabetes parts of our lives easier – all because #WeAreNotWaiting.

The power of visualizing your data, your way

by Dana Lewis

Sometimes, it’s the little things that make a big difference – even little glimpses of data, or little improvements to ways that you can control the way you access and view your data (and generate alarms).

For example, I recently had a conversation with a few people in the #WeAreNotWaiting community about the different watch faces that exist for displaying CGM data; and about how much I like my #DIYPS watch face. A few reasons why:

  • It’s a little more discreet than some watch faces showing BG data, so the average person won’t glance at my watch and see a large number.
  • It pulls from the #DIYPS interface, so I can see what I’m predicted to be, and any current recommendations (such as carbs, temp basal, or bolus needed).
DIYPS watchface showing Dana M. Lewis's OpenAPS data

It’s data-heavy, but I like having all this information without having to pull my CGM out and run calculations in my head; or pull out my phone and pull up a web page to #DIYPS; etc.

One of the many cool things about the #WeAreNotWaiting community is how together we have learned and created so many new ways to visualize our data, on various devices (tablets, phones, smart watches) and various size screens. And so when I hear that someone’s not wanting a smart watch, or isn’t using it for diabetes related things, sometimes I think it’s a matter of them finding the right tools to build their own display that works for them. Several times a week I hear about various people working on new, interesting DIY diabetes projects, and it’s awesome that we have tech to improve the tools we have – and excellent social media channels to communicate about these projects.

Related to that, I wanted to share an update – recently Milos, Jason, and others have done some really amazing work to visualize basal rates in Nightscout. (If you use Nightscout, you can get this in the 0.8.2 release – see here for more details.) This means it also can pull in temporary basal rates that are used in #OpenAPS, so you can get a nice visual showing the adjusted basal rate compared to normal scheduled basal rates – and see why it might be needed – on top of display of BG data and everything else that Nightscout offers.

Showing a fake drop in CGM glucose data that is a compression drop

In this example, it also shows how OpenAPS deals with compression drops, or how it might react to other flukey data. Remember, we designed OpenAPS to only enact 30 minute temporary basal rates in a way that is the safest possible thing to do, even if it loses communication. But if it keeps communication, and the system sees a drop and a return to the normal pattern from before (see visual), it can counteract a low temp with higher temp, or vice versa.

The visualization of temp basals in Nightscout (another example here) is an excellent improvement over how I previously used to check and see what OpenAPS had been doing. I have a watchface (similar to the above #DIYPS one) that shows me what the loop is doing currently, but when I wake up in the morning, I was mostly using a basic screen like the below to see the positive, negative, and net temp basal rates on an hourly basis and comparing that to my CGM graph to get an understanding of what happened.

Less insulin needed and OpenAPS reduced accordingly

Visualizing basal rates in Nightscout is a seemingly minor change, but every time we make a change like this that allows me to contextualize all of my data in one place (on a single glanceable watchface; or on the Nightscout screen); it saves a few seconds or minutes that add up to a lot of time saved every day, week, month, and additional year that I’m dealing with diabetes – a big win.

Building diabetes technology is like building a mountain bike

by Dana Lewis

I’ve had the opportunity to meet some fantastic people through our work with #DIYPS and #OpenAPS, one of whom is Eric Von Hippel, an MIT professor and researcher who work on user innovation. He shared a great example of user innovation that I was previously unfamiliar with, but is an awesome parallel for what we in the diabetes community are doing.

History: bike manufacturers used to make bikes for riding on flat surfaces. Some people wanted to ride their bikes down mountains, but existing bikes weren’t too comfortable (they didn’t have spring-based seats – ouch!). So, bikers started customizing and modifying the bikes they had. Eventually, bike manufacturers saw the demand and started building mountain bikes with the same features that the original mountain bikers had used. (And if you don’t like my paraphrased version of this story, Wikipedia is always your friend!)

The parallels:

We in the diabetes community have seen a series of needs that are not being met with our existing, FDA approved medical devices that are out on the market. From not-loud-enough alarms to not enabling us to track critical information like temporary basal rate history on the pump itself, these are the needs that drove me (and Scott) to first build #DIYPS and then to close the loop. At the same time, the need for remote data access is what drove John Costik and then the other Nightscout founders and developers to build an amazing, community-based open source tool to enable real-time remote data sharing.

Are there commercial products coming to market or are in the market that meet some of these needs now? Sure. But remember, I’ve had diabetes since 2002. In 2013, when Scott and I first started working to solve my need for louder alarms, there was NO commercial solution available for either remote data access or alarm customization. Thus the need for #DIYPS, which we built in 2013, and Nightscout, which blossomed in early 2014. And even though tools like Dexcom SHARE and MiniMed Connect have come to market (and in some cases, more quickly with help from the community communicating to the FDA about the critical importance of these tools), they came in 2015, which is a long time to wait for new tools when you’re dealing with diabetes 24/7/365. And when we managed to close the loop, again with help from the amazing #wearenotwaiting community, in December of 2014? Well, it’s now nearing the end of a year (and with amazing continued results from #OpenAPS not just for me, but for 13 additional people and potentially more to come soon), and we are AT LEAST a year and a half, if not more, away from any commercial device to reach the market. Not to mention: I’m not sure that the first generation of closed loops commercially available will be good enough for me.

The commercial entities are getting there. And, I always want to give them credit – I have a closed loop, but I can’t have one without a solidly working insulin pump and an excellent CGM system. They are, for the most part (with the exception of some missing features), making good, solid safe products for me to use.

The manufacturers are also starting to be open to more conversations. Not just “listening”, which they’ve sort-of/maybe done in drips over the past, but actual two-way conversations where we can share the needs that we know of in the community, and discuss what can be incorporated into their commercial product pipeline more quickly. This is progress starting to be made, and I’m excited to see more of it. It seems like there is a refreshed mindset and energy in the industry, as well as an understanding that we all care deeply about safety and that we’re all in this together to make life with diabetes less of a burden – like riding downhill on a mountain bike rather than a road bike.

“Should I build an artificial pancreas?” (It’s a personal question)

by Dana Lewis

Given that many (7 almost 8!) of us have closed the loop with OpenAPS, and sometimes show pictures of great overnights like the below, and given the fact that diabetes is a complicated, annoying, unfair disease, there is a lot of interest in closing the loop. Scott and I definitely get that, which is why we started the #OpenAPS movement.

I have been asked more and more lately, “Are you going to make this available to less tech-savvy people?” and “Should I build one?”.

Less insulin needed and OpenAPS reduced accordingly

The answer to the first question is emotionally hard, because a DIY build of a medical device that auto-adjusts insulin will always involve some technical knowledge – or at the very least, a growth mindset and willing to learn many new things to build a technical knowledge in order to proceed through the murky process of building a not-100%-documented artificial pancreas. You don’t have to be a programmer or an engineer; but you do have to have time and energy to spend learning as well as doing. I say this every day: the DIY part is important.

(And, I know people always want to hear “yes! It’ll be out on X date, and just as easy as installing Nightscout.” But it’s not as easy as installing something like Nightscout and never will be.)

That leads into the second question as an explanation of why it’s not as easy as we would wish:

Even if you have a very technical background, you’ll still spend time learning new languages, new pieces of software, and building pieces of your own. Things will break, things will need to be improved, and you need to have the knowledge of what’s going on and understand the logic of what you’re trying to achieve at each stage in order to be able to troubleshoot both the code part of things and the diabetes part of things.

It is hard. And it is a lot of work.

What you don’t see when someone says they’ve (DIY) closed the loop:

For every “I had such a great night” picture someone posts, that probably represents at least (10?+) hours of working on building or troubleshooting their system. Scott and I have each spent hundreds of hours working with my system, from trouble shooting, to building in new features, to reaffirming that things are all working as planned, etc. That figure should be a bit lower for new people as a result of our efforts, but it will never be as easy as just plugging something in, giving it your weight, and letting it take over. The system only does what you program it to do.

I often say this is “not a set-and-forget” system. And this is also true in the wearability of it. Right now, I use a Raspberry Pi and Carelink USB stick to communicate with my pump. They’re great, but the separate power source I also have to keep charged, plus keeping the USB in range of my pump, plus making sure it’s all working, can be a headache sometimes. (Which is why I’m so glad we made an offline mode, to reduce one of the biggest headaches of using the system.) When I’m on the go during the day, sometimes I don’t take it with me, or I choose to stop and un-power it and resume it when I get home. At this point, I wouldn’t be surprised if most people use it for nighttime use only (at least for the most part). But even with nighttime use only, there’s still constant changing of the code (in some cases daily), tweaking, altering, fixing, breaking, and un-breaking various parts.

Did I mention it was a lot of work?

And does a closed loop prevent all lows and highs? No.

It’s important to realize this is not a cure. I work really hard to do eating soon mode before meals to prevent spikes from the amount of carbs I choose to eat. I still have to test my blood sugar and calibrate my CGMs multiple times a day. I still have to change out my pump site every 2-3 days, and deal with the normal hassles of wonky pump sites, etc. I still have some highs – although the loop helps me handle them and I spend less time above range. And I still have some lows – although usually they’re from human error related to bolusing, the loop helps prevent them from always being a true low and/or blunting the drop so I don’t require as much correction. But diabetes is still a good amount of work, even with a closed loop.

Is it worth it to self-build an artificial pancreas?

This is a personal question. It’s a lot of work, with risk involved.

For me, I have decided and continue to decide that it’s worth it.

But only you can decide if the work and the risk are worth the potential rewards for you.

Does the FDA care more about safety than people with diabetes do?

by Dana Lewis

Today my inbox was suddenly flooded with links to a video with some commentary about artificial pancreas technology at a conference by a representative of the U.S. FDA. The implication many people are getting after watching the video clip is that this FDA representative is implying that people are being unsafe by building their own artificial pancreas. He mentions it is consumer prerogative to build an artificial pancreas – which is correct. The implication of his analogy is that changing your car and killing yourself is similar to a DIY artificial pancreas effort.

The scary takeaway from the video, in my opinion, as well as other public comments in the past, is the implication that the FDA cares more about the potential harms of taking action than the almost certain harms of inaction. And it’s increasingly frustrating that the FDA appears to imply publicly that those of us in the #wearenotwaiting community are doing things unsafely as a result of taking action.

Safety is what drives the #wearenotwaiting movement. In my case, I refuse to sleep another night with the fear that I won’t wake up in the morning because there’s not an FDA-approved system on the market that will wake me up if my life is in danger, let alone a system that can take action and change the situation to be more safe. So I built my own (#DIYPS), because the current FDA-approved CGM devices were not (and still are not) loud enough to wake me up at night, putting me at risk of dying in my sleep. And yes, it ultimately turned into an artificial pancreas – with the same goal of ensuring I wake up every morning, safely (alive). That is my prerogative for sure.

But I fail to see why the FDA, which collectively has no particular knowledge of these systems (especially as they have no jurisdiction, acknowledged on all fronts, over what I do myself – it’s my prerogative), is making public statements implying that these types of systems are categorically unsafe.

As a matter of fact, every DIY system I’ve seen is safer than the FDA-approved standard of care available for people with diabetes. The thousands of people using Nightscout, which is currently a DIY remote view-only monitoring system? Provides more safety and security for people with diabetes, not to mention it is helping achieve better outcomes for people with diabetes than they were able to achieve before with the standard of diabetes care as it exists today. (This was originally for the most part because of restricted access to data, although while that has improved there’s still interoperability issues getting access to real-time data in the same place from the 3+ average devices a person with diabetes uses…unless they have Nightscout or another DIY tool running.) The dozens of people working on their DIY version of an artificial pancreas system (many of whom are collaborating and sharing data in the #OpenAPS community)? These systems are safer than the standard of care, which is to let an insulin pump continue to overdose you if you are dangerously low while you sleep.

(You can see some of my personal data from #DIYPS, before we closed the loop, here and more about outcomes after we closed the loop and had #OpenAPS here. My closed loop artificial pancreas system continues to work excellently nine plus months in, and you can continue to watch my outcomes as I post them to Twitter regularly using the #DIYPS and #OpenAPS hashtags. I’ve also shared the other powerful lessons that DIY tech has helped me learn about diabetes care that helps all people with diabetes, regardless of technology.)

Are there risks to DIY efforts? Yes. But there’s risks to living with diabetes regardless. And as a person with diabetes, I am well aware of the risks that I choose to take. Diabetes is a disease in which you carry around large amounts of a lethal drug in your pocket that you are supposed to inject daily in order to save your life. As a person with diabetes, we are nothing but aware of the multitude of risks of living with this chronic disease 24/7/365.

In fact, even without a DIY artificial pancreas system, I am at risk every day simply from using my FDA-approved insulin pump that does not accurately track how much insulin I am given. (Read more here about how most insulin pumps on the market calculate IOB only from boluses, and often do not provide a record let alone incorporate any temporary adjustments to your basal rates and do not in any case track the impact of suspending your pump completely.)

And as someone who has founded the #OpenAPS movement, with the goal of an open and transparent effort to make safe and effective basic Artificial Pancreas System (APS) technology widely available to more quickly improve and save as many lives as possible and reduce the burden of type 1 diabetes…..we approach it with safety first in mind, and is a big part of why the DIY part is critical and is a part of our number one priority of safety.

Not everyone will choose to go the DIY route. In fact, most people do not and I am told all the time “Oh, I would never do that.” And that’s fine! Everyone can choose what they want for themselves.

But technology has made it increasingly feasible for those of us who want to improve our own safety to do so, because the industry and the FDA are not moving quickly enough to meet our needs.

That, indeed, is our prerogative – to increase our own safety.

Context – give me my data (on my device)

by Dana Lewis

Today I saw that Medtronic announced a partnership with IBM. You can read about it on Twitter, where I first saw it, or elsewhere online. There’s lots of news articles and PR about it, too, which I haven’t read yet in great detail.

My initial reaction:

Pointing out that I can't get temp basal histories on my insulin pump

Additional context:

When I reduce my insulin (either by “suspending” the pump’s activity altogether, or by reducing my basal rate with a “temp” or temporary basal rate), there’s no record of it visible on my insulin pump.

None, at all.

Suspended for 15 minutes while I’m in the shower? No record of it if I accidentally resume insulin activity before checking to see when I suspended it.

Same for if I go running and activate a reduce basal rate (again, a “temp”) of 0.3u/hour instead of my usual 1.3/hour. That’s 1u less of insulin than I normally get. If I cancel it, or if that hour ends without me noticing it?

No record at all.

Which means if my blood glucose skyrockets an hour later, it will take me much longer to catch up with insulin if I don’t realize that I’m -1u (negative one unit) below what my body is used to.

Suspending your pump for 3 hours to go swimming? Same deal. Your body has less insulin than it’s used to, but you have to manually and mentally keep track of it.

The reverse is true as well – if you are sick and your body is more resistant to insulin than usual, and you use a higher basal rate than your usual as a way to additionally correct for a high BG?

No record of the additional insulin you’re putting into your body above your baseline basal profile.

THIS IS DANGEROUS.

And yet this is the FDA approved medical device that everyone is happy that I’m using? Even with critical flaws that endanger my life every day?

And the world has a problem with patients “hacking” or otherwise finding ways to access this critical data since we can’t get it from our approved devices?

This is backwards.

Medtronic and other pump brands track how much “insulin on board” (aka IOB) you have…but this number is wrong, because it doesn’t calculate the lack of insulin if you adjust your basal rates (examples above).

This is something I’ve been doing with #DIYPS to compensate for the inaccessibility of data from my FDA-approved medical device. Instead, I have to calculate for myself the “net” IOB number that takes into account any ‘negative’ corrections from suspending or negative or positive temp basal rates. These make a huge difference in my diabetes care.

We’ve learned from talking to people about #DIYPS for a year and a half that many people don’t use temporary basal rates, even though they’re very effective to ward off future lows and highs.

Why?

For one thing, it’s because there’s no record in their pumps. It’s too hard, and too much guesswork when there’s no record.

I don’t understand why the pump companies seem to ignore this. (If someone has a pump that tracks net IOB and/or shows a history of temporary basal rates and suspension, let me know. I’m familiar mainly with Medtronic’s pumps.)

This is not ok.

So while I think there’s a lot of potential for Medtronic to do more things with diabetes data (like this or this) through this partnership with IBM’s Watson? In the meantime, I’d like them to start with something much more simple – and with guaranteed impact.

Give me, the patient, my data that I need – directly on my medical device – so I can safely take care of myself and better manage my diabetes.

(Note – I realize FDA approval cycles on pumps take a long time, and this is unlikely to get fixed in current pumps. But future pumps? This should be fixed across the industry. And in the meantime? Companies can and should make it much easier to access data from the pumps via their approved uploader methods and make it easier to read the data. Right now, it’s not even easy to see the data off your pump. Let’s change this.)