Understanding the Difference Between Open Source and DIY in Diabetes

There’s been a lot of excitement (yay!) about the results of the CREATE trial being published in NEJM, followed by the presentation of the continuation results at EASD. This has generated a lot of blog posts, news articles, and discussion about what was studied and what the implications are.

One area that I’ve noticed is frequently misunderstood is how “open source” and “DIY” are different.

Open source means that the source code is openly available to view. There are different licenses with open source; most allow you to also take and reuse and modify the code however you like. Some “copy-left” licenses commercial entities to open-source any software they build using such code. Most companies can and do use open source code, too, although in healthcare most algorithms and other code related to FDA-regulated activity is proprietary. Most open source licenses allow free individual use.

For example, OpenAPS is open source. You can find the core code of the algorithm here, hosted on Github, and read every line of code. You can take it, copy it, use it as-is or modify it however you like, because the MIT license we put on the code says you can!

As an individual, you can choose to use the open source code to “DIY” (do-it-yourself) an automated insulin delivery system. You’re DIY-ing, meaning you’re building it yourself rather than buying it or a service from a company.

In other words, you can DIY with open source. But open source and DIY are not the same thing!

Open source can and is usually is used commercially in most industries. In healthcare and in diabetes specifically, there are only a few examples of this. For OpenAPS, as you can read in our plain language reference design, we wanted companies to use our code as well as individuals (who would DIY with it). There’s at least one commercial company now using ideas from the OpenAPS codebase and our safety design as a safety layer against their ML algorithm, to make sure that the insulin dosing decisions are checked against our safety design. How cool!

However, they’re a company, and they have wrapped up their combination of proprietary software and the open source software they have implemented, gotten a CE mark (European equivalent of FDA approval), and commercialized and sold their AID product to people with diabetes in Europe. So, those customers/users/people with diabetes are benefitting from open source, although they are not DIY-ing their AID.

Outside of healthcare, open source is used far more pervasively. Have you ever used Zoom? Zoom uses open source; you then use Zoom, although not in a DIY way. Same with Firefox, the browser. Ever heard of Adobe? They use open source. Facebook. Google. IBM. Intel. LinkedIn. Microsoft. Netflix. Oracle. Samsung. Twitter. Nearly every product or service you use is built with, depends on, or contains open source components. Often times open source is more commonly used by companies to then provide products to users – but not always.

So, to more easily understand how to talk about open source vs DIY:

  • The CREATE trial used a version of open source software and algorithm (the OpenAPS algorithm inside a modified version of the AndroidAPS application) in the study.
  • The study was NOT on “DIY” automated insulin delivery; the AID system was handed/provided to participants in the study. There was no DIY component in the study, although the same software is used both in the study and in the real world community by those who do DIY it. Instead, the point of the trial was to study the safety and efficacy of this version of open source AID.
  • Open source is not the same as DIY.
  • OpenAPS is open source and can be used by anyone – companies that want to commercialize, or individuals who want to DIY. For more information about our vision for this, check out the OpenAPS plain language reference design.
Venn diagram showing a small overlap between a bigger open source circle and a smaller DIY circle. An arrow points to the overlapping section, along with text of "OpenAPS". Below it text reads: "OpenAPS is open source and can be used DIY. DIY in diabetes often uses open source, but not always. Not all open source is used DIY."

Continuation Results On 48 Weeks of Use Of Open Source Automated Insulin Delivery From the CREATE Trial: Safety And Efficacy Data

In addition to the primary endpoint results from the CREATE trial, which you can read more about in detail here or as published in the New England Journal of Medicine, there was also a continuation phase study of the CREATE trial. This meant that all participants from the CREATE trial, including those who were randomized to the automated insulin delivery (AID) arm and those who were randomized to sensor-augmented insulin pump therapy (SAPT, which means just a pump and CGM, no algorithm), had the option to continue for another 24 weeks using the open source AID system.

These results were presented by Dr. Mercedes J. Burnside at #EASD2022, and I’ve summarized her presentation and the results below on behalf of the CREATE study team.

What is the “continuation phase”?

The CREATE trial was a multi-site, open-labeled, randomized, parallel-group, 24-week superiority trial evaluating the efficacy and safety of an open-source AID system using the OpenAPS algorithm in a modified version of AndroidAPS. Our study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14 percentage points higher among those who used the open-source AID system (95% confidence interval [CI], 9.2 to 18.8; P<0.001) compared to those who used sensor augmented pump therapy; a difference that corresponds to 3 hours 21 minutes more time spent in target range per day. The system did not contribute to any additional hypoglycemia. Glycemic improvements were evident within the first week and were maintained over the 24-week trial. This illustrates that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID. This initial study concluded that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS, a widely used open-source AID solution, is efficacious and safe. These results were from the first 24-week phase when the two groups were randomized into SAPT and AID, accordingly.

The second 24-week phase is known as the “continuation phase” of the study.

There were 52 participants who were randomized into the SAPT group that chose to continue in the study and used AID for the 24 week continuation phase. We refer to those as the “SAPT-AID” group. There were 42 participants initially randomized into AID who continued to use AID for another 24 weeks (the AID-AID group).

One slight change to the continuation phase was that those in the SAPT-AID used a different insulin pump than the one used in the primary phase of the study (and 18/42 AID-AID participants also switched to this different pump during the continuation phase), but it was a similar Bluetooth-enabled pump that was interoperable with the AID system (app/algorithm) and CGM used in the primary outcome phase.

All 42 participants in AID-AID completed the continuation phase; 6 participants (out of 52) in the SAPT-AID group withdrew. One withdrew from infusion site issues; three with pump issues; and two who preferred SAPT.

What are the results from the continuation phase?

In the continuation phase, those in the SAPT-AID group saw a change in time in range (TIR) from 55±16% to 69±11% during the continuation phase when they used AID. In the SAPT-AID group, the percentage of participants who were able to achieve the target goals of TIR > 70% and time below range (TBR) <4% increased from 11% of participants during SAPT use to 49% during the 24 week AID use in the continuation phase. Like in the primary phase for AID-AID participants; the SAPT-AID participants saw the greatest treatment effect overnight with a TIR difference of 20.37% (95% CI, 17.68 to 23.07; p <0.001), and 9.21% during the day (95% CI, 7.44 to 10.98; p <0.001) during the continuation phase with open source AID.

Those in the AID-AID group, meaning those who continued for a second 24 week period using AID, saw similar TIR outcomes. Prior to AID use at the start of the study, TIR for that group was 61±14% and increased to 71±12% at the end of the primary outcome phase; after the next 6 months of the continuation phase, TIR was maintained at 70±12%. In this AID-AID group, the percentage of participants achieving target goals of TIR >70% and TBR <4% was 52% of participants in the first 6 months of AID use and 45% during the continuation phase. Similarly to the primary outcomes phase, in the continuation phase there was also no treatment effect by age interaction (p=0.39).

The TIR outcomes between both groups (SAPT-AID and AID-AID) were very similar after each group had used AID for 24 weeks (SAPT-AID group using AID for 24 weeks during the continuation phase and AID-AID using AID for 24 weeks during the initial RCT phase).. The adjusted difference in TIR between these groups was 1% (95% CI, -4 to 6; p=-0.67). There were no glycemic outcome differences between those using the two different study pumps (n=69, which was the SAPT-AID user group and 18 AID-AID participants who switched for continuation; and n=25, from the AID-AID group who elected to continue on the pump they used in the primary outcomes phase).

In the initial primary results (first 24 weeks of trial comparing the AID group to the SAPT group), there was a 14 percentage point difference between the groups. In the continuation phase, all used AID and the adjusted mean difference in TIR between AID and the initial SAPT results was a similar 12.10 percentage points (95% CI, p<0.001, SD 8.40).

Similar to the primary phase, there was no DKA or severe hypoglycemia. Long-term use (over 48 weeks, representing 69 person-years) did not detect any rare severe adverse events.

CREATE results from the full 48 weeks on open source AID with both SAPT (control) and AID (intervention) groups plotted on the graph.

Conclusion of the continuation study from the CREATE trial

In conclusion, the continuation study from the CREATE trial found that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS is efficacious and safe with various hardware (pumps), and demonstrates sustained glycaemic improvements without additional safety concerns.

Key points to takeaway:

  • Over 48 weeks total of the study (6 months or 24 weeks in the primary phase; 6 months/24 weeks in the continuation phase), there were 64 person-years of use of open source AID in the study, compared to 59 person-years of use of sensor-augmented pump therapy.
  • A variety of pump hardware options were used in the primary phase of the study among the SAPT group, due to hardware (pump) availability limitations. Different pumps were also used in the SAPT-AID group during the AID continuation phase, compared to the pumps available in the AID-AID group throughout both phases of trial. (Also, 18/42 of AID-AID participants chose to switch to the other pump type during the continuation phase).
  • The similar TIR results (14 percentage points difference in primary and 12 percentage points difference in continuation phase between AID and SAPT groups) shows durability of the open source AID and algorithm used, regardless of pump hardware.
  • The SAPT-AID group achieved similar TIR results at the end of their first 6 months of use of AID when compared to the AID-AID group at both their initial 6 months use and their total 12 months/48 weeks of use at the end of the continuation phase.
  • The safety data showed no DKA or severe hypoglycemia in either the primary phase or the continuation phases.
  • Glycemic improvements from this version of open source AID (the OpenAPS algorithm in a modified version of AndroidAPS) are not only immediate but also sustained, and do not increase safety concerns.
CREATE Trial Continuation Results were presented at #EASD2022 on 48 weeks of use of open source AID

Findings from the world’s first RCT on open source AID (the CREATE trial) presented at #ADA2022

September 7, 2022 UPDATEI’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or view an author copy here. You can also see a Twitter thread here, if you are interested in sharing the study with your networks.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NEJMoa2203913


(You can also see a previous Twitter thread here summarizing the study results, if you are interested in sharing the study with your networks.)

TLDR: The CREATE Trial was a multi-site, open-labeled, randomized, parallel-group, 24-week superiority trial evaluating the efficacy and safety of an open-source AID system using the OpenAPS algorithm in a modified version of AndroidAPS. Our study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14 percentage points higher among those who used the open-source AID system (95% confidence interval [CI], 9.2 to 18.8; P<0.001) compared to those who used sensor augmented pump therapy; a difference that corresponds to 3 hours 21 minutes more time spent in target range per day. The system did not contribute to any additional hypoglycemia. Glycemic improvements were evident within the first week and were maintained over the 24-week trial. This illustrates that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID. This study concluded that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS, a widely used open-source AID solution, is efficacious and safe.

The backstory on this study

We developed the first open source AID in late 2014 and shared it with the world as OpenAPS in February 2015. It went from n=1 to (n=1)*2 and up from there. Over time, there were requests for data to help answer the question “how do you know it works (for anybody else)?”. This led to the first survey in the OpenAPS community (published here), followed by additional retrospective studies such as this one analyzing data donated by the community,  prospective studies, and even an in silico study of the algorithm. Thousands of users chose open source AID, first because there was no commercial AID, and later because open source AID such as the OpenAPS algorithm was more advanced or had interoperability features or other benefits such as quality of life improvements that they could not find in commercial AID (or because they were still restricted from being able to access or afford commercial AID options). The pile of evidence kept growing, and each study has shown safety and efficacy matching or surpassing commercial AID systems (such as in this study), yet still, there was always the “but there’s no RCT showing safety!” response.

After Martin de Bock saw me present about OpenAPS and open source AID at ADA Scientific Sessions in 2018, we literally spent an evening at the dinner table drawing the OpenAPS algorithm on a napkin at the table to illustrate how OpenAPS works in fine grained detail (as much as one can do on napkin drawings!) and dreamed up the idea of an RCT in New Zealand to study the open source AID system so many were using. We sought and were granted funding by New Zealand’s Health Research Council, published our protocol, and commenced the study.

This is my high level summary of the study and some significant aspects of it.

Study Design:

This study was a 24-week, multi-centre randomized controlled trial in children (7–15 years) and adults (16–70 years) with type 1 diabetes comparing open-source AID (using the OpenAPS algorithm within a version of AndroidAPS implemented in a smartphone with the DANA-i™ insulin pump and Dexcom G6® CGM), to sensor augmented pump therapy. The primary outcome was change in the percent of time in target sensor glucose range (3.9-10mmol/L [70-180mg/dL]) from run-in to the last two weeks of the randomized controlled trial.

  • This is a LONG study, designed to look for rare adverse events.
  • This study used the OpenAPS algorithm within a modified version of AndroidAPS, meaning the learning objectives were adapted for the purpose of the study. Participants spent at least 72 hours in “predictive low glucose suspend mode” (known as PLGM), which corrects for hypoglycemia but not hyperglycemia, before proceeding to the next stage of closed loop which also then corrected for hyperglycemia.
  • The full feature set of OpenAPS and AndroidAPS, including “supermicroboluses” (SMB) were able to be used by participants throughout the study.

Results:

Ninety-seven participants (48 children and 49 adults) were randomized.

Among adults, mean time in range (±SD) at study end was 74.5±11.9% using AID (Δ+ 9.6±11.8% from run-in; P<0.001) with 68% achieving a time in range of >70%.

Among children, mean time in range at study end was 67.5±11.5% (Δ+ 9.9±14.9% from run-in; P<0.001) with 50% achieving a time in range of >70%.

Mean time in range at study end for the control arm was 56.5±14.2% and 52.5±17.5% for adults and children respectively, with no improvement from run-in. No severe hypoglycemic or DKA events occurred in either arm. Two participants (one adult and one child) withdrew from AID due to frustrations with hardware issues.

  • The pump used in the study initially had an issue with the battery, and there were lots of pumps that needed refurbishment at the start of the study.
  • Aside from these pump issues, and standard pump site/cannula issues throughout the study (that are not unique to AID), there were no adverse events reported related to the algorithm or automated insulin delivery.
  • Only two participants withdrew from AID, due to frustration with pump hardware.
  • No severe hypoglycemia or DKA events occurred in either study arm!
  • In fact, use of open source AID improved time in range without causing additional hypoglycemia, which has long been a concern of critics of open source (and all types of) AID.
  • Time spent in ‘level 1’ and ‘level 2’ hyperglycemia was significantly lower in the AID group as well compared to the control group.

In the primary analysis, the mean (±SD) percentage of time that the glucose level was in the target range (3.9 – 10mmol/L [70-180mg/dL]) increased from 61.2±12.3% during run-in to 71.2±12.1% during the final 2-weeks of the trial in the AID group and decreased from 57.7±14.3% to 54±16% in the control group, with a mean adjusted difference (AID minus control at end of study) of 14.0 percentage points (95% confidence interval [CI], 9.2 to 18.8; P<0.001). No age interaction was detected, which suggests that adults and children benefited from AID similarly.

  • The CREATE study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy.
  • This difference reflects 3 hours 21 minutes more time spent in target range per day!
  • For children AID users, they spent 3 hours 1 minute more time in target range daily (95% CI, 1h 22m to 4h 41m).
  • For adult AID users, they spent 3 hours 41 minutes more time in target range daily (95% CI, 2h 4m to 5h 18m).
  • Glycemic improvements were evident within the first week and were maintained over the 24-week trial. Meaning: things got better quickly and stayed so through the entire 24-week time period of the trial!
  • AID was most effective at night.
Difference between control and AID arms overall, and during day and night separately, of TIR for overall, adults, and kids

One thing I think is worth making note of is that one criticism of previous studies with open source AID is regarding the self-selection effect. There is the theory that people do better with open source AID because of self-selection and self-motivation. However, the CREATE study recruited a diverse cohort of participants, and the study findings (as described above) match all previous reports of safety and efficacy outcomes from previous studies. The CREATE study also found that the greatest improvements in TIR were seen in participants with lowest TIR at baseline. This means one major finding of the CREATE study is that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID.

This therefore means there should be NO gatekeeping by healthcare providers or the healthcare system to restrict AID technology from people with insulin-requiring diabetes, regardless of their outcomes or experiences with previous diabetes treatment modalities.

There is also no age effect observed in the trail, meaning that the results of the CREATE Trial demonstrated that open-source AID is safe and effective in children and adults with type 1 diabetes. If someone wants to use open source AID, they would likely benefit, regardless of age or past diabetes experiences. If they don’t want to use open source AID or commercial AID…they don’t have to! But the choice should 100% be theirs.

In summary:

  • The CREATE trial was the first RCT to look at open source AID, after years of interest in such a study to complement the dozens of other studies evaluating open source AID.
  • The conclusion of the CREATE trial is that open-source AID using the OpenAPS algorithm within a version of AndroidAPS, a widely used open-source AID solution, appears safe and effective.
  • The CREATE trial found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy; a difference that reflects 3 hours 21 minutes more time spent in target range per day.
  • The study recruited a diverse cohort, yet still produced glycemic outcomes consistent with existing open-source AID literature, and that compare favorably to commercially available AID systems. Therefore, the CREATE Trial indicates that a range of people with type 1 diabetes might benefit from open-source AID solutions.

Huge thanks to each and every participant and their families for their contributions to this study! And ditto, big thanks to the amazing, multidisciplinary CREATE study team for their work on this study.


September 7, 2022 UPDATE – I’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or like all of the research I contribute to, access an author copy on my research paper.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NE/Moa2203913

Note that the continuation phase study results are slated to be presented this fall at another conference!

Findings from the RCT on open source AID, the CREATE Trial, presented at #ADA2022

Automated Insulin Delivery: How artificial pancreas “closed loop” systems can aid you in living with diabetes (introducing “the APS book” by @DanaMLewis)

Tl;dr – I wrote a book about artificial pancreas systems / hybrid and fully closed loop systems / automated insulin delivery systems! It’s out today – you can buy a print copy on Amazon; a Kindle copy on Amazon; check out all the content on the web or your phone here; or download a PDF if you prefer.

A few months ago, I saw someone share a link to one of my old blog posts with someone else on Facebook. Quite old in fact – I had written it 5+ years ago! But the content was and is still relevant today.

It made me wonder – how could we as a diabetes community, who have been innovating and exploring new diabetes technology such as closed loop/artificial pancreas systems (APS), package up some of this knowledge and share it with people who are newer to APS? And while yes, much of this is tucked into the documentation for DIY closed loop systems, not everyone will choose a DIY closed loop system and also therefore may not see or find this information. And with regards to some of the things I’ve written here on DIYPS.org, not everyone will be lucky enough to have the right combination of search terms to end up on a particular post to answer their question.

Automated_Insulin_Delivery_by_DanaMLewis_example_covers_renderingThus, the idea for a book was born. I wanted to take much of what I’ve been writing here, sharing on Facebook and Twitter, and seeing others discuss as well, and put it together in one place to be a good starting place for someone to learn about APS in general. My hope is that it’s more accessible for people who don’t know what “DIY” or “open source” diabetes is, and it’s findable by people who also don’t know or don’t consider themselves to be part of the “diabetes online community”.

APSBook_NowAvailable_DanaMLewisIs it perfect? Absolutely not! But, like most of the things in the DIY community…the book is open source. Seriously. Here’s the repository on Github! If you see a typo or have suggestions of content to add, you can make a PR (pull request) or log an issue with content recommendations. (There’s instructions on the book page here with how to do either of those things!) I plan to make rolling updates to it, so you can see on the change log page what’s changed between major versions.)

It’s the first book out there that I know of on APS, but it won’t be the only one. I hope this inspires or moves more people to share their knowledge, through blogs or podcasts or future books, with the rest of our community and loved ones who want and need to learn more about managing type 1 diabetes.

“I will immediately recommend this book not just to people looking to use a DIY closed loop system, but also to anybody looking to improve their grasp on the management of type 1 diabetes, whether patient, caregiver, or healthcare provider.”

Aaron Neinstein, MD
Endocrinologist, UCSF

And as always, I’m happy to share what I’ve learned about the self-publishing process, too. I previously used CreateSpace for my children’s books, which got merged with Amazon’s Kindle Direct Publishing (KDP), and there was a learning curve for KDP for both doing the print version and doing the Kindle version. I didn’t get paid to write this book – and I didn’t write it for a profit. Like my children’s books, I plan to use any proceeds to donate copies to libraries and hospitals, and send any remaining funds to Life For A Child to help ensure as many kids as possible have access to insulin, BG monitoring supplies, and education.

I’m incredibly grateful for many people for helping out with and contributing to this book. You can see the full acknowledgement section with my immense thanks to the many reviewers of early versions of the book! And ditto for the people who shared their stories and experiences with APS. But special thanks go in particular to Scott for thorough first editing and overall support of every project I bring up out of the blue; to Tim Gunn for beautiful cover design of the book; and to Aaron Kowalski to be kind enough to write this amazing foreword.

Amazon_Button_APSBook_DanaMLewis

4 years DIY closed looping with #OpenAPS – what changed and what hasn’t

It’s hard to express the magnitude of how much closed looping can improve a person with diabetes’ life, especially to someone who doesn’t have diabetes or live closely with someone that does. There are so many benefits – and so many way beyond the typically studied “A1c improvement” and “increased time in range”. Sure, those happen (and in case you haven’t seen it, see some of the outcomes from various international studies looking at DIY closed loop outcomes). But everything else…it’s hard to explain all of the magic that happens in real life, that’s made so much richer by having technology that for the most part keeps diabetes out of the way, and more importantly: off the top of your mind.

Personally, my first and most obvious benefit, and the whole reason I started DIYing in the first place, was to have the peace of mind to sleep safely at night. Objective achieved, immediately. Then over time, I got the improvements in A1c and time in range, plus reduction in time spent doing diabetes ‘stuff’ and time spent thinking about my own diabetes. The artificial pancreas ‘rigs’ got smaller. We improved the algorithm, to the point where it can handle the chaos that is everything from menstrual cycle to having the flu or norovirus.

More recently, in the past ~17 months, I’ve achieved an ultimate level of not doing much diabetes work that I never thought was possible: with the help of faster insulin and things like SMB’s (improved algorithm enhancements in OpenAPS), I’ve been able do a simple meal announcement by pressing a button on my watch or phone..and not having to bolus. Not worrying about precise carb counts. Not worrying about specific timing of insulin activity. Not worrying about post-meal lows. Not worrying about lots of exercise. And the results are pretty incredible to me:

But I remember early on when we had announced that we had figured out how to close the loop. We got a lot of push back saying, well, that’s good for you – but will it work for anyone else? And I remember thinking about how if it helped one other person sleep safely at night..it would be worth the amount of work it would take to open source it. Even if we didn’t know how well it would work for other people, we had a feeling it might work for some people. And that for even a few people who it might work for, it was worth doing. Would DIY end up working for everyone, or being something that everyone would want to do? Maybe not, and definitely not. We wouldn’t necessarily change the world for everyone by open sourcing an APS, but that could help change the world for someone else, and we thought that was (and still is) worth doing. After all, the ripple effect may help ultimately change the world for everyone else in ways we couldn’t predict or expect.

Ripple_effect_DanaMLewisThis has become true in more ways than one.

That ‘one other person’ turned into a few..then dozen..hundreds..and now probably thousand(s) around the world using various DIY closed loop systems.

And in addition to more people being able to choose to access different DIY systems with more pumps of choice, CGMs of choice, and algorithm of choice, we’ve also seen the ripple effect in the way the world works, too. There is now, thankfully, at least one company who is evaluating open source code; running simulations with it; and where it is out-performing their original algorithm or code components, utilizing that knowledge to improve their system. They’re also giving back to the open source diabetes community, too. Hopefully more companies will take this approach & bring better products more quickly to the market. When they are ready to submit said products, we know at least U.S. regulators at the FDA are ready to quickly review and work with companies to get better tools on the market. That’s a huge change from years ago, when there was a lot of finger pointing and what felt like a lot of delay preventing newer technology from reaching the market. The other change I’m seeing is in diabetes research, where researchers are increasingly working directly with patients from the start and designing better studies around the things that actually matter to people with diabetes, including analyzing the impact and outcomes of open source technology.

After five years of open source diabetes work, and specifically four years of DIY closed looping, it finally feels like the ripples are ultimately helping achieve the vision we had at the start of OpenAPS, articulated in the conclusion of the OpenAPS Reference Design:

OpenAPS_Reference_Design_conclusionIs there still more work to do? Absolutely.

Even as more commercial APS roll out, it takes too long for these to reach many countries. And in most parts of the world, it’s still insanely hard and/or expensive to get insulin (which is one of the reasons Scott and I support Life For A Child to help get insulin, supplies, and education to as many children as possible in countries where otherwise they wouldn’t be able to access it – more on that here.). And even when APS are “approved” commercially, that doesn’t mean they’ll be affordable or accessible, even with health insurance. So I expect our work to continue, not only to support ongoing improvements with DIY systems directly; but also with encouraging and running studies to generalize knowledge from DIY systems; hopefully seeing DIY systems approved to work with existing interoperable devices; helping any company that will listen to improve their systems, both in terms of algorithms but also in terms of usability; helping regulators to see both what’s possible as well as what’s needed to successfully using these types of system in the real world. I don’t see this work ending for years to come – not until the day where every person with diabetes in every country has access to basic diabetes supplies, and the ability to choose to use – or not – the best technology that we know is possible.

But even so, after four years of DIY closed looping, I’m incredibly thankful for the quality of life that has been made possible by OpenAPS and the community around it. And I’m thankful for the community for sharing their stories of what they’ve accomplished or done while using DIY closed loop systems. It’s incredible to see people sharing stories of how they are achieving their best outcomes after 45 years of diabetes; or people posting from Antartica; or after running marathons; or after a successful and healthy pregnancy where they used their DIY closed loop throughout; or after they’ve seen the swelling in their eyes go done; etc.

The stories of the real-life impacts of this type of technology are some of the best ripple effects that I never want to forget.

Running and fueling for runs with type 1 diabetes

This blog post is not for you. (Well that sounds mean, doesn’t it? It’s not meant to be mean. But this post is written for a very small subset of people like me who are stumbling around on page 16 of Google trying to find someone sharing experiences and specific details around methods (both successful and less so) for fueling for longer endurance events such as full marathons or ultramarathons with type 1 diabetes. So – please don’t be offended, but also don’t be surprised if you don’t find this post very useful!)

I’ve started running again, and more, this year, and am now to the point where I’m considering running another full marathon sometime next year. As I adventure into running longer distances, and more miles, I’m reflecting on what I did in my first full marathon that worked related to diabetes, and what I want to try to do differently. This post is logging some of my experiences and notes to date, in honor of fellow page-16-of-Google-seekers, rather than waiting til after I run another full (if I do) and there continuing to be not much info out there.

Some background on my running:

I’m not a runner. And not a good runner. I never liked running. But, I walked the Seattle half marathon in December 2012 and thought it might be fun to then walk the full marathon in December 2013. However, I also tried snowboarding for the first time in January 2013 and majorly damaged my knee. I could barely walk the few blocks to work every day, let alone do my normal activities. It took several months, and several PT sessions, to get back to normal. But part of my frustration and pain manifested into the idea that I should recover enough to still walk that full marathon in December. And in order to be off the course by the time it closed, I would need to run a little bit. And I could barely walk, and never ran, so I would need to do some training to be able to run a mile or two out of the 26.2 I planned to otherwise walk. So I set off to teach myself how to run with the idea of walk/running the full, which evolved into a plan to run/walk it, and mostly eventually run it. And that’s what I did.

Now – this marathon was December 2013. This was right when we created DIYPS, and a year before we closed the loop, so I was in full, old-school traditional manual diabetes mode. And it sucked quite a bit. But now, almost 5 years later, with the benefit of everything I’ve learned from DIYPS and OpenAPS about insulin and food timing etc., here’s what I realized was happening – and why – in some of my training runs.

What I worried about was going low during the runs. So, I generally would set a low temporary basal rate to reduce insulin during the run, and try to run before dinner instead of after (to reduce the likelihood of running with a lot of active insulin in my body). I would also eat some kind of snack – I think for energy as well as making sure I didn’t go low. I would also carry a bottle of Gatorade to drink along the way.

With the benefit of 5 years of lots of learning/thinking about all the mechanics of diabetes, here’s what was happening:

Per the visualization, the carbs would hit in about 15 minutes. If I reduced insulin at the time of the run, it would drive my blood sugar up as well, over a longer time frame (after around 45+ minutes as the lack of insulin really started to kick in and previous basal impact tailed off). The combination of these usually meant that I would rise toward the middle or end of my short and medium runs, and end up high. In longer runs, I would go higher, then low – and sip gatorade, and have some roller coaster after that.

Now, this was frustrating in training runs, but I did ok for my long runs and my marathon had pretty decent BGs with no lows. However, knowing everything I know now, and commencing a new burst of running, I want to try to do better.

Here’s what I’ve been doing this year in 2018:

My original interest in running was to set a mileage goal for the year, because I didn’t run very much last year (around 50 miles, mostly throughout summer), and I wanted to try to run more regularly throughout the year to get a more regular dose of physical activity. (I am very prone to looking at Seattle weather in October-December and January-March and wanting to stay inside!) That mileage goal was ambitious for me since I didn’t plan to race/train for any distance. To help me stick to it, I divided it by 12 to give myself monthly sub-goals that I would try to hit as a way to stay on top of making regular progress to the goal.

(Ps – pro tip – it doesn’t matter how small or big your goal is. If you track % progress toward whatever your mileage goal is, it’s really nice! And it allows you to compete/compare progress, even if your friends have a much bigger mileage goal than you. That way everyone can celebrate progress, and you don’t have to tell people exactly what your mileage goal might be. What’s tiny for you is big for others; and what’s big for you may be small to others – and that doesn’t matter at all!)

This has worked really well. The first few months I scraped by in keeping up with my monthly goal. Except for February, when I had three weeks of flu and bronchitis, so I surged in March to finish February’s miles and March’s miles. I then settled back into a regular amount, meeting my monthly goals…and then surged again in August, so I was able to finish my yearly mileage in the middle of September! Wahoo! I didn’t plan to stop there, though, so I planned to keep running, and that’s where the idea of running the Seattle half (always the Sunday after Thanksgiving) popped up again, and maybe a full next year. I started adding some longer runs (two 7.5 miles; a 9.35 miler, and now a 13 miler) over the past month, and have felt really good about those, which has enabled me to start thinking more carefully about what I did last time BG-wise and why this time is so much easier.

Earlier in the year, even on my short runs (one mile or so), I quickly realized that because of the shorter peak of Fiasp, I was less likely to have previous insulin activity drive me low during the run. Within the first handful of runs, I stopped eating a snack or some carbs before the run. I also stopped setting a super high target an hour before my run. I gradually moved into just avoiding >1.5u of insulin on board before short runs; and for longer runs, setting a target of ~110 about 30 minutes before I walked out the door, mostly to avoid any of that insulin activity dosed that would kick in right after I started running. (Keep in mind when I talk about setting targets: I’m using OpenAPS, my DIY closed loop system that does automatic insulin dosing; and for fellow DIY closed loop users, I’m also using exercise mode settings so I can set lower targets like 110 and the targets also automatically adjust my sensitivity and recalculate IOB accordingly. So without those settings, I’d probably set the target to 130 or so.)

And this has worked quite well for me.

Is it perfect? No, I do still go low sometimes..but probably <10% of my runs instead of 50% of them, which is a huge improvement. Additionally, because of having OpenAPS running to pick up the rebound, there’s not usually much of a rebound and resulting roller coaster like I would have in 2013. Additionally, because autosensitivity is running, it picks up within a few hours of any additional sensitivity to insulin, and I don’t have any overnight lows after running. Yay!

However, that all assumes I’m running at a normal-for-my-body or slower speed.

There’s a nice (annoying) phenomenon that if you sprint/run faster than your body can really handle, your liver is going to dump and your BG will spike as a result:

I didn’t ever notice this in 2013, but I’ve now run enough and at varying paces to really understand what my fitness level is, and see very obvious spikes due to surges like this when I’m sprinting too fast. Some days, if I run too fast (even for a mile), I’ll have a surge up to 180 or 200 mg/dL, and that’ll be higher than my BG is for the rest of that 24 hour period. Which is annoying. Funny, but annoying. Not a big deal, because after my run OpenAPS can take care of bringing my down safely.

But other than the running-too-fast-spikes, my BGs have been incredible during and following my runs. As I thought about contributing factors to what’s working well, this is what’s likely been contributing:

  • with a mix of Fiasp & another short-acting insulin, I’m less likely to have the ‘whoosh’ effect of any IOB
  • but I’m also not starting with much IOB, because I tend to run first thing, or several hours after a meal
  • and of course, I have a DIY closed loop that takes care of any post-run sensitivity and insulin adjustments automatically

As I thought more about how much I’ve been running first thing in the morning/day, and usually not eating breakfast, that made me start reading about fasted long runs, or glycogen depleted runs, or low carb runs. People call them all these things, and I’m putting them in the post for my fellow page-16-of-Google-seekers. I call it “don’t eat breakfast before you run” long runs.

Now, some caveats before I go further into detail about what’s been working for me:

  • Your Diabetes May Vary (YDMV). in fact, it will. and so will your fitness level. what works for you may not be this. what works for you will probably not work for me. So, use this as input as one more blog post that you’ve read about a potential method, and then tweak and try what works for you. And you do you.
  • I’m not doing low carb. (And different people have different definitions of low carb, but I don’t think I’m meeting any of the definitions). What I’m talking about is not eating breakfast, a snack, or a meal before my runs in the morning. When I return from runs, I eat lunch, or a snack/meal, and the rest of my day is the usual amount/type of food that I would eat. (And since I have celiac, often times my gluten free food can be higher carb than a typical diet may be. It depends on whether I’m eating at home or eating out.) So, don’t take away anything related to overall carb consumption, because I’m not touching that! That’s a different topic. (And YDMV there, too.)
  • What I’m doing doesn’t seem to match anything I’ve read for non-T1D runners and what they do (or at least, the ones who are blogging about it).

Most of the recommendations I’ve read for glycogen depletion runs is to only do it for a few of your long runs in a marathon training cycle; that you should still eat breakfast before a full marathon; and you should only do fasted/glycogen depletion for slow, easy long runs.

I’m not sure yet (again, not in a full marathon cycle training), but I actually think based on my runs to date that I will do ok (or better) if I start without breakfast, and take applesauce/gatorade every once in a while as I feel I need it for energy, and otherwise managing my BG line. If I start a downtick, I’d sip some carbs. If I started dropping majorly, I’d definitely eat more. But so far, managing BG rather than trying to prescriptively plan carbs (for breakfast, or the concept of 30-60 per hour), works a lot better for me.

Part of the no-breakfast-works-better-for-me might be because the longevity of insulin in your body is actually like 6 hours (or more). Most non-T1D runners talk about a meal 3 hours before the start of your race. And they’re right that the peak and the bulk of insulin would be gone by then, but you’d still have a fair bit of residual insulin active for the first several hours of your race, and the body’s increased sensitivity to that insulin during exercise is likely what contributes to a lot of low BGs in us T1 runners. There’s also a lot of talk about how fasting during training runs teaches your body to better burn fat; and how running your race (such as a marathon) where you do carb during the race (whether that’s to manage BGs or more proactively) will make your body feel better since it has more fuel than you’re used to. That’s probably true; but given the lower insulin action during a run (because you’ve been fasted, and you may be on a lower temp basal rate to start), you’re likely to have a larger spike from a smaller amount of carbs, so the carb-ing you do before or during these long runs or a marathon race may need to be lower than what a non-T1D might do.

tl;dr – running is going better for me and BG management has been easier; I’m going to keep experimenting with some fasted runs as I build up to longer mileage; and YDMV. Hope some of this was helpful, and if you’ve done no-breakfast-long-runs-or-races, I’d love to hear how it worked for you and what during-race fueling strategy you chose as a result!

More open innovation coming soon?

This is a big deal: JDRF just announced funding for companies to open up their device protocols, with an explicit mention of projects including OpenAPS.

This is something we’ve been asking companies for over many years, but even the most forward-thinking diabetes device companies are still limiting patients to read-only retrospective access to the patient’s own data. That’s a start, but it isn’t enough.  We need all device makers to take the next step toward full and open interoperability: participating in open-protocol development of pumps and AP systems. If funding from a major organization like JDRF is what will be needed to prioritize this, great: we’re really excited to see them doing so.

Many of us in the diabetes community have chosen to accept the risk of a flawed device, because of the net risk reduction -and quality of life improvements – that come from being able to DIY closed loop. But that doesn’t mean we’re 100% happy with that.

  • We shouldn’t have to bandaid our pumps – literally – with tape.
  • We shouldn’t have to buy them second hand.
  • We should be able to use in-warranty devices that aren’t physically broken.

In order to use our medical devices in the safest and most effective way possible, we need the ability to remotely and safely control our devices – and understand them – as we see fit.  That means the makers of the medical devices we rely on need to openly document the communications protocols their devices use, so that any informed patient, or any company or organization operating on their behalf, can safely interact with the device.

It’s a big deal for JDRF to put resources into helping companies figure out how to do this, and ease liability and regulatory concerns. Thanks to everyone who’s been a vocal advocate in the DIY community; in organizations like JDRF; and individuals advocating at the medical device companies as well.  And props to the FDA, who last month released official guidance encouraging device makers to “design their devices with interoperability as an objective” and “clearly specify the relevant functional, performance, and interface characteristics to the user.”

We all have the same goals – to make life better, and safer, for those of us living with type 1 diabetes. I’m excited to see more efforts like this that further align all of our activities toward these goals.

To the diabetes device companies: we’ve long said we are happy to help if you want to figure out how to do this. Hopefully, you already have ideas about how to do this smartly and safely. But if you need help, let us know – we’re happy to help, because #WeAreNotWaiting and neither should you.

 

How I change pump sites

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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