Presentations and poster content from @DanaMLewis at #ADA2019

June 3, 2019December 22, 2022 by Dana Lewis

Like I did last year, I want to share the work being presented at #ADA2019 with those who are not physically there! (And if you’re presenting at #ADA2019 or another conference and would like suggestions on how to share your content in addition to your poster or presentation, check out these tips.) This year, I’m co-author on three posters and an oral presentation.

1056-P in category 12-D Clinical Therapeutics/New Technology–Insulin Delivery Systems, Preliminary Characterization of Rhythmic Glucose Variability In Individuals With Type 1 Diabetes, co-authored by Dana Lewis and Azure Grant.
- Come see us at the poster session, 12-1pm on Sunday! Dana & Azure will be presenting this poster.
76-OR, In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems, co-authored by Andreas Melmer, Thomas Züger, Dana Lewis, Scott Leibrand, Christoph Stettler, and Markus Laimer.
- Come hear our presentation in room S-157 (South, Upper Mezzanine Level), 2:15-2:30 pm on Saturday!
117-LB, DIWHY: Factors Influencing Motivation, Barriers and Duration of DIY Artificial Pancreas System Use Among Real-World Users, co-authored by Katarina Braune, Shane O’Donnell, Bryan Cleal, Ingrid Willaing, Adrian Tappe, Dana Lewis, Bastian Hauck, Renza Scibilia, Elizabeth Rowley, Winne Ko, Geraldine Doyle, Tahar Kechadi, Timothy C. Skinner, Klemens Raille, and the OPEN consortium.
- Come see us at the poster session, 12-1pm on Sunday! Scott will be presenting this poster.
78-LB, Detailing the Lived Experiences of People with Diabetes Using Do-it-Yourself Artificial Pancreas Systems – Qualitative Analysis of Responses to Open-Ended Items in an International Survey, co-authored by Bryan Cleal, Shane O’Donnell, Katarina Braune, Dana Lewis, Timothy C. Skinner, Bastian Hauck, Klemens Raille, and the OPEN consortium.
- Come see us at the poster session, 12-1pm on Sunday! Bryan Cleal will be presenting this poster.

See below for full written summaries and pictures from each poster and the oral presentation.

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First up: the biological rhythms poster, formally known as 1056-P in category 12-D Clinical Therapeutics/New Technology–Insulin Delivery Systems, Preliminary Characterization of Rhythmic Glucose Variability In Individuals With Type 1 Diabetes!

Lewis_Grant_BiologicalRhythmsT1D_ADA2019

As mentioned in this DiabetesMine interview, Azure Grant & I were thrilled to find out that we have been awarded a JDRF grant to further this research and undertake the first longitudinal study to characterize biological rhythms in T1D, which could also be used to inform improvements and personalize closed loop systems. This poster is part of the preliminary research we did in order to submit for this grant.

There is also a Twitter thread for this poster:

Poster from #ADA2019

Background:

Human physiology, including blood glucose, exhibits rhythms at multiple timescales, including hours (ultradian, UR), the day (circadian, CR), and the ~28-day female ovulatory cycle (OR).
Individuals with T1D may suffer rhythmic disruption due not only to the loss of insulin, but to injection of insulin that does not mimic natural insulin rhythms, the presence of endocrine-timing disruptive medications, and sleep disruption.
However, rhythms at multiple timescales in glucose have not been mapped in a large population of T1D, and the extent to which glucose rhythms differ in temporal structure between T1D and non-T1D individuals is not known.

Data & Methods:

The initial data set used for this work leverages the OpenAPS Data Commons. (This data set is available for all researchers – see www.OpenAPS.org/data-commons)
All data was processed in Matlab 2018b with code written by Azure Grant. Frequency decompositions using the continuous morlet wavelet transformation were created to assess change in rhythmic composition of normalized blood glucose data from 5 non-T1D individuals and anonymized, retrospective CGM data from 19 T1D individuals using a DIY closed loop APS. Wavelet algorithms were modified from code made available by Dr. Tanya Leise at Amherst College (see http://bit.ly/LeiseWaveletAnalysis)

Results:

Inter and Intra-Individual Variability of Glucose Ultradian and Circadian Rhythms is Greater in T1D

Figure_BiologicalRhythms_Lewis_Grant_ADA2019

Figure 1. Single individual blood glucose over ~ 1 year with A.) High daily rhythm stability and B.) Low daily rhythm stability. Low glucose is shown in blue, high glucose in orange.

Figure 2. T1D individuals (N=19) showed a wide range of rhythmic power at the circadian and long-period ultradian timescales compared to individuals without T1D (N=5).

A). Individuals’ CR and UR power, reflecting amplitude and stability of CRs, varies widely in T1D individuals compared to those without T1D. UR power was of longer periodicity (>= 6 h) in T1D, likely due to DIA effects, whereas UR power was most commonly in the 1-3 hour range in non-T1D individuals (*not shown). B.) On average, both CR and UR power were significantly higher in T1D (p<.05, Kruskal Wallis). This is most likely due to the higher amplitude of glucose oscillation, shown in two individuals in C.

Conclusions:

This is the first longitudinal analysis of the structure and variability of multi-timescale biological rhythms in T1D, compared to non-T1D individuals.
Individuals with T1D show a wide range of circadian and ultradian rhythmic amplitudes and stabilities, resulting in higher average and more variable wavelet power than in a smaller sample of non-T1D individuals.
Ultradian rhythms of people with T1D are of longer periodicity than individuals without T1D. These analyses constitute the first pass of a subset of these data sets, and will be continued over the next year.

Future work:

JDRF has recently funded our exploration of the Tidepool Big Data Donation Project, the OpenAPS Data Commons, and a set of non-T1D control data in order to map biological rhythms of glucose/insulin.
We will use signal processing techniques to thoroughly characterize URs, CRs, and ORs in the glucose/insulin for T1D; evaluate if stably rhythmic timing of glucose is associated with improved outcomes (lower HBA1C); and ultimately evaluate if modulation of insulin delivery based on time of day or time of ovulatory cycle could lead to improved outcomes.
Mapping population heterogeneity of these rhythms in people with and without T1D will improve understanding of real-world rhythmicity, and may lead to non-linear algorithms for optimizing glucose in T1D.

Acknowledgements:

We thank the OpenAPS community for their generous donation of data, and JDRF for the grant award to further this work, beginning in July 2019.

Contact:

Feel free to contact us at Dana@OpenAPS.org or azuredominique@berkeley.edu.

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Next up, 78-LB, Detailing the Lived Experiences of People with Diabetes Using Do-it-Yourself Artificial Pancreas Systems – Qualitative Analysis of Responses to Open-Ended Items in an International Survey, co-authored by Bryan Cleal, Shane O’Donnell, Katarina Braune, Dana Lewis, Timothy C. Skinner, Bastian Hauck, Klemens Raille, and the OPEN consortium.

78-LB_LivedExperiencesDIYAPS_OPEN_ADA2019

There is also a Twitter thread for this poster:

Poster from OPEN survey on lived experiences

Introduction

There is currently a wave of interest in Do-it-Yourself Artificial Pancreas Systems (DIYAPS), but knowledge about how the use of these systems impacts on the lives of those that build and use them remains limited. Until now, only a select few have been able to give voice to their experiences in a research context. In this study we present data that addresses this shortcoming, detailing the lived experiences of people using DIYAPS in an extensive and diverse way.

Methods

An online survey with 34 items was distributed to DIYAPS users recruited through the Facebook groups “Looped” (and regional sub-groups) and Twitter pages of the Diabetes Online Community (DOC). Participants were posed two open-ended questions in the survey, where personal DIYAPS stories were garnered; including knowledge acquisition, decision-making, support and emotional aspects in the initiation of DIYAPS, perceived changes in clinical and quality of life (QoL) outcomes after initiation and difficulties encountered in the process. All answers were analyzed using thematic content analysis.

Results

In total, 886 adults responded to the survey and there were a combined 656 responses to the two open-ended items. Knowledge of DIYAPS was primarily obtained via exposure to the communication fora that constitute the DOC. The DOC was also a primary source of practical and emotional support (QUOTES A). Dramatic improvements in clinical and QoL outcomes were consistently reported (QUOTES B). The emotional impact was overwhelmingly positive, with participants emphasizing that the persistent presence of diabetes in everyday life was markedly reduced (QUOTES C). Acquisition of the requisite devices to initiate DIYAPS was sometimes problematic and some people did find building the systems to be technically challenging (QUOTE D). Overcoming these challenges did, however, leave people with a sense of accomplishment and, in some cases, improved levels of understanding and engagement with diabetes management (QUOTE E).

Conclusion

The extensive testimony from users of DIYAPS acquired in this study provides new insights regarding the contours of this evolving phenomenon, highlighting factors inspiring people to adopt such solutions and underlining the transformative impact effective closed-loop systems bring to bear on the everyday lives of people with diabetes. Although DIYAPS is not a viable solution for everyone with type 1 diabetes, there is much to learn from those who have taken this route, and the life-changing results they have achieved should inspire all with an interest in artificial pancreas technology to pursue and dream of a future where all people with type 1 diabetes can reap the benefits that it potentially provides.

Also, see this word cloud generated from 665 responses in the two open-ended questions in the survey:

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Next up is 117-LB, DIWHY: Factors Influencing Motivation, Barriers and Duration of DIY Artificial Pancreas System Use Among Real-World Users, co-authored by Katarina Braune, Shane O’Donnell, Bryan Cleal, Ingrid Willaing, Adrian Tappe, Dana Lewis, Bastian Hauck, Renza Scibilia, Elizabeth Rowley, Winne Ko, Geraldine Doyle, Tahar Kechadi, Timothy C. Skinner, Klemens Raille, and the OPEN consortium.

There is also a Twitter thread for this poster:

DIWHY Poster at ADA2019

Background

Until recently, digital innovations in healthcare have typically followed a ‘top-down’ pathway, with manufacturers leading the design and production of technology-enabled solutions and patients involved only as users of the end-product. However, this is now being disrupted by the increasing influence and popularity of more ‘bottom-up’ and patient-led open source initiatives. A primary example is the growing movement of people with diabetes (PwD) who create their own “Do-it-Yourself” Artificial Pancreas Systems (DIY APS) through remote-control of medical devices employing an open source algorithm.

Objective

Little is known about why PwD leave traditional care pathways and turn to DIY technology. This study aims to examine the motivations of current DIYAPS users and their caregivers.

Research Design and Methods

An online survey with 34 items was distributed to DIYAPS users recruited through the Facebook groups “Looped” (and regional sub-groups) and Twitter pages of the “DOC” (Diabetes Online Community). Self-reported data was collected, managed and analyzed using the secure REDCap electronic data capture tools hosted at Charité – Universitaetsmedizin Berlin.

Results

1058 participants from 34 countries (81.3 % Europe, 14.7 % North America, 6.0 % Australia/WP, 3.1 % Asia, 0.1 % Africa), responded to the survey, of which the majority were adults (80.2 %) with type 1 diabetes (98.9 %) using a DIY APS themselves (43.0 % female, 56.8 % male, 0.3 % other) with a median age of 41 y and an average diabetes duration of 25.2y ±13.3. 19.8 % of the participants were parents and/or caregivers of children with type 1 diabetes (99.4 %) using a DIY APS (47.4 % female, 52.6 % male) with a median age of 10 y and an average diabetes duration of 5.1y ± 3.8. People used various DIYAPS (58.2 % AndroidAPS, 28.5 % Loop, 18.8 % OpenAPS, 5.7 % other) on average for a duration of 10.1 months ±17.6 and reported an overall HbA1c-improvement of -0.83 % (from 7.07 % ±1.07 to 6.24 % ±0.68 %) and an overall Time in Range improvement of +19.86 % (from 63.21 % ±16.27 to 83.07 % ±10.11). Participants indicated that DIY APS use required them to pay out-of-pocket costs in addition to their standard healthcare expenses with an average amount of 712 USD spent per year.

Primary motivations for building a DIYAPS were to improve the overall glycaemic control, reduce acute and long-term complication risk, increase life expectancy and to put diabetes on ‘auto-pilot’ and interact less frequently with the system. Lack of commercially available closed loop systems and improvement of sleep quality was a motivation for some. For caregivers, improvement of their own sleep quality was the leading motivation. For adults, curiosity (medical or technical interest) had a higher impact on their motivation compared to caregivers. Some people feel that commercial systems do not suit their individual needs and prefer to use a customizable system, which is only available to them as a DIY solution. Other reasons, like costs of commercially available systems and unachieved therapy goals played a subordinate role. Lack of medical or psychosocial support was less likely to be motivating factors for both groups.

Conclusions

Our findings suggest that people using Do-it-Yourself Artificial Pancreas systems and their caregivers are highly motivated to improve their/their children’s diabetes management through the use of this novel technology. They are also able to access and afford the tools needed to use these systems. Currently approved and available commercial therapy options may not be sufficiently flexible or customizable enough to fulfill their individual needs. As part of the project “OPEN”, the results of the DIWHY survey may contribute to a better understanding of the unmet needs of PwD and current challenges to uptake, which will, in turn, facilitate dialogue and collaboration to strengthen the involvement of open source approaches in healthcare.

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This is a written version of the oral presentation, In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems, co-authored by Andreas Melmer, Thomas Züger, Dana Lewis, Scott Leibrand, Christoph Stettler, and Markus Laimer.

Thread from “Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems”, presented by Andreas Melmer, incl. @dcberne1 colleagues @ChriStettler, @thomaszueger, @markuslaimer, & @scottleibrand & @DanaMLewis. #ADA2019

— Dana M. Lewis | #OpenAPS 🤖 (@danamlewis) June 8, 2019

APSComponents_Melmer_ADA2019

Artificial Pancreas Systems (APS) now exist, leveraging a CGM sensor, pump, and control algorithm. Faster insulin can play a role, too. Traditionally, APS is developed by commercial industry, tested by clinicians, regulated, and then patients can access it. However, DIYAPS is designed by patients for individual use.

There are now multiple different kinds of DIYAPS systems in use: #OpenAPS, Loop, and AndroidAPS. There are differences in hardware, pump, and software configurations. The main algorithm for OpenAPS is also used in AndroidAPS. DIYAPS can work offline; and also leverage the cloud for accessing or displaying data, including for remote monitoring. OnlineOffline_Melmer_ADA2019

This study analyzed data from the OpenAPS Data Commons (see more here). At the time this data set was used, there were n=80 anonymized data donors from the #OpenAPS community, with a combined 53+ years worth of CGM data.

TIR_PostLooping_Melmer_ADA2019 Looking at results for #OpenAPS data donors post-looping initiation, CV was 35.5±5.9, while eA1c was 6.4±0.7. TIR (3.9-10mmol/L) was 77.5%. Time spent >10 was 18.2%; time <3.9 was 4.3%.

SubcohortData_Melmer_ADA2019 We selected a subcohort of n=34 who had data available from before DIY closed looping initiation (6.5 years combined of CGM records), as well as data from after (12.5 years of CGM records).

For these next set of graphs, blue is BEFORE initiation (when just on a traditional pump); red is AFTER, when they were using DIYAPS.

TIR_PrePost_Melmer_ADA2019 Time in a range significantly increased for both wider (3.9-10 mmol/L) and tighter (3.9-7.8 mmol/L) ranges.

TOR_PrePost_Melmer_ADA2019 Time spent out of range decreased. % time spent >10 mmol/L decreased -8.3±8.6 (p<0.001); >13 mmol/L decreased -3.3±5.0 (p<0.001). Change in % time spent <3.9 mmol/L (-1.1±3.8 (p=0.153)), and <3.0 mmol/L (-0.7±2.2 (p=0.017)) was not significant.

We also analyzed daytime and nightime (the above was reflecting all 24hr combined; these graphs shows the increase in TIR and decrease in time out of range for both day and night).

Hypoglemic_event_reduction_Melmer_ADA2019 There were less CGM records in the hypoglycemic range after initiating DIYAPS.

Conclusion: this was a descriptive study analyzing available CGM data from #OpenAPS Data Commons. This study shows OpenAPS has potential to support glycemic control. However, DIYAPS are currently not regulated/approved technology. Further research is recommended.

(Note: a version of this study has been submitted and accepted for publication in the Journal of Diabetes. Obesity, and Metabolism.)

Missing metrics in diabetes measurement by @DanaMLewis

February 20, 2019 by Dana Lewis

“May I ask what your A1c is?”

This is a polite, and seemingly innocuous question. However, it’s one of my least favorite questions taken at face value. Why?

Well, this question is often a proxy for some of the following questions:

How well are *you* doing with DIY closed loop technology?
How well could *I* possibly do with DIY closed loop technology?
What’s possible to achieve in real-world life with type 1 diabetes?

But if I answered this question directly with “X.x%”, it leaves out so much crucial information. Such as:

What my BG targets are
- Because with DIY closed loop tech like OpenAPS, you can choose and set your own target.
- (That’s also one of the reasons why the 2018 OpenAPS Outcomes Study is fascinating to me, because people usually set high, conservative targets to start and then gradually lower them as they get comfortable. However, we didn’t have a way to retrospectively sleuth out targets, so those are results are even with the amalgamation of people’s targets being at any point they wanted at any time.)
What type of lifestyle I live
- I don’t consider myself to eat particularly “high” or “low” carb. (And don’t start at me about why you choose to eat X amount of carbs – you do you! and YDMV) Someone who *is* eating a lot higher or lower carb diet compared to mine, though, may have a different experience than me.
- Someone who is not doing exercise or activity may also have a different experience then me with variability in BGs. Sometimes I’m super active, climbing mountains (and falling off of them..more detail about that here) and running marathons and swimming or scuba diving, and sometimes I’m not. That activity is not so much about “being healthy”, but a point about how exercise and activity can actually make it a lot harder to manage BGs, both due to the volatility of the activity on insulin sensitivity etc.; but also because of the factor of going on/off of insulin for a period of time (because my pump is not waterproof).
What settings I have enabled in OpenAPS
- I use most of the advanced settings, such as “superMicroBoluses” (aka SMB – read more about how it works here); with a higher than default “maxSMBBasalMinutes”; and I also use all of the advanced exercise settings so that targets also nudge sensitivity in addition to autosensitivity picking up any changes after exercise and other sensitivity-change-inducing activities or events. I also get Pushover alerts to tell me if I need any carbs (and how many), if I’m dropping faster and expected to go below my target, even with zero temping all the way down.
What my behavioral choices are
- Timing of insulin matters. As I learned almost 5 years ago (wow), the impact of insulin timing compared to food *really* matters. Some people still are able to do and manage well with “pre-bolusing”. I don’t (as explained there in the previous link). But “eating soon” mode does help a lot for managing post-meal spikes (see here a quick and easy visual for how to do “eating soon”). However, I don’t do “eating soon” regularly like I used to. In part, because I’m now on a slightly-faster insulin that peaks in 45 minutes. I still get better outcomes when I do an eating-soon, sure, but behaviorally it’s less necessary.
- The other reason is because I’ve also switched to not bolusing for meals.
  - (The exceptions being if I’m not looping for some reason, such as I’m in the middle of switching CGM sensors and don’t have CGM data to loop off of.)

These settings and choices are all crucial information to understanding the X.x% of A1c.

Diabetes isn’t just the average blood glucose value. It’s not just the standard deviation or coefficient of variation or % time in range or how much BG fluctuates.

Diabetes impacts so much of our daily life and requires so much cognitive burden for us, and our loved ones. That’s part of the reasons I appreciate so much Sulka & his family being candid about how their A1C didn’t change, but the amount of work required to achieve it did (way fewer manual corrections). And ditto for Jason & the Wittmer family for sharing about the change in the number of school nurse visits before/after using OpenAPS. (See both of their stories in this post)

For me, my quality of life metric has always been first about sleep: can I sleep safely and with peace of mind at night? Yes. Then – how long can I safely sleep? (The answer: a lot. Yay!) But over time, my metrics have also evolved to consider how I can cut down (like Sulka) on the amount of work it takes to achieve my ideal outcomes, and find a happy balance there.

As I mentioned in this podcast recently, other than changing my pump site (here’s how I change mine) and soaking and swapping my CGM sensors (psst – soak your sensor!), I usually only take a few diabetes-related actions a day. They’re usually on my watch, pressing a button to either enable a temp target or entering carbs when I sit down to eat.

That’s a huge reduction in physical work, as well as amount of time spent thinking/planning/doing diabetes-related things. And when life happens – because I get the flu or the norovirus or I fall off a mountain and break my ankle – I don’t worry about diabetes any more.

So when I’m asked about A1c, my answer is not a simple “X.x%”. (And not just for the reason I’m annoyed by how much judging and shaming goes on around A1c, although that influences it, too.) I usually remind people that I first started with an “open loop” for a year, and that dropped my A1c by X%. And then I closed the loop, which reduced my A1c further. And we made OpenAPS even better over the last four years, which reduced it further. And then I completely stopped bolusing! And got less lows…and kept the same A1c.

And then I ask them what they’d really like to know. If it’s a fellow person with diabetes or a loved one, we talk about what problems they might be having or what areas they’d like to improve or what behaviors they’d like to change, if any. That’s usually way more effective than hearing “X.x%” of an A1c, and them wondering silently how to get there or what to do differently if someone wants to change things. (Or for clinicians who ask me, it turns into a discussion about choices and behaviors and tradeoffs that patients may choose to make.)

Remember, your diabetes may (and will) vary (aka, YDMV). Your lifestyle, the phase of life you’re in, your priorities, your body and health, and your choices will ALL be different than mine. That’s not bad in any way: that’s just the way it is. The behaviors I choose and the work I’m willing to do (or not do) to achieve *my* goals (and what my goals are), will be different than what you choose for yours.

And that’s therefore why A1c is not “enough” to me as a metric and something that we should compare people on, even though A1c is the “same” for everyone: because the work, time spent, behavioral tradeoffs, and goals related to it will all vary.

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

December 2, 2018December 20, 2022 by Dana Lewis

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:

We should be measuring and reducing user burden with AID in addition to improving TIR and A1c

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.

This 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:

Is 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

October 5, 2018December 20, 2022 by Dana Lewis

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!)

Showing number of runs per week with dips during travel weeks

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!

Accomplishing 78% of my yearly run goal so far

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:

Sprinting can drive BGs up

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!

Presentations and poster content from @DanaMLewis at #2018ADA

June 23, 2018December 22, 2022 by Dana Lewis

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

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

First up: the autosensitivity poster!

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

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

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

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

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

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

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

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

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

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

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

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

Criteria for using the #OpenAPS Data Commons:

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

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

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

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

I’m also thrilled to share a new tool we’ve created: an #OpenAPS simulator to allow us to more easily back-test and compare settings changes & feature changes in #OpenAPS code.

We pulled a recent week of data for n=1 adult PWD who does no-bolus, rough carb entry meal announcements, and ran the simulator to predict what the outcomes would be for no-bolus and no meal-announcement.

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

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

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

Michelle Litchman, Perry Gee, Lesly Kelly, and myself have a paper pending review analyzing social-media-reported outcomes & themes from DIY community.
There are also multiple other posters about DIY outcomes here at #2018ADA:
- See 983-P on learning curves in DIY AP (Crocket);
- 964-P on real-world outcomes from Korea (Choi et al);
- and 993-P on #OpenAPS outcomes in Italy (Tonolo et al).
There are many topics of interest in DIY community we’d like to see studies on, and have data for. These include: “eating soon” (optimal insulin dosing for lesser post-prandial spikes); and variability in sensitivity for various ages, pregnancy, and menstrual cycle.
I’m also thrilled to announce funding will be awarded to OPEN (a new collaboration on Outcomes of Patients’ Evidence, with Novel, DIY-AP tech), a 36-month international collaboration assessing outcomes, QOL, further development, access of real-world AP tech, etc. (More to come on this soon!)

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

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

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

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

—

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

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

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

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

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

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

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

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

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

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

June 8, 2018 by Dana Lewis

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

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

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

Example encouraging use of photography in content slides

Encouraging photography in the footer of my slides

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

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

—

Where you’ll find me at #2018ADA

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

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

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

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

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

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

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

April 5, 2018December 20, 2022 by Dana Lewis

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

Example of how getting out of bed - rather than dawn phenomenon hormones - can increase BG levels.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

March 26, 2018December 20, 2022 by Dana Lewis

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

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

I immediately was envisioning a number of things:

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

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

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

And here’s what it looked like on me:

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

Sticky notes with data scratched out and a USB stick with data from non-diabetes science experiments

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

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

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

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

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

RIP, original looping pump

March 5, 2018December 20, 2022 by Dana Lewis

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

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

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

The edge of my pump's reservoir section broke off and I tried to tape it back on.

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

—

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

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

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

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

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

Vitamin D and insulin sensitivity

February 9, 2018 by Dana Lewis

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

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

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

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

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

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

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

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

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

DIYPS.org

#WeAreNotWaiting to make the world a better place

#OpenAPS

Presentations and poster content from @DanaMLewis at #ADA2019

Missing metrics in diabetes measurement by @DanaMLewis

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

Running and fueling for runs with type 1 diabetes

Presentations and poster content from @DanaMLewis at #2018ADA

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

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

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

RIP, original looping pump

Vitamin D and insulin sensitivity

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