(Things I didn’t realize were involved in open-sourcing a DIY artificial pancreas: writing “yes you can” style self-help blog posts to encourage people to take the first step to TRY and use the open source code and instructions that are freely available….for those who are willing to try.)
You are the only thing holding yourself back from trying. Maybe it’s trying to DIY closed loop at all. Maybe it’s trying to make a change to your existing rig that was set up a long time ago. Maybe it’s doing something your spouse/partner/parent has previously done for you. Maybe it’s trying to think about changing the way you deal with diabetes at all.
Trying is hard. Learning is hard. But even harder (I think) is listening to the negative self-talk that says “I can’t do this” and perhaps going without something that could make a big difference in your daily life.
99% of the time, you CAN do the thing. But it primarily starts with being willing to try, and being ok with not being perfect right out of the gate.
I blogged last year (wow, almost two years ago actually) about making and doing and how I’ve learned to do so many new things as part of my OpenAPS journey that I never thought possible. I am not a traditional programmer, developer, engineer, or anything like that. Yes, I can code (some)…because I taught myself as I went and continue to teach myself as I go. It’s because I keep trying, and failing, then trying, and succeeding, and trying some more and asking lots of questions along the way.
Here’s what I’ve learned in 3+ years of doing DIY, technical diabetes things that I never thought I’d be able to accomplish:
You don’t need to know everything.
You really don’t particularly need to have any technical “ability” or experience.
You DO need to know that you don’t know it all, even if you already know a thing or two about computers.
(People who come into this process thinking they know everything tend to struggle even more than people who come in humble and ready to learn.)
You only need to be willing to TRY, try, and try again.
It might not always work on the first try of a particular thing…
If you think you can’t, you’re right – but it’s not that you can’t, it’s that you’re not willing to even try.
This list of things gets proved out to me on a weekly basis.
I see many people look at the #OpenAPS docs and think “I can’t do that” (and tell me this) and not even attempt to try.
What’s been interesting, though, is how many non-technical people jumped in and gave autotune a try. Even with the same level of no technical ability, several people jumped in, followed the instructions, asked questions, and were able to spin up a Linux virtual machine and run beta-level (brand new, not by any means perfect) code and get output and results. It was amazing, and really proved all those points above. People were deeply interested in getting the computer to help them, and it did. It sometimes took some work, but they were able to accomplish it.
OpenAPS, or anything else involving computers, is the same way. (And OpenAPS is even easier than most anything else that requires coding, in my opinion.) Someone recently estimated that setting up OpenAPS takes only 20 mouse clicks; 29 copy and paste lines of code; 10 entries of passwords or logins; and probably about 15-20 random small entries at prompts (like your NS site address or your email address or wifi addresses). There’s a reference guide, documentation that walks you through exactly what to do, and a supportive community.
You can do it. You can do this. You just have to be willing to try.
However, I think it’s worth recapping some of the amazing work that’s been done in the OpenAPS ecosystem over the past year, sometimes behind the scenes, because there are some key features and tools that have been added in that seem small, but are really impactful for people living with DIY closed loops.
Advanced meal assist (aka AMA)
This is an “advanced feature” that can be turned on by OpenAPS users, and, with reliable entry of carb information, will help the closed loop assist sooner with a post-meal BG rise where there is mis-timed or insufficient insulin coverage for the meal. It’s easy to use, because the PWD only has to put carbs and a bolus in – then AMA acts based on the observed absorption. This means that if absorption is delayed because you walk home from dinner, have gastroparesis, etc., it backs off and wait until the carbs actually start taking effect (even if it is later than the human would expect).
We also now have the purple line predictions back in Nightscout to visualize some of these predictions. This is a hallmark of the original iob-cob branch in Nightscout that Scott and I originally created, that took my COB calculated by DIYPS and visualized the resulting BG graph. With AMA, there are actually 3 purple lines displayed when there is carb activity. As described here in the OpenAPS docs, the top purple line assumes 10 mg/dL/5m carb (0.6 mmol/L/5m) absorption and is most accurate right after eating before carb absorption ramps up. The line that is usually in the middle is based on current carb absorption trends and is generally the most accurate once carb absorption begins; and the bottom line assumes no carb absorption and reflects insulin only. Having the 3 lines is helpful for when you do something out of the ordinary following a meal (taking a walk; taking a shower; etc.) and helps a human decide if they need to do anything or if the loop will be able to handle the resulting impact of those decisions.
The approach with a “preferences” file
This is the file where people can adjust default safety and other parameters, like maxIOB which defaults to 0 during a standard setup, ultimately creating a low-glucose-suspend-mode closed loop when people are first setting up their closed loops. People have to intentionally change this setting to allow the system to high temp above a netIOB = 0 amount, which is an intended safety-first approach.
One particular feature (“override_high_target_with_low”) makes it easier for secondary caregivers (like school nurses) to do conservative boluses at lunch/snack time, and allow the closed loop to pick up from there. The secondary caregiver can use the bolus wizard, which will correct down to the high end of the target; and setting this value in preferences to “true” allows the closed loop to target the low end of the target. Based on anecdotal reports from those using it, this feature sounds like it’s prevented a lot of (unintentional, diabetes is hard) overreacting by secondary caregivers when the closed loop can more easily deal with BG fluctuations. The same for “carbratio_adjustmentratio”, if parents would prefer for secondary caregivers to bolus with a more conservative carb ratio, this can be set so the closed loop ultimately uses the correct carb amount for any needed additional calculations.
Autosens is a feature that has to be turned on specifically (like AMA) in order for people to utilize it, because it’s making adjustments to ISF and targets and looping accordingly from those values. It also have safety caps that are set and automatically included to limit the amount of adjustment in either direction that autosens can make to any of the parameters.
Thanks to Intel, we were introduced to a board designer who collaborated with the OpenAPS community and inspired the creation of the “Explorer Board”. It’s a multipurpose board that can be used for home automation and all kinds of things, and it’s another tool in the toolbox of off-the-shelf and commercial hardware that can be used in an OpenAPS setup. It’s enabled us, due to the built in radio stick, to be able to drastically reduce the size of an OpenAPS setup to about the size of two Chapsticks.
As soon as we were working on the Explorer Board, I envisioned that it would be a game changer for increasing access for those who thought a Pi was too big/too burdensome for regular use with a DIY closed loop system. I knew we had a lot of work to do to continue to improve the setup process to cut down on the friction of the setup process – but balancing that with the fact that the DIY part of setting up a closed loop system was and still is incredibly important. We then worked to create the oref0-setup script to streamline the setup process. For anyone building a loop, you still have to set up your hardware and build a system, expressing intention in many places of what you want to do and how…but it’s cut down on a lot of friction and increased the amount of energy people have left, which can instead be focused on reading the code and understanding the underlying algorithm(s) and features that they are considering using.
The OpenAPS “docs” are an incredible labor of love and a testament to dozens and dozens of people who have contributed by sharing their knowledge about hardware, software, and the process it takes to weave all of these tools together. It has gotten to be very long, but given the advent of the Explorer Board hardware and the setup scripts, we were able to drastically streamline the docs and make it a lot easier to go from phase 0 (get and setup hardware, depending on the kind of gear you have); to phase 1 (monitoring and visualizing tools, like Nightscout); to phase 2 (actually setup openaps tools and build your system); to phase 3 (starting with a low glucose suspend only system and how to tune targets and settings safely); to phase 4 (iterating and improving on your system with advanced features, if one so desires). The “old” documentation and manual tool descriptions are still in the docs, but 95% of people don’t need them.
IFTTT and other tool integrations
It’s definitely worth calling out the integration with IFTTT that allows people to use things like Alexa, Siri, Pebble watches, Google Assistant (and just about anything else you can think of), to easily enter carbs or “modes” for OpenAPS to use, or to easily get information about the status of the system. (My personal favorite piece of this is my recent “hack” to automatically have OpenAPS trigger a “waking up” mode to combat hormone-driven BG increases that happen when I start moving around in the morning – but without having to remember to set the mode manually!)
..and that was all just things the community has done in 2016! There are some other exciting things that are in development and being tested right now by the community, and I look forward to sharing more as this advanced algorithm development continues.
Autosensitivity (or “autosens”, for short hand) is an advanced feature that can optionally be enabled in OpenAPS.
We know how hard it is for a PWD (person with diabetes) to pay attention to all the numbers and all the things and realize when something is “off”. This could be a bad pump site, a pump site going bad, hormones from growth, hormones from menstrual cycles, sensitivity from exercise the day before, etc. So at the beginning of the year, Scott and I started brainstorming with the community about automatically detecting when the PWD is more or less sensitive to insulin than normal, and adjusting accordingly. Building on the success we’d had in DIYPS with fixed “sensitivity” and “resistance” modes, we built the feature to assess how sensitive or resistant the body is (compared to normal), rather than just a binary mode that sets a predefined response.
How OpenAPS calculates autosensitivity/how it works
It looks at each BG data point for the last 24 hours and calculates the delta (actual observed change) over the last 5 minutes. It then compares it to “BGI” (blood glucose impact, which is how much BG *should* be dropping from insulin alone), and assesses the “deviations” (differences between the delta and BGI).
When sensitivity is normal and basals are well tuned, we expect somewhere between 45-50% of non-meal deviations to be negative, and the remaining 50-55% of deviations should be positive. (To exclude meal-related deviations, we exclude overly large deviations from the sample.) So if you’re outside of that range, you are probably running sensitive or resistant, and we want to adjust accordingly. The output of the detect-sensitivity code is a single ratio number, which is then used to adjust both the baseline basal rate as well as the insulin sensitivity factor (and, optionally, BG targets).
Autosens is designed to detect to food-free downward drift, due to basal rates being too high for the current state of the body, and will adjust basals downward to compensate. The other meal-assist related portion of the algorithms do a pretty good job of dealing with larger than expected post-meal spikes due to resistance: auto-sensitivity mostly comes into play for resistance when you’re sick or otherwise riding high even without food.
Does this calculate basals?
No. Similar to everything else in OpenAPS, this works from your established basals – meaning the baseline basal rates in your pump are what the sensitivity calculations are adjusting from. If you run a marathon and your sensitivity is normally 40, it might adjust your sensitivity to 60 (meaning 1u of insulin would drop your BG an expected 60mg/dl instead of 40 mg/dl) and temporarily adjust your baseline basal rate of 1u to .6u/hour, for example.
This algorithm is simply saying “there’s something going on, let’s adjust proportionately to deal with the lower-than-usual or higher-than-usual sensitivity, regardless of cause”. It easily detects “your basals are too high and/or your ISF is too low” or “your basals are too low and/or your ISF is too high”, but actually differentiating between the effect of basal and ISF is a bit more difficult to do with a simple algorithm like this, so we’re working on a number of new algorithms and tools (see “oref0 issue 99” for our brainstorming on basal tuning and the subsequent issues linked from there) to tackle this in the future.
#OpenAPS’s autosensitivity adjustments during norovirus
After I got over the worst of the norovirus, I started looking at what OpenAPS was calculating for my sensitivity during this time. I was especially curious what would happen during the 2-3 days when I was eating very little.
My normal ISF is 40, but OpenAPS gradually calculated the shift in my sensitivity all the way to 50. That’s really sensitive, and in fact I don’t remember ever seeing a sensitivity adjustment that dramatic – but makes sense given that I usually don’t go so long without eating. (Usually when I notice I’m a little sensitive, I’ll check and see that autosens has been adjusting based on an estimated 43 or so sensitivity.)
And in later days, as expected when sick, I shifted to being more resistant. So autosens continued to assess the data and began adjusting to an estimated sensitivity of 38 as my body continued fighting the virus.
It is so nice to have the tools to automatically make these assessments and adjustments, rather than having to manually deal with them on top of being sick!
When I first started throwing up over the first 8 hours, as is pretty normal for norovirus, I first worried about going low, because obviously my stomach was empty.
Nope. I never went lower than about 85 mg/dl. Even when I didn’t eat at all for > 24 hours and very little over the course of 5 days.
After that, I worried about going high as my body was fighting off the virus.
Nope. I never went much higher than a few minutes in the 160s. Even when I sipped Gatorade or gasp, ate two full crackers at the end of day two and didn’t bolus for the carbs.
The closed loop (as designed – read the OpenAPS reference design for more details) observed the rising or dropping BGs and adjusted insulin delivery (using temporary basal rates) up or down as needed. I sometimes would slowly rise to 150s and then slowly head back down to the 100s. I only once started dropping slowly toward the 80s, but leveled off and then slowly rose back up to the 110s.
None of this (\/\/\/\/\) crazy spiking and dropping fast that causes me to overreact.
No fear for having to force myself to drink sugar while in the midst of the worst of the norovirus.
No worries, diabetes-wise, at all. In fact, it didn’t even OCCUR to me to test or think about ketones (I’m actually super sensitive and can usually feel them well before they’ll register otherwise on a blood test) until someone asked on Twitter.
#OpenAPS balanced BGs during a stomache bug flawlessly. Never low, never high. Even w no boluses for sips of Gatorade and the like. Amazing.
I was talking with my father-in-law (an ER doc) and listening to him explain how anti-nausea medications (like Zofran) has reduced ER visits. And I think closed loop technology will similarly dramatically reduce ER visits for people with diabetes when sick with things like norovirus and flu and that sort of thing. Because instead of the first instance of vomiting causing a serious spiral and roller coaster of BGs, the closed loop can respond to the BG fluctuations in a safe way and prevent human overreaction in either direction.
3 days of hardly any food when dealing w/ norovirus. Mind blowing for daily avg BG ranges to be 108-113 with 92-97% time in range. #OpenAPS
This isn’t what you hear about when you look at various reports and articles (like hey, OpenAPS mentioned in The Lancet this week!) about this type of technology – it’s either general outcome reports or traditional clinical trial results. But we need to show the full power of these systems, which is what I experienced over the past week.
I’m reassured now for the future that norovirus, flu, or anything else I may get will likely be not as hard to deal with as it was for the first 12 years of living with diabetes when getting sick. That’s more peace of mind (in addition to what I get just being able to safely sleep every night) that I never expected to have, and I’m incredibly thankful for it.
(I’m also thankful for the numerous wonderful people who share their stories about how this technology impacts their lives – check out this wonderful video featuring the Mazaheri family to see what a difference this is making in other people’s lives. I’m so happy that the benefits I see from using DIY technology are available to so many other people, too. At latest count, there are (n=1)*174 other people worldwide using DIY closed loop technology, and we collectively have over half a million real-world hours using closed loop technology.)
I have now lived with diabetes for more than half of my life.
That also means I have now lived less than half of my life without diabetes.
This somehow makes the passing of another year living with diabetes seem much more impactful to me. Maybe not to you, or to someone else with a different experience of living with diabetes and a different timeline of life before and after diagnosis…but to me this is a big one.
(That’s almost as significant a marker of a “with” vs. “without” comparison as living “with” vs. “without” diabetes.)
And because I ended up with type 1 diabetes, I found out that doing things for other people and the communities you’re a part of is a powerful way to help yourself, both in the short term and the long term. That’s what drove me to figure out a way to take #DIYPS closed loop and make it something open source. And by doing that, I learned so much more about open source, and have been able to partner with incredible people innovating in hardware and software. These collaborations have resulted in an incredibly rich community of passionate people I like to call #OpenAPS-ers.
While #OpenAPS is by no means a cure, and no artificial pancreas will be a cure, they provide an immeasurably improved quality of life that a lot of us didn’t realize was possible with diabetes. Someone told me he can get the same results for his child living with diabetes, but with #OpenAPS it requires about 85% less work. And given the enormous time and cognitive burden of diabetes, this is a HUGE reduction.
And now doors are opening for us collectively to make even more of a significant impact on the diabetes community, and our fellow patient communities. Yesterday, while at the White House Frontiers conference, NIH Director Dr. Francis Collins was in the audience during my panel. At the end of the day, he stopped me to ask questions about my experiences and perspective on the FDA and what we need from the government. I was able to talk with him about the need for FDA & other parts of the government to help foster and support open source innovation. We talked about the importance of data access for patients, and the need for data visibility on commercially approved medical devices.
This is not just a need of people with diabetes (although it’s certainly very applicable for all of the manufacturers with pipelines full of artificial pancreas products): these are universal needs of people dealing with serious health conditions.
Given what I heard yesterday, it’s working. The #WeAreNotWaiting spirit is infusing our partners in these other areas. We are planting seeds, building relationships, and working in collaboration with those at the FDA, NIH, HHS in addition to those in industry and academia. I know they were working toward these same goals before, but social media has helped raise up our collective voices about the burning need to make things better, sooner, for more people.
So if I have to live the rest of my life at a ratio where more than half of it has been spent living with diabetes, I look forward to continuing to work to get to an 85% reduction in the burden of daily life with diabetes for everyone.
It’s been a busy couple (ok, more than couple) of months since we last blogged here related to developments from #DIYPS and #OpenAPS. (For context, #DIYPS is Dana’s personal system that started as a louder alarms system and evolved into an open loop and then closed loop (background here). #OpenAPS is the open source reference design that enables anyone to build their own DIY closed loop artificial pancreas. See www.OpenAPS.org for more about that specifically.)
We’ve instead spent time spreading the word about OpenAPS in other channels (in the Wall Street Journal; on WNYC’s Only Human podcast; in a keynote at OSCON, and many other places like at the White House), further developing OpenAPS algorithms (incorporating “eating soon mode” and temporary targets in addition to building in auto-sensitivity and meal assist features), working our day jobs, traveling, and more of all of the above.
Some of the biggest improvements we’ve made to OpenAPS recently have been usability improvements. In February, someone kindly did the soldering of an Edison/Rileylink “rig” for me. This was just after I did a livestream Q&A with the TuDiabetes community, saying that I didn’t mind the size of my Raspberry Pi rig. I don’t. It works, it’s an artificial pancreas, the size doesn’t matter.
That being said… Wow! Having a small rig that clips to my pocket does wonders for being able to just run out the door and go to dinner, run an errand, go on an actual run, and more. I could do all those things before, but downsizing the rig makes it even easier, and it’s a fantastic addition to the already awesome experience of having a closed loop for the past 18 months (and >11,000 hours of looping). I’m so thankful for all of the people (Pete on Rileylink, Oscar on mmeowlink, Toby for soldering my first Edison rig for me, and many many others) who have been hard at work enabling more hardware options for OpenAPS, in addition to everyone who’s been contributing to algorithm improvements, assisting with improving the documentation, helping other people navigate the setup process, and more!
That leads me to today. I just finished participating in a month-long usability study focused on OpenAPS users. (One of the cool parts was that several OpenAPS users contributed heavily to the design of the study, too!) We tracked every day (for up to 30 days) any time we interacted with the loop/system, and it was fascinating.
At one point, for a stretch of 3 days, we counted how many times we looked at our BGs. Between my watch, 3 phone apps/ways to view my data, the CGM receivers, Scott’s watch, the iPad by the bed, etc: dozens and dozens of glances. I wasn’t too surprised at how many times I glance/notice my BGs or what the loop is doing, but I bet other people are. Even with a closed loop, I still have diabetes and it still requires me to pay attention to it. I don’t *have* to pay attention as often as I would without a closed loop, and the outcomes are significantly better, but it’s still important to note that the human is still ultimately in control and responsible for keeping an eye on their system.
That’s one of the things I’ve been thinking about lately: the need to set expectations when a loop comes out on the commercial market and is more widely available. A closed loop is a tool, but it’s not a cure. Managing type 1 diabetes will still require a lot of work, even with a polished commercial APS: you’ll still need to deal with BG checks, CGM calibrations, site changes, dealing with sites and sensors that fall out or get ripped out… And of course there will still be days where you’re sensitive or resistant and BGs are not perfect for whatever reason. In addition, it will take time to transition from the standard of care as we have it today (pump, CGM, but no algorithms and no connected devices) to open and/or closed loops.
This is one of the things among many that we are hoping to help the diabetes community with as a result of the many (80+ as of June 8, 2016!) users with #OpenAPS. We have learned a lot about trusting a closed loop system, about what it takes to transition, how to deal if the system you trust breaks, and how to use more data than you’re used to getting in order to improve diabetes care.
As a step to helping the healthcare provider community start thinking about some of these things, the #OpenAPS community submitted a poster that was accepted and will be presented this weekend at the 2016 American Diabetes Association Scientific Sessions meeting. This will be the first data published from the community, and it’s significant because it’s a study BY the community itself. We’re also working with other clinical research partners on various studies (in addition to the usability study, other studies to more thoroughly examine data from the community) for the future, but this study was a completely volunteer DIY effort, just like the entire OpenAPS movement has been.
Our hope is that clinicians walk away this weekend with insight into how engaged patients are and can be with their care, and a new way of having conversations with patients about the tools they are choosing to use and/or build. (And hopefully we’ll help many of them develop a deeper understanding of how artificial pancreas technology works: #OpenAPS is a great learning tool not only for patients, but also for all the physicians who have not had any patients on artificial pancreas systems yet.)
Stay tuned: the poster is embargoed until Saturday morning, but we’ll be sharing our results online beginning this weekend once the embargo lifts! (The hashtag for the conference is #2016ADA, and we’ll of course be posting via @OpenAPS and to #OpenAPS with the data and any insights coming out of the conference.)
This post was written months ago for Prescribe Design, and will also be posted/made available there as a collection of their stories by and about patients who design, but I am also posting here for anyone new to #DIYPS and/or wondering about how #OpenAPS came into existence.
About the author: Dana Lewis is the creator of #DIYPS, the Do-It-Yourself Pancreas System, and a founder of the #OpenAPS movement. (Learn more about the open source artificial pancreas movement at OpenAPS.org.) Dana can be found online at @DanaMLewis, #DIYPS, and #OpenAPS on Twitter, and also on LinkedIn.
Diabetes is an invisible illness that’s not often noticeable, and may be considered to be “easy” compared to other diseases. After all, how hard can it be to track everything you eat, check your blood glucose levels, and give yourself insulin throughout the day?
What most people don’t realize is that managing diabetes is an extremely complex task; numerous variables influence your blood glucose levels throughout the day, from food to activity to sleep to your hormones. Some of these things are easier to measure than others, and some are easier to influence than others, as I’ve learned over the past 13 years of living with type 1 diabetes.
Diabetes technology certainly helps – and those of us with access to insulin pumps and continuous glucose monitors are thankful that we have this technology to better help us manage our disease. But this technology is still not a cure. After I run a marathon, my blood sugar is likely to run low overnight for the next few nights. And the devices I use to help me manage still have major flaws.
For example, my continuous glucose monitor (CGM) gives me a reading of my blood glucose every 5 minutes – but I have to pay attention to it in order to see what is going on (pulling the device from my pocket and pressing a button to see my numbers). And what happens when I go to sleep? I am sleeping, rather than paying attention to my blood sugar.
Sure, you can set alarms, and if your blood glucose (BG) goes above or below your personal threshold, an alarm will sound. That’s great, unless you’re a sound sleeper like me who doesn’t always hear these sounds in my sleep – and unfortunately there’s no way on the device to make the alarms louder.
For years, I worried every night when I went to sleep that I would have a low blood sugar, not hear the alarm, and not wake up in the morning. And since I moved across the country for work, and lived by myself, it could potentially be hours before someone realized I didn’t show up for work, and days before someone decided to check on me inside my apartment.
I was worried about “going low” overnight, and I kept asking the device manufacturers for louder alarms. The manufacturers usually responded, “the alarms are loud enough, most people wake up to them!” This was frustrating, because clearly I’m not one of those people.
I realized that if only I could get my CGM data off my device in real-time, I could make a louder alarm by using my phone or my laptop instead of having to rely on the existing medical device volume settings. It would be as easy as using a basic service like IFTTT or an app like “Pushover” that allows you to customize alerts on an iPhone.
However, for the longest time, I couldn’t get my data off of my device. (In fact, for years I had NO access to my own medical device data, because the FDA-approved software only ran on Windows computers, and I had a Mac.) But in November 2013, I by chance found someone who tweeted about how had managed to get his son’s data off the CGM in real-time, and he was willing to share his code with me. And this changed everything.
(At the time, my continuous glucose monitor only had FDA-approved software that could be used on a Windows computer. Since I had a Mac, when my endocrinologist asked for diabetes data, I took a picture with my iPhone and pasted the images into Excel, and printed it out for him. Data access is an ongoing struggle.)
My design “ah-ha” became a series of “wow, what if” statements. At every stage, it was very easy to see what I wanted to do next and how to iterate, despite the fact that I am not a designer and I am not a traditional engineer. I had no idea that within a year I would progress from making those louder alarms to building a full hybrid closed loop artificial pancreas (one that would auto-adjust the levels on my insulin pump overnight).
Once I had my CGM data, I originally wanted to be able to send my data to Scott (my then-boyfriend and now husband, who lived 20 miles away at the time) to see, but I didn’t want him to get alarms any time I was merely one point above or below my target threshold. What was important for him to know was if I wasn’t responding to alarms. We set up the system so that Scott could see whether or not I was taking action on a low reading, which I signaled by pressing a button. If the system alerted to Scott that I was not responding to a low reading, he could call and check on me, drive 20 miles to see me, or call 911 if necessary. (Luckily, he never needed to call 911 or come over, but within a week of building the first version of the system, he called me when my blood sugar was below 60 and I hadn’t woken up yet to the alarms.)
I realized next that if I was already pushing a button on the web interface (pictured), I might as well add three buttons and show him what action I was taking (more insulin, less insulin, or eating carbohydrates) in case I accidentally did the wrong thing in my sleep. I also customized the system so that I could log exactly how much insulin I was taking or how much I was eating.
Because I was entering every action I took (insulin given, any food eaten), we realized that this data could fuel real-time predictions and give precise estimates of where my blood sugar would be 30, 60, or 90 minutes in the future. As a result, I could see where my blood glucose level would be if I didn’t take action, and make sure I didn’t overcorrect when I did decide to take action. This was helpful during the day, too. The CGM has alarm thresholds that notify you if you cross the line; but #DIYPS will predict ahead of time that I am likely to go out of range, and will recommend action to help prevent me from crossing the threshold.
The system worked great and generated many alarms that woke me up at night. (Ironically, we generated so many alarms that Scott would periodically change the sound of the alarm without telling me, because my body would get used to ignoring the same sound over time!) The next step was deciding to get a smart watch (in my case, a Pebble) so I could see my data on my watch, and reduce the amount of time I spent pulling my CGM receiver out of my pocket and pressing the button to turn the screen on. With a watch, it was also easier to see real-time push alerts that the system would send me to tell me to take action. As a result, I was able to begin to spend less time throughout the day worrying about my blood sugar, and more time living my life while the system ran in the background, updating every few minutes and alerting me as to when I needed to pay attention when something changed.
People often ask what my health care provider thinks. He didn’t appear very interested in hearing about this system when I first mentioned it, but he was glad to hear I was having positive outcomes with it.
More significantly, I had a lot of other people with diabetes interested in it and wanting to know how they could get it.
As a patient, I can only design tools and technology for myself; but because it would be seen by the FDA as a class III medical device (and making dosing recommendations from a CGM rather than a blood glucose meter, which the CGM is not approved for), I can not distribute it to other people to use as it would have to first be reviewed and regulated by the FDA.
We also kept iterating on #DIYPS and the algorithms I use to predict when my blood sugar is going to end up high or low. By the time we made it to November of 2014, we realized that we had a well-tested system that did an excellent job giving precise recommendations of adjusting insulin levels. If only we had a way to talk to my insulin pump, we theorized that we could turn it into a fully closed loop artificial pancreas – meaning that instead of only allowing my insulin pump to give me a pre-determined amount of insulin throughout the night, a closed loop system would instead take into account my blood sugar and make the automatic needed adjustments to give me more or less insulin as needed to keep me in range.
With the help of Ben West, another developer we met while working on Nightscout, who has spent years working on tools to communicate with diabetes devices, we were able to take a carelink USB stick and use it to communicate with my insulin pump. Plugged into a raspberry pi (a small, pocket sized computer), the carelink USB stick could pull from our algorithms, read from the pump, write commands (in the form of temporary basal rates for 30 minutes), read back the results, update the algorithm and generate new predictions and action items, and then do the same process over and over again.
And so, with the help of various community members, we had closed the loop with our artificial pancreas. And once I had it turned on, testing, and working, it was hard to convince me to take it off. This was December of 2014. More than a year and a half later, I’m still wearing and using it every day and night.
There are definitely challenges to having self-designed a device. There are usability issues, such as the burden of keeping it powered and extra supplies to haul around. But as a patient, and as the designer, I can constantly iterate and make improvements to algorithms or the device setup itself and make it better as I go, all while having the benefit of this lifesaving technology (and more importantly, having the peace of mind to be able to go to sleep safely at night).
And, I have the ability to communicate and spread the word that this type of DIY technology is possible. I frequently talk with others who are interested in building their own artificial pancreas system as part of the OpenAPS movement. Like #DIYPS, I can’t give away an #OpenAPS implementation or build someone else an artificial pancreas. But through #OpenAPS, the community has collectively published a reference design, documentation and code, and established a community to support those who are choosing to do an n=1 implementation, following the reference design we have shared. As of the beginning of May 2016, there have been a total of 56+ people who have decided to close the loop by building individual OpenAPS implementations, with more in progress. You can read more here about the risks and how it is a personal decision to decide to build your own system; each person has to decide if the work to DIY and the risk is worth the potential reward.
For me, this definitely has been and is worth the time and effort. It’s worth noting that I am glad there are traditionally designed devices going into clinical trials and are in the pipeline to be made available to more people. But the timeline for this is years away (2017-2018), so I am also glad that the technology (including social media to enable our community to connect and design new tools together) is where it is today.
You don’t have to be an engineer, or formally trained, to spot a problem with disease management or quality of life and build a solution that works for you. Who knows – the solution that works for you may also work for other people. We can design the very tools we need to make our lives with diabetes, and other diseases, so much better – and we shouldn’t wait to do so.
There were the first few nights of “wow! this works! I wake up at night when I’m high/low”. Then there were the first 100 nights that involved more iteration, testing, and improvements as we built it out more. And then suddenly it had been a year of using #DIYPS, and it was awesome to see how my average BG and a1c were down – and stayed down – while equally as important, my % time in range was up and stayed up. Not to mention, the quality of life improvements of having better nights of sleep were significant.
Year two has been along the same lines – more improvements on A1c/average BGs, time in range, and sleep – but heavily augmented by the fact that I now have a closed loop. If you follow me on Twitter or have checked out the hashtag, you might be tired of seeing me post CGM graphs. At this point, they all look very similar:
(It’s worth noting that I still use #DIYPS, especially during the day to trigger “eating-soon” mode or basically get a snapshot glance at what my BGs are predicted to be, especially if I plan to go out without my loop in tow.)
As of 12/31/15 as I write this post, there are 22 people who have told me that they have a closed loop running that’s based on the OpenAPS reference design. I make a big deal about marking the date when I make a statement about the number of people running #OpenAPS (i.e. (n=1)*22), because every time I turn around, someone else seems to have done it!
There are dozens of other amazing stories like this in the #WeAreNotWaiting community. As we look to the new year, and I start to wonder about what might be next, I realize the speed of technology and the spirit of ingenuity in this community makes it impossible to predict exactly what we’ll see in 2016.
What I do know is this: we’ll see more people closing the loop, and we’ll see more ways to close the loop, using devices other than the Raspberry Pi, Carelink stick and Medtronic pump. We’ll see more new ways to communicate with old & new diabetes devices and more ways to make the diabetes parts of our lives easier – all because #WeAreNotWaiting.
Sometimes, it’s the little things that make a big difference – even little glimpses of data, or little improvements to ways that you can control the way you access and view your data (and generate alarms).
For example, I recently had a conversation with a few people in the #WeAreNotWaiting community about the different watch faces that exist for displaying CGM data; and about how much I like my #DIYPS watch face. A few reasons why:
It’s a little more discreet than some watch faces showing BG data, so the average person won’t glance at my watch and see a large number.
It pulls from the #DIYPS interface, so I can see what I’m predicted to be, and any current recommendations (such as carbs, temp basal, or bolus needed).
It’s data-heavy, but I like having all this information without having to pull my CGM out and run calculations in my head; or pull out my phone and pull up a web page to #DIYPS; etc.
One of the many cool things about the #WeAreNotWaiting community is how together we have learned and created so many new ways to visualize our data, on various devices (tablets, phones, smart watches) and various size screens. And so when I hear that someone’s not wanting a smart watch, or isn’t using it for diabetes related things, sometimes I think it’s a matter of them finding the right tools to build their own display that works for them. Several times a week I hear about various people working on new, interesting DIY diabetes projects, and it’s awesome that we have tech to improve the tools we have – and excellent social media channels to communicate about these projects.
Related to that, I wanted to share an update – recently Milos, Jason, and others have done some really amazing work to visualize basal rates in Nightscout. (If you use Nightscout, you can get this in the 0.8.2 release – see here for more details.) This means it also can pull in temporary basal rates that are used in #OpenAPS, so you can get a nice visual showing the adjusted basal rate compared to normal scheduled basal rates – and see why it might be needed – on top of display of BG data and everything else that Nightscout offers.
The visualization of temp basals in Nightscout (another example here) is an excellent improvement over how I previously used to check and see what OpenAPS had been doing. I have a watchface (similar to the above #DIYPS one) that shows me what the loop is doing currently, but when I wake up in the morning, I was mostly using a basic screen like the below to see the positive, negative, and net temp basal rates on an hourly basis and comparing that to my CGM graph to get an understanding of what happened.
Visualizing basal rates in Nightscout is a seemingly minor change, but every time we make a change like this that allows me to contextualize all of my data in one place (on a single glanceable watchface; or on the Nightscout screen); it saves a few seconds or minutes that add up to a lot of time saved every day, week, month, and additional year that I’m dealing with diabetes – a big win.
I’ve had the opportunity to meet some fantastic people through our work with #DIYPS and #OpenAPS, one of whom is Eric Von Hippel, an MIT professor and researcher who work on user innovation. He shared a great example of user innovation that I was previously unfamiliar with, but is an awesome parallel for what we in the diabetes community are doing.
History: bike manufacturers used to make bikes for riding on flat surfaces. Some people wanted to ride their bikes down mountains, but existing bikes weren’t too comfortable (they didn’t have spring-based seats – ouch!). So, bikers started customizing and modifying the bikes they had. Eventually, bike manufacturers saw the demand and started building mountain bikes with the same features that the original mountain bikers had used. (And if you don’t like my paraphrased version of this story, Wikipedia is always your friend!)
Are there commercial products coming to market or are in the market that meet some of these needs now? Sure. But remember, I’ve had diabetes since 2002. In 2013, when Scott and I first started working to solve my need for louder alarms, there was NO commercial solution available for either remote data access or alarm customization. Thus the need for #DIYPS, which we built in 2013, and Nightscout, which blossomed in early 2014. And even though tools like Dexcom SHARE and MiniMed Connect have come to market (and in some cases, more quickly with help from the community communicating to the FDA about the critical importance of these tools), they came in 2015, which is a long time to wait for new tools when you’re dealing with diabetes 24/7/365. And when we managed to close the loop, again with help from the amazing #wearenotwaiting community, in December of 2014? Well, it’s now nearing the end of a year (and with amazing continued results from #OpenAPS not just for me, but for 13 additional people and potentially more to come soon), and we are AT LEAST a year and a half, if not more, away from any commercial device to reach the market. Not to mention: I’m not sure that the first generation of closed loops commercially available will be good enough for me.
The commercial entities are getting there. And, I always want to give them credit – I have a closed loop, but I can’t have one without a solidly working insulin pump and an excellent CGM system. They are, for the most part (with the exception of some missing features), making good, solid safe products for me to use.
The manufacturers are also starting to be open to more conversations. Not just “listening”, which they’ve sort-of/maybe done in drips over the past, but actual two-way conversations where we can share the needs that we know of in the community, and discuss what can be incorporated into their commercial product pipeline more quickly. This is progress starting to be made, and I’m excited to see more of it. It seems like there is a refreshed mindset and energy in the industry, as well as an understanding that we all care deeply about safety and that we’re all in this together to make life with diabetes less of a burden – like riding downhill on a mountain bike rather than a road bike.