Edison Foundation honors #OpenAPS community with 2017 Edison Innovation Award

One of my favorite things about open source projects is the amazing humans behind it. OpenAPS came into existence because of numerous open source efforts; and has continued to evolve in both software and hardware improvements because of ongoing contributions in the open source world.

Some of the contributors and their stories are fairly well known (John Costik’s work to pull data from the CGM originally, which allowed Scott/me to create #DIYPS; Ben West’s work to study pump RF communications and create tools to communicate with the pump, in addition to his work on the building blocks that make up the openaps toolkit). Others have worked on areas that have drastically changed the trajectory of our community’s tools, as well. And two of the individuals who we also owe repeated thanks for facilitating our ability to utilize pocket-sized pancreas are Oskar Pearson and Pete Schwamb.

  • Oskar wanted to find a way to replace the Carelink stick, which has dismal range. (How dismal? Back in the day, I used to have a Pi on the bedside table connected to a Carelink stick under the mattress, plus another Carelink hanging over the middle of my bed to try to keep me looping all night in case I rolled over.) He ultimately leveraged some of Ben and Pete’s other work and created “mmeowlink“, which enabled other radio sticks (think TI cc1111 stick & other radios using the cc1110 and cc1111 chipsets) to similarly communicate with our loopable pumps.  He was also (I think) one of the first users of the Intel Edison for OpenAPS. When he shared his pictures showing the potential down sized rigs, jaws dropped across the Internet. This led to a bunch of new hardware options for OpenAPS rigs; Pi/Carelink was no longer the sole go-to. You could pick Pi/TI; Edison/TI; Pi/Slice of Radio; etc. And the range on these radio sticks is such that a single rig and radio stick can read (in most cases) across room(s). It greatly improved the reliability of real-world looping, and especially was a game changer for those on the go!
  • Pete is a wizard in the world of device RF communications. He created the RileyLink (named after his daughter) to bridge communications between a phone and any Sub-1 GHz device (like say, an insulin pump…). But he’s also done some other stellar projects – like subg_rfspy (general purpose firmware for cc111x for sub-GHz RF comms, which is what is leveraged in mmeowlink); and also ccprog (which enables you to flash the cc1110 radio chip on an Explorer board (see below) without having any separate equipment). And, as someone who has been building boards and decoding RF stuff for years..he’s also incredibly generous with sharing his knowledge with other people building open source hardware boards, including with those of us who collaborated on the Explorer Board.

In addition, there have been other people outside the OpenAPS community who have been touched by our stories or by diabetes in their families and have also stepped up to contribute to open source projects. This is how the Explorer Board came into existence. Someone from Intel had stumbled across OpenAPS on Twitter and reached out to meet up with Scott and me when he was in Seattle; he invited a hardware board designer he knew (Morgan Redfield) to stop by the meetup and offered to help initiate development of a smaller board. And amazingly, that’s exactly what happened. Morgan collaborated with us & others like Pete to design, build, and iterate on a small, open source hardware board (the “900 Mhz Explorer Board“) for the Edison that had a built in cc1110 radio, further allowing us to reduce the size of our rigs.

Eventually others at Intel heard about this collaboration, and we (OpenAPS and Morgan) were nominated for the Edison Foundation’s 2017 Edison Innovation Award for “Best Use of the Intel Edison Module”.

I was blown away to find out tonight that we are honored to actually receive the award on behalf of everyone who made these projects possible. I’m incredibly proud of this community and the dozens of people who have contributed in so many ways to a) make DIY artificial pancreases a thing and b) make it more feasible for hundreds of people to be DIYing these themselves with open source software and hardware. (And, this is very much in line with Thomas Edison’s work – the Edison Foundation spoke tonight about how Edison really created the group collaboration and innovation process!)

Big thanks to Intel and the Edison Foundation for highlighting the community’s efforts…and endless hugs and ongoing appreciation for everyone who has contributed to OpenAPS and other #WeAreNotWaiting projects!

Write It Do It: Tips for Troubleshooting DIY Diabetes Devices (#OpenAPS or otherwise)

When I was in elementary school, I did Science Olympiad. (Are you surprised? Once a geek, always a geek…) One of my favorite “events” was “Write It Do It”, where one person would get a sculpture/something constructed (could be Legos, could be other stuff) and you had to write down instructions for telling someone else how to build it. Your partner got your list of instructions, the equipment, and was tasked with re-building the structure.

Building open source code and tools is very similar, now that I look back on the experiences of having built #DIYPS and then working on #OpenAPS. First step? Build the structure. Second step? Figure out how to tell someone ELSE how to do it. (That’s what the documentation is). But then when someone takes the list of parts and your instructions off elsewhere, depending on how they interpreted the instructions…it can end up looking a little bit different. Sometimes that’s ok, if it still works. But sometimes they skip a step, or you forget to write down something that looks obvious to you (but leaves them wondering how one part got left out) – and it doesn’t work.

Unlike in Science Olympiad, where you were “scored” on the creation and that was that, in DIY diabetes this is where you next turn to asking questions and troubleshooting about what to change/fix/do next.

But, sometimes it’s hard.

If you’re the person building a rig:

  • You know what you’re looking at, what equipment you used to get here, what step you’re on, what you’ve tried that works and what hasn’t worked.
  • You either know “it doesn’t work” or “I don’t know what to do next.”

If you’re the troubleshooter:

  • You only know generally how it can/should work and what the documentation says to do; but you only know as much about the specific problem is shared with you in context of a question.

As someone who spends a lot of time in the troubleshooter role these days, trying to answer questions or assist people in getting past where they’re stuck, here are my tips to help you if you’re building something DIY and are stuck.

Tips_online_troubleshooting_DIY_diabetes_DanaMLewis

DO:

  1. Start by explaining your setup. Example: “I’m building an Edison/Explorer Board rig, and am using a Mac computer to flash my Edison.”
  2. Explain the problem as specifically as you can. Example: “I am unable to get my Edison flashed with jubilinux.”
  3. Explain what step you’re stuck on, and in which page/version of the docs. Example: “I am following the Mac Edison flashing instructions, and I’m stuck on step 1-4.” Paste a URL to the exact page in the docs you’re looking at.  Clarify whether your problem is “it doesn’t work” or “I don’t know what to do next.”
  4. Explain what it’s telling you and what you see. Pro tip: Copy/paste the output that the computer is telling you rather than trying to summarize the error message. Example: “I can’t get the login prompt, it says “can’t find a PTY”.”
    (This is ESPECIALLY important for OpenAPS’ers who want help troubleshooting logs when they’ve finished the setup script – the status messages in there are very specific and helpful to other people who may be helping you troubleshoot.)
  5. Be patient! You may have tagged someone with an @mention; and they may be off doing something else. But don’t feel like you must tag someone with an @mention – if you’re posting in a specific troubleshooting channel, chances are there are numerous people who can (and will) help you when they are in channel and see your message.
  6. Be aware of what channel you’re in and pros/cons for what type of troubleshooting happens where.
    My suggestions:

    1. Facebook – best for questions that don’t need an immediate fix, or are more experience related questions. Remember you’re also at the mercy of Facebook’s algorithm for showing a post to a particular group of people, even if someone’s a member of the same group. And, it’s really hard to do back-and-forth troubleshooting because of the way Facebook threads posts. However, it IS a lot easier to post a picture in Facebook.
    2. Gitter – best for detailed, and hard, troubleshooting scenarios and live back-and-forth conversations. It’s hard to do photos on the go from your mobile device, but it’s usually better to paste logs and error output messages as text anyway (and there are some formatting tricks you can learn to help make your pasted text more readable, too). Those who are willing to help troubleshoot will generally skim and catch up on the channel when they get back, so you might have a few hours delay and get an answer later, if you still haven’t resolved or gotten an answer to your question from the people in channel when you first post.
    3. Email groups – best for if no one in the other channels knows the questions, or you have a general discussion starter that isn’t time-constrained
  7. Start with the basic setup, and come back and customize later. The documentation is usually written to support several kinds of configurations, but the general rule of thumb is get something basic working first, and then you can come back later and add features and tweaks. If you try to skip steps or customize too early, it makes it a lot harder to help troubleshoot what you’re doing if you’re not exactly following the documentation that’s worked for dozens of other people.
  8. Pay it forward. You may not have a certain skill, but you certainly have other skills that can likely help. Don’t be afraid to jump in and help answer questions of things you do know, or steps you successfully got through, even if you’re not “done” with your setup yet. Paying it forward as you go is an awesome strategy J and helps a lot!

SOME THINGS TO TRY TO AVOID:

  1. Avoid vague descriptions of what’s going on, and using the word “it”. Troubleshooter helpers have no idea which “it” or what “thing” you’re referring to, unless you tell them. Nouns are good :) . Saying “I am doing a thing, and it stopped working/doesn’t work” requires someone to play the game of 20 questions to draw out the above level of detail, before they can even start to answer your question of what to do next.
  2. Don’t get upset at people/blame people. Remember, most of the DIY diabetes projects are created by people who donated their work so others could use it, and many continue to donate their time to help other people. That’s time away from their families and lives. So even if you get frustrated, try to be polite. If you get upset, you’re likely to alienate potential helpers and revert into vagueness (“but it doesn’t work!”) which further hinders troubleshooting. And, remember, although these tools are awesome and make a big difference in your life – a few minutes, or a few hours, or a few days without them will be ok. We’d all prefer not to go without, which is why we try to help each other, but it’s ok if there’s a gap in use time. You have good baseline diabetes skills to fall back on during this time. If you’re feeling overwhelmed, turn off the DIY technology, go back to doing things the way you’re comfortable, and come back and troubleshoot further when you’re no longer feeling overwhelmed.
  3. Don’t go radio silent: report back what you tried and if it worked. One of the benefits of these channels is many people are watching and learning alongside you; and the troubleshooters are also learning, too. Everything from “describing the steps ABC way causes confusion, but saying XYZ seems to be more clear” and even “oh wow, we found a bug, 123 no longer is ideal and we should really do 456.” Reporting back what you tried and if it resolved your issue or not is a very simple way to pay it forward and keep the community’s knowledge base growing!
  4. Try not to get annoyed if someone helping out asks you to switch channels to continue troubleshooting. Per the above, sometimes one channel has benefits over the other. It may not be your favorite, but it shouldn’t hurt you to switch channels for a few minutes to resolve your issue.
  5. Don’t wait until you’re “done” to pay it forward. You definitely have things to contribute as you go, too! Don’t wait until you’re done to make edits (PRs) to the documentation. Make edits while they’re fresh in your mind (and it’s a good thing to do while you’re waiting for things to install/compile ;)).

These are the tips that come to mind as I think about how to help people seek help more successfully online in DIY diabetes projects. What tips would you add?

Traveling through airport security with diabetes devices (with or without #OpenAPS)

tl;dr: Put your #OpenAPS or other artificial pancreas rigs through the x-ray machine; it’s a small computer and a battery.

Traveling through airport security with your diabetes devices and artificial pancreas rigs (#OpenAPS)

I travel quite a bit these days, so it’s pretty routine for me to pack up my diabetes gear and backup supplies and whisk away to the airport and the next adventure. In fact, in 2016 I think I went through airport security 44+ times, in several countries. I have never had any issues with my #OpenAPS (DIY hybrid closed loop artificial pancreas) rigs – even when I carry multiples. Here are some tips on what gear should be put where, who should be told what during the security process, and how to further simplify (as much as is possible with diabetes!) the airport security experience when traveling with diabetes.

A list of diabetes gear you’re probably packing for your trip:

  • BG meter
  • Test strips
  • Lancet(s)
  • Pump sites
  • Reservoirs
  • CGM sensors
  • CGM receiver
  • Tape for sites/sensors
  • Syringes as back up
  • Anti-nausea meds
  • Depending on the length of your trip, backup pump/transmitter/meter/receiver/etc.
  • Snacks
  • Extra batteries to power your phone for uploading BGs
  • (Uploader phone if you’re still using an uploader to Nightscout)
  • Artificial pancreas rig (i.e. #OpenAPS rig, whether that’s a Raspberry Pi or Explorer Board setup, or a Rileylink)
  • Insulin
  • Extra insulin
  • Juice for lows

Out of that list? Here are the only things I would pull out of your bag.

  • Insulin/extra insulin*
  • Juice for lows**

Everything else (yes, including your CGM receiver; yes, including your pancreas rigs) can stay in your bag and go through the x-ray.

*If you have a single bottle of insulin, it’s under the liquid (3oz) limits, so you don’t technically need to pull it out. But if you are carrying numerous bottles/pens/etc., if you have them separately bagged and can pull out separately, I would do so in order to reduce the risk of them flagging your bag for needing additional screening.

** Yes, you have a medical need for liquid and can take juice through security. HOWEVER, I *highly* recommend having this in a baggie and pulled out of your bag so it is separate. They’ll often pick that up, examine it, and if you say “medical liquid for diabetes”, it’s fine. Sometimes you’ll get pulled for a pat down, but not always. And, this usually prevents them from having to dig through your bags to find the juice and go through all your things. (Which is annoying, not to mention time consuming).

My second “HOWEVER” related to juice: I’ve stopped carrying juice for lows when I air travel. Yes, it only takes an extra couple of minutes or whatever for them to check things out, but I’d rather not have any hassle if I can avoid it. I instead have switched to Starbursts, Skittles, and similar. (They’re super fast acting for me, and actually make it easier to do a small 4g correction vs having to bust open an entire 15g juice box that can’t really be saved for later.) I have those in my pocket or easily accessible in an outer pocket of the bag that will go under my seat on the plane. You can of course still carry juice, but think about if that’s really worth the hassle/effort and if there’s an alternative (glucose tabs, small wrapped candies, etc.) that might be easier for treating lows when traveling. YDMV, of course.

(My favorite carrying-juice-through-security story is this: I was traveling to somewhere in Europe while in college (well before my DIY closed loop days), and I had a large baggie jam-packed with 8 or 9 juice boxes and a bottle of insulin. Despite telling them that I had diabetes and was traveling internationally and this was medically necessary in case of low BGs, the TSA agent said “how many juice boxes could you possibly need in an 11 hour flight? You wouldn’t use more than one, right?” It was *really* hard not to laugh.)

What about insulin pumps? Do you take it off?

  • I currently am wearing an insulin pump that does not alarm in 99% of metal detectors because it’s not made with lots of metal. I also have TSA Pre-Check, which means 95% of the time when traveling in the U.S. I am only asked to go through a metal detector. So right before I walk up to security, I take my pump that’s usually clipped to an outer pants pocket and clip it inside my waist band and underneath my shirt. If it doesn’t alarm, then I proceed like a usual traveler to get my bags and be on my way.
  • If I am randomly selected by the metal detector to instead go through the body scanner:
    • YDMV/YMMV, but there are no guarantees that the body scanners will not break your pump. And if you have a super special limited edition rare pump that does a special thing (like those that enable you to DIY closed loop), as I do, it may make you decide that a pat down is better than risking your pump, since if it DOES break due to scanner interference, TSA sure isn’t going to pay to fix it/get you a new one, and a new one wouldn’t allow you to DIY closed loop anyway.
    • So, if I get randomly selected, I stop right there and say “opt out”. Say it to whoever is pointing you over to the body scanner, they’ll posssibly read you a script to confirm you want to opt out, and just keep saying “yes, I opt out” and “that’s fine” to the “but then you have to have a pat down!”. They’ll order up a same-gender TSA agent who will come get you, escort you around the body scanners, and you’ll get your pat down. The usual applies – if you want, you can ask for a private area for your pat down. I usually don’t care, but if you do, make sure you keep an eye on your bags and ask for those to come with you so they’re not left out in the open for anyone to accidentally take. (They’re usually pretty good about that, though.)
    • For the pat down, they’ll ask you about sensitive areas/medical devices. This is the time to point out your pump; tell them (pat the area) where it’s connected, and ditto for patting/pointing out your CGM sensor if you have one. They’ll be extra careful then to not accidentally catch their hands on those areas.
    • At the end, they’ll go swab their gloves, then come back and ask you to pat/touch your pump and then let them swab your hands.
  • If you don’t have Pre-Check, the above will likely happen every time. So if you’re an opt-out-of-body-scanner-type and travel more than 2 times a year…IMO Pre-Check is worth the money. (And think about getting Global Entry, which comes with Pre-Check included, and also gets you expedited return to the country after traveling abroad).
  • If you have a metal-cased pump (or any other pump, and just want this instead of the metal detector or the body scanner), you can ask for a hand inspection of your pump. Different manufacturers say different things about whether x-ray and body scanners are ok/not ok, so check with them and also go with your gut about what you’d like to do with your pump.  Keep in mind that the radiation your carry-on luggage gets from the hand-luggage x-ray is about 100 times what your body gets from a backscatter x-ray, so if you’re concerned about x-ray radiation damaging your pump, it should not be sent through the scanner with your carry-on luggage.

What about a doctor’s note?

I have never carried a doctor’s note, and have not had an issue in the 14+ years I’ve been flying with diabetes – including in dozen of international airports. YDMV, and if you’d feel more comfortable with one, you can get one from your doctor. But for what it’s worth, I don’t travel with one.

What about international airports?

The only thing to know about international airports is they have similar guidelines about liquids, so plan to also pull out your juice and toiletries from your bag. Same rules apply for keeping rigs, supplies, etc. in your bag otherwise. I’ve never had an issue based on pancreas rigs internationally, either. They’re small computers and batteries, so both TSA and international security are used to seeing those in the x-ray.

(Let me know what other travel-related questions you have, and I’ll keep adding to this post if it’s helpful. Happy traveling!)

The only thing to fear is fear itself

(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:

  1. You don’t need to know everything.
  2. You really don’t particularly need to have any technical “ability” or experience.
  3. You DO need to know that you don’t know it all, even if you already know a thing or two about computers.
  4. (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.)
  5. You only need to be willing to TRY, try, and try again.
  6. It might not always work on the first try of a particular thing…
  7. …but there’s help from the community to help you learn what you need to know.
  8. The learning is a big piece of this, because we’re completely changing the way we treat our diabetes when we go from manual interventions to a hybrid closed loop (and we learned some things to help do it safely).
  9. You can do this – as long as you think you can.
  10. 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.

Autotune (automatically assessing basal rates, ISF, and carb ratio with #OpenAPS – and even without it!)

What if, instead of guessing needed changes (the current most used method) basal rates, ISF, and carb ratios…we could use data to empirically determine how these ratios should be adjusted?

Meet autotune.

What if we could use data to determine basal rates, ISF and carb ratio? Meet autotune

Historically, most people have guessed basal rates, ISF, and carb ratios. Their doctors may use things like the “rule of 1500” or “1800” or body weight. But, that’s all a general starting place. Over time, people have to manually tweak these underlying basals and ratios in order to best live life with type 1 diabetes. It’s hard to do this manually, and know if you’re overcompensating with meal boluses (aka an incorrect carb ratio) for basal, or over-basaling to compensate for meal times or an incorrect ISF.

And why do these values matter?

It’s not just about manually dosing with this information. But importantly, for most DIY closed loops (like #OpenAPS), dose adjustments are made based on the underlying basals, ISF, and carb ratio. For someone with reasonably tuned basals and ratios, that’s works great. But for someone with values that are way off, it means the system can’t help them adjust as much as someone with well-tuned values. It’ll still help, but it’ll be a fraction as powerful as it could be for that person.

There wasn’t much we could do about that…at first. We designed OpenAPS to fall back to whatever values people had in their pumps, because that’s what the person/their doctor had decided was best. However, we know some people’s aren’t that great, for a variety of reasons. (Growth, activity changes, hormonal cycles, diet and lifestyle changes – to name a few. Aka, life.)

With autosensitivity, we were able to start to assess when actual BG deltas were off compared to what the system predicted should be happening. And with that assessment, it would dynamically adjust ISF, basals, and targets to adjust. However, a common reaction was people seeing the autosens result (based on 24 hours data) and assume that mean that their underlying ISF/basal should be changed. But that’s not the case for two reasons. First, a 24 hour period shouldn’t be what determines those changes. Second, with autosens we cannot tell apart the effects of basals vs. the effect of ISF.

Autotune, by contrast, is designed to iteratively adjust basals, ISF, and carb ratio over the course of weeks – based on a longer stretch of data. Because it makes changes more slowly than autosens, autotune ends up drawing on a larger pool of data, and is therefore able to differentiate whether and how basals and/or ISF need to be adjusted, and also whether carb ratio needs to be changed. Whereas we don’t recommend changing basals or ISF based on the output of autosens (because it’s only looking at 24h of data, and can’t tell apart the effects of basals vs. the effect of ISF), autotune is intended to be used to help guide basal, ISF, and carb ratio changes because it’s tracking trends over a large period of time.

Ideally, for those of us using DIY closed loops like OpenAPS, you can run autotune iteratively inside the closed loop, and let it tune basals, ISF, and carb ratio nightly and use those updated settings automatically. Like autosens, and everything else in OpenAPS, there are safety caps. Therefore, none of these parameters can be tuned beyond 20-30% from the underlying pump values. If someone’s autotune keeps recommending the maximum (20% more resistant, or 30% more sensitive) change over time, then it’s worth a conversation with their doctor about whether your underlying values need changing on the pump – and the person can take this report in to start the discussion.

Not everyone will want to let it run iteratively, though – not to mention, we want it to be useful to anyone, regardless of which DIY closed loop they choose to use – or not! Ideally, this can be run one-off by anyone with Nightscout data of BG and insulin treatments. (Note – I wrote this blog post on a Friday night saying “There’s still some more work that needs to be done to make it easier to run as a one-off (and test it with people who aren’t looping but have the right data)…but this is the goal of autotune!” And as by Saturday morning, we had volunteers who sat down with us and within 1-2 hours had it figured out and documented! True #WeAreNotWaiting. :))

And from what we know, this may be the first tool to help actually make data-driven recommendations on how to change basal rates, ISF, and carb ratios.

How autotune works:

Step 1: Autotune-prep

  • Autotune-prep takes three things initially: glucose data; treatments data; and starting profile (originally from pump; afterwards autotune will set a profile)
  • It calculates BGI and deviation for each glucose value based on treatments
  • Then, it categorizes each glucose value as attributable to either carb sensitivity factor (CSF), ISF, or basals
  • To determine if a “datum” is attributable to CSF, carbs on board (COB) are calculated and decayed over time based on observed BGI deviations, using the same algorithm used by Advanced Meal Asssit. Glucose values after carb entry are attributed to CSF until COB = 0 and BGI deviation <= 0. Subsequent data is attributed as ISF or basals.
  • If BGI is positive (meaning insulin activity is negative), BGI is smaller than 1/4 of basal BGI, or average delta is positive, that data is attributed to basals.
  • Otherwise, the data is attributed to ISF.
  • All this data is output to a single file with 3 sections: ISF, CSF, and basals.

Step 2: Autotune-core

  • Autotune-core reads the prepped glucose file with 3 sections. It calculates what adjustments should be made to ISF, CSF, and basals accordingly.
  • For basals, it divides the day into hour long increments. It calculates the total deviations for that hour increment and calculates what change in basal would be required to adjust those deviations to 0. It then applies 20% of that change needed to the three hours prior (because of insulin impact time). If increasing basal, it increases each of the 3 hour increments by the same amount. If decreasing basal, it does so proportionally, so the biggest basal is reduced the most.
  • For ISF, it calculates the 50th percentile deviation for the entire day and determines how much ISF would need to change to get that deviation to 0. It applies 10% of that as an adjustment to ISF.
  • For CSF, it calculates the total deviations over all of the day’s mealtimes and compares to the deviations that are expected based on existing CSF and the known amount of carbs entered, and applies 10% of that adjustment to CSF.
  • Autotune applies a 20% limit on how much a given basal, or ISF or CSF, can vary from what is in the existing pump profile, so that if it’s running as part of your loop, autotune can’t get too far off without a chance for a human to review the changes.

(See more about how to run autotune here in the OpenAPS docs.)

What autotune output looks like:

Here’s an example of autotune output.

OpenAPS autotune example by @DanaMLewis

Autotune is one of the things Scott and I spent time on over the holidays (and hinted about at the end of my development review of 2016 for OpenAPS). As always with #OpenAPS, it’s awesome to take an idea, get it coded up, get it tested with some early adopters/other developers within days, and continue to improve it!

A big thank you to those who’ve been testing and helping iterate on autotune (and of course, all other things OpenAPS). It’s currently in the dev branch of oref0 for anyone who wants to try it out, either one-off or for part of their dev loop. Documentation is currently here, and this is the issue in Github for logging feedback/input, along with sharing and asking questions as always in Gitter!

 

 

OpenAPS feature development in 2016

It’s been two years since my first DIY closed loop and almost two years since OpenAPS (the vision and resulting ecosystem to help make artificial pancreas technology, DIY or otherwise, more quickly available to more people living with diabetes) was created.  I’ve spent time here (on DIYPS.org) talking about a variety of things that are applicable to people who are DIY closed looping, but also focusing on things (like how to “soak” a CGM sensorr and how to do “eating soon” mode) that may be (in my opinion) universally applicable.

OpenAPS feature development in 2016

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.

  1. Advanced meal assist (aka AMA)
    1. 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).
    2. 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.
  2. The approach with a “preferences” file
    1. 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.
    2. 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.
  3. Autosensitivity
    1. I’ve written about autosensitivity before and how impressive it has been in the face of a norovirus and not eating to have the closed loop detect excessive sensitivity and be able to deal with it – resulting in 0 lows. It’s also helpful during other minor instances of sensitivity after a few active days; or resistance due to hormone cycles and/or an aging pump site.
    2. 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.
  4. Tiny rigs
    1. 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.
  5. Setup scripts
    1. 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.
  6. Streamlined documentation
    1. 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.
  7. IFTTT and other tool integrations
    1. 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.

Happy New Year, everyone!

Automating “wake up” mode with IFTTT and #OpenAPS to blunt morning hormonal rises

tl;dr – automate a trigger to your #OpenAPS rig to start “wake up” mode (or “eating soon”, assuming you eat breakfast) without you having to remember to do it.

Yesterday morning, I woke up and headed to my desk to start working. Because I’m getting some amazing flat line overnights now, thanks to my DIY closed loop (#OpenAPS), I’m more attuned to the fact that after I wake up and start moving around, my hormones kick in to help wake me up (I guess), and I have a small BG rise that’s not otherwise explained by anything else. (It’s not a baseline basal problem, because it happens after I wake up regardless of it being 6am or 8am or even 10:30am if I sleep in on a weekend. It’s also more pronounced when I feel sleep deprived, like my body is working even harder to wake me up.)

Later in the morning, I took a break to jot down my thoughts in response to a question about normal meal rises on #OpenAPS and strategies to optimize mealtimes. It occurred to me later, after being hyper attuned to my lunch results, that my morning wake-up rise up from 1oo perfectly flat to ~140 was higher than the 131 peak I hit after my lunchtime bowl of potato soup.

Hmm, I thought. I wish there was something I could do to help with those morning rises. I often do a temporary target down to 80 mg/dL (a la “eating soon” mode) once I spot the rise, but that’s after it’s already started and very dependent on me paying attention/noticing the rise.

I also have a widely varied schedule (and travel a lot), so I don’t like the idea of scheduling the temp target, or having recurring calendar events that is yet another thing to babysit and change constantly.

What I want is something that is automatically triggered when I wake up, so whether I pop out of the bed or read for 15 minutes first, it kicks in automatically and I (the non-morning person) don’t have to remember to do one more thing. And the best trigger that I could think of is when I end Sleep Cycle, the sleep tracking app I use.

I started looking online to see if there was an easy IFTTT integration with Sleep Cycle. (There’s not. Boo.) So I started looking to see if I could stick my Sleep Cycle data elsewhere that could be used with IFTTT. I stumbled across this blog post describing Sleep Cycle -> iOS Apple HealthKit -> UP -> Google Spreadsheet -> Zapier -> Add to Google Calendar. And then I thought I would add another IFTTT trigger for when the calendar entry was added, to then send “waking up” mode to #OpenAPS. But I don’t need all of the calendar steps. The ideal recipe for me then might be Sleep Cycle ->  iOS Health Kit -> UP -> IFTTT sends “waking up mode” -> Nightscout -> my rig. However, I then learned that UP doesn’t necessarily automatically sync the data from HealthKit, unless the app is open. Hmm. More rabbit holing. Thanks to the tweet-a-friend option, I talked to Ernesto Ramirez (long time QS guru and now at Fitabase), who found the same blog post I did (above) and when I described the constraints, then pointed me to Hipbone to grab Healthkit sleep data and stuff it into Dropbox.

(Why Sleep Cycle? It is my main sleep tracker, but there’s IFTTT integration with Fitbit, Jawbone Up, and a bunch of other stuff, so if you’re interested in this, figure out how to plug your data into IFTTT, otherwise follow the OpenAPS docs for using IFTTT to get data into Nightscout for OpenAPS, and you’ll be all set. I’m trying to avoid having to go back to my Fitbit as the sleep tracker, since I’m wearing my Pebble and I was tired of wearing 2 things. And for some reason my Pebble is inconsistent and slow about showing the sleep data in the morning, so that’s not reliable for this purpose. )

Here’s how I have enabled this “wake up” mode trigger for now:

  1. If you’re using Sleep Cycle, enable it to write sleep analysis data to Apple HealthKit.
  2. Download the Hipbone app for iPhone, connect it with your Dropbox, and allow Hipbone to read sleep data from HealthKit.
  3. Log in or create an account in IFTTT.com and create a recipe using Dropbox as the trigger, and Maker as the action to send a web request to Nightscout. (Again, see the OpenAPS docs for using IFTTT triggers to post to Nightscout, there’s all kinds of great things you can do with your Pebble, Alexa, etc. thanks to IFTTT.) To start, I made “waking up” soon a temporary target to 80 for 30 minutes.

Guess what? This morning, I woke up, ended sleep cycle, and ~10-11 minutes later got notifications that I had new data in Dropbox and checked and found “waking up” mode showing in Nightscout! Woohoo. And it worked well for not having a hormone-driven BG rise after I started moving around.

First "waking up" mode in #OpenAPS automation success

Ideally, this would run immediately, and not take 10-11 minutes, but it went automatically without me having to open Hipbone (or any other app), so this is a great interim solution for me until we find an app that will run more quickly to get the sleep data from HealthKit.

We keep finding great ways to use IFTTT triggers, so if you have any other cool ones you’ve added to your DIY closed loop ecosystem, please let me know!

Half life

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.

I’m happy to have context, though, to help me keep things in perspective. For example, I’ve now lived with a closed loop artificial pancreas (or automated insulin delivery) system for almost two full years.

(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.

 

Why you should post that in Gitter

“You should post that question in Gitter.” –something @DanaMLewis says a lot

I realize it’s not always obvious to those who are newer to #WeAreNotWaiting or #OpenAPS why I am often pointing people to ask their questions in Gitter.

Let me explain:

There is a Gitter chat channel where most of the #OpenAPS and other DIY development conversations happens. (There are several other channels, so if you pop onto the main OpenAPS one, we’re happy to point you to another if there’s another one already built for another or related project.)

Gitter connects with Github, so you can use the same username to log in and ask a question. And very importantly, it’s public, so *anyone* can see the conversations in channel – even if you decide not to log in. See for yourself – click here to view the chat channel. This is very key for an open source project: anyone can jump in and check things out.

Transparency & archiving community knowledge
It also means that questions can be asked – and answered – openly, so that when someone has the same question, they can often find an existing answer with a little bit of searching.

(Tip: there’s a Gitter app for your phone, and for your desktop, both of which I use on the go – but for searching back in the channel, the web Gitter interface has a better search experience, or you can also use Google to search through the archives.)

Faster responses from a smarter, broader, worldwide community
There’s another key reason why asking a question in an open channel is helpful for Q&A. Two words: time zones. There are now (n=1)*104+ people around the world with DIY closed loops, and a lot of them pay it forward and provide guidance and also help answer questions.

If you send one individual a question in a private channel, you have to cross your fingers and hope they’re a) awake b) not working and c) otherwise available to respond.

But if you ask your question openly in a public channel, anyone out of the large community with the answer can jump in and answer more quickly. Given that we have a worldwide community and people across many time zones, this means faster answers and more “ah-ha’s” as people collaboratively work through new and already-documented-sticky-points in the build process.

So if you ask an individual a question in a private channel, you might get a “You should ask that in Gitter!” response. And this is why. :) You’re welcome as always to ping me across any channel, but often when there’s a good question, I’m going to want you to re-post in Gitter, anyway, so people can benefit from having the knowledge (including any answers from the community) archived for the next person who has the same or similar question.
4 reasons to post OpenAPS questions in Gitter

Our take on how to DIY closed loop, safely

You will often see similar growth and evolution cycles across any type of online community, and the closed loop community is following this growth cycle as expected. Much like how Nightscout went from one very hard way to setup to get your CGM data in the cloud, to ultimately having dozens of DIY options and now more recently, multiple commercial options, closed looping is following similar trends. OpenAPS was the first open source option for people who wanted to DIY loop, and now there are a growing number of ways to build or run closed loops! And next year, there should be at least one commercial option publicly available in the U.S. followed by several more options in 2018 on the commercial market. Awesome! This is exactly the progress we were hoping to see, and facilitate happening more quickly, by making our work & encouraging others to make their work open source.

We’ve learned a lot (from building our own closed loop and watching others do so through OpenAPS) that we think is relevant to anyone who pursues DIY closed looping, regardless of the technology option they choose. This thought process and approach will likely also be relevant to those who switch to a closed loop commercial option in the future, so we wanted to document some of the thought process that may be involved.

Approaching closed looping safely

Before considering closed looping, people should know:

  • A (hybrid or even full) closed loop is not a cure. There will be a learning curve, much like switching to a pump for the first time.
  • Even after you get comfortable with a closed loop, there will still sometimes be high or low BGs, because we are still dealing with insulin that peaks in 60-90 minutes; we’ll still get kinked pump sites or pooled insulin; and we’ll still have hormones that drive our BGs up and down very rapidly in ways we can’t predict, but must react to. Closed looping helps a lot, but there’s still a lot that goes into managing diabetes.

Before using a DIY closed loop, people should consider:

  • Identifying or creating the method to visualize their data in a way they are comfortable with, both for real-time monitoring of loop activity and retrospective monitoring. This is a key component of DIY looping.
  • Running in “open loop” mode, where the system provides recommendations and you spend days or weeks analyzing and comparing those recommendations to how you would calculate and choose to take action manually.
  • Based on watching the “open loop” suggestions, decide your safety limits: you should set max basal and bolus rates, as well as max net IOB limits where relevant. Start conservative, knowing you can change them over time as you watch and validate how a particular DIY loop works with your body and your lifestyle.

Getting started with a DIY closed loop, people should think about the following:

  • Understand how it works, so you know how to fix it. Remember, by pursuing a DIY closed loop, you are responsible for it and the operation of it. No one is forcing you to do this; it’s one of many choices you can and will make with regards to how you personally choose to manage your diabetes.
  • But even more importantly, you need to understand how it works so you can choose if you need to step in and take manual action. You should understand how it works so you can validate “this is what it should be doing” and “I am getting the output and outcomes that I would expect if I were doing this decision making manually”.
  • Often, people will get frustrated by diabetes and take actions that the loop then has to compensate for. Or they’ll get lax on when they meal bolus, or not enter carbs into the system, etc. You will get much better results by putting better data into the system, and also by having a better understanding of insulin timing in your body, especially at meal times. Using techniques like “eating soon mode” will dramatically help anyone, with or without a closed loop, reduce and limit severity of meal spikes. Ditto goes for having good CGM “calibration hygiene” (h/t to Pete for this phrase) and ensuring you have thought about the ramifications of automating insulin dosing based on CGM data, and how you may or may not want to loop if you doubt your CGM data. (Like “eating soon”, ‘soaking’ a CGM sensor may yield you better first day results.)
  • Start with higher targets for the loop than you might correct to manually.
  • Move first from an “open loop” mode to a “low glucose suspend” type mode first, where max net IOB is 0 and/or max basal is set at or just above your max daily scheduled basal, so it low temps to prevent and limit lows, but does not high temp above bringing net IOB back to 0.
  • Gradually increase max net IOB above 0 (and/or increase max basal) every few days after several days without low BGs; similarly, adjust targets down 10 points for every few days gone without experiencing low BGs.
  • Test basic algorithms and adjust targets and various max rates before moving on to testing advanced features. (It will be a lot easier to troubleshoot, and learn how a new feature works, if you’re not also adjusting to closed looping in its entirety).
This is our (Dana & Scott‘s) take on things to think about before and when pursuing a closed loop option. But there’s about a hundred others running around the world with closed loops, too, so if you have input to share with people that they should consider before looping, leave a comment below! :) And if you’re looking to DIY closed loop before a commercial solution is available, you might also be interested in checking out the OpenAPS Reference Design and some FAQs related to OpenAPS.