Making it possible for researchers to work with #OpenAPS or general Nightscout data – and creating a complex json to csv command line tool that works with unknown schema

This is less of an OpenAPS/DIYPS/diabetes-related post, although that is normally what I blog about. However, since we created the #OpenAPS Data Commons on Open Humans, to allow those of us who desire to donate our diabetes data to research, I have been spending a lot of time figuring out the process from uploading your data to how data is managed and shared securely with researchers. The hardest part is helping researchers figure out how to handle the data – because we PWDs produce a lot of data :) . So this post explains some of the challenges of the data management to get it to a researcher-friendly format. I have been greatly helped over the years by general purpose open-source work from other people, and one of the things that helps ME the most as a non-traditional programmer is plain language posts explaining the thought process by behind the tools and the attempted solution paths. Especially because sometimes the web pages and blog posts pop higher in search than nitty gritty tool documentation without context. (Plus, I’ve been taking my own advice about not letting myself hold me back from trying, even when I don’t know how to do things yet.) So that’s what this post is!

Background/inspiration for the project and the tools I had to build:

We’re using Nightscout, which is a remote data-viewing platform for diabetes data, made with love and open source and freely available for anyone with diabetes to use. It’s one of the best ways to display not only continuous glucose monitor (CGM) data, but also data from our DIY closed loop artificial pancreases (#OpenAPS). It can store data from a number of different kinds and brands of diabetes devices (pumps, CGMs, manual data entries, etc.), which means it’s a rich source of data. As the number of DIY OpenAPS users are growing, we estimate that our real-world use is overtaking the amount of total hours of data from clinical trials of closed loop artificial pancreas systems.  In the #WeAreNotWaiting spirit of moving quickly (rather than waiting years for research teams to collect and analyze their own data) we want to see what we can learn from OpenAPS usage, not only by donating data to help traditional researchers speed up their work, but also by co-designing research studies of the things of most value to the diabetes community.

Step 1: Data from users to Open Humans

I thought Step 1 would be the hardest. However, thanks to Madeleine Ball, John Costik, and others in the Nightscout community, a simple Nightscout Data Transfer App was created that enables people with Nightscout data to pop it into their Open Humans accounts. It’s then very easy to join different projects (like the OpenAPS Data Commons) and share your data with those projects. And as the volunteer administrator of the OpenAPS Data Commons, it’s also easy for me to provide data to researchers.

The biggest challenge at this stage was figuring out how much data to pull from the API. I have almost 3 years worth of DIY diabetes data, and I have numerous devices over time uploading all at once…which makes for large chunks of data. Not everyone has this much data (or 6-7 rigs uploading constantly ;)). Props to Madeleine for the patience in working with me to make sure the super users with large data sets will be able to use all of these tools!

Step 2: Sharing the data with researchers

This was easy. Yay for data-sharing tools like Dropbox.

Step 3: Researchers being able to use the data

Here’s where thing started to get interesting. We have large data files that come in json format from Nightscout. I know some researchers we will be working with are probably very comfortable working with tools that can take large, complex json files. However…not all will be, especially because we also want to encourage independent researchers to engage with the data for projects. So I had the belated realization that we need to do something other than hand over json files. We need to convert, at the least, to csv so it can be easily viewed in Excel.

Sounds easy, right?

According to basic searches, there’s roughly a gazillion ways to convert json to csv. There’s even websites that will do it for you, without making you run it on the command line. However, most of them require you to know the types of data and the number of types, in order to therefore construct headers in the csv file to make it readable and useful to a human.

This is where the DIY and infinite possibility nature of all the kinds of diabetes tools anyone could be using with Nightscout, plus the infinite ways they can self-describe profiles and alarms and methods of entering data, makes it tricky. Just based on an eyeball search between two individuals, I was unable to find and count the hundred+ types of data entry possibilities. This is definitely a job for the computer, but I had to figure out how to train the computer to deal with this.

Again, json to csv tools are so common I figured there HAD to be someone who had done this. Finally, after a dozen varying searches and trying a variety of command line tools, I finally found one web-based tool that would take json, create the schema without knowing the data types in advance, and convert it to csv. It was (is) super slick. I got very excited when I saw it linked to a Github repository, because that meant it was probably open source and I can use it. I didn’t see any instructions for how to use it on the command line, though, so I message the author on Twitter and found out that it didn’t yet exist and was a not-yet-done TODO for him.

Sigh. Given this whole #WeAreNotWaiting thing (and given I’ve promised to help some of the researchers in figuring this out so we can initiate some of the research projects), I needed to figure out how to convert this tool into a command line version.

So, I did.

  • I taught myself how to unzip json files (ended up picking `gzip -cd`, because it works on both Mac and Linux)
  • I planned to then convert the web tool to be able to work on the command line, and use it to translate the json files to csv.

But..remember the big file issue? It struck again. So I first had to figure out the best way to estimate the size and splice or split the json into a series of files, without splitting it in a weird place and messing up the data. That became jsonsplit.sh, a tool to split a json file based on the size you give it (and if you don’t specify, it defaults to something like 100000 records).

FWIW: 100,000 records was too much for the more complex schema of the data I was working with, so I often did it in smaller chunks, but you can set it to whatever size you prefer.

So now “all” I had to do was:

  • Unzip the json
  • Break it down if it was too large, using jsonsplit.sh
  • Convert each of these files from json to csv

Phew. Each of these looks really simple now, but took a good chunk of time to figure out. Luckily, the author of the web tool had done much of the hard json-to-csv work, and Scott helped me figure out how to take the html-based version of the conversion and make it useable in the command line using javascript. That became complex-json2csv.js.

Because I knew how hard this all was, and wanted other people to be able to easily use this tool if they had large, complex json with unknown schema to deal with, I created a package.json so I could publish it to npm so you can download and run it anywhere.

I also had to create a script that would pass it all of the Open Humans data; unzip the file; run jsonsplit.sh, run complex-json2csv.js, and organize the data in a useful way, given the existing file structure of the data. Therefore I also created an “OpenHumansDataTools” repository on Github, so that other researchers who will be using Nightscout-based Open Humans data can use this if they want to work with the data. (And, there may be something useful to others using Open Humans even if they’re not using Nightscout data as their data source – again, see “large, complex, challenging json since you don’t know the data type and count of data types” issue. So this repo can link them to complex-json2csv.js and jsonsplit.sh for discovery purposes, as they’re general purpose tools.) That script is here.

My next TODO will be to write a script to take only slices of data based on information shared as part of the surveys that go with the Nightscout data; i.e. if you started your DIY closed loop on X data, take data from 2 weeks prior and 6 weeks after, etc.

I also created a pull request (PR) back to the original tool that inspired my work, in case he wants to add it to his repository for others who also want to run his great stuff from the command line. I know my stuff isn’t perfect, but it works :) and I’m proud of being able to contribute to general-purpose open source in addition to diabetes-specific open source work. (Big thanks as always to everyone who devotes their work to open source for others to use!)

So now, I can pass researchers json or csv files for use in their research. We have a number of studies who are planning to request access to the OpenAPS Data Commons, and I’m excited about how work like this to make diabetes data more broadly available for research will help improve our lives in the short and long term!

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!

Autosensitivity (automatically adjusting insulin sensitivity factor for insulin dosing with #OpenAPS)

There’s a secret behind why #OpenAPS was able to deal so well with my BGs during norovirus. Namely, “autosensitivity”.

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!

 

Sick days solved with a DIY closed loop #OpenAPS

Ask me about the time I got a norovirus over Thanksgiving.

As expected, it was TERRIBLE. Even though the source of the norovirus was cute, the symptoms aren’t. (You can read about the symptoms from the CDC if you’ve never heard of it before.)

But, unexpectedly, it was only terrible on the norovirus symptoms front. My BGs were astoundingly perfect. So much so that I didn’t think about diabetes for 3 days.

Let me explain.

Since I use an OpenAPS DIY closed loop “artificial pancreas”, I have a small computer rig that automatically reads my CGM and pump and automatically adjusts the insulin dosing on my pump.

OpenAPS temp basal adjustments during day 2 norovirus November 2016
Showing the net basal adjustments made on day 2 of my norovirus – the dotted line is what my basals usually are, so anything higher than that dotted line is a “high” temp and anything lower is a “low” temp of various sorts.
  • 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.

Why this matters

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.

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.)

What a FDA approved commercial hybrid closed loop artificial pancreas system (670G) means for #OpenAPS

You probably heard that a commercial hybrid closed loop (the 670G) has been approved by the U.S. FDA and, like everyone else, are wondering what that means for #OpenAPS.

First, here’s our initial reaction:

And here are some longer form thoughts:

  • Yes, this is exciting. FDA moved months more quickly then expected (hmm, we are sensing a theme when the #WeAreNotWaiting community is involved ;)) to get this tech approved. And as we’ve experienced (check out this self-reported outcomes study with better outcomes than the pivotal trial for this new device), the results of using a hybrid closed loop are outstanding. It’s disappointing that they won’t be ready to ship until Spring 2017, but…
  • …This means the company has time to work on user guides and usability. As we’ve told every device company we’ve encountered, we (the #OpenAPS community) are happy to share everything we’ve learned. And we have learned a lot, including what it takes to trust a system, how much info is needed to help determine if additional human action is needed, what to do in all kinds of real-world situations, and more. We hope the companies continue to work with people with diabetes who have experience with this technology from both clinical trials and the DIY world, where we’ve racked up 350,000+ hours with this type of technology. Because setting expectations with users for this technology will be key for successful and sustained adoption.

This doesn’t really mean anything for #OpenAPS, though. The first generation of AP technology is similar to #OpenAPS in that it’s a hybrid closed loop that still requires the human to input carbs into the system, but it unfortunately has a set point that can not be adjusted below 120mg/dl.  For many people, this is not a big deal. But for others, this will be a deal breaker. For DIYers, that lack of customization will likely be frustrating. And for many families, the lack of remote data visualization may be another deal breaker. And, like with all new technology and devices, getting this stuff covered by insurance may be an uphill battle. So while optimistically this enables many people in the U.S. to finally access this technology (yay) without having to DIY, it won’t necessarily be truly available to everyone from a cost or access perspective for many years to come. So #OpenAPS and other DIY technology may still be needed from a cost/access perspective to continue to help fill gaps compared to current status quo with basic, non-connected diabetes devices (i.e. standalone pump and CGMs).

I also know that many of the parents of kids with T1D are disappointed, because the initial approval is for kids 14+, and it even notes that the system is not recommended for kids <7 or those taking less than 8u of insulin every day (usually young kids). I asked, suspecting it was related to occlusion, but it sounds more like they just don’t have enough data to say for sure that the system is safe with that small amount of insulin, and they’re working on additional studies to get data in that area.

Ditto, too, for more studies allowing different set points. They stuck with a 120mg/dl set point in order to speed to approval, but fingers crossed they get other studies done and new approvals from FDA before this device ships in the spring – that would be awesome. And I was glad to hear that they do have an “exercise” target of 150. That’s a bit of good…but I’m still hesitant that it is enough. From my personal experience knowing net IOB (here’s why net IOB matters) an hour before and when starting exercise is required information to help me decided whether or not I will need carbs to prevent lows during exercise. I don’t think this device will report on net IOB, but I admittedly haven’t seen the device and hopefully I’ll be proved wrong and the data available will be good enough for this purpose!

So in summary: this is good news. But we still need more FDA approved commercial options, and even with a single “commercial approved option”, it’s still ~6+ months away from reaching the hands of people with diabetes…so we as a #WeAreNotWaiting movement continue to have work to do to help speed up the processes for getting enhanced diabetes technology approved and available on the market, with access to view data the ways we need it.

*(Yes, in the title of the post I called it a commercial hybrid closed loop artificial pancreas system. It’s a hybrid closed loop, as is #OpenAPS, but it’s also on the road/part of the suite of more complex artificial pancreas technology. I realize to many PWDs “artificial pancreas” means a lot of different things. Quite certainly, regardless of definition, an artificial pancreas or hybrid closed loop still requires a lot of work. It’s not a cure by any stretch of the imagination. But it’s easy for the media to describe it as an AP, and I also find it a lot easier to describe the small device accompanying my pump when strangers ask as an “artificial pancreas” followed by an explanation rather than saying “hybrid closed loop”.

If anything, I think having the media broadly categorize it as an AP will encourage the diabetes community to ask more questions about what exactly this tech does, leading to greater understanding and better expectations about what the device will/won’t be able to do. So this may result in a good thing.)

#OpenAPS rigs are shrinking in size

My newest #OpenAPS rig is roughly the size of two sticks of Chapstick.

Think about that, especially in context of my earliest rig of a Raspberry Pi, Carelink stick, battery with enough power to last a full 12 hour day (or more), and the bulk that it added to my bag. I was happy to carry it, but once Oskar started working on a smaller rig with better range, for many people it was a game changer!

Components of an #OpenAPS implementation: pump; CGM; Raspberry Pi with battery and a radio communication device

And now we have another option with a new open source hardware board called the “915MHz Edison Explorer Board“. It’s a board designed to hold an Intel Edison (the ‘mini computer’), and it also has a 900MHz antenna on it – which means we can use it to talk to the insulin pump. This eliminates the need for a separate ‘radio stick’ – like the Carelink or a TI or similar. This is a huge improvement!

What carrying the new rig looks like:

This is what a full rig setup looks like:

  1. Insulin pump
  2. CGM
  3. Explorer Board rig

…and that’s all that’s strictly required. You can use openxshareble to read BG data from the receiver directly, but that’s currently the flakiest part of my setup, so I still recommending hotspotting your phone to pull BG data down from the cloud – and more importantly, so you can use Nightscout or similar to visualize what the loop is doing.

So, today’s post is about the new, shiny, smaller rig, and I know everyone wants to know how to get the parts to build their own!

**Update** – You can order an Explorer Board here. . Keep in mind Edison and battery are not included, so if you don’t already have an Edison, you’ll want to order one of those, too.

Improved #OpenAPS docs in the works, too!

Also, stay tuned – we have a new setup script and guide being developed and tested to streamline the setup of an OpenAPS implementation using this board or any of the previous hardware. These new docs will streamline the installation and configuration of the components required for anyone to build a new OpenAPS implementation for themselves, so they can more easily focus on testing the algorithm and decision making process that’s a critical part of DIY looping.

 

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.

OpenAPS poster cited in Nature!

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

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

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

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

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

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

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

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

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

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

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

How to “soak” a new CGM sensor for better first day BGs

Having used a CGM for years (and years and years, and years before that), and having chosen to build a DIY system that provides smart alerts and recommendations based on said CGM data (learn more about my #DIYPS system here) and ultimately using CGM data to build the open source closed loop system that automates insulin delivery (find out more about #OpenAPS here)…I’ve learned a few things about how to get the best data out of my sensors. Currently, I’m using Dexcom, so this applies to the Dexcom sensors.

The biggest thing I do to get better first day results from a continuous glucose monitor (CGM) sensor is to “soak” my sensors. Here’s what I mean by this:

Normally, you’d expect to see a person with one CGM sensor on their body, like this:

Dana Lewis_first day CGM sensor illustrationHowever, 12-24 hours before I expect my sensor to end, I insert my next sensor into my body. To protect the sensor (you don’t want the sensor filament itself to get torn off or lost in your body), I plop an old (“dead” battery) transmitter on it.

If you don’t have an old/dead transmitter, you could try taping over it – the goal is just to protect the sensor filament from ripping.

Dana Lewis_last day CGM sensor soak illustrationThe next day, when my sensor session ends:

  • I take the “live” transmitter off the old sensor, and remove the old sensor from my body. I hit “stop sensor” on my receiver, if it hasn’t already stopped itself.
  • I gently remove the “dead”/old transmitter from ‘new’ sensor.
  • I then stick the “live” transmitter onto the new sensor.
  • I hit “start sensor” on my receiver.

Dana Lewis_last day CGM sensor soak change illustrationThe outcome (for me) has always been significantly improved “first day” BG readings from the sensor. This works great when you can plan ahead and your outfits (don’t judge, sometimes you have important outfits like a wedding dress to plan around) and skin real estate support two sites on your body for 24 hours or so. This doesn’t work if you rip a sensor out by accident, so in those scenarios I go ahead and put a new sensor on, put the ‘live’ transmitter on, and hit ‘start’ to get through the 2 hour calibration period as soon as possible to get back to having live data. (All the while knowing that the first day is going to be more “meh” than it would be otherwise.)