DIY

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

by Dana Lewis

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

by Dana Lewis

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

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

I immediately was envisioning a number of things:

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

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

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

DanaMLewis EGG for scale

And here’s what it looked like on me:

DanaMLewis wearing an ambulatory EGG

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

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

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

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

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

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

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

RIP, original looping pump

by Dana Lewis

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

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

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

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

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

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

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

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

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

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

Vitamin D and insulin sensitivity

by Dana Lewis

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

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

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

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

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

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

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

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

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

Making changes in diabetes is hard by DanaMLewis

Women in open source make a difference

by Dana Lewis

I was incredibly honored to find out that I had been nominated for the 2018 Women in Open Source award (and even more blown away to learn that I am one of the finalists). I wasn’t familiar with the award, and when I looked it up to learn more about it, the finalist list for the last few years gives me some serious imposter syndrome! Aside from that, there were a few things that caught my eye – and one of them was a citation from a study that found that only 11% of people contributing in open source are women.

To me, this number both makes sense, and doesn’t.

Why it makes sense (to me): open source can be hard on women.

I’ve been doing things in open source since 2014, falling into it because of DIYPS and because of getting to know people like Ben who are passionate open source advocates. Because I was helped by open source work, it was a key driver for my own passion behind making our work with OpenAPS open source, and is why I’m currently working on developing a series of open source tools to help researchers working with diabetes data. I don’t know that I would have done anything open source had I not found the perfect series of projects that led me there.

While there are many great people in the diabetes open source community, in the middle of 2014 I wrote this blog post about being female and being discounted. It was a hard post to write. But I felt it was important, because one of things both Scott and I noticed is a lot of the attitudes behind this seemed to be subconscious: directing technical questions about our project to Scott only; refusing to direct any substantial questions to me even after Scott specifically would redirect questions to me, etc. The only way I saw to (begin to) deal with the problem was to address it head on.

And, for me, things have improved with time. But it hasn’t gone away, and it still requires active addressing about once a month or so. And yes – these are (relatively) minor problems compared to what some women experience in open source, or in tech. But it’s some of the most common, frustrating friction that can easily drive women away when they get tired of experiencing stuff like that. And when they go away, it’s a loss for everyone.

Why this number doesn’t make sense (to me): women contribute incredible value to open source, and are high-volume contributors, especially if you look beyond the narrow definition applied to open source coding.

Every where I turn, I see women participating in open source. I see Kate Farnsworth and Christine Deltrap, two incredible individuals who have made watchfaces used by thousands of families to remote monitor their children’s blood sugars. I see Katie DiSimone, who has written hundreds of lines of documentation, and answers hundreds of technical troubleshooting questions across several channels. I see Mad Price Ball, who leads the Open Humans Foundation with open source work (*and* is an amazing mentor to women like me, who have non-traditional development backgrounds). I see Karen Sandler, a fierce advocate for making software open source, who herself is a finalist for the WOS award, too!

I also see a lot of my own contributions in open source, especially in the early days when Scott was the one doing most of the committing to Github for the tools we were building. Those were part of why I was told I was discounted in 2014, because my work didn’t “count”. Today when someone goes and looks at Github, if they look at the wrong toolkit (or just one, for example), it gets said that “Dana didn’t do anything on OpenAPS”. (Heh).

So I know there are also other women out there whose work is being overlooked when counting who’s doing open source. However, this type of work is absolutely crucial to open source projects, and these contributors drive an incredible amount of value. I’m glad the Red Hat Women in Open Source Awards site acknowledged this, and made this list:

  • Code and programming.
  • Quality assurance and bug triage.
  • Involvement in open hardware.
  • System administration and infrastructure.
  • Design, artwork, user experience, and marketing.
  • Documentation, tutorials, and other communications.
  • Translation and internationalization.
  • Open content.
  • Community advocacy and community management.
  • Intellectual property advocacy and legal reform.
  • Open source methodology.

The list was partially to help encourage people to nominate women; and also to help women to recognize all of the activities they do that’s open source. And it was helpful to me, too. Because of that list, instead of a handful of key examples of open source activity by women, I can instead name dozens of women. I bet you can, too. There’s so much incredible open source activity and value that happens in places outside of commit history, and if we want to recognize and acknowledge the work of everyone in open source communities, we should do a better job of acknowledging *all* of these types of activities and not just recognizing individuals (male or female) who have a traditional code-based commit history.

So if you’re reading this, it’s likely you’re a supporter of women in open source communities. Thank you for that! But I’d like to ask you to do two specific things.

1) Actively recognize the women working with you in open source. The internet can be a hard place to be, let alone work, when you are female. Help lift women up; recognize their work; and help them grow their skills.

2) Ok, this one is optional :) But if you’ve read all this way, you might consider clicking here and going to the Women in Open Source Awards site and voting for one the finalists in each category. It’s one vote per email address. Thanks!

Acknowledging all contributions in open source by DanaMLewis

More than 3 years of DIY closed looping with #OpenAPS

by Dana Lewis

I’ve been using a DIY closed loop (OpenAPS) for 1,152 days. That’s over three years (from December 2014) of looping. That’s 1,152 nights of being able to sleep without worrying about dying in my sleep. That’s 1,152 mornings of getting to text my mom because – and when – I want to, not because it’s the thing that keeps her from worrying that I’m dead. It’s immeasurable peace of mind, in addition to the best outcomes I’ve ever had in 15 years of diabetes. And it’s gotten better since the very beginning.

Here’s where we started, and where we’ve come since then:

Here’s what hasn’t changed:

  • It is 100% do it yourself (aka, DIY). There’s no company or entity who will hand you a fully functional DIY closed loop. You get to build it yourself, which is why (among other reasons) comparing the costs of a DIY system to the cost of a potential commercial system is like apples and horseradish. But it also means you have ultimate control over your system, your algorithm, how it works, and what settings it has. There’s ultimate transparency, not just in what you’re using, but in understanding the path any feature or tool took in development from initial idea, all the way to it being a piece of code that the community is actively using. And you get to choose which pieces you use.
  • It’s driven by the spirit of paying it forward. In code and in documentation, in the interactions among the community across numerous online channels, you see incredible gratitude and appreciate sharing between members of the community. Because we can remember what it’s like to not have this technology, and we see the difference it makes. You hear stories of people succeeding at all day soccer camps or in running marathons; at school; at work; people having healthy pregnancies; and all other number of beautiful stories framed in gratitude for having technology that helps make life more about life, and less about diabetes. As Cameron said last night, “I’ve gotten use[d] to the day to day normalcy of OpenAPS, but it’s the “this is gonna be bad” and then “oh. Maybe not” that get me now. :)”

I’ve been looping for 1,152 days and I’m still blown away with appreciation for everyone in this community who codes, collaborates, documents, shares, troubleshoots, and together help us all overcome some of the many challenges in living well with diabetes. Without this community, there wouldn’t now be >500+ people worldwide accessing DIY closed loop technology. And that’s worth more to me than my own closed looping. <3

3 years of closed looping with #OpenAPS by @DanaMLewis

Quantified sickness when you have #OpenAPS and the flu

by Dana Lewis

Getting “real people sick*” is the worst. And it can be terrifying when you have type 1 diabetes, and know the sickness is both likely to send your blood sugars rocketing sky high, as well as leave you exhausted and weak and that much harder to deal with a plummeting low.

*(Scott hates this term because he doesn’t like the implication that PWD’s aren’t real. We’re real, all right. But I like the phrase because it differentiates between feeling bad from blood sugar-related reasons, and the kind of sickness that anyone can get.)

In February 2014, Scott got home from a conference on Friday, and on Saturday complained about being tired with a headache. By Sunday, I started feeling weary with a sore throat. By Monday morning, I had a raging fever, chills, and the bare minimum of energy required to drag myself into the employee health clinic and get diagnosed with the flu. And since they knew I was single and lived by myself, the conversation went from “here’s your prescription for Tamiflu” to “but you can’t be by yourself, maybe we should find a bed for you in the hospital” because of how sick I was. Luckily, I called Scott and asked him to come pick me up and let me stay at his place. And there I stayed in complete misery for several days, the sickest I’d ever been. I remember at one point on the second day, waking up from a fitful doze and seeing Scott standing across the room with his laptop on a dresser, using it as a standing desk because he was so worried about me that he didn’t want to leave the room at that point. It was that bad.

Luckily, I survived. (And good thing, right, given that we went on to build OpenAPS, yes? ;)) This year’s flu experience was different. This year I was real-people sick, but without the diabetes-related fear that I’d so often experienced in the past. My blood sugars were perfectly managed by OpenAPS. I didn’t go low. It didn’t matter if I didn’t eat, or did eat (potato soup, ice cream, and frozen fruit bars were the foods of choice). My BGs stayed almost entirely in range. And because they were so in range that it was odd, I started watching the sensitivity ratio that is calculated by autosensitivity to see how my insulin sensitivity was changing over the course of the sickness. And by day 5, I finally felt good enough to share some of that data (aka, tweet). Here’s what I found from this year’s flu experience:

  • Night 1 was terrible, because I got hardly any deep sleep (45 minutes, whereas 2+h is my usual average per night) and kept waking up coughing. I also was 40% insulin resistant all night long and into Day 2, meaning it took 40% more insulin than usual to keep my BGs at target.
  • Night 2 was even worse – ZERO deep sleep. Ahhhh! It was terrible. Resistance also nudged up to 50%.
  • Night 3 – hallelujah, deep sleep returned. I ended up getting 4h53m of deep sleep, and also was able to sleep for closer to 2 hour blocks at a time, with less coughing. Also, going into night 3 was pretty much the only “high” I had of being sick – up around 180 for a few hours. Then it fell off a cliff and whooshed down to the bottom of my target, marking the drastic end of insulin resistance. After that, insulin sensitivity was fairly normal.
  • Night 4 yielded more deep sleep (>5 hours), and a tad bit of insulin sensitivity (~10%), but it’s unclear whether that’s totally sickness related or more related to the fact that I wasn’t eating much in day 3 and day 4.
  • Night 5 felt like I was going backward – 1h36m of deep sleep, tons of coughing, and interestingly a tad bit of insulin resistance (~20%) again. Night 6 (last night) I supposedly got plenty of deep sleep again (>4h), but didn’t feel like it at all due to coughing. BGs are still perfectly in range, and insulin sensitivity back to usual.

This was all done still with no-bolus, and just carb announcement when I ate whatever it was I was eating. In several cases there was negative IOB on board, but I didn’t have the usual spikes that I would normally see from that. I had 120 carbs of gluten free biscuits and gravy yesterday, and I didn’t go higher than 130mg/dl.

In-range BGs shown on CGM graph thanks to OpenAPS

It’s a weird feeling to have been this sick, and have perfectly normal blood sugars. But that’s why it’s so interesting to be able to look at other data beyond average, time in range, and A1c – we now have the tools and the data to be able to dive in and really understand more about what our bodies are doing in sick situations, whether it’s norovirus or the flu.

I’m thinking if everyone shared their data from when they had the flu, or norovirus, or strep throat, or whatever – we might be able to start to analyze and detect patterns of resistance and otherwise sensitivity changes over the course of typical illness. This way, when someone gets sick with diabetes, we’d know generally “expect around XX% resistance for Days 1-3, and then expect a drop off that looks like this on Day 4”, etc.

That would be way better than the traditional ways of just bracing yourself for sky-high highs and terrible lows with no understanding or ability to make things better during illness. The peace of mind I had during the flu this year was absolutely priceless. Some people will be able to get that with DIY closed loop technology; but as with so many other things we have learned and are learning from this community, I bet we can find ways to help translate these insights to be of benefit for all people with diabetes, regardless of which therapies they have access to or decide to use.

—

Want to help? Been sick? Consider donating your data to my diabetes sick-day analysis project. What you should do:

  1. If you’re using a closed loop, donate your data to the OpenAPS Data Commons. You can do all your data (yay!), or just the time frame you’ve been sick. Use the “message the project owner” feature to anonymously message and share what kind of illness you had, and the dates of sickness.
  2. Not using a closed loop, but have Nightscout? Donate your data to the Nightscout Data Commons, and do the same thing: Use the “message the project owner” feature to anonymously message and share what kind of illness you had, and the dates of sickness.

As we have more people who identify batches of sick-day data, I’ll look at what insights we can find around sensitivity changes before, during, and after sickness, plus other insights we can learn from the data.

The last set of book/CGM-robot-related surprises :)

by Dana Lewis

As I mentioned when I wrote about the process of writing my children’s book about diabetes devices and how all people are different – I had to keep a lot of secrets. The topic of the book was a surprise from my mom. The fact that I wrote a book at all was a secret from my brother, sister-in-law, and niece.

As I started to add other fun things, like turning the CGM-robot into a “stuffy” (which you can get on Etsy), I also wanted to do something special as a surprise for my aunt. After all, she is  the one who donated her time and her efforts for the beautiful illustrations in the book!

robot illustration @DanaMLewisI love the CGM robot so much, because it’s such a cute illustration, and it also was something that was 100% my aunt’s idea (I just asked for some kind of illustration for the opening page, and it’s totally her creation). So when I was speaking at an event and they happened to be giving out found-object robots as awards, I knew I had found my gift idea.

 

Robot sculptures that inspired my robot CGM character

So, I almost immediately tweeted and began looking for a dead/broken CGM receiver. I had to be vague about it, though, given the topic of the book being a secret for my mom. I told the people who were reaching out with offers of busted receivers that this was for an “art project”, not hacking, so they didn’t get their hopes up about anything else!

I had reached out to the creator of the found-object robots, but she wasn’t available until January. Ouch. I knew that timeline wouldn’t work. I started thinking if maybe I could find someone local to help me create the robot. Then Scott had the idea of trying to do it with Lego’s. So on a cold, rainy, typical Seattle Friday night, off to the Lego store we went!

Let’s just say, an adult couple without kids is not the typical Lego store customer. However, I started poking around at the bins of for-sale Lego pieces, looking for pieces to help build the shapes that I wanted. The Lego store employees sidled up and said, “do you mind if we ask what you’re making?” and also began to help scout the right size and type of pieces after I showed them the illustration of the CGM robot.

And so it began to come together:

CGM robot build of legos by DanaMLewisCGM robot build of legos by DanaMLewisCGM robot build of legos by DanaMLewisWe bought a container of Lego pieces, plus some spares because why not, and brought them home. I played around with my receiver (blue) while I was waiting for the broken receiver someone had kindly donated to arrive.

CGM lego robot in blue by DanaMLewisThen the broken receiver arrived, and the robot was finished! While the Lego store will never ever tell you to glue Lego pieces, our helpful Lego store staffer passed on what other customers have told him for keeping pieces connected – a bit of super glue. So we (thanks, Scott! ;)) used super glue to attach the receiver to the back support piece and for all the major joints, so it wouldn’t fall apart in transit.

CGM lego robot in black by DanaMLewisWhen I went to see my family, taking the books and the stuffy, I also took the robot in bubble wrap, and sent it home with my mom. My mom went to take the signed copy of the book to my aunt today, and surprised her with the robot!

So, the last secret is out :).

Makers gonna make…a book about diabetes devices? Kids book written by @DanaMLewis

by Dana Lewis

book inspirationLast year after Christmas, I was running around my parents’ backyard with my niece when she spotted my CGM sensor on my arm and asked what it was. I’m always struck when my niece and nephews have noticed my diabetes devices, and am interested to see what “new” humans think about and how they encounter things and what they mean. In this case, I explained the CGM and we went back to running around, but it stuck in my mind for a few days.

I also remember the excitement and attention any time a kids’ book has a character with diabetes in it, or a storyline of diabetes, because there’s just not much out there. I was diagnosed at 14, but I love seeing PWDs in the wild and like the idea of more diabetes inclusion in materials for all ages.

So, I wrote a kids book, with the goal of introducing the concept of diabetes devices and more broadly, how people are different in different ways. I talked my incredible artist aunt into illustrating this book. :)

This book is primarily for me and my niece and nephews, but I know there might be a few other people who like the idea, too (even as there may be a few people who sniff at the idea*). I investigated the publishing options and decided to go with self-publishing, which would allow for:

  • The cheapest copies for me as the author, to be able to give to my various family members who want them.
  • The ability to make it available to other people who want copies.
  • The ability to price said copies so it’s accessible and reasonable to order easily.
  • (It’s actually cheaper for you to order this on Amazon directly to your house, than it is for you to ask me for an author-priced copy and for me to go through the hoopla of getting it to ship.)
  • Every two copies purchased via Amazon yields an author-priced copy that I plan to donate to libraries, hospitals, etc. (If you’d like to sponsor 10+ books for a library system, feel free to ping me about the easiest way to do that.) I’m planning to use any “profits” from the book to pay for copies that I’m donating.

I’ve been working on it off and on for the past few months as my aunt illustrated it, and got to give a copy to my brother and niece as a total surprise to read when we were in Alabama this past weekend. So now that the cat is out of the bag, the book is available online! The book, “Carolyn’s Robot Relative” (that’s me!), is available on Amazon here (note that’s an Amazon affiliate link). (There’s also now a German-translated copy with the title, “Ist Carolyns Tante ein Roboter?” – see the German version on Amazon.de here!)

—

robot illustration @DanaMLewisI also *love* the robot illustration that my aunt made with the CGM as the main body of the robot.  I reached out to someone on Etsy who does custom “stuffies” – and it turns out, she has a diabetes connection, too! So, you can get a stuffed CGM robot if you or your kids like it, for $20. Here is the link to the listing on Etsy. (I don’t make any money from this; I paid $20 for my first one, but had worked with her on pricing so it would be reasonable for people to get if they liked it!)

CGM robot stuffy from Carolyn's Robot Relative by DanaMLewisCGM robot stuffy from Carolyn's Robot Relative by DanaMLewisThe stuffy with the book – it’s an awesome sized stuffy!

And because I have also been playing with code fabric on Spoonflower (see tweet thread here, or this blog post here) and know they do fabric as well as gift wrap…I uploaded the CGM robot there so I could turn it into wrapping paper, too. Here’s the link to see it on Spoonflower.

CGM robot giftwrap preview! available on Spoonflower as fabric, gift wrap/wrapping paper, or wallpaper—

I learned a lot in the research process about self-publishing options and the route I took that I wanted to share here, especially for anyone who sniffs and goes troll on me about putting this out there.

*Tl;dr – self-publishing is easy, and if you don’t like my book, go make a better one yourself! :) The more books, the better!

—

Some background on the publishing process & how I made the book:

I chose self-publishing with CreateSpace on Amazon. They now have this new “Kindle Direct Publishing” (KDP) program that’s similar, but less tested than CreateSpace, and seems to be higher cost for author copies. I never figured out what the benefits are of that, and chose CS.

I generally Google’d a bunch of questions and ended up on the CS forums, too, and read up on different programs to use to create the book, etc.

My process:

  • I wrote the book test in Microsoft Word, then translated it into a Google spreadsheet so I could visualize the left/right layout of the flow of text, as well as start to identify where I had ideas about what images to use.
    Example_storyflow_spreadsheet_Dana_Lewis
  • My aunt began illustrating, and sending me pictures. Fun fact – all of the images in the book are put in via iPhone photos -> AirDrop -> my computer -> inserted! No fancy graphics. (Although I did open a few of the images in Preview and change the white balance, since each photo was taken in different lighting, in a weak attempt to balance the colors of the pictures side by side.)
  • I started dropping them into a Microsoft Word document. The one thing the CS forums warned about was making sure the images were high enough res. The images were…but later in the upload process, it complained about the DPI being low. I switched to Microsoft PowerPoint (doing the same thing I did in Word to create the custom page size to work with bleed, trim, etc.) and dropped the images in the same way, and PPT doesn’t compress the images and it was fine. Word was problematic. It didn’t take much time to switch back and forth, but if I did it again, I’d start with PPT because they generally seem to get that images need to be full sized.
  • (A workaround if you take screenshots and need to insert images – you can use Preview to go in and adjust the size and make it >300 DPI that CS prefers, before inserting the images into PPT).
  • I placed text boxes on top of the images.
  • Once done, I saved as a PDF, and then went to upload to CS. I uploaded and tweaked and viewed the Digital Proofreader tool about a dozen times the first day I did it, as I wanted to move text a tad up or down, and as I resolved the complaints about DPI not being great.
  • (You do the same process for the cover image, and CS is pretty good about telling you how to calculate your spine size for the number of pages in the book, and adding that in to the front/back cover size to calculate what you need. You can also get a sized template from them, and then use images and cover it up so it’s sized perfectly.)
  • Once you’re happy with what’s uploaded to the system, you submit to CS for review (takes 24 hours). You then get to review another digital proof, and a PDF version, and then get the chance to order a physical proof copy!

Tl;dr version 2 – it was actually super easy, even for someone who’s not a graphic designer, to do this. This was a great method to work with an illustrator with simple iPhone photos of awesome illustrations and turn them into a book. You could probably also scan and do all kinds of fancy stuff…but for a basic book, the basic process described above works great. It actually doesn’t take much time in terms of placing text or uploading and tweaking your file.

The hardest part was calculating the size of the pages and deciding on whether to do with bleed or without bleed.

The other hardest part was keeping the topic of the book a secret from my mom for 10 months, because I thought she’d get a bigger kick out of being surprised with the book’s topic and contents when she had a finished copy in her hands. Sorry, Mom! Hopefully you thought it’s worth it. :)

front and back of "Carolyn's Robot Relative" by @DanaMLewis

Why Open Humans is an essential part of my work to change the future of healthcare research

by Dana Lewis

I’ve written about Open Humans before; both in terms of how we’re creating Data Commons there for people using Nightscout and DIY closed loops like OpenAPS to donate data for research, as well as building tools to help other researchers on the Open Humans platform. Madeleine Ball asked me to share some more about the background of the community’s work and interactions with Open Humans, along with how it will play into the Opening Pathways grant work, so here it is! This is also posted on the OpenHumans blog. Thanks, Madeleine, and Open Humans!

—

 

So, what do you like about Open Humans?

Health data is important to individuals, including myself, and I think it’s important that we as a society find ways to allow individuals to be able to chose when and how we share our data. Open Humans makes that very easy, and I love being able to work with the Open Humans team to create tools like the Nightscout Data Transfer uploader tool that further anonymizes data  uploads. As an individual, this makes it easy to upload my own diabetes data (continuous glucose monitoring data, insulin dosing data, food info, and other data) and share it with projects that I trust. As a researcher, and as a partner to other researchers, it makes it easy to build Data Commons projects on Open Humans to leverage data from the DIY artificial pancreas community to further healthcare research overall.

Wait, “artificial pancreas”? What’s that?

I helped build a DIY “artificial pancreas” that is really an “automated insulin delivery system”. That means a small computer & radio device that can get data from an insulin pump & continuous glucose monitor, process the data and decide what needs to be done, and send commands to adjust the insulin dosing that the insulin pump is doing. Read, write, read, rinse, repeat!

I got into this because, as a patient, I rely on my medical equipment. I want my equipment to be better, for me and everyone else. Medical equipment often isn’t perfect. “One size fits all” really doesn’t fit all. In 2013, I built a smarter alarm system for my continuous glucose monitor to make louder alarms. In 2014, with the partnership of others like Ben West who is also a passionate advocate for understanding medical devices, I “closed the loop” and built a hybrid closed loop artificial pancreas system for myself. In early 2015, we open sourced it, launching the OpenAPS movement to make this kind of technology more broadly accessible to those who wanted it.

You must be the only one who’s doing something like this

Actually, no. There are more than 400+ people worldwide using various types of DIY closed loop systems – and that’s a low estimate! It’s neat to live during a time when off the shelf hardware, existing medical devices, and open source software can be paired to improve our lives. There’s also half a dozen (or more) other DIY solutions in the diabetes community, and likely other examples (think 3D-printing prosthetics, etc.) in other types of communities, too. And there should be even more than there are – which is what I’m hoping to work on.

So what exactly is your project that’s being funded?

I created the OpenAPS Data Commons to address a few issues. First, to stop researchers from emailing and asking me for my individual data. I by no means represent all other DIY closed loopers or people with diabetes! Second, the Data Commons approach allows people to donate their data anonymously to research; since it’s anonymized, it is often IRB-exempt. It also makes this data available to people (patient researchers) who aren’t affiliated with an organization and don’t need IRB approval or anything fancy, and just need data to test new algorithm features or investigate theories.

But, not everyone implicitly knows how to do research. Many people learn research skills, but not everyone has the wherewithal and time to do so. Or maybe they don’t want to become a data science expert! For a variety of reasons, that’s why we decided to create an on-call data science and research team, that can provide support around forming research questions and working through the process of scientific discovery, as well as provide data science resources to expedite the research process. This portion of the project does focus on the diabetes community, since we have multiple Data Commons and communities of people donating data for research, as well as dozens of citizen scientists and researchers already in action (with more interested in getting involved).

What else does Open Humans have to do with it?

Since I’ve been administering the Nightscout and OpenAPS Data Commons, I’ve spent a lot of time on the Open Humans site as both a “participant” of research donating my data, as well as a “researcher” who is pulling down and using data for research (and working to get it to other researchers). I’ve been able to work closely with Madeleine and suggest the addition of a few features to make it easier to use for research and downloading large data sets from projects. I’ve also been documenting some tools I’ve created (like a complex json to csv converter; scripts to pull data from multiple OH download files and into a single file for analysis; plus writing up more details about how to work with data files coming from Nightscout into OH), also with the goal of facilitating more researchers to be able to dive in and do research without needing specific tool or technical experience.

It’s also great to work with a platform like Open Humans that allows us to share data or use data for multiple projects simultaneously. There’s no burdensome data collection or study procedures for individuals to be able to contribute to numerous research projects where their data is useful. People consent to share their data with the commons, fill out an optional survey (which will save them from having to repeat basic demographic-type information that every research project is interested in), and are done!

Are you *only* working with the diabetes community?

Not at all. The first part of our project does focus on learning best practices and lessons learned from the DIY diabetes communities, but with an eye toward creating open source toolkit and materials that will be of use to many other patient health communities. My goal is to help as many other patient health communities spark similar #WeAreNotWaiting projects in the areas that are of most use to them, based on their needs.

How can I find out more about this work?
Make sure to read our project announcement blog post if you haven’t already – it’s got some calls to action for people with diabetes; people interested in leading projects in other health communities; as well as other researchers interested in collaborating! Also, follow me on Twitter, for more posts about this work in progress!