This. Matters. (Why I continue to work on #OpenAPS, for myself and for others)

If you give a mouse a cookie or give a patient their data, great things will happen.

First, it was louder CGM alarms and predictive alerts (#DIYPS).

Next, it was a basic hybrid closed loop artificial pancreas that we open sourced so other people could build one if they wanted to (#OpenAPS, with the oref0 basic algorithm).

Then, it was all kinds of nifty lessons learned about timing insulin activity optimally (do eating soon mode around an hour before a meal) and how to use things like IFTTT integration to squash even the tiniest (like from 100mg/dL to 140mg/dL) predictable rises.

It was also things like displays, button, widgets on the devices of my choice – ranging from being able to “text” my pancreas, to a swipe and button tap on my phone, to a button press on my watch – not to mention tinier sized pancreases that fit in or clip easily to a pocket.

Then it was autosensitivity that enabled the system to adjust to my changing circumstances (like getting a norovirus), plus autotune to make sure my baseline pump settings were where they needed to be.

And now, it’s oref1 features that enable me to make different choices at every meal depending on the social situation and what I feel like doing, while still getting good outcomes. Actually, not good outcomes. GREAT outcomes.

With oref0 and OpenAPS, I’d been getting good or really good outcomes for 2 years. But it wasn’t perfect – I wasn’t routinely getting 100% time in range with lower end of the range BG for a 24hour average. ~90% time in range was more common. (Note – this time in range is generally calculated with 80-160mg/dL. I could easily “get” higher time in range with an 80-180 mg/dL target, or a lot higher also with a 70-170mg/dL target, but 80-160mg/dL was what I was actually shooting for, so that’s what I calculate for me personally). I was fairly happy with my average BGs, but they could have been slightly better.

I wrote from a general perspective this week about being able to “choose one” thing to give up. And oref1 is a definite game changer for this.

  • It’s being able to put in a carb estimate and do a single, partial bolus, and see your BG go from 90 to peaking out at 130 mg/dL despite a large carb (and pure ballpark estimate) meal. And no later rise or drop, either.
  • It’s now seeing multiple days a week with 24 hour average BGs a full ~10 or so points lower than you’re used to regularly seeing – and multiple days in a week with full 100% time in range (for 80-160mg/dL), and otherwise being really darn close to 100% way more often than I’ve been before.

But I have to tell you – seeing is believing, even more than the numbers show.

I remember in the early days of #DIYPS and #OpenAPS, there were a lot of people saying “well, that’s you”. But it’s not just me. See Tim’s take on “changing the habits of a lifetime“. See Katie’s parent perspective on how much her interactions/interventions have lessened on a daily basis when testing SMB.

See this quote from Matthias, an early tester of oref1:

I was pretty happy with my 5.8% from a couple months of SMB, which has included the 2 worst months of eating habits in years.  It almost feels like a break from diabetes, even though I’m still checking hourly to make sure everything is connected and working etc and periodically glancing to see if I need to do anything.  So much of the burden of tight control has been lifted, and I can’t even do a decent job explaining the feeling to family.

And another note from Katie, who started testing SMB and oref1:

We used to battle 220s at this time of day (showing a picture flat at 109). Four basal rates in morning. Extra bolus while leaving house. Several text messages before second class of day would be over. Crazy amount of work [in the morning]. Now I just have to brush my teeth.

And this, too:

I don’t know if I’ve ever gone 24 hours without ANY mention of something that was because of diabetes to (my child).

Ya’ll. This stuff matters. Diabetes is SO much more than the math – it’s the countless seconds that add up and subtract from our focus on school/work/life. And diabetes is taking away this time not just from a person with diabetes, but from our parents/spouses/siblings/children/loved ones. It’s a burden, it’s stressful…and everything we can do matters for improving quality of life. It brings me to tears every time someone posts about these types of transformative experiences, because it’s yet another reminder that this work makes a real difference in the real lives of real people. (And, it’s helpful for Scott to hear this type of feedback, too – since he doesn’t have diabetes himself, it’s powerful for him to see the impact of how his code contributions and the features we’re designing and building are making a difference not just to BG outcomes.)

Thank you to everyone who keeps paying it forward to help others, and to all of you who share your stories and feedback to help and encourage us to keep making things better for everyone.

 

Why guess when you don’t have to? (#OpenAPS logs & why they’re handy)

One of the biggest benefits (in my very biased opinion) of a DIY closed loop is this: it’s designed to be understandable to the person using it.

You don’t have to guess “what did it do at 2am?” or “why did it do a temp basal and not an SMB?”

Well, you COULD guess – but you don’t have to. Guessing is a choice ;).

Because we’ve been designing a system that a person has to decide to trust, it provides information about everything it’s doing and the information it has. That’s what “the logs” are for, and you can get information from “the logs” from a couple of places:

  • The OpenAPS “pill” in Nightscout
  • Secondary logging sources like Papertrail
  • Information that shows up on your Pebble watch
  • The full logs from SSH’ing into a rig (usually what we mean when we ask, “what do your logs say?”)

Here’s an example of the information the OpenAPS pill provides me in Nightscout:

Example OpenAPS pill info in Nightscout

This tells me that at 11:03 am, my BG was 121; I had no carbs on board; was dropping a tiny bit as expected and was likely going to end up slightly below my target; and the current temporary basal rate running was about equivalent to what OpenAPS thought I needed at the time. I had 0.47 netIOB, all from basal adjustments. It also specifies some of the eventual numbers that are what trigger the “purple line predictions” displayed in Nightscout, so if you can’t tell where the line is (90 or 100?), you can use the pill information to determine that more easily.

(Here’s the instructions for setting up Nightscout for OpenAPS)

Here’s an example of a log from Papertrail and what it tells us:

Example papertrail usage for OpenAPS

This example is from Katie, who described her daughter’s patterns in the morning: when Anna leaves her rig in the bedroom and went to take a shower, you can see the tune change at around 6:55, meaning she’s out of range of the rig. After the shower, getting dressed, and getting back to the rig around 7:25, it goes back to “normal” tuning (which means reading and writing to the pump as usual).

Papertrail is handy for figuring out if a rig is working or not remotely and high level why it might not be, especially if it’s a communication or power problem. But I generally find it to be most helpful when you know what kind of problem it is, and use it to drill down on a particular thing. However, it’s not going to give you absolutely all the details needed for every problem – so make sure to read about how to access the traditional logs, too, and be able to do that on the go.

(Here’s the instructions for getting Papertrail going for OpenAPS)

Here’s what the logs ported to my Pebble tell me:

OpenAPS logs on Pebble watch @DanaMLewis example

There’s several helpful things that display on my watch (using the excellent “Urchin” watchface designed by Mark Wilson, which you can customize to suit your personal preference): BGs, basal activity, and then some detailed text, similar to what’s in the OpenAPS pill (current BG, the change in BG, timestamp of BG, my netIOB, my eventual BGs, and any temp basal activity). In this case, it’s easy for me to glance and see that I was a bit low for a while; am now flat but have negative net IOB so it’s been high temping a bit to level out the netIOB.

(I’ve always preferred a data-rich watchface – even back in the days of “open looping” with #DIYPS:)

https://twitter.com/danamlewis/status/652566409537433600/photo/1

(Here’s more about the Urchin watchface)

Here’s what the full logs from the rig tell me:

Example OpenAPS logs from the rig

This has a LOT of information in it (which is why it’s so awesome). There are messages being shared by each step of the loop – when it’s listening for “silence” to make sure it can talk successfully to the pump; refreshing pump history; checking the clocks on devices and for fresh BGs; and then processing through the math on what the BG is, where it’s headed, and what needs to happen. You can also see from this example where autosensitivity is kicking in, adjust basals slightly up, target down, and sensitivity down, etc. (And for those who aren’t testing oref1 features like SMB and UAM yet, you’ll get a glimpse of how there’s now additional information in the logs about if those features are currently enabled.)

(Here are some other logs you can watch, and how to run them)

Pro tip for OpenAPS users: if you’re logged into your rig, you just have to type l (the letter “L” but lower case) for it to bring up your logs!

So if you find yourself wondering: what did OpenAPS do/why did it do <thing>? Instead of wondering, start by looking at the logs.

And remember, if you don’t know what the problem is – the full logs are the best source of information for spotting what the main problem is. You can then use the information from the logs to ask about how to resolve a particular problem (Gitter is great for this!)– but part of troubleshooting well/finding out more is taking the first step to pull up your logs, because anyone who is going to help you troubleshoot will need that information to figure out a solution.

And if you ever see someone say “RTFL”, instead of “read the manual” or “read the docs”, it means “read the logs”. 😉 :)

Introducing oref1 and super-microboluses (SMB) (and what it means compared to oref0, the original #OpenAPS algorithm)

For a while, I’ve been mentioning “next-generation” algorithms in passing when talking about some of the work that Scott and I have been doing as it relates to OpenAPS development. After we created autotune to help people (even non-loopers) tune underlying pump basal rates, ISF, and CSF, we revisited one of our regular threads of conversations about how it might be possible to further reduce the burden of life with diabetes with algorithm improvements related to meal-time insulin dosing.

This is why we first created meal-assist and then “advanced meal-assist” (AMA), because we learned that most people have trouble with estimating carbs and figuring out optimal timing of meal-related insulin dosing. AMA, if enabled and informed about the number of carbs, is a stronger aid for OpenAPS users who want extra help during and following mealtimes.

Since creating AMA, Scott and I had another idea of a way that we could do even more for meal-time outcomes. Given the time constraints and reality of currently available mealtime insulins (that peak in 60-90 minutes; they’re not instantaneous), we started talking about how to leverage the idea of a “super bolus” for closed loopers.

A super bolus is an approach you can take to give more insulin up front at a meal, beyond what the carb count would call for, by “borrowing” from basal insulin that would be delivered over the next few hours. By adding insulin to the bolus and then low temping for a few hours after that, it essentially “front shifts” some of the insulin activity.

Like a lot of things done manually, it’s hard to do safely and achieve optimal outcomes. But, like a lot of things, we’ve learned that by letting computers do more precise math than we humans are wont to do, OpenAPS can actually do really well with this concept.

Introducing oref1

Those of you who are familiar with the original OpenAPS reference design know that ONLY setting temporary basal rates was a big safety constraint. Why? Because it’s less of an issue if a temporary basal rate is issued over and over again; and if the system stops communicating, the temp basal eventually expires and resume normal pump activity. That was a core part of oref0. So to distinguish this new set of algorithm features that depart from that aspect of the oref0 approach, we are introducing it as “oref1”. Most OpenAPS users will only use oref0, like they have been doing. oref1 should only be enabled specifically by any advanced users who want to test or use these features.

The notable difference between the oref0 and oref1 algorithms is that, when enabled, oref1 makes use of small “supermicroboluses” (SMB) of insulin at mealtimes to more quickly (but safely) administer the insulin required to respond to blood sugar rises due to carb absorption.

Introducing SuperMicroBoluses (or “SMB”)

The microboluses administered by oref1 are called “super” because they use a miniature version of the “super bolus” technique described above.  They allow oref1 to safely dose mealtime insulin more rapidly, while at the same time setting a temp basal rate of zero of sufficient duration to ensure that BG levels will return to a safe range with no further action even if carb absorption slows suddenly (for example, due to post-meal activity or GI upset) or stops completely (for example due to an interrupted meal or a carb estimate that turns out to be too high). Where oref0 AMA might decide that 1 U of extra insulin is likely to be required, and will set a 2U/hr higher-than-normal temporary basal rate to deliver that insulin over 30 minutes, oref1 with SMB might deliver that same 1U of insulin as 0.4U, 0.3U, 0.2U, and 0.1U boluses, at 5 minute intervals, along with a 60 minute zero temp (from a normal basal of 1U/hr) in case the extra insulin proves unnecessary.

As with oref0, the oref1 algorithm continuously recalculates the insulin required every 5 minutes based on CGM data and previous dosing, which means that oref1 will continually issue new SMBs every 5 minutes, increasing or reducing their size as needed as long as CGM data indicates that blood glucose levels are rising (or not falling) relative to what would be expected from insulin alone.  If BG levels start falling, there is generally already a long zero temp basal running, which means that excess IOB is quickly reduced as needed, until BG levels stabilize and more insulin is warranted.

Safety constraints and safety design for SMB and oref1

Automatically administering boluses safely is of course the key challenge with such an algorithm, as we must find another way to avoid the issues highlighted in the oref0 design constraints.  In oref1, this is accomplished by using several new safety checks (as outlined here), and verifying all output, before the system can administer a SMB.

At the core of the oref1 SMB safety checks is the concept that OpenAPS must verify, via multiple redundant methods, that it knows about all insulin that has been delivered by the pump, and that the pump is not currently in the process of delivering a bolus, before it can safely do so.  In addition, it must calculate the length of zero temp required to eventually bring BG levels back in range even with no further carb absorption, set that temporary basal rate if needed, and verify that the correct temporary basal rate is running for the proper duration before administering a SMB.

To verify that it knows about all recent insulin dosing and that no bolus is currently being administered, oref1 first checks the pump’s reservoir level, then performs a full query of the pump’s treatment history, calculates the required insulin dose (noting the reservoir level the pump should be at when the dose is administered) and then checks the pump’s bolusing status and reservoir level again immediately before dosing.  These checks guard against dosing based on a stale recommendation that might otherwise be administered more than once, or the possibility that one OpenAPS rig might administer a bolus just as another rig is about to do so.  In addition, all SMBs are limited to 1/3 of the insulin known to be required based on current information, such that even in the race condition where two rigs nearly simultaneously issue boluses, no more than 2/3 of the required insulin is delivered, and future SMBs can be adjusted to ensure that oref1 never delivers more insulin than it can safely withhold via a zero temp basal.

In some situations, a lack of BG or intermittent pump communications can prevent SMBs from being delivered promptly.  In such cases, oref1 attempts to fall back to oref0 + AMA behavior and set an appropriate high temp basal.  However, if it is unable to do so, manual boluses are sometimes required to finish dosing for the recently consumed meal and prevent BG from rising too high.  As a result, oref1’s SMB features are only enabled as long as carb impact is still present: after a few hours (after carbs all decay), all such features are disabled, and oref1-enabled OpenAPS instances return to oref0 behavior while the user is asleep or otherwise not engaging with the system.

In addition to these safety status checks, the oref1 algorithm’s design helps ensure safety.  As already noted, setting a long-duration temporary basal rate of zero while super-microbolusing provides good protection against hypoglycemia, and very strong protection against severe hypoglycemia, by ensuring that insulin delivery is zero when BG levels start to drop, even if the OpenAPS rig loses communication with the pump, and that such a suspension is long enough to eventually bring BG levels back up to the target range, even if no manual corrective action is taken (for example, during sleep).  Because of these design features, oref1 may even represent an improvement over oref0 w/ AMA in terms of avoiding post-meal hypoglycemia.

In real world testing, oref1 has thus far proven at least as safe as oref0 w/ AMA with regard to hypoglycemia, and better able to prevent post-meal hyperglycemia when SMB is ongoing.

What does SMB “look” like?

Here is what SMB activity currently looks like when displayed on Nightscout, and my Pebble watch:

First oref1 SMB OpenAPS test by @DanaMLewisFirst oref1 SMB OpenAPS test as seen on @DanaMLewis pebble watch

How do features like this get developed and tested?

SMB, like any other advanced feature, goes through extensive testing. First, we talk about it. Then, it becomes written up in plain language as an issue for us to track discussion and development. Then we begin to develop the feature, and Scott and I test it on a spare pump and rig. When it gets to the point of being ready to test it in the real world, I test it during a time period when I can focus on observing and monitoring what it is doing. Throughout all of this, we continue to make tweaks and changes to improve what we’re developing. After several days (or for something this different, weeks) of Dana-testing, we then have a few other volunteers begin to test it on spare rigs. They follow the same process of monitoring it on spare rigs and giving feedback and helping us develop it before choosing to run it on a rig and a pump connected to their body. More feedback, discussion, and observation. Eventually, it gets to a point where it is ready to go to the “dev” branch of OpenAPS code, which is where this code is now heading. Several people will review the code and approve it to be added to the “dev” branch. We will then have others test the “dev” branch with this and any other features or code changes – both by people who want to enable this code feature, as well as people who don’t want this feature (to make sure we don’t break existing setups). Eventually, after numerous thumbs up from multiple members of the community who have helped us test different use cases, that code from the “dev” branch will be “approved” and will go to the “master” branch of code where it is available to a more typical user of OpenAPS.

However, not everyone automatically gets this code or will use it. People already running on the master branch won’t get this code or be able to use it until they update their rig. Even then, unless they were to specifically enable this feature (or any other advanced feature), they would not have this particular segment of code drive any of their rig’s behavior.

Where to find out more about oref1, SMB, etc.:

  • We have updated the OpenAPS Reference Design to reflect the differences between oref0 and the oref1 features.
  • OpenAPS documentation about oref1, which as of July 13, 2017 is now part of the master branch of oref0 code.
  • Ask questions! Like all things developed in the OpenAPS community, SMB and oref1-related features will evolve over time. We encourage you to hop into Gitter and ask questions about these features & whether they’re right for you (if you’re DIY closed looping).

Special note of thanks to several people who have contributed to ongoing discussions about SMB, plus the very early testers who have been running this on spare rigs and pumps. Plus always, ongoing thanks to everyone who is contributing and has contributed to OpenAPS development!