insulin pump

12 years & counting – are we there yet?

by Dana Lewis

Today marks 12 years of living with diabetes. (Like everyone else, I wonder what to do on yet another diabetes anniversary – celebrate? Cry? Eat gluten-free cupcakes? All three things?) And interestingly, I’m almost at the one year mark of living with diabetes powered by #DIYPS.

When I was diagnosed, my blood glucose meter was bigger in all dimensions than the iPhone 6+.  Luckily, that’s changed, and my meter is now about the size of my Pebble smart watch – and that watch actually shows my my blood glucose readings from my CGM every 5 minutes.

Even better, in the last year, we’ve built a system that will not only give me a number, but help me track changes and push alerts so I can pay attention when needed…but otherwise live my life and not let diabetes get in the way of the things I actually want to be doing (running marathons, reading marathons, saving the world, etc.).

What hasn’t changed, though? My insulin pump. I’m pretty sure it’s a slightly different purple color than it was 11 or so years ago, but not much else has changed. For 11+ years I’ve been wanting:

  • To be able to see (on the pump) a HISTORY of any temporary basal rates
  • To be able to have “days of the week” programmable basal rates. (As you can imagine, my Tuesdays and Saturdays look a little different!)
  • To be able to have “net IOB” calculated and displayed to reflect the true amount of insulin in my body

For 11 years, I didn’t question the lack of progress, much, though. That’s just the way it was. I imagined it would probably be this way until we got a cure for diabetes in (5 or insert your magical number here) years.

I’m glad this is no longer the way it is and that I have tools like #DIYPS. But I hope that medical device companies will get out of their rut and the way they’ve been doing innovation and business for eons and do better. Because what they’re doing right now is not good enough. And #wearenotwaiting.

An artificial pancreas may not be good enough – #DIYPS perspective

by Dana Lewis

I didn’t expect to end up with #DIYPS when Scott and I first started building a system that would make louder CGM alarms. (If you are new to #DIYPS, read this post to learn more about it!)

But, as we added ‘snooze’ features for the loud alarms; the ability to input carbohydrates and insulin into the system; figured out that knowing your carbohydrate absorption rate is critical for managing diabetes as is the timing of the insulin activity in your body during mealtimes; and realizing that net IOB (factoring in temporary basal rates) matters…we ended up with today’s version of #DIYPS.

After the first 100 days, we knew #DIYPS was as good as the bionic pancreas was performing in clinical trials. And after several more months, I’ve had sustained results that show the value and impact of this type of system.

But, as more news and attention lately is focused on the “bionic pancreas” and APS (artificial pancreas systems), I have started to wonder… will those be good enough (for me)?

APS/bionic pancreas will not be a cure – it will not take away diabetes. It will make managing diabetes easier for many people.

But since I have #DIYPS, those systems may not be good enough (initially, for me).

The big difference between #DIYPS and APS is automation. With #DIYPS, I make all decisions and push all buttons to add or reduce insulin. There is still a “human in the loop”.

My hope would be able to add the ability for DIYPS to automatically set basal rates to zero temporarily overnight in advance of predicted low BG, to help prevent lows without having to wake up from a deep sleep and take that action myself. (And as Medtronic’s Low Glucose Suspend (LGS) technology has shown to the world and FDA, the risk of stopping insulin delivery for up to two hours while someone is sleeping is lower than it would be to continue providing insulin to them if they are not waking up and are low.)

If we’re able to make that happen with #DIYPS, I may be one of those people who doesn’t want to jump on board for day 1 of the first market available artificial pancreas system or bionic pancreas. (Although I reserve the right to change my mind :D). After all, at this point it would be a battle of the algorithms to see what system might work best for the most people. (And since I have my own algorithms that Scott and I developed, they might be better than the alternative for me.)

I also can imagine that there will be many people who won’t implicitly trust the/a system to make decisions for them all the time. They’ll still want a “human in the loop”.

#DIYPS-type systems that are “decision assist systems” may play an important role as a stepping stone platform for people to get comfortable with this new generation of technology that can predict lows and highs and make recommendations for them to take action.

Eventually, maybe everyone will be on an APS or bionic pancreas. Even setting aside the difficult questions of cost, availability, and insurance coverage, I know that one technology will not fit every person with diabetes (like how CGM in the Cloud is not for everyone); one system will not meet the needs of everyone; and none of us will be ultimately satisfied until we’ve prevented and cured all types of diabetes and eradicated the impact of any complications from diabetes.

But until we get there, #wearenotwaiting.

“Micro” highs and lows (they’re not really all the same)

by Dana Lewis

More thinking on what a snapshot of diabetes data means to me – this time on ‘micro’ highs or lows. @danamlewis #DIYPS http://bit.ly/1lt2ijE

—

I went to take a snapshot of my 24 hour CGM graph, because I was pleased with the outcome of the past 24 hours: no major lows or highs. According to the picture, it’s a “no hitter” (not hitting the high or low lines). However, I have the high alarm set to 170 right now, so to me knowing that I peaked around 150 overnight, including the slow crawl up to it, means it wasn’t a true no-hitter.

But, isn’t it still worth celebrating? No major overnight alarms to wake me up. No juicy juice or temp basals or reduced sleep or waking up feeling like I was dehydrated and apt to develop ketones. Diabetes, for a day, wasn’t a big deal. Isn’t this the ultimate goal of living with diabetes – living well, and not letting it stop us from living our lives and doing what we’re striving to do?

I tweeted the CGM picture with this caption:

Because it’s true. A 400 and 121 are both technically, medically speaking both “high” and “highs”. But are they the same? No.

(And same goes for any single data point – 121 could be flat, going up, or sliding down – the trend is what matters, regardless of what FDA has agreed to at this point in time. )

So, I’ve decided to categorize things as “micro” highs and lows when I’m sliding slightly below my comfortable range (like floating in low 80s or 70s and feeling low symptoms) or rising above what’s “normal” (80-120 is burned into my brain as normal), but may not warrant taking a picture as a “high”.

Semantics? Maybe. But as we talk about what these numbers represent, our ability and willingness to have a conversation about them online, including defeating data-shaming, I think it’s worth continually reframing and gaining more perspective on what diabetes data means to each of us.

What is #DIYPS (Do-It-Yourself Pancreas System)?

by Dana Lewis

#DIYPS (the Do-It-Yourself Pancreas System) was created by Dana Lewis and Scott Leibrand in the fall of 2013.

Curious about building a closed loop for yourself? Head to OpenAPS.org!

#DIYPS was originally developed with the goal of solving a well-known problem with an existing FDA-approved medical device. We originally set out to figure out a way to augment continuous glucose monitor (CGM) alerts, which aren’t loud enough to wake heavy sleepers, and to alert a loved one if the patient is not responding.

We were able to solve those problems and include additional features such as:

  •  Real-time processing of blood glucose (BG), insulin on board, and carbohydrate decay
  •  Customizable alerts based on CGM data and trends
  •  Real-time predictive alerts for future high or low BG states (hours in advance)
  •  Continually updated recommendations for required insulin or carbs
  • ..and as of December 2014, we ‘closed the loop’ and have #DIYPS running as a closed loop artificial pancreas.
  • (And, as of February 2015, we also launched #OpenAPS, an open and transparent effort to make safe and effective basic Artificial Pancreas System (APS) technology widely available. For more details, check out OpenAPS.org)

You can read this post for more details about how the #DIYPS system works.

While #DIYPS was invented for purposes of better using a continuous glucose monitor (CGM) and initially tailored for use with an insulin pump, what we discovered is that the concepts behind #DIYPS can actually be used with many types of diabetes technology. It can be utilized by those with:

  • CGM and insulin pump
  • CGM and multiple daily injections (MDI) of insulin
  • no CGM (fingerstick testing with BG meter) and insulin pump
  • no CGM (fingerstick testing with BG meter) and multiple daily injections (MDI) of insulin

Here are some frequently asked questions about #DIYPS:

  1. Q: “I love it. How can I get it?“A: #DIYPS is n=1, and because it is making recommendations based on CGM data, we previously have said that we can not publicly post the code to enable someone else to utilize #DIYPS as-is. There’s two things to know. 1) To get some of the same benefits from #DIYPS as an “open loop” system, with alerts and BG visualizations, you can get Nightscout, which includes all of the publicly-available components of #DIYPS, including the ability to upload Dexcom CGM data, view it on any web browser and on a Pebble watch, and get basic alarms for high and low BG – and depending on which features you enable, you can also get predictive alarms. Some of the other core #DIYPS features (“eating-soon mode“, and sensitivity/resistance/activity modes) are now available in Nightscout. 2) If you are interested in your own closed loop system, check out OpenAPS.org and review the available OpenAPS documentation on Github to help you determine if you have the necessary equipment and help you determine whether you want to do the work to build your own implementation.
  2. Q: “Does #DIYPS really work?”A: Yes! For N=1, using the “open loop” style system with predictive alerts and notifications, we’ve seen some great results. Click here to read a post about the results from #DIYPS after the first 100 days – it’s comparable to the bionic pancreas trial results. Or, click here to read our results after using #DIYPS for a full year. We should probably update this to talk about the second year of using #DIYPS, but the results from the first year have been sustained (yay!) and also augmented by the fact that we closed the loop and have the system auto-adjusting basal rates while I sleep.
  3. Q: “Why do you think #DIYPS works?”A: There could be some correlation with increased timed/energy spent thinking about diabetes compared to normal. (We’d love to do some small scale trials comparing people who use CGMs with easy access to time-in-range metrics and/or eAG data, to compare this effect). And, #DIYPS has also taught us some key lessons related to pre-bolusing for meals and the importance of having insulin activity in the body before a meal begins. You should read 1) this post that talks about our lessons learned from #DIYPS; 2) this post that gives a great example of how someone can eat 120 grams of carbohydrates of gluten-free pizza with minimal impact to blood glucose levels with the help of #DIYPS; 3) this post that will enable you to find out your own carbohydrate absorption rate that you can start using to help you decide when and how you bolus/inject insulin to handle large meals or 4) this post talking about how you can manually enact “eating-soon mode”. And of course, the key reason #DIYPS works (in open loop mode) is because it reduces the cognitive load for a person with diabetes by constantly providing push notifications and real time alerts and predictions about what actions a person with diabetes might need to consider taking. (Read more detail from this post about the background of the system.)
  4. Q: “Awesome!  What’s next?“A: If you haven’t read about #OpenAPS, we encourage you to check it out at OpenAPS.org. This is where we have taken the inspiration and lessons learned from using #DIYPS in manual mode (i.e. before we closed the loop), and from our first version of a closed loop and are paying it forward with a community of collaborators to make it possible for other people to close the loop! Some new features we wanted for #DIYPS that will likely still happen, but integrated with Nightscout and/or #OpenAPS include:
    • calculation of insulin activity and carb absorption curves (and from there, ISF & IC ratios, etc.) from historical data
    • better-calibrated BG predictions using those calculated absorption curves (with appropriate error bars representing predictive uncertainty)
    • recommendations for when to change basal rates, based on observed vs. predicted BG outcomes
    • integration with activity tracking and calendar data
    • closing the loop – done as of December 2014! :) and made possible for more than (n=1) with #OpenAPS as of February 2015

    We also are collaborating with medical technology and device companies, the FDA, and other projects and organizations like Tidepool, to make sure that the ideas, insights, and features inspired by our original work on #DIYPS get integrated as widely as possible. Stay tuned (follow the #DIYPS hashtag, Dana Lewis & Scott Leibrand on Twitter, and keep an eye on this blog) for more details about what we’re up to.

  5. Q: “I love it. What can I do to help the #DIYPS project? (or #openAPS)”A: #DIYPS is still a Dana-specific thing, but #OpenAPS is open source and a great place to contribute. First and foremost, if you have any ability to code (or a desire to learn), we need contributors to both the Nightscout project as well as #OpenAPS. There are many things to work on, so we need as many volunteers, with as many different types of skills, as we can get.  For those who are less technical, the CGM in the Cloud Facebook group is a great place to start. For those who are technical and/or want to close the loop for themselves, check out OpenAPS.org, join the openaps-dev google group, and hop on the #intend-to-bolus channel on Gitter. If you want to contact us directly, you can reach out to us on Twitter (@DanaMLewis @ScottLeibrand) or email us (dana@openAPS.org and scott@openAPS.org). We’d also love to know if you’re working on a similar project or if you’ve heard of something else that you think we should look into for a potential #OpenAPS feature or collaboration.

– Dana Lewis & Scott Leibrand

#DIYPS findings can help people with type 1 diabetes, regardless of tech, better manage post-meal blood glucose

by Scott Leibrand

Over the past few months, while developing #DIYPS, we have discovered a few things about mealtime blood glucose (BG) levels that we thought would be worth sharing more broadly. Using the insights, tips, and techniques below, it should be possible for many people with type 1 diabetes to more effectively keep their post-meal (post-prandial) BG levels in range. Here’s what we’ve learned…

After an initial carb absorption delay…

When carbohydrates are consumed, as part of a meal or snack, or to correct a low-blood-glucose (BG) situation, it causes BG to rise, but that rise is both delayed and gradual. In developing #DIYPS’s model, we discovered that for n=1, there is a delay of approximately 15 minutes between carb consumption and when BG starts to rise (as measured by a Dexcom G4 CGM, which actually measures subcutaneous interstitial-fluid glucose levels).

Subsequent carb absorption rate is constant…

In addition, we discovered that the rate at which BG rises after carb consumption is fairly constant, both across food types and over time. For n=1, we observed that carbohydrates are digested and absorbed at a rate of approximately 30g/hour (0.5g/minute), and that this rate is relatively constant beginning after the initial 15-minute lag, and lasting until the last of the carbs are absorbed (up to 4 hours later, in the case of a large 120-carb meal).

Regardless of glycemic index…

We also observed that, for real-world meals, glycemic index (GI) doesn’t matter much for carb absorption rate. Our initial testing was performed on high-GI foods used to correct low BG (juice and Mountain Dew) and a milkshake consumed without corrective insulin while participating in an unrelated clinical trial (to try to detect any endogenous insulin production, which was not present). However, in subsequent real-world use of #DIYPS, we’ve observed the same for almost all meal types. It seems that for meals containing at least some sugar, starch, or other highly accessible form of carbohydrates, the body seems to begin digesting and absorbing the most accessible carbs immediately, and is able to break down low-glycemic-index carbohydrates by the time the higher-GI foods are absorbed.

Additionally, insulin activity at mealtime matters a lot…

While developing and testing #DIYPS (and working to bring A1c down a full percentage point), we also observed that the level of insulin activity at the start of a meal matters a great deal in determining whether BG rises significantly as the meal carbs are absorbed by the body.

Pre-boluses can help…

One fairly common technique for dealing with this, among people with type 1 diabetes, is the pre-bolus. This generally means estimating insulin requirements for an upcoming meal, and bolusing or injecting fast-acting insulin approximately 15 minutes before the meal. Such pre-boluses do help somewhat in preventing large spikes in BG immediately after meals, but in our experience an 80-point BG rise (from 80mg/dl to 160mg/dl, for example) is still common for meals consumed “on an empty stomach” with little or no extra insulin on board (IOB) prior to the pre-bolus.

But pre-bolusing can sometimes be dangerous…

Also, pre-bolusing can contribute to dangerously low BG (hypoglycemia) if a meal is delayed or if you end up eating fewer carbs than expected.

And what really matters is not IOB, but insulin activity…

Based on observing the differences in post-meal (post-prandial) BG between empty-stomach meals and those where insulin was already on board and at full activity from small snacks or correction boluses 1-2h prior to the meal[1], it appears that what actually makes the most difference for post-meal BG is not how much insulin is on board (IOB) at the start of meal time, but rather how much insulin has already been absorbed into the bloodstream and is at full activity.

Because the liver needs insulin when the carbs first hit…

When carbohydrates are initially absorbed by the small intestine, they are directed into the portal vein and pass through the liver before reaching the rest of the body’s circulatory system. The liver is designed to absorb any excess glucose out of the blood at that point, storing it for later release. The mechanism by which the liver does so is dependent on two factors: the presence of higher glucose levels in the portal vein than in general circulation (indicative of ongoing carb absorption), and the presence of sufficient active insulin. If enough insulin is fully active, the liver can absorb ingested carbs just as fast as they can be absorbed from the intestine. If not, then the glucose passes through the liver into general circulation, and cannot be subsequently absorbed by this mechanism, but must be absorbed later by peripheral tissues once insulin levels get high enough.

And a 15m pre-bolus doesn’t get insulin active fast enough…

Even fast-acting insulin does not reach peak activity for 60-90 minutes after injection, since it must be absorbed through subcutaneous tissue into the bloodstream. This means that, if no insulin is on board from previous boluses, the pre-bolus insulin doesn’t really kick in for 30 minutes or more after the start of the meal. In the time it takes pre-bolus insulin to kick in, the body might absorb 15-20g of carbohydrates, resulting in a 60-80 mg/dl rise in BG.

So, some insulin is needed sooner…

So we need to get enough insulin active at mealtime to allow the liver to do its job, but not so much as to cause low BG before or after the meal. The best way we’ve found to do this is to do a small early pre-bolus about an hour prior to a meal. We calculate the size of the early pre-bolus based on the current BG, by determining how much insulin we can safely add and still stay above 80 mg/dl for 1-2 hours. In our case, that means assuming that up to 75% of the insulin activity will occur before the meal carbs kick in. So for a BG of 110 mg/dl an hour prior to the meal, and a correction ratio of 40 mg/dl per unit of insulin, it would be safe to bolus 1 unit of insulin. That 1 U then ramps up to peak activity right at mealtime, and largely prevents any substantial rise in BG immediately after the meal.

Finally, we can time mealtime insulin based on carb absorption…

Once we’re almost ready to eat and it’s time for a normal pre-bolus, it’s important not to over-bolus for all our carbs all at once. Doing so (particularly after a successful early pre-bolus for a large meal) risks causing post-meal low BG, as insulin activity can peak before all the meal carbs can be absorbed. For example, a large meal with 90g of carbohydrates would take 3 hours to absorb, but insulin activity often peaks after 60-90 minutes. So what we have developed and incorporated into #DIYPS is a relatively simple meal-bolus algorithm that dramatically reduces high- and low-BG situations after meals. The most important feature of this algorithm can be implemented manually without #DIYPS, either via multiple boluses or via a combo/dual/“square wave” bolus. The key idea is to bolus only for those carbs that will be absorbed by the time the insulin hits peak activity. So if you have a large meal, you might decide to bolus at mealtime for only the first 30g of carbs initially (minus any prebolus), since those will be absorbed over the first 60 minutes. If the meal totals 60g of carbs, you will then want to bolus for the next 30g of carbs over the next hour, possibly via a continual delayed (“square wave”) bolus, or by doing one or more manual bolus(es) after the meal is over. (The latter strategy is similar to what #DIYPS does, in 0.5U increments, as it allows you to react if you eat more or fewer carbs than expected, or if BG rises or falls unexpectedly in the interim.)

Resulting in almost flat BG, even after large meals…

By combining all these strategies, and providing real-time feedback and alerts when boluses, temp basals, or carbs are necessary, #DIYPS has allowed us to largely eliminate post-meal hyperglycemia, while minimizing risk of hypoglycemia. As detailed previously, this has helped reduce A1c by a full percentage point, reduce BG standard deviation, and increase time in range. While such impressive results are hard to achieve without assistance, it definitely should be possible for many people with diabetes to improve their own ability to manage post-meal BG levels by adopting some of the same techniques into their own self-care.

Note: there have been some updated posts since this original post about how to easily do the “eating soon” mode approach. Take a look at these illustrations for more details on it!

Footnote

[1] A few weeks ago, we were enjoying a weekend down in Portland. Normally, Dana eats a fairly low-carb breakfast, small snacks throughout the day as needed, and ends up with a fair bit of insulin activity at lunch and dinner time. That weekend, however, we had two very large spikes in postprandial blood glucose, after a late brunch, and then again after dinner, following a hike at Multnomah Falls. The next day, we saw a much smaller post-meal rise after brunch, as she had corrected a morning low with juice, and then bolused to prevent a rebound, so that insulin was near full activity at brunch time. Subsequently, we have incorporated these insights into #DIYPS’s “eating soon” mode, and have been able to much more consistently prevent large BG spikes immediately after meals.

#DIYPS, pathways to Artificial Pancreas Systems, and diabetes technology for all

by Dana Lewis

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#DIYPS=on path to artificial pancreas but not limited to those using newest diabetes tech. http://bit.ly/1mMS7LA @danamlewis @scottleibrand

—

Like many others, we’ve been reading the latest in the New York Times about the impact of diabetes technology innovation on the cost of managing the disease – not to mention the reactions to the piece, the response to the reactions, the reactions to that, etc.

We believe there is a better way forward, and #WeAreNotWaiting to make it happen.  Innovation can happen in a low-cost way, and can be scaled to support a broad patient audience, without contributing to or requiring significantly increased healthcare costs. #DIYPS for example (check out these results) was developed by two individuals, not an organization, with the goal of solving a well-known problem with an existing FDA-approved medical device. As recounted here (from Scott) and here (from Dana), we set out to figure out a way to augment continuous glucose monitor (CGM) alerts, which aren’t loud enough to wake heavy sleepers, and to alert a loved one if the patient is not responding.

We were able to solve those problems, and include additional features such as:

  •  Real-time processing of BG, insulin on board, and carbohydrate decay
  •  Customizable alerts based on CGM data and trends
  •  Real-time predictive alerts for future high or low BG states (hours in advance)
  •  Continually updated recommendations for required insulin or carbs

While #DIYPS was invented for purposes of better using a continuous glucose monitor (CGM) and initially tailored for use with an insulin pump, what we discovered is that #DIYPS can actually be used with many types of diabetes technology. It can be utilized by those with:

  • CGM and insulin pump
  • CGM and multiple daily injections (MDI) of insulin
  • no CGM (fingerstick testing with BG meter) and insulin pump
  • no CGM (fingerstick testing with BG meter) and multiple daily injections (MDI) of insulin

We think this type of device-agnostic software/technology is critical as we work on pathways to the artificial pancreas systems (APS). While we hope APS is out on the market soon (10 years ago would’ve been nice :)), we know it will take several years to a decade. And, even when it comes out, APS will be expensive. It may not be covered by insurance. Even with insurance, people may not be able to afford it. And even if everyone could afford it, some people may prefer not to use it.

—

We believe technology like #DIYPS can, and must, scale to take advantage of real-time data from CGMs, insulin pumps, and any other new diabetes technology, and help patients achieve the best possible health and quality-of-life outcomes while waiting for APS systems to become available.  But at the same time, we want to build these types of tools so that anyone with any combination of diabetes tools can use them to better self-manage their own particular condition. For example, availability of bolus calculator tools is often limited to those with pumps.  #DIYPS can be used as a simple bolus calculator, with the added benefit that it can keep track of carb absorption and allow the user to calculate correction accurate boluses while still digesting a meal.  Packaged into a simple web or app interface, this would allow people to do the same type of quick data input and calculations to be able to verify/support their mental math.

While #DIYPS is a very effective prototype, we don’t expect it to be the only interface that everyone with Type 1 diabetes uses.  Rather, we hope to integrate it with projects like Tidepool that will allow anyone, with any kind of meter, pump, or CGM, to easily upload their data, and then use any number of tools like #DIYPS to interact with their own data and get both real-time and historical insights from it that will let them improve their own diabetes self-care.  However, to make this possible, we need all medical device makers to open up their devices to allow patients real-time programmatic access to their own data. (A good example – there’s no access to temp basal history on Medtronic pumps!)

We need people and companies with innovative ideas to focus on making those ideas available as device-agnostic software, not solely as a proprietary feature on a single non-interoperable medical device.  And most of all, we need everyone to focus on making the fruits of innovation available as widely as possible, even to patients without the financial resources to buy cutting-edge hardware.

After all, #DIYPS is proof that low-cost innovation can have big results.

Initial findings from #DIYPS after 100 days and comparing it to a bionic pancreas

by Scott Leibrand

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#DIYPS + @danamlewis = as good as a bionic pancreas! Reduced avg. BG & time spent <60mg/dl. Details: http://bit.ly/1qF6qRp @scottleibrand

 —

Recently, diaTribe published a summary of results presented by Dr. Ed Damiano at #ATTD2014 showing how their bionic pancreas closed loop artificial pancreas system (APS) improved average glucose levels and halved time spent <60 mg/dl in patients with Type 1 Diabetes (T1D), compared to when those same patients were not wearing the system.  The numbers are impressive: mean glucose was reduced from 159 mg/dl to 133 mg/dl in the Beacon Hill study (n=20 adults, 5 days data per patient, for a total of 100 bionic pancreas days).  If that were sustained over the long term and converted to an A1c value, that would represent a 0.9% improvement, from 7.1% to 6.2%. As diaTribe points out, “Those results are unheard of for any diabetes drug or device.”

Given those results, we wanted to see how the bionic pancreas results compared to the results for #DIYPS, and the results were equally impressive: 90-day eAG was better than in any of the study control groups prior to using the system (before #DIYPS =146 mg/dl) and eAG was further reduced to better than any of the bionic pancreas treatment groups (after #DIYPS = 128 mg/dl) after utilizing #DIYPS.  Time spent <60 mg/dl was already lower than for any of the bionic pancreas treatment groups (before #DIYPS = 1.2%), but was reduced still further to 0.9% with #DIYPS.

@danamlewis 30 day estimated average glucose values. Note – #DIYPS began in December.

(Note that for #DIYPS, n=1 at the moment.  These results are specific to a single highly motivated individual, who was already doing everything possible to manage T1D.)

The pre-DIYPS control condition included wearing and using both a continuous glucose monitor (CGM) and an insulin pump.  It is not clear from the diaTribe article whether the subjects in the bionic pancreas study under their “Usual Care” or “Supervised Camp Care” control conditions included the use of such technology, or whether they were using the more common methods of fingerstick meters and multiple daily injections (MDI) of insulin.  If “Usual Care” included patients on MDI, much of the benefit attributed to the bionic pancreas could be attainable using CGM+pump therapy as well.  The #DIYPS data, however, shows significant improvements even compared to CGM+pump in the control condition.

While n=1 (vs. n=20 and n=32 in the two bionic pancreas studies), the #DIYPS data shows the effects of much longer term usage of the #DIYPS system.  The total time using the system (90-100 days) was equivalent between our data and the Beacon Hill study. Also, because #DIYPS has been in use for 100 days now, we have now seen the results in actual A1c values, not just eAG-calculated Projected A1c.  The improvement from pre-DIYPS A1c to the first post-DIYPS A1c data point is consistent with the improvement shown by the calculated eAG results. (Note that we plan to validate with additional data from A1c testing to show the actual improvement in A1c attributable to #DIYPS, and hopefully validating the sustainability of using the system).

Differences between the bionic pancreas & #DIYPS

Part of what makes the bionic pancreas so promising is that it is able to dose glucagon (through a second pump) to correct low or falling BGs. Because #DIYPS uses only an existing FDA-approved CGM, relies on the patient to dose their own insulin through an FDA-approved insulin pump, and is not using glucagon, #DIYPS might not be expected to be able to prevent hypoglycemia as well as the bionic pancreas. However, our data show that #DIYPS’ predictive alarms and proactive correction suggestions allow a patient to prevent hypoglycemia (BGs <60 mg/dl) even better than the bionic pancreas can do. (#DIYPS also suggests, when possible, using temporary basal rates, which often reduces the extra carbohydrates that have to be consumed to prevent or correct a low BG).  Less-motivated patients using #DIYPS may not be able to prevent low BGs quite as effectively, but this is still an important demonstration that such improvements in control are possible before new glucagon pump technology is available on the market in the future.

Differences between bionic pancreas, artificial pancreas systems (APS), and #DIYPS

And finally, and most importantly as a distinction of the difference from the bionic pancreas, #DIYPS does not automatically dose insulin.  It is solely an alerting system (with predictive alerting and real-time calculation abilities), which relies on the user to both validate any suggested actions, and to actually dose any insulin, reduce insulin, or consume any carbohydrates required to manage their BGs.  This means #DIYPS is not an artificial pancreas system (APS), and does not provide the ability  for you to “forget about diabetes” that is such a powerful promise of true APS systems like the bionic pancreas. Yet it does reduce the overall cognitive load of diabetes (and provides additional security mechanisms such as alerting loved ones if you don’t respond to alarms over time). And, #DIYPS shows that better software and alerting can result in dramatic improvements in blood glucose levels, even without automatic dosing of insulin.

What’s next for #DIYPS

As you can tell, we are excited about the promise of #DIYPS for helping people with diabetes (PWD) manage their disease.  But, as mentioned above, all of this is currently being done in our spare time, with no funding or institutional support.  We, and a small group of like-minded individuals and non-profits scattered across the Internet, have decided that #WeAreNotWaiting for research labs and medical device companies to develop a full APS system and get it approved by the FDA (which is still probably 5 years away). Instead, we are doing what we can to make progress now.

But the next step is critical: we need to make this technology available to the people whose quality of life – and possibly even whose lives – depend on it. (This is why we originally set out to build #DIYPS – to help people wake up to overnight CGM alerts who sleep through the one-size-fit-all alarms coming from the device).  Recently we received an email from someone in Europe whose MD tells her that her severe nocturnal hypoglycemia is life threatening, and sent her on a mission to find a system that can wake her up from a severe low and contact a loved one if she doesn’t respond.  In order to help people like her, we need to begin working with researchers and doctors, and hopefully even get funding to develop #DIYPS into a scalable system that can help any PWD manage their diabetes better.

If you are someone (or know anyone) who can help with any aspect of that effort, please reach out to us on Twitter or directly by email. Otherwise, please stay tuned for more updates.

–Dana Lewis and Scott Leibrand

 

 

 

#DIYPS and Pizza, and wondering why we judge people with diabetes for how and what they eat

by Scott Leibrand

What’s your reaction if you read that someone with Type 1 diabetes just ate 110 carbs worth of pizza for dinner?

Go ahead, answer the question out loud (or write it down) before continuing.

No, really. Did you say it out loud? Or at least think about it?

Now, what if you find out that their BG never spiked (only rose 10 points) and then glided along in range (80<in range<150) for the rest of the night?

Were they lucky? Was it a fluke? Or was it the way that it (eating food) actually should work?

If you’re like many of us, your initial reaction (the one we asked you to say out loud so you wouldn’t mis-remember it later) was probably something along the lines of “that isn’t very responsible”. It just *makes sense* to judge someone for eating food that is “so obviously bad” for them. But, is the food bad for them? Or is what we’re trying to say (think) that the food is likely to lead to “bad” or out of range BGs, therefore it’s not a good idea to consume (or consume so much)?

Maybe we shouldn’t be blaming people with type 1 diabetes for not eating “right” or “trying hard enough” to get the health outcomes they want (and we all want for them). Maybe we all need to start working on putting together all the technology that already exists, in a way that actually allows people with T1D to live a normal life and worry less about constantly managing their BGs. The way #DIYPS does for me.

We also need to start working on changing the intuitive attitude that the problem is a lack of “compliance” (related – read this great post from Kerri on “compliance”) with diabetes management/treatment. Instead, why don’t we all work with patients to understand what is difficult for them about managing their diabetes, and what changes we might be able to make in the processes, systems, and technology they use to make it easier and more effective to do so.

(You may be wondering where this blog post came from. It’s related to #DIYPS – I tested the system one night by eating several slices of a frozen gluten-free pizza, which while convenient is often higher carb than the already-high-carb food. And, my instinct was not to talk exactly about how much and what I ate, because I’ve experienced so many times over the years a judgement from observers (with or without diabetes) about what I personally choose to consume – whether it’s a bite (or a correctly portioned serving) of dessert, pizza, or whatever someone thinks is not acceptable for someone with diabetes. Scott was surprised by the guarded way I was choosing to document and characterize this test, and this post is the result of our discussion.)

Thanks to #DIYPS, I’ve found (several times, the above scenario has proved not to be a fluke!) that I can eat large meals full of carbohydrates, and have no or minimal spike in my blood glucose. It doesn’t matter if it’s high protein, high fat, a mix, or lots of sugar (like a milkshake). And that’s changed the way I feel about talking about large-carb/”non-diabetes friendly” meals.

There’s a well-known stigma related to food for people with diabetes, but no one seems to know a way to remove the stigma. We’re wondering if tools like #DIYPS (and being able to see the data and more outcomes when someone DOES eat pizza and *is* ok BG-wise) will help change the conversation?

–Dana Lewis

#DIYPS goes mobile

by Scott Leibrand

We’ve made progress to enable #DIYPS for on-the-go use.

Previously, we’ve only been able to use it when at home and able to plug the CGM in to an old laptop running Windows. Now, we can get real time predictive alerts anywhere, thanks to an unlocked Moto G that we plug the CGM into. Thanks again to John’s help and his code, the data from the CGM can now be uploaded to the cloud from the phone. We originally planned to use the Moto G on WiFi only, but because of other phone upgrades we ended up finding a cheap way to get data on the Moto G, and will be testing it with data for the next ~6 months.

One challenge for #DIYPS going mobile is that this is another device and more “stuff” a PWD has to carry around. For now, I’m using this case so I can easily drop it in or pull it out of my larger bag that I carry around on most days, while keeping the cords secure. (I’ve found the Moto G’s charger and cords don’t plug in as securely as other devices that we’re used to.)

diyps_goes_mobile

Next steps for #DIYPS mobile testing include using the Moto G and #DIYPS while out running, since I don’t have to rely on WiFi for #DIYPS. It’ll take a few more weeks before I can run regularly due to the cold, rainy Seattle weather, but this will be the most effective road test (pun intended) of the mobile capabilities, and will enable me to test #DIYPS for the first time during intense exercise.

We are also working with Brian, who’s helping us test the system and clean up the #DIYPS code.  We want to get it modular enough for more people to install and use the code more easily, and figure out a better UI (with graphs!). We also want to plug into the frameworks John and Tidepool are using, so we can take advantage of all the great work they’re doing as well.

Stay tuned for more updates.  And if you have some interest and expertise and want to help out, please reach out to us on Twitter or directly.

–Dana Lewis and Scott Leibrand

A DIY Artificial Pancreas System? Are we crazy?

by Scott Leibrand

The Do-It-Yourself Pancreas System (#DIYPS) is a tongue-in-cheek name that recognizes a serious fact: people with (type 1) diabetes (PWDs) have been acting as their own pancreas for years.  What a healthy pancreas does using subcellular machinery in islet beta cells, and a full closed-loop artificial pancreas system (APS) attempts to do automatically using medical technology, are the same things that PWDs have been doing, on their own, with whatever help they can get.  We recognize that for many PWDs the available help is not yet enough, so we are not waiting.

Medical device companies are working on new Artificial Pancreas Device System components, and researchers are conducting clinical trials of artificial pancreas systems incorporating various levels of closed-loop technology to do anything from automatically suspend basal insulin when BGs are below a pre-set threshold (available now in the Medtronic MiniMed 530G) to a fully closed-loop bionic pancreas system that automatically doses insulin (and even glucagon, in one trial) to provide full 24/7 BG control.  However, full closed loop systems are still years away, so there is a serious unmet need for better tools to help people with diabetes manage in the meantime.

The #DIYPS is designed for immediate deployment, using a device-agnostic web-based interface, and optionally ingesting data from any of a user’s existing medical devices using commodity off-the-shelf hardware (such as a Moto G Android phone connected to a Dexcom G4 via USB).  In fact, interactive use does not even require automatic data upload capabilities: once the system is configured for their insulin sensitivity and other user-specific parameters, a user with any sort of CGM or meter can manually enter BG levels, boluses/injection amounts, and carb counts and get an immediate estimate of their resulting BG levels.  In addition, the system will estimate insulin on board (IOB, meaning insulin still working in the body) and unabsorbed carbs, so a later follow-up assessment only requires updating the BG data to figure out if any additional action is required.

The #DIYPS can be used immediately to empower PWDs to prevent many instances of out-of-range BG levels.

The #DIYPS is not a substitute for a true closed-loop Artificial Pancreas*.  It is not a medical device, and does not dose insulin.  However, it does fill two currently unmet needs.  Firstly, none of the CGMs on the market currently support robust enough alerting features to awaken many patients when they experience low BG while sleeping.  Such nocturnal hypoglycemia unawareness is a serious risk for many people with diabetes, and better tools are required to deal with it.  The MiniMed 530G’s Low Glucose Suspend feature is a huge step forward there, but even it doesn’t help with the converse problem: at other times users will end up going an entire night with high BG, and never wake up to their CGM’s high BG alarms.  The #DIYPS’s alert system certainly cannot replace a CGM’s alarms (it’s not a medical device, and various interruptions in connectivity can prevent it from alarming), but it does have the ability to send notifications that can trigger alarms (i.e. on an iPad or phone) loud enough to wake the deepest sleepers. It can also function as a de facto remote alert or monitoring system, allowing family or loved ones in different locations to check and make sure a PWD is responding to alarms. Until device manufacturers get FDA approval for loud enough devices, this alone is enough reason for many people to want to put together their own alerting system like the #DIYPS – and was the original reason for building it.

Secondly, it is currently completely up to patients (with occasional help from their doctors) to determine how much insulin is required at any given time, and decide when additional carbs are required to prevent a low.  Performing such calculations manually every few hours, without ever being able to take a break, is a major problem for the quality of life of many people with diabetes.  There are very few effective tools available today to help, so such calculations are usually done in the user’s head, and are very prone to human error.  (The few tools available are also often not in one system, creating barriers for effective use.) In addition, many factors such as stress, sickness and varying activity levels can impact BG levels.  This combination of factors often results in frequent (often daily) instances of low or high BG levels.  The #DIYPS system provides a framework to support the user in making the best decision in every situation, while reducing the cognitive load on the user, and reduce the risk from harder-to-calculate factors like stress or varying activity levels.  It also helps the user to pay more attention and take action when they are at risk for hypoglycemia (predicted based on CGM, IOB, and carb data input into the system), while allowing them to continue living their life normally, with less worry about managing diabetes.

Finally, the #DIYPS system is designed to be device and data source agnostic, and to integrate with frameworks like Tidepool to allow users to do things that have never been possible before.  As new and better medical devices are released, our hope is that all of them will support methods for allowing a user to export their own data, so that users can truly take advantage of all the innovation we are starting to see in this space.

But today, the #DIYPS is just a prototype system, and has only been development tested with a single user.  In order to determine whether it can truly fulfill the unmet need described above for a wider population, additional development and testing are required.  At this point, we are looking to begin collaborating with anyone who has the skills and interest required to move the project forward.  We are not yet ready to begin allowing (and helping) a large number of users to begin testing the system themselves, but hope to open it up (and hopefully integrate it with the efforts underway at Tidepool.org, among others) in a manner that will allow as many people as possible to begin safely using the system as soon as possible.  However, this is currently only a 2-person side project, so if you think it’s worthwhile to move forward more quickly, I would encourage you to get involved (if you have technical or other skills that would be helpful) or pass along the word to others who might be interested and able to help.  (And if you haven’t already, you might also want to read How I Became a DIY Artificial Pancreas System Builder.)

P.S. A special thank you to John Costik, who graciously provided the drivers we’ve been using to pull data off the Dexcom CGM to a Windows PC, and who is working on perfecting the Android (Moto G) drivers as well.  We wouldn’t have done any of this if it wasn’t possible to get data off the Dexcom CGM in real time.  We’d also like to thank members of the diabetes online community for their support, encouragement, and feedback: we know y’all will be pivotal in driving this forward.  :-)

–Scott Leibrand and Dana Lewis

*Update in December 2014: We wrote this post in February, when #DIYPS was truly a “human in the loop” decision-assist system. However, as of December 2014, we closed the loop with #DIYPS, and also have a version of #DIYPS that is essentially a closed loop artificial pancreas. You can read more about our work on the closed loop artificial pancreas version of #DIYPS here.