#DIYPS was developed 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 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
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
Here are some frequently asked questions about #DIYPS:
- Q: “I love it. How can I get it?”
A: Right now, #DIYPS is n=1, and because it is making recommendations based on CGM data, we can’t publicly post the code to enable someone else to utilize #DIYPS. We’re working to further develop #DIYPS, and also figure out which features we could break out and make widely available as soon as possible. That will likely mean integrating and collaborating with medical technology and device companies, and also with open-source projects like Nightscout and organizations like Tidepool. Stay tuned (easiest way is to follow the #DIYPS hashtag on Twitter or keep an eye on this blog) for more details about what we’re up to.
- Q: “Does #DIYPS really work?” A: Yes! For N=1, 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.
- 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; and 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. And of course, the key reason #DIYPS works 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.)
- Q: “I love it. What can I do to help the #DIYPS project?”
A: We’d love to know if you’re interested in helping! Reach out to us on Twitter (@DanaMLewis @ScottLeibrand and #DIYPS) or email us here. 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 #DIYPS feature or collaboration.
There is also a need for contributors to the CGM in the Cloud / Nightscout project. Among other things, we would love to have help implementing the most broadly applicable #DIYPS features into Nightscout.