Loop is a do-it-yourself closed-loop insulin delivery system, which consists of 4 components:
- Open-source Loop app running on an iPhone
- CGM (Dexcom G6, G5, or G4+share, or Medronic Enlite)
- A compatible insulin pump, including older Medtronic pumps and (as of April 22, 2019) Omnipod (Eros pods, non-DASH)
- RileyLink, an open-source hardware device, which serves as a bridge between iPhone’s Bluetooth Low Energy (BLE) radio and pump’s radio.
Assuming all required components are available, assembling the system is pretty simple: download the Loop source code and install the app on the iPhone using Xcode (Apple developer tool) on a Mac computer. Detailed, easy-to-follow directions are available in the Loop documentation. No programming or any other special technical skills are required.
During normal operation, there is no need to touch the pump at all. Everything is done through the Loop app on the iPhone or, optionally, through a companion Apple Watch app. In short, the user manually enters meals, and accepts or overrides bolus recommendations, while corrections are performed automatically by the Loop closed-loop algorithm.
The main Loop screen shows graphs of recent CGM data and predicted BG, active insulin (“insulin on board” or IOB), insulin delivery, and active carbs (“carbs on board” or COB). Tapping on the plate icon in the lower left corner brings up a carb entry menu, which allows the user to enter meal time (which could in the future, or in the past), and food type with expected absorption time. The user should be aware of the fact that “carbs” entered should reflect everything one needs to bolus for (including protein and fat effects). Large mixed carb/protein/fat meals often take multiple hours to digest. After a meal is entered, Loop suggests a bolus, which the user may accept or override at will.
Every 5 minutes, Loop updates its BG prediction, calculates a correction dose, and delivers the correction in the form of a temporary basal rate, with the goal of steering the eventual BG towards the user specified correction range, while avoiding lows.
Is Loop Effective?
It is estimated that more than 1,000 people, including adults and kids, have been using Loop for extended periods of time now. Informal feedback and reports have generally been very positive, reflecting improved A1c or other bg control metrics with less effort, improved sleep, and improved quality of life.
Is Loop Safe?
Taking insulin in any form is risky, and it is impossible to guarantee that any approach or system is absolutely safe. Loop has been designed and implemented by people who have T1D or who have kids with T1D, and who very well understand the importance of delivering insulin safely. Loop safety features include the following:
- In case of failures, the system defaults back to normal, manual pump operation
- Bolusing requires fingerprint or face recognition identification
- There are limits for maximum allowed bolus, and maximum allowed basal rate
- Loop interface is designed to be transparent to the user, and easy to use
- Open-source code benefits from reviews and feedback by many users
Is Loop for Me?
- Loop is an experimental DIY system not approved for therapy. If you are looking for a perfect system backed up by clinical trials, regulatory approvals, and 24/7 technical support, Loop is not for you.
- Currently compatible pumps are older-generation out-of-warranty Medronic pumps, which can be difficult to find. As of April 22, 2019, Omnipod (Eros, non-DASH) pods are also supported.
- Beyond basic familiarity with computers, no special skills are required. Nevertheless, people may find DIY concepts and language difficult or intimidating. However, technical support offered by the community is outstanding. As is usually the case, motivation is far more important than any technical skills or aptitude.
- The non-profit Tidepool is working on a version of Loop to be submitted to FDA for approval in future. It is envisioned that the FDA approved Tidepool Loop would be available on the Apple app store and work with in-warranty, compatible insulin pumps. As reported by diaTribe, Tidepool and Insulet have announced a partnership to support Omnipod DASH in Tidepool Loop.
Every 5 minutes, Loop updates its prediction of future BG values over a time period equal to the duration of insulin action. Based on the predicted BG, insulin doses (both boluses and temp basals) are calculated to drive eventual BG into the user specified correction range, while not allowing BG to fall below a user-specified minimum BG level (called Suspend Threshold) at any point in time. BG predictions are based on insulin entries, carb entries and two correction factors (Glucose Momentum and Retrospective Correction), which are attempting to mitigate various unmodeled effects.
Insulin absorption model
In IOB calculations, Loop algorithms take into account boluses and temp basals, as well as nonlinear absorption curves based on published data. Several choices are available to the user, including legacy curves (based on Walsh’s Pumping Insulin book), as well as scalable exponential curves that are found to better fit actual absorption:
Dynamic carb absorption model
The meal entry includes an expected absorption time, which Loop uses only as an initial guess. Based on insulin activity, and observed CGM data, Loop calculates and displays to the user the predicted and the observed carb absorption. Below is an example of a relatively large 90g carbs+protein+fat meal that takes about 5 hours to digest. Based on the observed data, predicted carb absorption is dynamically updated, leading to improved BG predictions and dosing.
Loop playground: contributions to the BG prediction
All factors contributing to BG predictions are clearly visible to the user on a Loop playground screen. One may turn any of the effects on and off and observe changes in predictions. In addition to the impact of insulin and carbs, the Loop algorithm includes two corrective factors. Glucose Momentum blends in the average slope in BG over past 15 minutes into prediction over next 30 minutes, while Retrospective Correction blends in the observed prediction error over past 30 minutes into the prediction over the next 60 minutes. These corrective features are attempting to mitigate unmodeled effects such as carb entry errors, unmodeled carb absorption, effects of exercise, increased or reduced sensitivity, etc.
Loop is well integrated with the Apple Health app, including storage and retrieval of BG, carb, and insulin data between Loop and Health. Loop currently relies on the user entered parameters such as default basal rates, carb ratios, and insulin sensitivities. Future versions may include estimation these parameters, automating carb absorption time entries based on meal composition (which could be based on other iOS apps), and other algorithm or usability improvements.
User blogs, posts and reports
- SeeMyCGM, blog by Katie DiSimone, T1D parent, and a major contributor in the DIY closed-loop community
- Judy Hoskins’s Loop story
- Why We Chose to #Loop #WeAreNotWaiting post by Lorraine, T1D parent
- My new diabetes, a blog post by Renza Scibilia
- Welcome to Looping post by Erik Douds, an endurance athlete
- How I Loop: Two Years of Using An iPhone App To Automate My Insulin Delivery, a diaTribe post by Adam Brown.
- Loop documentation, to get started read introduction and requirements
- Loop Tips, a companion to the Loop documentation, provides tips on how Loop is recommending the actions being taken, and how one can improve blood glucose outcomes on Loop.
- Loop code: download complete code as a zip file from the Loop github site, and install using Apple Xcode app on a Mac following the instructions. You may also take a look at the 16min video How to build the Loop app by Katie DiSimone.
- Community technical support and forums: Looped group on Facebook (need to request to enter), and Loop Zulip channel (open to view, need to sign up to post)
- History of Loop by the original creator and developer, Nate Racklyeft
- Building Amazing Diabetes Apps talk by Nate Racklyeft
- Other DIY closed loop systems: OpenAPS, AndroidAPS
- OpenOmni: efforts to enable Omnipod to be used in DYI closed-loop systems are under way. Technical progress can be viewed on the openomni github site.
- Nightscout, one of the first #WeAreNotWaiting projects, an open-source system that allows real time access to CGM data via personal website, smartwatch viewers, or apps and widgets available for smartphones. Loop communicates to Nightscout, which integrates all user data: CGM, carbs, Loop actions, etc. allowing real-time web based visualization, sharing and remote monitoring, and a wide range of analysis reports.
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