As of September 26, 2019, there are more than (n=1)*1,569+ individuals around the world with various types of DIY closed loop implementations (that we know of). This number continues to grow, as does the number of options for various types of DIY closed loops!
We currently estimate this collective DIY community has more than 15,800,000+ real-world “loop hours”. (This rough calculation for estimating loop hours is based on approximately 8 hours per 24 hour period for someone who uses the system overnight; and 20 hours per 24 hour period for someone who may use the system 24/7, to account for any downtime.)
This means #OpenAPS and other DIY closed loop users experience fewer highs, less severe lows, and more “time in range”: most users self-report less of both highs and lows, plus more time in range, AND hbA1c reductions – not to mention the quality of life improvements associated with having a system that can auto-adjust basal rates overnight while they sleep.
It means #OpenAPS users have more peace of mind to sleep at night for everyone in the family.
See below for a variety of self-reported, retrospective, observational, and prospective studies completed over the past few years.
2016 Self-reported OpenAPS Outcomes Study
A poster with data from the OpenAPS community was presented in June at the 2016 American Diabetes Association Scientific Sessions meeting. You can read this post for full insights from the poster, or these highlights:
While using OpenAPS, self-reported outcome measures (by 18 of the first 40 users) showed:
- median HbA1c dropped from 7.1% to 6.2%
- median percent time in range (80-180 mg/dL) increased from 58% to 81%
- All but one respondent reported some improvement in sleep quality, and 56% reported a large improvement
2018 Outcomes studies
Additional studies have been done, both in the community and by traditional researchers, looking at outcomes from those using DIY closed loop studies. In addition to the 2016 self-reported outcomes study, in 2018 another outcomes study was presented by the community, showing similar results (n=20). Additionally, two other sets of researchers from Italy (n=30) and Korea (n=20; pediatric population) also presented data analyses with similar outcomes. A summary of these studies and their reduction in A1c, time spent in hypo- and hyperglycemia, as well as improvements in time in range, is visualized below.
Also in 2018, Litchman et al. published in JDST a retrospective netnography study over a period of two years, suggesting a correlation between OpenAPS usage and improved A1c and quality of life. See https://doi.org/10.1177/1932296818795705 for the full publication.
There have been other studies performed in 2019.
- Braune et al published “Real-World Use of Do-it-Yourself Artificial Pancreas Systems in Children and Adolescents: Self-Reported Clinical Outcomes” in JMIR mHealth and uHealth 7.7 (2019): e14087. doi:10.2196/14087
- Melmer et al published a retrospective analysis of n=80 with 19,495 days worth of CGM data. See: “Glycemic Control in Individuals with Type 1 Diabetes Using an Open Source Artificial Pancreas System (OpenAPS)” in Diabetes, Obesity and Metabolism (2019). doi:10.1111/dom.13810
- Toffanin et al published a study using the UVA Padova simulator for in silico safety trials of the OpenAPS algorithm. See “In silico trials of an open-source android-based artificial pancreas: a new paradigm to test safety and efficacy of do-it-yourself systems.” Diabetes Technology and Therapeutics ja (2019). doi:10.1089/dia.2019.0375
- And, Koutsovasilis et al presented a prospective study of the OpenAPS algorithm at EASD in 2019. See: “Clinical evaluation of a closed-loop insulin delivery system on glycaemic control in adults with type 1 diabetes”