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. 2021 Jun 7;23(6):e25409.
doi: 10.2196/25409.

Why #WeAreNotWaiting-Motivations and Self-Reported Outcomes Among Users of Open-source Automated Insulin Delivery Systems: Multinational Survey

Katarina Braune  1   2 , Katarzyna Anna Gajewska  3   4 , Axel Thieffry  5 , Dana Michelle Lewis  6 , Timothée Froment  7 , Shane O'Donnell  7 , Jane Speight  8   9 , Christel Hendrieckx  8   9 , Jasmine Schipp  8   10 , Timothy Skinner  8   10 , Henriette Langstrup  11 , Adrian Tappe  12 , Klemens Raile  1 , Bryan Cleal  13
Affiliations

Why #WeAreNotWaiting-Motivations and Self-Reported Outcomes Among Users of Open-source Automated Insulin Delivery Systems: Multinational Survey

Katarina Braune et al. J Med Internet Res. .

Abstract

Background: Automated insulin delivery (AID) systems have been shown to be safe and effective in reducing hyperglycemia and hypoglycemia but are not universally available, accessible, or affordable. Therefore, user-driven open-source AID systems are becoming increasingly popular.

Objective: This study aims to investigate the motivations for which people with diabetes (types 1, 2, and other) or their caregivers decide to build and use a personalized open-source AID.

Methods: A cross-sectional web-based survey was conducted to assess personal motivations and associated self-reported clinical outcomes.

Results: Of 897 participants from 35 countries, 80.5% (722) were adults with diabetes and 19.5% (175) were caregivers of children with diabetes. Primary motivations to commence open-source AID included improving glycemic outcomes (476/509 adults, 93.5%, and 95/100 caregivers, 95%), reducing acute (443/508 adults, 87.2%, and 96/100 caregivers, 96%) and long-term (421/505 adults, 83.3%, and 91/100 caregivers, 91%) complication risk, interacting less frequently with diabetes technology (413/509 adults, 81.1%; 86/100 caregivers, 86%), improving their or child's sleep quality (364/508 adults, 71.6%, and 80/100 caregivers, 80%), increasing their or child's life expectancy (381/507 adults, 75.1%, and 84/100 caregivers, 84%), lack of commercially available AID systems (359/507 adults, 70.8%, and 79/99 caregivers, 80%), and unachieved therapy goals with available therapy options (348/509 adults, 68.4%, and 69/100 caregivers, 69%). Improving their own sleep quality was an almost universal motivator for caregivers (94/100, 94%). Significant improvements, independent of age and gender, were observed in self-reported glycated hemoglobin (HbA1c), 7.14% (SD 1.13%; 54.5 mmol/mol, SD 12.4) to 6.24% (SD 0.64%; 44.7 mmol/mol, SD 7.0; P<.001), and time in range (62.96%, SD 16.18%, to 80.34%, SD 9.41%; P<.001).

Conclusions: These results highlight the unmet needs of people with diabetes, provide new insights into the evolving phenomenon of open-source AID technology, and indicate improved clinical outcomes. This study may inform health care professionals and policy makers about the opportunities provided by open-source AID systems.

International registered report identifier (irrid): RR2-10.2196/15368.

Keywords: artificial pancreas; automated insulin delivery; diabetes; diabetes technology; digital health; do-it-yourself; medical device regulation; mobile health; motivation; online communities; open-source; patient-led; peer support; sleep quality; user-led.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the Unified Competing Interest form and declare the following: KB has received research grants from the Berlin Institute of Health, SPOKES Wellcome Trust, Stiftung Charité, and the German Diabetes Association (Deutsche Diabetes Gesellschaft). She has served as a speaker and advisory board member for Medtronic Diabetes and Hi.Health and received fees for medical consulting and public speaking from Roche Diabetes Care, Dexcom, Medtronic Diabetes, Diabeloop, BCG Digital Ventures, and Bertelsmann Stiftung, outside the submitted work. DL reports grants from the Robert Wood Johnson Foundation, JDRF, personal fees from Lilly, Diabeloop, Roche Diabetes Care, and Novo Nordisk and Tandem, outside the submitted work. J Speight has served on advisory boards for Janssen, Medtronic, Roche Diabetes Care, and Sanofi Diabetes; her research group (Australian Centre for Behavioural Research in Diabetes) has received honoraria for this advisory board participation and has also received unrestricted educational grants and in-kind support from Abbott Diabetes Care, AstraZeneca, Medtronic, Roche Diabetes Care, and Sanofi Diabetes. J Speight has also received sponsorship to attend educational meetings from Medtronic, Roche Diabetes Care, and Sanofi Diabetes and consultancy income or speaker fees from Abbott Diabetes Care, AstraZeneca, Medtronic, Novo Nordisk, Roche Diabetes Care, and Sanofi Diabetes, all outside the submitted work. KR is an advisory board member of Lilly Diabetes Care and Abbott Diabetes Care outside the submitted work. KB, DL, SO, A Tappe, and KAG are members of the open-source AID web-based community. All other authors have no conflict of interest to declare.

Figures

Figure 1
Figure 1
Motivations for building an open-source automated insulin delivery system. The x-axis shows the percentage of responses for each motivation question (y-axis). Bar colors represent the degree of relevance ranging from “does not apply at all” to “fully applies.” The left and right columns show the responses of caregivers of children with diabetes and adults with diabetes, respectively. Responses are ranked from the most frequently endorsed motivations (top) to the less frequently endorsed (bottom).
Figure 2
Figure 2
Positive effects of open-source automated insulin delivery on clinical outcomes: average self-reported glycated hemoglobin (%; y-axis) for all 310 respondents, before and after open-source automated insulin delivery (x-axis) distinguished by orange and green colors, respectively. The left side is displayed as a density plot, with horizontal lines indicating quartiles. The right side depicts the data as a scatter plot. DIYAPS: Do-it-Yourself Artificial Pancreas System; HbA1c: glycated hemoglobin.
Figure 3
Figure 3
Self-reported time in range (%; x-axis) of adults and children with diabetes, before and after implementing an open-source automated insulin delivery system. The left side is displayed as a density plot, with horizontal lines indicating quartiles. The right side depicts the data as a scatter plot.
Figure 4
Figure 4
Improvements in self-reported glycated hemoglobin levels associated with open-source automated insulin delivery, independent of age or gender: relation between average glycated hemoglobin levels (%; y-axis) and age (x-axis), shown separately for female and male respondents (top and bottom rows, respectively). Colors separate average glycated levels before (orange) and after (green) open-source automated insulin delivery implementation. Each point represents one respondent after filtering of responses (the Methods section). Solid lines and their gray areas represent the trend and standard error for the respective groups. AID: automated insulin delivery; HbA1c: glycated hemoglobin.
Figure 5
Figure 5
Improvements in self-reported glycated hemoglobin levels associated with open-source automated insulin delivery, independent of age or gender: the y-axis shows the difference of average glycated hemoglobin levels after open-source automated insulin delivery, compared with before its implementation. Colors distinguish respondents with reduced average glycated hemoglobin (blue) from those with unchanged or increased glycated hemoglobin (red). AID: automated insulin delivery; HbA1c: glycated hemoglobin.
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