Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems.

BACKGROUND: Open-source automated insulin delivery (OS-AID) systems combine commercially available insulin pumps and continuous glucose monitors with open-source algorithms to automate insulin dosing for people with insulin-requiring diabetes. Two data sets (OPEN and the OpenAPS Data Commons) contain anonymized OS-AID user data. METHODS: We assessed glycemic variability (GV) outcomes in the OPEN data set and characterized it alongside a comparison to the n = 122 version of the OpenAPS Data Commons. Glucose data are analyzed using an unsupervised machine learning algorithm for clustering, and GV metrics are quantified using statistical tests for distribution comparison. Demographic data are also analyzed quantitatively. RESULTS: The n = 75 OPEN data set contains 36 827 days worth of data. Mean TIR is 82.08% (TOR < 70: 3.66%; TOR > 180: 14.3%). LBGI (P < .05) differs by gender whereas HBGI distributions are similar (P > .05). GV metrics (except TOR < 70, LBGI) show a statistically significant difference (P < .05) between data sets. CONCLUSIONS: Both the OPEN and OpenAPS Data Commons data sets show TOR < 70, TIR, and TOR > 180 within recommended goals, adding additional evidence of real-world efficacy of OS-AID. Future research should evaluate in more detail potential data set differences and relationships between individual patterns of user behaviors and GV outcomes.

Barriers to uptake of Open-Source automated insulin delivery Systems: Analysis of socioeconomic factors and perceived challenges of adults with type 1 diabetes from the OPEN survey.

AIMS: Social and technical trends are empowering people with diabetes to co-create or self-develop medical devices and treatments to address their unmet healthcare needs, for example, open-source automated insulin delivery (AID) systems. This study aims to investigate the perceived barriers towards adoption and maintaining of open-source AID systems. METHODS: This is a multinational study based on a cross-sectional, retrospective web-based survey of non-users of open-source AID. Participants (n = 129) with type 1 diabetes from 31 countries were recruited online to elicit their perceived barriers towards building and maintaining of an open-source AID system. RESULTS: Sourcing the necessary components, lack of confidence in one's own technology knowledge and skills, perceived time and energy required to build a system, and fear of losing healthcare provider support appear to be major barriers towards the uptake of open-source AID. CONCLUSIONS: This study identified a range of structural and individual-level barriers to uptake of open-source AID. Some of these individual-level barriers may be overcome over time through the peer support of the DIY online community as well as greater acceptance of open-source innovation among healthcare professionals. The findings have important implications for understanding the possible wider diffusion of open-source diabetes technology solutions in the future.

Open-source Web Portal for Managing Self-reported Data and Real-world Data Donation in Diabetes Research: Platform Feasibility Study.

BACKGROUND: People with diabetes and their support networks have developed open-source automated insulin delivery systems to help manage their diabetes therapy, as well as to improve their quality of life and glycemic outcomes. Under the hashtag #WeAreNotWaiting, a wealth of knowledge and real-world data have been generated by users of these systems but have been left largely untapped by research; opportunities for such multimodal studies remain open. OBJECTIVE: We aimed to evaluate the feasibility of several aspects of open-source automated insulin delivery systems including challenges related to data management and security across multiple disparate web-based platforms and challenges related to implementing follow-up studies. METHODS: We developed a mixed methods study to collect questionnaire responses and anonymized diabetes data donated by participants-which included adults and children with diabetes and their partners or caregivers recruited through multiple diabetes online communities. We managed both front-end participant interactions and back-end data management with our web portal (called the Gateway). Participant questionnaire data from electronic data capture (REDCap) and personal device data aggregation (Open Humans) platforms were pseudonymously and securely linked and stored within a custom-built database that used both open-source and commercial software. Participants were later given the option to include their health care providers in the study to validate their questionnaire responses; the database architecture was designed specifically with this kind of extensibility in mind. RESULTS: Of 1052 visitors to the study landing page, 930 participated and completed at least one questionnaire. After the implementation of health care professional validation of self-reported clinical outcomes to the study, an additional 164 individuals visited the landing page, with 142 completing at least one questionnaire. Of the optional study elements, 7 participant-health care professional dyads participated in the survey, and 97 participants who completed the survey donated their anonymized medical device data. CONCLUSIONS: The platform was accessible to participants while maintaining compliance with data regulations. The Gateway formalized a system of automated data matching between multiple data sets, which was a major benefit to researchers. Scalability of the platform was demonstrated with the later addition of self-reported data validation. This study demonstrated the feasibility of custom software solutions in addressing complex study designs. The Gateway portal code has been made available open-source and can be leveraged by other research groups.

Real-world evidence on clinical outcomes of people with type 1 diabetes using open-source and commercial automated insulin dosing systems: A systematic review.

AIMS: Several commercial and open-source automated insulin dosing (AID) systems have recently been developed and are now used by an increasing number of people with diabetes (PwD). This systematic review explored the current status of real-world evidence on the latest available AID systems in helping to understand their safety and effectiveness. METHODS: A systematic review of real-world studies on the effect of commercial and open-source AID system use on clinical outcomes was conducted employing a devised protocol (PROSPERO ID 257354). RESULTS: Of 441 initially identified studies, 21 published 2018-2021 were included: 12 for Medtronic 670G; one for Tandem Control-IQ; one for Diabeloop DBLG1; two for AndroidAPS; one for OpenAPS; one for Loop; three comparing various types of AID systems. These studies found that several types of AID systems improve Time-in-Range and haemoglobin A1c (HbA1c ) with minimal concerns around severe hypoglycaemia. These improvements were observed in open-source and commercially developed AID systems alike. CONCLUSIONS: Commercially developed and open-source AID systems represent effective and safe treatment options for PwD of several age groups and genders. Alongside evidence from randomized clinical trials, real-world studies on AID systems and their effects on glycaemic outcomes are a helpful method for evaluating their safety and effectiveness.

Barriers to Uptake of Open-Source Automated Insulin Delivery Systems: Analysis of Socioeconomic Factors and Perceived Challenges of Caregivers of Children and Adolescents With Type 1 Diabetes From the OPEN Survey.

Background: As a treatment option for people living with diabetes, automated insulin delivery (AID) systems are becoming increasingly popular. The #WeAreNotWaiting community plays a crucial role in the provision and distribution of open-source AID technology. However, while a large percentage of children were early adopters of open-source AID, there are regional differences in adoption, which has prompted an investigation into the barriers perceived by caregivers of children with diabetes to creating open-source systems. Methods: This is a retrospective, cross-sectional and multinational study conducted with caregivers of children and adolescents with diabetes, distributed across the online #WeAreNotWaiting online peer-support groups. Participants-specifically caregivers of children not using AID-responded to a web-based questionnaire concerning their perceived barriers to building and maintaining an open-source AID system. Results: 56 caregivers of children with diabetes, who were not using open-source AID at the time of data collection responded to the questionnaire. Respondents indicated that their major perceived barriers to building an open-source AID system were their limited technical skills (50%), a lack of support by medical professionals (39%), and therefore the concern with not being able to maintain an AID system (43%). However, barriers relating to confidence in open-source technologies/unapproved products and fear of digital technology taking control of diabetes were not perceived as significant enough to prevent non-users from initiating the use of an open-source AID system. Conclusions: The results of this study elucidate some of the perceived barriers to uptake of open-source AID experienced by caregivers of children with diabetes. Reducing these barriers may improve the uptake of open-source AID technology for children and adolescents with diabetes. With the continuous development and wider dissemination of educational resources and guidance-for both aspiring users and their healthcare professionals-the adoption of open-source AID systems could be improved.

Do transportation network companies decrease or increase congestion?

This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.

Vascular Endothelial Growth Factor (VEGF) Induced Downstream Responses to Transient Receptor Potential Vanilloid 1 (TRPV1) and 3-Iodothyronamine (3-T1AM) in Human Corneal Keratocytes.

This study was undertaken to determine if crosstalk among the transient receptor potential (TRP) melastatin 8 (TRPM8), TRP vanilloid 1 (TRPV1), and vascular endothelial growth factor (VEGF) receptor triad modulates VEGF-induced Ca2+ signaling in human corneal keratocytes. Using RT-PCR, qPCR and immunohistochemistry, we determined TRPV1 and TRPM8 gene and protein coexpression in a human corneal keratocyte cell line (HCK) and human corneal cross sections. Fluorescence Ca2+ imaging using both a photomultiplier and a single cell digital imaging system as well as planar patch-clamping measured relative intracellular Ca2+ levels and underlying whole-cell currents. The TRPV1 agonist capsaicin increased both intracellular Ca2+ levels and whole-cell currents, while the antagonist capsazepine (CPZ) inhibited them. VEGF-induced Ca2+ transients and rises in whole-cell currents were suppressed by CPZ, whereas a selective TRPM8 antagonist, AMTB, increased VEGF signaling. In contrast, an endogenous thyroid hormone-derived metabolite 3-Iodothyronamine (3-T1AM) suppressed increases in the VEGF-induced current. The TRPM8 agonist menthol increased the currents, while AMTB suppressed this response. The VEGF-induced increases in Ca2+ influx and their underlying ionic currents stem from crosstalk between VEGFR and TRPV1, which can be impeded by 3-T1AM-induced TRPM8 activation. Such suppression in turn blocks VEGF-induced TRPV1 activation. Therefore, crosstalk between TRPM8 and TRPV1 inhibits VEGFR-induced activation of TRPV1.