Cooper Drew, Ubben Tebbe, Knoll Christine, Ballhausen Hanne, O'Donnell Shane, Braune Katarina, Lewis Dana
Department of Pediatric Endocrinology and Diabetes, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Berlin Institute of Health, Berlin, Germany.
JMIR Diabetes. 2022 Mar 31;7(1):e33213. doi: 10.2196/33213.
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.
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.
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.
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.
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.
糖尿病患者及其支持网络开发了开源自动胰岛素输送系统，以帮助管理糖尿病治疗，改善生活质量和血糖控制结果。在#WeAreNotWaiting这个话题标签下，这些系统的用户已经产生了大量知识和真实世界数据，但研究在很大程度上尚未对其加以利用；此类多模式研究的机会仍然存在。
我们旨在评估开源自动胰岛素输送系统几个方面的可行性，包括跨多个不同基于网络的平台的数据管理和安全相关挑战，以及开展随访研究相关挑战。
我们开展了一项混合方法研究，收集参与者（包括通过多个糖尿病在线社区招募的成年和儿童糖尿病患者及其伴侣或护理人员）捐赠的问卷回复和匿名糖尿病数据。我们通过我们的网络门户（称为网关）管理前端参与者互动和后端数据管理。来自电子数据采集（REDCap）和个人设备数据聚合（Open Humans）平台的参与者问卷数据被假名化并安全地链接起来，存储在一个使用开源和商业软件的定制数据库中。参与者随后可以选择让他们的医疗保健提供者参与研究，以验证他们的问卷回复；数据库架构在设计时特别考虑到了这种可扩展性。
在研究着陆页的1052名访客中，930人参与并完成了至少一份问卷。在对自我报告的临床结果进行医疗保健专业人员验证纳入研究后，又有164人访问了着陆页，其中142人完成了至少一份问卷。在可选的研究项目中，7对参与者-医疗保健专业人员二元组参与了调查，97名完成调查的参与者捐赠了他们的匿名医疗设备数据。
该平台在参与者可访问的同时保持了对数据法规的合规性。网关实现了多个数据集之间自动数据匹配系统的形式化，这对研究人员来说是一个重大好处。通过后来增加自我报告数据验证，展示了该平台的可扩展性。这项研究证明了定制软件解决方案在解决复杂研究设计方面的可行性。网关门户代码已开源提供，可供其他研究团队使用。
