Open source artificial pancreas systems (OpenAPS) have gained considerable interest in the diabetes community. We analyzed continuous glucose monitoring (CGM) records of 80 OpenAPS users with type 1 diabetes (T1D). A total of 19 495 days (53.4 years) of CGM records were available. Mean glucose was 7.6 ± 1.1 mmol/L, time in range 3.9-10 mmol/L was 77.5 ± 10.5%, 10 mmol/L was 18.2 ± 11.0% and > 13.9 mmol/L was 4.1 ± 4.0%, respectively. In 34 OpenAPS users, additional CGM records were obtained while using sensor-augmented pump therapy (SAP). After changing from SAP to OpenAPS, lower mean glucose (-0.6 ± 0.7; P < 0.0001), lower estimated HbA1c (-0.4 ± 0.5%; P < 0.0001), higher time in range 3.9-10 mmol/L (+9.3 ± 9.5%; P < 0.0001), less time < 3.0 mmol/L (-0.7 ± 2.2%; P = 0.0171), lower coefficient of variation (-2.4 ± 5.8; P = 0.0198) and lower mean of daily differences (-0.6 ± 0.9 mmol/L; P = 0.0005) was observed. Glycaemic control using OpenAPS was comparable with results of more rigorously developed and tested AP systems. However, OpenAPS was used by a highly selective, motivated and technology-adept cohort, despite not being approved for the treatment of individuals with T1D.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/dom.13810

Glycaemic control in individuals with type 1 diabetes using an open source artificial pancreas system (OpenAPS)

textCollaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who “give” their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

/pdf/data-sharing-and-privacy-issues-in-neuroimaging-research-4j92jfzkqf.pdf

Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed

Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making and revolutionizing the field of health care. A major open challenge that AI will need to address before its integration in the clinical routine is that of algorithmic bias. Most AI algorithms need big datasets to learn from, but several groups of the human population have a long history of being absent or misrepresented in existing biomedical datasets. If the training data is misrepresentative of the population variability, AI is prone to reinforcing bias, which can lead to fatal outcomes, misdiagnoses, and lack of generalization. Here, we describe the challenges in rendering AI algorithms fairer, and we propose concrete steps for addressing bias using tools from the field of open science.

Addressing bias in big data and AI for health care: A call for open science.

Introduction
Previous studies have measured individuals’ willingness to share personal information stored in electronic health records (EHRs) with health care providers, but none has measured preferences among patients when they are allowed to determine the parameters of provider access.

Patient Preferences in Controlling Access to Their Electronic Health Records: a Prospective Cohort Study in Primary Care

The menstrual cycle is characterized by predictable patterns of physiological change across timescales. Although patterns of reproductive hormones across the menstrual cycle, particularly ultradian rhythms, are well described, monitoring these measures repeatedly to predict the preovulatory luteinizing hormone (LH) surge is not practical. In the present study, we explored whether non-invasive measures coupled to the reproductive system: high frequency distal body temperature (DBT), sleeping heart rate (HR), sleeping heart rate variability (HRV), and sleep timing, could be used to anticipate the preovulatory LH surge in women. To test this possibility, we used signal processing to examine these measures in 45 premenopausal and 10 perimenopausal cycles alongside dates of supra-surge threshold LH and menstruation. Additionally, urinary estradiol and progesterone metabolites were measured daily surrounding the LH surge in 20 cycles. Wavelet analysis revealed a consistent pattern of DBT and HRV ultradian rhythm (2-5 h) power that uniquely enabled anticipation of the LH surge at least 2 days prior to its onset in 100% of individuals. Together, the present findings reveal fluctuations in distal body temperature and heart rate variability that consistently anticipate the LH surge, suggesting that automated ultradian rhythm monitoring may provide a novel and convenient method for non-invasive fertility assessment.

/pdf/ultradian-rhythms-in-heart-rate-variability-and-distal-body-5ck4ng7hfj.pdf

Ultradian rhythms in heart rate variability and distal body temperature anticipate onset of the luteinizing hormone surge.

Background Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals). Results To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects. Conclusions Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.

Open Humans: A platform for participant-centered research and personal data exploration.

Background: Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for fields of human subject research ranging from the social sciences to precision medicine. With this potential wealth of data come practical problems - such as how to merge data streams from various sources - as well as ethical problems - how can people responsibly share their personal information?
Results: To address these problems we present Open Humans, a community-based platform that enables personal data collections across data streams, enables individuals to take control of their personal data, and enables academic research as well as patient-led projects. We showcase data streams that Open Humans combines - such as personal genetic data, wearable activity monitors, GPS location records and continuous glucose monitor data - along with use cases of how that data is used by various participants. 
Conclusions: Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources as well as how these data can be ethically used by academic and citizen scientists.

https://www.biorxiv.org/content/biorxiv/early/2019/05/02/469189.full.pdf

Beau Gunderson

Papers

Open Humans: A platform for participant-centered research and personal data exploration.

Open Humans: A platform for participant-centered research and personal data exploration