Abstract
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Despite the exponential rise in the number of smartphone applications, cloud computing, and innovations in glucose sensing and insulin delivery, the day-to-day reality of type 1 diabetes remains one of tedium, unpredictability, and fear. A direct result of this frustration has been the (in this case) “hacking” of a popular continuous glucose monitor (CGM) to enable remote monitoring of glucose levels—so-called do-it-yourself (DIY) diabetes care. Therefore, and in terms of real-world experiences, the article by Lee et al. 2 in this issue is welcome as there is a need to evaluate the impact of using DIY systems for people with diabetes and their families and also what clinicians and industry can learn from the experiences of the #wearenotwaiting community.
Nightscout (also known as CGM in the cloud) was created by parents of children with type 1 diabetes with the simple aim of allowing for remote monitoring of their children's glucose levels. Beginning with the Dexcom G4 CGM, Nightscout is an open source, DIY project that “allows real-time access to glucose data through personal websites, smartwatch viewers, apps, and widgets available for smartphones.” This approach can now also be applied to devices from Medtronic and the Dexcom G5 system. 3 In the article by Lee and colleagues, the experiences of almost 1200 members of the CGM in the Cloud Facebook Group were captured using an online survey with comparisons made between users and nonusers. 2 The overall conclusions were positive, with Nightscout users reporting lower HBA1c levels and diabetes distress and an improved quality of life. These important benefits seemed to be based on the ability of the system to deliver 24 h access to real-time CGM data for multiple family members across multiple devices, allowing them to take preventative action based on trends in rising or falling glucose levels. A key self-management change was more frequent use of rapid-acting insulin boluses.
Overall, participants used less frequent self-monitored blood glucose levels and, therefore, presumably were comfortable using the CGM results as a basis for insulin dose calculations—predating the recent food and drug administration approval for the Dexcom G5 CGM system to replace finger-stick measurements for treatment decisions. 4 It was also noteworthy that almost half of the survey respondents used the “raw” and “unfiltered” CGM data not usually available with commercial systems. The median setup cost was modest at $155 (independent of device costs), and the majority of the users had no “significant technology/programming” expertise. Nevertheless, setting the system up was described as “not a simple consumer experience” with around a third of survey participants deciding not to begin using Nightscout and 7% discontinuing the system once they had started it.
Clearly, reports such as this one have major limitations, many of which are acknowledged by the authors. Applying a survey to an already self-selected group (the CGM in the Cloud Facebook community) will inevitably produce a biased sample. The approach to data analysis was not on an intention-to-treat bias and there was a significant amount of missing information (not completely surprising, given that the survey contained around 140 questions). However, the survey has highlighted key and current problem areas in modern diabetes self-management, specifically the challenges faced overnight, in the workplace or at school, preparing for and participating in physical exercise and travel. These “diabetes moments” are clearly common for adults and children living with diabetes, yet focused information and guidance on how to prepare and deal with them are difficult to obtain outside of clinic visits, although this is starting to change (e.g.,
Also reported by Lee et al., 2 DIY systems such as Nightscout present undeniable concerns surrounding safety, access, and privacy, despite the active and technologically competent user base. One primary worry is that when people design devices for their own use and then share code and building plans with other people, the latter may not be as skilled and, therefore, use the devices incorrectly. The report from Lee et al. also highlighted that awareness of and referral to DIY systems for diabetes self-management are infrequently provided by clinicians. The reasons for this are likely multifactorial, but presumably related to a lack of an evidence base from randomized controlled clinical trials beyond an n = 1 approach. As a corollary, the development of smartphone applications for health, in the absence of evidence of effectiveness, has been labeled as “pure snake-oil”. 4 Clinicians may also be fearful of their own lack of knowledge on the technical aspects of such systems and the potential for “losing face.”
Another potential concern is that the availability of DIY technologies could lead to individuals trying technology before other evidence-based approaches, most notably structured education that has been shown to be effective in diabetes self-management. Furthermore, the basic requirements to move to DIY systems require access to CGM and other devices in the first place. In the United States, CGM is not used universally by people with type 1 diabetes, and in other countries CGM reimbursement is simply not in place. The potential for a third party hacking into a home-made system is also a concern and this applies to any diabetes technology.
Overall, for any new technology for diabetes, DIY or not, to facilitate widespread adoption, five major hurdles must be overcome: demonstration of (a) privacy to satisfy legal regulators of personal information, (b) security to preserve safety and satisfy product safety regulators, (c) clinical benefit to satisfy clinicians, (d) usability (which can be defined as a combination of effectiveness, efficiency, and satisfaction to satisfy people with diabetes), and (e) economic benefit to satisfy payers. 6 Of course, a system such as Nightscout should ideally be accessible by a wide variety of people with diabetes. In actuality, users in this survey were from a fairly narrow group, as the overwhelming majority were white, educated, and insured, and mostly parents of children with type 1 diabetes. Obviously these characteristics are not universal across the global diabetes village. Recent reports have highlighted that in the United States, minority participation in research and innovation for both type 1 and type 2 diabetes remains suboptimal. 6,7 Also the use of social media to disseminate the information about the project and the survey would exclude individuals who are not frequent users of social media—widening the so-called digital divide.
In theory, DIY users might, themselves, become disadvantaged with time if they continue to use their own version while more user friendly, effective, and validated commercial systems reach the marketplace. Nightscout is not the only example of DIY technology innovation for diabetes. One other high-profile group is the open artificial pancreas system (openAPS). 8 According to their website, their aim is “an open and transparent effort to make safe and effective basic Artificial Pancreas System technology widely available.” 9 This group does recognize important concerns around the DIY approach, namely safety, stating “the ultimate answer to ‘Is it safe?’ will be something each individual decides for themselves.”
Nightscout, openAPS, and other DIY technologies are clearly empowering and effective and for the individuals involved are providing undoubted progress. For clinicians, regulators, and payers, this new approach to innovation in healthcare raises new challenges and dilemmas—what type and how much regulation are required, and how can a haves versus have-nots situation be prevented? There is also a need to validate these new approaches, to agree on metrics of success, and to create robust evidence of their effectiveness. The latter will still require traditional randomized, controlled clinical trials. In the meantime, it would make sense to create a database of all users of DIY technologies to make sure that important decisions are made on the basis of hard data and not opinion.