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IoT Is Watching You
Published in Salvatore Volpe, Health Informatics, 2022
Salvatore G. Volpe, Paul Quigley
MIoT interventions fall under two broad categories: clinical services and operational support. Clinical services can be broken down further into monitoring and CDS, which more or less serve as different checkpoints along the chronological timeline of care delivery. Patients can be monitored at the point of care with body sensors or remotely through integrations with mHealth apps and wearables (Figure 19.4).14,16 Once transmitted by an IoT sensor node, the data can be analyzed by ML algorithms that may prompt an alert about dangerously high blood glucose levels or notify a clinician of increased fluid retention or changes in blood oximetry – data that suggests an increased likelihood for congestive heart failure. ML and CDS help to circumvent information overload and avoid the costly process of manually labeling all of medical data that enters from the MIoT system.17 For a sense of scale, Stanford Medicine predicted that by the end of 2020, over 2,314 exabytes (2,314 × 109 gigabytes) of data would have been generated from the health sector globally.18 CDS tools have the capacity to send and receive information from patients as well as clinicians. There is a growing community of patients who are leveraging the bidirectionality of MIoT systems in open-source technologies for diabetes self-management (OpenAPS)19 or other chronic conditions such as HIV and COPD.20
Mobile Technology to Facilitate Self-Management and Independence among Adolescents and Young Adults with Disabilities – Best Practices and the State of the Science 1
Published in Christopher M. Hayre, Dave J. Muller, Marcia J. Scherer, Everyday Technologies in Healthcare, 2019
Michelle A. Meade, Marisa J. Perera
Open source refers to freely available code that can be used without (many) restrictions (https://opensource.org/osd). Providing open access can foster growth and flexibility, allowing the technology to develop in ways driven by a user community with both time and resources. At its core, though, open source is a licencing approach and a distribution technique that does not take away the need for potential users to learn about your product or the potential the product may have for their own work and objectives. As such, the impact of the product will be a function of one’s ability to build a community around it and to deal with ancillary issues such as identifying and addressing problems and keeping track of versions. The large volume of content available through open-source platform should also be considered as it heightens the importance of having a product that is attention grabbing. Examples of communities and projects that have been built around open-source content include Project Possibility (out of UCLA), FEVA (out of the University of Michigan) and OpenAPS. Project Possibility, in particular, includes many people with disabilities developing and sharing code freely.
Closed-loop insulin delivery: update on the state of the field and emerging technologies
Published in Expert Review of Medical Devices, 2022
Prior to the availability of commercial hybrid closed-loop systems, a community of people with type 1 diabetes and their families developed open source artificial pancreas systems, or ‘Do-It-Yourself’ (DIY) systems. The three main systems, OpenAPS, AndroidAPS and Loop were used by around 1,500 people with type 1 diabetes in 2019 worldwide [90]. Even now that commercial hybrid closed-loop therapy is available, these low-cost systems remain an attractive option for those who have the know-how and skills to build and maintain them with little assistance from healthcare professionals, who are not able to support unregulated systems. Observational studies show improvements in glycemic control with all systems, but no randomized clinical trial data exists at present [91]. The results of a 6-month randomized controlled parallel design study using a locked version of OpenAPS are awaited [92].
Artificial pancreas systems: experiences from concept to commercialisation
Published in Expert Review of Medical Devices, 2022
David L. Rodríguez-Sarmiento, Fabian León-Vargas, Maira García-Jaramillo
PWT1D have waited several decades to access the first hybrid commercial AID system. However, during this long process, other developments related to this technology arose with noncommercial approaches known as do-it-yourself (DIY) AID systems. The main motivation of the DIY-AID developers was to accelerate and democratize access to advanced diabetes treatments. The first DIY-AID system, the well-known OpenAPS, was launched in early 2015 followed by other versions such as Android APS or Loop. These systems include hardware and software configurations that allow an individual to fully determine how the system adjusts insulin delivery, choosing which insulin pump, CGM system, and control algorithm to use, along with additional hardware (if a radio bridge is needed for a particular pump) or software (if the pump can communicate through a mobile device). DIY-AID systems leverage already approved insulin pumps and CGM systems, adding the necessary technological components to make the system interoperable [157], achieving more than an 80% TIR without the need for regular meal announcements or accurate carbohydrate counting, while preventing hypoglycemia [158].
Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy
Published in Expert Review of Medical Devices, 2020
The DIY movement began in 2013 when a community of people with type 1 diabetes and their families worked together online to promote the development of open-source diabetes management systems using the hashtag ‘#WeAreNotWaiting’ [75]. These DIY hybrid closed-loop systems use open-source software, namely OpenAPS, AndroidAPS, and Loop. Unlike commercial systems, they are not regulated and no clinical trial data exists. They may require a hardware radio ‘bridge’ (i.e. RileyLink) to communicate between the pump and the algorithm controller. Lack of oversight and clear lines of accountability as well as the absence of clinical trial data are clear drawbacks to DIY systems. However, continuous user-driven optimization of the system, low-cost availability, and high interoperability will continue to make these algorithms an attractive option for some people with type 1 diabetes [75].