TELERA—Asynchronous TELEmedicine for Patients With Rheumatoid Arthritis: Study Protocol for a Prospective, Multi-Center, Randomized Controlled Trial

Innovative strategies are needed to adequately assess and monitor disease activity of patients with rheumatoid arthritis (RA) in times of scarce appointments. The aim of the TELERA study is to evaluate the feasibility and performance of asynchronous telemedicine visits based on patient-generated data and patient's drug history. RA patients use a medical app, ABATON, that captures the results of a self-performed quick CRP-test, joint-count, and electronic patient-reported outcomes in between visits. This is a prospective, multi-center, randomized controlled trial performed in four German university centers. The estimated sample size is 120 patients. The main outcome is the agreement of rheumatologists' treatment decisions based on asynchronous telemedicine patient-generated data with traditional in-person rheumatology clinic-based decisions and with patient suggestions. The TELERA trial will provide evidence regarding the implementation of remote care in rheumatology.Clinical Trial Registration: This clinical trial was registered at German Registry for Clinical Trials (DRKS). http://www.drks.de/DRKS00016350, identifier: DRKS00024928.

Adolescents’ Experiences of Using a Smartphone Application Hosting a Closed-loop Algorithm to Manage Type 1 Diabetes in Everyday Life: Qualitative Study

Background: Closed-loop technology may help address health disparities experienced by adolescents, who are more likely to have suboptimal glycemic control than other age groups and, because of their age, find diabetes self-management particularly challenging. The CamAPS FX closed-loop has sought to address accessibility and usability issues reported by users of previous prototype systems. It comprises small components and a smartphone app used to: announce meal-time boluses, adjust (“boost” or “ease-off”) closed-loop insulin delivery, customize alarms, and review/share data. We explored how using the CamAPS FX platform influences adolescents’ self-management practices and everyday lives. Methods: Eighteen adolescents were interviewed after having ≥6 months experience using the closed-loop platform. Data were analyzed thematically. Results: Participants reported feeling less burdened and shackled by diabetes because closed-loop components were easier to carry/wear, finger-pricks were not required, the smartphone app provided a discreet and less stigmatizing way of managing diabetes in public, and they were able to customize alarms. Participants also reported checking and reviewing data more regularly, because they did so when using the smartphone for other reasons. Some reported challenges in school settings where use of personal phones was restricted. Participants highlighted how self-management practices were improved because they could easily review glucose data and adjust closed-loop insulin delivery using the “boost” and “ease-off” functions. Some described how using the system resulted in them forgetting about diabetes and neglecting certain tasks. Conclusions: A closed-loop system with small components and control algorithm on a smartphone app can enhance usability and acceptability for adolescents and may help address the health-related disparities experienced by this age group. However, challenges can arise from using a medical app on a device which doubles as a smartphone. Trial registration: Closed Loop From Onset in Type 1 Diabetes (CLOuD); NCT02871089; https://clinicaltrials.gov/ct2/show/NCT02871089

Digital Medical Device Companion (MyIUS) for New Users of Intrauterine Systems: App Development Study

Background Women choosing a levonorgestrel-releasing intrauterine system may experience changes in their menstrual bleeding pattern during the first months following placement. Objective Although health care professionals (HCPs) can provide counseling, no method of providing individualized information on the expected bleeding pattern or continued support is currently available for women experiencing postplacement bleeding changes. We aim to develop a mobile phone–based medical app (MyIUS) to meet this need and provide a digital companion to women after the placement of the intrauterine system. Methods The MyIUS app is classified as a medical device and uses an artificial intelligence–based bleeding pattern prediction algorithm to estimate a woman’s future bleeding pattern in terms of intensity and regularity. We developed the app with the help of a multidisciplinary team by using a robust and high-quality design process in the context of a constantly evolving regulatory landscape. The development framework consisted of a phased approach including ideation, feasibility and concept finalization, product development, and product deployment or localization stages. Results The MyIUS app was considered useful by HCPs and easy to use by women who were consulted during the development process. Following the launch of the sustainable app in selected pilot countries, performance metrics will be gathered to facilitate further technical and feature updates and enhancements. A real-world performance study will also be conducted to allow us to upgrade the app in accordance with the new European Commission Medical Device legislation and to validate the bleeding pattern prediction algorithm in a real-world setting. Conclusions By providing a meaningful estimation of bleeding patterns and allowing an individualized approach to counseling and discussions about contraceptive method choice, the MyIUS app offers a useful tool that may benefit both women and HCPs. Further work is needed to validate the performance of the prediction algorithm and MyIUS app in a real-world setting.

Every Byte Matters

Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-style, habits and interests of the wearer. This data is typically synchronized with a companion app running on a smartphone over a Bluetooth (Classic or Low Energy) connection. In this work, we investigate what can be inferred from the metadata (such as the packet timings and sizes) of encrypted Bluetooth communications between a wearable device and its connected smartphone. We show that a passive eavesdropper can use traffic-analysis attacks to accurately recognize (a) communicating devices, even without having access to the MAC address, (b) human actions (e.g., monitoring heart rate, exercising) performed on wearable devices ranging from fitness trackers to smartwatches, (c) the mere opening of specific applications on a Wear OS smartwatch (e.g., the opening of a medical app, which can immediately reveal a condition of the wearer), (d) fine-grained actions (e.g., recording an insulin injection) within a specific application that helps diabetic users to monitor their condition, and (e) the profile and habits of the wearer by continuously monitoring her traffic over an extended period. We run traffic-analysis attacks by collecting a dataset of Bluetooth communications concerning a diverse set of wearable devices, by designing features based on packet sizes and timings, and by using machine learning to classify the encrypted traffic to actions performed by the wearer. Then, we explore standard defense strategies against traffic-analysis attacks such as padding, delaying packets, or injecting dummy traffic. We show that these defenses do not provide sufficient protection against our attacks and introduce significant costs. Overall, our research highlights the need to rethink how applications exchange sensitive information over Bluetooth, to minimize unnecessary data exchanges, and to research and design new defenses against traffic-analysis tailored to the wearable setting.

Medical Device Regulation Efforts for mHealth Apps during the COVID-19 Pandemic—An Experience Report of Corona Check and Corona Health

Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a “medical app”. However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team—especially software engineers—are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted.

Treatment Effect of Type 2 Diabetes Patients in Outpatient Department Based on Blockchain Electronic Mobile Medical App

As the pace of people’s lives accelerates, there are more and more diabetic patients. This research mainly explores the treatment effect of type 2 diabetic patients based on blockchain electronic mobile medical app. Considering that it is more realistic to adopt an off-chain storage solution, the blockchain-based medical data sharing platform in this study adopts an off-chain storage solution. Only key information is stored in the blockchain network, and all medical data will be in the cloud space. For storage, cloud storage uses Aliyun’s OSS storage service, which can be expanded infinitely. The cloud operation module is responsible for all operations that interact with cloud storage. The chain code can call the cloud operation module to upload the user’s encrypted medical data and user ID to Alibaba Cloud’s OSS. The chain code will return the storage address of the medical data and the authorized access address is sent to the blockchain network for consensus on the chain. The message processing module provides information processing functions such as chat information processing, APP use reminders, and health tips. The indicator recording module includes indicator recording functions including 6 indicators of blood sugar, medication, diet, weight, exercise, and sleep. The main function of the indicator analysis module is to display the curve trends of the 6 indicators recorded by the patient in three days, one week, and one month. Comparing the change range of the mean value of glycosylated hemoglobin at the beginning and end of the two groups of patients, it can be found that the change range of glycosylated hemoglobin in the intervention group is −6.04%, while the change range of the control group is only −3.26%. The impact of the mobile medical app designed in this study will indeed be reflected in the patient’s blood sugar control and help patients to better control blood sugar.