Designing the Health-related Internet of Things: Ethical Principles and Guidelines

Abstract

Abstract:The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols.

Participatory Disease Surveillance Systems: Ethical Framework

“…This could affect the expected benefits that big data can bring to society by increasing the regulatory burden on public health surveillance studies [1]. Furthermore, as stressed in the study by Mittelstadt, the GDPR classifies “data concerning health” as a “special category of personal data” [77]. As this category includes any personal data that reveals information on the health status (physical or mental) of participants [77], health-related information gathered from Influenzanet participants or similar epidemics forecasting studies might—until properly anonymized—fall in this special category.…”

“…Furthermore, as stressed in the study by Mittelstadt, the GDPR classifies “data concerning health” as a “special category of personal data” [77]. As this category includes any personal data that reveals information on the health status (physical or mental) of participants [77], health-related information gathered from Influenzanet participants or similar epidemics forecasting studies might—until properly anonymized—fall in this special category. It is, thus, possible that detailed limitations to health data usage are imposed in the future because of the protective stance endorsed by the GDPR [77].…”

Evaluation of a Rehabilitation System for the Elderly in a Day Care Center

“…Currently, new measurements technologies (portable, inexpensive, with network connectivity, and also for complex body movement [8]) are applied to the field of game and augmented reality [9]. These new devices in the context of augmented reality suggest that these tools can be used to prevent falls in the elderly population thanks to the regular monitoring of fall risk [10][11][12][13]. Physical exercise protocols carried out by using video games combining exercise, known as exergames [14,15], generate a lot of interest in the field of fall prevention especially when it is based on low-cost devices such as Nintendo Wii, Microsoft Kinect, or Asus Xtion.…”

Ethical Considerations for Movement Mapping to Identify Disease Transmission Hotspots

“…In all situations, investigators must put protective measures in place to avoid breaches in confidential mobility data that might cause unintended inferences about a user’s life ( 14 ). …”