Data Management and Privacy Policy of COVID-19 Contact-Tracing Apps: Systematic Review and Content Analysis.

BACKGROUND: COVID-19 digital contact-tracing apps were created to assist public health authorities in curbing the pandemic. These apps require users' permission to access specific functions on their mobile phones, such as geolocation, Bluetooth or Wi-Fi connections, or personal data, to work correctly. As these functions have privacy repercussions, it is essential to establish how contact-tracing apps respect users' privacy. OBJECTIVE: This study aimed to systematically map existing contact-tracing apps and evaluate the permissions required and their privacy policies. Specifically, we evaluated the type of permissions, the privacy policies' readability, and the information included in them. METHODS: We used custom Google searches and existing lists of contact-tracing apps to identify potentially eligible apps between May 2020 and November 2021. We included contact-tracing or exposure notification apps with a Google Play webpage from which we extracted app characteristics (eg, sponsor, number of installs, and ratings). We used Exodus Privacy to systematically extract the number of permissions and classify them as dangerous or normal. We computed a Permission Accumulated Risk Score representing the threat level to the user's privacy. We assessed the privacy policies' readability and evaluated their content using a 13-item checklist, which generated a Privacy Transparency Index. We explored the relationships between app characteristics, Permission Accumulated Risk Score, and Privacy Transparency Index using correlations, chi-square tests, or ANOVAs. RESULTS: We identified 180 contact-tracing apps across 152 countries, states, or territories. We included 85.6% (154/180) of apps with a working Google Play page, most of which (132/154, 85.7%) had a privacy policy document. Most apps were developed by governments (116/154, 75.3%) and totaled 264.5 million installs. The average rating on Google Play was 3.5 (SD 0.7). Across the 154 apps, we identified 94 unique permissions, 18% (17/94) of which were dangerous, and 30 trackers. The average Permission Accumulated Risk Score was 22.7 (SD 17.7; range 4-74, median 16) and the average Privacy Transparency Index was 55.8 (SD 21.7; range 5-95, median 55). Overall, the privacy documents were difficult to read (median grade level 12, range 7-23); 67% (88/132) of these mentioned that the apps collected personal identifiers. The Permission Accumulated Risk Score was negatively associated with the average App Store ratings (r=-0.20; P=.03; 120/154, 77.9%) and Privacy Transparency Index (r=-0.25; P<.001; 132/154, 85.7%), suggesting that the higher the risk to one's data, the lower the apps' ratings and transparency index. CONCLUSIONS: Many contact-tracing apps were developed covering most of the planet but with a relatively low number of installs. Privacy-preserving apps scored high in transparency and App Store ratings, suggesting that some users appreciate these apps. Nevertheless, privacy policy documents were difficult to read for an average audience. Therefore, we recommend following privacy-preserving and transparency principles to improve contact-tracing uptake while making privacy documents more readable for a wider public.

Robotics in Nursing: A Scoping Review.

PURPOSE: Driven by the shortage in qualified nurses and the high percentage of aging populations, the past decade has witnessed a significant growth in the use of robots in nursing, especially in countries like Japan. This article is a scoping review of the different tracks in which robots are used in nursing. Whereas assistive robots are used for physical care, including service and monitoring tasks, social assistive robots focus on the cognitive and emotional well-being of patients in need of companionship. METHODS: A total of six electronic databases were used in the search for journal papers and conference proceedings. The key words used in searching the databases were nursing OR nurses, AND robots OR robotics. Topics covering surgical robotics, nursing education robotics, and clinical procedures were excluded. FINDINGS: A total of 1,758 articles were retrieved, from which 69 articles were included in the final review. The analysis of the chosen papers led to the categorization of robots into two main categories: assistive robots and social assistive robots. CONCLUSIONS: After a detailed review of the state of the art in assistive robots and social assistive robots, an insight into the future of robotics in this field is provided. The recommendations include the need to intensify research on human robot interaction, greater focus on monitoring robots, and analysis of the psychological barriers that need to be surmounted to achieve more tolerance and higher acceptance of robots. CLINICAL RELEVANCE: For researchers and developers to provide suitable technological solutions, a full understanding of robotics in nursing is needed. An overview of the most recent applications and their proper categorization is key to finding areas for contribution.