Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.

Background: The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels. Objective: In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals’ perceived stress reactivity moderates stress level changes while using TYS. Methods: Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity. Results: Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04). Conclusions: TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.

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.

Background: Nowadays, visual analytics are mainly performed by programming approaches and viewing the results on a desktop monitor. However, due to the capabilities of smart glasses, new user interactions and representation possibilities become possible. This refers especially to 3D visualizations in the medical field as well as the industry domain, as valuable depth information can be related to the complex real-world structures and related data, which is also denoted as immersive analytics. However, the applicability of immersive analytics and its drawbacks, especially in the context of mixed reality, are quite unexplored. Objective: In order to validate the feasibility of immersive analytics for the aforementioned purposes, we designed and conducted a usability study with 60 participants. More specifically, we evaluated the effects of spatial sounds, performance changes from one analytics task to another, expert status, and compared an immersive analytics approach (i.e., a mixed-reality application) with a desktop-based solution. Methods: Participants had to solve several data analytics tasks (outlier’s detection and cluster recognition) with the developed mixed-reality application. Thereby, performance measures regarding time, errors, and movement patterns were evaluated. The separation into groups (low performer vs. high performer) was performed using a mental rotation pretest. Results: When solving analytic tasks in mixed reality, participants changed their movement patterns in the mixed reality setting significantly, while the use of spatial sounds reduced the handling time significantly, but did not affect the movement patterns. Furthermore, the usage of mixed reality for cluster recognition is significantly faster than the desktop-based solution (i.e., an 2D approach). Moreover, the results obtained with self-developed questionnaires indicate (1) that wearing smart glasses is perceived as a potential stressor and (2) that the utilization of sounds is perceived very differently by the participants. Altogether, industry and researchers should consider immersive analytics as a suitable alternative compared to traditional approaches.

The widespread use of smart mobile devices (e.g., in clinical trials or online surveys) offers promising perspectives with respect to the controlled collection of high-quality data. The design, implementation and deployment of such mobile data collection applications, however, is challenging in several respects. First, various mobile operating systems need to be supported, taking the short release cycles of vendors into account as well. Second, domain-specific requirements need to be flexibly aligned with mobile application development. Third, usability styleguides need to be obeyed. Altogether, this turns both programming and maintaining mobile applications into a costly, time-consuming, and error-prone endeavor. To remedy these drawbacks, a model-driven framework empowering domain experts to implement robust mobile data collection applications in an intuitive way was realized. The design of this end-user programming framework is based on experiences gathered in real-life mobile data collection projects. Facets of various stakeholders involved in such projects are discussed and an overall architecture as well as its components are presented. In particular, it is shown how the framework enables domain experts (i.e., end users) to flexibly implement mobile data collection applications on their own. Overall, the framework allows for the effective support of mobile services in a multitude of application domains.