Background Adoption and evaluation of contact tracing tools based on information and communications technology may expand the reach and efficacy of traditional contact tracing methods in fighting COVID-19. The Dutch Ministry of Health, Welfare and Sports initiated and developed CoronaMelder, a COVID-19 contact tracing app. This app is based on a Google/Apple Exposure Notification approach and aims to combat the spread of the coronavirus among individuals by notifying those who are at increased risk of infection due to proximity to someone who later tests positive for COVID-19. The app should support traditional contact tracing by faster tracing and greater reach compared to regular contact tracing procedures. Objective The main goal of this study is to investigate whether the CoronaMelder is able to support traditional contact tracing employed by public health authorities. To achieve this, usability tests were conducted to answer the following question: is the CoronaMelder user-friendly, understandable, reliable and credible, and inclusive? Methods Participants (N=44) of different backgrounds were recruited: youth with varying educational levels, youth with an intellectual disability, migrants, adults (aged 40-64 years), and older adults (aged >65 years) via convenience sampling in the region of Twente in the Netherlands. The app was evaluated with scenario-based, think-aloud usability tests and additional interviews. Findings were recorded via voice recordings, observation notes, and the Dutch User Experience Questionnaire, and some participants wore eye trackers to measure gaze behavior. Results Our results showed that the app is easy to use, although problems occurred with understandability and accessibility. Older adults and youth with a lower education level did not understand why or under what circumstances they would receive notifications, why they must share their key (ie, their assigned identifier), and what happens after sharing. In particular, youth in the lower-education category did not trust or understand Bluetooth signals, or comprehend timing and follow-up activities after a risk exposure notification. Older adults had difficulties multitasking (speaking with a public health worker and simultaneously sharing the key in the app). Public health authorities appeared to be unprepared to receive support from the app during traditional contact tracing because their telephone conversation protocol lacks guidance, explanation, and empathy. Conclusions The study indicated that the CoronaMelder app is easy to use, but participants experienced misunderstandings about its functioning. The perceived lack of clarity led to misconceptions about the app, mostly regarding its usefulness and privacy-preserving mechanisms. Tailored and targeted communication through, for example, public campaigns or social media, is necessary to provide correct information about the app to residents in the Netherlands. Additionally, the app should be presented as part of the national coronavirus measures instead of as a stand-alone app offered to the public. Public health workers should be trained to effectively and empathetically instruct users on how to use the CoronaMelder app.

BACKGROUND Adoption and evaluation of ICT-based contact tracing tools may expand the reach and efficacy of traditional contact tracing methods in fighting COVID-19. The Dutch Ministry of Health, Welfare and Sports (HWS) initiated and developed a COVID-19 contact tracing app: CoronaMelder. This app is based on Google/Apple exposure notification approach and aims to combat the spread of the COVID-19 virus among citizens, by notifying citizens who were at increased risk of infection because they were close by someone who was later tested positive for COVID-19. The app should support the traditional contact tracing by quicker tracing and reaching more people than regular contact tracing procedures. OBJECTIVE The main goal of this study is to investigate whether the CoronaMelder is able to support traditional contact tracing of Public Health Authorities (PHAs). To achieve this, usability tests were conducted aimed at answering the following question: Is the CoronaMelder user-friendly, understandable, reliable and credible, and inclusive? METHODS Participants (n=44) with different backgrounds were recruited: young people with a lower or higher level of education, young people with an intellectual disability, migrants, adults (40-64 years) and elderly (65> years) via convenience sampling in the CoronaMelder test region Twente, The Netherlands. The app was evaluated with scenario-based think-aloud usability tests with additional interviews. Findings were recorded via voice recordings, observation notes, the Dutch User Experience Questionnaire (UEQ-Dutch) and some participants wore eye trackers to measure gaze behavior. RESULTS Our results show that the app is easy to use. Yet, problems occurred with understandability and accessibility. Elderly and young people with a lower level of education do not understand why or when they receive notifications, or why they must share the key, and what happens after sharing. Especially young people with a lower level of education did not trust and understand the Bluetooth signals, timing and follow-up activities after risk exposure notification and elderly had difficulties in multitasking (contact with PHAs simultaneously with sharing key in app). PHAs appeared unprepared to be supported by the app in traditional contact tracing, because their telephone conversation protocol lacks guidance, explanation, and empathy. CONCLUSIONS The study indicated that the app is easy to use, but participants have misconceptions about its functioning. The perceived lack of clarity led to misconceptions of the app, mostly regarding its usefulness or privacy-preserving mechanisms. Tailored and target group specified communication, in forms of public campaigns or social media, is necessary to provide correct information about the app to Dutch citizens. Additionally, the app should be presented as part of the package of national corona measures, instead of just as a stand-alone app provided to the public. To succeed, PHA workers should be trained to effectively and empathically instruct users to warn others by using the CoronaMelder app.

Maintaining mental health can be quite challenging, especially when exposed to stressful situations. In many cases, mental health problems are recognized too late to effectively intervene and prevent adverse outcomes. Recent advances in the availability and reliability of wearable technologies offer opportunities for continuously monitoring mental states, which may be used to improve a person’s mental health. Previous studies attempting to detect and predict mental states with different modalities have shown only small to moderate effect sizes. This limited success may be due to the large variability between individuals regarding e.g., ways of coping with stress or behavioral patterns associated with positive or negative feelings. A study was set up for the detection of mental states based on longitudinal wearable and contextual sensing, targeted at investigating between-subjects variations in terms of predictors of mental states and variations in how predictors relate to mental states. At the end of March 2022, 16 PhD candidates from the Netherlands started to participate in the study. Over nine months, we collected data in terms of their daily mental states (valence and arousal), continuous physiological data (Oura ring) and smartphone data (AWARE framework including GPS and smartphone usage). From the raw data, we aggregated daily values for each participant in terms of sleep, physical activity, mental states, phone usage and GPS movement. First results (six months into the study at the time of writing) indicate that almost all participants show a large variability in ratings of daily mental states, which is a prerequisite for predictive modeling. Direction, strength and standard deviations of Spearman correlations between valence, arousal and the different variables suggest that several predictors of valence and arousal are more subject dependent than others. In future analyses, we will test and compare different versions of predictive modeling to highlight the potential of wearable technologies for mental state monitoring and the personalized prediction of the development of mental problems.