With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.

https://www.mdpi.com/1660-4601/19/13/7979/pdf?version=1656658079

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

Since the early stage of the current pandemic, digital contact tracing (DCT) through mobile phone apps, called “Immuni”, has been introduced to complement manual contact tracing in Italy. Until 31 December 2021, Immuni identified 44,880 COVID-19 cases, which corresponds to less than 1% of total COVID-19 cases reported in Italy in the same period (5,886,411). Overall, Immuni generated 143,956 notifications. Although the initial download of the Immuni app represented an early interest in the new tool, Immuni has had little adoption across the Italian population, and the recent increase in its download is likely to be related to the mandatory Green Pass certification for conducting most daily activities that can be obtained via the application. Therefore, Immuni failed as a support tool for the contact tracing system. Other European experiences seem to show similar limitations in the use of DTC, leaving open questions about its effectiveness, although in theory, contact tracing could allow useful means of “proximity tracking”.

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“Immuni” and the National Health System: Lessons Learnt from the COVID-19 Digital Contact Tracing in Italy

Background In several countries, contact tracing apps (CTAs) have been introduced to warn users if they have had high-risk contacts that could expose them to SARS-CoV-2 and therefore could develop COVID-19 or further transmit the virus. For CTAs to be effective, sufficient critical mass of users is needed. Until now, adoption of these apps in several countries has been limited, resulting in questions on which factors refrain app uptake or stimulate app use discontinuation. Objective This study investigates individuals' reasons for not using or stop using a CTA, more particularly the app Coronalert. Users' and nonusers' attitudes towards the app's potential impact was assessed in Belgium. To further stimulate interest and potential use of a CTA, the study also investigated population's interest for new functionalities. Methods An online survey was administered in Belgium, to a sample of 1,850 respondents aged 18 to 64. Data were collected between 30 October and 2 November 2020. Sociodemographic differences were assessed between users and nonusers. Both groups' attitude towards CTAs potential impact and their acceptance of new app functionalities were analysed. Results Our data show that 64.9% (1201/1850) of our respondents were nonusers of the CTA under study (individuals who did not install the app, downloaded the app but did not activate is, uninstalled the app). While we did not find any sociodemographic differences between users and nonusers, attitudes towards the app and its functionalities seemed to differ. The main reasons for not downloading and using the app are a perceived lack of advantages (31.1%, 308/991), worries about privacy (29.3%, 290/991) and, to a lesser extent, not having a smartphone (18.5%, 183/991). Users of a CTA agree more on the potential of such apps to mitigate the consequences of the pandemic. Overall, nonusers find the possibility of extending the CTA with future functionalities less acceptable than current users. However, also among users, acceptability tends to differ. Among users, functionalities relating to access and control, such as digital certificates or 'green cards' for events, were less accepted (55.2%, 358/649) than functionalities focusing on informing citizens about the spread of the virus (69.8%, 453/649) or making an appointment to get tested (80.9%, 525/649). Conclusions Our results show that current users are more convinced of the CTA's utility and more inclined to accept new app features then nonusers. Moreover, nonusers have more CTA related privacy concerns. Therefore, to further stimulate app adoption and use its potential advantages and privacy-preserving mechanisms, need to be stressed. Building further knowledge on forms of resistance among nonusers is important to respond to these barriers through the app's further development, related policy, and communication campaigns.

COVID-19 digital contact tracing: a cross-sectional survey on drivers of nonuse, use discontinuation and acceptance of app options.

Background and Aim: Health Information Technology helps individuals prevent COVID-19. Many information technology applications have been developed to prevent Coronavirus infection globally;however, the quality level of these applications is uncertain. This study aims to evaluate the usability of the Mask mobile application, which is designed to fight COVID-19. Materials and Methods: The Mask mobile application is evaluated in two phases in this study. In the first phase, five experts performed the evaluation by Jacob Nielsen's 10 general principles, and in the second phase, 124 participants evaluated the Mask mobile application. Data collection tools were the System Usability Scale (SUS) questionnaire. Results: Mask mobile application was poor in experts evaluating it in terms of error prevention, flexibility, and efficiency. In the user-based test method, the user score of this application was 89 out of 100, which was equal to an excellent ranking and grade A. Conclusion: The results have indicated that the usability of the Mask mobile application to prevent COVID-19 has been excellent. As a result of the study, mobile application developers might improve or modify their existing mobile health application designs to achieve optimal outcomes. © 2021, This is an original open-access article distributed under the terms of the Creative Commons Attribution-noncommercial 4.0 International License which permits copy and redistribution of the material just in noncommercial usages with proper citation.

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Usability Evaluation of the Mask Mobile Application: The Official Application of the Iranian Government

Background: Many have proposed the use of Bluetooth technology to help scale up contact tracing for COVID-19. However, much remains unknown about the accuracy of this technology in real-world settings, the attitudes of potential users, and the differences between delivery formats (mobile app vs carriable or wearable devices).
Objective: We pilot tested 2 separate Bluetooth contact tracing technologies on a university campus to evaluate their sensitivity and specificity, and to learn from the experiences of the participants.
Methods: We used a convergent mixed methods study design, and participants included graduate students and researchers working on a university campus during June and July 2020. We conducted separate 2-week pilot studies for each Bluetooth technology. The first was for a mobile phone app (“app pilot”), and the second was for a small electronic “tag” (“tag pilot”). Participants validated a list of Bluetooth-identified contacts daily and reported additional close contacts not identified by Bluetooth. We used these data to estimate sensitivity and specificity. Participants completed a postparticipation survey regarding appropriateness, usability, acceptability, and adherence, and provided additional feedback via free text. We used tests of proportions to evaluate differences in survey responses between participants from each pilot, paired t tests to measure differences between compatible survey questions, and qualitative analysis to evaluate the survey’s free-text responses.
Results: Among 25 participants in the app pilot, 53 contact interactions were identified by Bluetooth and an additional 61 by self-report. Among 17 participants in the tag pilot, 171 contact interactions were identified by Bluetooth and an additional 4 by self-report. The tag had significantly higher sensitivity compared with the app (46/49, 94% vs 35/61, 57%; P<.001), as well as higher specificity (120/126, 95% vs 123/141, 87%; P=.02). Most participants felt that Bluetooth contact tracing was appropriate on campus (26/32, 81%), while significantly fewer participants felt that using other technologies, such as GPS or Wi-Fi, was appropriate (17/31, 55%; P=.02). Most participants preferred technology developed and managed by the university rather than a third party (27/32, 84%) and preferred not to have tracing apps on their personal phones (21/32, 66%), due to “concerns with privacy.” There were no significant differences in self-reported adherence rates across pilots.
Conclusions: Convenient and carriable Bluetooth technology may improve tracing efficiency while alleviating privacy concerns by shifting data collection away from personal devices. With accuracy comparable to, and in this case, superior to, mobile phone apps, such approaches may be suitable for workplace or school settings with the ability to purchase and maintain physical devices.

Pilot Evaluations of Two Bluetooth Contact Tracing Approaches on a University Campus: Mixed Methods Study.

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.

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The Dutch COVID-19 Contact Tracing App (the CoronaMelder): Usability Study.

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The Dutch COVID-19 Contact Tracing App (the CoronaMelder): Usability Study (Preprint)

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.

Lea Berkemeier

Papers

The Dutch COVID-19 Contact Tracing App (the CoronaMelder): Usability Study.

The Dutch COVID-19 Contact Tracing App (the CoronaMelder): Usability Study (Preprint)

Towards continuous mental state detection in everyday settings: investigating between-subjects variations in a longitudinal study