BackgroundDuring the COVID-19 pandemic, protective measures have been prescribed to prevent or slow down the spread of the SARS-CoV-2 virus and protect the population. Individuals follow these measures to varying degrees. We aimed to identify factors influencing the extent to which protective measures are adhered to.MethodsA cross-sectional survey (telephone interviews) was undertaken between April and June 2021 to identify factors influencing the degree to which individuals adhere to protective measures. A representative sample of 1,003 people (age >16 years) in two Austrian states (Carinthia, Vorarlberg) was interviewed. The questionnaire was based on the Health Belief Model, but also included potential response-modifying factors. Predictors for adherent behavior were identified using multiple regression analysis. All predictors were standardized so that regression coefficients (β) could be compared.ResultsOverall median adherence was 0.75 (IQR: 0.5–1.0). Based on a regression model, the following variables were identified as significant in raising adherence: higher age (β = 0.43, 95%CI: 0.33–0.54), social standards of acceptable behavior (β = 0.33, 95%CI: 0.27–0.40), subjective/individual assessment of an increased personal health risk (β = 0.12, 95%CI: 0.05–0.18), self-efficacy (β = 0.06, 95%CI: 0.02–0.10), female gender (β = 0.05, 95%CI: 0.01–0.08), and low corona fatigue (behavioral fatigue: β = −0.11, 95%CI: −0.18 to −0.03). The model showed that such aspects as personal trust in institutions, perceived difficulties in adopting health-promoting measures, and individual assessments of the risk of infection, had no significant influence.ConclusionsThis study reveals that several factors significantly influence adherence to measures aimed at controlling the COVID-19 pandemic. To enhance adherence, the government, media, and other relevant stakeholders should take the findings into consideration when formulating policy. By developing social standards and promoting self-efficacy, individuals can influence the behavior of others and contribute toward coping with the pandemic.

As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success.

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As the driving is shifting towards automation, the maximization of related benefits would profit from improved user acceptance of the new technology. Studies suggest a strong connection between acceptance and trust in technical solutions. We investigate the improvement of user trust to driving automation through demonstrations that carried on a sophisticated driving simulator. The study correlates subjective data with objective psycho-physiological measurements. The multi-factorial and multivariate analysis of variance investigates the influence of learning effects and pre-experience with ADAS on trust. Results show improvement in trust through user interaction with a human-machine interface of the demonstrated AD system, hence illustrating the relevance of human-centered development processes. The conclusion is supported by the observation of driver cardiac signals.