Individuals’ use of eHealth services has increased significantly. However, the recent pandemic of coronavirus disease 2019 (COVID-19) has resulted in a significant reallocation of health resources and support. This study investigated the impact of service quality dimensions on individuals’ continued trust in eHealth during COVID-19. A decision-making trial and evaluation laboratory (DEMATEL) approach was used to identify and analyse the causal relationships between service quality dimensions and individuals’ continued trust in eHealth services. A total of 134 eHealth users (78 males and 56 females; aged 29–61 years) responded to the DEMATEL questionnaire. The results showed a variation in the impact of service quality factors on individuals’ continued trust in eHealth services. This study found three core factors (responsiveness, assurance and tangibility) that influence individuals’ continued trust in eHealth services. Other secondary factors (e.g. content quality, reliability, efficiency and hedonic benefits) were found to be primarily influenced by the core factors. The identified relationships in this study can aid the decision-making process of healthcare providers and increase the efficiency of healthcare delivery.

Differences in individuals’ psychological and cognitive characteristics have been always found to play a significant role in influencing our behavior and preferences. While a number of studies have identified the impact of these characteristics on individuals’ visual design preferences, understanding how emotional intelligence (EI) would influence this process is yet to be explored. This study investigated the link between individuals’ EI dimensions (eg, emotionality, self-control, sociability, and well-being) and their eye movement behavior in an attempt to build a prediction model for visual design preferences. A total of 136 participants took part in this study. The feature selection and prediction of EI and eye movement data were performed using the genetic search method in conjunction with the bagging method. The results showed that participants high in self-control and emotionality exhibited different eye movement behaviors when performing five visual selection tasks. The prediction results (93.87% accuracy) revealed that specific eye parameters can predict the link between certain EI dimensions and preferences for visual design. This study adds new insights into human–computer interaction, EI, and rational choice theories. The findings also encourage researchers and designers to consider EI in the development of intelligent and adaptive systems.

Abstract Although several studies have looked at the relationship between emotional characteristics and viewing behaviour, understanding how emotional intelligence (EI) contributes to individuals’ viewing behaviour is not clearly understood. This study examined the viewing behaviour of people (74 male and 80 female) with specific EI profiles while viewing five facial expressions. An eye-tracking methodology was employed to examine individuals’ viewing behaviour in relation to their EI. We compared the performance of different machine learning algorithms on the eye-movement parameters of participants to predict their EI profiles. The results revealed that EI profiles of individuals high in self-control, emotionality, and sociability responded differently to the visual stimuli. The prediction results of these EI profiles achieved 94.97% accuracy. The findings are unique in that they provide a new understanding of how eye-movements can be used in the prediction of EI. The findings also contribute to the current understanding of the relationship between EI and emotional expressions, thereby adding to an emerging stream of research that is of interest to researchers and psychologists in human–computer interaction, individual emotion, and information processing.

Social media sites, such as Twitter, provide the means for users to share their stories, feelings, and health conditions during the disease course. Anemia, the most common type of blood disorder, is recognized as a major public health problem all over the world. Yet very few studies have explored the potential of recognizing anemia from online posts. This study proposed a novel mechanism for recognizing anemia based on the associations between disease symptoms and patients' emotions posted on the Twitter platform. We used k-means and Latent Dirichlet Allocation (LDA) algorithms to group similar tweets and to identify hidden disease topics. Both disease emotions and symptoms were mapped using the Apriori algorithm. The proposed approach was evaluated using a number of classifiers. A higher prediction accuracy of 98.96 % was achieved using Sequential Minimal Optimization (SMO). The results revealed that fear and sadness emotions are dominant among anemic patients. The proposed mechanism is the first of its kind to diagnose anemia using textual information posted on social media sites. It can advance the development of intelligent health monitoring systems and clinical decision-support systems.

Abstract Anxiety and self-regulation are the most common problems among the college student population. There are few attempts found in the literature to promote the development of students’ cognitive and metacognitive abilities in online learning environments. In addition, mechanisms for overcoming or reducing individuals’ anxiety in a computer-mediated environment is yet to be fully characterized. This study was conducted to investigate the potential of integrating the concept of flow into the design of a Metalearner (MTL) to help reduce anxiety and increase self-regulation among students. The design of MTL was based on the development of adaptive strategies to balance between the challenge of the task and user skills. A total of 260 participants were asked to use the system and respond to an online questionnaire that asked about flow antecedents, experience, and consequences. The structural model results showed that incorporating flow into the design of MTL can help reduce anxiety and improve self-regulation among students. Our findings can be used to enrich students’ online learning experience and inform designers and developers of learning systems about the importance of regulating task complexity according to the challenge/skills balance. This would help learners to process the presented information meaningfully and to make the inferences necessary for understanding the learning content.

Individual preferences for learning environments can be linked to a specific behavior. The tendency of such behavior can somehow be associated with an individual’s ability to cognitively engage in the learning process without being distracted by other stimuli. An online continuous adaptation mechanism (OCAM) of learning contents was developed in order to regulate the presentation of learning contents based on changes in the learner’s aptitude level. This was claimed to stimulate a better cognitive and emotional response among learners, thus stimulating their engagement. A total of 41 students (36 male and 5 female; age 20–25 years) participated in this study. The results revealed that learners’ levels of concentration and cognitive load were positively influenced by the OCAM, which significantly increased their engagement. Our findings can be used to inform designers and developers of online learning systems about the importance of regulating the presentation of learning contents according to the aptitude level of individual learners. The proposed OCAM can improve learners’ ability to process specific information meaningfully and make the inferences necessary for understanding the learning content.

Information processing speed affects reading performance and interaction with text. Understanding how column type in an online context influences reading effectiveness can help us to identify less effective layouts. This study explored the visual perception of 23 participants while they read text arranged in a multi-column layout. Two tasks (repeated reading and non-repeated reading) were designed and assessed to have the same level of difficulty. Information was organised according to three types of column layout (one, two, or three columns). Eye movement analysis showed that participants performed best in a three-column layout for repeated reading, and with one column for normal reading. We also found that the repeated reading technique reduced readers’ distraction and therefore increased their visual performance, which in turn increased information processing, regardless of column layout. These findings with regard to single- and multi-column layouts can help suggest effective reading configurations for online readers and provide insights for human–computer interaction theories on human interaction with different typographic elements.

It is a well-known fact that users vary in their preferences and needs. Therefore, it is very crucial to provide the customisation or personalisation for users in certain usage conditions that are more associated with their preferences. With the current limitation in adopting perceptual processing into user interface personalisation, we introduced the possibility of inferring interface design preferences from the user’s eye-movement behaviour. We firstly captured the user’s preferences of graphic design elements using an eye-tracker. Then we diagnosed these preferences towards the region of interests to build a prediction model for interface customisation. The prediction models from eye-movement behaviour showed a high potential for predicting users’ preferences of interface design based on the paralleled relation between their fixation and saccadic movement. This mechanism provides a novel way of user interface design customisation and opens the door for new research in the areas of human–computer interaction and decision-making.