Fitness apps are persuasive tools developed to motivate physical activity. Despite their popularity, there is little work on how social-cognitive characteristics such as culture, household size, physical activity level, perceived self-efficacy and social support influence users’ willingness to use them and preference (personal vs. social). Knowing these relationships can help developers tailor fitness apps to different socio-cultural groups. Hence, we conducted two studies to address the research gap. In the first study (n = 194) aimed at recruiting participants for the second study, we asked participants about their app preference (personal vs. social), physical activity level and key demographic variables. In the second study (n = 49), we asked participants about their social-cognitive beliefs about exercise and their willingness to use a fitness app (presented as a screenshot). The results of the first study showed that, in the collectivist group (Nigerians), people in large households were more likely to be active and use the social version of a fitness app than those in small households. However, in the individualist group (Canadians/Americans), neither the preference for the social or personal version of a fitness app nor the physical activity level depended on the household size. Moreover, in the second study, in the individualist model, perceived self-efficacy and perceived self-regulation have a significant total effect on willingness to use a fitness app. However, in the collectivist model, perceived social support and outcome expectation have a significant total effect on the target construct. Finally, we found that females in individualist cultures had higher overall social-cognitive beliefs about exercise than males in individualist cultures and females in collectivist cultures. The implications of the findings are discussed.

In this paper, we explore an approach to feedback which could allow those learning creative digital media practices in remote and asynchronous environments to receive rich, multi-modal, and interactive feedback upon their creative artefacts. We propose the show-and-tell feedback interface which couples graphical user interface changes (the show) to a text-based explanations (the tell). We describe the rationale behind the design and offer a tentative set of design criteria. We report the implementation and deployment into a real-world educational setting using a prototype interface developed to allow either traditional text-only feedback or our proposed show-and tell feedback across four sessions. The prototype was used to provide formative feedback upon music students’ coursework resulting in a total of 103 pieces of feedback. Thematic analysis was used to analyse the data obtained through interviews and focus groups with both educators and students (i.e., feedback givers and receivers). Recipients considered show-and-tell feedback to possess greater clarity and detail in comparison with the single modality text-only feedback they are used to receiving. We also report interesting emergent issues around control and artistic vision, and we discuss how these issues could be mitigated in future iterations of the interface.

Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) have been well researched due to their easy system configuration, little or no user training and high information transfer rates. To elicit an SSVEP, a repetitive visual stimulus (RVS) is presented to the user. The properties of this RVS (e.g., frequency, luminance) have a significant influence on the BCI performance and user comfort. Several studies in this area in the last one-and-half decades have focused on evaluating different stimulus parameters (i.e., properties). However, there is little research on the synthesis of the existing studies, as the last review on the subject was published in 2010. Consequently, we conducted a scoping review of related studies on the influence of stimulus parameters on SSVEP response and user comfort, analyzed them and summarized the findings considering the physiological and neurological processes associated with BCI performance. In the review, we found that stimulus type, frequency, color contrast, luminance contrast and size/shape of the retinal image are the most important stimulus properties that influence SSVEP response. Regarding stimulus type, frequency and luminance, there is a trade-off between the best SSVEP response quality and visual comfort. Finally, since there is no unified measuring method for visual comfort and a lack of differentiation in the high-frequency band, we proposed a measuring method and a division of the band. In summary, the review highlights which stimulus properties are important to consider when designing SSVEP BCIs. It can be used as a reference point for future research in BCI, as it will help researchers to optimize the design of their SSVEP stimuli.

This study explores the conventional ‘funneling’ method by introducing two extra locations beyond the virtual reality (VR) controller boundaries, terming it the extended funneling technique. Thirty-two participants engaged in a localization task, with their responses recorded using eye-tracking technology. They were tasked with localizing a virtual ping-pong ball as it bounced both within and outside their virtual hands on a virtual board. Both the experimental and control groups received simultaneous spatial audio and vibrotactile feedback. The experimental group received vibrotactile feedback with extended funneling, while the control group received vibrotactile feedback without funneling for comparison. The results indicate that the experimental group, benefiting from the extended funneling technique, demonstrated a significantly higher accuracy rate (41.79%) in localizing audio–vibrotactile stimuli compared to the control group (28.21%). No significant differences emerged in embodiment or workload scores. These findings highlight the effectiveness of extended funneling for enhancing the localization of sensory stimuli in VR.

A chatbot, or else a conversational agent (CA), is a technology that is used in order to imitate the process of a conversation between a human being and a software application for supporting specific services. The utilization of this technology has been increasing considerably over the past five years, particularly in education where CAs are mostly utilized as teaching assistants that provide educational content. This paper aims to contribute to the existing body of knowledge by systematically reviewing the support provided by chatbots both to educational institutions and their students, investigating their capabilities in further detail, and highlighting the various ways that this technology could and should be used in order to maximize its benefits. Emphasis is given to analyzing and synthesizing the emerging roles of CAs, usage recommendations and suggestions, student’s desires, and challenges recorded in the literature. For this reason, a systematic literature review (SLR) was carried out using the PRISMA framework in order to minimize the common biases and limitations of SLRs. However, we must note that the SLR presented has specific limitations, namely using only Scopus as a search engine, utilizing a general search query, and selecting only journal articles published in English in the last five years.

Immersive Unit Visualization is an emergent form of visualization that arose from Immersive Analytics where, unlike traditional visualizations, each data point is represented by an individual visual mark in an immersive virtual environment. This practice has focused almost exclusively on virtual reality, excluding augmented reality (AR). This article develops and tests a prototype of an Immersive Unit Visualization (Floating Companies II) with two AR devices: head-mounted display (HMD) and hand-held display (HHD). Results from the testing sessions with 20 users were analyzed through qualitative research analysis and thematic coding indicating that, while the HHD enabled a first contact with AR visualization on a familiar device, HMD improved the perception of hybrid space by supporting greater stability of virtual content, wider field of view, improved spatial perception, increased sense of immersion, and more realistic simulation, which had an impact on information reading and sense-making. The materialization of abstract quantitative values into concrete reality through its simulation in the real environment and the ludic dimension stand out as important opportunities for this type of visualization. This paper investigates the aspects distinguishing two experiences regarding data visualization in hybrid space, and characterizes ways of seeing information with AR, identifying opportunities to advance information design research.

Public warning systems are an essential element of safe cities. However, the functionality of neither traditional nor digital emergency warnings is understood well enough from the perspective of citizens. This study examines smart city development from the perspective of safety by exploring citizens’ viewpoints. It investigates people’s perceptions of the ways in which they obtain warnings and information about emergencies involving health risks. Data were collected in the form of focus group interviews and semi-structured interviews in Finland, Germany, and Greece. The results suggest that people place a lot of trust in their social network, receiving text messages, and their ability to use web-based search engines in order to obtain public warnings. The study discusses the challenges identified by citizens in the use of conventional radio and television transmissions and sirens for public warnings. Based on the results, citizens demonstrate informed ignorance about existing mobile emergency applications. Our results imply that it is not sufficient to build emergency communication infrastructure: the development of smart, safe cities requires continuous work and the integration of both hard and soft infrastructure-oriented strategies, i.e., technological infrastructure development including digitalisation and education, advancement of knowledge, and participation of people. Both strategic aspects are essential to enable people to take advantage of novel digital applications in emergency situations.

Relational cues are extracts from actual verbal dialogues that help build the therapist–patient working alliance and stronger bond through the depiction of empathy, respect and openness. ECAs (Embodied conversational agents) are human-like virtual agents that exhibit verbal and non-verbal behaviours. In the digital health space, ECAs act as health coaches or experts. ECA dialogues have previously been designed to include relational cues to motivate patients to change their current behaviours and encourage adherence to a treatment plan. However, there is little understanding of who finds specific relational cues delivered by an ECA helpful or not. Drawing the literature together, we have categorised relational cues into empowering, working alliance, affirmative and social dialogue. In this study, we have embedded the dialogue of Alex, an ECA, to encourage healthy behaviours with all the relational cues (empathic Alex) or with none of the relational cues (neutral Alex). A total of 206 participants were randomly assigned to interact with either empathic or neutral Alex and were also asked to rate the helpfulness of selected relational cues. We explore if the perceived helpfulness of the relational cues is a good predictor of users’ intention to change the recommended health behaviours and/or development of a working alliance. Our models also investigate the impact of individual factors, including gender, age, culture and personality traits of the users. The idea is to establish whether a certain group of individuals having similarities in terms of individual factors found a particular cue or group of cues helpful. This will establish future versions of Alex and allow Alex to tailor its dialogue to specific groups, as well as help in building ECAs with multiple personalities and roles.

The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the realm of scientific publication recommendations. In a content-based scientific publication RS, recommendations are composed by observing the features of users and papers. Content-based recommendation encompasses three primary steps, namely, item representation, user modeling, and recommendation generation. A crucial part of generating recommendations is the user modeling process. Nevertheless, this step is often neglected in existing content-based scientific publication RS. Moreover, most existing approaches do not capture the semantics of user models and papers. To address these limitations, in this paper we present a transparent Recommendation and Interest Modeling Application (RIMA), a content-based scientific publication RS that implicitly derives user interest models from their authored papers. To address the semantic issues, RIMA combines word embedding-based keyphrase extraction techniques with knowledge bases to generate semantically-enriched user interest models, and additionally leverages pretrained transformer sentence encoders to represent user models and papers and compute their similarities. The effectiveness of our approach was assessed through an offline evaluation by conducting extensive experiments on various datasets along with user study (N = 22), demonstrating that (a) combining SIFRank and SqueezeBERT as an embedding-based keyphrase extraction method with DBpedia as a knowledge base improved the quality of the user interest modeling step, and (b) using the msmarco-distilbert-base-tas-b sentence transformer model achieved better results in the recommendation generation step.

Integration of technology within problem-based learning curricula is expanding; however, information regarding student experiences and attitudes about the integration of such technologies is limited. This study aimed to evaluate pre-clinical medical student perceptions and use patterns of the “Road to Birth” (RtB) software, a novel program designed to support human maternal anatomy and physiology education. Second-year medical students at a large midwestern American university participated in a prospective, mixed-methods study. The RtB software is available as a mobile smartphone/tablet application and in immersive virtual reality. The program was integrated into problem-based learning activities across a three-week obstetrics teaching period. Student visuospatial ability, weekly program usage, weekly user satisfaction, and end-of-course focus group interview data were obtained. Survey data were analyzed and summarized using descriptive statistics. Focus group interview data were analyzed using inductive thematic analysis. Of the eligible students, 66% (19/29) consented to participate in the study with 4 students contributing to the focus group interview. Students reported incremental knowledge increases on weekly surveys (69.2% week one, 71.4% week two, and 78.6% week three). Qualitative results indicated the RtB software was perceived as a useful educational resource; however, its interactive nature could have been further optimized. Students reported increased use of portable devices over time and preferred convenient options when using technology incorporated into the curriculum. This study identifies opportunities to better integrate technology into problem-based learning practices in medical education. Further empirical research is warranted with larger and more diverse student samples.