This brief report presents an experiment testing the effect of immersion on emotional responses and cognitive genre categorisation of film viewers. Immersion of a film presentation was varied by presenting an animated movie either in a 3D-viewing condition (low immersive condition) or in a CAVE condition (high immersive condition, comparable to virtual reality experience). Viewers rated their emotions and categorised the movies into four basic film genres (action, drama, comedy, and non-fiction). Two distinct types of emotion were measured: Fictional World emotions (e.g., sadness) in response to the presented fictional events and Artefact emotions in response to the film as an artefact (e.g., fascination). Results showed that stronger immersion led to more intense emotions but did not influence genre categorisation. In line with expectations, both types of emotional response were intensified by high immersion. The results are explained by suggesting that highly immersive cinema has its impact on a basic dimension of emotion, namely arousal that underlies both types of emotions.

Background: Promoting health behaviors and preventing chronic diseases through a healthy lifestyle among those with a low socioeconomic status (SES) remain major challenges. eHealth interventions are a promising approach to change unhealthy behaviors in this target group. Objective: This review aims to identify key components, barriers, and facilitators in the development, reach, use, evaluation, and implementation of eHealth lifestyle interventions for people with a low SES. This review provides an overview for researchers and eHealth developers, and can assist in the development of eHealth interventions for people with a low SES. Methods: We performed a scoping review based on Arksey and O’Malley’s framework. A systematic search was conducted on PubMed, MEDLINE (Ovid), Embase, Web of Science, and the Cochrane Library, using terms related to a combination of the following key constructs: eHealth, lifestyle, low SES, development, reach, use, evaluation, and implementation. There were no restrictions on the date of publication for articles retrieved upon searching the databases. Results: The search identified 1323 studies, of which 42 met our inclusion criteria. An update of the search led to the inclusion of 17 additional studies. eHealth lifestyle interventions for people with a low SES were often delivered via internet-based methods (eg, websites, email, Facebook, and smartphone apps) and offline methods, such as texting. A minority of the interventions combined eHealth lifestyle interventions with face-to-face or telephone coaching, or wearables (blended care). We identified the use of different behavioral components (eg, social support) and technological components (eg, multimedia) in eHealth lifestyle interventions. Facilitators in the development included iterative design, working with different disciplines, and resonating intervention content with users. Facilitators for intervention reach were use of a personal approach and social network, reminders, and self-monitoring. Nevertheless, barriers, such as technological challenges for developers and limited financial resources, may hinder intervention development. Furthermore, passive recruitment was a barrier to intervention reach. Technical difficulties and the use of self-monitoring devices were common barriers for users of eHealth interventions. Only limited data on barriers and facilitators for intervention implementation and evaluation were available. Conclusions: While we found large variations among studies regarding key intervention components, and barriers and facilitators, certain factors may be beneficial in building and using eHealth interventions and reaching people with a low SES. Barriers and facilitators offer promising elements that eHealth developers can use as a toolbox to connect eHealth with low SES individuals. Our findings suggest that one-size-fits-all eHealth interventions may be less suitable for people with a low SES. Future research should investigate how to customize eHealth lifestyle interventions to meet the needs of different low SES groups, and should identify the components that enhance their reach, use, and effectiveness.

A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care.

Despite the growing potential of artificial intelligence (AI) in improving clinical decision making, patients' perspectives on the use of AI for their care decision making are underexplored. In this paper, we investigate patients’ preferences towards the autonomy of AI in assisting healthcare decision making. We conducted interviews and an online survey using an interactive narrative and speculative AI prototypes to elicit participants’ preferred choices of using AI in a pregnancy care context. The analysis of the interviews and in-story responses reveals that patients’ preferences for AI autonomy vary per person and context, and may change over time. This finding suggests the need for involving patients in defining and reassessing the appropriate level of AI assistance for healthcare decision making. Departing from these varied preferences for AI autonomy, we discuss implications for incorporating patient-centeredness in designing AI-powered healthcare decision making.

While there has been increasing interest in the use of gamification in mental health care, there is a lack of design knowledge on how elements from games could be integrated into existing therapeutic treatment activities in a manner that is balanced and effective. To help address this issue, we propose a design process framework to support the development of mental health gamification. Based on the concept of experienced game versus therapy worlds, we highlight 4 different therapeutic components that could be gamified to increase user engagement. By means of a Dual-Loop model, designers can balance the therapeutic and game design components and design the core elements of a mental health care gamification. To support the proposed framework, 4 cases of game design in mental health care (eg, therapeutic protocols for addiction, anxiety, and low self-esteem) are presented.

Background: People with a low socioeconomic position (SEP) are less likely to benefit from eHealth interventions, exacerbating social health inequalities. Professionals developing eHealth interventions for this group face numerous challenges. A comprehensive guide to support these professionals in their work could mitigate these inequalities. Objective: We aimed to develop a web-based guide to support professionals in the development, adaptation, evaluation, and implementation of eHealth interventions for people with a low SEP. Methods: This study consisted of 2 phases. The first phase involved a secondary analysis of 2 previous qualitative and quantitative studies. In this phase, we synthesized insights from the previous studies to develop the guide’s content and information structure. In the second phase, we used a participatory design process. This process included iterative development and evaluation of the guide’s design with 11 professionals who had experience with both eHealth and the target group. We used test versions (prototypes) and think-aloud testing combined with semistructured interviews and a questionnaire to identify design requirements and develop and adapt the guide accordingly. Results: The secondary analysis resulted in a framework of recommendations for developing the guide, which was categorized under 5 themes: development, reach, adherence, evaluation, and implementation. The participatory design process resulted in 16 requirements on system, content, and service aspects for the design of the guide. For the system category, the guide was required to have an open navigation strategy leading to more specific information and short pages with visual elements. Content requirements included providing comprehensible information, scientific evidence, a user perspective, information on practical applications, and a personal and informal tone of voice. Service requirements involved improving suitability for different professionals, ensuring long-term viability, and a focus on implementation. Based on these requirements, we developed the final version of “the inclusive eHealth guide.” Conclusions: The inclusive eHealth guide provides a practical, user-centric tool for professionals aiming to develop, adapt, evaluate, and implement eHealth interventions for people with a low SEP, with the aim of reducing health disparities in this population. Future research should investigate its suitability for different end-user goals, its external validity, its applicability in specific contexts, and its real-world impact on social health inequality.