Background: There is a high prevalence of children and young people (CYP) experiencing mental health (MH) problems. Owing to accessibility, affordability, and scalability, an increasing number of digital health interventions (DHIs) have been developed and incorporated into MH treatment. Studies have shown the potential of DHIs to improve MH outcomes. However, the modes of delivery used to engage CYP in digital MH interventions may differ, with implications for the extent to which findings pertain to the level of engagement with the DHI. Knowledge of the various modalities could aid in the development of interventions that are acceptable and feasible.Objective: This review aimed to (1) identify modes of delivery used in CYP digital MH interventions, (2) explore influencing factors to usage and implementation, and (3) investigate ways in which the interventions have been evaluated and whether CYP engage in DHIs.Methods: A literature search was performed in the Cochrane Library, Excerpta Medica dataBASE (EMBASE), Medical Literature Analysis and Retrieval System Online (MEDLINE), and PsycINFO databases using 3 key concepts “child and adolescent mental health,” “digital intervention,” and “engagement.” Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed using rigorous inclusion criteria and screening by at least two reviewers. The selected articles were assessed for quality using the mixed methods appraisal tool, and data were extracted to address the review aims. Data aggregation and synthesis were conducted and presented as descriptive numerical summaries and a narrative synthesis, respectively.Results: This study identified 6 modes of delivery from 83 articles and 71 interventions for engaging CYP: (1) websites, (2) games and computer-assisted programs, (3) apps, (4) robots and digital devices, (5) virtual reality, and (6) mobile text messaging. Overall, 2 themes emerged highlighting intervention-specific and person-specific barriers and facilitators to CYP’s engagement. These themes encompassed factors such as suitability, usability, and acceptability of the DHIs and motivation, capability, and opportunity for the CYP using DHIs. The literature highlighted that CYP prefer DHIs with features such as videos, limited text, ability to personalize, ability to connect with others, and options to receive text message reminders. The findings of this review suggest a high average retention rate of 79% in studies involving various DHIs.Conclusions: The development of DHIs is increasing and may be of interest to CYP, particularly in the area of MH treatment. With continuous technological advancements, it is important to know which modalities may increase engagement and help CYP who are facing MH problems. This review identified the existing modalities and highlighted the influencing factors from the perspective of CYP. This knowledge provides information that can be used to design and evaluate new interventions and offers important theoretical insights into how and why CYP engage in DHIs.

User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption. It has been suggested that AI-enabled systems must go beyond technical-centric approaches and towards embracing a more human-centric approach, a core principle of the human-computer interaction (HCI) field. This review aims to provide an overview of the user trust definitions, influencing factors, and measurement methods from 23 empirical studies to gather insight for future technical and design strategies, research, and initiatives to calibrate the user-AI relationship. The findings confirm that there is more than one way to define trust. Selecting the most appropriate trust definition to depict user trust in a specific context should be the focus instead of comparing definitions. User trust in AI-enabled systems is found to be influenced by three main themes, namely socio-ethical considerations, technical and design features, and user characteristics. User characteristics dominate the findings, reinforcing the importance of user involvement from development through to monitoring of AI-enabled systems. Different contexts and various characteristics of both the users and the systems are also found to influence user trust, highlighting the importance of selecting and tailoring features of the system according to the targeted user group’s characteristics. Importantly, socio-ethical considerations can pave the way in making sure that the environment where user-AI interactions happen is sufficiently conducive to establish and maintain a trusted relationship. In measuring user trust, surveys are found to be the most common method followed by interviews and focus groups. In conclusion, user trust needs to be addressed directly in every context where AI-enabled systems are being used or discussed. In addition, calibrating the user-AI relationship requires finding the optimal balance that works for not only the user but also the system.

Trust plays an essential role in all human relationships. However, measuring trust remains a challenge for researchers exploring psychophysiological signals. Therefore, this article aims to systematically map the approaches used in studies assessing trust with psychophysiological signals. In particular, we examine the numbers and frequency of combined psychophysiological signals, the primary outcomes of previous studies, and the types and most commonly used data analysis techniques for analyzing psychophysiological data to infer a trust state. For this purpose, we employ a systematic mapping review method, through which we analyze 51 carefully selected articles (studies focused on trust using psychophysiology). Two significant findings are as follows: (1) Psychophysiological signals from EEG(electroencephalogram) and ECG(electrocardiogram) for monitoring peripheral and central nervous systems are the most frequently used to measure trust, while audio and EOG(electro-oculography) psychophysiological signals are the least commonly used. Moreover, the maximum number of psychophysiological signals ever combined so far is three (2). Most of which are peripheral nervous system monitoring psychophysiological signals that are low in spatial resolution. (3) Regarding outcomes: there is only one tool proposed for assessing trust in an interpersonal context, excluding trust in a technology context. Moreover, there are no stable and accurate ensemble models that have been developed to assess trust; all prior attempts led to unstable but fairly accurate models or did not satisfy the conditions for combining several algorithms (ensemble). In conclusion, the extent to which trust can be assessed using psychophysiological measures during user interactions (real-time) remains unknown, as there several issues, such as the lack of a stable and accurate ensemble trust classifier model, among others, that require urgent research attention. Although this topic is relatively new, much work has been done. However, more remains to be done to provide clarity on this topic.

Recently there has been an increasing demand for technologies (automated and intelligent machines) that brings benefits to organizations and society. Similar to the widespread use of personal computers in the past, today’s needs are towards facilitating human-machine technology appropriation, especially in highly risky and regulated industries like robotics, manufacturing, automation, military, finance, or healthcare. In this context, trust can be used as a critical element to instruct how human-machine interaction should occur. Considering the context-dependency and multidimensional trust, this study seeks to find a way to measure the effects of perceived trust in a collaborative robot (cobot), regardless of its literal credibility as a real person. This article aims at translating, adapting, and validating a Human-Computer Trust Scale (HCTM) in human-robot interaction (HRI) context and its application to cobots. The Human-Robot Interaction Trust Scale (HRITS) involved 239 participants and included eleven items. The 2nd order CFA with a general factor called “trust” have proven to be empirically robust ( ; ; ; and ) [ ; , and ]; results indicated a good measurement of the general factor trust, and the model satisfied the criteria for measure trust. An analysis of the differences in perceptions of trust by gender was conducted using a -test. This analysis showed that statistical differences by gender exist ( ). This study’s results allowed for a better understanding of trust in HRI, specifically regarding cobots. The validation of a Portuguese scale for trust assessment in HRI can give a valuable contribution to designing collaborative environments between humans and robots.

System architecture plays a crucial role in the establishment of Digital Government infrastructure. Over recent decades, various architectures have been introduced by scholars for the establishment of Digital Government infrastructure. However, there is no uniform agreement on Digital Government architecture concepts required for Digital Government infrastructure. To more thoroughly examine the Digital Government architecture introduced in this article, we collected 103 papers published between 2003 and 2020 retrieved from five leading literature databases. To conduct our research, we followed best practice scholarly accepted guidelines for researchers. Per the guidelines, we formulated research questions and employed an approach based on specific inclusion and exclusion criteria to meet our research goals. Our study found evidence that there is a lack of knowledge in terms of the state-of-the-art in Digital Government infrastructure and its challenges concerning existing Digital Government architectures. We identified a set of primary Digital Government architecture characteristics and building blocks on which the Digital Government infrastructures are built. These components are meant to improve the design of future Digital Government systems and applications. Furthermore, our research revealed a need for designing a reference architecture to provide government organizations with the best practice knowledge of already existing Digital Government architectures.

The transition from Industry 4.0 to Industry 5.0 highlights recent European efforts to design intelligent devices, systems, and automation that can work alongside human intelligence and enhance human capabilities. In this vision, human–machine interaction (HMI) goes beyond simply deploying machines, such as autonomous robots, for economic advantage. It requires societal and educational shifts toward a human-centric research vision, revising how we perceive technological advancements to improve the benefits and convenience for individuals. Furthermore, it also requires determining which priority is given to user preferences and needs to feel safe while collaborating with autonomous intelligent systems. This proposed human-centric vision aims to enhance human creativity and problem-solving abilities by leveraging machine precision and data processing, all while protecting human agency. Aligned with this perspective, we conducted a systematic literature review focusing on trust and trustworthiness in relation to characteristics of humans and systems in human–robot interaction (HRI). Our research explores the aspects that impact the potential for designing and fostering machine trustworthiness from a human-centered standpoint. A systematic analysis was conducted to review 34 articles in recent HRI-related studies. Then, through a standardized screening, we identified and categorized factors influencing trust in automation that can act as trust barriers and facilitators when implementing autonomous intelligent systems. Our study comments on the application areas in which trust is considered, how it is conceptualized, and how it is evaluated within the field. Our analysis underscores the significance of examining users’ trust and the related factors impacting it as foundational elements for promoting secure and trustworthy HRI.