Background: The ongoing adoption and use of digital interventions offer promising opportunities to meet the growing demand for mental health support. The effectiveness, implementation, and usage of these interventions depend on how well they are designed and evaluated. However, given the emerging nature of design research in this area, there is still no clear consensus on the specific principles and guidelines for developing digital mental health interventions (DMHIs). There seems to be a lack of clarity regarding the best practices for designing and evaluating these tools. Objective: We aimed to investigate and report on the design principles and evaluation approaches used in digital interventions specific to mental health care. Additionally, we sought to outline how these principles and approaches are applied in research. Methods: This scoping review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews. The literature search was performed in 2 electronic databases, SCOPUS and Web of Science, across 3 iterations from January 2024 to January 2025. A total of 2 independent reviewers screened and selected papers based on predefined inclusion and exclusion criteria, followed by data extraction from the selected studies. The data were then synthesized by categorizing the papers according to the primary research aim of each study. The inclusion criteria covered studies involving populations with mental health challenges or users of DMHIs, any digital tools for mental health care, and principles or strategies related to the design, evaluation, or implementation of DMHIs. Results: Our search identified 401 papers, of which 17 met the inclusion criteria for this review. Among these, 11 focused on evaluation studies, while 6 covered both design and evaluation studies (mixed). An iterative user-centered development process, expert inclusion, usability testing, specification of design elements, and user tracking and feedback were identified as common design principles used in studies focused on DMHIs. Evaluation approaches were shaped by the evaluation goal, which influenced the chosen methodologies. We also summarize the recommendations for implementation highlighted in some studies. Based on our findings, we propose 8 guidelines emphasizing stakeholder involvement in the development process and the need for clear justifications for design decisions, among other considerations. Conclusions: Design principles used in DMHI development include user-centered development, expert inclusion, and usability testing, while evaluation approaches often rely on randomized controlled trials to assess efficacy. Qualitative and mixed-method approaches are commonly adopted by studies to capture user experience and bridge both process and outcome measures. We recommend that future research explicitly report its design justification and adopt a multiperspective approach in the research and design of DMHIs.

While mental health apps can help to promote adolescents’ mental health, prevent mental health problems, and reduce symptoms, maintaining sufficient user engagement with these apps remains challenging. This is often caused by a mismatch between the needs and preferences of adolescents and what the apps offer. Therefore, we need a better understanding of (i) adolescents’ needs and preferences and (ii) potential differences based on user characteristics. To this end, we qualitatively and quantitatively analyzed a dataset describing the user experience of 1312 Dutch adolescents (12–25 years) from the general population after they interacted for several weeks with a gamified mHealth app (the Grow It! app) that aims to promote momentary emotional awareness, reflection, and adaptive coping. A total of 4833 free-text survey responses spanning five user experience survey questions were analyzed using an inductive and iterative coding process, while accounting for intercoder reliability. We used (i) a thematic analysis to identify adolescents’ needs and preferences related to the app, and (ii) an exploratory quantitative analysis of the subthemes to investigate potential differences in which needs and preferences were mentioned by adolescents based on demographics. Through our thematic analysis, we identified three overarching themes related to the app’s design: usability , psychological impact , and meaningful interactive features . Furthermore, we identified two overarching themes that related to the adolescents’ motivation to use the app: intrinsic (de)motivators , and social–environmental factors impacting usage . Each of these themes consisted of four subthemes. Our exploratory statistical analysis shed light on several differences in how frequently these subthemes were mentioned based on age, sex, and educational level. By synthesizing our insights, we identify five design implications that can help tailor future mHealth apps to adolescents’ needs and preferences. These include concrete suggestions to personalize self-monitoring, include actionable insights, align content with personal needs, implement meaningful interactive features (e.g., competitions, gamification, and social communication), and make apps appealing to the entire target group.

Validating claims and replicating findings on the impact of artificial social agents (ASA), such as virtual agents, conversational agents, and social robots, requires a standardised measurement instrument that researchers can employ in different settings and for various agents. Such an instrument would allow researchers to evaluate their agents and establish insights beyond their specific study context. Therefore, we present the long and short versions of the ASA questionnaire (ASAQ) for evaluating human-ASA interaction on 19 constructs, such as the agent's believability, sociability, and coherence. It has been developed by an international workgroup with more than 100 ASA-researchers over multiple years who identified community-relevant constructs and associated questionnaire items and examined the questionnaire's reliability, validity, and interpretability. The result is a questionnaire that can capture more than 80% of the constructs that studies in the intelligent virtual agent community investigate, with acceptable levels of reliability, content validity, construct validity, and cross-validity. We suggest that ASA-researchers use the ASAQ short version to report their agent's psychographic information and the ASAQ long version to analyse any constructs in-depth that are specifically relevant to their agent or study. Finally, this paper gives instructions for practical use, such as sample size estimations, and how to interpret and present results.

Background: Smoking and physical inactivity compromise health, especially in combination. Interventions to promote smoking cessation and increased physical activity (PA) often lack impact, especially in the long term. Digital future-self interventions (FSIs), which prompt individuals to imagine who they do and do not want to become (ie, their desired and undesired future selves), show promise in encouraging sustainable changes in both behaviors. However, knowledge of user experiences with digital FSIs is limited. A deeper understanding of these experiences could help optimize FSIs, enhancing their efficacy in supporting smoking cessation and increased PA sustainably. Objective: This study examined behavioral, cognitive, and affective experiences with digital FSIs focused on smoking, PA, or both. Potential differences in user experiences based on behavior (smoking vs PA), polarity (desired vs undesired future self), and modality (verbal vs visual description of future selves) were explored. Methods: Secondary analyses of quantitative and qualitative survey data from 3 studies using digital FSIs as a means to encourage smoking cessation or increase PA were conducted. In study 1, participants (N=144) thought about how it would be to complete the FSI. In studies 2 (N=447) and 3 (N=87), they completed an FSI. Each study highlighted different aspects of user experiences with FSIs, namely, behavioral (eg, time spent), cognitive (eg, mental effort exerted), or affective (eg, emotions) experiences. Quantitative and qualitative findings were integrated for a comprehensive interpretation. Results: Regarding behavioral experiences, participants completed future-self tasks promptly (mean 6.64, SD 8.30 minutes), spent less time completing the desired- versus undesired-future-self (P<.001; η _p ²=0.227) and verbal versus visual (P=.03; η _p ²=0.060; quantitative) tasks, and integrated the tasks into their lives (qualitative). Despite tasks being preparatory and not actively encouraging behavior change, multiple participants reported implementing changes in their smoking or PA (qualitative). Regarding cognitive experiences, moderate effort (mean 5.85/10, SD 2.56) was exerted on the tasks regardless of behavior (P=.69; η _p ²=0.002), modality (P=.45; η _p ²=0.004), or polarity (P=.69; η _p ²=0.002; quantitative). Experiences of task difficulty were inconsistent across studies, individuals, and tasks, although mental visualization and describing one’s future self using images were consistently reported as challenging (quantitative and qualitative). Future-self tasks were reported to prompt cognitive processes such as contemplating consequences of smoking and PA behavior (qualitative). Regarding affective experiences, desired- and undesired-future-self tasks elicited different emotions (P<.001; η _p ²=0.630; quantitative). Desired-future-self tasks were perceived as enjoyable and happiness inducing, whereas undesired-future-self tasks were perceived as confronting and unpleasant, evoking feelings of sadness, fear, and anger (quantitative and qualitative). Conclusions: Digital FSIs appeared to be a time-efficient, feasible, and acceptable way of strengthening identities as a means to encourage smoking cessation and PA. Findings support continued implementation of digital FSIs, although further research is required to optimize their operationalization. Avenues in that regard are proposed and discussed.

Background: Every month, around 3800 people complete an anonymous self-test for suicidal thoughts on the website of the Dutch suicide prevention helpline. Although 70% score high on the severity of suicidal thoughts, <10% navigate to the web page about contacting the helpline. Objective: This study aimed to test the effectiveness of a brief barrier reduction intervention (BRI) in motivating people with severe suicidal thoughts to contact the suicide prevention helpline, specifically in high-risk groups such as men and middle-aged people. Methods: We conducted a fully automated, web-based, randomized controlled trial. Respondents with severe suicidal thoughts and little motivation to contact the helpline were randomly allocated either to a brief BRI, in which they received a short, tailored message based on their self-reported barrier to the helpline (n=610), or a general advisory text (care as usual as the control group: n=612). Effectiveness was evaluated using both behavioral and attitudinal measurements. The primary outcome measure was the use of a direct link to contact the helpline after completing the intervention or control condition. Secondary outcomes were the self-reported likelihood of contacting the helpline and satisfaction with the received self-test. Results: In total, 2124 website visitors completed the Suicidal Ideation Attributes Scale and the demographic questions in the entry screening questionnaire. Among them, 1222 were randomized into the intervention or control group. Eventually, 772 respondents completed the randomized controlled trial (intervention group: n=369; control group: n=403). The most selected barrier in both groups was “I don’t think that my problems are serious enough.” At the end of the trial, 33.1% (n=122) of the respondents in the intervention group used the direct link to the helpline. This was not significantly different from the respondents in the control group (144/403, 35.7%; odds ratio 0.87, 95% CI 0.64‐1.18, P=.38). However, the respondents who received the BRI did score higher on their self-reported likelihood of contacting the helpline at a later point in time (B=0.22, 95% CI 0.12‐0.32, P≤.001) and on satisfaction with the self-test (B=0.27, 95% CI 0.01‐0.53, P=.04). For male and middle-aged respondents specifically, the results were comparable to that of the whole group. Conclusions: This trial was the first time the helpline was able to connect with high-risk website visitors who were hesitant to contact the helpline. Although the BRI could not ensure that those respondents immediately used the direct link to the helpline at the end of the trial, it is encouraging that respondents indicated that they were more likely to contact the helpline at a later point in time. In addition, this low-cost intervention provided greater insight into the perceived barriers to service. Follow-up research should be focused on identifying the added value of other components (eg, video or photo material) in the BRI and increasing its effectiveness, especially for men and middle-aged people.

In the published article, there was an error in Table 5. For each second construct/dimension, the means are swapped between Chinese and English data, which is caused by an error in the underlying R script. Consequently, the plus and minus signs for the delta and CI values are also wrong. The corrected Table 5 and its caption appear below. Construct/dimension rating difference between mixed-international English-speaking and Chinese mother-tongue groups. Δ Score are pairwise differences between Chinese and mother-tongue cultural background and mixed-international cultural background taken from the posterior distribution. M, mean; SD, standard deviation; CI, credible interval. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Child helplines offer a safe and private space for children to share their thoughts and feelings with volunteers. However, training these volunteers to help can be both expensive and time-consuming. In this demo, we present Lilobot, a conversational agent designed to train volunteers for child helplines. Lilobot’s reasoning is based on the Belief-Desire-Intention (BDI) model, which simulates, for example, a bullied child who contacts the helpline through text. Users engage with Lilobot in a role-play format, taking on the volunteer’s role. Through this system, volunteers can practice applying the Five Phase Model, a conversational strategy helplines use. The training tool includes a trainer interface for monitoring and modifying Lilobot’s interactions. Trainers can also create new conversational scenarios through an authoring tool. An initial evaluation led to enhancements in Lilobot’s knowledge base and intent recognition, addressing the main issues encountered by participants. The components used to implement the system were Java Spring for the BDI model and the authoring tool, Rasa for Natural Language Understanding, PostgreSQL for the database, and Vue.js for the front-end. This tool aims to provide volunteers with consistent, interactive training, enhancing their counselling skills in a controlled environment.