Background: A major challenge in providing mental health interventions for young people is making such interventions accessible and appealing to those most in need. Online and app-based forms of therapy for mental health are burgeoning. It is therefore crucial to identify features that are most effective and engaging for young users. Objectives: This study reports a systematic review and meta-analysis of digital mental health interventions and their effectiveness in addressing anxiety and depression in young people to determine factors that relate to outcomes, adherence, and engagement with such interventions. Methods: A mixed methods approach was taken, including a meta-analysis of 9 randomized controlled trials that compared use of a digital intervention for depression in young people to a no-intervention control group, and 6 comparing the intervention to an active control condition. A thematic analysis and narrative synthesis of 41 studies was also performed. Results: The pooled effect size of digital mental health interventions on depression in comparison to a no-intervention control was small (Cohen’s d = 0.33, 95% CI 0.11 to 0.55), while the pooled effect size of studies comparing an intervention group to an active control showed no significant differences (Cohen’s d = 0.14, 95% CI -.04 to 0.31). Pooled effect sizes were higher when supervision was involved (studies with no-intervention controls: Cohen’s d = 0.52, 95% CI 0.23 to 0.80; studies with active control: Cohen’s d = 0.49, 95% CI -0.11, 1.01). Engagement and adherence rates were low. Qualitative analysis revealed that users liked interventions with a game-like feel and relatable, interactive content. Educational materials were perceived as boring, and users were put off by non-appealing interfaces and technical glitches. Conclusions: Digital interventions work better than no intervention to improve depression in young people when results of different studies are pooled together. However, these interventions may only be of clinical significance when use is highly supervised. Digital interventions do not work better than active alternatives regardless of the level of support. Future interventions need to move beyond the use of digital educational materials, considering other ways to attract and engage young people and to ensure relevance and appeal.

Background: Suicide is one of the most frequent causes of death in young people worldwide. Depression lies at the root of this issue, a condition that has a significant negative impact on the lives of those who experience it and on society more generally. However, 80% of affected young people do not obtain professional help for depression and other mental health issues. Therefore, a key challenge is to find innovative and appealing ways to engage young people in learning to manage their mental health. Research suggests that young people prefer to access anonymous Web-based programs rather than get face-to-face help, which has led to the development of numerous smartphone apps. However, the evidence indicates that not all of these apps are effective in engaging the interest of young people who are most in need of help. Objective: The study aimed to investigate young people’s response to six currently available smartphone apps for mental health and to identify features that young people like and dislike in such apps. Methods: Focus groups were conducted with 23 young people aged 13 to 25 years in which they viewed and used six smartphone apps for mental health. A general inductive approach following a realist paradigm guided data analysis. Results: The results revealed that young people value autonomy and the opportunity to personalize experiences with these apps above other things. Finding a balance between simplicity and informativeness is also an important factor. Conclusions: App developers need to consider using participant-design frameworks to ensure that smartphone apps are providing what young people want in a mental health app. Solutions to the need for personalization and increasing user engagement are also crucially needed.

This paper describes a new technique called EncCon for visualizing and navigating large hierarchical information. This technique consists of two components: visualization and navigation. Visualization uses a fast enclosure + connection method to calculate the geometrical layout for the display of large hierarchies in a two-dimensional space. Our technique uses a rectangular division algorithm for recursively positioning the graph. This visualization aims to maximize the utilization of display space while retaining a good geometrical layout as well as a clear (explicit) presentation of the hierarchical structure of graphs. This paper also presents an experimental evaluation of EncCon's layout algorithm. Besides the layout algorithm, EncCon uses a new focus + context viewing technique for the navigation of large hierarchies. We use the zooming + layering concept to achieve the focus + context viewing, rather than the traditional enlarge + embedded concept, which is used by most of the available focus + context techniques. Technically, it employs semi-transparency to achieve the display of two layers of information in z-coordination at the same visualization. Both context view and detail view are drawn at two separate layers. These layers are then displayed in an overlapped manner at the same physical screen space.

The traditional solutions to the stock market security are not sufficient in identifying attackers and further attack plans from the analysis of existing events.Therefore, it is difficult for analysts to prevent future unexpected events or frauds by only monitoring the realtime trading information. The event-driven fraud detection in financial market could not help analysts to find attack plans and the further intention of attackers. This paper proposed a new framework of visual analytics for stock market security. The proposed solution consists of two stages: 1) visual surveillance of market performance, and 2) behavior-driven visual analysis of trading networks.In the first stage, we use a 3D treemaps to monitor the real-time stock market performance and to identify a particular stock that produced an unusual trading pattern. We then move to the next stage: social network visualization to conduct behavior-driven visual analysis of suspected pattern. Through the visual analysis of social (or trading)network, analysts may finally identify the attackers (the sources of the fraud), and further attack plans.

This paper describes a new approach, space-optimized tree, for the visualization and navigation of tree-structured relational data. This technique can be used especially for the display of very large hierarchies in a two-dimensional space. We discuss the advantages and limitations of current techniques of tree visualization. Our strategy is to optimize the drawing of trees in a geometrical plane and maximize the utilization of display space by allowing more nodes and links to be displayed at a limited screen resolution. Space-optimized tree is a connection+enclosure visualization approach that recursively positions children of a subtree into polygon areas and still uses a node–link diagram to present the entire hierarchical structure. To be able to handle the navigation of large hierarchies, we use a new hybrid viewing technique that combines two viewing methods, the modified semantic zooming and a focus+context technique. While the semantic zooming technique can enlarge a particular viewing area by filtering out the rest of tree structure from the visualization, the focus+context technique allows the user to interactively focus, view and browse the entire visual structure with a reasonable high-density display.