Work place health support interventions can help support our aging work force, with mApps offering cost-effectiveness opportunities. Previous research shows that health support apps should offer users enough newness and relevance each time they are used. Otherwise the ‘eHealth law of attrition’ applies: 90 % of users are lost prematurely. Our research study builds on this prior research with further investigation on whether a mobile health quiz provides added value for users within a hybrid service mix and whether it promotes long term health? We developed a hybrid health support intervention solution that uses a mix of electronic and physical support services for improving health behaviours, including a mobile micro-learning health quiz. This solution was evaluated in a multiple-case study at three work sites with 86 users. We find that both our mobile health quiz and the overall hybrid solution contributed to improvements in health readiness, −behaviour and -competence. Users indicated that the micro-learning health quiz courses provided new and relevant information. Relatively high utilization rates of the health quiz were observed. Participants indicated that health insights were given that directly influenced every day health perceptions, −choices, coping and goal achievement strategies, plus motivation and self-norms. This points to increased user health self-management competence. Moreover, even after 10 months they indicated to still have improved health awareness, −motivation and -behaviours (food, physical activity, mental recuperation). A design analysis was conducted regarding service mix efficacy; the mobile micro-learning health quiz helped fulfil a set of key requirements that exist for designing ICT-enabled lifestyle interventions, largely in the way it was anticipated.

The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model. We describe a method for identifying GLOs and apply it to identify five classes of GLOs, which can be flexibly combined to re-create six canonical graph visualization techniques. We discuss advantages of the GLO model, including potentially discovering new, effective network visualization techniques and easing the engineering challenges of building multi-technique graph visualization applications. Finally, we implement the GLOs that we identified into the GLO-STIX prototype system that enables an analyst to interactively explore a graph by applying GLOs.

There is a wealth of visualization techniques available for graph and network visualization. However, each of these techniques was designed for a specific task. Many graph visualization techniques and the transitions between them can be specified using a set of operations on the visualization elements such as positioning or resizing nodes, showing or hiding edges, or showing or hiding axes. We term these operations Graph-Level Operations or GLOs. Our goal is to identify and provide a comprehensive set of these operations in order to better support the broadest range of graph and network analysis tasks. Here we present early results of our work, including a preliminary set of operations and an example application of GLOs in transitioning between familiar graph visualization techniques.

Greater patient involvement in the healthcare process is a key pillar of the patient centered health-care model. However, observations of a healthcare facility identified limited communication as a barrier for engagement between healthcare providers and patients and caregivers, particularly during the discharge process. The use of a personalized healthcare application for facilitating more thorough and personal communication between healthcare providers, patients, and caregivers is presented as a possible improvement to the healthcare communication process centered on the discharge process. To improve communication with healthcare providers, patients and caregivers are intended to use the application during the initial visit, in waiting times after diagnosis and discharge, as well as at home and on subsequent visits. Improved communication between these groups through means of augmented discharge instructions and improved processes can serve the goals of increased patient satisfaction and health outcomes.