Design and evaluation of a dashboard to support the comprehension of the progression of patients with dementia in day centers.

BACKGROUND AND OBJECTIVE: In day centers, people with dementia are assigned to specific groups to receive care according to the progression of the disease. This article presents the design and evaluation of a dashboard aimed at facilitating the comprehension of the progression of people with dementia to support decision-making of healthcare professionals (HCPs) when determining patient-group assignment. MATERIALS AND METHOD: A participatory design methodology was followed to build the dashboard. The grounded theory methodology was utilized to identify requirements. A total of 8 HCPs participated in the design and evaluation of a low-fidelity prototype. The perceived usefulness and perceived ease of use of the high-fidelity prototype was evaluated by 15 HCPs (from several day centers) and 38 psychology students utilizing a questionnaire based on the technology acceptance model. RESULTS: HCPs perceived the dashboard as extremely likely to be useful (Mdn=6.5 out of 7) and quite likely to be usable (Mdn=6 out of 7). Psychology students perceived the dashboard as quite likely to be useful and usable (both with Mdn=6). CONCLUSIONS: Making use of a participatory design helped foster in HCPs a sense of ownership of the dashboard, thus facilitating its acceptance. The creation of low-fidelity and high-fidelity prototypes led to identifying valuable, timely, and specific feedback at different stages of the development process as well as to establishing a set of lessons learned for the development of dashboards in the healthcare domain.

Search for an Appropriate Behavior within the Emotional Regulation in Virtual Creatures Using a Learning Classifier System.

Emotion regulation is a process by which human beings control emotional behaviors. From neuroscientific evidence, this mechanism is the product of conscious or unconscious processes. In particular, the mechanism generated by a conscious process needs a priori components to be computed. The behaviors generated by previous experiences are among these components. These behaviors need to be adapted to fulfill the objectives in a specific situation. The problem we address is how to endow virtual creatures with emotion regulation in order to compute an appropriate behavior in a specific emotional situation. This problem is clearly important and we have not identified ways to solve this problem in the current literature. In our proposal, we show a way to generate the appropriate behavior in an emotional situation using a learning classifier system (LCS). We illustrate the function of our proposal in unknown and known situations by means of two case studies. Our results demonstrate that it is possible to converge to the appropriate behavior even in the first case; that is, when the system does not have previous experiences and in situations where some previous information is available our proposal proves to be a very powerful tool.