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Self-expression is central to mental well-being and mental health therapy. Art therapy offers a wide range of expressive mechanisms, allowing individuals to process their emotions when traditional therapies prove unsuccessful. However, a lack of expertise or comfort with artistic expression, along with cost and waiting times, may hinder one's ability to receive needed mental health support. Creative machines can offer novel therapeutic approaches enabling the bereaved to engage in creative expression as and when needed. In this paper, we apply a co-creative songwriting system, ALYSIA, as a new form of therapy for those who had recently suffered the loss of a loved one. We evaluate the utility of this creative system in aiding bereaved individuals through user testing. The utility of collaborative creative systems for adaptation to bereavement is discussed and may have implications for other therapeutic applications. [ABSTRACT FROM AUTHOR]

Carers - people who provide regular support for a friend or relative who could not manage without them - frequently report high levels of stress. Good emotional support could help relieve this stress. This study uses seven scenarios that depict different types of stress and acquires emotional support messages for them. We then categorize and evaluate the emotional support for different types of stress. We found that telling the carer they are appreciated and offering support are the best types of emotional support. Additionally, we found that how well a supporter sympathises with a situation affects the type of support they consider suitable. We describe and evaluate an algorithm that selects different categories of support to be used by an intelligent virtual agent to provide emotional support to carers experiencing different types of stress. [ABSTRACT FROM AUTHOR]

This study examined how emotional proximity and gender affect people's information requirements when someone that they know is chronically or critically ill. In an online study, participants were asked what information they would want to receive about members of their social network in three categories: someone who was very close, someone who was not so close, and someone who was not close at all. Our results show that the information that people want can be predicted from their gender and emotional proximity to the network member. The closer the relationship with the patient, the more information people want. Women want more information than men. We propose a model for the socially intelligent communication of health information across the social network, and discuss areas for its application. [ABSTRACT FROM AUTHOR]