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Traditional approaches to psychotherapy emphasise face-to-face contact between patients and therapists. In contrast, current computerised approaches tend to minimise this contact. This can limit the range of mental health difficulties for which computerised approaches are effective. Here, we explore an alternative approach that integrates face-to-face contact, electronic contact, online collaboration, and support for between-session activities. Our discussion is grounded in the design of a platform to deliver psychotherapy for depression. We report findings of an 11-month pragmatic study in which 17 patients received treatment for depression via the platform. Results show how design decisions had a significant impact on the dynamics of therapeutic sessions and the establishment of patient-therapist relationships. For example, the use of instant messaging for synchronous, in-session contact slowed communication, but also provided a valuable space for reflection and helped to maintain session focus. We discuss the impact of flexibility and the potential of integrated approaches to both enhance and reduce patient engagement.
Seeking help is often an important step in addressing mental health difficulties. Evidence suggests that positive help-seeking experiences contribute to an increased likelihood of future help-seeking and achieving improved outcomes. However, help-seeking is a complex process. Alongside traditional sources, digital technologies offer many pathways to help. Using a mixed methods approach across two studies, this paper explores key design factors for online mental health resources that can support young people's help-seeking. First, a large online survey (n=1308) highlighted challenges and identified common help-seeking scenarios, including information-seeking, person-centred approaches and crisis situations. Using survey data, personas were developed to represent different help-seekers - each characterised by a particular help-seeking scenario. The personas were then used in co-design workshops to facilitate further exploration of help-seeking needs. Four key design considerations were identified: connectedness, accessible information, personalisation, and immediacy. Based on our findings, we provide design recommendations that are grounded in existing theories of help-seeking.
Depression is a leading cause of disability worldwide, which has inspired the design of mobile health (mHealth) applications for disease monitoring, prediction, and diagnosis.Less mHealth research has, however, focused on the treatment of depressive disorders. Clinical evidence shows that depressive symptoms can be reduced through a behavior change method known as Behavioral Activation (BA). This paper presents MUBS; a smartphone-based system for BA, which specifically contributes a personalized content-based activity recommendation model using a unique list of validated activities. An 8-week feasibility study with 17 depressive patients provided detailed insight into how MUBS provided inspiration and motivation for planning and engaging in more pleasant activities, thereby facilitating the core components of BA. Based on this study, the paper discusses how recommender technology can be used in the design of mHealth technology for BA.
Much human-computer interaction work related to depression focuses on the population level (e.g., studying social media hashtags related to depression) or evaluates prototypes for digital interventions to manage depression. However, little is known about how people living with depression perceive and manage technology use, such as time spent on social media per day. For this study, we interviewed 30 individuals living with depression to explore their technology and social media use. We find that these individuals demonstrated emergent practices related to self-regulation, such as learning to monitor and adjust technology use to improve their emotional, cognitive, and behavioral health. Our findings add a human-centered viewpoint to the relationship between living with depression and technology and social media use. We present design implications of these findings for better empowering individuals with depression to encourage their natural inclinations to self-regulate technology and social media use.
Online mental health interventions are increasingly important in providing access to, and supporting the effectiveness of, mental health treatment. While these technologies are effective, user attrition and early disengagement are key challenges. Evidence suggests that integrating a human supporter into such services mitigates these challenges, however, it remains under-studied how supporter involvement benefits client outcomes, and how to maximize such effects. We present our analysis of 234,735 supporter messages to discover how different support strategies correlate with clinical outcomes. We describe our machine learning methods for: (i) clustering supporters based on client outcomes; (ii) extracting and analyzing linguistic features from supporter messages; and (iii) identifying context-specific patterns of support. Our findings indicate that concrete, positive and supportive feedback from supporters that reference social behaviors are strongly associated with better outcomes; and show how their importance varies dependent on different client situations. We discuss design implications for personalized support and supporter interfaces.