The Pacific Island Countries (PICs) are classified as Low to Middle Income Countries. Recently depression and suicide have emerged as pressing public health concerns in PICs. The suicide rate in these countries is twice that in neighboring Australia, and in Fiji, a leading PIC, the suicide rate is one the highest worldwide. Further, there are only 15 – 20 qualified psychiatrists to serve a more than 11 million people in the PICs. Since training qualified psychiatrists is expensive and lengthy, the government of Fiji is wishes to use their Community Health Nurses (CHNs) as the front line in providing mental health services. Since CHNs receive very little training in mental health, it wants to urgently develop mobile health (mHealth) systems to improve CHNs performance for early identification of depression and suicide risk. However, a barrier to the efficacious design of mHealth tools is that very little research has been done that directly examines the nature of the cognitive processes underlying diagnostic screening and management for mental health disorders, taking into account technology, use of evidence- based guidelines, and socio-cultural factors. An overarching goal of our research is to find more efficient and effective ways to deliver mental health care by CHNs by understanding the nature of their decision-making in screening and managing of mental health disorders with and without clinical guideline support. Specifically, we will elucidate the cognitive mechanisms, including the use of knowledge and the strategies underlying CHNs performance in diagnostic and management tasks. We will investigate the differences in these mechanisms for CHNs under three conditions: 1) Current standard of care, using existing training and texts, 2) Using paper-based screening tools; and 3) Using step-by-step algorithmic screening guidelines on smart phones. We will also gather data on CHNs acceptance, usability, and perceived workload when using smart phones. Results of this research will lead to a better understanding of cognitive mechanisms underlying use of mHealth technologies by CHNs, leading to an actionable and generalizable framework for design and development of effective mHealth systems for early identification of depression and suicide risk in the PICs. In addition, we will enhance mHealth research capacity in Fiji through development of a conceptual framework and specific strategies to improve research competencies and skills. We propose to use a “train the trainers approach” resulting in a cadre of researchers in Fiji capable of performing rigorous mHealth research sensitive to socio-cultural factors and help bridge the gap between research and practice.

Due to very high rates of depression and suicide in The Pacific Island Countries (PICs), there is an urgent need to develop new tools and approaches to identify and provide care to those at risk. In this application we will use modern cognitive science techniques to design and test mobile mental health tools to assist Community Health Nurses, who currently receive little or no training in mental health, for this purpose. To sustain and continuously improve these tools, we will also enhance mobile health research capacity in Fiji (a leading PIC), by training a core group in the principles and practices of mobile health research and development.