Automated Text Messaging to Improve Depression Treatment in Low-Income Settings

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DESCRIPTION (provided by applicant): Poor adherence to depression treatments (psychotherapy and pharmacotherapy) limits their effectiveness in community settings. Problems with adherence are especially pronounced in low-income settings. Innovative and cost-effective methods are needed to improve adherence to treatments and maximize mental health resources. Mobile phone based text messaging (or short messaging service: SMS) is a ubiquitous technology that has been used in various health applications across socioeconomic status. This technology has the potential to increase the fidelity of mental health treatments via increased adherence. The proposed research project will test whether adding an automated SMS adjunct to group cognitive behavioral therapy (CBT) for depression can increase adherence (homework adherence, attendance, medication adherence) and further reduce depression symptoms. The SMS adjunct will 1) prompt patients to monitor mood, thoughts and behaviors, 2) will provide medication and appointment reminders and 3) will send personalized CBT based tips. The information that patients provide will be used within the clinical setting to highlight interrelations between thoughts, behaviors and symptoms. The results of the research project will inform an R01 to do further testing of health information technology (HIT) applications in low-income settings. The experience gained through this award will complement previous training and prepare me for a successful clinical research career in the application of health information technologies to mental health services in low-income communities. This K23 (Mentored Patient-Oriented Career Development Award) application delineates a training and research plan seeking to improve depression treatment in low-income communities through the use of text messaging as an adjunct to psychotherapy. The applicant is seeking advanced training in 1) community based mental health services research, 2) health information technology and 3) mixed methods research via mentorship from Kurt C. Organista, Ricardo F. Mu¿oz, and Patricia A. Arean. To achieve further expertise in these areas, various training experiences are proposed with a research trial serving as the core of the career development plan.