Depression is the second leading cause of disability and has the highest burden of disease in the US. The PI's career goal is to become an independent investigator designing, evaluating, and implementing novel technology-based interventions for the treatment and prevention of depression. Behavioral intervention technologies (BITs), showing great promise in treating depression, require experts who can integrate an understanding of empirically-based techniques for behavior change with the effective design and application of technologies. This proposal outlines a plan to achieve this goal, through the culmination of training and research plans into a successful R01 proposal. The training plan takes full advantage of the candidate's strong institutional support and environment at Northwestern University's Feinberg School of Medicine and the NIMH- funded Center for Behavioral Intervention Technologies, which is led by the primary mentor for this proposal. Training goals necessary to the PI's career goal create expertise in: 1) creation and evaluation of BITs, especially for use in existing healthcare systems;2) user-centered design and usability testing;3) dissemination and implementation;and 4) advanced professional development. This plan builds on the PI's background in clinical psychology, including his past research on Internet-based, self-help interventions for mental and behavioral health including depression treatment and prevention, well-being promotion, and smoking cessation, and a clinical focus on cognitive-behavioral treatments. In this K08, the PI will expand his work to include new modes (e.g., mobile application) and settings (community clinics) of dissemination of BITs. The long-term goal of the research is to integrate BITs into existing healthcare settings thus increasing the efficacy of existing psychological treatments for depression. To this end, the research plan will develop a technology-based treatment support system (TSS) with both patient and therapist-facing features to be used as an adjunct for cognitive-behavioral therapy for depression thus increasing its efficacy. This will be achieved through the following specific aims: 1) conduct user-centered design and usability testing to refine features and tools of the TSS and determine feasibility and acceptability of their use patients and therapists;2) conduct a randomized pilot trial of the TSS as an adjunct to depression treatment compared to regular treatment alone;and 3) obtain preliminary data assessing efficacy the TSS, changes in mechanisms to be related to efficacy, and system-level factors that would facilitate or retard its adoption, implementation, and sustainability. These studies are expected to advance the design of BITs, improve and increase their use in clinical settings, and ultimately increase the impact of evidence-based practices. Furthermore, these studies will provide the preliminary data for an R01 to conduct a larger RCT. The PI will obtain critical interdisciplinary skills through mentorship from experts in psychology and engineering. Through these experiences, the PI will gain the expertise necessary for a successful career as an independent investigator.
Public Health Relevance Statement Depression affects about 121 million people worldwide and approximately 1 in every 15 people in the US will experience an episode of major depressive disorder each year. Efficacious treatments for depression exist, yet it is critical to develop resources that ensure people receive and use effective therapeutic strategies. Technology-based interventions including mobile applications can increase the efficacy of existing treatments for depression by promoting the use of therapeutic strategies and lessons in real-world settings.
|Ng, Ada; Reddy, Madhu; Zalta, Alyson K et al. (2018) Veterans' Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study. JMIR Ment Health 5:e10415|
|Bruehlman-Senecal, Emma; Aguilera, Adrian; Schueller, Stephen M (2017) Mobile Phone-Based Mood Ratings Prospectively Predict Psychotherapy Attendance. Behav Ther 48:614-623|
|Yarosh, Svetlana; Schueller, Stephen Matthew (2017) ""Happiness Inventors"": Informing Positive Computing Technologies Through Participatory Design With Children. J Med Internet Res 19:e14|
|Adkins, Elizabeth C; Zalta, Alyson K; Boley, Randy A et al. (2017) Exploring the potential of technology-based mental health services for homeless youth: A qualitative study. Psychol Serv 14:238-245|
|Schueller, Stephen M; Aguilera, Adrian; Mohr, David C (2017) Ecological momentary interventions for depression and anxiety. Depress Anxiety 34:540-545|
|Stiles-Shields, Colleen; Montague, Enid; Lattie, Emily G et al. (2017) Exploring User Learnability and Learning Performance in an App for Depression: Usability Study. JMIR Hum Factors 4:e18|
|Mohr, David C; Zhang, Mi; Schueller, Stephen M (2017) Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annu Rev Clin Psychol 13:23-47|
|Mohr, David C; Lyon, Aaron R; Lattie, Emily G et al. (2017) Accelerating Digital Mental Health Research From Early Design and Creation to Successful Implementation and Sustainment. J Med Internet Res 19:e153|
|Saeb, Sohrab; Cybulski, Thaddeus R; Schueller, Stephen M et al. (2017) Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles. J Med Internet Res 19:e118|
|Schueller, Stephen M; Stiles-Shields, Colleen; Yarosh, Lana (2017) Online Treatment and Virtual Therapists in Child and Adolescent Psychiatry. Child Adolesc Psychiatr Clin N Am 26:1-12|
Showing the most recent 10 out of 27 publications