ABSTRACT Individuals living with likely incurable cancer (ILLIC) are a heterogeneous, growing subpopulation of cancer survivors who have unique survivorship care needs. Principal among these is the need for psychosocial treatment. Treatment of depression is particularly critical as up to half of ILLIC report depressive symptoms, with negative sequalae including lower quality of life, desire for hastened death, and suicidality. Numerous trials and meta-analyses have documented that evidence-based psychosocial treatment improves depression outcomes for ILLIC. However, multilevel barriers limit access. Thus, ILLIC need feasible, accessible depression treatment options. Brief depression screeners are now routinely administered in oncology settings. These data can be used to proactively identify survivors in need of psychosocial treatment. Efficient identification (ID) of ILLIC, though, is more challenging. Data necessary to determine likelihood of curability are recorded in unstructured EHR fields, necessitating labor-intensive, manual chart review to identify ILLIC. To realize the goal of proactive ID and delivery of scalable depression care for ILLIC, accurate, efficient, automated ID approaches are needed. Self- guided digital mental health interventions (DMHIs) can be paired with proactive ID to deliver scalable depression treatment. Our team previously adapted one evidence-based depression treatment, Behavioral Activation, for delivery via a DMHI called “Moodivate” and demonstrated that Moodivate is a feasible, acceptable, and efficacious DMHI. Thus, a proactive treatment delivery model using a self-guided DMHI such as Moodivate may be a promising approach to deliver evidence-based depression treatment to ILLIC. [[We have confirmed feasibility and acceptability of this approach via a recent pilot trial in which we specifically tailored Moodivate to the unique needs of ILLIC and tested all methods proposed herein.]] Importantly, a sustainable model must address the chronic evidence-to-practice gap that limits psychosocial care delivery. Thus, implementation outcomes and determinants must be concurrently evaluated. Directly aligned with RFA-CA-22-027, we propose a Hybrid Type I effectiveness-implementation trial to: 1) comprehensively assess the effectiveness of a proactive ID + DMHI approach among ILLIC, 2) gather information on intervention delivery to guide implementation best practices, and 3) develop an EHR-derived phenotype of likely incurable cancer. Our diverse stakeholder advisory board, which includes ILLIC, oncology providers, and organizational leaders, has guided and refined this proposal to ensure its clinical relevance and will continue to partner with our team on all aspects of the study design, implementation, and dissemination of study findings. This program of research has the potential to expand evidence-based psychosocial treatment access in a manner that is scalable across oncology settings and ultimately decrease the undue burden of depression shouldered by ILLIC.

PROJECT NARRATIVE Individuals living with likely incurable cancer need feasible, accessible evidence-based depression treatment options. To address this need, we propose a Hybrid Type I effectiveness-implementation trial to: 1) comprehensively assess the effectiveness of a proactive identification (ID) + digital mental health intervention (DMHI) approach among individuals living with likely incurable cancer, 2) gather information on intervention (proactive ID + DMHI) delivery to guide best practices for implementation, and 3) develop an Electronic Health Record-derived phenotype of likely incurable cancer to enhance ID of individuals living with likely incurable cancer. This program of research has the potential to expand evidence-based psychosocial treatment access in a manner that is scalable across oncology settings and will enhance scientific knowledge for improving comprehensive survivorship care for individuals living with likely incurable cancer.