Grant Details
Grant Number: |
1R01CA281740-01A1 Interpret this number |
Primary Investigator: |
Dahne, Jennifer |
Organization: |
Medical University Of South Carolina |
Project Title: |
Evaluation of a Proactive Identification and Digital Mental Health Intervention Approach to Address Unmet Psychosocial Needs of Individuals Living with Likely Incurable Cancer |
Fiscal Year: |
2024 |
Abstract
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.
Publications
None