Project Abstract
Psychological distress is prevalent among older adults with serious illness, affecting approximately 30-50% of patients. Psychological distress is associated with greater symptom burden, poorer quality of life, higher health care utilization, and potentially inappropriate medication use. Despite its clinical significance, access to evidence-based psychological care remains limited due to workforce shortages, Medicare access constraints, transportation barriers, cost, and poor scalability of traditional in-person psychotherapy. As a result, psychological outcomes in serious illness care remain suboptimal.
Dialectical Behavior Therapy (DBT) is a principle-based, skills-focused intervention with strong transdiagnostic efficacy across psychiatric and medical populations. DBT's emphasis on strengthening self-efficacy to manage negative emotions, a core mechanism underlying diverse forms of psychological distress, makes it particularly well suited to serious illness populations characterized by multimorbidity and fluctuating symptomatology. However, DBT has limited research in serious illness, largely due to its reliance on frequent sessions with trained providers.
This project will evaluate the feasibility, acceptability, and preliminary efficacy of a video and AI-chatbot-delivered DBT skills and coaching (AI-DBT) for older adults with serious illness and elevated psychological distress. AI-DBT was developed and refined through prior funded studies and delivers brief DBT skills videos combined with coaching via a multilingual, constrained-logic conversational agent accessible by phone or text. In a pilot randomized controlled trial (n=80), participants with serious illness and psychological distress will be randomized to usual care or usual care plus AI-DBT.
Aim 1 will assess feasibility and acceptability using recruitment, retention, engagement metrics, participant-reported satisfaction, and qualitative interviews. Aim 2 will examine preliminary efficacy on the mechanistic target of self-efficacy to manage emotions and exploratory clinical outcomes including psychological distress, anxiety, depression, and quality of life.This study will generate critical early-stage data to support a future R01 and advance scalable, mechanism-driven psychosocial care for people living with serious illness.