Predictors of Treatment Preferences Among Community Dwelling Older Adults with Depression

Abstract

Older adults with depression experience substantial disability and mortality, yet mental health service utilization remains disproportionately low relative to need. Diagnostic challenges and lower rates of treatment initiation further contribute to this disparity. Understanding treatment preferences may help align care with older adults’ perspectives on acceptability and feasibility. Task-sharing, in which trained lay providers deliver care, can increase access to mental health services in community settings, though patient perceptions of these approaches remain understudied. This study addressed two research questions. RQ1 examined demographic and clinical factors associated with preferences for active (e.g., psychotherapy, medication) versus complementary (e.g., exercise, spiritual practices) treatment approaches. RQ2 evaluated whether perceived treatment credibility differed between lay-delivered and clinician-delivered interventions following initial exposure. This was a secondary cross-sectional analysis of baseline data from a randomized controlled trial (Do More, Feel Better). For RQ1 (N = 326), logistic regression models examined associations with treatment preference. For RQ2 (N = 252), Wilcoxon rank-sum tests compared credibility ratings across four Likert-scale items after the first treatment session. Most participants (74.5%) preferred active treatment approaches. Prior experience with both psychotherapy and medication was the strongest correlate of this preference. Estimates for demographic and clinical characteristics were less precise after adjustment. Credibility ratings were high and comparable between lay-delivered and clinician-delivered interventions. These findings suggest that prior treatment experience may shape treatment preferences and that lay-delivered interventions can achieve early credibility comparable to clinician-delivered care. Attention to patient preferences and perceived acceptability may support scalable depression care models for older adults.