Individual and program Characteristics May Drive Variability in Outcomes After Caregivers Participate in a Tailored Support Intervention. (August 2022)

• Record Type: Journal Article
• Authors: Shepherd-Banigan, Megan; Jones, Kelley A.; Sullivan, Caitlin; Wang, Ke; Clark, Amy G.; Van Houtven, Courtney; Olsen, Jennifer M.
• Is Part Of: Journal of applied gerontology. Volume 41:Number 8(2022)
• Page Start: 1960
• Page End: 1970
• Publication Date: 2022-08
• Subjects: caregiving -- veterans -- health outcomes -- intervention -- depression -- machine learning -- burden -- problem solving; Gerontology -- Periodicals; 362.6
• DOI: 10.1177/07334648221091564
• Languages: English
• ISSNs: 0733-4648

Abstract

Critically needed programs designed to support family caregivers have shown inconsistent reductions in stress and burden. To explore drivers of improvement in caregiver outcomes after participation in a support intervention we analyzed data from a one-on-one, tailored problem-solving intervention targeting caregiver wellbeing (2015–2019, n = 503). We explored data patterns across 21 individual, household, and program-level variables using elastic net regression to identify drivers of improvements, and their relative importance. Baseline subjective burden, baseline depressive symptom scores, baseline caregiver problem solving, African American race, and site and coach fixed effects were the most consistent drivers of changes across the explored caregiver outcomes. Caregiver and program characteristics may be promising avenues to target to decrease distress and burden during intervention design. Interventions focusing on highly distressed caregivers may lead to greater improvements. More research is needed to identify how site or interventionists characteristics drive positive intervention effects.