Testing a Strategy to Improve the Effects of Caregiving for Caregivers of Persons with Dementia
Testing a Strategy to Improve the Effects of Caregiving for Caregivers of Persons with Dementia
The Three Good Things (3GT) is a gratitude list intervention that is empirically supported (Emmons & McCullough, 2003; Seligman et al., 2005) and is a low-cost, easily accessible tool for medical and mental providers to recommend and dementia caregivers to implement independently. Thus, this study aims to investigate the feasibility of use of 3GT in the dementia caregiving population and investigate potential therapeutic mechanisms of the intervention.
The hypothesis predicts that Caregivers of persons diagnosed with dementia who are randomized into the intervention group (3GT) will report reduced levels of depression, burden, burnout, show an increase in levels of gratitude, increased positive emotion, and increased positive reframing (Brief COPE: Positive Reinterpretation scale and Positive Aspects of Caregiving measure) as compared to baseline levels, and post-follow up assessment in 15 days. The participants will be recruited from local community partners and Amazon MTurk. They will be randomized into 2 groups; one group will be asked to reflect on three good things that happened that day, and the other group will be asked to reflect on early memories. Participants will be reminded via text message daily for 15 days and asked to document their three good things or early memories on a Qualtrics link that will be provided in the text message. Additionally, they will be asked to answer 10 mood-related questions daily. An ANOVA will be used to assess the pre and post intervention differences between groups for depression, burden, burnout, gratitude, positive emotion, and positive reframing. Multilevel modeling will be used to assess the relationships between gratitude and depression at the within-subjects level and then between-subjects by intervention condition. Identified demographic factors will be added to covary at both levels of analysis. Data will be de-identified and stored on a University of Central Florida local hard drive, which requires special permission and password-protected profiles.
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