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The effects of an internet-based self-compassion writing intervention for adults with mental illness
Description
Multiple studies have found that writing with self-compassion about a difficult event helps promote mental health and improve affect in college students and non-clinical populations (Johnson & O'Brien, 2013; Leary et al, 2007; Shapira & Mongrain, 2010). This study investigated whether a self-compassion writing intervention would lead to increases in self-compassion and proactive coping and reductions in depression and physical symptoms in a sample of individuals with different types of mental illness. This study also looked more broadly at the feasibility of conducting an online randomized trial on individuals with mental illness, including psychotic disorders, on Amazon MTurk. Individuals with schizophrenia, schizoaffective disorder, bipolar disorder and/or depression on Amazon MTurk were recruited and randomly assigned to either a (1) treatment condition in which participants wrote with self-compassion or a (2) neutral condition in which participants wrote about how they spent their time. Participants were asked to write for 20 minutes each day for three consecutive days. Outcome measures were administered at baseline, after the three-day intervention, and one month later. Computerized linguistic analysis (LIWC; Pennebaker et al., 2015) was also used to analyze participants' writing to determine if the intervention had the intended effect. Both the treatment and control groups showed significant improvements in self-compassion, proactive coping, general mental health and physical health following the intervention and both groups showed significant improvements in self-compassion, proactive coping and general mental health between the post-test and 1-month follow-up. In addition, the self-compassion writing group's positive affect improved significantly more than the control group after the wave 1 writing intervention and the control group's negative affect improved significantly more than the self-compassion writing group after the wave 2 writing intervention. Overall, the results suggest both self-compassion writing and writing about how one spends one's time may be beneficial for individuals with mental illness with different needs. Moreover, it was found Amazon MTurk may not be a reliable platform for recruiting individuals with psychotic disorders, and that the prevalence of individuals with any mental illness on MTurk may be equal or greater than the prevalence of any mental illness in the general population.
ContributorsUrken, Debra (Author) / LeCroy, Craig W. (Thesis advisor) / Holschuh, Jane (Committee member) / Stalker, Katie (Committee member) / Arizona State University (Publisher)
Created2018
Description
The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit individual differences by allowing within-person slopes to vary across individuals. This study examined the statistical performance of the contextual model with a random slope for longitudinal within-person fluctuation data.
A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
ContributorsWurpts, Ingrid Carlson (Author) / Mackinnon, David P (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Created2016