Expanding the foundation for populationbased anxiety management interventions

Jessica Morrow Lipschitz, University of Rhode Island

Abstract

Anxiety is the most prevalent mental illness and treatments are effective but underutilized. Failure to design treatments that proactively reach individuals at varying levels of readiness may be one driver of under-utilization. The Transtheoretical Model of behavior change (TTM) offers a framework for designing treatments tailored to readiness to engage in exposure, the process of gradually approaching feared stimuli and the central behavioral component of evidence-based anxiety treatments. This study sought to develop the essential building blocks for applying the TTM to anxiety by developing a set of measures of core TTM constructs (Stage of Change, Decisional Balance, Self-efficacy, and Processes of Change) relevant for increasing approach behaviors in individuals with anxiety disorders. ^ ^ Measurement development entailed qualitative methods for item development and refinement followed by a series of quantitative analyses. The Stage of Change measure was validated against external constructs such as treatment seeking behavior, anxiety severity, and quality of life. As expected, a chi-square test indicated that individuals in Action and Maintenance were significantly more likely to be in treatment than those in the pre-Action stages. ANOVA results indicated that individuals in Action or Maintenance reported significantly lower levels of anxiety (F(1, 592) = 5.06, p=.025, η2=.01) and significantly higher quality of life (F(1, 592) = 8.20, p<.01, η 2=.01) than those in pre-Action stages.^ Measures for Decisional Balance and Self-efficacy were developed using split-half, cross-validation procedures. In these, a series of Principle Component Analyses (PCAs) were conducted with half of the sample to narrow the item set and explore factor structure, and Confirmatory Factor Analyses (CFA) was conducted on the second half of the sample to confirm factor structure and item loadings. For Decisional Balance, PCA supported two, 5-item factors, and CFA indicated a two-factor correlated model was the best fit to the data, χ²(35)=80.82, p<.01, CFI=.94, RMSEA =.7 with Pros α=.87 and Cons α=.75. For Self-efficacy, PCA supported one, 6-item factor, and CFA further supported this structure, χ²(9)=30.39, p<.01, CFI=.98, RMSEA=.088, α=.90. Multivariate analyses indicated significant stage-construct relationships in expected directions with the exception of Cons, which showed no significant cross-Stage differences.^ For Processes of Change, a series of iterative CFAs were conducted to narrow the item set, and then additional CFAs were conducted on the final set of items to determine which factor structure was the best fit to the data. A 10-factor, fully correlated model was the best fit to the data, χ²(360)=905.82, p<.01, CFI=.94, RMSEA =.51. Factor loadings were strong, ranging from 0.53 to 0.85, and internal consistency was acceptable to good (? ranged from to .69 to .88). Effect sizes for differences in POC across Stage were mostly in the medium range, indicating that POC represent important behavior change strategies for reducing anxiety-based avoidance. ^ Overall results support the validity of the measures developed and laid the foundation for applying the TTM to anxiety-based avoidance. Implications for application of the TTM to anxiety-based avoidance are discussed. Future research should explore the relationship between these measures and treatment outcomes longitudinally and examine the effectiveness of TTM-tailored feedback in the context of a computer-based intervention.^

Subject Area

Psychology

Recommended Citation

Jessica Morrow Lipschitz, "Expanding the foundation for populationbased anxiety management interventions" (2015). Dissertations and Master's Theses (Campus Access). Paper AAI3716714.
http://digitalcommons.uri.edu/dissertations/AAI3716714

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