The role of psychological distress in the relationship between the severity of pressure injury and pain intensity in hospitalized adults.

AIMS: To examine the effect of psychological distress in mediating the relationship between the severity of pressure injury and pain intensity in hospitalized adults. BACKGROUND: Despite the prevalence of pressure injury (previously known as pressure ulcers) in hospitalized adults, the current knowledge of pain associated with pressure injury is limited and findings are inconsistent. There is also a lack of understanding of the relationship between psychological distress and pain from pressure injury. DESIGN: Retrospective cross-sectional secondary analysis of data from electronic health records. METHODS: The data were retrieved from the third day of admission in the period between 2013 - 2016 through the Integrated Data Repository (IDR). Electronic health records were reviewed to collect data as needed. The mediation effect was tested by using path analysis implemented through Mplus. RESULTS: Path analysis revealed that the severity of pressure injuries and psychological distress have significant direct effects on pain intensity in hospitalized adults. However, the relationship between the severity of pressure injury and pain intensity was not significantly mediated by psychological distress. CONCLUSION: Hospitalized adults who have more severe pressure injury and more treatments for psychological distress experienced greater pain intensity. Healthcare providers must pay attention to treating psychological distress among hospitalized adults to manage pain. Further study is needed to validate these findings and it should incorporate more appropriate measures of psychological distress. The lack of standardized nursing documentation in electronic health records severely limits the usefulness of data from electronic health records for nursing research.

Cusp catastrophe model: a nonlinear model for health outcomes in nursing research.

BACKGROUND: Although health outcomes may have fundamentally nonlinear relationships with relevant behavioral, psychological, cognitively, or biological predictors, most analytical models assume a linear relationship. Furthermore, some health outcomes may have multimodal distributions, but most statistical models in common use assume a unimodal, normal distribution. Suitable nonlinear models should be developed to explain health outcomes. OBJECTIVE: The aim of this study is to provide an overview of a cusp catastrophe model for examining health outcomes and to present an example using grip strength as an indicator of a physical functioning outcome to illustrate how the technique may be used. Results using linear regression, nonlinear logistic model, and the cusp catastrophe model were compared. METHODS: Data from 935 participants from the Survey of Midlife Development in the United States (MIDUS) were analyzed. The outcome was grip strength; executive function and the inflammatory cytokine interleukin-6 were predictor variables. RESULTS: Grip strength was bimodally distributed. On the basis of fit and model selection criteria, the cusp model was superior to the linear model and the nonlinear logistic regression model. The cusp catastrophe model identified interleukin-6 as a significant asymmetry factor and executive function as a significant bifurcation factor. CONCLUSION: The cusp catastrophe model is a useful alternative for explaining the nonlinear relationships commonly seen between health outcome and its predictors. Considerations for the use of cusp catastrophe model in nursing research are discussed and recommended.

Cusp Catastrophe Polynomial Model: Power and Sample Size Estimation.

Guastello's polynomial regression method for solving cusp catastrophe model has been widely applied to analyze nonlinear behavior outcomes. However, no statistical power analysis for this modeling approach has been reported probably due to the complex nature of the cusp catastrophe model. Since statistical power analysis is essential for research design, we propose a novel method in this paper to fill in the gap. The method is simulation-based and can be used to calculate statistical power and sample size when Guastello's polynomial regression method is used to cusp catastrophe modeling analysis. With this novel approach, a power curve is produced first to depict the relationship between statistical power and samples size under different model specifications. This power curve is then used to determine sample size required for specified statistical power. We verify the method first through four scenarios generated through Monte Carlo simulations, and followed by an application of the method with real published data in modeling early sexual initiation among young adolescents. Findings of our study suggest that this simulation-based power analysis method can be used to estimate sample size and statistical power for Guastello's polynomial regression method in cusp catastrophe modeling.