Applying Latent Growth Curve Modeling to the Investigation of Individual Differences in Cardiovascular Recovery From Stress

Objective This paper provides an introduction to latent growth curve (LGC) modeling, a modern method for analyzing data resulting from change processes such as cardiovascular recovery from stress. LGC models are superior to traditional approaches such as repeated measures analysis of variance and simple change scores. Methods The basic principles of LGC modeling are introduced and applied to data from 167 men and women whose systolic blood pressure was assessed before, during, and after the cold pressor and evaluated speech stressors and who had completed the Cook-Medley Hostility Inventory. Results The LGC models revealed that systolic blood pressure recovery follows a different nonlinear trajectory after speech relative to the cold pressor. The difference resulted not from the initial decline at the completion of the stressor, but from higher levels at the end of the stressor and slower rate of change in decline for the speech. Hostility predicted the trajectory for speech but not for cold pressor. This relationship did not differ as a function of gender, although men had larger systolic blood pressure responses than women to both stressors. Conclusions LGC modeling yields an understanding of the processes and predictors of change that is not attainable through traditional statistical methods. Although our application concerns cardiovascular recovery from stress, LGC modeling has many other potential applications in psychosomatic research.