Applying latent growth curve modeling to the investigation of individual differences in cardiovascular recovery from stress.
Applying latent growth curve modeling to the investigation of individual differences in cardiovascular recovery from stress.
复制标题
应用潜在生长曲线模型来研究心血管从压力中恢复的个体差异。
DOI：
10.1097/01.psy.0000107886.51781.9c
复制
发表时间：
2004
影响因子：
3.3
通讯作者：
Schneiderman,Neil
中科院分区：
文献类型：
--
作者：
Llabre,MariaM;Spitzer,Susan;Siegel,Scott;Saab,PatriceG;Schneiderman,Neil
本文介绍了潜在生长曲线（LGC）建模，这是一种分析变化过程（如心血管从压力中恢复）所产生数据的现代方法。LGC模型上级传统的方法，如重复测量方差分析和简单的变化scores. MethodsLGC模型的基本原理，并应用于167名男性和女性的数据，他们的收缩压评估之前，期间，在冷加压和评估言语应激源以及完成Cook-混合泳敌意清单。结果LGC模型显示，收缩压恢复遵循不同的非线性轨迹后的讲话相对于冷加压。这种差异不是由于压力源完成时的初始下降，而是由于压力源结束时的较高水平和演讲下降的较慢变化率。敌意预测了语言的轨迹，但不是冷压。这种关系并没有不同的性别的功能，虽然男性有较大的收缩压反应比女性两个stresss.ConclusionsLGC建模产生的过程和预测的变化，是无法通过传统的统计方法实现的理解。尽管我们的应用涉及心血管从压力中恢复，但LGC建模在心身研究中还有许多其他潜在应用。
ObjectiveThis 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.MethodsThe 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.ResultsThe 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.ConclusionsLGC 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.