How to assess and interpret everyday life salivary cortisol measures: A tutorial on practical and statistical considerations

日常生活 心理学 集合(抽象数据类型) 心理干预 发展心理学 临床心理学 认知心理学 应用心理学 计算机科学 政治学 精神科 程序设计语言 法学
作者
Martin Stoffel,Andreas Neubauer,Beate Ditzen
出处
期刊:Psychoneuroendocrinology [Elsevier BV]
卷期号:133: 105391-105391 被引量:32
标识
DOI:10.1016/j.psyneuen.2021.105391
摘要

Salivary cortisol has been the central marker in psychoneuroendocrinological stress research for three decades. Given the technological possibilities to assess data in ecologically valid circumstances, many studies have implemented longitudinal assessments of salivary cortisol in study participants' everyday life. Such studies bear the potential to understand real-life associations of cortisol with psychological traits, states, and health variables. Furthermore, changes in the neuroendocrine regulation and in cortisol reactivity can be used to evaluate the effects of behavioral interventions in real-life circumstances. While standardized paradigms have been developed to measure cortisol in laboratory settings, there is high heterogeneity in the assessment, statistical processing, and interpretation of everyday life cortisol measures. This methodological tutorial aims at summarizing important knowledge which had been accumulated during the past two decades and which could be used to set up an ambulatory assessment study focusing on salivary cortisol in everyday life. Practical advice for possible strategies at all stages of the research process is outlined in detail. Additionally, an example on how to statistically process cortisol data in a multilevel framework (including syntax) is provided. In these analyses, we investigate within- and between-person research questions regarding the association between stress and cortisol in daily life. Thus, the present work (a) can be used as tutorial for setting up everyday life studies focusing on the assessment of salivary cortisol, and (b) can be useful to avoid inconsistencies in study planning, data assessment and data processing in future studies.
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