Chronic stress and executive functioning: A specification-curve analysis
Section snippets
Background
Living in a technologized and globalized world, we are often faced with tasks of complex cognitive nature that require executive functioning (EF), such as cognitive flexibility, monitoring, or goal pursuing. In addition to these cognitive demands, several studies noted an increase of stress-levels in everyday life [26,86], which could lead to a chronification of stress in many cases [26]. More importantly, the high prevalence of chronic stress [11,25,35,86] is not only associated with numerous
Sample
Data collection was part of the initial assessment for the Dresden longitudinal study of chronic stress and cognitive control (StressCog) in November and December 2016. After approval was granted by the local ethics committee (dossiers EK23012016, IRB00001473 and IORG0001076), the recruiting process of the StressCog cohort started. Contact information of 8400 eligible participants between the ages of 25 and 55 years old were provided by the population registry of the City of Dresden. Besides
Measurement model
Descriptive statistics and correlations for the performance measures of the three cognitive tasks are presented in Table 1. Split-half reliabilities (Spearman-Brown corrected) were high, ranging between 0.81 ≤ r ≤ .97. Intercorrelations between the Number-Letter, 2-Back and Go/Nogo task outcomes ranged between 0.01 ≤ r ≤ .32. This is similar to the correlations between EF domains shifting, updating and inhibition reported in ([51]; −0.05 ≤ r ≤ 0.34). Our measurement model showed an excellent
Discussion
The present study aimed to further our understanding of the relationship between chronic stress and EF. For this purpose, we fitted a structural equation model, in which a latent common EF factor was predicted by objective chronic stress (i.e., HCC) and subjective chronic stress (i.e., self-reported chronic stress) on data from a large cross-sectional sample of young and middle-aged adults. Additionally, in order to assess the impact of different analytical choices on such models, we conducted
Research data
All raw data as well as the R script are available under https://osf.io/fzbrw/ [64].
Funding
This work was supported by the German Research Foundation (DFG, Grant No. SFB 940/2 awarded to Sören Enge., Robert Miller & Clemens Kirschbaum).
Declaration of Competing Interest
The authors have no competing interests to declare.
Acknowledgement
We thank Clemens Kirschbaum for valuable comments and discussions on an earlier version of this manuscript, as well as Kelly Schaunsland, Christiane Wesarg, Marion Augustin, and Rebekka Reetz for their assistance in the data collection and Florian Rupprecht for his assistance in data preprocessing.
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These authors contributed equally to this work.