Dataset Information

Covariation of psychobiological stress regulation with valence and quantity of social interactions in everyday life: disentangling intra- and interindividual sources of variation.

ABSTRACT: While the overall effects of social relationships on stress and health have extensively been described, it remains unclear how the experience of social interactions covaries with the activity of psychobiological stress in everyday life. We hypothesized that the valence as well as quantitative characteristics of social interactions in everyday life would attenuate psychobiological stress. Sixty healthy participants provided data for the analyses. Using an ecological momentary assessment design, participants received 6 prompts on their smartphone for 4 days. At each prompt, they reported on social interactions since the last prompt (any occurrence, frequency, duration, quality, and perceived social support), current subjective stress, and provided one saliva sample for the analyses of cortisol (sCort) and alpha-amylase (sAA). Experiencing any contact within days as well as higher daily levels of contact quality and perceived social support were associated with reduced levels of sCort. Furthermore, on a daily level, experiencing at least one contact in-between prompts more often as well as having more contacts on average attenuated the sAA output. Perceived social support and contact quality as well as higher daily contact durations were associated with lower subjective stress. For sCort, daily levels of stress moderated the effects of experiencing any contact within days while daily perceived social support moderated the effects of subjective stress. For sAA, experiencing at least one contact in-between prompts more often on a daily level moderated the effects of subjective stress. There were no between-person effects throughout all analyses. The results show ecologically valid evidence for direct attenuating effects of social interactions on psychobiological stress as well as for the stress-buffering hypothesis in everyday life. Increasing the quantity and improving the valence of social interactions on an intrapersonal level can possibly reduce psychobiological stress and prevent its consequences.

SUBMITTER: Stoffel M

PROVIDER: S-EPMC8423684 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThere is currently no way to verify the quality of a multiple sequence alignment that is independent of the assumptions used to build it. Sequence alignments are typically evaluated by a number of established criteria: sequence conservation, the number of aligned residues, the frequency of gaps, and the probable correct gap placement. Covariation analysis is used to find putatively important residue pairs in a sequence alignment. Different alignments of the same protein family give different results demonstrating that covariation depends on the quality of the sequence alignment. We thus hypothesized that current criteria are insufficient to build alignments for use with covariation analyses.Methodology/principal findingsWe show that current criteria are insufficient to build alignments for use with covariation analyses as systematic sequence alignment errors are present even in hand-curated structure-based alignment datasets like those from the Conserved Domain Database. We show that current non-parametric covariation statistics are sensitive to sequence misalignments and that this sensitivity can be used to identify systematic alignment errors. We demonstrate that removing alignment errors due to 1) improper structure alignment, 2) the presence of paralogous sequences, and 3) partial or otherwise erroneous sequences, improves contact prediction by covariation analysis. Finally we describe two non-parametric covariation statistics that are less sensitive to sequence alignment errors than those described previously in the literature.Conclusions/significanceProtein alignments with errors lead to false positive and false negative conclusions (incorrect assignment of covariation and conservation, respectively). Covariation analysis can provide a verification step, independent of traditional criteria, to identify systematic misalignments in protein alignments. Two non-parametric statistics are shown to be somewhat insensitive to misalignment errors, providing increased confidence in contact prediction when analyzing alignments with erroneous regions because of an emphasis on they emphasize pairwise covariation over group covariation.

Dataset Information

Covariation of psychobiological stress regulation with valence and quantity of social interactions in everyday life: disentangling intra- and interindividual sources of variation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets