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Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events

Various sociodemographic, psychosocial, cognitive, and life event factors are associated with mental wellbeing; however, it remains unclear which measures best explain variance in wellbeing in the context of related variables. This study uses data from 1017 healthy adults from the TWIN-E study of we...

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Autores principales: Chilver, Miranda R., Champaigne-Klassen, Elyse, Schofield, Peter R., Williams, Leanne M., Gatt, Justine M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076502/
https://www.ncbi.nlm.nih.gov/pubmed/37019908
http://dx.doi.org/10.1038/s41598-023-32588-3
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author Chilver, Miranda R.
Champaigne-Klassen, Elyse
Schofield, Peter R.
Williams, Leanne M.
Gatt, Justine M.
author_facet Chilver, Miranda R.
Champaigne-Klassen, Elyse
Schofield, Peter R.
Williams, Leanne M.
Gatt, Justine M.
author_sort Chilver, Miranda R.
collection PubMed
description Various sociodemographic, psychosocial, cognitive, and life event factors are associated with mental wellbeing; however, it remains unclear which measures best explain variance in wellbeing in the context of related variables. This study uses data from 1017 healthy adults from the TWIN-E study of wellbeing to evaluate the sociodemographic, psychosocial, cognitive, and life event predictors of wellbeing using cross-sectional and repeated measures multiple regression models over one year. Sociodemographic (age, sex, education), psychosocial (personality, health behaviours, and lifestyle), emotion and cognitive processing, and life event (recent positive and negative life events) variables were considered. The results showed that while neuroticism, extraversion, conscientiousness, and cognitive reappraisal were the strongest predictors of wellbeing in the cross-sectional model, while extraversion, conscientiousness, exercise, and specific life events (work related and traumatic life events) were the strongest predictors of wellbeing in the repeated measures model. These results were confirmed using tenfold cross-validation procedures. Together, the results indicate that the variables that best explain differences in wellbeing between individuals at baseline can vary from the variables that predict change in wellbeing over time. This suggests that different variables may need to be targeted to improve population-level compared to individual-level wellbeing.
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spelling pubmed-100765022023-04-07 Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events Chilver, Miranda R. Champaigne-Klassen, Elyse Schofield, Peter R. Williams, Leanne M. Gatt, Justine M. Sci Rep Article Various sociodemographic, psychosocial, cognitive, and life event factors are associated with mental wellbeing; however, it remains unclear which measures best explain variance in wellbeing in the context of related variables. This study uses data from 1017 healthy adults from the TWIN-E study of wellbeing to evaluate the sociodemographic, psychosocial, cognitive, and life event predictors of wellbeing using cross-sectional and repeated measures multiple regression models over one year. Sociodemographic (age, sex, education), psychosocial (personality, health behaviours, and lifestyle), emotion and cognitive processing, and life event (recent positive and negative life events) variables were considered. The results showed that while neuroticism, extraversion, conscientiousness, and cognitive reappraisal were the strongest predictors of wellbeing in the cross-sectional model, while extraversion, conscientiousness, exercise, and specific life events (work related and traumatic life events) were the strongest predictors of wellbeing in the repeated measures model. These results were confirmed using tenfold cross-validation procedures. Together, the results indicate that the variables that best explain differences in wellbeing between individuals at baseline can vary from the variables that predict change in wellbeing over time. This suggests that different variables may need to be targeted to improve population-level compared to individual-level wellbeing. Nature Publishing Group UK 2023-04-05 /pmc/articles/PMC10076502/ /pubmed/37019908 http://dx.doi.org/10.1038/s41598-023-32588-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chilver, Miranda R.
Champaigne-Klassen, Elyse
Schofield, Peter R.
Williams, Leanne M.
Gatt, Justine M.
Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events
title Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events
title_full Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events
title_fullStr Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events
title_full_unstemmed Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events
title_short Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events
title_sort predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076502/
https://www.ncbi.nlm.nih.gov/pubmed/37019908
http://dx.doi.org/10.1038/s41598-023-32588-3
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