| Title: |
Predicting wellbeing over one year using sociodemographic factors, personality, health behaviours, cognition, and life events |
| Authors: |
Chilver, MR; Champaigne-Klassen, E; Schofield, PR; Williams, LM; Gatt, JM |
| Source: |
urn:ISSN:2045-2322 ; Scientific reports, 13, 1 |
| Publisher Information: |
Nature Research |
| Publication Year: |
2023 |
| Collection: |
UNSW Sydney (The University of New South Wales): UNSWorks |
| Subject Terms: |
5205 Social and Personality Psychology; 52 Psychology; Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD); Brain Disorders; Neurosciences; Aging; Mental Health; Dementia; Basic Behavioral and Social Science; Social Determinants of Health; Neurodegenerative; Behavioral and Social Science; Alzheimer's Disease; Acquired Cognitive Impairment; Clinical Research; 2.3 Psychological; social and economic factors; 3 Good Health and Well Being; Adult; Humans; Cross-Sectional Studies; Sociodemographic Factors; Personality; Cognition; Health Behavior; anzsrc-for: 5205 Social and Personality Psychology; anzsrc-for: 52 Psychology |
| 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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
https://hdl.handle.net/1959.4/unsworks_83443 |
| DOI: |
10.1038/s41598-023-32588-3 |
| Availability: |
https://hdl.handle.net/1959.4/unsworks_83443; https://unsworks.unsw.edu.au/bitstreams/c338c002-3f41-4b33-8f6a-2a7fa2ac281f/download; https://doi.org/10.1038/s41598-023-32588-3 |
| Rights: |
open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY ; https://creativecommons.org/licenses/by/4.0/ ; free_to_read ; © The Author(s) 2023 This Nature Research 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/. |
| Accession Number: |
edsbas.1E21B751 |
| Database: |
BASE |