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Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study

Objective: The objective of the current study is to investigate how Big Five personality traits could predict the risk of multiple sclerosis (MS) diagnosis in 7 years. Methods: A binary logistic regression was used to analyze data from 17,791 participants who responded to questions at Wave 3 (collec...

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Detalles Bibliográficos
Autor principal: Kang, Weixi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861722/
https://www.ncbi.nlm.nih.gov/pubmed/36675611
http://dx.doi.org/10.3390/jcm12020682
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author Kang, Weixi
author_facet Kang, Weixi
author_sort Kang, Weixi
collection PubMed
description Objective: The objective of the current study is to investigate how Big Five personality traits could predict the risk of multiple sclerosis (MS) diagnosis in 7 years. Methods: A binary logistic regression was used to analyze data from 17,791 participants who responded to questions at Wave 3 (collected between 2011 to 2012) and Wave 10 (collected between 2018 to 2019) using a binary logistic regression from UKHLS with a mean age of 47.01 (S.D. = 16.31) years old with 42.62% males. Results: The current study found that Openness (OR = 0.68, p < 0.01, 95% C.I. (0.51, 0.89)) and Conscientiousness (OR = 0.70, p < 0.05, 95% C.I. (0.52, 0.93)) are positively associated with a reduced risk of MS diagnosis in 7 years. Conclusion: Health professionals can use findings from the current study as evidence for developing tools for assessing the risk of MS, and providing interventions for people who may be at high risk of MS based on their personality traits.
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spelling pubmed-98617222023-01-22 Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study Kang, Weixi J Clin Med Brief Report Objective: The objective of the current study is to investigate how Big Five personality traits could predict the risk of multiple sclerosis (MS) diagnosis in 7 years. Methods: A binary logistic regression was used to analyze data from 17,791 participants who responded to questions at Wave 3 (collected between 2011 to 2012) and Wave 10 (collected between 2018 to 2019) using a binary logistic regression from UKHLS with a mean age of 47.01 (S.D. = 16.31) years old with 42.62% males. Results: The current study found that Openness (OR = 0.68, p < 0.01, 95% C.I. (0.51, 0.89)) and Conscientiousness (OR = 0.70, p < 0.05, 95% C.I. (0.52, 0.93)) are positively associated with a reduced risk of MS diagnosis in 7 years. Conclusion: Health professionals can use findings from the current study as evidence for developing tools for assessing the risk of MS, and providing interventions for people who may be at high risk of MS based on their personality traits. MDPI 2023-01-15 /pmc/articles/PMC9861722/ /pubmed/36675611 http://dx.doi.org/10.3390/jcm12020682 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Brief Report
Kang, Weixi
Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study
title Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study
title_full Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study
title_fullStr Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study
title_full_unstemmed Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study
title_short Personality Traits Predict 7-Year Risk of Diagnosis of Multiple Sclerosis: A Prospective Study
title_sort personality traits predict 7-year risk of diagnosis of multiple sclerosis: a prospective study
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861722/
https://www.ncbi.nlm.nih.gov/pubmed/36675611
http://dx.doi.org/10.3390/jcm12020682
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