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Insomnia and personality traits

Grant number: 21/11574-4
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): November 01, 2021
Effective date (End): October 31, 2023
Field of knowledge:Humanities - Psychology - Fundamentals and Assessments in Psychology
Principal Investigator:Renatha El Rafihi Ferreira
Grantee:Bárbara Araújo Conway
Host Institution: Instituto de Psiquiatria Doutor Antonio Carlos Pacheco e Silva (IPq). Hospital das Clínicas da Faculdade de Medicina da USP (HCFMUSP). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
Associated research grant:18/19506-5 - Acceptance and commitment therapy for insomnnia, AP.JP


Insomnia is a sleep disorder with a high prevalence in the population that can lead to serious consequences for physical and mental health. Some personality traits can be considered predisposing factors for the development of insomnia and perpetuators of insomnia. Some studies are still inconclusive as to which features show the most direct correlations to the severity of insomnia. Identifying and analyzing the most prevalent personality traits in people with insomnia can help in planning proper treatments. The aim of this study is to assess the correlation between insomnia and personality traits. Adults between 18 and 59 years old will participate in the study and the estimated minimum sample size will be of 200 participants (n = 200). Participants will be divided into two groups: a group with chronic insomnia and a group of controls without complaints of insomnia. To assess the personality traits, the Revised NEO Five Factors Inventory NEO-FFI-R (for short) will be used. To assess the severity of insomnia, the Insomnia Severity Index (ISI) will be used. The application of the instruments will be carried out for both groups. T-tests will be performed for continuous variables and the chi-square test for ordinal data. Pearson's correlation analysis will be used to investigate the correlation of various factors with the severity of the insomnia and multiple regression analysis will be used to determine the single influence of each factor. (AU)

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