Fatigue, sleep quality and mental health symptoms in Brazilian women during the COVID-19 pandemic: longitudinal study.
Gabriel Bernardi Dos SantosAna Carolina Sartorato BelezaTatiana de Oliveira SatoCristiano CarvalhoPaula Regina Mendes da Silva SerrãoPublished in: Scientific reports (2022)
To assess the impact of the COVID-19 pandemic on the variables of sleep quality, fatigue, anxiety, and depression in healthy Brazilian women. Longitudinal observational study conducted through an online questionnaire with women in 2020 and 2021. The Pittsburgh Sleep Quality Index, the Fatigue Severity Scale and the Hospital Anxiety and Depression Scale were used. The data were analyzed descriptively and the comparison between the data obtained in the first and second evaluation was performed using the McNemar test. A logistic regression was applied to test the association between the variables that showed a significant difference. A total of 235 women responded to the questionnaires. There was a significant increase in fatigue between the two moments (p < 0.05). In the first assessment, depression (OR: 2.39; 95% CI: 1.14-4.99), anxiety (OR: 2.68; 95% CI: 1.37-5.22) and sleep quality (OR: 4.01; 95% CI: 1.71-9.67) were associated with fatigue. In the second assessment, depression (OR: 2.93; 95% CI: 1.19-7.18) and anxiety (OR: 2.69; 95% CI: 1.27-5.71) were associated with fatigue. There was an impact on biopsychosocial aspects during the COVID-19 pandemic, with worsening of fatigue symptoms within a 6-month interval. In addition, fatigue was associated with symptoms of depression and anxiety, and worse sleep quality in the first year of the pandemic, remaining associated with symptoms of depression and anxiety in the second year of the pandemic in the country.
Keyphrases
- sleep quality
- depressive symptoms
- polycystic ovary syndrome
- physical activity
- mental health
- sars cov
- pregnancy outcomes
- coronavirus disease
- healthcare
- type diabetes
- cervical cancer screening
- electronic health record
- insulin resistance
- machine learning
- cross sectional
- metabolic syndrome
- adipose tissue
- big data
- data analysis
- acute care
- adverse drug