Maternal Adverse Childhood Experiences and Perceived Stress During Pregnancy: The Role of Personality.
Kristin FieldsLucia M CiciollaSamantha AddanteGina EratoAshley QuigleyStephanie N Mullins-SweattKarina M ShrefflerPublished in: Journal of child & adolescent trauma (2023)
This study explores the role of personality traits in the relationship between maternal adverse childhood experiences (ACEs) and perceived stress during pregnancy. Pregnancy can be a stressful time for new mothers. ACEs have been associated with elevated levels of pregnancy stress, and have also been linked to the Big Five dimensions of personality, including a positive association with neuroticism. The Big Five have also been associated with perceptions of stress, and there is evidence to suggest that personality may be one mechanism through which ACEs disrupt psychosocial functioning during pregnancy. The sample included 177 pregnant girls and women (ages 15-40) from two prenatal clinics serving diverse and low-income patients. Participants completed online questionnaires on perceived stress, ACEs, and the Ten Item Personality Inventory. Results of a path analysis and test of mediation showed significant indirect effects from ACEs to perceived stress mediated independently by neuroticism and conscientiousness. Mothers with high ACEs reported higher neuroticism and lower conscientiousness, and in turn, experienced high levels of perceived stress during pregnancy. High neuroticism and low conscientiousness associated with early adverse experiences increase the risk for perceived stress during pregnancy. Screening for ACEs may help identify mothers at risk for perinatal stress and provide the opportunity for additional support for maternal emotion regulation and mental health.
Keyphrases
- mental health
- social support
- depressive symptoms
- physical activity
- pregnancy outcomes
- stress induced
- end stage renal disease
- primary care
- chronic kidney disease
- adipose tissue
- mental illness
- heat stress
- social media
- prognostic factors
- polycystic ovary syndrome
- machine learning
- skeletal muscle
- patient reported outcomes