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Neonatal pain-related stress and developmental outcomes in preterm infants

Grant number: 13/16728-3
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): October 21, 2013
Effective date (End): August 20, 2014
Field of knowledge:Humanities - Psychology - Psychological Treatment and Prevention
Principal Investigator:Maria Beatriz Martins Linhares
Grantee:Beatriz Oliveira Valeri Pereira da Silva
Supervisor: Ruth Grunau
Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Research place: University of British Columbia, Vancouver (UBC), Canada  


Neonates hospitalized in a neonatal intensive care unit (NICU) are exposed to many painful and stressful procedures. Repeated experience of pain in early development can have long-term effects on vulnerable newborns. The aims of the present study are to examine the neonatal pain-related stress to predict child self-rating of pain sensation at school-age, to examine the association of the parent´s and child´s pain ratings, and to verify the association of parenting stress, depression and anxiety with parent ratings of their child´s pain at school age. The sample will comprise 52 preterm-born children at 7 years of age who were hospitalized in the Neonatal Intensive Care Unit at neonatal age. Neonatal pain-related stress is defined as the number of skin-breaking procedures at neonatal age, adjusted for neonatal clinical confounders variables. Child self-ratings of pain at 7 years-age will be assessed according to the Color Analog Scale and the Facial Affective Scale. Parent ratings of pain will be measure using the visual analogue scale. Parenting Stress will be examine following the Parenting Stress Index III. Mother´s anxiety and depression scores will be evaluated according to State-Trait Anxiety Inventory-State Beck Depression Inventory II, respectively. Statistical analyses will be done using multivariate analyses performed by generalized linear modeling and hierarchical regression analyses. (AU)

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