Why the study?
Do sleep efficiency, respiratory sinus arrhythmia reactivity, and family income interact to predict cognitive performance in school-aged children?
Do sleep efficiency, respiratory sinus arrhythmia reactivity, and family income interact to predict cognitive performance in school-aged children?
Optimal sleep efficiency and parasympathetic nervous system reactivity (RSA reactivity) may protect against poor cognitive performance in children from lower-income families.
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May support targeted interventions in lower-income children; hypothesis-generating and requires longitudinal confirmation before practice change.
A health disparities view suggests that low family income status acts as a risk factor for poor cognitive functioning. A biosystems view suggests that poor sleep and poor stress response system functioning are also risk factors. These views are rarely integrated to test multiplicative risk or protective effects from social-cultural and biological variables. We investigated interactions among familial income, children's sleep and respiratory sinus arrhythmia reactivity (RSA reactivity, indexing parasympathetic nervous system reactivity) in the prediction of cognitive performance of school-aged children. Participants were 282 children (146 boys; 35% African American and 65% European American; M age = 9.42 years, SD = .71). Mothers reported on family income. Children's sleep quality (efficiency) and duration (minutes) were assessed via a week of actigraphy. Children's RSA reactivity to an attention demanding and frustrating star tracing challenge was assessed in the lab. Children completed standardized cognitive assessments examining attention, processing speed, and crystallized cognitive functioning. Findings show that more optimal sleep efficiency and RSA reactivity interact to confer protection against poor cognitive performance, particularly for children from lower income homes. Results build on the literature and suggest that interactions between biological systems and socioeconomic variables are key for understanding children's cognitive performance.
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Staton et al. (2014) studied this question.
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