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Published on: October 2, 2019
Which objective sleep elements predict children's perceptions of good sleep quality? A preliminary investigation
Christine J So1, Cara A Palmer2, Rogelio D Gonzalez1
1Department of Psychology, University of Houston, 8461 Calhoun Road, Houston, Texas, USA.
Insights
Children’s sleep quality perceptions are linked to wake after sleep onset (WASO) and N1 sleep stage percentage, not just sleep duration. Optimal sleep quality in children is associated with less than 30 minutes of WASO and 2-6% N1 sleep.
Area of Science:
- Pediatric Sleep Medicine
- Sleep Research
- Child Psychology
Background:
- Objective sleep parameters influencing children's perceived sleep quality remain largely unidentified.
- Current sleep recommendations for children primarily emphasize sleep duration.
- Limited child-specific studies exist on objective sleep metrics and subjective sleep quality.
Purpose of the Study:
- To investigate objective sleep continuity and architectural variables associated with higher sleep quality ratings in school-aged children.
- To explore linear and nonlinear relationships between objective sleep measures and children's subjective sleep quality.
Main Methods:
- Utilized polysomnography and actigraphy in 52 healthy, prepubertal children (7-11 years).
- Collected sleep data during one unattended overnight study at home.
- Analyzed associations using polynomial regression models.
Main Results:
- Total sleep time, sleep onset latency, and sleep efficiency were not correlated with children's sleep quality ratings.
- Wake After Sleep Onset (WASO) demonstrated a curvilinear relationship with sleep quality; ratings were high when WASO was below approximately 30 minutes.
- N1 sleep percentage (N1%) showed a quadratic association, with optimal sleep quality reported when N1% ranged between 2% and 6%.
Conclusions:
- Findings support expert recommendations for WASO values predicting high sleep quality in children, pending replication.
- Further research is needed to understand objective sleep elements and other factors influencing children's sleep quality perceptions.
- Nonlinear models are crucial for exploring the complex relationship between objective sleep and subjective quality in pediatric populations.
Objectives:
Objective sleep elements that underlie child ratings of sleep quality are largely unknown. Child-based sleep recommendations, therefore, typically focus on duration. An expert panel recently provided specific recommendations regarding objective sleep parameters that correspond with higher quality sleep, but child-based studies from which to draw conclusions were notably limited. The present study used actigraphy and polysomnography to explore sleep continuity and architectural variables that correspond with higher ratings of sleep quality in a sample of school-aged children.
Methods:
Fifty-two healthy, prepubertal children (aged 7-11 years) completed one night of unattended ambulatory polysomnography at home with concurrent actigraphy and provided sleep quality ratings the following morning. Associations between sleep variables and subjective ratings were examined using polynomial regression models to examine potential linear and nonlinear relationships.
Results:
In contrast to findings among adults, total sleep time, sleep onset latency, and sleep efficiency values were unrelated to child ratings of sleep quality. Wake after sleep onset (WASO) showed a curvilinear (reversed j-shaped) relationship such that perceptions of sleep quality were high when WASO values were less than approximately 30 minutes. For sleep architecture, N1% showed a significant quadratic association with sleep quality such that N1% between 2% and 6% corresponded with high sleep quality ratings.
Conclusions:
Our findings support expert recommendations regarding WASO values that predict high quality sleep in children, but also await replication. There is need for additional research aimed at understanding objective sleep elements and other influences of children's perceptions of sleep quality using linear and nonlinear models.
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