Effect of " Caring and Nursing " on Significantly Improving Children's Sleep Monitoring Compliance

2021 ◽  
Keyword(s):  
Sleep Monitoring ◽  
Children’S Sleep

scholarly journals 0134 Self-Reported Sleep is Associated with Central, But Not Peripheral Blood Pressure Values in Healthy Children

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A53-A53
Author(s):  
F R Berube ◽  
E K Katulka ◽  
M N D’Agata ◽  
F Patterson ◽  
S J Ives ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Pulse Wave ◽  
Poor Quality ◽  
Self Report ◽  
Healthy Children ◽  
Sleep Monitoring ◽  
Sleep Health ◽  
Quality Sleep ◽  
Children’S Sleep ◽  
Peripheral Blood Pressure

Abstract Introduction Shortened and poor quality sleep have emerged as nontraditional risk factors for the development of high blood pressure (BP) in adults, but it is unclear if these relations exist in younger children. Self-report and objective sleep measurements are both clinically relevant and may inform interventions to improve sleep in this population, but do not always coincide with one another. The purpose of this study was to evaluate both self-reported and objective sleep metrics and their associations with central and peripheral BP values in younger children. Methods Participants included 21 healthy 7-12-year-old children (10±0.5 yrs, 10M/11F). Self-reported sleep was evaluated using the Children’s Sleep Health Questionnaire and a total sleep score was generated, where a higher score indicates worse sleep (a score >41 indicates a pediatric sleep disorder). Objective sleep was recorded for 7 consecutive days and nights outside of the laboratory via wrist accelerometry and reported as sleep duration (SD) and sleep efficiency (SE). Following sleep monitoring, peripheral BP was measured and using pulse wave analysis (PWA) central BP was estimated, both of which were averaged over 3 trials. Pearson’s r correlations were used to assess relations between self-reported sleep score, objective sleep metrics, and BP values. Significance was set at p<0.05. Results Self-reported sleep score averaged 40±1 points, objective SD averaged 7.9±0.1 hours/night, and SE averaged 82±2%. Sleep score was significantly associated with central systolic and diastolic BP (r = .485, p = 0.03, and r = .517, p = 0.02, respectively), but not peripheral BP values. Objective SD and SE were not significantly associated with central or peripheral BP values. Conclusion In this sample, self-reported sleep score, but not objective sleep metrics, was associated with higher central BP values in healthy children age 7–12. Support Provided in part by P20GM113125.

Influence of Children's Sleep on Maternal Sleep, Stress, and Psychopathology

2010 ◽  
Author(s):  
Danelle Hodge ◽  
Charles D. Hoffman ◽  
Dwight P. Sweeney
Keyword(s):  
Maternal Sleep ◽  
Children’S Sleep

Pediatric recurring pain in the community: the role of children’s sleep and internalizing symptoms

2021 ◽  
Author(s):  
Samantha A. Miadich ◽  
Reagan S. Breitenstein ◽  
Mary C. Davis ◽  
Leah D. Doane ◽  
Kathryn Lemery-Chalfant
Keyword(s):  
Internalizing Symptoms ◽  
Children’S Sleep

scholarly journals Later sleep timing predicts accelerated summer weight gain among elementary school children: a prospective observational study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Jennette P. Moreno ◽  
Javad Razjouyan ◽  
Houston Lester ◽  
Hafza Dadabhoy ◽  
Mona Amirmazaheri ◽  
...  
Keyword(s):  
Physical Activity ◽  
Observational Study ◽  
Sleep Duration ◽  
Obesity Prevention ◽  
Prospective Observational Study ◽  
Light Exposure ◽  
Activity Level ◽  
Activity Levels ◽  
School Year ◽  
Children’S Sleep

Abstract Objectives and background Social demands of the school-year and summer environment may affect children’s sleep patterns and circadian rhythms during these periods. The current study examined differences in children’s sleep and circadian-related behaviors during the school-year and summer and explored the association between sleep and circadian parameters and change in body mass index (BMI) during these time periods. Methods This was a prospective observational study with 119 children ages 5 to 8 years with three sequential BMI assessments: early school-year (fall), late school-year (spring), and beginning of the following school-year in Houston, Texas, USA. Sleep midpoint, sleep duration, variability of sleep midpoint, physical activity, and light exposure were estimated using wrist-worn accelerometry during the school-year (fall) and summer. To examine the effect of sleep parameters, physical activity level, and light exposure on change in BMI, growth curve modeling was conducted controlling for age, race, sex, and chronotype. Results Children’s sleep midpoint shifted later by an average of 1.5 h during summer compared to the school-year. After controlling for covariates, later sleep midpoints predicted larger increases in BMI during summer, (γ = .0004, p = .03), but not during the school-year. Sleep duration, sleep midpoint variability, physical activity levels, and sedentary behavior were not associated with change in BMI during the school-year or summer. Females tended to increase their BMI at a faster rate during summer compared to males, γ = .06, p = .049. Greater amounts of outdoor light exposure (γ = −.01, p = .02) predicted smaller increases in school-year BMI. Conclusions Obesity prevention interventions may need to target different behaviors depending on whether children are in or out of school. Promotion of outdoor time during the school-year and earlier sleep times during the summer may be effective obesity prevention strategies during these respective times.

scholarly journals Home sleep monitoring based on wrist movement data processing

2021 ◽  
Vol 183 ◽  
pp. 696-705
Author(s):  
Qiang Pan ◽  
Damien Brulin ◽  
Eric Campo
Keyword(s):  
Data Processing ◽  
Wrist Movement ◽  
Sleep Monitoring ◽  
Movement Data

569 Correlation between age and initial findings of sleep disordered breathing in children with achondroplasia

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A224-A225
Author(s):  
Fayruz Araji ◽  
Cephas Mujuruki ◽  
Brian Ku ◽  
Elisa Basora-Rovira ◽  
Anna Wani
Keyword(s):  
Sleep Apnea ◽  
Primary Care Physicians ◽  
Sleep Disordered Breathing ◽  
Upper Airway ◽  
Sleep Study ◽  
Surgical Interventions ◽  
Screening Guidelines ◽  
Obstructive Sleep ◽  
Children’S Sleep ◽  
Disordered Breathing

Abstract Introduction Achondroplasia (ACH) occurs approximately 1 in 20,000–30,000 live births. They are prone to sleep disordered breathing specifically due to the upper airway stenosis, enlarged head circumference, combined with hypotonia and limited chest wall size associated with scoliosis at times. The co-occurrence of sleep apnea is well established and can aide in the decision for surgical intervention, however it is unclear at what age children should be evaluated for sleep apnea. Screening is often delayed as during the daytime there is no obvious gas exchange abnormalities. Due to the rareness of this disease, large studies are not available, limiting the data for discussion and analysis to develop guidelines on ideal screening age for sleep disordered breathing in children with ACH. Methods The primary aim of this study is to ascertain the presence of sleep disorder breathing and demographics of children with ACH at time of first polysomnogram (PSG) completed at one of the largest pediatric sleep lab in the country. The secondary aim of the study is to identify whether subsequent polysomnograms were completed if surgical interventions occurred and how the studies differed over time with and without intervention. Retrospective review of the PSGs from patients with ACH, completed from 2017–2019 at the Children’s Sleep Disorders Center in Dallas, TX. Clinical data, demographics, PSG findings and occurrence of interventions were collected. Results Twenty-seven patients with the diagnosis of ACH met criteria. The average age at the time of their first diagnostic PSG was at 31.6 months of age (2.7 years), of those patients 85% had obstructive sleep apnea (OSA),51% had hypoxemia and 18% had hypercapnia by their first diagnostic sleep study. Of those with OSA, 50% were severe. Majority were females, 55%. Most of our patients were Hispanic (14%), Caucasian (9%), Asian (2%), Other (2%), Black (0%). Each patient had an average of 1.9 PSGs completed. Conclusion Our findings can help create a foundation for discussion of screening guidelines. These guidelines will serve to guide primary care physicians to direct these patients to an early diagnosis and treatment of sleep disordered breathing. Support (if any):

614 The Development and Psychometrics of an Assessment of Children’s Sleep Environments

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A241-A242
Author(s):  
Jack Peltz ◽  
Ronald Rogge ◽  
Joseph Buckhalt ◽  
Lori Elmore-Staton
Keyword(s):  
Online Survey ◽  
Sleep Problems ◽  
Clinical Work ◽  
Sleep Health ◽  
Insufficient Sleep ◽  
Hygiene Behavior ◽  
Sleep Environment ◽  
Healthy Functioning ◽  
Total Scale Score ◽  
Children’S Sleep

Abstract Introduction Approximately half of school-aged children (ages 5–18) get either insufficient sleep during school nights or barely meet the required amount of sleep expected for healthy functioning (National Sleep Foundation, 2014).This percentage increases as children develop into adolescents (National Sleep Foundation, 2006). Accordingly, sleep problems and insufficient sleep are so pervasive that they could be considered an epidemic due to their adverse impact on children’s mental and physical health (Owens, 2015; Shochat et al., 2014). Fundamental to children’s sleep health is their sleep environment (Billings et al., 2019; Spilsbury et al., 2005). Despite its importance, however, there remains a noticeable absence of valid and reliable assessments of this construct. The current study sought to develop a measure of children’s sleep environments to support research and clinical work on youth’s sleep health. Methods A total of 813 parents (Mage = 40.6, SD = 8.6; 72% female) completed an online survey regarding their child’s (Mage = 10.5, SD = 3.8; 45% female) sleep environment and sleep-related behavior. The majority of families identified as Caucasian (approximately 80%). Parents reported fairly high annual incomes (Median = $75,000), but 28.2% of families reported incomes less than $50,000. A total of 18 items (total scale score; alpha = .74) were selected from a pool of 38 items developed from previous research that examined aspects of the sleep environment and were entered into an exploratory factor analysis from which 4 factors emerged: general sleep environment (10 items, alpha = .91), sleeping alone vs. with siblings (2 items, alpha = .78), presence of electronic screens (4 items, alpha = .75), and emotional environment (2 items, alpha = .80). Results The subscales demonstrated distinct patterns of correlations with related constructs, and unique predictive variance in explaining children’s daytime sleepiness even after controlling for children’s sleep hygiene, behavior problems, and sleep problems. Conclusion The current study is one of the first to demonstrate a valid/reliable assessment of children’s sleep environments. Not only will this measure provide researchers with an assessment of a fundamental influence on children’s sleep, but it will also enable clinicians to better measure this construct and support effective sleep health recommendations. Support (if any):

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