Study Objective: The Epworth Sleepiness Scale (ESS) has been used to detect patients with potential sleep disordered breathing (SDB). Recently, a 4-Variable screening tool was proposed to identify patients with SDB, in addition to the STOP and STOP-Bang questionnaires. This study evaluated the abilities of the 4-Variable screening tool, STOP, STOP-Bang, and ESS questionnaires in identifying subjects at risk for SDB. Methods: A total of 4,770 participants who completed polysomnograms in the baseline evaluation of the Sleep Heart Health Study (SHHS) were included. Subjects with RDIs ≥ 15 and ≥ 30 were considered to have moderate-to-severe or severe SDB, respectively. Variables were constructed to approximate those in the questionnaires. The risk of SDB was calculated by the 4-Variable screening tool according to Takegami et al. The STOP and STOP-Bang questionnaires were evaluated including variables for snoring, tiredness/sleepiness, observed apnea, blood pressure, body mass index, age, neck circumference, and gender. Sleepiness was evaluated using the ESS questionnaire and scores were dichotomized into < 11 and ≥ 11. Results:The STOP-Bang questionnaire had higher sensitivity to predict moderate-to-severe (87.0%) and severe (70.4%) SDB, while the 4-Variable screening tool had higher specifi city to predict moderate-to-severe and severe SDB (93.2% for both). Conclusions:In community populations such as the SHHS, high specifi cities may be more useful in excluding low-risk patients, while avoiding false positives. However, sleep clinicians may prefer to use screening tools with high sensitivities, like the STOP-Bang, in order to avoid missing cases that may lead to adverse health consequences and increased healthcare costs. S C I E N T I f I C I N V E S T I g A T I O N SP rimary care providers frequently decide whether or not patients are referred for obstructive sleep apnea evaluations. Due to fi nancial constraints, this decision must be made quickly and accurately during short patient visits. Accurate screening for sleep disordered breathing (SDB) is necessary to properly identify at-risk patients. Several tools have been proposed to rapidly identify these patients. Anecdotally, the Epworth Sleepiness Scale (ESS) has been used by primary care providers to identify patients with potential sleep disorders. However, the ESS was developed to measure propensity for sleep onset rather than the likelihood of SDB.1,2 Takegami et al. 3 proposed a 4-Variable screening tool with high sensitivity (0.93) and high specifi city (0.66) for determining SDB severity. This scale utilizes gender, blood pressure (BP), body mass index (BMI), and snoring. In addition, the STOP and STOP-Bang questionnaires, 4,5 two simple 4-and 8-item tools, also have been used to screen for SDB. However, these tools have been validated in different populations and clinical settings with differing results, leaving the clinician to wonder which tool best screens for SDB. We aimed to investigate this question by comparing the results of these 4 tools, ut...

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Children with reduced amounts of sleep (≤ 7.5 h/night) had an increased risk for higher body weight in early adolescence. Similarly, children who slept ≤ 7.5 h/night had higher risk of being anxious or depressed or having learning problems in early adolescence.

Objective-To determine the incidence and remission of sleep disordered breathing in adolescent children.Study design-319 children completed two home polysomnograms approximately 5 years apart. Sleep disordered breathing (SDB) was determined to be present if a child had a respiratory disturbance index ≥ 1 event per hour associated with a ≥3% oxygen desaturation. Subjective symptoms such as witnessed apnea, excessive daytime sleepiness, difficulty initiating and maintaining sleep, and habitual loud snoring were considered present if they occurred frequently or almost always. BMI percentiles were calculated using CDC childhood growth charts adjusted for sex and age.Results-The mean age at assessment was 8.5 years at Baseline and 13.7 years at Follow-up respectively. Incident SDB was more common in boys (OR=3.93, p=.008, CI= 1. 41-10.90). Children with Prevalent SDB were more likely to be boys (OR=2.48, p=.006) and had a greater increase in BMI percentile change (OR 1.01, p=.034). Children with Prevalent SDB also had 3.41 greater odds of developing obesity from Baseline to Follow-up in comparison with children with Prevalent NoSDB.Conclusions-Adolescent boys are more likely to have persistent and incident SDB than girls. Children with prevalent SDB are more likely to have developed obesity. These risks are similar to those observed in adults.Correspondence: James L. Goodwin, PhD, 1501 N. Campbell AHSC 245030, The University of Arizona, Tucson, AZ 85724-5030, Phone: 520-621-5001, FAX: 520-626-6970, jamieg@arc.arizona.edu. Reprint request author : James L. Goodwin Edited by RW and WFB The authors declare no conflicts of interest.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Sleep disordered breathing (SDB) in children has been associated with a number of physiological, neurocognitive, and behavioral problems. 1-4 Moreover, there is increasing evidence that sleep disordered breathing has an important adverse impact on a child's physical and cognitive health and social development. 1,[5][6][7] Although there are several studies specifying the prevalence of snoring and other sleep related problems in children, 8-10 11, 12 there is little evidence documenting the incidence and remission of symptoms associated with sleep disorders in children, such as witnessed apnea, excessive daytime sleepiness, habitual snoring, and insomnia. [13][14][15] Furthermore, there are no data to date indicating the incidence and remission rates of SDB in a large sample of children using polysomnography as well as identifying factors associated with incidence or remission. This gap in the rese...

A slight increase in severity of sleep disordered breathing was seen over 5 years; this was not associated with worsening of quality of life. However, subjective symptoms of quality of sleep and daytime sleepiness were associated with declining quality of life.

Introduction The impact of sleep on quality of life (QoL) has been well documented; however, there is a great need for reliable QoL measures for persons with obstructive sleep apnea (OSA). We compared the QoL scores between the 36-Item Short Form of the Medical Outcomes Survey (SF-36), Calgary Sleep Apnea Quality of Life Index (SAQLI), and Functional Outcomes Sleep Questionnaire (FOSQ) in persons with OSA. Methods A total of 884 participants from the Sleep Heart Health Study second examination, who completed the SF-36, FOSQ, and SAQLI, and in-home polysomnograms, were included. The apnea hypopnea index (AHI) at 4% desaturation was categorized as no OSA (<5 /hour), mild to moderate OSA (5–30 /hour) and severe OSA (>30 /hour). QoL scores for each questionnaire were determined and compared by OSA severity category and by gender. Results Participants were 47.6% male, 49.2% (n=435) had no OSA, 43.2% (n=382) had mild to moderate OSA, and 7.6% (n=67) had severe OSA. Participants with severe OSA were significantly older (mean age = 63.7 years, p <.0001), had higher BMI (mean = 34.3 kg/m2, p <.0001) and had lower SF-36 Physical Component scores (PCS) (45.1) than participants with no OSA (48.5) or those with mild to moderate OSA (46.5, p= .006). When analyzed according to gender, no significant differences were found in males for QoL by OSA severity categories. However, females with severe OSA had significantly lower mean scores for the SAQLI (5.4, p= .006), FOSQ (10.9, p= .02), and SF-36 PCS (37.7, p<.0001) compared to females with no OSA (6.0, 11.5, 44.6) and those with mild to moderate OSA (5.9, 11.4, 48, respectively). Females with severe OSA also had significantly higher mean BMI (41.8 kg/m2,) than females with no OSA (26.5 kg/m2) or females with mild to moderate OSA (30.6 kg/m2, p<.0001). The SF-36 PCS and Mental Component Scores (MCS) were correlated with the FOSQ and SAQLI (r=.37 PCS vs FOSQ; r=.31 MCS vs FOSQ; r=.42 PCS vs SAQLI; r=.52 MCS vs SAQLI; and r=.66 FOSQ vs SAQLI, p<.001 for all correlations). Linear regression analyses, adjusting for potential confounders, indicated that the impact of OSA severity on QoL is largely explained by the presence of daytime sleepiness. Conclusion The impact of OSA on QoL differs between genders with a larger effect on females and is largely explained by the presence of daytime sleepiness. Correlations among QoL instruments are not high and various instruments may assess different aspects of QoL.

Hispanic ethnicity and parental reports of TST were found to be the most closely associated with BMI z-score. Decreased TST and increased caffeine intake and screen time may result in higher obesity risk in the adolescent population.

Research comparing parental report of sleep times to objectively obtained polysomnographic evidence of sleep times in schoolchildren is lacking. This report compares habitual sleep time and objectively recorded sleep time and sleep latency with parental reports of sleep time immediately after a night of polysomnography in elementary schoolchildren. Unattended home polysomnograms (PSG) were obtained from 480 children. On the night of the PSG, a parent was asked to complete a Sleep Habits Questionnaire, which inquired about the habitual total sleep time (HABTST) and habitual sleep onset latency (HABSOL) of his/her child on both school days and nonschool days. On the morning after the PSG, the parent was asked to estimate the total sleep time (ESTTST) and sleep onset latency (ESTSOL) of his/her child on the night of the recording. Comparisons were made to actual total sleep time (PSGTST) and sleep latency (PSGSOL) on the PSG. The sample was comprised of 50% girls, 42.3% Hispanic, and 53% aged 6-8 years. The mean HABTST, ESTTST, and PSGTST were 578, 547, and 480 min, respectively. HABTST was greater than both ESTST and PSGTST (p < 0.001). Moreover, ESTTST was greater than PSGTST (p < 0.001). The mean HABSOL, ESTSOL, and PSGSOL were 15, 17, and 11 min. ESTSOL was longer than PSGSOL (p < 0.001). There were no gender differences. However, Hispanic parents reported significantly less HABTST in their children than Caucasian parents (566 vs 587 min, p < 0.001). Parents of schoolchildren in this population-based sample substantially overestimated their children's actual total sleep time and sleep onset latency.