Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Dec 1.
Published in final edited form as: Health Psychol. 2016 Jun 2;35(12):1298–1306. doi: 10.1037/hea0000369

Is Daytime Napping Associated With Inflammation In Adolescents?

Karen P Jakubowski a, Martica H Hall a,b, Anna L Marsland a, Karen A Matthews a,b
PMCID: PMC5118199  NIHMSID: NIHMS777074  PMID: 27253429

Abstract

Objective

Daytime napping has been associated with poor health outcomes in adults. It is not known whether daytime napping is similarly linked to adverse health in adolescents, although many report napping. The present study evaluated associations between daytime napping and two markers of increased inflammation, high sensitivity C-reactive protein (hs-CRP) and interleukin-6 (IL-6), in healthy high school students.

Methods

234 black and white high school students completed a week of actigraph and diary measures of sleep and napping and provided a fasting blood sample. Napping measures were the proportion of days napped and the average minutes napped across one week during the school year.

Results

Linear regressions adjusted for age, sex, race, average nocturnal sleep duration, time between sleep protocol and blood draw, and BMI percentile demonstrated that proportion of days napped measured by actigraphy [B(SE)=.41(.19), p<.05] across the full week was positively associated with IL-6. Higher proportions of school days napped between 2 p.m. and 6 p.m. [B(SE)=.40(.20), p<.05] and between 6 p.m. and 10 p.m. [B(SE)=.57(.28), p<.05] were associated with increased IL-6. No associations emerged between average actigraphy-assessed nap duration and either study outcome. Diary-reported napping was unrelated to either study outcome.

Conclusions

Actigraphy-assessed napping and IL-6 are associated but the direction of the relationship remains to be determined. Overall, napping is an important factor to consider in order to better understand the relationship between short sleep and cardiovascular health in adolescents.

Keywords: adolescence, napping, sleep duration, inflammation

Introduction

Adolescents do not get enough sleep, and may make up for lost sleep by napping during the day. Evidence suggests adolescents need as much as nine hours of sleep per night, but many adolescents fail to achieve that amount, with some studies reporting that adolescents obtain fewer than eight hours per night (Carskadon, Wolfson, Acebo, Tzischinsky, & Seifer, 1998; Gau & Soong, 1995; Iglowstein, Jenni, Molinari, & Largo, 2003; Matthews, Hall, & Dahl, 2014; McKnight-Eily et al., 2011; Mercer, Merritt, & Cowell, 1998; Roberts, Roberts, & Duong, 2009; Roberts, Roberts, & Xing, 2011; Wolfson & Carskadon, 1998). A number of explanations for this sleep pattern have been postulated, including a circadian phase delay during puberty (Carskadon, Viera, & Acebo, 1993; Crowley, Acebo, & Carskadon, 2007), increased utilization of technology before bed (Cain & Gradisar, 2010; Gradisar et al., 2013), and increased time spent in social and extra-curricular activities (National Sleep Foundation, 2006). In response to short sleep, many adolescents report taking naps (Calamaro, Mason, & Ratcliffe, 2009; National Sleep Foundation, 2011), which have been associated with shorter nocturnal sleep duration (Fischer, Nagai, & Teixeira, 2008).

In adults, short sleep and napping are associated with cardiovascular morbidity and mortality. Meta-analyses indicate associations between short self-reported sleep and increased risk for morbidity and mortality from coronary heart disease and stroke (Cappuccio, Cooper, D’Elia, Strazzullo, & Miller, 2011), as well as all-cause mortality (Cappuccio, D’Elia, Strazzullo, & Miller, 2010; Gallicchio & Kalesan, 2009). Although the literature is limited, daytime napping is associated with worse cardiovascular outcomes in adults, even in relatively healthy samples without a history of cardiovascular disease (CVD), stroke, or cancer. Adults with a regular daytime napping habit had an elevated risk of cardiac events (Campos & Siles, 2000; Stang et al., 2012), CVD mortality (Tanabe et al., 2010) and mortality from all causes (Leng et al., 2014; Tanabe et al., 2010). Indeed, a recent meta-analysis indicates that daily daytime napping is associated with increased risk of CVD and all-cause mortality (Yamada, Hara, Shojima, Yamauchi, & Kadowaki, 2015).

Recent studies have examined whether associations of short sleep with cardiovascular risk observed in adults extend to adolescents. The data in adolescents and children are strongest for associations of short sleep and obesity (Cappuccio et al., 2008; Chen, Beydoun, & Wang, 2008; Gauralet et al., 2011; Patel & Hu, 2008), followed by increased insulin resistance (Flint et al., 2007; Javaheri et al., 2011; Koren et al., 2011; Matthews et al., 2012; Spruyt, Molfese, & Gozal, 2011) and ambulatory blood pressure (Javaheri, Storfer-Isser, Rosen & Redline, 2008; Mezick, Hall, & Matthews, 2012). Overall, it appears that short sleep duration in adolescence is related to poorer cardiovascular health at least for the above risk factors, indicating that the health risks of short sleep may begin earlier than adulthood (Matthews & Pantesco, in press).

Few sleep studies in adolescents have focused on inflammatory markers, which are important risk factors for CVD (e.g., Blake & Ridker, 2002; Danesh et al., 2008; Danesh et al., 2000; Hartman & Frishman, 2014). One study found an inverse relationship between self-reported sleep duration (average number of hours including school nights and weekend nights) and C-reactive protein (CRP) levels, but not interleukin 6 (IL-6), among adolescents aged 13 to 17 years (Martinez-Gomez et al., 2011). A second study conducted in a community sample of 4 to 10 year olds, reported that short actigraphy-assessed sleep duration on school days and weekends was associated with higher CRP levels (Spruyt et al., 2011). Finally, in a study of black and white high school students, shorter actigraphy-assessed weekday sleep duration predicted risk of inclusion in a high-risk CRP group (CRP values > 3 mg/L), particularly in white adolescents and females, relative to black adolescents and males (Hall, Lee, & Matthews, 2015). Overall, there is a paucity of research on inflammatory markers, particularly IL-6.

Why might napping be important to consider in understanding markers of systemic inflammation and cardiovascular risk among adolescents? First, napping may contribute to shorter nighttime sleep duration, a known covariate of cardiovascular risk. Second, napping may alter circadian rhythms of cardiovascular risk factors (e.g., blood pressure), in addition to causing disruptions in the wake-sleep cycle (Borbély, 1982). In the case of inflammation, IL-6 exhibits diurnal variation with levels peaking in the late afternoon (Izawa, Miki, Liu, Ogawa, 2013; Vygontzas et al., 2005; Vygontzas et al., 1997). Furthermore, it is also known that IL-6 communicates with the central nervous system to induce behavioral changes that include sleep dysregulation and the experience of fatigue (Cho, Bower, Kiefe, Seeman, & Irwin, 2012). Thus, napping after school/in the evening may disrupt circadian rhythms and result in increased levels of IL-6, further dysregulating sleep. Finally, evidence suggests a relationship between IL-6 and body mass index (BMI) in adults with excessive daytime sleepiness and apnea symptoms, with IL-6 related to greater fatigue and sleepiness among obese individuals (Vygontzas et al., 1997). As a result, it is possible that the relationship between napping and inflammation varies by BMI, a known risk factor for CVD.

The current study evaluates the relationship of napping and inflammation in a sample of black and white high students that was the basis of prior papers on short nocturnal sleep duration and higher CRP (Hall et al., 2015), elevated ambulatory blood pressure (Mezick et al., 2012), and insulin resistance (Matthews et al., 2012). The present study has three objectives. First, we assessed the associations of napping frequency and duration with hs-CRP and IL-6, two widely-examined markers of inflammation. Second, we examined whether the associations of napping and inflammation were independent of nighttime sleep duration and obesity. Finally, we explored whether associations of napping with inflammation varied by time of day, particularly with IL-6 since it has a clear diurnal pattern. We hypothesized that (1) increased proportion of days napped and average minutes napped across the weeklong study period (including both weekdays and weekends) would be associated with elevated inflammatory markers, independent of nighttime sleep duration and obesity, and (2) napping after school or in the evening (on schooldays/weekdays only) would be more strongly associated with increased inflammation than naps in the morning/early afternoon. Exploratory analyses examined whether relationships between napping and inflammation varied by race, gender, or BMI percentile.

Methods

Participants

A sample of 250 adolescents between the ages of 14 and 19 were enrolled from a single public high school from November 2008 through May 2011 (excluding summers and school vacations). Adolescents attended a public high school that served a lower to middle class community. Participants were recruited from health classes for an adolescent health project designed to measure risk factors for cardiovascular disease and sleep. Approval of the research project was obtained from the school district superintendent, school principal, and the University of Pittsburgh Institutional Review Board. Participants (or parents/legal guardians for students under the age of 18) provided written informed consent prior to any research procedures. Assent was obtained for students under age 18. A parent or guardian verified the student was free of hypertension, diabetes, cardiovascular or kidney disease, and also not taking medications for emotional problems, diabetes, high blood pressure, or medications known to affect the cardiovascular system or sleep. Per parent report, adolescents took medications for the following conditions: ADHD (n=1), acid reflux and irritable bowel syndrome (n=1), Crohn’s disease and osteopenia (n=1), birth control (n=4), or took medications as needed for asthma (n=6), seasonal allergies (n=6), and migraine (n=2). We did not obtain data regarding chronic pain unless it required medication.

Exclusion criteria for the present study were: less than five nights of actigraphy data (given evidence that five or more nights of adequate data are necessary to provide reliable estimates of actigraphy-assessed sleep in adolescents; Acebo et al., 1999), CRP values > 10 mg/L (a value that could be indicative of acute infection), report of illness at time of blood draw, or not having at least one inflammatory outcome. Thus, as noted below, the final analytic sample included 234 participants (48% male and 56% black) who met study criteria. For details regarding the full study sample, see Matthews et al. (2014).

Overview of Procedure

Parents of students expressing interest in the study were contacted for a screening telephone interview to determine interest and eligibility (i.e., confirm that the student was free of cardiovascular or kidney disease, not taking medications for emotional problems or diabetes, or medications known to affect the cardiovascular system or normal sleep). After obtaining informed consent from the parent/guardian and the student, the student was scheduled to complete a seven-day study protocol during the school year. Staff obtained anthropometric measurements, including height and weight. On a separate day, participants had a venous blood draw in a recumbent position. The blood draw was performed in the morning by trained personnel after verifying that the student had been fasting for at least eight hours and was not taking medications for infectious disease within three days of the draw. Participants who reported being ill were rescheduled. For the sleep assessment, participants wore an actigraph on their non-dominant wrist continuously for seven days and nights. They reported napping behavior on a handheld computer each evening, and also completed a battery of psychosocial questionnaires. Days on which the adolescent reported feeling ill during the sleep assessment were removed from analyses.

Measures

Actigraphy

Actigraph devices are worn on the wrist and record movements/accelerations; using activity counts and diary records of bedtime and wake time, periods of sleep and wake can be estimated. The Mini-mitter actiwatch model AW-16 (Philips Respironics, Bend, OR) was used to collect sleep/wake activity continuously over seven days and nights. Subjects were instructed to wear the watch on the non-dominant arm and to press an event marker when they tried to go to sleep. Actigraphs were configured to collect data during 1-minute epochs. Stored data were downloaded into the Actiware software program (version 5.57) for processing and analysis. The medium threshold (default) was selected to detect nocturnal sleep periods of at least 3 hours in duration based upon sleep onset and offset using the 10-minute criterion of quiescence. Nighttime rest periods were set using the data reported by the participant in their morning and evening sleep diaries as the time they “tried to go to sleep” and the “time they finally awoke”. Sleep periods occurring within 30 minutes of the major nocturnal sleep interval (either 30 minutes prior to sleeping or after waking) that were at least 15 minutes in duration were combined with the major sleep interval (i.e., if a 6-hour sleep interval was detected from 12 a.m. – 6 a.m., and a 20-minute sleep interval was detected beginning at 11:30 p.m., the 20-minute interval was combined with the major sleep interval. The new major sleep interval would become 11:30 p.m. – 6 a.m.). All subsequent sleep variables were then calculated from data within these set sleep periods. Sleep duration was calculated as actual sleep time from initial sleep onset to final sleep offset, excluding periods of wakefulness throughout the night. The actiwatch has been used extensively in research studies, and has been validated against polysomnography measures for nocturnal sleep episodes (Kushida et al., 2001; Tryon, 2004).

Given that there is no accepted criterion for scoring actigraphy-assessed naps, we used the same criterion used to determine nocturnal sleep periods to designate periods of napping, i.e. ten minutes of quiescence; nap periods were measured in minutes. In a comparison of naps of at least 15 minutes in duration measured concurrently by actigraphy (using the medium threshold setting) and by polysomnography (PSG) in healthy adults aged 18 to 35 years, results suggested good accuracy, sensitivity, and specificity for the detection of nap and non-nap (resting wake) periods (Kanady, Drummond, & Mednick, 2011); the accuracy for nap and non-nap periods, respectively, was 85% and 77%. Two napping variables were used in primary analyses: proportion of days with at least one nap across the study period (i.e., total number of days with at least one nap divided by number of days) and average minutes napped per day across the study period (i.e., total number of minutes napped across the study period divided by the number of days).

The nap timing variables used in exploratory analyses were measured on school days only. Given that adolescents have the opportunity to sleep in later on weekends or free days, it is likely that sleep need and napping might be different on weekends. Additionally, adolescents had more data points for school days versus weekend/free days, thus the estimates for school day timing of naps were expected to be more reliable. The three exploratory nap timing variables were as follows: 1) proportion of school days with at least one nap that began during the period after the adolescent woke up until 2 p.m. (i.e., yes/no a nap occurred between waking and 2 p.m. divided by the number of school days with actigraphy data), 2) proportion of school days with at least one nap that began between 2 p.m. and 6 p.m. (i.e., yes/no a nap began between 2 p.m. and 6 p.m. divided by the number of school days with actigraphy data), and 3) proportion of school days with at least one nap that began between 6 p.m. and 10 p.m. (i.e., yes/no a nap began between 6 p.m. and 10 p.m. divided by the number of school days with actigraphy data). These time periods were chosen to reflect the period during which adolescents would normally be in school, the period immediately after school, and in the period in the evening before the adolescent went to bed, respectively.

Daily diary

Adolescents completed a sleep diary on a handheld computer each morning after awakening. Each night before going to bed, they reported the number of naps they took each day and the total number of minutes they spent napping in their diary. The minimum length for accepted diary-reported naps was 10 minutes, in order to maintain consistency with actigraphy-assessed data. Diary-reported variables were the same as calculated for actigraphy. Although there were modest correlations between actigraphy and diary proportion of days napped and average minutes napped (rs = .45 to .59, all ps < .001), on average adolescents demonstrated fewer but longer diary-reported naps.

High sensitivity C-reactive protein (hs-CRP)

Hs-CRP prospectively predicts cardiovascular events in both healthy subjects and those with coronary disease (Danesh et al., 2000). It was selected as a summary marker of systemic inflammation, as it has a long half-life and is detectable at low levels. CRP was measured turbidimetrically by measuring increased absorbance when the CRP in the sample reacts with anti-CRP antibodies. The intra- and inter-assay coefficients of variation were 5.5% and 3.0%, respectively. Due to skewness, hs-CRP was log transformed after adding 1.

Interleukin-6 (IL-6)

IL-6 predicts risk of coronary artery disease (Danesh et al., 2008) and is known to mediate the amplification of pro-inflammatory signals within atherosclerotic plaque (Blake & Ridker, 2002). IL-6 was determined using a high-sensitivity enzyme-linked immunosorbent assay (ELISA). The intra- and inter-assay coefficients of variation were 6.6% and 4.9%, respectively. Due to positive skewness, IL-6 was log transformed after adding 1. Hs-CRP and IL-6 values were modestly correlated (r=.43, p<.001).

Additional variables

Adolescents self-reported age, gender, and race. Participants reported whether or not they had smoked cigarettes in the past month using the CDC Youth Risk Behavior Surveillance System Survey (Centers for Disease Control and Prevention, 2007). Responses were categorized as 0 = did not smoke cigarettes, 1 = smoked cigarettes on one day or more in the past 30 days. Family socioeconomic status was determined from parental/caregiver report on the Hollingshead Four Factor Index (Hollingshead, 1975). This scale measures socioeconomic status by coding paternal and maternal years of education and highest attained degree, as well as current occupation for both parents (if contributing to the household income) to yield an overall score. BMI percentile was used as a covariate in relevant analyses to adjust for overall obesity; this measure indicates the relative position of the adolescent’s BMI among adolescents of the same sex and age using charts provided by the Centers for Disease Control (2011). Due to skewness, BMI percentile was square-root transformed after subtracting the BMI percentile value from 100 [square root (100 – BMI percentile)], so that lower values reflect higher BMI percentiles.

The timing of the blood draw in relation to the sleep assessment varied from 0 days (i.e., blood draw occurred during the week-long sleep study) to 96 days (M = 10.88 days, SD = 12.71 days). Approximately 75% of the sample completed the blood draw within two weeks of the sleep protocol, and 94% within one month of the sleep protocol. Thus, we included a control for the length of time between conclusion of the week-long sleep protocol and completion of the blood draw. Due to skewness, this variable was log transformed.

Analytic Plan

Sixteen participants were excluded from analyses because of malfunctioning actigraph watches (n=1), less than 5 days of actigraphy data (n=2), BMI values that fell more than 4 standard deviations from the mean (n=2), missing IL-6 data (n=1), CRP values > 10 mg/L (a value that could be indicative of acute infection; n=8), or report of illness at time of blood draw (n=2). These exclusions resulted in a final sample of 234 adolescents; see Table 1 for characteristics of the full analytic sample. Preliminary analyses indicated no relationship between napping and family socioeconomic status, as measured by total family score on the Hollingshead Four Factor Index (Hollingshead, 1975), so it was not included as a covariate in the present study. Differences in napping, nocturnal sleep, and inflammatory markers were examined by gender and race, as well as the interaction of gender and race, using 2 × 2 analysis of variance for continuous data. Unadjusted correlations were conducted between nocturnal sleep, napping variables, and BMI percentile. For primary analyses, hierarchical linear regressions were conducted to measure associations between inflammatory markers and two separate nap variables: proportion of days napped and average minutes napped across the weeklong study period (including both weekday/schoolday and weekend data); nap variables were measured by both actigraphy and daily diary. Additional analyses were conducted to examine whether the timing of daytime naps on schooldays was associated with inflammatory markers. For this purpose, separate hierarchical linear regression analyses were conducted using the proportion of school days with at least one nap that began between 1) waking and 2 p.m., 2) 2 p.m. and 6 p.m., and 3) 6 p.m. and 10 p.m. All primary and exploratory analyses were analyzed using separate hierarchical regression models adjusted for age, gender, race, time between sleep protocol and blood draw, nocturnal sleep duration throughout the weeklong study period (or average actigraphy-assessed nocturnal sleep duration on school nights for exploratory analyses), and BMI percentile entered in Step 1, followed by the nap variable of interest. Additional analyses explored moderation of napping and inflammatory marker relationships by race, gender, and BMI percentile. Results reflect unstandardized coefficients and standard error values. P-values were considered statistically significant at <.05.

Table 1.

Sample Characteristics

All (N = 234) Black White
Mean (SD) unless noted Male (n = 65) Female (n = 67) Male (n = 47) Female (n = 55)
Age, years 15.7 (1.3) 15.7 (1.2) 15.7 (1.4) 15.7 (1.5) 15.6 (1.2)
Family Hollingshead Totala 30.5 (11.6) 33.6 (12.0) 30.9 (11.7) 29.0 (11.6) 27.8 (10.2)
Smokers in last 30 days, N (%)a 60 (26%) 14 (22%) 12 (18%) 14 (30%) 20 (36%)
Actigraphy Measures
Nocturnal sleep duration, hours
 Full weeka,b,c 6.4 (0.8) 6.2 (0.8) 6.3 (0.6) 6.4 (0.7) 6.9 (0.9)
 Weekendsa,b,c 7.4 (1.2) 7.3 (1.1) 7.3 (1.3) 7.2 (1.3) 8.0 (1.2)
 School nightsa,b 6.0 (0.9) 5.7 (0.8) 5.9 (0.7) 6.1 (0.8) 6.3 (1.0)
Proportion of days napped
 Full weekb .36 (.23) .32 (.23) .44 (.23) .32 (.22) .33 (.21)
 Wake time to 2 p.m. (school days) .11 (.16) .10 (.15) .14 (.18) .13 (.19) .09 (.13)
 2 p.m. to 6 p.m. (school days)a,b .19 (.22) .18 (.23) .25 (.23) .10 (.14) .20 (.21)
 After 6 p.m. (school days) b .11 (.15) .10 (.15) .12 (.15) .06 (.12) .14 (.17)
Average minutes napped (full week)a,b 23.4 (21.6) 19.4 (20.1) 32.3 (25.4) 16.9 (16.8) 22.9 (18.9)
Diary Measures
Nocturnal sleep duration, hours
 Full week a 7.4 (0.9) 7.3 (0.9) 7.2 (0.9) 7.5 (0.8) 7.7 (1.0)
 Weekends 8.7 (1.4) 8.7 (1.4) 8.4 (1.6) 8.7 (1.2) 8.9 (1.5)
 School nightsa 6.8 (1.1) 6.7 (1.1) 6.7 (1.0) 7.0 (1.0) 7.0 (1.2)
Proportion of days napped
 Full weekb .18 (.19) .14 (.17) .23 (.18) .14 (.18) .20 (.21)
 Wake time to 2 p.m. (school days) .05 (.10) .04 (.10) .06 (.10) .05 (.08) .06 (.11)
 2 p.m. to 6 p.m. (school days)b .11 (.15) .10 (.14) .15 (.17) .07 (.15) .13 (.15)
 After 6 p.m. (school days)b .02 (.06) .01 (.04) .04 (.09) .01 (.04) .03 (.06)
 Average minutes napped (full week)b 23.6 (27.4)) 18.9 (27.0) 30.3 (26.3) 17.2 (24.9) 26.4 (29.7)
Cardiovascular Risk Factors
BMI percentile (M, SD) 78.7 (22.3) 79.4 (19.8) 81.8 (19.6) 81.3 (24.0) 72.3 (26.0)
hs-CRP, mg/L; Mdn,(IQR)d 0.6 (0.2, 1.5) 0.4 (0.2, 0.9) 0.6 (0.2, 2.3) 0.9 (0.4, 1.6) 0.6 (0.3, 1.3)
IL-6, mg/L; Mdn,(IQR)a,b 1.0 (0.7, 1.6) 0.8 (0.5, 1.6) 1.0 (0.7, 1.7) 1.1 (0.7, 1.7) 1.1 (0.7, 1.7)
Open in a new tab

Note. Mdn = Median. IQR = Interquartile Range (25th, 75th percentiles). BMI = Body Mass Index; hs-CRP = high sensitivity C-reactive protein; IL-6 = interleukin 6; Full week = school days and free days.

a

Race main effect from ANOVAS or logistic regression, p<.05

b

Gender main effect from ANOVAS or logistic regression, p<.05

c

Race × gender interaction from 2 (race) by 2 (gender) ANOVA, p<.05

d

Raw values are shown in table; hs-CRP and IL-6 values were natural log transformed prior to analyses

Results

Sample Characteristics

The analytic sample was composed of 65 black males, 67 black females, 47 white males, and 55 white females. Their average age was approximately 16 years (Table 1). The sample was from low to middle class as evidenced by their family Hollingshead scores (range: 10–54); black adolescents had somewhat higher family Hollingshead scores, relative to whites.

As shown in Table 1, on average, adolescents were overweight. Approximately one-quarter of the sample reported smoking cigarettes in the past thirty days. Significant race differences emerged for IL-6, such that white adolescents demonstrated higher IL-6 values, relative to black adolescents (Table 1). More white adolescents reported smoking (33%), relative to black adolescents (20%). Smoking in the past month (yes/no) was unrelated to inflammation, although adolescents who smoked demonstrated a greater proportion of days napped before 2 p.m. [F(1,230)=9.95, p<.01].

Nocturnal Sleep and Napping Characteristics

As demonstrated by actigraphy measures, adolescents slept on average 6.4 hours (range: 4.3–9.2) at night across the full week, and 6.0 hours (range: 3.4–8.6) on school nights. As reported elsewhere based on the full sample (Matthews et al., 2014), there were significant race and gender differences in actigraphy-assessed nocturnal sleep duration across the full week and on school nights, with black adolescents and males demonstrating shorter duration, relative to white adolescents and females. White female adolescents demonstrated the longest actigraphy-assessed nocturnal sleep across the full week and on weekends. As demonstrated by diary measures, adolescents slept on average 7.4 hours (range: 4.8–9.6) at night across the full week, and 6.8 hours (range: 2.9–9.5) on school nights. As reported elsewhere based on the full sample (Matthews et al., 2014), there were significant race differences in diary-reported nocturnal sleep duration across the full week and on school nights, with black adolescents demonstrating shorter duration, relative to white adolescents.

Napping was a common behavior (see also Jakubowski, Hall, Lee, & Matthews, in press; Table 1). As demonstrated by actigraphy measures, eighty-five percent of adolescents demonstrated at least one nap across the week-long study period. On average, adolescents napped 36% (range: 0–100%) of days and 23 minutes per day (range: 0–111.9 minutes) by actigraphy. There were significant race and gender differences with regard to actigraphy-assessed napping across the full week. Females napped more days, relative to males. Females and black adolescents demonstrated significantly increased average minutes napped across the study period, relative to males and white adolescents, respectively. With regard to diary-reported napping, sixty-two percent of adolescents reported at least one nap in their diary during the week-long study period. On average, adolescents napped 18% (range: 0–86%) of days and 24 minutes (range: 0–114.4 minutes) by diary report. Females napped more days and also took longer naps by diary report, relative to males.

Bivariate Correlations

Correlations among nocturnal sleep and napping variables are presented in Table 2. The longer the average nocturnal sleep duration across the full week, the fewer days napped across the full week and the shorter the average minutes napped across the full week. The longer the average nocturnal sleep duration on school nights, the fewer proportion of school days with a nap that began between before 2 p.m., between 2 p.m. and 6 p.m, or between 6 p.m. and 10 p.m. The proportion of days napped during all three time periods were modestly correlated. The pattern of results was similar for diary-reported and actigraphy-assessed nocturnal sleep and napping variables. BMI percentile was unrelated to nocturnal sleep or napping variables, by actigraphy or diary report.

Table 2.

Correlations among primary study variables

1 2 3 4 5 6 7 8
1 Nocturnal sleep duration (full week) – .84** −.20** −.23** −.08 −.26** −.04 .04
2 Nocturnal sleep duration (school nights) .81** – −.26** −.29** −.12* −.29** −.04 .04
3 Average minutes napped across the week −.25** −.29** – .83** .34** .72** .27** .09
4 Prop. days napped (full week) −.22** −.29** .73** – .51** .81** .40** .08
5 Prop. schooldays napped before 2 p.m. −.10 −.12* .27** .45** – .04 .21** .02
6 Prop. schooldays napped 2 p.m. to 6 p.m. −.14** −.22** .61** .62** .23** – .08 .09
7 Prop. schooldays napped after 6 p.m. −.12* −.19** .38** .44** .12* .24** – −.002
8 BMI % (low) −.003 −.02 .01 .08 .06 −.05 .01 –
Open in a new tab

Note. Actigraphy-assessed nocturnal sleep and napping variables presented below the diagonal; diary-reported nocturnal sleep and napping variables presented above the diagonal. Full week = school days and free days; Prop. = proportion; BMI % = sqrt (100-BMI percentile).

*

p < .05.

**

p < .01.

Association of Actigraphy-Assessed Napping and Inflammatory Outcomes

hs-CRP

As shown in Table 3, there were no significant associations between the proportion of days napped or the average minutes napped across the full week and hs-CRP. Similarly, no significant associations between napping and hs-CRP were found when models were re-analyzed using a categorical variable of inclusion in a high-risk CRP group (CRP values ≤ 3mg/L versus > 3 mg/L), a cutoff that has been associated with risk for cardiovascular events (Park et al., 2012); data not shown.

Table 3.

Hierarchical linear regressions showing the contributions of age, gender, race, average nocturnal sleep duration, BMI percentile, and timing of blood draw to the prediction of log transformed inflammatory markers

Actigraphy Napping Diary Napping
ln hs-CRP ln IL-6 ln hs-CRP ln IL-6
Step 1
 (Constant) .05 (1.1) −.46 (.63) −.11 (1.0) −.23 (.63)
 Age (years) .06 (.06) .06 (.03)* .07 (.05) .04 (.03)
 Gender (0=male, 1=female) .29 (.14)** .07 (.09) .29 (.14)** .08 (.09)
 Race (0=white, 1=black) −.33 (.15)** −.13 (.09) −.28 (.14)* −.16 (.09)
 Average nocturnal sleep duration (hours) −.10 (.09) .04 (.06) −.07 (.08) .03 (.05)
 BMI percentile (low) −.25 (.03)** −.10 (.02)** −.25 (.03)** −.10 (.02)**
 Timing of blood draw .05 (.07) −.08 (.04)* .06 (.07) −.07 (.04)
Step 2 of separate models
 Proportion of days napped (full week) .32 (.32) .41 (.19)** −.43 (.39) .04 (.24)
 Average minutes napped (full week) .002 (.003) .003 (.002) −.004 (.003) .000 (.002)
Open in a new tab

Note. Values reflect unstandardized coefficient (standard error); hs-CRP = high sensitivity C-reactive protein; IL-6 = interleukin 6; Full week = free days and school days. Timing of blood draw, hs-CRP, and IL-6 values were natural log transformed prior to analyses. BMI percentile (low) = sqrt (100-BMI percentile).

*

p < .1.

**

p < .05.

IL-6

As shown in Table 3, increased proportion of days napped was significantly associated with higher IL-6 following adjustment for age, gender, race, average actigraphy-assessed nocturnal sleep duration, BMI percentile, and time between sleep protocol and blood draw [B(SE)=.41(.19), p=.031]. There was no significant association between average actigraphy-assessed minutes napped across the study period and IL-6.

Nap timing and inflammation

On average, 52% of adolescents napped at least once on school days between wake time and 2 p.m., 66% napped between 2 p.m. and 6 p.m., and 50% napped after 6 p.m. As seen in Table 1, adolescents napped 11% of days between wake time and 2 p.m., 19% of days between 2 p.m. and 6 p.m., and 11% of days after 6 p.m. Females and black adolescents demonstrated significantly higher proportion of school days with a nap that began between 2 p.m. and 6 p.m., relative to males and white adolescents. Females demonstrated a greater proportion of days with a nap that began between 6 p.m. and 10 p.m., relative to males.

We conducted exploratory analyses to evaluate whether the timing of daytime naps was associated with inflammatory markers. Entering the three nap periods individually into the second step of the analytic model revealed a significant positive association between the proportion of school days with a nap that began between 2 p.m. and 6 p.m. [B(SE)=.40(.20), p=.048] and between 6 p.m. and 10 p.m. [B(SE)=.57(.28), p=.044] and IL-6 values. There was no significant relationship between proportion of school days with a nap that began during the school day (after wake time to 2 p.m.) and IL-6 [B(SE)=.39(.26), p>.1]. There were also no significant associations between timing of daytime naps and hs-CRP.

Additional analyses were conducted looking exclusively at naps that occurred between 7:30 a.m. (school start time) and 2 p.m., in order to account for the possibility that the period between wake time and 7:30 a.m. might be an extension of the previous nights’ sleep. Naps during this period might make up for inadequate sleep and would be consistent with delayed sleep phase in adolescence. After this analytic constraint, the pattern of results was the same; morning/school day naps were not significantly associated with hs-CRP or IL-6 (data not shown).

Association of Diary-Reported Napping and Inflammatory Outcomes

As shown in Table 3, there were no significant associations between the proportion of days with a diary-reported nap or the average minutes of diary-reported napping and hs-CRP or IL-6. Given these results, we did not test for associations between the timing of diary-measured naps and inflammatory outcomes, although descriptive information concerning the timing of diary-reported naps can be found in Table 1.

Moderation by Race, Gender and BMI Percentile

We tested whether the relationship between napping and inflammatory markers varied by race, gender or BMI percentile. For the 42 tests, only one was statistically significant. A significant interaction emerged between gender and diary-reported average minutes napped on hs-CRP [B(SE)=.01(.01), p=.033]. Simple slope analyses revealed that more napping was significantly associated with lower hs-CRP among males [B(SE)=−.01(.004), p=.012], but not among females. Given the large number of moderation analyses conducted, we will not further discuss this finding.

Discussion

This study used actigraphy and daily diary measures of daytime napping and nocturnal sleep to evaluate whether napping was associated with elevated inflammatory markers in healthy black and white adolescents. Results indicated partial support for our hypotheses, with more days napped by actigraphy, particularly school days with naps that began between 2 p.m. and 6 p.m. or between 6 p.m. and 10 p.m., relating to higher IL-6. Contrary to expectations, actigraphy-assessed napping was not associated with hs-CRP, and no associations emerged between diary-reported napping and either hs-CRP or IL-6.

Although actigraphy-assessed proportion of days napped was associated with IL-6, this measure of napping was not similarly associated with hs-CRP. As previously described, research suggests that IL-6 is a “sleep factor” and its concentration in peripheral circulation is subject to diurnal variation (Izawa et al., 2013; Vygontzas et al., 2005; Vygontzas et al., 1997). In contrast, CRP levels do not show circadian variation, showing greater stability among disease-free individuals (Meier-Ewert et al., 2001). Given its association with sleep loss and sleepiness, Vygontzas and colleagues (2005) have suggested that IL-6 is a marker of sleep need, which may explain why IL-6 was associated with napping but not with nocturnal sleep in the current sample. In contrast, CRP has been associated with short sleep in this sample (Hall et al., 2014), but not with napping. It is important to note that IL-6 plays a role in communicating with the central nervous system (CNS) to result in “sickness behaviors” that include sleep disruption and fatigue (Cho et al., 2012). In contrast, CRP does not cross the blood brain barrier to influence CNS processes, except following brain injury. Thus, although they are both peripheral markers of inflammation, IL-6 and CRP differentially affect the CNS, which might explain their different relationships with actigraphy-assessed napping.

Results suggested that actigraphy-assessed naps that began after school (between 2 p.m. and 6 p.m.) or in the evening (between 6 p.m. and 10 p.m.) were related to higher IL-6. For adolescents who start the school day with higher sleep need, particularly when regularly obtaining inadequate sleep duration on school nights, the need for sleep will build throughout the school day until they have an opportunity to nap. Thus, it may be that after school adolescents’ sleep drive is higher and there are more opportunities to nap. Or, given diurnal variation in IL-6, which is higher in the afternoon/evening and is a putative “sleep factor,” it is possible that adolescents are more likely to nap when IL-6 values are higher. However, it is important to note that although as many adolescents napped during the school day as during the evening, morning naps were not associated with inflammatory markers. This finding may lend support for the hypothesis that adolescents are more likely to nap when IL-6 values are higher. Future examination of the direction and temporal profile of this effect is warranted.

Finally, we found that actigraphy, but not diary, measures of napping were associated with inflammatory markers. These methods provide somewhat different information about napping, although they are moderately correlated (rs = .45 to .59, all ps < .001). Actigraphy measures captured more frequent, but shorter, nap episodes. In contrast, diary measures captured fewer, but longer, nap episodes, perhaps including those that were “preventative”, such as naps taken prior to anticipated short nocturnal sleep. Our results are supported by adult data which indicated that actigraphs recorded more naps, relative to subjective measures (Lockley, Skene, & Arendt, 1999). Additionally, there are important differences regarding the tracking of actigraphy versus diary naps. Students complete their diary napping records at the end of the evening and may recall only the more salient, longer naps taken when they are very tired. Actigraphy includes shorter napping intervals recorded “online” and do not require recall; these naps may include those out of the awareness of the adolescent.

The present study has several limitations. First, inflammatory markers were measured at only one time point. Second, the cross-sectional nature of the study precludes causal/clinical inference. Third, since actigraphy involves measurement of accelerations (Van Wouwe, Valk, Veenstra, 2011), we cannot be certain that some periods recorded as “sleep” were not actually very still moments in wake (e.g., watching TV). Thus, it is possible that the aforementioned relationship between actigraphy-assessed napping and IL-6 reflects periods of low activity and IL-6, and consequently our results may speak more broadly to associations between sedentary behaviors and inflammatory markers. However, if periods of napping in our data were actually still moments while awake, it is likely that results would have also suggested associations with morning naps on schooldays, a time when adolescents may doze in class; notably, we did not find associations with morning naps in our data. Finally, it is possible that the association between actigraphy-assessed proportion of days napped and IL-6 is a product of multiple comparisons. Thus, it will be important for future studies to validate actigraphy-assessed measures of napping in adolescent samples and to use additional measures, such as metabolic equivalents, that may be more sensitive to differentiate periods of low activity from periods of napping.

To our knowledge, the present study is the first to investigate the relationship between daytime napping and inflammatory markers in healthy adolescents. Overall, results provided partial support for this association, with evidence that proportion of days napped by actigraphy relates positively to circulating levels of IL-6, but not CRP among this sample of adolescents, while diary-reported naps were not associated with either inflammatory marker. Although it is premature to make clinical recommendations on the basis of these findings, they provide initial evidence that actigraphy-assessed napping associates with increased levels of a marker of inflammation that is known to predict health risk. If replicated in future work, the current findings may provide a pathway linking napping to poorer health outcomes. In conclusion, actigraphy-assessed napping is an important factor to consider in order to better understand the relationship between short sleep and cardiovascular health in adolescents.

Supplementary Material

1

Acknowledgments

This work was supported by National Institutes of Health (HL025767, HL007560).

References

  1. Acebo C, Sadeh A, Seifer R, Tzischinsky O, Wolfson AR, Hafer A, Carskadon MA. Estimating sleep patterns with activity monitoring in children and adolescents: How many nights are necessary for reliable measures? Sleep. 1999;22:95–103. doi: 10.1093/sleep/22.1.95. [DOI] [PubMed] [Google Scholar]
  2. Borbély AA. A two-process model of sleep regulation. Human Neurobiology. 1982;1:195–204. [PubMed] [Google Scholar]
  3. Blake GJ, Ridker PM. Inflammatory bio-markers and cardiovascular risk prediction. Journal of Internal Medicine. 2002;252:283–294. doi: 10.1046/j.1365-2796.2002.01019.x. [DOI] [PubMed] [Google Scholar]
  4. Cain N, Gradisar M. Electronic media use and sleep in school-aged children and adolescents: A review. Sleep Medicine. 2010;11:735–742. doi: 10.1016/j.sleep.2010.02.006. [DOI] [PubMed] [Google Scholar]
  5. Calamaro CJ, Mason TBA, Ratcliffe SJ. Adolescents living the 24/7 lifestyle: Effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics. 2009;123:e1005–e1010. doi: 10.1542/peds.2008-3641. [DOI] [PubMed] [Google Scholar]
  6. Campos H, Siles X. Siesta and the risk of coronary heart disease: results from a population-based, case-control study in Costa Rica. International Journal of Epidemiology. 2000;29:429–437. [PubMed] [Google Scholar]
  7. Cappuccio FP, Cooper D, D’Elia L, Strazzullo P, Miller MA. Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. European Heart Journal. 2011;32:1484–1492. doi: 10.1093/eurheartj/ehr007. [DOI] [PubMed] [Google Scholar]
  8. Cappuccio FP, D’Elia L, Strazzullo P, Miller MA. Sleep duration and all-cause mortality: A systematic review and meta-analysis of prospective studies. Sleep. 2010;33:585–592. doi: 10.1093/sleep/33.5.585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cappuccio FP, Taggart FM, Kandala NB, Currie A, Peile E, Stranges S, Miller MA. Meta-analysis of short sleep duration and obesity in children and adults. Sleep. 2008;31:619–626. doi: 10.1093/sleep/31.5.619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Carskadon MA, Viera C, Acebo C. Association between puberty and delayed phase preference. Sleep. 1993;16:258–262. doi: 10.1093/sleep/16.3.258. [DOI] [PubMed] [Google Scholar]
  11. Carskadon MA, Wolfson AR, Acebo C, Tzischinsky O, Seifer R. Adolescent sleep patterns, circadian timing, and sleepiness at a transition to early school days. Sleep. 1998;21:871–881. doi: 10.1093/sleep/21.8.871. [DOI] [PubMed] [Google Scholar]
  12. Centers for Disease Control and Prevention. 2007 Youth Risk Behavior Survey. 2007 www.cdc.gov/yrbss. Accessed 7/12/13.
  13. Chen X, Beydoun MA, Wang Y. Is sleep duration associated with childhood obesity? A systematic review and meta-analysis. Obesity. 2008;16:265–274. doi: 10.1038/oby.2007.63. [DOI] [PubMed] [Google Scholar]
  14. Cho HJ, Bower JE, Kiefe CI, Seeman TE, Irwin MR. Early life stress and inflammatory mechanisms of fatigue in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Brain, Behavior, and Immunity. 2012;26:859–865. doi: 10.1016/j.bbi.2012.04.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Crowley SJ, Acebo C, Carskadon MA. Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Medicine. 2007;8:602–612. doi: 10.1016/j.sleep.2006.12.002. [DOI] [PubMed] [Google Scholar]
  16. Danesh J, Kaptoge S, Mann AG, Sarwar N, Wood A, Angelman SB, Gudnason V. Long-term interleukin-6 levels and subsequent risk of coronary heart disease: Two new prospective studies and a systematic review. PLoS Medicine. 2008;5:e78. doi: 10.1371/journal.pmed.0050078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Danesh J, Whincup P, Walker M, Lennon L, Thomson A, Appleby P, Pepys MB. Low grade inflammation and coronary heart disease: prospective study and updated meta-analyses. British Journal of Medicine. 2000;321:199–204. doi: 10.1136/bmj.321.7255.199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fischer FM, Nagai R, Teixeira LR. Explaining sleep duration in adolescents: the impact of socio-demographic and lifestyle factors and working status. Chronobiology International. 2008;25:359–372. doi: 10.1080/07420520802110639. [DOI] [PubMed] [Google Scholar]
  19. Flint J, Kothare SV, Mamoon Z, Suarez E, Adams R, Legido A, De Luca F. Association between inadequate sleep and insulin resistance in obese children. Journal of Pediatrics. 2007;150:364–369. doi: 10.1016/j.jpeds.2006.08.063. [DOI] [PubMed] [Google Scholar]
  20. Gallicchio L, Kalesan B. Sleep duration and mortality: a systematic review and meta-analysis. Journal of Sleep Research. 2009;18:148–158. doi: 10.1111/j.1365-2869.2008.00732.x. [DOI] [PubMed] [Google Scholar]
  21. Garaulet M, Ortega FB, Ruiz JR, Rey-Lopez JP, Beghin L, Manios Y, Moreno LA. Short sleep duration is associated with increased obesity markers in European adolescents: effect of physical activity and dietary habits. The HELENA study. International Journal of Obesity. 2011;35:1308–1317. doi: 10.1038/ijo.2011.149. [DOI] [PubMed] [Google Scholar]
  22. Gau SF, Soong WT. Sleep problems of junior high school students in Taipei. Sleep. 1995;18:667–673. doi: 10.1093/sleep/18.8.667. [DOI] [PubMed] [Google Scholar]
  23. Gradisar M, Wolfson AR, Harvey AG, Hale L, Rosenberg R, Czeisler CA. The sleep and technology use of Americans: Findings from the National Sleep Foundation’s 2011 Sleep in America Poll. Journal of Clinical Sleep Medicine. 2013;9:1291–1299. doi: 10.5664/jcsm.3272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hall MH, Lee L, Matthews KA. Sleep duration during the school week is associated with C-reactive protein risk groups in healthy adolescents. Sleep Medicine. 2015;16:73–78. doi: 10.1016/j.sleep.2014.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hartman J, Frishman WH. Inflammation and atherosclerosis: A review of the role of interleukin-6 in the development of atherosclerosis and the potential for targeted drug therapy. Cardiology in Review. 2014;22:147–151. doi: 10.1097/CRD.0000000000000021. [DOI] [PubMed] [Google Scholar]
  26. Hollingshead AB. 1975 Four factor index of social status. New Haven, CT: Yale University; 1975. [Google Scholar]
  27. Iglowstein I, Jenni OG, Molinari L, Largo R. Sleep duration from infancy to adolescence: reference values and generational trends. Pediatrics. 2003;111:302–307. doi: 10.1542/peds.111.2.302. [DOI] [PubMed] [Google Scholar]
  28. Izawa S, Miki K, Liu X, Ogawa N. The diurnal patterns of salivary interleukin-6 and C-reactive protein in healthy young adults. Brain, Behavior, and Immunity. 2013;27:38–41. doi: 10.1016/j.bbi.2012.07.001. [DOI] [PubMed] [Google Scholar]
  29. Jakubowski KP, Hall MH, Lee L, Matthews KA. Temporal relationships between napping and nocturnal sleep in healthy adolescents. Behavioral Sleep Medicine. doi: 10.1080/15402002.2015.1126595. in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Javaheri S, Storfer-Isser A, Rosen CL, Redline S. Sleep quality and elevated blood pressure in adolescents. Circulation. 2008;118:1034–1040. doi: 10.1161/CIRCULATIONAHA.108.766410. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Javaheri S, Storfer-Isser A, Rosen CL, Redline S. Association of short and long sleep durations with insulin sensitivity in adolescents. Journal of Pediatrics. 2011;158:617–623. doi: 10.1016/j.jpeds.2010.09.080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kanady JC, Drummond SP, Mednick SC. Actigraphic assessment of a polysomnographic-recorded nap: a validation study. Journal of Sleep Research. 2011;20:214–222. doi: 10.1111/j.1365-2869.2010.00858.x. [DOI] [PubMed] [Google Scholar]
  33. Koren D, Levitt Katz LE, Brar PC, Gallagher PR, Berkowitz RI, Brooks LJ. Sleep architecture and glucose and insulin homeostasis in obese adolescents. Diabetes Care. 2011;34:2442–2447. doi: 10.2337/dc11-1093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kushida CA, Chang A, Gadkary C, Guilleminault C, Carrillo O, Dement WC. Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients. Sleep Medicine. 2001;2:389–396. doi: 10.1016/s1389-9457(00)00098-8. [DOI] [PubMed] [Google Scholar]
  35. Leng Y, Wainwright NWJ, Cappuccio FP, Surtees PG, Hayat S, Luben R, Khaw KT. Daytime napping and the risk of all-cause and cause-specific mortality: a 13-year follow-up of a British population. American Journal of Epidemiology. 2014 doi: 10.1093/aje/kwu036. Advance online publication. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lockley SW, Skene DJ, Arendt J. Comparison between subjective and actigraphic measurement of sleep and sleep rhythms. Journal of Sleep Research. 1999;8:175–183. doi: 10.1046/j.1365-2869.1999.00155.x. [DOI] [PubMed] [Google Scholar]
  37. Martinez-Gomez D, Eisenmann JC, Gomez-Martinez S, Hill EE, Zapatera B, Veiga OL, Marcos A. Sleep duration and emerging cardiometabolic risk markers in adolescents. The AFINOS study. Sleep Medicine. 2011;12:997–1002. doi: 10.1016/j.sleep.2011.05.009. [DOI] [PubMed] [Google Scholar]
  38. Matthews KA, Dahl RE, Owens JF, Lee L, Hall M. Sleep duration and insulin resistance in healthy black and white adolescents. Sleep. 2012;35:1353–1358. doi: 10.5665/sleep.2112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Matthews KA, Hall M, Dahl RE. Sleep in healthy black and white adolescents. Pediatrics. 2014;133:e1189–e1196. doi: 10.1542/peds.2013-2399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Matthews KA, Pantesco EM. Sleep characteristics and cardiovascular risk in children and adolescents: An enumerative review. Sleep Medicine. doi: 10.1016/j.sleep.2015.06.004. in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. McKnight-Eily LR, Eaton DK, Lowry R, Croft JB, Presley-Cantrell L, Perry GS. Relationships between hours of sleep and health-risk behaviors in US adolescent students. Preventive Medicine. 2011;53:271–273. doi: 10.1016/j.ypmed.2011.06.020. [DOI] [PubMed] [Google Scholar]
  42. Mercer PW, Merritt SL, Cowell JM. Differences in reported sleep need among adolescents. Journal of Adolescent Health. 1998;23:259–263. doi: 10.1016/s1054-139x(98)00037-8. [DOI] [PubMed] [Google Scholar]
  43. Meier-Ewert HK, Ridker PM, Rifai N, Price N, Dinges DF, Mullington JM. Absence of diurnal variation of C-reactive protein concentrations in healthy human subjects. Clinical Chemistry. 2001;47:426–430. [PubMed] [Google Scholar]
  44. Mezick EJ, Hall M, Matthews KA. Sleep duration and ambulatory blood pressure in black and white adolescents. Hypertension. 2012;59:747–752. doi: 10.1161/HYPERTENSIONAHA.111.184770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. National Sleep Foundation. Communications Technology in the Bedroom. 2011 http://www.sleepfoundation.org/sites/default/files/sleepinamericapoll/SIAP_2011_Summary_of_Findings.pdf. Accessed 5/13/15.
  46. National Sleep Foundation. Sleep in America poll: Summary of Findings. 2006 Available from: http://www.sleepfoundation.org/sites/default/files/2006_summary_of_findings.pdf. Accessed 5/13/15.
  47. Park HE, Cho GY, Chun EJ, Choi SI, Lee SP, Kim HK, Park YB. Can C-reactive protein predict cardiovascular events in asymptomatic patients? Analysis based on plaque characterization. Atherosclerosis. 2012;224:201–207. doi: 10.1016/j.atherosclerosis.2012.06.061. [DOI] [PubMed] [Google Scholar]
  48. Patel SR, Hu FB. Short sleep duration and weight gain: a systematic review. Obesity. 2008;16:643–653. doi: 10.1038/oby.2007.118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Roberts RE, Roberts CR, Duong HT. Sleepless in adolescence: prospective data on sleep deprivation, health and functioning. Journal of Adolescence. 2009;32:1045–1057. doi: 10.1016/j.adolescence.2009.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Roberts RE, Roberts CR, Xing Y. Restricted sleep among adolescents: prevalence, incidence, persistence, and associated factors. Behavioral Sleep Medicine. 2011;9:18–30. doi: 10.1080/15402002.2011.533991. [DOI] [PubMed] [Google Scholar]
  51. Spruyt K, Molfese DL, Gozal D. Sleep duration, sleep regularity, body weight, and metabolic homeostasis in school-aged children. Pediatrics. 2011;127:e345–e352. doi: 10.1542/peds.2010-0497. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Stang A, Dragano N, Moebus S, Möhlenkamp S, Schmerund A, Kälsch H, Jöckel KH. Midday naps and the risk of coronary artery disease: results of the Heinz Nixdorf Recall Study. Sleep. 2012;35:1705–1712. doi: 10.5665/sleep.2248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Tanabe N, Iso H, Seki N, Suzuki H, Yatsuya H, Toyoshima H, Tamakoshi A, JACC Study Group Daytime napping and mortality, with a special reference to cardiovascular disease: the JACC study. International Journal of Epidemiology. 2010;39:233–243. doi: 10.1093/ije/dyp327. [DOI] [PubMed] [Google Scholar]
  54. Tryon WW. Issues of validity in actigraphic sleep assessment. Sleep. 2004;27:158–165. doi: 10.1093/sleep/27.1.158. [DOI] [PubMed] [Google Scholar]
  55. Van Wouwe NC, Valk PJL, Veenstra BJ. Sleep monitoring: a comparison between three wearable instruments. Military Medicine. 2011;176:811–816. doi: 10.7205/milmed-d-10-00389. [DOI] [PubMed] [Google Scholar]
  56. Vygontzas AN, Bixler EO, Lin HM, Prolo P, Trakada G, Chrousos GP. IL-6 and its circadian secretion in humans. Neuroimmunomodulation. 2005;12:131–140. doi: 10.1159/000084844. [DOI] [PubMed] [Google Scholar]
  57. Vygontzas AN, Papanicolaou DA, Bixler EO, Kales A, Tyson K, Chrousos GP. Elevation of plasma cytokines in disorders of excessive daytime sleepiness: role of sleep disturbance and obesity. Journal of Clinical Endocrinology and Metabolism. 1997;82:1313–1316. doi: 10.1210/jcem.82.5.3950. [DOI] [PubMed] [Google Scholar]
  58. Wolfson AR, Carskadon MA. Sleep schedules and daytime functioning in adolescents. Child Development. 1998;69:875–887. [PubMed] [Google Scholar]
  59. Yamada T, Hara K, Shojima N, Yamauchi T, Kadowaki T. Daytime napping and the risk of cardiovascular disease and all-cause mortality: a prospecitve study and dose-response meta-analysis. SLEEP. 2015;38:1945–1953. doi: 10.5665/sleep.5246. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS777074-supplement-1.docx (15.7KB, docx)

RESOURCES