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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Sch Nurs. Author manuscript; available in PMC 2017 April 1.
Published in final edited form as:
PMCID: PMC4779703
NIHMSID: NIHMS727403

Characteristics associated with Sleep Duration, Chronotype, and Social jet lag in Adolescents

Susan Kohl Malone, PhD, RN, NCSN, Post-doctoral fellow,1,4 Babette S. Zemel, PhD, Professor of Pediatrics,2,3 Charlene Compher, PhD, RD, CNSC, LDN, FADA, Professor of Nutrition Science,1 Margaret Souders, PhD, CRNP, Assistant Professor of Human Genetics,1,3 Jesse Chittams, MS, Biostatistician,1 Aleda Leis Thompson, BS, Biostatistician,1 and Terri H. Lipman, PhD, CRNP, FAAN, Miriam Stirl Endowed Term Professor of Nutrition Professor of Nursing of Children1,3

Abstract

Sleep is a complex behavior with numerous health implications. Identifying socio-demographic and behavioral characteristics of sleep are important for determining those at greatest risk for sleep-related health disparities. In this cross-sectional study, general linear models were used to examine socio-demographic and behavioral characteristics associated with sleep duration, chronotype, and social jet lag in adolescents. One hundred fifteen participants completed Phase I (self-reported sleep measures); 69 of these participants completed Phase II (actigraphy-estimated sleep measures). Black adolescents had shorter free night sleep than Hispanics. Youth with later chronotypes ate fewer fruits and vegetables, drank more soda, were less physically active, and took more daytime naps. Based on these findings, recommendations for individual support and school policies are provided.

Keywords: sleep duration, chronotype, adolescence

Introduction

Sleep is essential for the health, well-being, and academic success of adolescents (M. Hall et al., 2015; Noland, Price, Dake, & Telljohann, 2009). There are multiple dimensions of healthy sleep, including sufficient duration, regular sleep-wake patterns, efficiency, alertness, and satisfaction (Blunden & Galland, 2014). Insufficient sleep (less than eight hours) and irregular sleep-wake patterns are associated with depressed moods, risk taking behaviors, cardiovascular disease risk, and obesity (Arora & Taheri, 2015; Hall, Lee, & Matthews, 2015; Randler, 2011b; Wittmann, Dinich, Merrow, & Roenneberg, 2006). Yet, 62% of high school students report insufficient sleep (National Sleep Foundation, 2006b). Early school start times are incongruent with developmental shifts towards later sleep-wake times, or later chronotypes (Carskadon, Acebo, & Jenni, 2004; National Sleep Foundation, 2006b). This leads to irregular sleep-wake patterns, coined social jet lag, which are characterized by variations in school day and free day sleep-wake timing (Wittmann et al., 2006). Elucidating characteristics associated with insufficient sleep, later chronotype, and social jet lag in adolescents are important because life-long habits are established during this developmental period (Dahl, 2004).

Several socio-demographic characteristics have been associated with sleep in adolescents. Age and puberty-related declines in sleep duration, delays in chronotype, and increases in social jet lag have been consistently reported (Knutson, 2005; Olds, Blunden, Petkov, & Forchino, 2010). Evidence that Black adolescents report shorter sleep than other racial/ethnic groups is also emerging (Maslowsky & Ozer, 2013; Organek et al., 2015). However, conflicted evidence for sex differences in sleep duration exist and few studies have examined associations between poverty and sleep in adolescents (Lowry et al., 2012; Marco, Wolfson, Sparling, & Azuaje, 2011; Moore et al., 2011; Olds et al., 2010). Studies with objective sleep measures have rarely included Hispanic adolescents or chronotype and social jet lag sleep parameters (Marco et al., 2011; Moore et al., 2011). Elucidating socio-demographic differences in objectively measured adolescent sleep, inclusive of the growing Hispanic population and across multiple dimensions of sleep, is important because sleep disparities may be an important link to health disparities in adulthood (Ennis, Rios-Vargas, & Albert, 2011; Grandner, Chakravorty, Perlis, Oliver, & Gurubhagavatula, 2014).

Determining how sleep clusters with other behaviors is important for informing multi-behavioral approaches for improving sleep and health. Although chronotype is partially determined by biology, typical adolescent behaviors, such as late-night technology use, late–night socializing, and high fat diets may shift chronotypes later by delaying melatonin onset and lengthening the circadian cycle (Kohsaka et al., 2007; Roenneberg, Kuehnle, et al., 2007). This may have implications for health because late meal times (after 8pm) are associated with obesity risk and less successful weight loss interventions in adults (Baron, Reid, Kern, & Zee, 2011; Garaulet et al., 2013). Limited evidence of associations between sleep and other lifestyle behaviors (e.g. eating habits) in adolescents exists (Garaulet et al., 2011; Kauderer & Randler, 2013; Schaal, Peter, & Randler, 2010).

The purpose of this study was to examine socio-demographic and behavioral characteristics associated with sleep duration, chronotype, and social jet lag among racially/ethnically diverse 9th and 10th grade students using subjective (self-report) and objective (actigraphy) measures. The aim was to examine whether socio-demographic characteristics (age, race/ethnicity, sex, poverty, puberty) and behavioral characteristics (eating habits, physical activity, screen time) predicted short sleep, late chronotype, or social jet lag.

Methods

Study Design

This cross-sectional study examined predictors of sleep duration and chronotype and other lifestyle characteristics in 9th and 10th grade students. In Phase I, participants completed questionnaires to assess socio-demographic characteristics, sleep habits, eating habits, physical activity, and pubertal development. Phase I participants were invited to participate in Phase II for a more detailed assessment of sleep duration with seven days of wrist actigraphy. The University of Pennsylvania Institutional Review Board approved this study and the school district provided letters of support. Written parent/guardian informed consent and written student assent were obtained prior to participation.

Participants

Participants were recruited from a four-year comprehensive public high school located in a northeast US coastal city. Phase I recruitment took place during compulsory health and physical education classes, back to school night, and prior to selected winter sports practices between October 2013 and January 2014. Data were collected during this same time period. Phase II recruitment took place between January 2014 and April 2014. Data for Phase II were collected between February 2014 and June 2014. The time between Phase I and II varied between participants depending on student availability. See Table 1 for Phase I and II exclusion criteria.

Table 1
Exclusion Criteria

Measures

Sleep duration

Phase I

The Sleep Habits Survey is part of the School Sleep Habits Survey (Wolfson & Carskadon, 1998). Six items query students about their usual sleep-wake behavior over the previous two weeks. Questions about sleep duration on school nights and free nights (Friday and Saturday nights) require the participant to record the number of hours and minutes slept, not including time awake in bed (e.g. 8 hours 30 minutes). The Sleep Habits Survey has been validated with actigraphy in adolescents (Wolfson et al., 2003).

Phase II

Participants wore non-invasive watch-like devices to measure rest and activity on their non-dominant wrist (Acti-watch 2) and maintained a sleep diary concurrently for seven continuous days (Acebo et al., 1999; Sadeh, Sharkey, & Carskadon, 1994). Sleep diary information included duration of naps, bedtime, time sleep was attempted, time needed to fall asleep, frequency and duration of wake after sleep onset, actual morning wake time, desired morning wake time, time out of bed for the day, sleep quality (very poor to very good), and any other comments (e.g. illness, medication use). Diary based sleep data were used as a validity check for actigraphy based data. Discrepancies were reconciled with each participant using an established protocol. Briefly, participants were queried using open-ended questions for discrepancies greater than 15 minutes in diary and actigraphy data for sleep onset or sleep offset, nocturnal activity indicating wakefulness in actigraphy data, and daytime non-activity indicating rest in actigraphy data (excluding diary indicated watch removal or daytime nap).

Downloaded actigraphy data were analyzed using the manufacturer’s supplied software. Sleep duration was calculated as [(sleep onset – sleep offset) – sleep onset latency]. Sleep onset and sleep offset are proxy measures for the beginning and end of sleep. Sleep onset latency is a proxy measure for how long it took the participant to fall asleep. Average school night (Sunday through Thursday) and free night sleep durations (Friday and Saturday) were used in the analyses. Intraclass correlation coefficients (ICC) for wrist actigraphy and polysomnogram in adolescents range from 0.2 to 0.6 (Johnson et al., 2007). The investigator called participants each evening to encourage retention and adherence to the protocol. Ninety nine percent of participants had actigraphy data for at least seven nights (range 4 – 14 nights).

Total night sleep was calculated as [(mean school night sleep duration × 5) + (mean free night sleep duration × 2) / 7] from both questionnaire data for Phase I and actigraphy data for Phase II. Sleep durations were further categorized as insufficient (less than eight hours), borderline (eight to less than nine hours), and optimal (greater than or equal to nine hours) (National Sleep Foundation, 2006a). Continuous sleep duration variables were used in all analyses. School nights and free nights were also analyzed separately to compare sleep duration on school nights versus free nights. Free night actigraphy data was not available in one subject, reducing the sample size in Phase II free night , chronotype, and social jet lag analyses to 68.

Naps

In Phase I, two questions were added to the Sleep Habits Survey by the investigator because 38% of high school students have reported at least two naps in a two week period (National Sleep Foundation, 2006a). Participants responded “yes” or “no” to each question: “During the past 2 weeks, have you slept during the day on weekends?” and “During the past 2 weeks, have you slept during the day on school days?” In Phase II, actigraphy identified naps (rest periods between 6am and 6pm) and self-reported naps from sleep diaries were verified with each participant.

Chronotype

Chronotype preference was measured using the Morningness/Eveningness Questionnaire. Chronotype was measured by calculating the midpoint of sleep from the Munich Chronotype Timing Questionnaire (Phase I) and from actigraphy data (Phase II).

Morningness/Eveningness Questionnaire

The 10-item Morningness/Eveningness Questionnaire (M/E Q) estimates activity/rest preferences by querying participants about preferred timing for activities such as tests, physical activity, bedtimes etc. (Carskadon, Vieira, & Acebo, 1993; Giannotti, Cortesi, Sebastiani, & Ottaviano, 2002). Scores range on a continuum from 10 (extreme late) to 43 (extreme early). Cut-off scores for early and late chronoptypes have not been established, but some investigators have used the 10th and 90th centiles to determine early and late chronotypes (Giannotti et al., 2002). The M/E Q has been validated in children and adolescents (Carskadon et al., 1993; Giannotti et al., 2002).

Midpoint of sleep

The midpoint of sleep estimates chronotype based on sleep-wake timing (Roenneberg, Wirz-Justice, & Merrow, 2003). This estimate was corrected using the formula described by others to account for the fact that people often sleep longer on free days to compensate for shorter sleep on school days (Roenneberg, Allebrandt, Merrow, & Vetter, 2012). The midpoint of sleep has been validated with dim light melatonin onset in adolescents (Crowley, Acebo, Fallone, & Carskadon, 2006). In Phase I and II, the midpoint of sleep was calculated from participant responses to questions about sleep-wake timing from Munich Chronotype Timing Questionnaire (MCTQ) and actigraphy data respectively (Roenneberg et al. (2012).

Social jet lag

Social jet lag is the absolute difference in midpoints of sleep between school nights and free nights. Data from the MCTQ (for Phase I) and actigraphy (for Phase II) were used to calculate social jet lag (Wittmann et al., 2006).

Eating habits

Eating habits refer to the type of foods eaten and patterns of eating. This was measured using nine questions from the 2013 Youth Risk Behavior Survey that queried specific food intake (e.g. fruit consumption) and eating patterns (e.g. breakfast skipping) over the previous seven days (Centers for Disease Control and Prevention, 2014a). Servings per day were calculated for fruit juice, fruit and vegetables, soft drinks, and milk. Participants completed this survey during Phase I. Results were used in Phase I and Phase II analyses. Other types of self reported food intake questionnaires have been validated with 24-hour diet recalls in youth (r = 0.54) (Rockett et al., 1997).

Physical activity

Physical activity was measured using four questions from the 2013 Youth Risk Behavior Survey. These questions asked about moderate/vigorous physical activity, sports participation, and screen time (Centers for Disease Control and Prevention, 2014a). Participants completed this survey during Phase I. Results were used in Phase I and Phase II analyses. Self reports may underestimate the percentage of youth meeting moderate activity recommendations and overestimate the percentage of youth meeting vigorous activity recommendations compared to accelerometry data (Troped et al., 2007). Test-retest intra-class correlation coefficients range from 0.51 (moderate activity) to 0.46 (vigorous activity) (Troped et al., 2007). Self reported TV viewing questions have been validated with seven-day logs (Schmitz et al., 2004). Screen time data was missing on one participant, reducing the sample size for Phase I screen time analysis to 114.

Pubertal Category

The five to six item Pubertal Self Rating Scale estimates pubertal status in settings where Tanner staging by physical examination is not appropriate (e.g. schools). Questions about body hair growth, deepening voice, and facial hair growth for males or body hair growth, breast growth, and menstruation (yes/no) for females are used to estimate pubertal categories (Carskadon & Acebo, 1993). Responses range from “barely started” to “seems complete”. “Don’t know” was also a response option. Categories created from this scale are similar to Tanner stages where pre-pubertal and post-pubertal are comparable to Tanner Stage 1 and Tanner Stage 5 respectively (Brooks-Gunn, Warren, Rosso, & Gargiulo, 1987). Agreement rates with Tanner’s Sexual Maturation Scale (images used to depict Tanner’s five stages of pubertal development) range from 82% (males) to 88% (females). If individual items were missing, the average of the non-missing items were used to fill in the missing values for each participant (Olinsky, Chen, & Harlow, 2003).

Free/reduced lunch participation

Free/reduced lunch, available through the National School Lunch Program, is a federally assisted meal program administered through Food and Nutrition Service at the federal level and state education agencies at the state level. Family income must be less than or equal to 130% of the poverty level to qualify for free meals or between 130% and 185% of the poverty level to qualify for reduced price meals. In 2012 - 2013, a family of four with an income less than or equal to $29,965 would qualify for free meals and a family of four with an income less than $42,643 would qualify for reduced-price meals (US Department of Agriculture Food and Nutrition Services, 2012). Participation data was obtained from official school records and used as a proxy measure for poverty. Participant’s who “did not apply” for free/reduced lunch were coded as non-participants.

Race/ethnicity

Data were obtained from official school records that were based on parent/guardian report of the student’s race/ethnicity as White, Black, Hispanic, or Asian. Hispanics were chosen as the reference population based on the distribution of the cohort.

Sex

Participants self-reported as male or female.

Analysis

Descriptive statistics were calculated on all outcome and predictor variables of interest. Continuous variables were described as means and standard deviations and categorical variables as frequencies and percentages. Phase II midpoints of sleep, social jet lag, and sleep duration parameters were highly correlated because they were derived from the same actigraphy-estimated data. Thus, separate models were developed to test these relationships in Phase II analyses. Independent sample t tests, ANOVAs, Pearson’s product moment correlations, and Spearman correlations were used as appropriate to assess bivariate associations between independent variables (e.g. race/ethnicity, screen time) and dependent variables of interest (e.g. sleep duration, chronotype). Next, general linear models were used to build more formal multivariable models. Only variables significant at alpha level of 0.2 from the bivariate analysis were included in the final adjusted models. Statistical significance was set at an alpha level of 0.05 based on the two-tailed test. SPSS was used for statistical analysis (SPSS, version 22).

Results

Sample Characteristics

Written parent/guardian consent and written student assent was received from approximately 21% of 9th and 10th grade students (N=116) for Phase I. One student with an implausible midpoint of sleep (3:00 pm) was excluded leaving 115 participants [Hispanic (n = 43), White (n = 35), Black (n = 33), Asian (n = 4)]. Sixty percent of this sample provided written parent/guardian consent and written student assent to participate in Phase II (N = 70). One student, diagnosed with Type 2 diabetes, was excluded leaving 69 participants [Hispanic (n = 25), White (n = 26), Black (n = 15), Asian (n = 3)]. Each racial/ethnic group comprised about one third of the sample. Most participants were females (Phase I: 70%, Phase II: 66%), and approximately 65% participated in the free/reduced lunch program. The mean age of participants was 15.4 (range 13.4 – 16.8) for Phase I and 15.5 (range 13.7 – 16.9) for Phase II. Sample characteristics are presented in Table 2. Most participants reported insufficient sleep on school nights (less than eight hours) and optimal sleep on free nights (greater than or equal to 9 hours) (National Sleep Foundation, 2006a). Most participants did not meet the recommended guidelines for milk intake, fruit/vegetable intake, physical activity, or screen time (Centers for Disease Control and Prevention, 2012; US Department of Agriculture & US Department of Health and Human Services, 2010; US Department of Health and Human Services, 2008). See Table 3.

Table 2
Sample Characteristics
Table 3
Participants meeting Recommendations for selected Health Behaviors

Socio- Demographic Characteristics associated with Sleep

Race/ethnicity was a predictor of self-reported and actigraphy-estimated free night sleep duration. Black participants reported sleeping 1.3 hours less on average on free nights than Hispanic participants (p < 0.01) and 1.2 hours less when estimated by actigraphy (p = 0.01). Although not reaching statistical significance, there was a similar trend for shorter self-reported and actigraphy-estimated free night sleep duration in Black compared to White participants. No other statistically significant racial/ethnic differences for any self-reported or actigraphy-estimated sleep duration parameters were found. Age, sex, poverty, and pubertal category were not significantly associated with any self-reported or actigraphy-estimated sleep duration parameters. See Table 4.

Table 4
Adjusted Model for the Predictors of Self-reported Free Night Sleep Duration by Demographic Characteristics, Behavioral Characteristics, and Chronotype: Phase I (N = 114)

Age was a predictor of chrontoype preference (M/E Q). Older ages predicted later chronotype preferences. For every one-month increase in age, M/E Q scores decreased 1 unit (p = 0.03). See Table 5. Poverty, pubertal category, and sex were not significantly associated with chronotype preference in Phase I or II.

Table 5
Adjusted Model for the Prediction of the Morningness/Eveningness Questionnaire by Demographic and Behavioral Characteristics: Phase I (N = 115)

Sex was a predictor of chronotype (midpoint of sleep). Males had 0.7 hours later midpoints of sleep than females (Phase I: p = 0.02). Poverty, pubertal category, and age were not significantly associated with chronotype in Phase I or II.

Behavioral Characteristics associated with Sleep

Eating habits

Eating more fruits and vegetables was a predictor of shorter self-reported free night sleep duration. For every one serving increase in fruit/vegetable servings per day, free night sleep duration decreased 0.2 hours (p = 0.02). See Table 4. Fruit and vegetable consumption was not significantly associated with actigraphy-estimated free night sleep (p = 0.06).

Drinking more soda and eating fewer fruits and vegetables were predictors of later chronotype preferences. For every additional serving of soda per day, M/E Q scores decreased approximately 1 unit (Phase I and II: p = 0.05). For every one less serving of fruits/vegetables per day, M/E Q scores decreased 0.5 units (Phase II only: p = 0.03). See Table 5.

Physical activity

Reporting less physical activity was a predictor of later chronotype preferences and later chronotypes. For every additional day not physically active during the week, M/E Q scores decreased 0.3 units (Phase I: p = 0.05, see Table 5) and midpoints of sleep were 0.13 hours later (Phase I: p = 0.02). Although not reaching statistical significance, there was a similar trend for later midpoints of sleep, computed from actigraphy, and less physical activity.

Other Sleep Behaviors

Reporting less social jet lag was a predictor of shorter self-reported free night sleep duration. For every one-hour decrease in social jet lag, free night sleep duration decreased 0.3 hours (p = 0.05). See Table 4.

Taking naps on school days was a predictor of shorter actigraphy-estimated total night sleep. Adolescents taking naps on school days had 0.4 hours less total nocturnal sleep compared to adolescents not taking naps (p = 0.04). Although not reaching statistical significance, there was a similar trend for school day naps and shorter self-reported total night sleep.

Taking naps was a predictor of later chronotype preferences and later chronotypes. Participants reporting free day naps had 3 units lower M/E Q scores than non-nappers (Phase I only: p < 0.001). See Table 5. Participants who napped on school days had a 0.6-hour later midpoint of sleep (p = 0.02). A similar trend was also evidence for later midpoints of sleep, computed from self-report, and more school day napping.

Discussion

Only nine percent of study participants reported the recommended number of hours sleep on school nights compared to 11% (10th grade) and 17% (9th grade) nationally. Irregular sleep-wake times were also evident in study participants with 40% to 68% having greater than or equal to two hours of social jet lag. Additionally, over 40% reported school day naps. These data corroborate extensive findings of unhealthy sleep in adolescents that includes school night sleep duration falling short of the recommended 9 to 10 hours per night (Centers for Disease Control and Prevention). Socio-demographic predictors of short free night sleep and late chronotype were identified. Our findings also indicate that youth with later chronotypes engage in less healthy eating habits and physical activity behaviors. If these behavioral patterns persist over time, youth with later chronotypes may be at greater risk for obesity.

Black participants had significantly shorter free night sleep than Hispanic participants and there was a similar trend for shorter free night sleep in Black compared to White participants. This is consistent with previous reports of shorter subjectively and objectively measured school night and total night sleep in Black compared to White adolescents and shorter subjectively measured sleep in Black compared to Hispanic adolescents (Lowry et al., 2012; Matthews, Hall, & Dahl, 2014; Moore et al., 2011; Organek et al., 2015) Other studies have reported longer self-reported sleep in Black compared to White adolescents and no differences between Black and Hispanic adolescents (Organek et al., 2015; Williams, Zimmerman, & Bell, 2013). Disparate self-reported findings may stem from parent-reported versus self-reported sleep, total sleep (potentially including naps) versus nocturnal sleep, and one versus two sleep duration questions (Lauderdale, 2014; Short, Gradisar, Lack, Wright, & Chatburn, 2013). Blacks, but not Whites, have typically reported different sleep durations on weeknights and weekends (Lauderdale, 2014). The lack of statistical significance between Black and White youth in this study may have been driven by the small sample size. There were no significant racial/ethnic differences in free night sleep duration between White and Hispanic participants or other self-reported or actigraphy-estimated nocturnal sleep parameters.

These data contribute to the limited evidence of racial/ethnic differences in adolescent sleep duration and identify that differences may stem from differences in free night sleep. It is interesting that shorter free night sleep was also associated with more positive health behaviors (e.g. more fruits/vegetables, less social jet lag). Longer free night sleep in other racial/ethnic groups might represent greater compensation for short school night sleep, yet racial/ethnic differences in social jet lag were not evident. Hence whether shorter free night sleep in Black adolescents is less healthy is uncertain.

Results from this study also support nuanced sex differences in chronotype. The lack of sex differences in psychological preferences for later chronotypes, despite differences in sleep-wake times (e.g. males reporting later chronotypes) is consistent with earlier reports (Borchers & Randler, 2012; Koscec, Radosevic-Vidacek, & Bakotic, 2014; Randler, 2011a). It has been speculated that adolescent females may have greater restriction on late-night activities and/or have greater parental/guardian expectations for domestic help (i.e. babysitting younger siblings on weekends) that undergird these behavioral differences in sleep-wake times for chronotype but not psychological preferences in chronotype (Borchers & Randler, 2012).

Results for this study are consistent with one earlier study indicating that poverty was not a predictor of short sleep or irregular sleep patterns in adolescents (Moore et al., 2011). A broader socio-economic estimate, beyond parental income, may be necessary to elucidate factors that contribute to differences in adolescent sleep, such as parent education and parent employment status (Marco et al., 2011).

There is limited evidence that adolescents with later chronotypes are more likely to engage in unhealthy behaviors than adolescents with earlier chronotypes (Fleig & Randler, 2009; Schaal et al., 2010; Urban, Magyarodi, & Rigo, 2011). This study extends this evidence to racially diverse high school students. Similar to previous studies, eating fewer fruits/vegetables and drinking more soda predicted later chronotypes (Arora & Taheri, 2015; Fleig & Randler, 2009). Drinking more caffeinated beverages may help youth with later chronotype adapt better to daytime hours (Giannotti et al., 2002).

This study also found that less physical activity (days per week) predicted subjectively measured later chronotypes and a trend towards objectively measured later chronotypes. These findings are consistent with previous findings in Japanese and German adolescents (Gaina et al., 2006; Schaal et al., 2010). Lack of statistical significance with objectively estimated chronotype might have been due to the smaller sample size.

It was surprising that having a later chronotype did not predict greater social jet lag as reported in adults (Wittmann et al., 2006). However, having a later chronotype did predict more daytime naps. This is consistent with findings that Tiawanese and Italian adolescents with later chronotypes nap more than their earlier counterparts (Gau & Soong, 2003; Giannotti et al., 2002).

Although screen time was associated with later chronotypes in the univariate analyses, it did not predict later chronotypes in the multivariable models. This finding is inconsistent with one study reporting greater screen time in adolescents with later chronotypes (Kauderer & Randler, 2013). Disparate findings may be related to our small sample size.

Limitations

Compared to a nationally representative sample of high school students, this study enrolled more female participants (Davis, September 2013). There was also greater participation in the National School Lunch program (free/reduced lunch) compared to a nationally representative sample of students and a state-wide representative sample of students (Department of U.S. Department of Education, Institute of Education Sciences, & National Center for Education Statistics). Hence, the generalizability of these findings to male adolescents and more affluent socio-economic groups may be limited. Also, as a small cross-sectional study, the short and long term health implications of these sleep patterns and racial/ethnic differences in sleep duration remain undetermined.

Other factors that may have biased our findings are as follows. Recruitment efforts were undertaken through the athletic department to increase male participation. Written parent/guardian consent may have limited participation from some students (Tigges, 2003). Low reliability of the Pubertal Development Scale (Cronbach’s alpha 0.3 – 0.6) and the Mornigningness/Evenigness Questionnaire (Cronbach’s alpha 0.6) may have limited our ability to detect differences. Information on other factors that may have influenced sleep such as melatonin, parent/guardian employment, and depression was not collected.

Implications for School Nurses

School nurses have a vital role in improving adolescent sleep by intervening at the individual, local school, community, and national levels. At the individual level, school nurses can assess sleep duration in all adolescents using brief questionnaires, such as the Sleep Habits Survey, during health class or individual health visits. Nurses need to ask about school night and free night sleep. Importantly, sleep disparities that persist into adulthood are associated with hypertension, heart disease and increased mortality (Duggan, Reynolds, Kern, & Friedman, 2014; Grandner et al., 2014).

School nurses must advocate that recommendations to delay school start times be put into local school policies and participate in future research to further elucidate optimal school start times (Adolescent Sleep Working Group, 2014). Recommendations to delay school start times until 8:30 has been based on evidence that 8:30 start times are associated with longer school night sleep duration, improved attendance, reduced tardiness, and less daytime sleepiness (Danner & Phillips, 2008; Owens, Belon, & Moss, 2010; Short, Gradisar, Lack, Wright, Dewald, et al., 2013; Wahlstrom, 2002). Recent evidence from a geographically diverse sample of U.S. high schools, suggests that delays after 8 am may not lead to appreciable gains in sleep duration and that adolescent males in metropolitan areas reap the greatest gains in school start time delays (Paksarian, Rudolph, He, & Merikangas, 2015). Although the authors conclude with supporting 8:30 start time recommendations, this evidence underscores the need for more research to identify optimal start times for specific populations. Regardless, 86% of over 18,000 public high schools started school before 8:30 during the 2011-2012 school year (US Department of Education & National Center for Education Statistics). Hence, delaying school start times is a promising intervention for increasing school night sleep duration for a large number of adolescents. Initial steps include identifying barriers faced by school districts striving to implement these changes and networking with others to develop resources in overcoming these barriers (Center for Applied Research and Educational Improvement, 1998).

Innovative strategies to adapt schedules based on chronotype should be undertaken in conjunction with efforts to mitigate further shifts towards lateness. Nurses must educate school administrators, teachers, parents/guardians, and students about biological, developmental, and environmental factors that influence chronotype. Sleep education that includes chronotype, must be adequately represented in the health curriculum and presented to parents/guardians. Findings that youth with later chronotypes report poorer eating habits and less physical activity suggest that they may be at greater risk for health problems than their earlier chronotype counterparts. If replicated in future studies, strategies aimed at aligning daily activities with the later sleep-wake patterns characteristic of youth with later chronotypes and preventing shifts towards lateness may be important for future health.

To accomplish this, school nurses can work with health educators to help students determine their chronotype by completing the Morningness/Eveningness Questionnaire during health class. This information can be used to help students determine times that they will feel and perform best for certain activities, including exercise. For example, youth with earlier chronotypes will be best suited for morning physical activity, whereas, youth with later chronotypes will be better suited for afternoon physical activity. Advocating for innovative ways to integrate this type of flexibility into the physical education curriculum can influence adherence to regular physical activity regimes and attitudes towards participating in sports programs (Brown, 2008; Vitale, 2013). Together with delaying school start times; these changes will benefit youth with later chronotypes who may be combating sleepiness by drinking caffeinated beverages, such as soda, and being less physically active (Tran et al., 2014).

Changes in modern lifestyles are contributing to greater shifts towards lateness at the population level (Roenneberg, Kumar, & Merrow, 2007). Greater time spent indoors has dampened the strength of light/dark stimuli critical to regulating sleep-wake timing. Increasing exposure to outdoor daylight and limiting exposure to night-time light may mitigate shifts towards lateness (Vollmer, Michel, & Randler, 2012; West et al., 2011). Two hours of outdoor light exposure per day is needed to shift sleep-wake timing one hour earlier (Roenneberg & Merrow, 2007). Strategies to increase outdoor light exposure for adolescents include scheduling weekend athletic events during the day, promoting short times between classes and during lunch to be outdoors during the school day (Harada, 2002), and designing school buildings that enhance light exposure and increase outdoor spaces.

These interventions must be combined with limiting light at night exposure. Urban planning committees should seek innovative ways to reduce ambient light at night exposure, including the lighting of school athletic fields. School nurses should educate adolescents, parents/guardians, teachers, and school information technology personnel about strategies for reducing light exposure from electronic media. Although one study found that one-hour exposure to electronic media at night did not delay sleep-wake times in adolescents, most students report far greater media use (Centers for Disease Control and Prevention, 2014b; Heath et al., 2014). Limiting electronic devices in bedrooms, installing software programs to adjust computer screen displays based on the time of day, and adjusting screen settings to white text on black for night reading are other possible interventions (Calamaro, Mason, & Ratcliffe, 2009; Calamaro, Yang, Ratcliffe, & Chasens, 2012).

Parent/guardian monitoring over bedtimes, typically decline as adolescents progress through high school. Yet, adolescents whose parents/guardians enforce bedtimes have earlier sleep-wake times during the week (Randler & Bilger, 2009). Nurses should educate and encourage parents/guardians to enforce bedtimes throughout high school, including weekends.

Conclusion

Findings from this study corroborate extensive findings that adolescents do not sleep enough on school nights. School nurses should spearhead and support initiatives to delay high school start times, ensure sleep education is adequately represented in the health curriculum, and routinely assess school night and free night sleep. The Sleep Habits Survey is a brief, validated questionnaire that will identify youth at greatest risk for short school night and free night sleep.

Less healthy eating habits and physical activity patterns reported by youth with later chronotypes forebode poorer health if these behaviors persist into adulthood. The Morningness/Eveningness Questionnaire may be completed and scored briefly during health class. School nurses, together with health educators, can help students identify their chronotype preference relative to their peers. If these findings are replicated in larger studies, innovative individual and environmental strategies aimed at aligning activities with the later sleep-wake patterns characteristic of youth with later chronotypes and preventing further shifts towards lateness may be warranted.

Acknowledgements

We would like to thank Kathleen Celli, Mary Whalen, and the Long Branch High School administration, faculty, parents/guardians, and students for their help with recruitment and data collection. We would also like to acknowledge Dr. Allan Pack for providing insight on the analysis of this data and Dr. Philip Gehrman for providing additional feedback on analyzing actigraphy data.

Funding Support

This work was made possible through generous support from the National Association of School Nurses, National Institute of Nursing Research Ruth L. Kirschstein National Research Service Award (F31 NR014603), The Rockefeller University Heilbrunn Nurse Scholar Award, University of Pennsylvania School of Nursing Biobehavioral Research Center, and University of Pennsylvania Office of Nursing Research

References

  • 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(1):95–103. [PubMed]
  • Adolescent Sleep Working Group, Committee on Adolescence and Council on School Health School start times for adolescents. Pediatrics. 2014;134(3):642–649. doi: 10.1542/peds.2014-1697. [PubMed]
  • Arora T, Taheri S. Associations among late chronotype, body mass index and dietary behaviors in young adolescents. Int J Obes (Lond) 2015;39(1):39–44. doi: 10.1038/ijo.2014.157. [PubMed]
  • Baron KG, Reid KJ, Kern AS, Zee PC. Role of sleep timing in caloric intake and BMI. Obesity (Silver Spring, Md.) 2011;19(7):1374–1381. doi: 10.1038/oby.2011.100; 10.1038/oby.2011.100. [PubMed]
  • Blunden S, Galland B. The complexities of defining optimal sleep: Empirical and theoretical considerations with a special emphasis on children. Sleep Med Rev. 2014;18(5):371–378. doi: 10.1016/j.smrv.2014.01.002. [PubMed]
  • Borchers C, Randler C. Sleep-wake cycle of adolescents in Cote d'ivoire: Influence of age, gender, religion and occupation. Chronobiology International. 2012;29(10):1366–1375. doi: 10.3109/07420528.2012.741173; 10.3109/07420528.2012.741173. [PubMed]
  • Brooks-Gunn J, Warren MP, Rosso J, Gargiulo J. Validity of self-report measures of girls' pubertal status. Child Dev. 1987;58(3):829–841. [PubMed]
  • Brown F, Neft E, LaJambe C. Collegiate rowing crew performance varies by morningess-eveningness. Journal of Strength and Conditioning Research. 2008;22(6):1894–1900. [PubMed]
  • Calamaro CJ, Mason TB, Ratcliffe SJ. Adolescents living the 24/7 lifestyle: Effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics. 2009;123(6):e1005–1010. doi: 10.1542/peds.2008-3641. [PubMed]
  • Calamaro CJ, Yang K, Ratcliffe S, Chasens ER. Wired at a young age: The effect of caffeine and technology on sleep duration and body mass index in school-aged children. J Pediatr Health Care. 2012;26(4):276–282. doi: 10.1016/j.pedhc.2010.12.002. [PubMed]
  • Carskadon MA, Acebo C. A self-administered rating scale for pubertal development. J Adolesc Health. 1993;14(3):190–195. [PubMed]
  • Carskadon MAMA, Acebo C, Jenni OG. Regulation of adolescent sleep: Implications for behavior. Ann N Y Acad Sci. 2004;1021:276–291. doi: 10.1196/annals.1308.032. [PubMed]
  • Carskadon MA, Vieira C, Acebo C. Association between puberty and delayed phase preference. Sleep. 1993;16(3):258–262. [PubMed]
  • Center for Applied Research and Educational Improvement School start time study final report summary. 1998.
  • Centers for Disease Control and Prevention Sleep and sleep disorders: How much sleep do I need? 2013 Jul 1; Retrieved May 22, 2015, from http://www.cdc.gov/sleep/about_sleep/how_much_sleep.htm.
  • Centers for Disease Control and Prevention Youth Risk Behavior Surveillance: Questionnaires and item rationales. 2012. Web Page.
  • Centers for Disease Control and Prevention Adolescent and school health: YRBS questionnaires. 2014a 2014 Jul 9; Retrieved November 21, 2014, from http://www.cdc.gov/healthyyouth/yrbs/questionnaire_rationale.htm.
  • Centers for Disease Control and Prevention Youth online: High school YRBS. 2014b Retrieved November 15, 2014, 2014, from http://nccd.cdc.gov/youthonline/App/Default.aspx.
  • Crowley SJ, Acebo C, Fallone G, Carskadon MA. Estimating dim light melatonin onset (dlmo) phase in adolescents using summer or school-year sleep/wake schedules. Sleep. 2006;29(12):1632–1641. [PubMed]
  • Dahl RE. Adolescent brain development: A period of vulnerabilites and opportunities. Ann NY Acad Sci. 2004;1021:1–22. [PubMed]
  • Danner F, Phillips B. Adolescent sleep, school start times, and teen motor vehicle crashes. J Clin Sleep Med. 2008;4(6):533–535. [PubMed]
  • Davis J, Bauman K. School enrollment in the United States 2011: Population characteristics. Sep, 2013.
  • Duggan KA, Reynolds CA, Kern ML, Friedman HS. Childhood sleep duration and lifelong mortality risk. Health Psychol. 2014;33(10):1195–1203. doi: 10.1037/hea0000078. [PMC free article] [PubMed]
  • Ennis S, Rios-Vargas M, Albert N. The Hispanic population 2010 (U. S. Census Bureau, Trans.) 2011. pp. 1–16. U. S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau (Ed.), 2010 Census Briefs.
  • Fleig D, Randler C. Association between chronotype and diet in adolescents based on food logs. Eat Behav. 2009;10(2):115–118. doi: 10.1016/j.eatbeh.2009.03.002. [PubMed]
  • Gaina A, Sekine M, Kanayama H, Takashi Y, Hu L, Sengoku K, Kagamimori S. Morning-evening preference: Sleep pattern spectrum and lifestyle habits among Japanese junior high school pupils. Chronobiol Int. 2006;23(3):607–621. doi: 10.1080/07420520600650646. [PubMed]
  • Garaulet M, Gomez-Abellan P, Alburquerque-Bejar JJ, Lee YC, Ordovas JM, Scheer FA. Timing of food intake predicts weight loss effectiveness. Int J Obes (Lond) 2013;37(4):604–611. doi: 10.1038/ijo.2012.229. [PMC free article] [PubMed]
  • 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. Int J Obes (Lond) 2011;35(10):1308–1317. doi: 10.1038/ijo.2011.149. [PubMed]
  • Gau SF, Soong WT. The transition of sleep-wake patterns in early adolescence. Sleep. 2003;26(4):449–454. [PubMed]
  • Giannotti F, Cortesi F, Sebastiani T, Ottaviano S. Circadian preference, sleep and daytime behaviour in adolescence. J Sleep Res. 2002;11(3):191–199. [PubMed]
  • Grandner MA, Chakravorty S, Perlis ML, Oliver L, Gurubhagavatula I. Habitual sleep duration associated with self-reported and objectively determined cardiometabolic risk factors. Sleep Med. 2014;15(1):42–50. doi: 10.1016/j.sleep.2013.09.012. [PMC free article] [PubMed]
  • Hall M, Smagula SF, Boudreau RM, Ayonayon HN, Goldman SE, Harris T, Newman AB. Association between sleep duration and mortality is mediated by markers of inflammation and health in older adults: The Health, Aging and Body Composition Study. Sleep. 2015;38(2):189–195. [PubMed]
  • Hall MH, Lee L, Matthews KA. Sleep duration during the school week is associated with c-reactive protein risk groups in healthy adolescents. Sleep Med. 2015;16(1):73–78. doi: 10.1016/j.sleep.2014.10.005. [PMC free article] [PubMed]
  • Harada T, Morisane H, Takeuchi H. Effect of daytime light conditions on sleep habits and morningness–eveningness preference of Japanese students aged 12–15 years. Psychiatry and Clinical Neurosciences. 2002;56:225–226. [PubMed]
  • Heath M, Sutherland C, Bartel K, Gradisar M, Williamson P, Lovato N, Micic G. Does one hour of bright or short-wavelength filtered tablet screenlight have a meaningful effect on adolescents' pre-bedtime alertness, sleep, and daytime functioning? Chronobiol Int. 2014;31(4):496–505. doi: 10.3109/07420528.2013.872121. [PubMed]
  • Johnson NL, Kirchner HL, Rosen CL, Storfer-Isser A, Cartar LN, Ancoli-Israel S, Redline S. Sleep estimation using wrist actigraphy in adolescents with and without sleep disordered breathing: A comparison of three data modes. Sleep. 2007;30(7):899–905. [PubMed]
  • Kauderer S, Randler C. Differences in time use among chronotypes in adolescents. Biological Rhythm Research. 2013;44(4):601–608. doi: 10.1080/09291016.2012.721687.
  • Knutson KL. The association between pubertal status and sleep duration and quality among a nationally representative sample of U. S. Adolescents. Am J Hum Biol. 2005;17(4):418–424. doi: 10.1002/ajhb.20405. [PubMed]
  • Kohsaka A, Laposky AD, Ramsey KM, Estrada C, Joshu C, Kobayashi Y, Bass J. High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metabolism. 2007;6(5):414–421. doi: 10.1016/j.cmet.2007.09.006. [PubMed]
  • Koscec A, Radosevic-Vidacek B, Bakotic M. Morningness-eveningness and sleep patterns of adolescents attending school in two rotating shifts. Chronobiol Int. 2014;31(1):52–63. doi: 10.3109/07420528.2013.821128. [PubMed]
  • Lauderdale DS. Survey questions about sleep duration: Does asking seperately about weekdays and weekends matter? Behavioral Sleep Medicine. 2014;12:158–168. [PubMed]
  • Lowry R, Eaton DK, Foti K, McKnight-Eily L, Perry G, Galuska DA. Association of sleep duration with obesity among US high school students. Journal of Obesity. 20122012:476914. doi: 10.1155/2012/476914. [PMC free article] [PubMed]
  • Marco CA, Wolfson AR, Sparling M, Azuaje A. Family socioeconomic status and sleep patterns of young adolescents. Behav Sleep Med. 2011;10(1):70–80. doi: 10.1080/15402002.2012.636298. [PubMed]
  • Maslowsky J, Ozer EJ. Developmental trends in sleep duration in adolescence and young adulthood: Evidence from a national United States sample. J Adolesc Health. 2013 doi: 10.1016/j.jadohealth.2013.10.201. [PMC free article] [PubMed]
  • Matthews KA, Hall M, Dahl RE. Sleep in healthy black and white adolescents. Pediatrics. 2014;133(5):e1189–1196. doi: 10.1542/peds.2013-2399. [PMC free article] [PubMed]
  • Moore M, Kirchner HL, Drotar D, Johnson N, Rosen C, Redline S. Correlates of adolescent sleep time and variability in sleep time: The role of individual and health related characteristics. Sleep Med. 2011;12(3):239–245. doi: 10.1016/j.sleep.2010.07.020. [PMC free article] [PubMed]
  • National Sleep Foundation . Sleep in America poll. Sleep Foundation; Washington DC: 2006a.
  • National Sleep Foundation Summary of findings. 2006b Retrieved February 1, 2015, from http://sleepfoundation.org/sites/default/files/2006_summary_of_findings.pdf.
  • Noland H, Price JH, Dake J, Telljohann SK. Adolescents' sleep behaviors and perceptions of sleep. The Journal of School Health. 2009;79(5):224–230. doi: 10.1111/j.1746-1561.2009.00402.x. [PubMed]
  • Olds T, Blunden S, Petkov J, Forchino F. The relationships between sex, age, geography and time in bed in adolescents: A meta-analysis of data from 23 countries. Sleep Med Rev. 2010;14(6):371–378. doi: 10.1016/j.smrv.2009.12.002. [PubMed]
  • Olinsky A, Chen S, Harlow L. The comparative efficacy of imputation methods for missing data in structural equation modeling. European Journal of Operational Research. 2003;151(1):53–79. doi: 10.1016/s0377-2217(02)00578-7.
  • Organek K, Taylor D, Petrie T, Martin S, Greenleaf C, Dietch J, Ruiz J. Adolescent sleep disparities: Sex and racial/ethnic differences. Sleep Health. 2015 doi: 10.1016/j.sleh.2014.12.003.
  • Owens JA, Belon K, Moss P. Impact of delaying school start time on adolescent sleep, mood, and behavior. Arch Pediatr Adolesc Med. 2010;164(7):608–614. doi: 10.1001/archpediatrics.2010.96. [PubMed]
  • Paksarian D, Rudolph KE, He JP, Merikangas KR. School start time and adolescent sleep patterns: Results from the US national comorbidity survey-adolescent supplement. Am J Public Health. 2015;105(7):1351–1357. doi: 10.2105/AJPH.2015.302619. [PubMed]
  • Randler C. Age and gender differences in morningness–eveningness during adolescence. The Journal of Genetic Psychology. 2011a;172(3):302–308. [PubMed]
  • Randler C. Association between morningness-eveningness and mental and physical health in adolescents. Psychol Health Med. 2011b;16(1):29–38. doi: 10.1080/13548506.2010.521564. [PubMed]
  • Randler C, Bilger S. Associations among sleep, chronotype, parental monitoring, and pubertal development among German adolescents. J Psychol. 2009;143(5):509–520. doi: 10.3200/jrl.143.5.509-520. [PubMed]
  • Rockett HR, Breitenbach M, Frazier AL, Witschi J, Wolf AM, Field AE, Colditz GA. Validation of a youth/adolescent food frequency questionnaire. Prev Med. 1997;26(6):808–816. doi: 10.1006/pmed.1997.0200. [PubMed]
  • Roenneberg T, Allebrandt KV, Merrow M, Vetter C. Social jetlag and obesity. Curr Biol. 2012;22(10):939–943. doi: 10.1016/j.cub.2012.03.038. [PubMed]
  • Roenneberg T, Kuehnle T, Juda M, Kantermann T, Allebrandt K, Gordijn M, Merrow M. Epidemiology of the human circadian clock. Sleep Med Rev. 2007;11(6):429–438. doi: 10.1016/j.smrv.2007.07.005. [PubMed]
  • Roenneberg T, Kumar CJ, Merrow M. The human circadian clock entrains to sun time. Curr Biol. 2007;17(2):R44–45. doi: 10.1016/j.cub.2006.12.011. [PubMed]
  • Roenneberg T, Merrow M. Entrainment of the human circadian clock. Cold Spring Harb Symp Quant Biol. 2007;72:293–299. doi: 10.1101/sqb.2007.72.043. [PubMed]
  • Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: Daily temporal patterns of human chronotypes. J Biol Rhythms. 2003;18(1):80–90. [PubMed]
  • Sadeh A, Sharkey KM, Carskadon MA. Activity-based sleep-wake identification: An empirical test of methodological issues. Sleep. 1994;17(3):201–207. [PubMed]
  • Schaal S, Peter M, Randler C. Morningness-eveningness and physcial actvity in adolescents. Int J Sport Exercise Psychol. 2010;8(2):147–159.
  • Schmitz KH, Harnack L, Fulton JE, Jacobs DR, Jr., Gao S, Lytle LA, Van Coevering P. Reliability and validity of a brief questionnaire to assess television viewing and computer use by middle school children. J Sch Health. 2004;74(9):370–377. [PubMed]
  • Short MA, Gradisar M, Lack LC, Wright HR, Chatburn A. Estimating adolescent sleep patterns: Parent reports versus adolescent self-report surveys, sleep diaries, and actigraphy. Nat Sci Sleep. 2013;5:23–26. doi: 10.2147/nss.s38369. [PMC free article] [PubMed]
  • Short MA, Gradisar M, Lack LC, Wright HR, Dewald JF, Wolfson AR, Carskadon MA. A cross-cultural comparison of sleep duration between US and Australian adolescents: The effect of school start time, parent-set bedtimes, and extracurricular load. Health Educ Behav. 2013;40(3):323–330. doi: 10.1177/1090198112451266. [PMC free article] [PubMed]
  • Tigges BB. Parental consent and adolescent risk behavior research. J Nurs Scholarsh. 2003;35(3):283–289. [PubMed]
  • Tran J, Lertmaharit S, Lohsoonthorn V, Pensuksan WC, Rattananupong T, Tadesse MG, Williams MA. Daytime sleepiness, circadian preference, caffeine consumption and use of other stimulants among thai college students. J Public Health Epidemiol. 2014;8(6):202–210. doi: 10.5897/JPHE2014.0620. [PMC free article] [PubMed]
  • Troped PJ, Wiecha JL, Fragala MS, Matthews CE, Finkelstein DM, Kim J, Peterson KE. Reliability and validity of yrbs physical activity items among middle school students. Med Sci Sports Exerc. 2007;39(3):416–425. doi: 10.1249/mss.0b013e31802d97af. [PubMed]
  • U.S. Department of Education, Institute of Education Sciences, & National Center for Education Statistics Digest for education statistics. Retrieved June 3, 2014, from http://nces.ed.gov/programs/digest/d10/tables/dt10_044.asp.
  • Urban R, Magyarodi T, Rigo A. Morningness-eveningness, chronotypes and health-impairing behaviors in adolescents. Chronobiol Int. 2011;28(3):238–247. doi: 10.3109/07420528.2010.549599. [PMC free article] [PubMed]
  • US Department of Agriculture, & US Department of Health and Human Services . Dietary guidelines for Americans 2010. 7th US Government Printing Office; Washington DC: 2010.
  • US Department of Agriculture Food and Nutrition Services National school lunch program fact sheet. 2012 2012 Aug; Retrieved March 3, 2013, from http://www.fns.usda.gov/cnd/Lunch/AboutLunch/NSLPFactSheet.pdf.
  • US Department of Education, & National Center for Education Statistics Schools and staffing survey. Retrieved March 18, 2015, from http://nces.ed.gov/surveys/sass/tables/sass1112_201381_s1n.asp.
  • US Department of Health and Human Services . 2008 physical activity guidelines for Americans. US Department of Health and Human Services; Washington DC: 2008.
  • Vitale JC, Weydahl A. Influence of chronotype on responses to a standardized, self-paced walking task in the morning vs afternoon: A pilot study. Perceptual & Motor Skills: Exercise & Sport. 2013;116(3):1020–1028. G. doi: 10.2466/06.19.PMS.116.3.1020-1028. [PubMed]
  • Vollmer C, Michel U, Randler C. Outdoor light at night (lan) is correlated with eveningness in adolescents. Chronobiol Int. 2012;29(4):502–508. doi: 10.3109/07420528.2011.635232. [PubMed]
  • Wahlstrom K. Changing times: Findings from the first longitudinal study of later high school start times NASSP Bulletin. 2002;86:1–21.
  • West KE, Jablonski MR, Warfield B, Cecil KS, James M, Ayers MA, Brainard GC. Blue light from light-emitting diodes elicits a dose-dependent suppression of melatonin in humans. J Appl Physiol (1985) 2011;110(3):619–626. doi: 10.1152/japplphysiol.01413.2009. [PubMed]
  • Williams JA, Zimmerman FJ, Bell JF. Norms and trends of sleep time among US children and adolescents. JAMA Pediatr. 2013;167(1):55–60. doi: 10.1001/jamapediatrics.2013.423. [PubMed]
  • Wittmann M, Dinich J, Merrow M, Roenneberg T. Social jetlag: Misalignment of biological and social time. Chronobiol Int. 2006;23(1-2):497–509. doi: 10.1080/07420520500545979. [PubMed]
  • Wolfson AR, Carskadon MA. Sleep schedules and daytime functioning in adolescents. Child Dev. 1998;69(4):875–887. [PubMed]
  • Wolfson AR, Carskadon MA, Acebo C, Seifer R, Fallone G, Labyak SE, Martin JL. Evidence for the validity of a sleep habits survey for adolescents. Sleep. 2003;26(2):213–216. [PubMed]