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. Author manuscript; available in PMC: 2018 Feb 11.
Published in final edited form as: Chronobiol Int. 2016 Aug 11;33(9):1255–1266. doi: 10.1080/07420528.2016.1196697

Social jetlag, chronotype, and body mass index in 14 to 17 year old adolescents

Susan Kohl Malone a,d, Babette Zemel b,c, Charlene Compher a, Margaret Souders a,c, Jesse Chittams a, Aleda Leis Thompson a, Allan Pack d, Terri H Lipman a,c
PMCID: PMC5303560  NIHMSID: NIHMS831595  PMID: 27715325

Abstract

The relationship between sleep duration and obesity in adolescents is inconclusive. This may stem from a more complex relationship between sleep and obesity than previously considered. Shifts towards evening preferences, later sleep-wake times, and irregular sleep-wake patterns are typical during adolescence but their relationship to body mass index has been relatively unexplored. This cross sectional study examined associations between sleep duration, midpoint of sleep, and social jetlag (estimated from seven days of continuous actigraphy monitoring) and morningness/eveningness with body mass indexes (BMI z scores) and waist to height ratios in 14 to 17 year old adolescents. Seventy participants were recruited from 9th and 10th grades at a public high school. Participant characteristics were as follows: 74% female, 75% post-pubertal, 36% Hispanic, 38% White, 22% Black, 4% Asian, and 64% free/reduced lunch participants with a mean age of 15.5 (SD, 0.7). Forty one percent of the participants were obese (BMI ≥ 95th percentile); 54% were abdominally obese (waist to height ratio ≥ 0.5). Multivariable general linear models were used to estimate the association between the independent variables (school night sleep duration, free night sleep duration, midpoint of sleep (corrected), social jetlag, and morningness/eveningness) and the dependent variables (BMI z scores and waist to height ratios). Social jetlag positively associated with BMI z scores (p < 0.01) and waist to height ratios (p = 0.01). Midpoint of sleep (corrected) positively associated with waist to height ratios (p = 0.01). After adjusting for social jetlag, school night sleep duration was not associated with waist to height ratios or BMI z scores. Morningness/eveningness did not moderate the association between sleep duration and BMI z scores. Findings from this study suggest that chronobiological approaches to preventing and treating obesity may be important for accelerating progress in reducing obesity rates in adolescents.

Introduction

Obesity has a profound impact on the quality of life of adolescents (12–19 years) (Schwimmer, Burwinkle, & Varni, 2003; Taras & Potts-Datema, 2005). Obesity during adolescence forebodes a lifetime burden of obesity and increased risk for several chronic diseases (Gordon-Larsen, The, & Adair, 2010; Skinner, Perrin, Moss, & Skelton, 2015). Despite recent plateaus in adolescent obesity rates, these rates remain unacceptably high and there is a disturbing increase in obesity trends among Black and Hispanic adolescents and White adolescents from lower socioeconomic groups (Freedman, 2011). Furthermore, abdominal obesity (waist to height ratio ≥ 0.5) is increasing to a greater extent than generalized obesity (body mass index (BMI) ≥ 95th percentile) in youth (Li, Ford, Mokdad, & Cook, 2006; McCarthy, Jarrett, Emmett, & Rogers, 2005). Accelerating progress in reducing adolescent obesity rates requires a multifactorial approach that extends beyond diet and exercise (Chaput et al., 2010). Within this broader context, sleep has emerged as a potentially modifiable risk factor for obesity.

Several studies have reported an inverse relationship between self-reported sleep duration and obesity in adolescents, whereby shorter sleep duration is associated with a higher BMI (Guidolin & Gradisar, 2012). Plausible pathways for this association include altered appetite regulatory hormones and increased snacking (Al-Disi et al., 2010; Nedeltcheva et al., 2009; Spiegel, Tasali, Penev, & Van Cauter, 2004). However, adolescent studies have also reported a U-shaped association in females (Lowry et al., 2012), no association in males or females (Calamaro et al., 2010; Gates, 2013; Kong et al., 2013), and a positive association between self-reported sleep duration and BMI (Biggs & Dollman, 2007; Lowry et al., 2012; Lytle, Pasch, & Farbakhsh, 2011; Sun, Sekine, & Kagamimori, 2009). Few studies have estimated sleep duration using actigraphy (Gupta, Mueller, Chan, & Meininger, 2002; Moore et al., 2011). In sum, research supporting an association between short sleep and obesity in adolescence is inconclusive and findings are limited by the self-reported nature of sleep duration data in most studies (Guidolin & Gradisar, 2012).

Inconclusive evidence for the relationship between sleep duration and obesity in adolescents may also stem from a more complex relationship between sleep and obesity. During adolescence, there is a shift in sleep-wake timing towards later sleep onsets and sleep offsets and eveningness (Carskadon, Vieira, & Acebo, 1993). These development shifts are at odds with early waking required by high school start times (Carskadon, Acebo, & Jenni, 2004). On school days, adolescents with evening preferences go to bed late but are required to rise early. On free days, adolescents with evening preferences go to bed late and rise late (National Sleep Foundation, 2006). These chronic weekly shifts in sleep-wake times, coined social jetlag (Wittmann, Dinich, Merrow, & Roenneberg, 2006), resemble larger shifts in sleep-wake times typical of shift workers, a group known to suffer disproportionately from obesity (Suwazono et al., 2008). Social jetlag is greatest in individuals with evening preferences (Roenneberg, Allebrandt, Merrow, & Vetter, 2012). Yet associations between morningness/eveningness, social jetlag, and BMI have been relatively unexplored in adolescents.

This cross-sectional study examined the associations between actigraphy estimated sleep parameters (duration, midpoint of sleep, social jetlag), as well as, morningness/eveningness with BMI z scores and waist to height ratios in 9th and 10th grade students. We hypothesized that shorter sleep, later midpoints of sleep, greater social jetlag, and eveningness would be associated with higher BMI z scores and waist to height ratios. We also hypothesized that the association between short sleep and higher BMI z scores would be stronger in adolescents with evening preferences and greater social jetlag.

Materials and Methods

Participants were recruited from a coastal city public high school in New Jersey during 9th and 10th grade health and physical education classes, back-to-school night, and selected winter sports practices. Seventy 9th and 10th grade students, representing 13% of the 9th and 10th grade class, volunteered and provided written parent/guardian consent and written student assent. To be eligible to participate, individuals had to be full time 9th or 10th grade students with non-restricted participation in physical education class. Individuals were excluded for 1) self-reported pregnancy; 2) self-reported acute illness (defined as seven days prior to actigraphy monitoring); 3) self-reported diagnosis of a sleep disorder; 4) diagnosed medical condition affecting growth or development; or 5) diagnosed physical condition affecting diet and activity. Participants were compensated with a $15 gift card. The University of Pennsylvania Institutional Review Board approved this study.

Physical Characteristics

Anthropometric Measures

Standing height was measured with a portable stadiometer (Weigh and Measure LLC, ShorrBoard) on non-carpeted flooring to the nearest 0.1 cm following a standard protocol (Lipman et al., 2000; Lipman et al., 2004). Weight was measured on a calibrated digital scale (Health o meter 498 KL) to the nearest 0.1 kg with the participant wearing light indoor clothing (excluding shoes) (Gordon, 1988). As described by others, waist circumference (WC) was measured by the investigator (SKM) after students exposed their waist at the level of the umbilicus (Nambiar, Truby, Abbott, & Davies, 2009). Each measure was obtained three times. Means were used for calculating BMI and waist to height ratios ((Himes & Bouchard, 1989). A standardized z-score for BMI, adjusted for age and sex, was computed using the CDC 2000 BMI charts with the SAS software application and is presented as BMI z.(Control & Prevention, 2009). Waist to height ratio was calculated as waist circumference (cm)/height (cm).

Puberty

Pubertal development is associated with a shift towards later chronotypes (Carskadon et al., 1993). The 5 to 6 item Pubertal Self Rating Scale was used to assess pubertal development (Petersen, L, M, & A, 1988). Participants self-reported secondary sexual characteristics such as growth, body hair, and skin changes were anchored by the responses “not yet started” and “seems complete”. “Do not know” was also a response option. Responses were used to estimate pubertal categories (pre-puberty early puberty, mid-puberty, late puberty, post puberty) (Carskadon & Acebo, 1993). Pre-puberty is comparable to Tanner Stage 1 and post-puberty is comparable to Tanner Stage 5 (Brooks-Gunn, Warren, Rosso, & Gargiulo, 1987; Carskadon & Acebo, 1993). The Cronbach’s alpha, used to determine the internal consistency of this scale in our sample was 0.3 (females) and 0.6 (males). This is lower than the Cronbach’s alpha reported by others for this scale in largely Caucasian younger adolescent samples (Carskadon & Acebo, 1993; Petersen et al., 1988) The low Cronbach’s alpha in our sample may reflect the narrow range in data (Streiner, 2003) because the majority of the females selected post pubertal response options. This would be an expected stage of pubertal development in 9th and 10th grade females. The Cronbach’s alpha reported by others represented younger females where greater variability in pubertal development would be expected. Mean values of non-missing items were used to replace missing values on the Pubertal Self Rating Scale at the individual level (Olinsky, Chen, & Harlow, 2003).

Morningness/eveningness

The 10-item Morningness/Eveningness Scale provided an estimate of participant’s morning or evening preferences for activity and rest (Carskadon et al., 1993). Scores range from 10 to 43 with lower scores indicating greater eveningness (Carskadon et al., 1993; Giannotti, Cortesi, Sebastiani, & Ottaviano, 2002). The Morningness/Eveningness Scale has been validated with adolescents’ self-reported bed times and wake times (Carskadon et al., 1993; Giannotti et al., 2002). The Cronbach’s alpha for our sample was 0.6, which is slightly lower than that reported by others for this scale (Giannotti et al., 2002).

Sleep Measures

Participants wore actigraphs on their non-dominant wrist and maintained a sleep diary for seven continuous days (see Table 1) (Acebo et al., 1999; Sadeh, Sharkey, & Carskadon, 1994). Actigraphy data were collected at 1-min epochs in the proportional integration mode using Actiwatch 2 devices (Philips Respironics). Analyses were based on medium threshold for sleep/wake detection in Actiware 5.70 from the manufacturer (Cole-Kripke formula). Sleep diary responses were used as a cross reference for actigraphy-estimated sleep onset, sleep offset and daytime naps. Discrepancies between actigraphy-estimated and diary-reported sleep onset, sleep offset, and naps were reconciled with participants on an individual basis. Wrist actigraphy has been validated with polysomnography in adolescents (Johnson et al., 2007).

Table 1.

Sleep Characteristics of Sample

Total Sample (N = 69) Females (N = 51) Males (N = 18)
N (%) or M (SD) N (%) or M (SD) N (%) or M (SD)
Total night sleep (hours )^ 7.28 (0.76) 7.31 (0.77) 7.19 (0.74)
School night (hours)^ 7.07 (0.79) 7.08 (0.79) 7.07 (0.82)
 ≤ 6 9 (13) 6 (12) 3 (17)
 > 6 to ≤ 7 22 (32) 18 (35) 4 (22)
 > 7 to ≤ 8 28 (41) 19 (37) 9 (50)
 > 8 to ≤ 9 10 (15) 8 (16) 2 (11)
 > 9 0 0 0
School day naps
 yes 28 (41) 21 (41) 7 (39)
 no 41 (59) 30 (59) 11 (61)
Free night (hours) ^ a 8.11 (1.53) 8.27 (1.55) 7.68 (1.44)
 ≤ 6 5 (7) 4 (8) 1 (6)
 > 6 to ≤ 7 9 (13) 4 (8) 5 (28)
 > 7 to ≤ 8 19 (28) 12 (24) 7 (39)
 > 8 to ≤ 9 19 (28) 17 (33) 2 (11)
 > 9 16 (23) 13 (26) 3 (17)
Free day naps a
 yes 8 (12) 7 (14) 1 (6)
 no 61 (88) 44 (86) 17 (94)
Sleep onset (hours:minutes)
 School nights 23:17 (1:01) 23:15 (1:04) 23:49 (0:53)
 Free nights a 23:55 (1:15) 23:57 (1:21) 23:48 (0:57)
Sleep offset (hours:minutes)
 School days (Monday–Friday) 6:28 (00:42) 6:26 (0:43) 6:35 (0:40)
 Free days (Saturday–Sunday) a 8:20 (1:43) 8:35 (1:42) 7:38 (1:34)
Alarm used for waking (yes)
 School days (Monday – Friday)b 58 (84) 45 (88) 13 (72)
 Free days (Saturday – Sunday)c 7 (10) 4 (8) 3 (17)
Morningness/eveningness^ 26.88 (4) 26.57 (4.51) 27.78 (3.34)
Midpoint of Sleep – corrected (hour:minutes)a 3:29 (1:10) 3:34 (1:14) 3:15 (1:00)
Social jetlag (hours) a 1.3 (0.9) 1.47 (0.91) 0.88 (0.80)
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Notes. M = mean, SD = standard deviation

^

M (SD)

a

N = 68

b

N = 63

c

N = 62

Sleep duration

The formula ((sleep onset – sleep offset) – sleep onset latency) was used to calculate sleep duration for school nights (Sunday through Thursday nights) and free nights (Friday and Saturday nights). Total night sleep was calculated as ((mean school night sleep duration × 5) + (mean free night sleep duration × 2)/7). Mean school night, mean free night, and total night sleep durations were used in the analyses.

Midpoint of sleep (corrected)

The midpoint of sleep was computed from the mean sleep onset and the mean sleep offset on free nights. For individuals who slept longer on free nights than school nights, the midpoint of sleep on free nights was corrected to account for this oversleep using the formula described by others (Roenneberg et al., 2012). The unit of measurement for sleep duration was hours so any individual whose average free night sleep was 0.1 hours (or 10 minutes) longer on free nights than school nights had their midpoint of sleep corrected. As described by Roenneberg et al. (2012), this correction formula is not applicable for individuals who sleep shorter on free days. In these cases the midpoint of sleep on free days is used (Roenneberg et al., 2012). Midpoints of sleep are significantly correlated with dim light melatonin onset phase in adolescents indicating that the midpoint of sleep may be a useful circadian phase marker (Crowley, Acebo, Fallone, & Carskadon, 2006).

Social jetlag

Social jetlag was computed from the absolute difference between the mean midpoint of sleep on school nights and the mean midpoint of sleep on free nights (not corrected) (Wittmann et al., 2006).

Daytime naps

Actigraphy-estimated periods of inactivity between 6am and 6pm were cross referenced with diary reports and/or verified with individual participants to determine the presence/absence of daytime napping (yes/no).

Health Behaviors

The Youth Risk Behavior Survey (YRBS), established by the CDC, is a widely used instrument for monitoring self-reported health behaviors in 9th through 12th grade students across the US. Selected questions about eating habits, physical activity, and screen time from the 2013 YRBS were used in these analyses. The following behaviors were assessed: fruits and vegetables (servings per day comprised of fruit, green salad, carrots, potatoes, and other vegetables), fruit juice (servings per day), soda (servings per day), milk (servings per day), breakfast (days per week), moderate/vigorous physical activity (days per week in which 60 minutes of activity per day was done), sports team participation (number per year), and screen time (hours per day watching TV, playing video/computer games, and using a computer for non-school related activities).

Socio-demographics

Participants self-reported as male or female. Race/ethnicity and free/reduced lunch participation were gathered from official school records. Race/ethnicity was reported as White, Black, Hispanic, or Asian by parents/guardians. Free/reduced lunch participation was used as a proxy measure for poverty (US Department of Agriculture Food and Nutrition Services, 2012). Non-participants included those recorded as “did not apply”.

Procedures

Anthropometric and actigraphy measurements took place between February 2014 and June 2014. Actigraphy and sleep diary instruction took place in small groups during the school day and data collection began at 3pm on the instruction day. The investigator called participants daily at a pre-determined time to troubleshoot problems and encourage adherence to the study protocol. On the seventh day, participants returned actiwatches and sleep diaries to the investigator and the investigator performed all anthropometric measurements. Individual appointments to resolve discrepancies between diary and actigraphy data were arranged within one week of completing data collection. These appointments were scheduled to minimize disruption to students’ academic schedules and followed an established protocol for resolving discrepancies.

Data Analysis

Means and standard deviations were calculated for continuous variables; frequencies and percentages for categorical variables. Bivariate associations and correlations between the independent variables (e.g. social jetlag, race/ethnicity) and the dependent variables of interest (BMI z-scores, waist to height ratios) were examined using independent sample t tests, ANOVAs, and Pearson’s product correlations or Spearman’s rho correlations, as appropriate. Multivariable models for the dependent variables were constructed using general linear modeling. To maximize statistical power, the final adjusted models only included variables significant at alpha level of 0.2 from the univariate analysis. An alpha level of 0.05 based on the two-tailed test was set for statistical significance. The moderating effect of morningness/eveningness on BMI z-score was tested using the interaction terms morningness/eveningness*school night sleep duration and morningness/eveningness*free night sleep duration. SPSS version 22 was used for statistical analysis.

Results

Participant Characteristics

Of the 70 participants recruited, one student diagnosed with type 2 diabetes was excluded leaving 69 participants. Of the remaining 69 participants, one did not have free night actigraphy data which precluded the calculation of midpoint of sleep and social jet lag. This reduced the sample to 68 for analyses involving free night sleep duration, midpoint of sleep, and social jet lag. Briefly, participants were mostly female (74%), post-pubertal (75%), racially/ethnically diverse (36% Hispanic, 38% White, 22% Black, 4% Asian), free/reduced lunch participants (64%) with a mean age of 15.5 (SD, 0.7). Forty one percent of the participants were overweight or obese (BMI ≥ 85th percentile); 54% were abdominally obese (waist to height ratio ≥ 0.5).

Most participants (76%) had later midpoints of sleep on free days than school days. Most participants took daytime naps before 6pm on school or free days (N=36). Six participants took naps after 6pm only on school or free days. Table 1 lists the sleep characteristics for the total sample and separately for males and females. The univariate associations of each participant characteristic with midpoint of sleep (corrected) and social jetlag are listed in Table 2. Later midpoints of sleep (corrected) were associated with less sports team participation (b = −0.23, 95% CI = −0.46, 0.00, p = 0.05), more screen time (b = 0.11, 95% CI = 0.00, 0.22, p = 0.05) and higher waist to height ratios (b = 5.33, 95% CI = 0.93, 9.73, p = 0.02). Greater social jetlag was associated with females (b = −0.58, 95% CI = −1.07, −0.10, p = 0.02), post pubertal participants (b = −1.00, 95% CI = −1.69, −0.31, p = 0.01), higher BMI z scores (b = 0.26, 95% CI = 0.01, 0.50, p = 0.04), and higher waist to height ratios (b= 5.04, 95% CI = 1.71, 8.37, p < 0.01).

Table 2.

Participant characteristics and their association with midpoint of sleep and social jetlag

N (%) or M (SD) Midpoint of Sleepa
b (95% CI)		 Social Jetlaga
b (95% CI)
Socio-demographic Variables
Age 15.50 (0.65)^ −0.01 (−0.05, 0.03) −0.01(−0.04, 0.02)
Sex
 male 18 (26) −0.32 (−0.97, 0.33) −0.58 (−1.07, −0.10)**
 female 51 (74) reference reference
Race/ethnicity
 Hispanic 25 (36) reference reference
 White 26 (38) 0.42 (−0.25, 1.30) −0.06 (−0.58, 0.46)
 Black 15 (22) 0.31 (−0.47, 1.08) −0.30 (−0.90, 0.30)
 Asian 3 (4) −0.15 (−1.59, 1.30) 0.19 (−0.94, 1.32)
Free/reduced lunch participation
 yes 44 (64) reference reference
 no 25 (36) −0.10 (−0.70, 0.50) −0.05 (−0.52, 0.41)
Physical Characteristics
BMI z score 0.66 (0.93)^ 0.18 (−0.12, 0.49) 0.26 (0.01, 0.50)*
BMI percentile
 < 85th (non-obese) 41 (59) reference reference
 ≥85th (obese) 28 (41) 0.34 (−0.24, 0.92) 0.14 (−0.32, 0.59)
Waist to height ratio 0.50 (0.06)^ 5.33 (0.93, 9.73)* 5.04 (1.71, 8.37)**
 < 0.5 32 (46) −0.73 (−1.28, −0.19)** −0.76 (−1.16, −0.35)***
 ≥ 0.5 37 (54) reference reference
Pubertal Category
 mid 10 (14) −0.18 (−0.99, 0.62) −0.48 (−1.08, 0.12)
 late 7 (10) −0.88 (−1.81, 0.06) −1.00 (−1.69, −0.31)**
 post 52 (75) reference reference
Health Behavior Variables Median (IQR) Midpoint of Sleepa
b (95% CI)		 Social Jetlaga
b (95% CI)
Eating habits (servings per day)
 fruit/vegetables 1.86 (1.71) −0.05 (−0.18, 0.08) 0.01(−0.09, 0.11)
 milk 0.29 (0.71) −0.14 (−0.45, 0.18) −0.06 (−0.03, 0.15)
 soda 0.29 (0.71) 0.13 (−0.13, 0.38) 0.04 (−0.16, 0.24)
 juice 0.71 (0.71) −0.18 (−0.50, 0.13) −0.19 (−0.43, 0.05)
Ate breakfast (days per week) 4 (5) −0.04 (−0.15, 0.07) −0.06 (−0.14, 0.02)
Physically active for 60 minutes (days per week) 3 (4) −0.10 (−0.23, 0.03) −0.07 (−0.17, 0.03)
Played on a sports team (number per year) 1 (3) −0.23 (−0.46, 0.00)* −0.14 (−0.32, 0.04)
Screen time (hours per day) 5 (3) 0.11 (0.00, 0.22)* 0.06 (−0.03, 0.15)
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Notes. M = mean, SD = standard deviation, CI = confidence interval

^

M (SD)

a

N = 68

IQR = Interquartile range,

*

p<0.05

**

p<0.01

***

p < 0.001

Sleep Duration, Midpoint of Sleep (corrected), Social jetlag, Morningness/eveningness and Anthropometric Characteristics

Table 3 reports the associations between sleep duration, midpoint of sleep (corrected), social jetlag, and morningness/eveningness with BMI z scores. Characteristics, significant at alpha level of 0.2, identified from the univariate analyses (model 1), were used to build the multivariable model (model 2). Social jetlag was positively associated with BMI z scores (p = 0.01) after adjusting for variables that were influential in the univariate analysis: sex, fruit/vegetable intake, screen time, school night sleep duration, daytime naps (school days and free days). School night sleep duration was no longer associated with BMI z scores after adjusting for sex, fruit/vegetable intake, screen time, daytime naps (school days and free days), and social jetlag. Males had higher BMI z scores than females (p = 0.04). Morningness/eveningness did not moderate the association between sleep duration and BMI z scores (data not shown).

Table 3.

Relationship of Social jetlag to BMI z scores

BMI z score (Model 1) BMI z score (Model 2)
b (95% CI) b (95% CI)
Age −0.01 (−0.04, 0.02)
Sex (reference: female) 0.36 (−0.14, 0.86) 0.49 (0.01, 0.97)*
Race/ethnicity (reference: Hispanic)
 White −0.04 (−0.59, 0.48)
 Black −0.21 (−0.83, 0.40)
 Asian −0.53 (−1.68, 0.61)
Free/reduced lunch (reference: yes) −0.13 (−0.60, 0.33)
Pubertal category (reference: post)
 mid 0.29 (−0.36, 0.93)
 late 0.13 (−0.62, 0.88)
Eating Habits (servings per day)
 juice 0.001 (−0.24, 0.24)
 fruit/vegetables 0.09 (−0.02, 0.19) 0.07 (−0.02, 0.17)
 soda −0.08 (−0.28, 0.12)
 milk 0.06 (−0.19, 0.31)
Breakfast (days per week) 0.05 (−0.04, 0.13)
Physical Activity
 screen time (hours per day) −0.07 (−0.16, 0.02) −0.05 (−0.13, 0.04)
 days active (days per week) 0.06 (−0.04, 0.16)
 sports (number per year) 0.00 (−0.19, 0.19)
Sleep duration (hours)
 school nights 0.23 (−0.05, 0.51) 0.092 (−0.17, 0.36)
 free nights a 0.05 (−0.10, 0.20)
School day naps (reference: yes) 0.50 (0.05, 0.94) 0.28 (−0.17, 0.73)
Free day naps (reference: yes) 0.95 (0.28, 1.61) 0.49 (−0.22, 1.19)
Morningness/eveningness 0.03 (−0.02, 0.08)
Midpoint of sleep – corrected (hours) a 0.11 (−0.08, 0.31)
Social jetlag (hours) a 0.26 (0.01, 0.50) 0.33 (0.09, 0.57)**
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Notes. CI = confidence interval

a

N = 68

Model 1: unadjusted general linear model. Model 2: general linear model adjusted for covariates with alpha levels < 0.20 in the unadjusted general linear model (Model 1)

*

p < 0.05

**

p < 0.01.

Table 4 reports the associations between sleep duration, midpoint of sleep (corrected), social jetlag, and morningness/eveningness on waist to height ratios using the same model building strategies. Variables, significant at alpha level of 0.2, identified from the univariate analyses (model 1), were used to build the multivariable models (model 2: midpoint of sleep, model 3: social jetlag). Midpoint of sleep (corrected) and social jetlag remained positively associated with waist to height ratios after adjusting for variables that were influential in the univariate analysis (p = 0.01). Due to multicollinearity between midpoint of sleep (corrected) and social jet lag, separate general linear models were generated (see Table 4 model 2 (midpoint of sleep (corrected)) and Table 4 model 3 (social jetlag)). Greater fruit/vegetable intake was also associated with higher waist to height ratios (model 2: p = 0.01; model 3: p = 0.01).

Table 4.

Relationship of Midpoint of Sleep (corrected) and Social jetlag to Waist to Height Ratios

Waist to height ratios (Model 1) Waist to height ratios (Model 2: midpoint of sleep (corrected)) Waist to height ratios (Model 3: social jetlag)
b (95% CI) b (95% CI) b (95% CI)
Age 0 (−0.002, 0.002)
Sex (reference: female) −0.018 (−0.053, 0.016)
Race/ethnicity (reference: Hispanic)
 White −0.01 (−0.04, 0.03)
 Black −0.03 (−0.07, 0.02)
 Asian −0.06 (−0.13, 0.02)
Free/reduced lunch (reference: yes) −0.02 (−0.05, 0.01)
Pubertal category (reference: post)
 mid −0.01 (−0.06, 0.03)
 late −0.04 (−0.09, 0.01)
Eating Habits (servings per day)
 juice 0.003 (−0.01, 0.02)
 fruit/vegetables 0.01 (0.00, 0.01) 0.009 (0.003, 0.016)** 0.008 (0.002, 0.015)*
 soda −0.01 (−0.02, 0.01)
 milk 0.001 (−0.02, 0.02)
Breakfast (days per week) 0.002 (−0.004, 0.007)
Physical Activity
 screen time (hours per day) −0.001 (−0.01, 0.01)
 days active (days per week) −0.001 (−0.007, 0.006)
 sports (number per year) −0.010 (−0.023, 0.002) −0.01 (−0.022, 0.002) −0.011 (−0.023, 0.001)
Sleep duration (hours)
 school nights 0 (−0.02, 0.02)
 free nights 0.002 (−0.008, 0.012)
Naps: school days (reference: yes) 0.02 (−0.02, 0.05)
Naps: free days (reference: yes) 0.03 (−0.01, 0.08) 0.04 (−0.01, 0.08) 0.03 (−0.02, 0.07)
Morningness/eveningness 0.001 (−0.002, 0.005)
Midpoint of sleep- corrected (hours)a 0.015 (0.003, 0.028) 0.015 (0.003, 0.028)*
Social jetlag (hours)a 0.02 (0.01, 0.04) 0.02 (0.01, 0.04)*
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Notes. CI = confidence interval

a

N = 68

Model 1: unadjusted general linear model. Model 2 and Model 3: general linear model adjusted for covariates with alpha levels < 0.20 in the unadjusted general linear model (Model 1)

*

p < 0.05

**

p < 0.01.

Discussion

The aims of this study were to determine whether sleep duration, midpoint of sleep, social jet lag and/or morningness/eveningness associated with BMI z scores and waist to height ratios in 9th and 10th grade students. Our main finding was that greater social jetlag was associated with higher BMI z scores and higher waist to height ratios. This finding is consistent with one earlier report of a positive association between social jetlag and BMI in adults (Wong, Hasler, Kamarck, Muldoon, & Manuck, 2015) and extends the findings of a positive association reported by others that relied on self-reported sleep and self- reported anthropometric measures (Randler, Haun, & Schaal, 2013; Roenneberg et al., 2012).

Our finding is similar to reports of a positive association between irregular sleep-wake times (differences in bedtimes and wake times on school days and free days) and BMI in adolescent females (Lytle et al., 2011). Additionally, children with irregular sleep-wake times (greater than 45 minute delays in bedtimes on weekends) and long sleep durations had larger increases in BMI over time than children with regular sleep-wake times and long sleep durations (Miller et al., 2014). These findings suggest that regular sleep-wake patterns are important for maintaining a healthy BMI during growth and development. To date, only one study has reported that “sleeping in” on weekends was a beneficial compensatory mechanism for insufficient nocturnal sleep in Hong Kong children (Wing, Li, Li, Zhang, & Kong, 2009). This latter study relied on parental reports of bedtimes and wake times as well as parental reports of height and weight to estimate BMI z scores. Parental estimates of anthropometric measurements are often inaccurate (Harris, Kuramoto, Schulzer, & Retallack, 2009). These differences may explain the disparate findings from this study.

These findings are important because social jetlag is prevalent in adolescents. Eighty eight percent of adolescents report going to bed later on free nights than school nights and 44% of high school students report a two or more hour difference in bedtimes on free night and school nights (National Sleep Foundation, 2006). Our mean social jetlag of 1.3 hours (SD, 0.9 hours) was smaller than might be expected given this two hour difference in free night and school night bedtimes. This may stem from differences in self-reported sleep times and actigraphy monitored sleep times. Additionally, as midpoints of sleep become progressively later through 20 years of age, social jetlag increases (Roenneberg et al., 2007; Roenneberg et al., 2004). Thus, it may be expected that the social jetlag in our sample of 14–17 year olds will increase over time. This is concerning because obesity risk for shift workers increases based on years of exposure (Parkes, 2002). Our finding that social jetlag is associated with higher BMI z scores and waist to height ratios in youth during their first and second years of high school bodes poorly for reducing obesity rates in adolescents.

Our finding that later midpoints of sleep were associated with higher waist to height ratios raises further concerns for cardio-metabolic health. Youth with higher waist to height ratios (≥ 0.5) have adverse levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, and insulin, even in the presence of a healthy BMI (< 85th percentile) (Mokha et al., 2010). Norwegian female adolescents with higher waist measurements are more likely to become overweight over time (Bratberg, Nilsen, Holmen, & Vatten, 2007). Although the underlying mechanisms for the association between later midpoints of sleep and higher waist to height ratios are uncertain, later midpoints of sleep have been associated with fewer days of physical activity per week in adolescents (Malone et al., 2015).

Our hypotheses that later midpoints of sleep and eveningness would be associated with higher BMI z scores were not supported. This is inconsistent with one study of UK adolescents whereby eveningness was associated with a higher BMI (Arora & Taheri, 2015) and with another study of Australian children whereby later bedtimes and later wake times were associated with higher BMIs (Olds, Maher, & Matricciani, 2011). Disparate findings may be attributed to different measurement strategies. Arora and Taheri (2015) assessed morningness/eveningness with one question from the Horne–Ostberg Morningness/Eveningness Questionnaire (Horne & Ostberg, 1976). Olds, Mayer and Matricciani determined bedtimes and wake times from 2 day recall time survey data (Olds et al., 2011).

Higher BMI z scores in males in our study may reflect the inability of BMI measurements to distinguish fat mass from fat free mass. The higher BMI z scores in males may indicate the rapid gain in fat free mass that is part of normal adolescent male development rather than increased fat mass (Veldhuis et al., 2005). It is also possible that these data reflect the greater prevalence of obesity in adolescent males compared to females that has been reported in the US (Ogden, Carroll, Kit, & Flegal, 2014).

In sum, findings from this study suggest that regular sleep wake patterns may be important for preventing obesity in adolescents. Reaping the cardio-metabolic benefits of longer sleep for adolescents may hinge on establishing regular sleep-wake patterns and earlier sleep-wake times. To that end, delaying school start times 30 to 60 minutes can increase school night sleep duration and may improve the regularity of sleep-wake patterns (Hansen, Janssen, Schiff, Zee, & Dubocovich, 2005; Warner, Murray, & Meyer, 2008). However, the benefits of these delays for sleep duration vary by sex and urbanicity. Adolescent males from metropolitan areas have been reported to benefit most in terms of longer sleep duration from school start time delays (Paksarian, Rudolph, He, & Merikangas, 2015).

As a cross-sectional study, these findings represent associations and the direction of these relationships cannot be determined. Several factors limit the generalizability of these findings. Our sample consisted of more females and free/reduced lunch participants than state (New Jersey) and nationally representative samples of high school students (Davis & Bauman, September 2013; U.S. Department of Education, Institute of Education Sciences, & National Center for Education Statistics). This may limit the generalizability of these findings to male adolescents and more affluent socio-economic groups. However, greater racial/ethnic diversity in our sample than state and nationally representative samples of high school students is an important strength (Davis & Bauman, September 2013). Additionally, parent/guardian consent was required for participation, potentially limiting participation from some students (Tigges, 2003). Another limitation of this study was that neither self-reported school attendance nor official school attendance records were obtained to verify that students attended school. Hence, it is possible that a student’s absence my not reflect a school night for that particular individual. Future studies should consider reframing definitions of school nights and free nights to reflect these potential individual variations. Although estimating sleep parameters using actigraphy was a strength of our study, our sample size was small. It will be important to replicate these findings in larger samples using actigraphy-estimated, as well as, self-reported sleep parameters.

The associations identified in this study between social jetlag and BMI raise interesting questions about what aspects of sleep are most relevant to obesity risk and present novel opportunities for interventions if replicated in future studies. Interventions should target aligning the timing of specific behaviors (e.g. sleep-wake, activity-rest) with individual circadian rhythms to mitigate social jetlag. This approach resonates with growing interest in personalized preventative care. Furthermore, interventions aimed at reducing social jetlag and promoting earlier midpoints of sleep may have a broad sweeping impact on obesity and cardio-metabolic health at the population level because two thirds of the population report social jetlag and average midpoints of sleep have become progressively later over the past decade (Roenneberg et al., 2012).

Acknowledgments

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. Philip Gehrman for providing additional feedback on analyzing actigraphy data and Drs. David Dinges and Namni Goel for their input on this manuscript.

Footnotes

Declaration of Interest Statement

The author(s) disclosed the following financial support for the research, authorship, and/or publication of this article: This work was made possible through generous support from the National Association of School Nurses, Ruth L. Kirschstein National Research Service Award (F31NR014603), The Rockefeller University Heilbrunn Nurse Scholar Award, University of Pennsylvania School of Nursing Biobehavioral Research Center, University of Pennsylvania Office of Nursing Research and the National Institute of Health (T32 HL 7953-16). This funding source did not influence the study design, the collection, analysis or interpretation of data, the writing of the report, nor the decision to submit the article for publication.

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