J Youth Adolescence (2015) 44:271–284 DOI 10.1007/s10964-014-0149-0

EMPIRICAL RESEARCH

Sleep Characteristics, Body Mass Index, and Risk for Hypertension in Young Adolescents Hannah Peach • Jane F. Gaultney • Charlie L. Reeve

Received: 28 March 2014 / Accepted: 25 June 2014 / Published online: 8 July 2014 Ó Springer Science+Business Media New York 2014

Abstract Inadequate sleep has been identified as a risk factor for a variety of health consequences. For example, short sleep durations and daytime sleepiness, an indicator of insufficient sleep and/or poor sleep quality, have been identified as risk factors for hypertension in the adult population. However, less evidence exists regarding whether these relationships hold within child and early adolescent samples and what factors mediate the relationship between sleep and risk for hypertension. Using data from the Study of Early Child Care and Youth Development, the present study examined body mass index (BMI) as a possible mediator for the effects of school-night sleep duration, weekend night sleep duration, and daytime sleepiness on risk for hypertension in a sample of sixth graders. The results demonstrated gender-specific patterns. Among boys, all three sleep characteristics predicted BMI and yielded significant indirect effects on risk for hypertension. Oppositely, only daytime sleepiness predicted BMI among girls and yielded a significant indirect effect on risk for hypertension. The findings provide clarification for the influence of sleep on the risk for hypertension during early adolescence and suggest a potential need for gender-specific designs in future research and application endeavors.

H. Peach (&)
J. F. Gaultney
C. L. Reeve Health Psychology Program, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, USA e-mail: [email protected] J. F. Gaultney e-mail: [email protected] C. L. Reeve e-mail: [email protected]

Keywords Adolescent
Sleep
Gender difference
Obesity
Blood pressure
Hypertension

Introduction The field of developmental science widely recognizes the dynamic and multi-level processes that shape health and adjustment through the lifespan, particularly during childhood and adolescence (Bronfenbrenner 2005). Yet, among American youth the dynamic relationship between the increasingly common health problems of poor sleep (Mindell and Meltzer 2008), obesity, and hypertension (Riley et al. 2013) are unclear. Hypertension, the clinical term for chronically and abnormally high blood pressure, has been identified as a rising public health problem and a significant risk factor for cardiovascular disease, the leading cause of death in Americans (Hoyert and Xu 2012; Roger et al. 2012). Research has been devoted to examinations of the etiology and prevalence of hypertension in the American adult population, with studies demonstrating that blood pressure levels during childhood can predict hypertension and metabolic syndrome later in life (Martikainen et al. 2011; Mezick et al. 2012; Sun et al. 2007). Yet research on adolescent hypertension risk is largely lacking in comparison to adult literatures, and recognition of elevated blood pressure in adolescents is poor (Riley et al. 2013). Among adult patients, hypertension classifications stem from single clinical cut offs (e.g., 120/80 mmHg), while pediatric and adolescent patients’ blood pressure standards for prehypertension and hypertension are dependent upon age and gender, making recognition more difficult (Assadi 2012). Therefore, more research is needed on the examination of risk factors for elevated blood pressure in adolescents, utilizing age- and gender-adjusted blood pressure classifications.

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Within the adult population, sleep durations shorter than the recommended 7? h per night (National Sleep Foundation 2009) and/or poor sleep quality with or without the presence of a sleep disorder (Knutson 2012), have been identified as risk factors for a variety of health consequences, including hypertension (Wang et al. 2012). However, less research has been dedicated to the investigation of characteristics of poor sleep and risk for hypertension within nonclinical samples of youth. Developmental considerations of health posit meaningful interconnections between biological, behavioral, and psychosocial factors, and adolescence indeed marks a time period of changing sleep patterns and insufficient sleep due to biological, environmental, behavioral, and social influences (National Sleep Foundation 2006; O’Brien and Mindell 2005), with prevalence rates of adolescent sleep problems estimated at 25–40 % (Mindell and Meltzer 2008). Therefore, examining the association of sleep problems with risk for hypertension during late childhood/early adolescence proves relevant. Quantity of sleep is a commonly measured indicator of sufficient restoration, and while sleep needs vary by individual, recommended amounts of sleep required for optimal physiological function for all age groups have been well-established (National Heart, Lung, and Blood Institute 2012). Among adolescents, sleep quantity or duration can vary significantly between school-nights and weekend nights. Although middle and high school start times require students to awake early, adolescence is characterized by delayed phases of sleep and wake cycles caused by shifts in the circadian clock, as well as behavioral factors and social influences that delay sleep patterns, thus causing students to lose sleep during the week (Carskadon and Acebo 2002; Hansen et al. 2005). This marked difference between weekday and weekend sleep suggests school-aged adolescents compensate for school week sleep deprivation by sleeping longer on the weekends, and this weekdayweekend sleep difference may be important to health (Kim et al. 2012). Therefore, distinct school-night and weekend sleep durations are relevant within studies of adolescent health. Additionally, reported daytime sleepiness, a behavioral manifestation or indicator of insufficient sleep and/or poor sleep quality, has been recognized as important in health research (Parker et al. 2003); daytime sleepiness has been identified as an independent risk factor for hypertension (Goldstein et al. 2004) and cardiovascular mortality (Empana et al. 2009) among older adults. However, such research has not yet been expanded to younger populations. Though often related, sleep duration and daytime sleepiness are believed to represent independent factors given evidence showing that they are differentially associated with psychosocial variables (e.g., Hawkley et al. 2010) and serve as independent predictors of health and performance

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outcomes (Dewald et al. 2010; Empana et al. 2009; Goldstein et al. 2004; Pilcher et al. 1997). Sleep Characteristics and Blood Pressure Research suggests hypertension among youth is a growing public health problem (Assadi 2012), with data from the 2003–2006 National Health and Nutrition Examination Survey (Ostchega et al. 2009) suggesting that among American youth ages 8–17 years old, 13.6 % of boys and 5.7 % of girls are classified as at risk for hypertension. While high blood pressure is less common among youth versus adults, blood pressure follows a ‘‘tracking pattern’’ throughout development in which individuals with elevated blood pressure earlier in life are more likely to be diagnosed with hypertension in adulthood (He et al. 2008). Considering hypertension is a leading cause of death in the United States (Roger et al. 2012) and childhood blood pressure levels can track or continue into adulthood (Assadi 2012), it is important to clearly understand associations between poor sleep and risk for hypertension during youth and prioritize interventions on modifiable risk factors during childhood and adolescence (He et al. 2008). The extant evidence on sleep as a predictor of adolescent blood pressure and/or hypertension classifications has yielded mixed results. For example, Mezick et al. (2012) linked shorter actigraphy-assessed sleep to elevated blood pressure among 246 Black and White adolescents 14–19 years old, even after controlling for age, race, sex, and body mass index (BMI). Other research (Javaheri et al. 2008) has shown associations between actigraphy-assessed low weekday sleep efficiency (i.e., an objective measure of sleep quality) and increased odds of prehypertension among healthy adolescents ages 13–14 years old, even after adjusting for sex, BMI, and socioeconomic status. Studies (Wells et al. 2008) of an adolescent Brazilian birth cohort (N = 4452) ages 12–14 found associations between self-reported sleep duration and systolic blood pressure after adjusting for television viewing, maternal education, family social status, sex, birth weight, birth length, smoking and alcohol, consumption in pregnancy, maternal BMI and hours of physical activity, but the effect no longer reached significance after adjusting for BMI. Studies of younger children (3–10 years old) found differences in mean arterial pressure between children with parent-reported long versus short sleep durations that was independent of age, however this effect was no longer significant after adjusting for BMI and physical activity (Bayer et al. 2009). Similarly, Kaditis et al. (2005) reported age, gender, and BMI as significant predictors of systolic blood pressure in a large study (N = 760) of children and adolescents ages 4–14 years old, but also reported null associations between blood pressure and parent-reported snoring, a characteristic of sleep problems. Overall, such

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mixed findings may stem from variations in sleep measurements but suggest key patterns in which associations between sleep and blood pressure may not arise until early adolescence or later, and that BMI may play a key role in such associations; thus, the present study may shed light on mixed findings reported to date by explicitly testing indirect effects of several sleep characteristics on blood pressure via the pathway of BMI among early adolescents. Though the precise physiological mechanism by which sleep relates to elevated blood pressure has not yet been established, several pathways have been suggested within the literature, including a reduction in nocturnal blood pressure dipping that can result from fragmented or poor quality sleep, as well as sleep deprivation (Sayk et al. 2010). Other studies suggest that shorter sleep leads to longer exposures to elevated sympathetic nervous system arousal (Sapolsky 2004) and resulting waking physical and psychosocial stressors that parallel inadequate sleep and daytime sleepiness can generate increased salt retention (Gangwisch et al. 2006). Furthermore, inadequate sleep can lead to disrupted circadian rhythmicity and autonomic balance (Gangwisch et al. 2006), as well as diminished melatonin production (Zisapel 2007), all of which affect blood pressure levels. Most research has been conducted in adult samples; therefore it is unclear if these etiologies are also predictive of child and adolescent cases of elevated blood pressure. Hypertension in older populations typically stems from stiffening of the arteries and increased peripheral resistance, which corresponds with aging. However, hypertension in adolescents is driven primarily by increased sympathetic activity and cardiac output (Foe¨x et al. 2004); therefore mechanisms in which sleep contributes to elevated sympathetic activity and/or overworked cardiac output are plausible explanations for the sleepblood pressure link in early life. One such mechanism is the indirect pathway through obesity, defined within children as a BMI at or above the 95th percentile for children of the same age and sex (Barlow 2007). The Mediating Role of BMI Research has linked sleep duration and obesity within adolescent samples (Bo¨rnhorst et al. 2012; Garaulet et al. 2011; Gupta et al. 2002; Nielsen et al. 2011). However, some studies have yielded null findings (Calamaro et al. 2010; Hassan et al. 2011), while others demonstrate gender differences in which sleep duration is associated with obesity only among female adolescents (Lowry et al. 2012) or yields stronger patterns among males (Knutson 2005; Storfer-Isser et al. 2012). These mixed results may stem from variations in methodology, such as anthropometric data and body fat composition measurements (e.g., Garaulet et al. 2011) versus caregiver reports of height and

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weight (Hassan et al. 2011), and findings in which the presence or strength of associations vary by gender may be due to hormonal differences or depression that were not accounted for within analyses. Depression is more common among females, associated with poor sleep, and can increase risk for obesity (Lowry et al. 2012). Further, while Calamaro et al. (2010) found significant unadjusted associations between sleep duration and obesity, the association was no longer significant once depression was accounted in the model, highlighting this potential explanation for mixed results. Pubertal status is also a vital covariate to consider, for puberty marks considerable changes in sleep and circadian patterns, as well as sex differences regarding changes in body composition, metabolism, and endocrinology that could shape the influence of sleep on BMI (Knutson 2005) and may serve as a viable explanation for null findings in samples that did not conduct separate analyses by gender. Additionally, it has been proposed that the association between inadequate sleep and BMI is stronger during late childhood/early adolescence, and therefore children and adolescents may in fact be particularly vulnerable to the consequences of inadequate sleep (Danielsen et al. 2010; Knutson 2012). Aside from the influence of fatigue on reduced physical activity, studies have shown that inadequate sleep can contribute to the development of obesity by affecting levels of hormones leptin and ghrelin, which are involved in appetite regulation, by reducing energy expenditure, and by increasing subjective appetite (Knutson 2012). Obesity serves as a significant and widely recognized risk factor for concurrent elevated blood pressure among adolescents (e.g., Babinska et al. 2012; Oduwole et al. 2012), as well as adult hypertension (Virdis et al. 2009) by placing strain on cardiac output and over time leading to metabolic abnormalities and chronic kidney diseases, all of which contribute to the development of hypertension (Narkiewicz 2006). While research (e.g., Knutson 2012; Narang et al. 2012; Wells et al. 2008) has identified associations between sleep, BMI, and risk for hypertension, a mediation model is not yet well examined within the literature. Instead, obesity is often treated as a confounding variable in studies of adolescent sleep and elevated blood pressure (e.g., Li et al. 2009), and BMI is simply controlled during analyses rather than identified as a possible mechanism within the sleepblood pressure relationship. The biological mechanisms that connect inadequate sleep to obesity, as well as the repeatedly demonstrated link between obesity and hypertension risk, suggest a plausible path model (see Fig. 1) in which the relationship between sleep characteristics (operationalized as school-night sleep duration, weekend sleep duration, and daytime sleepiness) and risk for hypertension during adolescence emerges via associations with BMI.

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Fig. 1 Proposed path model in which the effects of schoolnight sleep duration, weekend sleep duration, and daytime sleepiness on risk for hypertension are mediated via associations with body mass index; the model does not account for possible bidirectionality

Hypotheses The purpose of the current study was to examine sleep characteristics as predictors of BMI and risk for hypertension among American adolescents and test the potential mediating role of BMI between sleep and risk for hypertension. We chose to examine the proposed model within a sample of sixth grade students because this age group is particularly at risk for consequences of poor sleep as a result of the transition from preadolescence to adolescence in which sleep patterns begin to change dramatically. Sixth grade serves as one of the youngest ages in which sleep durations shorten during the week, and weekday and weekend sleep durations diverge (Yang et al. 2005), often as a result of earlier school start times, later bedtimes, and extracurricular activities. Therefore, demonstrations of associations between sleep, BMI, and risk for hypertension as young as early adolescence could yield meaningful evidence for the earliest developmental period in which sleep can predict hypertension risk. We also chose to distinguish between school-night sleep duration and weekend sleep duration to account for the markedly longer ‘‘catch up’’ sleep that occurs during the weekend among adolescents (Carskadon and Acebo 2002; Hansen et al. 2005; Kim et al. 2012). Considering the differing hormonal changes that occur for boys and girls during this pubertal period that marks the beginning of adolescence, as well as varying patterns of the sleep-risk for hypertension relationship demonstrated within the literature, the present study tested the proposed model separately by gender. Further, well-established psychosocial and physiological characteristics related to sleep, BMI, and/or risk for hypertension must be controlled for in order to isolate the

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relationships between the primary variables. As stated previously, gender and pubertal development are potential confounds of the proposed model and are therefore included as covariates of the entire sample. Research suggests that prevalence rates of high BMI and risk for hypertension are higher among certain racial/ethnic groups or lowincome individuals (e.g., Burford et al. 2013; ScharounLee et al. 2009), therefore these demographic variables were included as covariates. Depression is also commonly linked to sleep problems among adolescents (e.g., Danielsson et al. 2013; Wolfson and Armitage 2009) and attention-deficit/hyperactivity disorder has been associated with both sleep (Cortese et al. 2008) and obesity (Gruber 2014), therefore both factors were included in the analyses. Lastly, unhealthy eating habits and low physical activity are common risk factors for high BMI and elevated blood pressure (Huh et al. 2011), thus these factors were considered within analyses, as well. Therefore, the following specific hypotheses were tested. We predict that school-night sleep duration, weekend sleep duration, and daytime sleepiness will independently predict BMI (H1), for prior research suggests associations between sleep and BMI in adolescents (e.g., Bo¨rnhorst et al. 2012) and associations between daytime sleepiness and health outcomes (e.g., Goldstein et al. 2004; Pilcher et al. 1997). We hypothesize that BMI will independently predict risk for hypertension (H2), as the increased sympathetic activity and cardiac output characterized by excessive body fat contribute to hypertension risk in adolescence (Babinska et al. 2012; Foe¨x et al. 2004). We also predict that school-night sleep duration, weekend sleep duration, and daytime sleepiness will yield independent, indirect effects on risk for hypertension via BMI (H3), for

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excessive body fat serves as a plausible pathway for previously demonstrated associations between sleep and elevated blood pressure in adolescents (e.g., Mezick et al. 2012). Lastly, we expect patterns will vary by gender, such that school-night sleep duration, weekend sleep duration, and daytime sleepiness will be more strongly associated with obesity and risk for hypertension among girls (H4) because prior research (e.g., Lowry et al. 2012) has shown stronger sleep-BMI associations among females.

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the full model with all covariates, 78 % of cases were missing data on all x-variables or all variables except x-variables. Considering maximum likelihood imputation can only utilize available data to estimate model parameters and standard errors, 541 participants were included in the final model. Covariate Measures Demographics

Method Participants The present study analyzed data drawn from the Study of Early Child Care and Youth Development. This longitudinal study conducted by the National Institute of Child Health and Human Development (NICHD) was designed to examine the relationship between child care experiences, child development, and well-being. Starting in 1991, 1,364 families were interviewed 1 month after the birth of a child, and data on these children’s developmental experiences were collected through 2007 in five phases. More extensive information regarding the recruitment, selection process, and eligibility requirements is available from previous publications (see NICHD Early Child Care Research Network 2005; http://www.nichd.nih.gov/ research/supported/seccyd/pages/overview.aspx). The present study included data from Phase III (2000–2004), which collected data from children in the sixth grade, as well as data collected 1 month after birth (i.e., gender and race/ethnicity). While the full sample of participants was included in analyses, data were missing at random across the set of covariate, predictor, and outcome variables. Full imputation maximum likelihood was employed during analyses, which utilizes all available data to estimate model parameters and standard errors (Buhi et al. 2008; Muthe´n and Muthe´n 2010). Maximum likelihood does not make assumptions about what values should be, but treats missing data as another parameter to be estimated (Buhi et al. 2008). Maximum likelihood also produces estimates that are consistent, asymptotically efficient and asymptotically normal, with the advantage of efficiency and production of the same results for the same set of data (Allison 2012). Lastly, the implementation of maximum likelihood does not require a series of decisionmaking processes for imputation and thus reduces user error, while also eliminating potential conflict between the imputation model and the analysis model because all calculations are computed under one model (Allison 2012). Using full maximum likelihood imputation in MPlus 6.1 statistical software (Muthe´n and Muthe´n 2010) to analyze

Parent-reported gender and race/ethnicity were collected in 1991. Age was calculated using parent-reported birth dates and the interview date of the Health and Physical Development Assessment. Race/ethnicity was dummy coded with White as the reference group. Family income was measured as parental report of the total pre-tax family income (for the year the child was in grade six; 2002–2003). Pubertal Development The Pubertal Development Scale (Petersen et al. 1988) asks mothers a series of 5 questions evaluating the degree to which a specific physical change (i.e. pimply skin, growth spurt, breast development, menstrual cycle, deepening of voice, and body/facial hair) had occurred in their child. Separate forms are used for boys and girls. Response scales ranged from 1 (specified development has not started) to 4 (development seems complete), and also provided a ‘‘don’t know’’ response, which was recoded as missing data. The item regarding female participants’ first menstrual period was coded dichotomously as 4 = yes and 1 = no. Mean scores were computed for complete data, with higher scores indicating more advanced stages of pubertal development. Prior assessments of this instrument (Petersen et al. 1988) demonstrate reasonable internal consistency (median Cronbach’s alpha = .77). Unhealthy Eating Habits An index of unhealthy eating habits was based on the child’s self-report responses to the Child’s Eating Habits and Body Self Image questionnaire (Brener et al. 2002). Four items pertaining to the consumption of unhealthy meals, snacks, and drinks generated the measure used in the current study. Children were asked to report how many times they had eaten or drank the following since yesterday: regular soda, hamburger/hotdog, French fries, and cookies/doughnuts. Responses were coded as 0 (not at all), 1 (only a little) 2 (a lot), and 3 (almost every day). Items were summed with possible scores ranging from 0 to 12 and higher scores indicating more unhealthy eating habits.

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The raw items used to create the index score had modest internal reliability (Cronbach’s alpha = .59). Depressive Symptoms Depression was measured via the Children’s Depression Inventory Short Form (Kovacs 1992), a widely used 10-item questionnaire with higher scorings indicating more depressive symptoms. Scores above 8 for girls and above 10 for boys are considered ‘‘well above average.’’ The instrument yielded adequate internal reliability within this sample (Cronbach’s alpha = .73).

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children and by the same child on various days. Data were averaged across all valid days for which the study child had data, yielding a measure of average minutes spent per day in moderate activity, which was used as a measurement of exercise in the present study. Intracorrelations and 95 % confidence intervals were used to analyze data from a sample of 30 children who wore the monitors for 12 h per day for 6 days, demonstrating adequate stability (r = 0.81–0.84) when 6 days of data were used and acceptable correlations (r = 0.75–0.78, CI 0.60–0.88) when at least 4 days of data were used. Primary Measures

Attention/Behavioral Problems Risk for Hypertension Problems with attention or behavior were measured via parental report of attention, behavior, or emotional problems as diagnosed by a professional. In parent interviews, mothers were asked ‘‘Since we talked to you in [date of last interview], has anyone suggested that [your child] see the school psychologist or other counselor or any other professional because of learning problems, emotional problems, or problems with attention, school work, or classroom behavior?’’ If mothers responded ‘‘yes,’’ and reported that the child visited a professional, interviewers then asked ‘‘Did they say that [your child] has attention, behavior, or emotional problems?’’ Responses were coded dichotomously as ‘‘yes’’ or ‘‘no.’’ Physical Activity Physical Activity Monitoring was used to measure activity over a period of 7 days during a typical school week. Each study child wore a single channel accelerometer that collected movement data by recording multiple accelerations, or changes in the child’s total body movement over a defined period of time, that indicated level of physical activity. Monitors were not worn at night while sleeping or during water related activities. A full day of activity monitor data was defined as the time frame beginning with the first nonzero accelerometer count after 5 a.m. and ending with whichever of the following criteria came first: 60 consecutive minutes of zero counts after 9 p.m.; 30 consecutive minutes of zero counts after 10 p.m.; or the last nonzero count prior to midnight. Accelerometer counts were computed as the total number of minutes calculated for each day, and invalid days were coded and removed. The number of minutes spent per day in moderate activity were divided by the number of minutes spent wearing the monitor; this provided a measure of the percentage of time the monitor was worn during moderate activity, as well as a means of comparing across the varying lengths of time the monitor was worn by different

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Continuous measures of systolic blood pressure and diastolic blood pressure were used to generate dichotomized risk for hypertension classifications. Blood pressure was measured by a nurse practitioner during the annual Health and Physical Development Assessment of the study. To preserve uniformity in data collection across sites, blood pressure was taken with the child seated, using the nondominant arm, and measured via a blood pressure cuff and stethoscope. Readings were taken a second time if the child appeared anxious or if the initial readings were high. When two readings were reported, the value of the analysis variable was set to the value of the second reading; this yielded raw measures of both systolic blood pressure and diastolic blood pressure. Using blood pressure standards for children and adolescents based on sex, age, and height, the present study computed blood pressure percentiles from growth charts published by the Centers for Disease and Prevention (CDC 2000). Using an established formula (National High Blood Pressure Education 2004), separate systolic and diastolic blood pressure percentiles were calculated as a function of age and sex. In children, prehypertension is defined as an average systolic or diastolic blood pressure level Cthe 90th percentile for sex, age, and height, while hypertension is defined as Cthe 95th percentile (National High Blood Pressure Education 2004). Considering these were not clinical diagnoses, we maintained a conservative approach to risk for hypertension measurement by collapsing prehypertension (9.2 % of sample) and hypertension (5.5 %) into one ‘‘risk for hypertension’’ classification. Body Mass Index BMI was computed from height and weight measurements recording during laboratory visits. Height was given in inches and centimeters; weight was given in pounds and kilograms. BMI was calculated by the original researchers,

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and the raw continuous values were included in the present study. Daytime Sleepiness Daytime sleepiness was measured via a subscale of the My Child’s Sleep Habits scale, which is a set of questions from the Children’s Sleep Habits Questionnaire (CSHQ; Owens et al. 2000). Measures were reported by each study child’s mother, who responded to four items referring to daytime sleepiness (i.e., My child seems tired during the day; My child suddenly falls asleep in the middle of watching TV, reading in a car, or other daily activities; My child naps during the day; Daytime sleepiness is a problem for my child). Response scales ranging from 1 (usually) to 3 (rarely) were reverse scored and averaged, such that higher scores indicated more daytime sleepiness problems. The raw items used to create the problem score had modest internal reliability (Cronbach’s alpha = 0.65). Prior research has found significant consistency among parent and child reports of daytime sleepiness using the CHSQ within this age group (Meltzer et al. 2012). Sleep Duration Sleep duration was reported by the study child using a scale developed by the original study researchers that included items adapted from the CSHQ. Questions were administered to study children in an interview format and included items regarding children’s typical bedtimes, amount of sleep, and difficulties going to sleep. Bedtimes and wake times were measured using the following format: ‘‘Do you have a usual bedtime on school nights? What time is your usual bedtime? What time do you usually wake up in the morning on school days?’’ Amount of sleep on schoolnights and weekend nights was calculated accordingly by original researchers. Research has demonstrated sufficient agreement between self-reported sleep duration and objective measurement, such as actigraphic monitoring (Gangwisch et al. 2006), and adolescent self-reports of sleep duration are more accurate than parent-reports, which tend to overestimate sleep duration (Short et al. 2013). Statistics All covariates (gender, age, income, race/ethnicity, unhealthy eating habits, pubertal development, physical activity, depressive symptoms, and attention/behavioral problems) were included in the proposed models. All analyses were conducted within the full sample, as well as separately by gender. To test the proposed mediation model, structural equation modeling was used, and analyses were conducted

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using MPlus 6.1 statistical software (Muthe´n and Muthe´n 2010). When multiple independent variables, mediators, and/or dependent variables are being tested, structural equation modeling yields the most comprehensive approach by allowing direct effects, indirect effects, and standard errors to be estimated with the presence of multiple predictors, mediators, or outcome measures. Confidence intervals (CIs) and significance testing for indirect effects were calculated via Theta parameterization, per recommendations for path analysis with categorical dependent variables (Muthe´n and Muthe´n 2010). Full mediation path models were tested within the full sample, and separately among boys and girls (Fig. 2).

Results Descriptive information (see Table 1) and Pearson productmoment correlations were computed using the IBM SPSS Statistics software package. Correlations among primary variables and covariates are shown in Table 2. All variables have reasonable means and variability given their scales. Average sleep duration exceeded the nationally recommended 8.5–9.25 h per night for this age group (National Sleep Foundation 2009). In the total sample, 14.8 % were classified as at risk for hypertension (15.7 % of boys; 13.9 % of girls). Overall, primary variables correlated with one another within the expected direction. Independent sample ttests showed no significant gender differences on BMI (t(915) = .58, p = .56), risk for hypertension (t(748) = .72, p = .47), or daytime sleepiness (t(989) = -1.58, p = .12). School-night sleep duration was slightly shorter among boys (M = 9.27 h) versus girls (M = 9.36 h) (t(1,010) = -1.94, p = .05), and only weekend sleep duration was significantly shorter among boys (M = 9.67 h) versus girls (M = 10.11 h) (t(1,010) = -4.33, p \ .001). The proposed model was then tested within the full sample, just girls, and just boys (see Table 3). Within the total sample, fit statistics suggest a very good-fitting model. The comparative fix index (CFI = .99) and the TuckerLewis index (TLI = .99) both exceeded the recommended cut-off of .90, and the root mean squared error of approximation (RMSEA = .01) was below the recommended cut-off of .05. The full model, including covariates, accounted for 18 % of the variance in risk for hypertension. As predicted, all three sleep variables significantly predicted BMI, such that every hour increase in school-night and weekend sleep duration predicted a .63 and .45 decrease in BMI, respectively. Similarly, a one unit increase in daytime sleepiness predicted a 2.84 increase in BMI, suggesting that an increase in frequency of daytime sleepiness from ‘‘rarely’’ to ‘‘sometimes,’’ or ‘‘sometimes’’ to ‘‘usually’’ is associated with higher BMI values of

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Fig. 2 Results from mediation models examined within gender subgroups. Path weights for all direct and indirect effects are reported in unstandardized regression coefficients. *p \ .05; **p \ .01; ***p \ .001. R2 values indicate the amount of variance in risk for hypertension accounted for by the full model. Path weights for covariates are not shown

Table 1 Demographic information of primary variables and covariates Variable

Total M (N)

N

Girls SD

Range

M (N)

Boys SD

Range

M (N)

(1,364)

(659)

(705)

White

(1,097)

(525)

(572)

Black

(176)

(86)

(90)

Asian

(22)

(12)

(10)

Other

SD

Range

Race

(69)

(36)

(33)

Attn./beh. prob.

7.1 % yes

5.3 % yes

8.9 % yes

Income

87,168.34

84,745.11

2,500–1,000,001

80,451.97

67,037.59

2,500–550,000

81,510.54

66,941.33

2,500–550,000

Eating habits

3.31

2.35

0–12

2.98

2.11

0–12

3.64

2.53

0–12

Pubertal dev.

1.86

.64

1–3.80

2.19

.64

1–3.80

1.49

.41

1–3

Physical activity

10

3.38

.61–25.7

9.29

3.23

.61–20.80

10.72

3.39

2.73–25.70

Depression

1.46

2.19

0–19

1.51

2.27

0–14

1.31

2.03

0–19

Age

12

.32

11.17–13.33

11.97

.30

11.17–13.33

12.02

.34

11.17–13.25

BMI School. sleep

20.85 9.31

4.95 .76

11.60–49.77 6.33–12

20.75 9.36

4.67 .78

13.96–37.37 7–12

20.94 9.27

5.23 .74

11.60–49.77 6.33–11.83

Weekend sleep

9.89

1.63

2.75–18

10.11

1.44

5–16

9.67

1.76

2.75–18

Sleepiness

1.13

.25

1–3

1.14

.26

1–3

1.12

.24

1–2.75

Risk for hyp.

14.8 % at risk

13.9 % at risk

15.7 % at risk

Values in parentheses indicate sample size. Attn./beh. prob. = Attention/behavior problems; Eating habits = Unhealthy eating habits; Pubertal dev. = Pubertal development; School. sleep = School-night sleep; Risk for hyp. = Risk for hypertension

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Table 2 Correlations of Primary Variables and Covariates BMI

School. sleep

Weekend sleep

Sleepiness

Race

Age

Attn./beh. prob.

Income

Eating habits

BMI

–

–

–

–

.13***

School–night sleep

-.15***

–

–

–

.12***

.02

-.01

-.13***

-.04

-.02

-.03

-.01

-.10**

Weekend sleep

-.08*

.23***

–

–

.09**

-.04

-.02

Sleepiness

.15***

-.08**

.03

–

.16***

Risk for hyp.

.30***

-.02

-.04

.12***

.06

Pub. dev.

Phys. activity

Depress.

-.09*

.11**

Total Sample

.09** -.03

.09** -.00

.09*** -.09***

-.13*** .03

-.02

-.02

-.14**

-.04

-.03

-.08

.22*** -.04

.09*

.01

.07*

.00

-.07*

.08*

.03

.06

-.02

.01

.01

Girls BMI

–

–

–

–

-.03

.02

School–night sleep

-.12*

–

–

–

-.09

.13***

-.01

-.04

-.04

-.09*

Weekend sleep

-.06

.21***

–

–

Sleepiness

.20***

-.09

.04

–

.15**

Risk for hyp.

.23***

.09

-.03

.14**

.07

BMI

–

–

–

–

School–night sleep

-.17***

–

–

–

Weekend sleep

-.10*

.24***

–

–

-.13**

Sleepiness

.11*

-.09

.00

–

.18***

Risk for hyp.

.35***

-.12*

-.04

.10

.06

.09*

.02 .10*

.12**

-.17***

.29*** -.09

.02

.10*

.14**

-.10* -.16***

.02

.03

-.09*

.08

.07

.06

.09

.03

-.03

-.03

-.04

-.04

-.01

.00

.13***

.05

-.03

-.16**

-.05

-.18**

.12***

-.16***

-.02

-.03

.02

-.11*

-.11*

.04

.12**

.01

-.02

.06

-.07

-.04

.02

-.01

.09

-.09

-.01

.05

.02

.02

-.00

-.05

-.06

.11*

-.02

.07

Boys

.10* -.03

.26***

Sleepiness = Daytime sleepiness; Risk for hyp. = Risk for hypertension; School. sleep = School-night sleep; Weeknd sleep = Weekend sleep; Attn./beh. prob. = Attention/ behavior problems; Eating habits = Unhealthy eating habits; Pub. dev. = Pubertal development; Phys. activity = Physical activity; Depress. = Depressive symptoms * p \ .05; ** p \ .005; *** p \ .001

approximately 3 units. As expected, BMI was significantly associated with risk for hypertension. School-night and weekend sleep duration yielded significant, negative indirect effects on risk for hypertension, while daytime sleepiness yielded a significant, positive indirect effect on risk for hypertension. Differing patterns emerged between the gender subgroups (see Fig.2; Table 3). Among girls, the overall fit indices (CFI = .93, TLI = .86, RMSEA = .03) indicated a good-fitting model. The full model accounted for 17 % of the variance in risk for hypertension. Interestingly, schoolnight and weekend sleep duration did not significantly predict BMI, nor did the sleep duration variables yield significant indirect pathways to risk for hypertension. Only daytime sleepiness significantly predicted BMI and yielded a significant indirect pathway to risk for hypertension. Among boys, fit indices (CFI = 1.00, TLI = .1.00, RMSEA = .00) exceeded recommended cut-offs, suggesting a very good-fitting model. The full model accounted for 24 % of the variance in risk for hypertension. Contrary to results within the subsample of girls, schoolnight sleep duration, weekend sleep duration, and daytime sleepiness all yielded significant pathways to BMI. Similarly, school-night sleep duration, weekend sleep duration, and daytime sleepiness yielded significant indirect associations with risk for hypertension.

Discussion Researchers who study adolescents have called for greater attention to the growing epidemic of hypertension among adolescents (Assadi 2012). Prehypertension in adolescents has been associated with the current presence of active disease, such as target organ damage and hypertrophy, and also places adolescents at risk for future hypertension (Redwine and Falker 2012) that can yield severe health and economic outcomes (CDC 2011). Studies have shown obesity as a substantial risk factor for hypertension (Babinska et al. 2012; Oduwole et al. 2012) and demonstrated sleep as a risk factor for elevated blood pressure in adolescence (e.g., Javaheri et al. 2008; Mezick et al. 2012). Yet, little research has examined if the sleep-blood pressure relationship is occurring as an indirect pathway through associations with BMI. Clarification of the relationship between sleep, BMI, and risk for hypertension during early adolescence could aid in identifying modifiable factors and designing health prevention and intervention programs to improve health outcomes and reduce risk for hypertension later in life. The present study contributes to this emerging literature by empirically demonstrating a mediation model in which BMI mediates the relationship between sleep characteristics and risk for hypertension in early adolescents. Further, the present study shows that these associations are gender-specific.

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J Youth Adolescence (2015) 44:271–284

Table 3 Full mediation models: direct, indirect, and total effects on risk for hypertension Total Sample

R2

Girls

Boys

BMI

Hyp.

BMI

Hyp.

.17

.18

.28

.17

BMI .21

Hyp. .24

School-night sleep Direct

-.63(.27)***

Indirect

-.35 (.36) -.06 (.03)*

-1.28 (.45)** -.03 (.03)

-.13 (.05)**

Weekend sleep Direct

-.45 (.13)**

Indirect Sleepiness Direct

-.33 (.22) -.04 (.01)**

2.84 (.72)***

Indirect

-.52 (.18)** -.03 (.02)

3.11 (.94)**

-.05 (.02)** 2.55 (1.78)*

.26 (.07)**

.26 (.10)**

.25 (.13)*

.09 (.01)***

.08 (.02)***

.10 (.01)***

BMI Direct

Total sample N = 541; Girls N = 292; Boys N = 249. These analyses have been corrected for age, gender, race/ethnicity, income, unhealthy eating habits, depression, attention/behavior problems, physical activity, and pubertal development. Path weights derived from unstandardized regression coefficients. SE for effects in (bolded parentheses). Sleepiness = Daytime sleepiness; Hyp. = Risk for hypertension. All significant indirect effects yielded confidence intervals that exclude zeros * p \ .05; ** p \ .01; *** p \ .001

The findings from the present study demonstrate that both sleep quantity and daytime sleepiness, an indicator of insufficient/poor quality sleep, served as direct risk factors for elevated BMI and indirectly predicted risk for hypertension in a sample of sixth graders, although the relative contribution of each sleep predictor varied by gender. Within the total sample, results from the path model suggested that school-night sleep duration, weekend sleep duration, and daytime sleepiness all directly predicted BMI and indirectly predicted risk for hypertension via associations with BMI. However, further analyses by gender suggested varying patterns; therefore conclusions regarding a sleep-BMI-blood pressure relationship without considering gender differences may be misleading. Differential gender patterns suggested that levels of BMI among girls may be sensitive to insufficient/poor quality sleep indicated by daytime sleepiness, but unaffected by the influence of sleep duration. Prior research has suggested that females may experience or be more susceptible to worse sleep quality versus men (Burgard and Ailshire 2013; Driver 2013) and have shown gender differences in sleep as young as adolescence (Mateo et al. 2012; Natal et al. 2009); however, to our knowledge, few studies have suggested differential gender patterns regarding the relationship between adolescent sleep variables and health outcomes, such as BMI. These novel findings suggest that future research should consider additional characteristics of sleep, beyond sleep quantity, as contributors to obesity among girls. Furthermore, daytime sleepiness as well as shortened school-night and weekend sleep durations emerged as

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meaningful predictors for boys, such that worse sleep characteristics served as a risk factor for higher BMI and, in turn, an indirect risk factor for risk for hypertension. Such findings are in line with prior studies that have shown stronger effects of sleep duration on BMI in males (Knutson 2005; Storfer-Isser et al. 2012). The strength of direct and indirect paths differed by gender and the effect sizes are small yet consistent in size to other studies of sleep and BMI in adolescents (e.g., Storfer-Isser et al. 2012). While associations of obesity and blood pressure with sleep may be small compared to effect sizes of risk factors such as nutrition, SES, and physical activity, small effect sizes can yield empirical and practical value, especially when results are consistent with other findings; smaller effect sizes can also represent trends that are strong enough to be apparent despite problems with measurement or methodology (Prentice & Miller 1992). Short sleep durations and daytime sleepiness may also yield cascading effects on adolescent health that are not captured within this model. For example, poor sleep hinders executive functioning processes that control impulsivity and decision-making (e.g., Ireland and Culpin 2006; Peach and Gaultney 2013; Sadeh et al. 2002) and thereby increase risky behaviors (e.g., Catrett and Gaultney 2009; Wong et al. 2010) that can further contribute to obesity and risk for hypertension. The overall findings suggest that sleep characteristics among adolescents are important factors to consider for weight and blood pressure management. Within the present study, a rich data set allowed for the control of important contributing factors to both BMI and

J Youth Adolescence (2015) 44:271–284

risk for hypertension, including race/ethnicity and family income, as well as behavioral and physiological influences such as eating habits, physical activity, attention/behavior problems, pubertal development, and depression. By accounting for these variables, analyses yielded the unique and independent effects of sleep on BMI and risk for hypertension above and beyond the influence of these related factors. The effect sizes can therefore be interpreted as exclusively due to the influence of sleep. Despite mixed findings within the adolescent health literature, present analyses provide support for prior research demonstrating a link between poor sleep and obesity (Bo¨rnhorst et al. 2012; Garaulet et al. 2011; Gupta et al. 2002; Nielsen et al. 2011), as well as poor sleep and elevated blood pressure (Javaheri et al. 2008; Mezick et al. 2012) in young adolescents, although gender plays a role in the strength and nature of these relationships. These findings highlight the importance of considering sleep characteristics as contributing factors to concurrent health during young adolescence, and coupled with research demonstrating tracking patterns of blood pressure into adulthood (Assadi 2012), the results suggest the possibility of poor sleep during early adolescence as a risk factor for elevated blood pressure later in life. While the present findings yield additional clarification for the interconnections between sleep, BMI, and risk for hypertension among young adolescents, several limitations of the present study are evident and provide direction for future research. Firstly, while blood pressure and BMI measurements met standard objective calculations, all sleep measures within the data set were self or parentreport. School-night and weekend sleep may be less reliable as a ‘‘typical’’ measure of sleep if home environments differ from week to week (e.g., when parents live apart and have shared custody). Additionally, internal consistency for daytime sleepiness was low, and this limited measurement tool only serves as a behavioral indicator of insufficient sleep and/or poor sleep quality; while other studies of daytime sleepiness have shown associations with a variety of health outcomes (e.g., Parker et al. 2003), additional research should include more validated and narrowed measures of such constructs. Future studies can replicate the present analyses using child-reported or teacherreported sleepiness, as well as objective measures of sleep, such as actigraphy or polysomnography. Secondly, the present study proposes a limited model. Prior research has examined bidirectionality between sleep and obesity in children (e.g., Magee et al. 2014), as well bidirectionality between disordered sleep and cardiovascular disease in adults (Kasai et al. 2012). Bidirectionality or disordered sleep were not addressed within the present study, therefore additional research is needed to clarify if BMI and risk for hypertension are in fact influencing sleep quality or quantity in some way. Furthermore, this research

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only demonstrates patterns of the influences of sleep within young adolescents. One cannot conclude causality or identify underlying physiological mechanisms from the present study. However, the clarification of such patterns is necessary in order to inform further research regarding causal mechanisms. The present findings provide justification for future research to examine causal sleep-blood pressure pathways and propose how and why such pathways vary between genders. For example, studies in adults suggest sex hormones may play a role in heart health (Coulter 2011), and although this study accounted for pubertal development, future research can examine hormonal activity as a possible underlying factor of these gender differences.

Conclusions The present findings contribute to a body of literature that report contrasting findings (e.g., Bo¨rnhorst et al. 2012; Calamaro et al. 2010; Garaulet et al. 2011; Gupta et al. 2002; Hassan et al. 2011; Knutson 2005; Lowry et al. 2012; Nielsen et al. 2011; Storfer-Isser et al. 2012). The analyses supported the proposed model and provide information regarding a possible pathway by which sleep may predict blood pressure through BMI at an early age. Considering that early adolescence provides a period of opportunity for prevention or early intervention for hypertension (Mezick et al. 2012), the clarification of pathways underlying the sleep-blood pressure link may inform the design of such prevention/intervention efforts. The present findings may also extend to examinations of the synergistic interactions of sleep and weight systems during adolescent development that can create either negative or positive spirals of functioning and health (Rofey et al. 2013). Such developmental frameworks recognize the dynamic and transitional period of change and maturation that occurs during adolescence and can aid in developing strategies to increase self-regulation in healthy sleep habits, bed times, and weight-management behaviors that can cumulatively impact the development of hypertension. Additional research could also consider the connections between these relationships and other developmentally-appropriate outcomes, such as sleep and obesity-related associations with academic performance (e.g., Stroebele et al. 2013). Furthermore, the emerging differential patterns between boys and girls, while preliminary findings, may suggest that application efforts as well as future research should be gender-specific. Efforts to lengthen regular sleep duration among boys may be protective against increasing BMI and blood pressure levels, while sources of daytime sleepiness among girls may need to be further explored and addressed. Future research and application efforts among adolescents

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should consider multiple measures of sleep as a significant influence on health that may help fine-tune future efforts to design interventions for obesity and hypertension development. Such efforts should also strive to consider the developmental stage of early adolescents, in which parents are influential in shaping their children’s developmental trajectories in ways such as initiating, sustaining, mediating and reacting to child-driven trajectories (Holden 2010). Identification and awareness of health risks early in development, while parents still provide some environmental context, can inform parents’ efforts to establish healthy behaviors by their child. By initiating and modeling healthy practices early in development, parents can direct the child’s developmental trajectory in a positive direction. As the developmental trajectory transitions from parent-driven to child-driven, parents can ‘‘pre-arm’’ children to manage their risk for hypertension. Author contributions HP conceived of the study, performed the statistical analysis, and drafted the manuscript. JG contributed to conception of the study, coordinated access to archival data set, and helped to draft the manuscript. CL participated in coordination of the statistical analysis, interpretation of the data, and drafting the manuscript. All authors read and approved the final manuscript.

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Hannah Peach is a doctoral student at the University of North Carolina at Charlotte. She received her Bachelors in Psychology from Clemson University. Her major research interests include the cognitive and physiological consequences of poor sleep in adolescence and emerging adulthood. Dr. Jane F. Gaultney is an Associate Professor at the University of North Carolina at Charlotte. She received her doctorate in Cognitive Developmental Psychology from Florida Atlantic University. Her major research interests include cognition and behavior in children, adults with sleep disorders, and the effect of sleepiness on children and adults. Dr. Charlie L. Reeve is Full Professor at the University of North Carolina at Charlotte. He received his doctorate in IndustrialOrganizational Psychology from Bowling Green State University. His major research interests include psychometrics and the application of the quantitative methods of the science of mental abilities.