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Waking up to sleep's role in obesity and blood pressure among Black adolescent girls in low-income, US urban communities: A longitudinal analysis

Open AccessPublished:February 11, 2022DOI:https://doi.org/10.1016/j.sleh.2021.12.001

      Abstract

      Objective

      To identify longitudinal bidirectional associations between unique sleep trajectories and obesity and hypertension among Black, adolescent girls.

      Design, setting, and participants

      Longitudinal data were from a randomized controlled trial (2009-2013) implemented in schools serving low-income communities aimed at preventing obesity among adolescent girls (mean age = 12.2 years (standard deviation ± 0.72).

      Measures

      Nocturnal sleep data were extracted from accelerometers at T1 (enrollment, n = 470), T2 (6-month, n = 348), and T3 (18-month follow-up, n = 277); height and weight were measured at T1-T3; and systolic/diastolic blood pressure at T1 and T3 using an oscillometric monitor. Multilevel models examined longitudinal associations. Finite mixture models identified sleep trajectory groups. Structural equation models examined whether T1 chronic disease risk predicted sleep profiles, and conversely, if sleep trajectories predicted T3 chronic disease risk. Data were analyzed in 2021.

      Results

      For each additional hour of sleep and 1% increase in efficiency there was a 7% lower risk of overweight/obesity at T1 and 6% lower risk at T2, but not at T3. Four sleep trajectories emerged: Worsened, Irregular, Improved, and Regular, with no demographic or metabolic differences between the trajectories. Improved sleep trajectory predicted lower diastolic percentile at T3 (b = −8.81 [95% confidence interval −16.23, −1.40]).

      Conclusions

      Group-based trajectories of sleep duration and quality provide information on modifiable factors that can be targeted in interventions to evaluate their impact on reducing chronic diseases and addressing disparities. Additional research is needed on samples beyond those recruited in the context of an intervention study.

      Keywords

      Introduction

      Insufficient sleep is common among adolescents, as most middle school-age (60%) and high school-age adolescents (78%) report sleeping less than the recommended 8 hours per night.
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      all of which may contribute to shorter sleep durations.
      However, lack of routines, crowded housing, neighborhood noise and lighting, and crime are among the social and environmental determinants of insufficient sleep duration and poor quality that disproportionally impact adolescents of color and low socioeconomic status.
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      Black adolescent girls in the United States report shorter sleep durations,
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      have more fragmented sleep,
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      than White adolescent girls and are at greatest risk for hypertension in adulthood.
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      Sufficient sleep is necessary for optimal daytime functioning, school performance, and wellbeing, thus placing adolescents from low-income backgrounds at higher risk for health disparities. Although most studies investigating health disparities compare outcomes across socioeconomic strata, studies that examine the variability within low-income groups are necessary to avoid perceptions of stereotypic health behavior based on race, age, sex, or socioeconomic status.
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      Short sleep duration and low sleep efficiency (ie, the percentage of time spent awake between sleep onset and offset) are important dimensions of pediatric sleep health
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      that may increase the risk of chronic diseases, such as obesity and hypertension.
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      It has been suggested that poor sleep disrupts hormone regulation leading to an energy imbalance and changes in body composition.
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      In turn, obesity may influence sleep through emotional regulation and stress.
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      Stress and depressive symptoms may mediate the association between weight stigma and sleep.
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      Furthermore, fat accumulation makes the upper airway vulnerable to collapse during sleep and can contribute to sleep disordered breathing, resulting in shorter and more fragmented sleep.
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      A recent cross-sectional study found that sleep duration and efficiency, measured with accelerometry, were inversely associated with blood pressure among adolescents.
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      Consistent late bedtimes, paired with longer light exposure, might disrupt the secretion schedule of melatonin and potentially raise average 24-h blood pressure.
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      Short sleep duration as a risk factor for hypertension: analyses of the first National Health and Nutrition Examination Survey.
      Although reverse-causality may be possible, few studies explore whether high blood pressure can reduce sleep duration or efficiency. Results from a retrospective study showed that children with hypertension reported significantly more difficulty initiating sleep than children with normal blood pressure.
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      Although causality cannot be inferred given the design of the referenced study, a possible mechanism for this association is that living with a chronic disease may act as a chronic stressor, thus disrupting sleep homeostasis.
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      The directional and mechanistic longitudinal associations between sleep (duration and efficiency) and obesity and hypertension are complex and remain unclear.
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      Weekly sleep trajectories and their associations with obesity and hypertension in the Hispanic/Latino population.
      Furthermore, there is heterogeneity in sleep trajectories indicated by intraindividual variability of sleep patterns throughout adolescence.
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      However, most research to date investigating changes in sleep over time uses population average trajectories,
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      Identifying typical trajectories in longitudinal data: modelling strategies and interpretations.
      assuming a uniform process in the development of sleep behavior and health outcomes.
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      Theories of multiple developmental pathways propose dynamic differences in progression to health outcomes (eg, sleep problems, or chronic risk).
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      A person-centered approach with longitudinal data, such as finite mixture modeling, can represent heterogeneity in sleep trajectories by identifying groups of people who may have similar developmental pathways of sleep over time. Although some studies have identified different sleep trajectories among adolescents,
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      Persistent short sleep from childhood to adolescence: child, parent and peer predictors.
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      they usually describe the trajectories based on one sleep dimension (eg, duration) without linking the sleep patterns with health indicators. Identifying distinct sleep patterns, risk factors, and implications for later health outcomes among adolescents would allow programs to design tailored strategies that prevent chronic diseases and address health disparities.
      This analysis remedies these gaps by analyzing longitudinal sleep behaviors among Black adolescent girls living in low-income, urban communities to examine the temporal relations among sleep (ie, duration and efficiency), body mass index z-score (BMIz), overweight/obesity, and systolic and diastolic blood pressure (SDBP). The aims of this study are:
      • 1.
        To examine the longitudinal relation between sleep habits (duration and efficiency) and chronic disease risk (BMIz, overweight or obesity, and SDBP).
      • 2.
        To identify unique trajectories of sleep patterns during adolescence and determine the bidirectional association between unique trajectories of sleep patterns and chronic disease risk.

      Methods

      Design

      Longitudinal data were from participants in Challenge! In Middle Schools, a multilevel school-based randomized controlled trial aimed at improving diet and physical activity using a mentorship model in schools serving low-income, urban communities.
      • Buckingham-Howes S
      • Armstrong B
      • Pejsa-Reitz MC
      • et al.
      BMI and disordered eating in urban, African American, adolescent girls: the mediating role of body dissatisfaction.
      The intervention was implemented over 4 years (2009-2013). Data collection occurred at 3 time points within each cohort: Time 1 (enrollment, fall), Time 2 (6-month follow-up, spring), and Time 3 (18-month follow-up, spring).

      Study sample

      The parent study enrolled 22 middle schools in a large urban public school district located within 7 miles of a YMCA, with predominantly Black students (70%), and majority (75%) eligible for free/reduced priced meals. Within each school, 20-40 adolescent girls in sixth and seventh grades who were able to participate in a physical education class, and whose parent/caregiver reads English were recruited to participate through mailings or in-person during lunch shifts. A total of 789 girls enrolled in the study. There were no weight inclusion/exclusion criteria. Adolescent girls signed a written informed assent. Adolescents’ primary caregivers signed a written informed consent. The study protocol was approved by the Institutional Review Boards (HP-00040540) at both the University and the city public school system where the study took place.

      Intervention

      In the multi-level intervention, schools were randomized to a school-wide intervention vs. control (1:1 randomization ratio). The school-wide intervention developed strategies to encourage physical activity and healthy eating throughout the school. Participating sixth and seventh grade girls in both intervention and control schools were randomized to small groups that addressed either physical activity and nutrition (small-group intervention) or stress-reduction (control). Thus, there were 4-level intervention groups with approximately 25% of sample in each: school-wide only, small group only, both school-wide and small group, and neither. Sleep was not incorporated into either the school-wide or small group interventions. At the conclusion of the trial, there were no significant changes in BMIz, the prevalence of obesity, or in body composition attributed to either the school-wide or small group interventions.

      Measures

      Overweight and obesity

      Height and weight were measured at 3 time points using a portable stadiometer (Shorr Productions, Olney, MD) and a weight scale (Tanita TBF-410, Arlington Heights, IL) to the nearest 0.1 cm and 0.1 kg in triplicate and then averaged. Participants were measured with their shoes removed and light clothing. BMI-for-age and -sex specific z-scores were calculated and compared to the CDC growth charts. Overweight or obesity were defined as BMI-for-age > 1 z-score.

      Systolic and diastolic blood pressure

      Blood pressure (systolic and diastolic) was objectively measured at 2 time points (Time 1 and 3) using a validated digital oscillometric blood pressure monitor (Omron, HEM-907XL, Bannockburn, IL). Blood pressure was measured at least twice, between a 3-minute rest, and then averaged with the participant in a seated position and right arm in an extended position resting at heart level with palm facing up. Age-, gender- and height-specific SDBP percentile were calculated following national standards normalized for US adolescents.
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      Determination of blood pressure percentiles in normal-weight children: some methodological issues.
      Prehypertension was defined as having systolic or diastolic blood pressure at or above the 90th percentile.

      Accelerometry

      A subset of participants was selected using a randomization procedure to wear an Actical accelerometer (n = 556), a widely used and validated proxy for sleep.
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      • Montgomery-Downs HE
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      • Walsh CM.
      Use of actigraphy for assessment in pediatric sleep research.
      Adolescent girls wore an omnidirectional accelerometer (Actical; Respironics, Inc.; Bend, OR) for at least 7 days on their nondominant ankle with a nonremovable hospital band (ie, no nonwear time) at 3 time points (Times 1-3).
      • Black MM
      • Hager ER
      • Le K
      • et al.
      Challenge! Health promotion/obesity prevention mentorship model among urban, black adolescents.
      Data from Actical were collected in 60-second epochs (time-sampling intervals) detecting movement in the 0.5- to 3.0-Hz range.

      Sleep metrics

      Sleep and wake measures were estimated from accelerometer data using the validated American Sleep Association (ASA) Sadeh algorithm for adolescents
      • Sadeh A
      • Sharkey M
      • Carskadon MA.
      Activity-based sleep-wake identification: an empirical test of methodological issues.
      based on a wake threshold of 40 (medium sensitivity). Only complete days (ie, full 24-hour periods) with a daily average of 80 counts/minute, and at least 3 nights/assessment
      • Quante M
      • Kaplan ER
      • Rueschman M
      • Cailler M
      • Buxton OM
      • Redline S.
      Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep.
      were included in the analysis. Data were truncated after 7 days (totaling maximum of 6 nights).
      Sleep onset was defined as the first of ten continuous minutes scored as sleep based on the algorithm occurring between 7:00 PM and 5:00 AM next day to capture delayed sleep schedules. Sleep offset was the last minute of sleep followed by ten continuous minutes of awake,
      • Zhou M
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      • Robinson TN.
      Sleep duration, timing, variability and measures of adiposity among 8- to 12-year-old children with obesity.
      and could occur anytime between 5:00 AM and 1:00 PM. Nocturnal sleep was the sum of minutes scored as sleep from sleep onset to offset, converted into hours. Wake time after sleep onset (WASO) was the number of wake minutes between sleep onset and offset. Sleep efficiency was the percentage of nocturnal sleep minutes out of the total sleep period. Supplemental Fig. 1 illustrates the participant flow from the baseline sample, visual inspection, reasons for nights excluded, and final analytical sample. Only 3 adolescents had missing sleep onset/offset, thus cut-offs were deemed plausible.

      Covariates

      Sociodemographic characteristics were collected at Time 1. Age was calculated in years using dates of birth and interview and treated as a continuous variable. Caregivers reported household size and annual household income (US$) via a self-administered questionnaire sent by mail. The income-to-poverty ratio was calculated using the weighted average poverty thresholds by the US Census Bureau specific to each cohort (2009-2013). A poverty ratio below 1 indicates that the average income for each respective family size is below the federal poverty threshold.
      At every time point (T1-3), the 2-item Tanner scale was used to assess puberty development. Girls reported sexual maturation on a scale from 1 to 4 for breast and pubic. Higher scores denoted more maturation. The average of the 2-item Tanner scale was used as a time-varying covariate.
      Moderate-to-vigorous physical activity (MVPA) data were extracted using Actical accelerometer and reduced using Actiware 9.0. Validated thresholds for MVPA were applied,
      • Hager ER
      • Treuth MS
      • Gormely C
      • Epps L
      • Snitker S
      • Black MM.
      Ankle accelerometry for assessing physical activity among adolescent girls: threshold determination, validity, reliability, and feasibility.
      yielding minutes of MVPA per day.

      Statistical analysis

      All analyses were conducted using Stata SE 16.1 software (StataCorp, College Station, TX). For all analyses, statistical significance was defined by a p value of <.05.
      Multilevel models assessed the longitudinal associations between sleep parameter (time-variant, independent variable) and chronic disease risk factors (ie, BMIz, overweight/obesity, and SDBP), using full-information maximum likelihood estimation with a random intercept to account for repeated measures over time (ICCBMIz = 0.96; ICCoverweight/obese=0.61; ICCsystolic = 0.48, ICCdiastolic = 0.47) and clustering of students within school (ICCBMIz = 0.006; ICCoverweight/obese=0.008; ICCsystolic = 0.09, ICCdiastolic = 0.11). To assess whether the association varied over time, an interaction term between time of assessment (T1-T3) and sleep parameters were included in all models. For the categorical outcome (overweight/obesity), multilevel models with Poisson distribution were conducted. Cohen's f2 effect size was calculated to estimate the magnitude of the longitudinal association between sleep and chronic disease risk factors, allowing for a direct comparison of the 2 regressors.
      Adolescents’ sleep trajectories were identified using a group-based trajectory model (finite mixture model) based on 2 adolescent-level mean sleep parameters (ie, nocturnal sleep hours and sleep efficiency), assessed longitudinally. Akaike information criterion statistic for model fit, the Bayesian information criteria, entropy, interpretability of the trajectories, and percentage of sample in the smallest profile were used to identify the number of trajectories. A restriction requiring at least 5% per trajectory was implemented to avoid small group sizes. Then, adolescents were assigned to a mutually exclusive trajectory based on the individual's maximum posterior probability. To assess the potential bidirectionality of the association between sleep trajectories and chronic disease risk factors, structural equation models with linear response variables (or Poisson for categorical outcome) were employed to examine if chronic disease risk factors at T1 were associated with sleep trajectory at T3 and conversely, generalized structural equation modeling with multinomial logistic models examined whether sleep trajectory at T1 was associated with later chronic disease risk factors at T3 (BMIz, SDBP).
      Inverse probability weighting was used to address potential bias due to loss to follow-up and to correct for the effects of missing data using the pweight option in Stata. For all analyses, there was no significant effect by intervention group on the association between sleep and chronic disease risk, nor was there baseline differences in sleep between the 4-level intervention groups, thus intervention group was treated as a covariate in all models. For the most parsimonious model, the following covariates were selected: adolescent's school grade (categorical), Tanner score (continuous, time-varying), cohorts of data collection (2009-2013), moderate-to-vigorous physical activity (minutes), and intervention group (time invariant). Covariates were chosen based on associations reported previously and significant associations with sleep and chronic disease risk factors.

      Results

      On average, at Time 1, adolescent girls were 12.1 years old (± 0.72) and most were at tanner stage of 3 (mean 3.2 points ± 0.96) (Table 1). Two percent were prehypertensive (systolic or diastolic blood pressure at or above 90th percentile) and 47.8% had overweight or obesity. On average, adolescent girls had 4.8 nights of valid sleep data (min: 3, max: 6), slept for 7.9 hours, average WASO of 67 minutes, and sleep efficiency at 87.9%. Sleep efficiency decreased by 1.4% and WASO increased by 6 minutes over time (p = .005) in unadjusted growth mixture models (Table 1). There were no statistically significant differences between the analytical sample and all available data (Supplemental Table 1).
      Table 1Descriptive information on adolescent characteristics, cardiometabolic outcomes, and sleep measures of analytical sample
      Time 1Time 2Time 3Change over time (p value)
      p value for change over time was derived from multilevel growth models (unadjusted).
      Adolescent characteristicsnMean (SD)nMean (SD)nMean (SD)
      Age (years)46412.1 (0.7)34812.6 (0.7)27613.2 (0.7)<.001
      School grade4646.4 (0.5)3486.5 (0.5)2767.4 (0.5)<.001
      Tanner score
      Mean score of the 2 Tanner items breast and pubic
      4203.2 (0.9)3263.5 (0.9)2643.6 (0.9)<.001
      Race Black (%)470100.0
      Poverty ratio (<1.0) (%)
      The income-to-poverty ratio was calculated according to the US Census Bureau, Weighted Average Poverty Thresholds, 2009-2012. Annual household income was reported by adolescent's caregiver (n = 261).
      11351.1
      Chronic disease risk outcomes
      Body mass index (z-score)
      Body mass index was calculated for age- and sex-specific z-score according to the CDC Child Growth Reference for girls.
      4640.9 (1.0)3481.0 (1.0)2760.9 (1.1).827
      Systolic blood pressure (mm Hg, Average)449104.4 (9.8)276106.5 (9.2).275
      Diastolic blood pressure (mm Hg, Average)44959.7 (7.9)27660.1 (8.0).673
      Systolic blood pressure (percentile)44939.9 (23.3)27638.1 (23.2).902
      Diastolic blood pressure (percentile)44944.9 (27.8)27646.2 (25.9).933
      Overweight or obese (%)22247.817249.413147.5.371
      Prehypertensive
      90th percentile for either SBP or DBP.
      (%)
      71.651.8.913
      Sleep parameters51470349276
      Total number of nights4.8 (0.7)4.7 (0.6)4.7 (0.5).010
      Nighttime sleep (hours)7.9 (1.2)7.6 (1.5)7.8 (1.6).092
      WASO (minutes)66.8 (49.1)76.3 (46.6)80.5 (50.8).005
      Sleep efficiency (%)
      Percent of time asleep during sleep period.
      87.9 (8.6)85.4 (10.9)84.9 (10.9).002
      SD, standard deviation; WASO, wake after sleep onset.
      – Not measured at Time 2 assessment.
      Time 1 (enrollment), Time 2 (6-month follow-up), Time 3 (18-month follow-up).
      a Mean score of the 2 Tanner items breast and pubic
      b The income-to-poverty ratio was calculated according to the US Census Bureau, Weighted Average Poverty Thresholds, 2009-2012. Annual household income was reported by adolescent's caregiver (n = 261).
      c Body mass index was calculated for age- and sex-specific z-score according to the CDC Child Growth Reference for girls.
      d 90th percentile for either SBP or DBP.
      e Percent of time asleep during sleep period.
      f p value for change over time was derived from multilevel growth models (unadjusted).

      Longitudinal associations between sleep and chronic disease risk factors

      The magnitude of the longitudinal associations between sleep and chronic disease risk factors was larger in models treating sleep parameters as predictors of health outcomes (Supplemental Table 2). Longer sleep duration and higher efficiency were associated with lower risk for having overweight/obesity at Time 1 and 2, but not at Time 3 (Table 2). For each one-minute increase in nocturnal sleep duration, there was a 7% lower risk for having overweight or obesity at Time 1 (RR = 0.93 [95% confidence interval [CI] 0.89, 0.97]) and 6% lower risk at Time 2 (RR = 0.94 [95% CI 0.91, 0.96]), but not at Time 3 (RR = 0.99 [95% CI 0.96, 1.03]). Similarly, for each one-percentage increase in sleep efficiency, there was a 1% lower risk for having overweight/obesity at Time 1 and 2 (RR = 0.99 [95% CI 0.98, 0.99]), but not at Time 3 (RR = 0.99 [95% CI 0.99, 1.01]). Longer sleep duration was cross sectionally associated with lower BMIz (b = −0.03 [95% CI −0.06, −0.01]), and systolic percentile (b = −1.42 [95% CI −2.75, −0.10]), but not longitudinally. Similarly, greater sleep efficiency was associated with lower BMIz (b = −0.003 [95% CI −0.006, −0.001]) at Time 1, but not at Time 2 and 3. WASO did not predict any chronic disease risk outcomes. Sleep duration, WASO, nor efficiency predicted diastolic percentile in the person-centered models.
      Table 2Associations between sleep behavior and chronic disease risk factors among adolescents across 3 time points
      Model
      Hierarchical models controlled for adolescent's school grade (6th-7th), minutes of moderate-to-vigorous physical activity, Tanner score, cohort of data collection (2009-2012), and intervention group. (n = 547).
      Body mass index z-score (BMIz) as chronic disease risk outcome
      Metabolic outcomeDifference in beta between time 1 and time 2Difference in beta between time 1 and time 3Difference in beta between time 2 and time 3Cohen's f2 effect size
      Time-variant predictorsb (95% CI)p valueb (95% CI)p valueb (95% CI)p valueb (95% CI)p value
      Sleep hours
       Time 1−0.033 (−0.054, −0.012).002ReferenceReference25.80
       Time 2−0.002 (−0.017, 0.012).7510.031 (0.011, 0.050).002Reference
       Time 30.004 (−0.025, 0.032).8080.036 (0.012, 0.061).0040.006 (−0.020, 0.032).656
      WASO
       Time 10.000 (−0.000, 0.001).143ReferenceReference25.54
       Time 20.000 (−0.000, 0.001).358−0.000 (−0.001, 0.001).687Reference
       Time 30.000 (−0.001, 0.001).599−0.001 (−0.001, 0.000).080−0.000 (−0.001, 0.000).247
      Sleep efficiency
       Time 1−0.003 (−0.006, −0.001).012ReferenceReference25.67
       Time 2−0.001 (−0.003, 0.001).3070.002 (−0.001, 0.005).123Reference
       Time 30.001 (−0.003, 0.004).7690.004 (0.001, 0.007).0250.002 (−0.002, 0.005).304
      Overweight or obesity as chronic disease risk outcome
      Sleep hoursRR (95% CI)p valueRR (95% CI)p valueRR (95% CI)p valueRR (95% CI)p value
       Time 10.931 (0.897, 0.966)<.001ReferenceReference0.001
       Time 20.941 (0.919, 0.964)<.0011.020 (0.921, 1.119).689Reference
       Time 30.996 (0.957, 1.036).8391.140 (1.009, 1.271).0311.118 (1.020, 1.216).013
      WASO
       Time 11.001 (1.000, 1.002).117ReferenceReference−0.001
       Time 21.001 (1.000, 1.002).0591.001 (0.998, 1.003).674Reference
       Time 31.001 (1.000, 1.002).1461.000 (0.998, 1.002).6051.000 (0.998, 1.002).786
      Sleep efficiency
       Time 10.994 (0.989, 0.998).010ReferenceReference0.001
       Time 20.993 (0.989, 0.997).0010.999 (0.986, 1.013).936Reference
       Time 30.997 (0.990, 1.003).3131.005 (0.991, 1.020).4701.006 (0.993, 1.018).355
      Systolic blood pressure percentile as chronic disease risk outcome
      Sleep hoursb (95% CI)p valueb (95% CI)p value
       Time 1−1.424 (−2.748, −0.101).035Reference0.26
       Time 31.099 (−1.059, 3.256).3182.630 (−0.180, 5.440).067
      WASO
       Time 10.008 (−0.027, 0.042).667Reference0.25
       Time 3−0.038 (−0.092, 0.016).166−0.046 (−0.099, 0.007).089
      Sleep efficiency
       Time 1−0.085 (−0.295, 0.124).423Reference0.24
       Time 30.137 (−0.204, 0.477).4310.222 (−0.159, 0.603).253
      Diastolic blood pressure percentile as chronic disease risk outcome
      Sleep hoursb (95% CI)p valueb (95% CI)p value
       Time 10.091 (−1.399, 1.582).904Reference0.25
       Time 3−0.589 (−2.642, 1.464).574−0.680 (−3.229, 1.868).601
      WASO
       Time 10.022 (−0.024, 0.068).345Reference0.27
       Time 30.036 (−0.007, 0.079).1020.014 (−0.045, 0.073).646
      Sleep efficiency
       Time 1−0.103 (−0.364, 0.158).439Reference0.27
       Time 3−0.196 (−0.439, 0.047).113−0.093 (−0.403, 0.217).556
      CI, confidence interval; WASO, wake after sleep onset.
      b represents the average marginal effect of change in BMIz that is produced by a 1-unit increase in sleep at a given time point.
      RR represents the exponentiated Poison coefficient (relative risk) for 1-unit increase in sleep at a given time point.
      Time 1 (enrollment), Time 2 (6-month follow-up), Time 3 (18-month follow-up).
      a Hierarchical models controlled for adolescent's school grade (6th-7th), minutes of moderate-to-vigorous physical activity, Tanner score, cohort of data collection (2009-2012), and intervention group. (n = 547).

      Adolescent sleep trajectory

      Results of the finite mixture model following the statistical criteria illustrated in the Supplemental Table 3 indicated a 4-sleep trajectory solution: (1) worsened sleep pattern with decrease in sleep duration and efficiency over 18-months of assessment (Worsened, 12.3% of the sample); (2) irregular sleep pattern with high variability in sleep duration and efficiency over time (Irregular, 10.0%); (3) improved sleep pattern with increase in sleep duration and efficiency over time (Improved, 6.5%); and (4) regular sleep pattern over time with low variability in sleep duration and efficiency (Regular, 71.2% of the sample) (Fig. 1). Girls in the Improved trajectory had a greater mean Tanner score at T1 (3.4 ± 1.1) compared to girls in the Worsened trajectory (mean = 3.2 ± 0.9) (p value for mixed level model <.001; Supplemental Table 4).
      Fig 1
      Fig. 1Four profile trajectory solution of the group-based trajectory model of quality (efficiency) and duration (hours) of nocturnal sleep.

      Associations between chronic disease risk factors with sleep trajectory

      Girls in the Improved sleep trajectory had the lowest sleep duration (M = 5.1 ± 1.3) at Time 1 and maintained an 8-hour average sleep duration for Times 2 and 3 (Supplemental Table 5). Chronic disease risk at Time 1 did not predict sleep trajectory at Time 3. Adolescents in the Improved sleep trajectory presented reduction in diastolic-for-age by 8.8 percentile (95% CI: −16.23, −1.40) at Time 3, after controlling for demographics, intervention group, BMIz, and diastolic percentile at enrollment (Table 3). Statistical significance remained after Bonferroni corrections for multiple testing. Sleep trajectories at time 1 did not predict BMIz, overweight/obesity, nor systolic percentile at Time 3.
      Table 3Association between chronic disease risk factors (body mass index z-score, overweight/obesity, systolic and diastolic percentile) with sleep trajectory
      BMIzOverweight or obesitySystolic percentileDiastolic percentile
      Chronic disease risk (Time 1) → sleep trajectory
      Generalized structural equation with multinomial logistic models with full information maximum likelihood estimation corrected missing data using the inverse probability weighted method controlled for adolescent's age at baseline, Tanner score (baseline), moderate-to-vigorous physical activity (baseline), and intervention group; n = 464. Systolic and Diastolic Percentile models were further controlled for body mass index z-score at baseline.
      RR (95 % CI)p valueRR (95 % CI)p valueRR (95% CI)p valueRR (95% CI)p value
      RegularReferenceReferenceReferenceReference
      Worsened0.93 (0.67, 1.30).6890.99 (0.97, 1.01).1600.89 (0.45, 1.78).7630.99 (0.98, 1.01).259
      Irregular0.75(0.53, 1.05).0981.01 (0.99, 1.01).4451.01 (0.40, 1.68).5911.01 (0.99, 1.01).599
      Improved1.05 (0.69, 1.61).7951.01 (0.98, 1.01).5910.99 (0.66, 3.56).3200.99 (0.98, 1.01).737
      Sleep trajectory →chronic disease risk (Time 3)
      Structural equation with linear response variables (or Poisson for categorical outcome) and full information maximum likelihood estimation controlled for adolescent's cardiometabolic factor at baseline, age at baseline, Tanner score (baseline), moderate-to-vigorous physical activity (baseline), and intervention group; n = 464. Systolic and Diastolic Percentile models were further controlled for body mass index z-score at baseline.
      b (95% CI)p valueRR (95% CI)p valueb (95% CI)p valueb (95% CI)p value
      RegularReferenceReferenceReferenceReference
      Worsened−0.04 (−0.20, 0.10).5640.96 (0.69, 1.32).795−4.70(−13.43, 4.02).291−0.33 (−7.55, 6.88).928
      Irregular−0.01 (−0.12, 0.11).9700.96 (0.75, 1.22).7361.94 (−6.08, 9.97).634−2.67(−11.56, 6.21).555
      Improved−0.01 (−0.13, 0.12).8670.95 (0.72, 1.26).742−4.80 (−17.26, 7.65).450−8.81 (−16.23, −1.40).020
      BMIz, body mass index z-score; RR, relative risk ratio; b, regression coefficient; CI, confidence interval.
      RR represents the exponentiated multinomial or Poison coefficient (relative risk) for one unit increase in cardiometabolic variable for the worsened or irregular trajectory relative to the regular trajectory (reference)
      b represents the change in the metabolic outcome that is produced by a sleep profile compared to regular trajectory (reference)
      Regular = Regular sleep trajectory; Worsened = Worsened sleep profile trajectory; Irregular = Irregular sleep trajectory; Improved = Improved sleep trajectory
      a Generalized structural equation with multinomial logistic models with full information maximum likelihood estimation corrected missing data using the inverse probability weighted method controlled for adolescent's age at baseline, Tanner score (baseline), moderate-to-vigorous physical activity (baseline), and intervention group; n = 464. Systolic and Diastolic Percentile models were further controlled for body mass index z-score at baseline.
      b Structural equation with linear response variables (or Poisson for categorical outcome) and full information maximum likelihood estimation controlled for adolescent's cardiometabolic factor at baseline, age at baseline, Tanner score (baseline), moderate-to-vigorous physical activity (baseline), and intervention group; n = 464. Systolic and Diastolic Percentile models were further controlled for body mass index z-score at baseline.

      Discussion

      Three major findings emerged from this longitudinal investigation of associations between sleep parameters and chronic disease risk factors among Black adolescent girls from a predominantly low-income background. First, greater sleep duration was associated with lower chronic disease risk cross sectionally (Time 1), and greater sleep duration and efficiency were associated with lower risk for having overweight/obesity over time. Second, 4 trajectories of sleep were identified. Third, chronic disease risk did not predict sleep trajectories, although Improved sleep trajectory (sleep duration and efficiency) predicted reduction in diastolic-for-age percentile at Time 3, after accounting for diastolic measures and BMIz at enrollment.
      Both variable-centered and person-centered approaches consistently indicated that sleep predicted chronic disease risk, but not the other way round. Despite the increased importance of sleep on cardiovascular health, clinical and public health guidelines (eg, American Heart Association Life's Simple 7) do not routinely promote sleep as a protective behavioral factor. In this study, longer and more efficient sleep was cross sectionally associated with lower BMIz and SDBP and longitudinally with lower risk for having overweight/obesity, even after accounting for physical activity. The findings from this investigation are consistent with many reviews and meta-analysis demonstrating the protective role of sleep on chronic disease risk in adolescents.
      • Sun J
      • Wang M
      • Yang L
      • Zhao M
      • Bovet P
      • Xi B.
      Sleep duration and cardiovascular risk factors in children and adolescents: a systematic review.
      The Improved sleep trajectory was associated with lower diastolic blood pressure percentile at Time 3, independent of age, puberty stage, intervention group, BMIz, and physical activity level. This is an encouraging finding, meaning that interventions to improve sleep behaviors during adolescence may have a positive influence on cardiovascular health. Although the underlying mechanism for the association between short sleep duration or efficiency and high blood pressure is not fully understood, it has been hypothesized that short sleep duration may influence cardiometabolic factors through inflammatory mechanisms.
      • Miller MA
      • Cappuccio FP.
      Inflammation, sleep, obesity and cardiovascular disease.
      Habitual short sleep duration has been shown to increase free fatty acids in plasma, which would in turn increase mitochondrial uncoupling, causing a reduction in high-energy phosphate level, manifested as diastolic dysfunction.
      • Rider OJ
      • Francis JM
      • Ali MK
      • et al.
      Effects of catecholamine stress on diastolic function and myocardial energetics in obesity.
      Thus, a longer duration of nocturnal sleep with greater efficiency could decrease free fatty acids in plasma and influence average blood pressure, especially diastolic blood pressure.
      • Rider OJ
      • Francis JM
      • Ali MK
      • et al.
      Effects of catecholamine stress on diastolic function and myocardial energetics in obesity.
      More clinical studies are needed to confirm the potential biological mechanism of sleep on blood pressure.
      Other studies evaluating sleep duration trajectories among adolescents classified most individuals as having a reduction in sleep duration over time.
      • Kramer Fiala Machado A
      • Wendt A
      • Baptista Menezes AM
      • Gonçalves H
      • Wehrmeister FC.
      Sleep duration trajectories from adolescence to emerging adulthood: findings from a population-based birth cohort.
      However, in this study, the majority was classified into the Regular trajectory; perhaps the 18-month follow-up period did not capture the sleep reduction that often occurs with pubertal onset and psychosocial alterations.
      • Gohil A
      • Hannon TS.
      Poor sleep and obesity: concurrent epidemics in adolescent youth.
      ,
      • Owens J
      Adolescent Sleep Working Group, Committee on Adolescence
      Insufficient sleep in adolescents and young adults: an update on causes and consequences.
      Conversely, results from the variable-centered approach indicated that efficiency, as opposed to duration of sleep, decreased over time. In the present study, girls who had a Worsened and Irregular sleep trajectories had a significant variation in sleep duration and efficiency over time, possibly due to lack of sleep routines (eg, irregular bedtimes). Consistent bedtime is an important protective factor to prevent weight gain, especially in the context of poverty.
      • Covington L
      • Armstrong B
      • Trude ACB
      • Black MM.
      Longitudinal associations among diet quality, physical activity and sleep onset consistency with body mass index z-score among toddlers in low-income families.
      Both sleep duration and efficiency were longitudinally associated with lower risk for having overweight/obesity. Most studies on sleep among adolescents have been cross-sectional
      • Zhou M
      • Lalani C
      • Banda JA
      • Robinson TN.
      Sleep duration, timing, variability and measures of adiposity among 8- to 12-year-old children with obesity.
      and used self-reported sleep.
      • Kramer Fiala Machado A
      • Wendt A
      • Baptista Menezes AM
      • Gonçalves H
      • Wehrmeister FC.
      Sleep duration trajectories from adolescence to emerging adulthood: findings from a population-based birth cohort.
      However, meta-analyses of prospective studies have consistently shown the inverse association between sleep duration and weight gain among children and adolescents.
      • Deng X
      • He M
      • He D
      • Zhu Y
      • Zhang Z
      • Niu W.
      Sleep duration and obesity in children and adolescents: evidence from an updated and dose-response meta-analysis.
      Of note, sleep duration nor efficiency were associated with chronic disease risk at the 18-month assessment (Time 3). The lack of the long-term association could be due to the loss to follow-up, leading to a reduced statistical power to detect true differences. Other studies could replicate these findings in other samples of adolescents that are followed up over 18 months, while examining potential moderators (eg, individual-level factors such as stress and environmental-level factors such as school start time). Contrary to what was observed in the variable-centered approach, the trajectory of sleep duration and efficiency over time did not predict chronic disease risk, like overweight/obesity, but predicted lower diastolic percentile. Girls in the Worsened trajectory had a significant decrease in sleep duration and efficiency between the 2 follow-ups, which may not have been enough time to influence chronic disease risk.
      A major strength of this study was the use of objectively measured sleep quantity (duration), efficiency, and chronic disease risk factors (BMIz and blood pressure) analyzed longitudinally. Further, a person-centered approach was used to identify groups of adolescent girls sharing similar developmental pathways of sleep longitudinally. This is a relatively novel method in sleep research and is typically characterized by higher levels of statistical power than comparable traditional methods applied to the same data.
      The use of group-based trajectory modeling comes with some limitations. Misclassification can occur in assigning trajectory membership. Irregular and Improved trajectories represented a small percentage of the population, although many studies identifying trajectories typically require at least 5% of the population per group. This was a secondary analysis with various exposures being tested and it is possible that type I error inflation was present. Although this study examined important domains of pediatric sleep such as duration and efficiency, future studies should consider additional domains such as satisfaction, napping, timing, and sleep routines.
      • Meltzer LJ
      • Williamson AA
      • Mindell JA.
      Pediatric sleep health: it matters, and so does how we define it.
      Furthermore, the present analysis incorporated weekend sleep into sleep measurements, given that most sleep data available were collected during weekdays (65%). Future studies should examine potential differences in sleep between weekday versus weekend among adolescents. Accelerometers are commonly used in population-based studies; however, they are less precise than the gold standard (PSG). Actical, placed on the wrist or ankle, yields similar or high intraclass correlation compared to PSG in sleep latency, total sleep time, and sleep efficiency. Environmental factors known to influence sleep such as housing conditions and temperature control, room-sharing, and screen use, as well as psychological factors such as stress, depression, and anxiety, were not evaluated. Obstructive sleep apnea syndrome is also known to impact sleep duration and efficiency and it should be measured in future studies. Seasonality is unlikely to have played a role in our study, as Actical data were collected when school was in session and not during periods of extreme weather conditions (Summer or Winter) or holiday breaks. Participant loss is a limitation, although attrition rate was typical of expectations in community- and school-based intervention trials. To address potential selection bias, inverse probability weighting and full-information likelihood estimation models were employed in all analyses. Lastly, although the parent study recruited adolescent from schools serving low-income communities, almost half of the sample had a family income-to-needs ratio > 1.0. However, data on household income were limited and self-reported by only 56% of the adult caregivers. Thus, poverty level may be underestimated. Additional clinical research is needed on samples of color and of low-income backgrounds, beyond those recruited in the context of an intervention study.

      Conclusions

      In summary, greater sleep duration and efficiency were longitudinally associated with lower risk of having overweight/obesity among a sample of Black adolescent girls. Girls followed 4 unique sleep trajectories through adolescence: Worsened, Decreased, Improved, and Regular. When linking sleep trajectories with health indicators, girls who improved sleep duration and efficiency over time also improved their blood pressure profile. Determining the sources of vulnerability in health behaviors experienced within each of these groups, such as short sleep duration and low sleep efficiency, provides opportunities to intervene to disrupt chronic disease risk and address disparities. Future interventions could aim to promote both sleep duration and sleep consolidation by employing multilevel preventive strategies to improve both individual-level factors (ie, sleep hygiene) and social and environmental factors (ie, noise, light, school start time) and test the effect on weight gain and blood pressure in adolescents.

      Declaration of conflict of interest

      The authors have declared no conflicts of interest.

      Funding

      The presented work had financial support from the American Heart Association (20POST35210825, PI: Trude) and the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) at the National Institutes of Health (NIH) (R01 HD054727, PI: Black).

      Trial registration

      Challenge! A Health Promotion/ Obesity Prevention Program for Teens. https://clinicaltrials.gov/ct2/show/NCT03103269.

      Ethics approval and consent to participate

      The study protocol was approved by the Institutional Review Boards (HP-00040540) at both the University and the city public school system where the study took place. Informed assent and consent were gathered from the adolescent and caregiver, respectively.

      Appendix. Supplementary materials

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