ArticlePDF Available

Parents Still Matter: The Influence of Parental Enforcement of Bedtime on Adolescents’ Depressive Symptoms

  • State University of New York at Brockport

Abstract and Figures

Study Objectives The aim of the current study was to test a multilevel mediation model that examined how adolescent sleep duration might be linked to depressive symptoms via their daytime energy levels. Furthermore, the study examined how parents’ enforcement of various types of bedtime rules predicted the duration of adolescent sleep. Methods A total of 193 adolescent (ages 14-17; Mage = 15.7 years old, SD = .94; 54.4% female; 71% Caucasian) and parent dyads completed baseline, online surveys, and adolescents also completed online 7-day, twice-daily (i.e., morning and evening) reports of their sleep duration (morning diary) and their energy levels and depressive symptoms throughout the day (evening diary). Parents (Mage = 47.6 years old, SD = 5.4; 80% female) completed assessments of enforcement of bedtime-related rules (i.e., bedtime, cessation of electronic media usage, prohibiting afternoon/evening caffeine consumption). Multilevel modeling enabled the testing of the mediation model both at the between-person level and within individuals. Results Results suggested that adolescents’ energy levels mediated the association between adolescents’ sleep duration and depressive symptoms. Furthermore, both greater enforcement of bedtimes and later school start times predicted longer sleep durations for adolescents, and were indirectly associated with adolescents’ depressive symptoms. Conclusions These findings underscore the importance of adolescents obtaining sufficient sleep to support their mental health and suggest a critical point of intervention for preventing or decreasing insufficient sleep. Given the diverse threats to adolescents’ sleep as well as adolescents’ desire for greater independence, collaborative, autonomy-promoting bedtime limit-setting is recommended to support adolescents’ well-being.
Content may be subject to copyright.
Submitted: 7 May, 2019; Revised: 28 October, 2019
© Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society.
All rights reserved. For permissions, please e-mail
O A
Parents still matter: the inuence of parental
enforcement of bedtime on adolescents’ depressive
JackS. Peltz1,*, RonaldD. Rogge2 and Heidi Connolly3
1Daemen College, 4380 Main Street, Amherst, NY 14226, 2Department of Clinical and Social Sciences in Psychology,
University of Rochester, Box 270266, Rochester, NY 14627 and 3Department of Pediatrics, University of Rochester
Medical Center, Rochester, NY 14627
*Corresponding author. Jack S.Peltz, Daemen College, 4380 Main Street, Amherst, NY 14226. Email:
Study Objectives: The aim of the current study was to test a multilevel mediation model that examined how adolescent sleep duration might be linked
to depressive symptoms via their daytime energy levels. Furthermore, the study examined how parents’ enforcement of various types of bedtime rules
predicted the duration of adolescent sleep.
Methods: A total of 193 adolescent (ages 14–17; Mage=15.7years old, SD=.94; 54.4% female; 71% Caucasian) and parent dyads completed baseline,
online surveys, and adolescents also completed online 7-day, twice-daily (i.e. morning and evening) reports of their sleep duration (morning diary)
and their energy levels and depressive symptoms throughout the day (evening diary). Parents (Mage=47.6years old, SD=5.4; 80% female) completed
assessments of enforcement of bedtime-related rules (i.e. bedtime, cessation of electronic media usage, prohibiting afternoon/evening caffeine
consumption). Multilevel modeling enabled the testing of the mediation model both at the between-person level and within individuals.
Results: Results suggested that adolescents’ energy levels mediated the association between adolescents’ sleep duration and depressive symptoms.
Furthermore, both greater enforcement of bedtimes and later school start times predicted longer sleep durations for adolescents, and were indirectly
associated with adolescents’ depressive symptoms.
Conclusions: These ndings underscore the importance of adolescents obtaining sufcient sleep to support their mental health and suggest a critical
point of intervention for preventing or decreasing insufcient sleep. Given the diverse threats to adolescents’ sleep as well as adolescents’ desire for
greater independence, collaborative, autonomy-promoting bedtime limit-setting is recommended to support adolescents’ well-being.
Key words: adolescence; sleep; bedtimes; depression; mental health
Statement of Signicance
The majority of adolescents suffer from insufcient sleep, which results in extensive behavioral, psychological, and physical problems. This
study builds on research that has examined the inuence of parent-set bedtimes and other threats to adolescents’ sleep and functioning.
Using multilevel modeling to highlight processes through which adolescents’ sleep duration might impact their well-being, we found that
greater enforcement of parent-set bedtimes and later school start times were associated with longer sleep duration for adolescents. In turn,
longer sleep duration predicted lower levels of depressive symptoms, via the mediating inuence of adolescents’ energy levels during the
day. The ndings provide an important avenue through which parents can intervene and defend against the myriad impediments to ado-
lescents’ sleep and mental health problems.
SLEEPJ, 2019, 1–11
doi: 10.1093/sleep/zsz287
Advance Access Publication Date: 29 November 2019
Original Article
Downloaded from by guest on 10 January 2020
2 | SLEEPJ, 2019, Vol. XX, No. XX
Adolescents’ insufcient sleep remains a critical risk factor for
the development of potential mental health problems [1–4].
Extensive research underscores the role that sleep plays in ado-
lescent academic, behavioral, and psychosocial functioning
[5–9], including clinical outcomes such as suicidal ideation [10,
11]. This epidemic of insufcient sleep experienced by most ado-
lescents highlights the need for research to identify potential
interventions for adolescent sleep problems [3, 6]. According
to the National Sleep Foundation’s 2014 Sleep in the Modern
Family Poll, approximately 25% of parents of 15–17year olds do
not have any formal sleep-related rules [12]. Although parents’
enforcement of rules related to bedtime, afternoon/evening caf-
feine intake, and prebedtime electronic media usage can yield
up to approximately an additional hour of sleep per night on
school nights, only 35% of parents of 15–17year olds enforce
such rules [12]. Given the functional consequences of adoles-
cents’ insufcient sleep [8], the current study sought to examine
how day-to-day uctuations in sleep duration might impact
adolescents’ mental well-being. Furthermore, given the limited
research on parents’ enforcement of sleep-related rule enforce-
ment, we further tested how different parent-enforced rules
might impact adolescents’ sleep duration.
Threats to adolescent sleep
School starttimes
Considering the ecology of adolescent sleep, the diverse threats
to adolescents obtaining sufcient sleep include early school
start times, prebedtime media consumption, poor sleep hygiene,
and chaotic family environments [7, 13–16]. Specically, the inu-
ence of school start times has emerged over the past decade as a
particularly salient feature of adolescents’ sleep ecology [13, 14].
Although interventions, such as delaying school start times [17],
have demonstrated sustained benets to adolescents’ sleep and
their levels of alertness and well-being, only approximately 14%
of high schools across America have moved their start times to
the American Academy of Pediatricians’ recommended start time
of 8:30 a.m. or later [6, 18]. Thus, a large majority of adolescents
struggle with the negative correlates of early school start times.
Challenging family contexts
Evidence also links chaotic households and parents’ dysfunc-
tional sleep-related beliefs as risk factors for the role that the
family environment might play in adolescents’ sleep and
mental health problems [19, 20]. Furthermore, despite the rela-
tive effectiveness of sleep hygiene-focused interventions to in-
crease adolescents’ sleep duration and physical well-being [21],
the use of these interventions remains limited, leaving a ma-
jority of adolescents to struggle with sleep problems unaided
in any formal way. Taken together, these threats to adolescents’
sleep potentially put a greater onus on parents to support their
teenagers in getting sufcient sleep. The current study therefore
sought to extend previous work by examining the more specic
roles that parents can play in this process.
Parent rule-setting
Parent-set bedtimes
In terms of adolescents’ psychosocial development, it is ex-
pected that parents’ inuence on their adolescents’ behavior
will diminish during the high school years, due in part to
parents’ decreased involvement in their children’s sleep rou-
tines as they enter adolescence [14, 22]. However, this fact does
not preclude the impact that parents and family environments
have on adolescents’ sleep [23, 24]. Multiple studies have sug-
gested that bedtime-related rules serve to extend adolescents’
sleep duration whereas not extending their sleep latency [25, 26].
For instance, Adam and colleagues demonstrated in a nationally
representative sample that family rules related to activities (e.g.
watching television, homework) were associated with longer
sleep durations for older children (ages 12–19) [25]. They did not,
however, specically examine bedtime-related rules or their
level of enforcement. Building on this work, in a cross-sectional
study with a sample of 5–17 year olds, Pyper and colleagues
found that both parents’ encouragement as well as enforce-
ment of bedtimes were associated with longer weekday sleep
duration [26]. The study, however, was limited by its reliance on
parent-reported sleep duration, which has been shown to over-
estimate children’s sleep duration [27].
Extending this work on adolescents’ longer sleep duration
due to parental rule-setting, multiple studies have examined the
consequences of insufcient sleep on both adolescents’ mental
health and daytime alertness [11, 27, 28]. In one of the rst
studies to specically assess parent-set bedtimes on adoles-
cents’ sleep duration and symptoms of depression, Gangwisch
and colleagues conducted in-home interviews and asked parents
to respond to the question, “What times does {name} have to go
to bed on weeknights?” [11] Despite demonstrating a signicant
and cross-sectional indirect association of parent-set bedtimes
on adolescents’ (ages 11–21) depressive symptoms via sleep dur-
ation, the specic question they used potentially introduced two
sources of bias, due to parents wanting to provide a socially de-
sirable response and to the implicit assumption that parents set
a bedtime for their child [27]. Building on the Gangwisch and
colleagues’ study [11], Short and colleagues found that adoles-
cents (ages 13–18) who reported a parent-set bedtime also re-
ported longer improved daytime alertness and less daytime
fatigue [27]. Despite the use of adolescents’ self-reports for
sleep duration and fatigue, this study did not assess parents’
perspectives on rule enforcement due to concerns about social
desirability. Furthermore, in relying upon adolescents’ reports
of the presence vs. absence of parent-set bedtimes, Short and
colleagues were unable to assess the true level and consistency
of their enforcement [27]. In addition to including multiple re-
porters (i.e. parents and adolescents) and by employing a novel
sleep diary methodology to capitalize on the temporal aspects
of measurement, the current study used a measure of parental
bedtime-related rules that attempted to minimize response bias
as it provided parents with a continuous scale of parents’ levels
of enforcement of these rules [29].
Other sleep-relatedrules
Other areas of potential parental intervention involve the
overwhelming presence of electronic media (e.g. smartphones,
televisions) in adolescents’ bedrooms and adolescents’ con-
sumption of caffeinated beverages in the afternoon/evening.
Given the negative inuence of light emitted from electronic de-
vices on the building of sleep pressure and preparing oneself for
sleep [30], numerous studies have found that prebedtime elec-
tronic media is associated with later bedtimes [31, 32], shorter
sleep duration [33], and increased daytime sleepiness [15].
Downloaded from by guest on 10 January 2020
Peltz etal. | 3
Although there is some evidence suggesting that the negative
inuence of screens on sleep is negligible [30, 34], enforcing rules
regarding the use of these electronic devices before bedtime can
yield longer sleep duration for adolescents, with nightly gains
ranging from 12 to 19min for each school night they were en-
forced [29]. Research on adolescents’ caffeine consumption sug-
gests similar negative outcomes on adolescent sleep duration
and functioning [35]. In this light, the current study extended
previous work by examining if, as an alternative to enforcing
bedtimes, parents could also support their adolescents’ sleep
and its inuence on their mental health by either enforcing
screen- or caffeine-related rules.
The costs of adolescents’ insufcient sleep on
mental health via daytimeenergy
One clear consequence of adolescents’ insufcient sleep is
their increased levels of sleepiness or daytime fatigue [4, 36–38].
Excessively sleepy adolescents are at greater risk of depressed
mood as self-reported sleepiness has been shown to predict the
onset and maintenance of depressive symptomatology [4, 5].
One potential mechanism through which adolescents’ sleepi-
ness might lead to depression includes being ill-equipped to
handle stressful or frustrating situations [2]. In a study of 385
adolescents (ages 13–18), Short and colleagues demonstrated a
signicant cross-sectional association between daytime sleepi-
ness and depressed mood; however, adolescents’ sleep dur-
ation did not signicantly predict levels of daytime sleepiness
[4]. Short and colleagues suggested that this lack of association
might be due to their sample’s relatively restricted range of sleep
quantity, but it also highlights the complexities of assessing
such duration vis-à-vis adolescents’ wide-ranging sleep needs
[4]. The current study sought to extend this line of research by
incorporating daily reports of adolescents’ sleep duration, en-
ergy levels, and depressive symptoms. By assessing adolescents’
sleep duration upon waking and their energy levels and depres-
sive symptoms in the evening, the current study optimized the
predictive nature of adolescents’ sleep quantity on these critical
The currentstudy
Given the important role that parents and the larger family en-
vironment play in shaping adolescents’ sleep and sleep habits
[24, 26, 29, 39], the current study is an investigation of the po-
tential impact of parents’ enforcement of sleep-related rules on
their adolescent’s sleep duration. Furthermore, in order to ex-
tend the mediational analyses of Gangwisch and colleagues [11]
and Short and colleagues [27], we chose to examine these as-
sociations through a short-term, longitudinal mediation model
that could simultaneously measure the inuence of parental
rule enforcement on adolescents’ sleep duration and the sub-
sequent process through which sleep duration indirectly inu-
ences adolescents’ depressive symptoms via their daily energy
levels. To this end, we capitalized on a 7-day sleep diary study
of 193 adolescents and their parents and incorporated recent
methodological advances in the multilevel testing of mediation
[40] that allowed us to control for between-person associations
when examining the temporal links between our constructs
within the diary data. Finally, to create a more ecologically valid
model, the models controlled for proximal factors related to
adolescent sleep (i.e. school start times and parent–child bed-
time related arguments). This design therefore allowed us to
examine the following hypotheses: (1) parents’ enforcement of
sleep-related rules would predict longer sleep durations for ado-
lescents controlling for school start times and parent–child ar-
guments about bedtime; (2) longer sleep duration would predict
higher levels of daytime energy; (3) higher energy levels would
predict lower levels of depressive symptoms; and (4) parents’
enforcement of sleep-related rules would indirectly predict ado-
lescents’ depressive symptoms via the mediating constructs of
sleep duration and daily energy levels.
Participants and recruitment
Participants were adolescent–parent dyads (n= 193), who were
recruited through direct solicitation (e.g. receiving a study bro-
chure following a brief presentation at school), emails to distri-
bution lists (e.g. parenting groups), and through ResearchMatch,
a national health volunteer registry that was created by sev-
eral academic institutions and supported by the U.S. National
Institutes of Health as part of the Clinical Translational Science
Award program. ResearchMatch has a large population of volun-
teers who have consented to be contacted by researchers about
health studies for which they may be eligible. In order to partici-
pate, adolescents had to be in 9–11th grades in either a public or
private day school within the United States, between the ages of
14 and 17, living 7days/week in the participating family’s house-
hold, and both parent and child had to agree to participate.
Families with adolescents with severe cognitive limitations (i.e.
developmental disabilities) were excluded from thestudy.
A total of 193 adolescents (Mage = 15.7 years old, SD =.94;
54.4% female) completed the baseline and 7-day sleep diary
surveys, and their parents (Mage= 47.6 years old, SD= 5.4; 80%
female) provided data from the baseline survey on bedtime-
related rule enforcement, frequency of arguments regarding
bedtime, and school start times. The adolescents reported being
in 9th (37%), 10th (32%), or 11th (31%) grade. The majority of ado-
lescents and parents identied as Caucasian (71% and 79% re-
spectively), with another 14% and 14% (respectively) identifying
as African American, 8% and 2% (respectively) identifying as
multiracial, 3% and 2% (respectively) identifying as Latino/a, 2%
and 2% (respectively) identifying as Asian American, and 2% and
1% (respectively) identifying as “other.” Parents had relatively
high levels of education, with approximately 42% reporting a
graduate degree, 35% with a BA/BS, 19% with some college or
an associate degree, and 4% with a high-school diploma or GED
or less. Mean income was $81 600 (SD=$27 800)with 17.6% of
families reporting incomes below the poverty level (i.e. equal to
or less than $45 000).
The study was approved by the local Institutional Review Board
and informed consent from parents and assent from adoles-
cents was obtained prior to participation. The baseline survey
took roughly 20–25 min to complete; respondents were com-
pensated $10 each as an incentive. During the baseline survey,
Downloaded from by guest on 10 January 2020
4 | SLEEPJ, 2019, Vol. XX, No. XX
parents and adolescents provided their own email addresses (to
obtain a dyadic sample), and parents set a start date for their
child to complete the 7-day sleep diary. After completing the
baseline survey, adolescents were invited to complete an online
7-day daily sleep diary. In order to optimize the temporal spa-
cing of sleep and daily mood reports, the sleep diary consisted
of both a morning and an evening portion. The morning diary
survey (e.g. sleep-related measures) was completed within an
hour of waking up, and the evening diary survey (e.g. daily func-
tioning and mood-related measures) was completed within an
hour of going to sleep. Due to the use of online surveys for the
diary, entries were time-stamped to verify that they were com-
pleted within the expected timeframe. Adolescent respondents
received $15 for completing a minimum of four morning and
evening diary entries, an entry to win a lottery prize (an iPad
mini) for every diary entry completed, and brief feedback on
their sleep (e.g. average bed/wake times based on the diary data
they provided) following the conclusion of the data collection.
On average, the parents and adolescents completed their base-
line surveys 8.4 days (SD = 5.7) before the adolescent began
the sleep diary. Atotal of 178 adolescents (92.2%) completed at
least 4 days of the daily diaries, with adolescents completing
on average approximately 11.7 diary entries out of a possible 14
(SD= 2.8). ANOVA and χ
2 analyses suggested that the respond-
ents participating in the daily diaries did not differ from par-
ticipants who only completed the baseline survey across all
primary variables and demographic covariates.
Sleep-related rules (baseline)
To assess the level of rules related to bedtime and other sleep-
related behaviors that parents enforced, parents completed
a 6-item scale (adapted from Buxton and colleagues [12, 29])
during the baseline assessment. The scale was comprised of
three domains of sleep-related rules for bedtime (1 item; “Which
comes closest to describing rules your child may have to follow
[regarding] the specic time he/she goes to bed?”), usage of elec-
tronic media (4 items; “Which comes closest to describing rules
your child may have to follow [regarding how late your child
can]”: watch television, use smart/cellphone, use computer/
tablet, play videogames), and consumption of caffeinated bev-
erages in the afternoon/evening (1 item; “Which comes closest
to describing rules your child may have to follow [regarding]
drinking colas, coffee, or other sources of caffeine in the after-
noon or evening?”). These items assessed the presence and
level of enforcement of rules in the household and were rated
on 4-point scales (“no formal rules” to “have rules, always en-
forced”). Responses were averaged across the 4 media-related
items such that higher scores indicated higher levels of media
rule enforcement (αmedia rules=.86).
Parent–child bedtime conict (baseline)
The level of disagreement about bedtime was reported by
parents at baseline with a 1-item measure (i.e. “Thinking about
the last month, how often have you and your high schooler
disagreed about bedtimes?”). The item was rated on a 7-point
response scale (0—“not at all in the last month” to 7—“more
than once a day”), with higher scores indicating higher levels of
disagreements regarding bedtime.
School start times (baseline)
Parents provided the start time for their child’s school in the
baseline survey.
Sleep duration (daily diary—morning)
Sleep duration was assessed in the morning diaries by calcu-
lating the daily differences between the time the child reported
going to sleep and waking up the next morning, with both
sleep latency (min) and wake-after-sleep-onset durations (min)
having been subtracted from each night’s time spent in bed.
Energy level (daily diary—evening)
Adolescents’ self-reported levels of energy were assessed in the
evening diaries with a 1-item measure (“indicate the number
that best describes how much energy you had today”) that was
rated on a 5-point response scale (1—“no energy” to 5—“full of
energy”). Higher scores indicated higher levels of energy.
Depressive symptoms (daily diary—evening)
To assess adolescents’ levels of depressive symptoms, respond-
ents self-reported on adapted versions of the Patient Health
Questionnaire-2 in the evening portion of their diary [41]. This
measure has demonstrated strong reliability and validity in ado-
lescent samples [42]. Respondents reported how much they had
been bothered by the following symptoms since waking up that
morning (“little interest or pleasure in doing things,” “feeling down,
depressed, or hopeless.”) The items were rated on 4-point response
scales (“not at all” to “nearly all day”), summed so that higher
scores indicated higher levels of depressive symptoms (α=.90).
Data analytic strategy
The repeated observations from the daily diaries represented
multiple assessments nested within adolescents. To appropri-
ately model the nested nature of the data, a multilevel SEM model
(Mplus, Version 8) [43], using a mediational framework [40], was
used. As depicted in our conceptual model (Figure 1), repeated as-
sessments within individual adolescents across time (i.e. 7-day
daily diary data) were modeled at level 1, and parent-reported
data, which served as predictors of adolescent sleep duration,
were modeled between families at level 2.Based on the best prac-
tices articulated by Preacher and colleagues [40], we employed a
1–1–1 mediational model that simultaneously included the three
different domains of parents’ sleep-related rule enforcement
(bedtime, screentime, caffeine consumption) as predictors of ado-
lescent sleep duration. Because many commonly used multilevel
modeling approaches are at high risk of conating the between-
and within-level components of mediational effects, the multi-
level SEM approach distinguishes the variation associated both
between-person (at level 2, representing between-family trait-
like differences on the variables in the model) and within-person
(i.e. the repeated assessments at level 1, representing state-like
uctuations of the variables on each day of the diary period) by
creating level 2 latent variables based on the level 1 predictors
(within-adolescent uctuations) that thereby represent the stable
portion of those constructs across the diary period for each
Downloaded from by guest on 10 January 2020
Peltz etal. | 5
adolescent (see Figure 1) [40]. In terms of our process model, this
multilevel framework allows for indirect effects to be tested both
at the between-family level (i.e. level 2, examining how latent
variables representing typical levels of the variables of interest
are associated across families) as well as at the within-adolescent
level (i.e. level 1, examining how within adolescent uctuations
in the variables covary across the days of the week; see Figure 1).
Using current best practices [44], we used asymmetric con-
dence intervals to test the signicance of the level 1 and level 2
mediational paths via RMediation [45].
To control for proximal factors that could also inuence ado-
lescent sleep, we also included parent–child arguments about
bedtime and school start times as between-family predictors of
adolescents’ sleep duration. Given that multilevel modeling is
tasked with parsing variance between levels (i.e. distinguishing
between-person differences from a within-person variation on
the variables being examined), all multilevel modeling tech-
niques are unable to provide standardized path coefcients.
However, to maximize the generalizability of the current nd-
ings, we prepared the data in a way that could provide approxi-
mations of standardized path coefcients within this multilevel
framework. We did this by standardizing all variables (i.e. con-
verting all predictors, controls, and outcomes to z-scores) before
entering them into the analysis (level 1 variables standardized
at level 1—across all participants and observations—and level
2 variables standardized at level 2—across all participants).
Thus, a level 1 effect of B= .50 would suggest that for every
one standard deviation higher on the predictor on a specic
day of the study, the model would predict outcome scores .50
standard deviations higher. As these are not truly standardized
coefcients (as the equations to estimate those do not yet exist
for multilevel models), we continue to use “B rather than “β
to present them. However, their interpretation is close to that
of standardized coefcients, providing estimates of standard-
ized effects from the model to place this work in context with
the previous literature. Overall model t was assessed with the
comparative t index (CFI [46]; values above .90 indicating good
t), the root-mean-square error of approximation (RMSEA [47];
values below .08 indicating good t) and the standardized root-
mean-square residual (SRMR [48]; values below .10 indicating
good t). The model t for the current analysis was very good.
Preliminary analyses
Descriptive statistics for the sample and intercorrelations
among the key variables are presented in Table 1. Although a
majority of the families reported rules concerning bedtime,
prebedtime electronic media usage, and caffeine consumption,
Figure 1. Conceptual model.
Downloaded from by guest on 10 January 2020
6 | SLEEPJ, 2019, Vol. XX, No. XX
47% of parent respondents reported having no enforced bed-
times, 30% of parents reported no enforced rules regarding
prebedtime screen usage, and 48% of parents reported no en-
forced rules regarding afternoon/evening caffeine consumption.
Consistent with this, on 74% of the evening diaries, adolescents
and parents agreed that there was no specic suggested or en-
forced bedtime. In providing data on their average daily sleep
duration across the 7 days of the daily diary, adolescents re-
ported an average of 19.4min (SD=14.6) of sleep latency and an
average of 3.3 (SD=4.5) min of waking following their episode(s)
of wake-after-sleep-onset. Taking into account both sleep la-
tency and post-WASO time awake, adolescents averaged 7.8h
of sleep per night (SD= 1.0), which is consistent with nation-
ally representative datasets of adolescents [29], with an average
bedtime of 10:58 p.m. (SD = 1.1) on weekdays and 11:16 p.m.
(SD=1.4) on weekends.
As shown in Table 1, higher levels of enforcement of bedtime-
related rules were positively associated with enforcement of
screen- and caffeine-related rules, bedtime disagreements, and
average daily sleep duration; higher levels of enforcement of
screen-related rules were positively associated with enforce-
ment of caffeine-related rules and higher levels of bedtime dis-
agreements. Based on the child-reported daily diary data, later
school start times were positively associated with longer average
sleep duration; longer average sleep durations were positively
associated with higher energy levels; and higher average levels
of daytime energy were associated with lower average levels of
depressive symptoms across the week. Taken as a set, these cor-
relations support the proposed multilevel SEM path models.
Predicting adolescent sleep duration
Turning to the unique portions of our model, Table 2 presents
the between-person path coefcients predicting adolescent
sleep duration (the dashed arrows in Figure 1) along with the
t indices for the model. Based on the data from the baseline
survey and consistent with our hypothesis, greater bedtime
rule enforcement by parents predicted longer adolescent sleep
durations (B= .10, SE= .05, p < .05; Table 2; Figure 2). Given the
standardized values used in the analyses, this result suggests
that for every 1 SD above-average levels of bedtime rule enforce-
ment across families, adolescents are predicted to extend their
sleep duration each night by approximately .10 SDs, or about
6.1 min. In addition, later school start times predicted longer
sleep duration (B= .12, SE=.04, p < .01), while higher levels of
parent–child disagreement concerning bedtime only marginally
predicted shorter adolescent sleep duration (B= −.07, SE =.04,
p < .07). These results suggest that for every 1 SD increase in school
start times (approximately 29 min), adolescents would be ex-
pected to extend their sleep by approximately 7.3min per night;
and for every 1 SD increase in parent–child disagreement about
bedtime, adolescents would be expected to decrease their sleep
by 4.3min per night. Contrary to our predictions, neither parents’
enforcement of evening screentime usage (B=−.04, SE=.06, ns)
nor enforcement of rules concerning afternoon/evening caffeine
consumption (B=.02, SE=.06, ns) signicantly predicted adoles-
cents’ sleep duration during the diary period after controlling for
the other effects in the model (Table 2; Figure 2).
Hypothesis 1: adolescent sleep duration predicting daytime
energy levels
Consistent with our hypothesis, adolescents’ sleep duration (as-
sessed each morning of the 7-day diary) signicantly predicted
their daytime energy levels (assessed each evening of the 7-day
diary) such that longer sleep durations were associated with
higher daytime energy levels both at the between-adolescent/
family level (B=.48, SE=.19, p < .01; Table 2; Figure 2) and at the
within-adolescent level (B=.17, SE=.03, p < .001).
Hypothesis 2: daytime energy levels predicting depressive
Consistent with our hypothesis, adolescents’ daytime energy
levels signicantly predicted their daily reports of depressive
symptoms (assessed each evening of the 7-day diary) such that
higher daytime energy levels were associated with lower levels
of depressive symptoms both at the between-adolescent/family
level (B= −.71, SE =.12, p < .001) and at the within-adolescent
level (B=−.25, SE=.04, p < .001).
Hypothesis 3: adolescents’ daytime energy levels mediate the
association of sleep duration on depressive symptoms
Consistent with our hypothesis, the indirect effect of adolescent
sleep duration on their depressive symptoms was signicant:
longer sleep duration predicted higher daytime energy levels,
which, in turn, predicted lower levels of adolescents’ depressive
symptoms (indirect effect=−.34, SE=.16, p < .05; 95% CI: LL=−.65,
Table 1. Psychometrics and bivariate correlations between study variables
Variables Range M SD Bivariate correlations
1 2 3 4 5 6 7
Assessed at baseline (parent-report)
1. Bedtime-related rules 0–3.0 0.9 1.0
2. Screen-related rules 0–3.0 1.0 1.0 0.62
3. Caffeine-related rules 0–3.0 1.2 1.3 0.34 0.58
4. School start time 6:55–9:30 7:56 29.3 0.03 0.06 −0.13
5. Bedtime disagreement 0–6.0 1.2 1.4 0.16 0.23 0.18 0.12
Assessed during daily diary (child-report)
6. Avg. daily sleep duration 4.4–11.3 7.8 1 0.20 0.07 0.01 0.18 −0.06
7. Avg. daily energy level 1.7–5.0 3.8 0.7 0.07 −0.02 −0.04 −0.05 −0.15 0.20
8. Avg. daily depressive symptoms 0–4.4 0.6 0.9 0.02 0.03 0.06 0.13 0.14 −0.02 −0.49
All bolded correlations are signicant at the p < .05 level. All diary-reported data have been averaged across all waves of follow-up.
Downloaded from by guest on 10 January 2020
Peltz etal. | 7
UL=−.04). This result suggests that adolescents with longer sleep
durations experienced higher levels of energy during the day and
ultimately reported lower levels of depressive symptoms that
same day. Thus, although the effect size of this indirect effect
is small to moderate in magnitude (.34), the results suggest that
it represents one of the mechanisms by which adolescent sleep
duration might inuence adolescent mental health functioning.
After controlling for those indirect paths, the direct effect of ado-
lescent sleep duration on their depressive symptoms was not sig-
nicant at the between-adolescent/family level (B=.21, SE=.22,
ns) and only marginally signicant at the within-adolescent level
(B=−.05, SE =.02, p < .06). Taken as a set, the results therefore
suggest that, even after controlling for more stable between-
person differences (by creating the latent variables at level 2),
daily uctuations in energy within adolescents mediated the ef-
fects of uctuations in each adolescent’s previous night’s sleep
duration predicting corresponding uctuations in their reports of
depressive symptoms at the end of each day.
Multistage mediation
Having the parent-reported levels of bedtime rule enforcement
and school start time as level 2 predictors allowed us to test a
Table 2. Coefcients for multilevel mediationmodels
Predicting sleep duration Model t
Baseline Predictors of Sleep Duration LL UL
Bedtime rule enforcement 0.10 0.05 0.01 0.20 344.1 (31) <0.001 0.02 0.96 0.07
Screentime rule enforcement −0.04 0.06 −0.15 0.07
Caffeine consumption rule enforcement 0.02 0.06 −0.09 0.13
Arguments about bedtime −0.07 0.04 −0.15 0.01
School start time 0.12 0.04 0.04 0.19
Mediation model (between-adolescents/families)
B SE 95% CI
Sleep duration energy levels 0.48 0.19 0.12 0.84
Sleep duration depressive symptoms 0.21 0.22 −0.22 0.64
Energy levels depressive symptoms −0.71 0.12 −0.95 −0.47
Indirect effect −0.34 0.16 −0.65 −0.04
Note. The predictors and outcome variables were standardized in their nal levels of the data prior to running the multilevel models. Thus, the regression coefcients
serve as rough approximations of standardized regression coefcients. All bolded results are signicant at the p < .05 level. RMSEA=root mean square error of ap-
proximation. CFI=comparative t index. SRMR=standardized root mean square residual.
Figure 2. Results of multilevel SEM mediation analyses.
Downloaded from by guest on 10 January 2020
8 | SLEEPJ, 2019, Vol. XX, No. XX
multi-stage mediational model linking both of those parent-
reported variables to adolescent depressive symptoms at a
between-family level. The results of these analyses suggested
that both bedtime rule enforcement (indirect effect = −.04,
SE=.02, p < .07; 93% CI: LL= −.086, UL= −.001) and school start
times (indirect effect=−.04, SE=.02, p < .05; 95% CI: LL= −.092,
UL = −.003) inuenced adolescents’ depressive symptoms via
the mediating variables of adolescent sleep duration and daily
energy levels. Thus, between-family differences in enforcing
bedtime rules were indirectly predictive of between-family dif-
ferences in adolescent depressive symptoms via its links to both
adolescent sleep duration and corresponding levels of daily en-
ergy reported across the diary period.
Building upon previous work that has linked parent-set bed-
times, adolescents’ daytime functioning, and mental well-being
[11, 27], the current study sought to provide a more rigorous
test of the inuence of parental limit-setting around bedtime
and the mechanisms that might ultimately lead to better psy-
chosocial outcomes for adolescents. Given that within and
between-person differences are typically confounded within
longitudinal models and can therefore generate spurious results
[49, 50], the current study made use of multilevel modeling to
distinguish those two distinct sources of variance. Our model
was therefore able to demonstrate both between- and within-
person associations for a process in which adolescent sleep
duration indirectly inuenced their daily levels of depressive
symptoms through their daytime energy levels. We also pro-
vided strong support for the use of parental enforcement of
bedtimes above and beyond the environmental impediments to
longer sleep durations for adolescents (i.e. earlier school start
times). This model capitalized on an adolescent-reported 7-day
daily diary that could capture the short-term longitudinal im-
pact of sleep duration (morning assessment) on daytime energy
levels (evening assessment), which were ultimately associated
with adolescents’ depressive symptoms (evening assessment).
Furthermore, given the interval between the baseline assess-
ment and the 7-day daily diary, our results suggest that both
the enforcement of bedtimes and school start times do have
a prospective association with adolescents’ daily sleep dur-
ation and indirectly impact adolescents’ depressive symptoms.
Building on the extensive links between adolescents’ insuf-
cient sleep and their mental health [4], the current results pro-
vide another avenue through which both adolescents’ sleep and
its subsequent impact on their mental health can be addressed.
Specically, parents that can appropriately create and main-
tain bedtimes for their adolescent-aged children increase not
only the opportunity for more sleep but also for greater daytime
alertness and mental well-being for these children.
First articulated in the “Perfect Storm” model originally de-
veloped by Carskadon [13, 14], the biological and psychosocial
factors that serve to ultimately decrease adolescents’ sleep dur-
ations continue to be a source of risk for their mental health
functioning. Fortunately, despite the challenges of parenting
adolescents, parents and caregivers are still available to exert
inuence over their teenage children. Relatively few parents,
however, actively enforce parent-set bedtimes or rules related
to sleep hygiene (e.g. ceasing use of electronic media in the
hours before bedtime, restrictions on the consumption of caf-
feinated beverages in the afternoon/evening) [12]. Although our
results support the use of parent-set and enforced bedtimes,
it is interesting that neither the enforcement of rules related
to prebedtime electronic media usage nor the enforcement of
afternoon/evening caffeine consumption predicted longer sleep
durations or other constructs in our models. There are perhaps
at least two reasons why these links did not emerge. First, sleep
hygiene related to prebedtime media usage and caffeine con-
sumption provides a guide for improving sleep, but not everyone
responds to the light emitted from screens or caffeine similarly.
For instance, for individuals with high enough sleep pressure,
the effects of screen-emitted light might be negligible. Second,
although there is extensive evidence supporting the negative
consequences of prebedtime electronic media usage on sleep
latency and duration [15, 51, 52], nuances within this body of
literature suggest that the level of interaction with electronic
media (e.g. watching television vs. playing videogames) might
have differential effects [53]. For example, multiple studies have
shown that the use of prebedtime electronic media did not im-
pact sleep onset latency or duration for both adolescents and
emerging adults [30, 34, 54, 55]. The lack of signicant ndings
for rules limiting technology’s usage impacting sleep in the
current study is therefore consistent with these ndings and
suggests that once other factors are controlled, the negative
association between technology use and poor sleep might be
more limited than originally envisioned. Screen-related bedtime
rules, whereas a critical component of proper sleep hygiene, ap-
pear to not have the same level of effect as setting a bedtime for
adolescents. Similarly, given the relative independence adoles-
cents are afforded, parents might have little control over what
their children are consuming after school despite their beliefs in
maintaining rules in this domain. As the “Perfect Storm” model
suggests [13, 14], it may ultimately be the interplay of multiple
sleep hygiene factors and not just the enforcement of one that
will yield the same results as parent-set bedtimes.
In an attempt to provide an ecologically valid depiction of
adolescent sleep, we included both school start times and
parent–child disagreements about bedtime in our models.
Consistent with the literature on school start times [6], earlier
start times predicted shorter sleep durations and were indir-
ectly linked with adolescents’ depressive symptoms. Although
the paths linking school start times emerged as slightly more ro-
bust than the paths linking bedtime rule enforcement to adoles-
cents’ depressive symptoms, it is important to note that parents
continue to provide a key source of an intervention despite the
negative inuence of earlier school starttimes.
In addition, in our bivariate correlations, the frequency of
bedtime disagreements was associated with both bedtime and
screen-related rule enforcement. These results speak to the
complexity of the family environment as well as to other con-
textual inuences when it comes to adolescents getting enough
sleep. Adolescence is a period marked by increasing autonomy
and independence, and setting limits, such as a bedtime, can be
considered a direct provocation to one’s teenage child. However,
unchecked autonomy may put adolescents further at risk for
insufcient sleep. For instance, research suggests that adoles-
cents with greater bedtime autonomy in addition to higher
frequencies of cell phone usage were most at risk for insuf-
cient sleep when compared to those adolescents who used their
cell phones less frequently [56]. In this light, intervening with
Downloaded from by guest on 10 January 2020
Peltz etal. | 9
adolescents to promote better and longer sleep might be best
served through a framework that draws on the principles of mo-
tivational interviewing and other individually tailored strategies
in order to simultaneously meet the needs of both adolescents
and their caregivers [57]. Furthermore, any parental limit-setting
on bedtimes needs to account for the developmental shifts in
adolescents’ sleep schedules. The sleep phase delay associated
with pubertal development, a hallmark of adolescence, remains
an integral factor of the “Perfect Storm” model and an essential
consideration when imposing bedtime limits [13, 14]. One could
expect that forcing a teenager to get to bed before they were
biologically ready might result in longer sleep onset latencies
and even to the development of insomnia. Although our sleep
duration measure assessed the net time asleep (and not simply
in bed), the current study did not include sleep latency as a pre-
dictor in its models due to its lack of association with either
bedtime or screen-related rule enforcement. However, previous
research has suggested that parent-set bedtimes are not associ-
ated with longer sleep onset latencies [27].
The current ndings add to a growing body of literature
examining links between sleep difculties and depressive
symptoms. As daytime fatigue or sleepiness represents a
symptom of both depression and insomnia, a growing body
of work has begun to conceptualize daytime energy levels as
a distinct construct in models—isolating that one facet as a
pivotal mechanism that potentially links sleep problems and
depression [4, 5, 36, 58, 59]. Thus, although daytime fatigue is
considered a symptom of depression, the current study built on
this growing body of work and distinguished daytime energy
levels as meaningfully distinct from other depressive symp-
toms. The current results therefore suggest that low-daytime
energy levels might represent the most proximal symptom
of depression linked to lack of sleep, highlighting a potential
underlying process linking these two domains of functioning
that warrants furtherstudy.
We must acknowledge several of the current study’s limita-
tions. First, despite efforts to improve on previous studies that
included assessments of parents’ enforcement of bedtime (e.g.
[11, 27]), our measure was also a participant to desirability bias.
Future studies should collect detailed information on bedtime
practices from all family members to triangulate agreement.
With that said, based on parents’ and children’s diary reports
of whether a bedtime limit was given (yes/no), parents and chil-
dren agreed that no specic bedtime limit was sent on 74% of
the nights surveyed. Second, all measures of adolescent sleep,
daytime functioning, and mental health symptoms were self-
reported, and thus may be confounded by response-biases.
Although we tried to limit such response-bias by employing
separate assessments for sleep and daily functioning (i.e. energy
levels and mood), future studies would benet from augmenting
self-report surveys with additional methods (e.g. actigraphy).
Likewise, only one parent reported on bedtime-related limit-
setting in their household, which provides only a limited
portrayal of the family environment. Second, although com-
parable to other samples recruited primarily via the internet
[60], the sample was predominately Caucasian, well-educated,
and economically advantaged, and ndings may only gener-
alize to a similar population. In addition, given that adolescent
participants were required to be living 7 days a week in the
participating family’s household, our results might not gener-
alize to youths who split time between two households. Future
studies should seek to examine these questions in more nation-
ally representative samples with a more diverse range of family
structures to ensure broad generalizability of the ndings.
Finally, our mediational model employing the daily diary as-
sessments supports a direction of effects from adolescent sleep
to daytime functioning to adolescent depressive symptoms.
Although the temporal spacing of our diary assessments (i.e.
morning to evening diary entries) supports the directionality of
our ndings linking sleep duration to daytime energy levels, it is
entirely possible that the opposite direction of inuence could
also emerge. Furthermore, the link between daily energy levels
and depressive symptoms was assessed concurrently in our
model as both of those constructs were assessed in the evening
diary, leaving the directionality of that association unclear. Thus,
although the current model offers partial longitudinal support
to the mediation model tested, future work (e.g. using experi-
ential momentary assessment) is needed to determine the true
directions of causality. Much support exists for the bidirectional
links between adolescent sleep and mental health functioning
[61, 62], and future investigations of this topic will ideally in-
clude models that can test reciprocal associations between
these constructs.
Despite these limitations, the current study extends re-
search on the positive influence of parent-set bedtime by
including reports from both parents and children, by min-
imizing autocorrelation through the use of separate as-
sessments of sleep and functioning, by distinguishing
between- and within-person differences within our models,
and by controlling for critical factors (i.e. school start times,
bedtime disagreements) within adolescents’ sleep environ-
ments. In the decade that has passed since some of the sem-
inal research on parent-set bedtimes first emerged [11, 27] ,
the epidemic of adolescent insufficient sleep has still not
abated [6]. Some would say that with the increasing preva-
lence of teenagers’ use of electronic media, the problem has
worsened [63]. Fortunately, supported by both the current
findings and other recent studies (e.g. [26, 39]), parents and
their enforcement of appropriate bedtimes should still be
considered an effective frontline intervention in the effort to
afford adolescents their much-neededsleep.
This investigation was supported with funding from the
National Sleep Foundation.
Conict of interest statement. None declared.
1. AlfanoCA, etal. Sleep problems and their relation to cogni-
tive factors, anxiety, and depressive symptoms in children
and adolescents. Depress Anxiety. 2009;26(6):503–512.
2. DahlRE, et al. Pathways to adolescent health sleep regula-
tion and behavior. J Adolesc Health. 2002;31(6 Suppl):175–184.
3. National Sleep Foundation. 2006 Sleep in America Poll:
summary of ndings. Washington, DC: National Sleep
Foundation; 2006.
4. Short MA, et al. The impact of sleep on adolescent de-
pressed mood, alertness and academic performance. J
Adolesc. 2013;36(6):1025–1033.
Downloaded from by guest on 10 January 2020
10 | SLEEPJ, 2019, Vol. XX, No. XX
5. FalloneG, etal. Sleepiness in children and adolescents: clin-
ical implications. Sleep Med Rev. 2002;6(4):287–306.
6. OwensJ, et al. Insufcient sleep in adolescents and young
adults: an update on causes and consequences. Pediatrics.
7. Peltz JS, et al. A process-oriented model linking ado-
lescents’ sleep hygiene and psychological functioning:
the moderating role of school start times. Sleep Health.
8. Shochat T, et al. Functional consequences of inadequate
sleep in adolescents: a systematic review. Sleep Med Rev.
9. SmaldoneA, et al. Sleepless in America: inadequate sleep
and relationships to health and well-being of our nation’s
children. Pediatrics. 2007;119(Suppl 1):S29–S37.
10. FitzgeraldCT, etal. Teen sleep and suicidality: results from
the youth risk behavior surveys of 2007 and 2009. J Clin Sleep
Med. 2011;7(4):351–356.
11. GangwischJE, et al. Earlier parental set bedtimes as a pro-
tective factor against depression and suicidal ideation.
Sleep. 2010;33(1):97–106.
12. National Sleep Foundation. Sleep in America Poll Sleep in the
Modern Family . Washington (DC): The Foundation; 2014.
13. Crowley SJ, et al. An update on adolescent sleep: new
evidence informing the perfect storm model. J Adolesc.
14. Carskadon MA. Sleep in adolescents: the perfect storm.
Pediatr Clin North Am. 2011;58(3):637–647.
15. Cain N, et al. Electronic media use and sleep in school-
aged children and adolescents: a review. Sleep Med.
16. LeBourgeoisMK, et al. The relationship between reported
sleep quality and sleep hygiene in Italian and American
adolescents. Pediatrics. 2005;115(1 Suppl):257–265.
17. Lo JC, et al. Sustained benets of delaying school start
time on adolescent sleep and well-being. Sleep. 2018;41(6).
18. Taie,S, etal. Characteristics of Public Elementary and Secondary
Schools in the United States: Results From the 2015–16 National
Teacher and Principal Survey. First Look. NCES 2017-072.
National Center for Education Statistics. 2017.
19. PeltzJS, etal. The moderating role of parents’ dysfunctional
sleep-related beliefs among associations between adoles-
cents’ pre-bedtime conict, sleep quality, and their mental
health. J Clin Sleep Med. 2019;15(2):265–274.
20. Peltz JS, et al. Adolescent sleep quality mediates family
chaos and adolescent mental health: a daily diary-based
study. J Fam Psychol. 2019;33(3):259–269.
21. Lin C-Y, et al. A cluster randomized controlled trial of a
theory-based sleep hygiene intervention for adolescents.
Sleep. 2018;41(11). doi:10.1093/sleep/zsy170.
22. MeltzerLJ, etal. Sleep in the family. Pediatr Clin North Am.
23. Bartel KA, et al. Protective and risk factors for ado-
lescent sleep: a meta-analytic review. Sleep Med Rev.
24. El-SheikhM, etal. Family functioning and children’s sleep.
Child Dev Perspect. 2017;11(4):264–269.
25. Adam EK, et al. Sleep timing and quantity in ecological
and family context: a nationally representative time-diary
study. J Fam Psychol. 2007;21(1):4–19.
26. Pyper E, et al. Do parents’ support behaviours pre-
dict whether or not their children get sufcient sleep?
Across-sectional study. BMC Public Health. 2017;17(1):432.
27. ShortMA, et al. Time for bed: parent-set bedtimes associ-
ated with improved sleep and daytime functioning in ado-
lescents. Sleep. 2011;34(6):797–800.
28. ShortMA, etal. How internal and external cues for bedtime
affect sleep and adaptive functioning in adolescents. Sleep
Med. 2019;59:1–6.
29. Buxton OM, et al. Sleep in the modern family: protective
family routines for child and adolescent sleep. Sleep Health.
30. Grønli J, etal. Reading from an iPad or from a book in bed:
the impact on human sleep. A randomized controlled
crossover trial. Sleep Med. 2016;21:86–92.
31. BrunborgGS, et al. The relationship between media use in
the bedroom, sleep habits and symptoms of insomnia. J
Sleep Res. 2011;20(4):569–575.
32. FossumIN, etal. The association between use of electronic
media in bed before going to sleep and insomnia symp-
toms, daytime sleepiness, morningness, and chronotype.
Behav Sleep Med. 2014;12(5):343–357.
33. Van den Bulck J. Television viewing, computer game
playing, and internet use and self-reported time to bed
and time out of bed in secondary-school children. Sleep.
34. TavernierR, et al. Sleep problems: predictor or outcome of
media use among emerging adults at university? J Sleep Res.
35. CalamaroCJ, etal. Adolescents living the 24/7 lifestyle: ef-
fects of caffeine and technology on sleep duration and day-
time functioning. Pediatrics. 2009;123(6):e1005–e1010.
36. Gibson ES, et al. “Sleepiness” is serious in adolescence:
two surveys of 3235 Canadian students. BMC Public Health.
37. MillmanRP. Excessive sleepiness in adolescents and young
adults: Causes, consequences, and treatment strategies.
Pediatrics. 2005;115(6):1774–1786.
38. National Institutes of Health. National Center on Sleep
Disorders Research and Ofce of Prevention, Education, and
Control; 1997.
39. Meijer AM, et al. Parenting matters: a longitudinal
study into parenting and adolescent sleep. J Sleep Res.
40. Preacher KJ, et al. A general multilevel SEM framework
for assessing multilevel mediation. Psychol Methods.
41. Löwe B, et al. Detecting and monitoring depression
with a two-item questionnaire (PHQ-2). J Psychosom Res.
42. Richardson LP, et al. Evaluation of the PHQ-2 as a brief
screen for detecting major depression among adolescents.
Pediatrics. 2010;125(5):e1097–e1103.
43. MuthénLK, etal. Mplus User’s Guide. 8th ed. Los Angeles, CA:
Muthén & Muthén; 2017.
44. MacKinnonDP, etal. Mediation analysis. Annu Rev Psychol.
45. Toghi D, et al. RMediation: an R package for medi-
ation analysis condence intervals. Behav Res Methods.
46. BentlerPM. Comparative t indexes in structural models.
Psychol Bull. 1990;107(2):238–246.
47. KlineRB. Principles and Practice of Structural Equation Modeling.
3rd ed. New York, NY: The Guilford Press; 2011.
48. HuL, etal. Cutoff criteria for t indexes in covariance struc-
ture analysis: conventional criteria versus new alterna-
tives. Struct Equ Model Multidiscip J. 1999;6(1):1–55.
Downloaded from by guest on 10 January 2020
Peltz etal. | 11
49. Hamaker EL, et al. A critique of the cross-lagged panel
model. Psychol Methods. 2015;20(1):102–116.
50. Keijsers L. Parental monitoring and adolescent problem
behaviors: how much do we really know? Int J Behav Dev.
51. FalbeJ, etal. Sleep duration, restfulness, and screens in the
sleep environment. Pediatrics. 2015;135(2):e367–e375.
52. Johansson AE, et al. Adolescent sleep and the impact of
technology use before sleep on daytime function. J Pediatr
Nurs. 2016;31(5):498–504.
53. GradisarM, etal. The sleep and technology use of Americans:
ndings from the National Sleep Foundation’s 2011 sleep in
America poll. J Clin Sleep Med. 2013;9(12):1291–1299.
54. HeathM, etal. Does one hour of bright or short-wavelength
ltered tablet screenlight have a meaningful effect on ado-
lescents’ pre-bedtime alertness, sleep, and daytime func-
tioning? Chronobiol Int. 2014;31(4):496–505.
55. vanderLelyS, etal. Blue blocker glasses as a countermeasure
for alerting effects of evening light-emitting diode screen ex-
posure in male teenagers. J Adolesc Health. 2015;56(1):113–119.
56. Tashjian SM, et al. Bedtime autonomy and cellphone use
inuence sleep duration in adolescents. J Adolesc Health.
57. Cassoff J, et al. School-based sleep promotion programs:
effectiveness, feasibility and insights for future research.
Sleep Med Rev. 2013;17(3):207–214.
58. LiuY, et al. Excessive daytime sleepiness among children
and adolescents: prevalence, correlates, and pubertal ef-
fects. Sleep Med. 2019;53:1–8.
59. BlakeMJ, etal. Mechanisms underlying the association be-
tween insomnia, anxiety, and depression in adolescence:
implications for behavioral sleep interventions. Clin Psychol
Rev. 2018;63:25–40.
60. PeltzJS, etal. Bidirectional associations between sleep and
anxiety symptoms in emerging adults in a residential col-
lege setting. Emerg Adulthood. 2017;5(3):204–215.
61. Roberts RE, et al. Depression and insomnia among ado-
lescents: a prospective perspective. J Affect Disord.
62. van Zundert RM, et al. Reciprocal associations between
adolescents’ night-time sleep and daytime affect and the
role of gender and depressive symptoms. J Youth Adolesc.
63. Mazzer K, et al. Longitudinal associations between time
spent using technology and sleep duration among adoles-
cents. J Adolesc. 2018;66:112–119.
Downloaded from by guest on 10 January 2020
... Of these, seven studies [13,17,30,53,54,62,63] evaluated the longitudinal impact of positive family relational factors on adolescents' sleep quality. For a subset of five studies [13,30,53,62,63], it was possible to compute an overall effect size of this relation. ...
... Of these, seven studies [13,17,30,53,54,62,63] evaluated the longitudinal impact of positive family relational factors on adolescents' sleep quality. For a subset of five studies [13,30,53,62,63], it was possible to compute an overall effect size of this relation. Results, summarized in Table 3, showed a significant but small effect (r = 0.14, p < 0.001). ...
... Moreover, 10 studies evaluated the longitudinal impact of negative family relational factors on adolescents' sleep quality over time, and for a subset of six studies [31,37,57,59,60,63] it was possible to compute an overall effect size of this relation. Results, summarized in Table 3, showed a significant but small effect (r = −0.08, ...
Full-text available
Family is one of the primary socialization contexts influencing adolescents’ psychological health. In this regard, a crucial indicator of adolescents’ health is their sleep quality. Nevertheless, it is still unclear how multiple family factors (i.e., demographic and relational) are intertwined with adolescents’ sleep quality. For this reason, this systematic review with meta-analysis aims to comprehensively summarize and integrate previous longitudinal research investigating the reciprocal relation between demographics (e.g., family structure) and positive (e.g., family support) and negative (e.g., family chaos) relational family factors and adolescents’ sleep quality. Several search strategies were applied, and a final set of 23 longitudinal studies that matched the eligibility criteria were included in this review. The total number of participants was 38,010, with an average age at baseline of 14.7 years (SD = 1.6, range: 11–18 years). On the one hand, the meta-analytic results showed that demographic factors (e.g., low socio-economic status) were not related to adolescents’ sleep quality at a later time point. On the other hand, positive and negative family relational factors were positively and negatively related to adolescents’ sleep, respectively. Furthermore, the results suggested that this association could be bidirectional. Practical implications and suggestions for future research are discussed.
... However, research on adolescents' smartphone location has been largely unexplored, which poses a major shortcoming in this line of research. In the few existing studies, parental control was defined as rules regarding the time and duration of media use (Peltz, Rogge, and Connolly 2020;Pieters et al. 2014;Smith et al. 2017;Sormunen, Turunen, and Tossavainen 2016). However, research including different parental mediation activities (active vs. restrictive) about smartphone use and digital devices simultaneously is virtually nonexistent (cf. ...
... Findings from cross-sectional survey studies showed that adolescents obtain more sleep when parents restrict electronic media use before bedtime (Pieters et al. 2014;Smith et al. 2017;Sormunen, Turunen, and Tossavainen 2016). A more recent diary study among adolescents aged 14-17 indicated that parental enforcement of screen-related bedtime rules was not predictive of adolescents' sleep duration, daily energy levels, and depressive symptoms (Peltz, Rogge, and Connolly 2020). However, no research thus far has investigated the effectiveness of different parental mediation styles on smartphone nighttime location. ...
... With the study findings, we provided further evidence that restrictive mediation might not be a valuable strategy to reduce smartphone use in bed in this particular age group of early adolescence. Thus, our study findings add to previous literature hinting at an ineffective strategy of restrictive mediation to reduce children's and adolescents' overinvolvement with the smartphone (Hefner et al. 2019) or sleep duration (Peltz, Rogge, and Connolly 2020). Particularly during adolescence, when children grow older, they become more independent from their parents and less open to restrictive measures of smartphone use. ...
This article seeks to explain the longitudinal associations of taking the smartphone to bed on adolescents’ daytime tiredness and physical well-being. We examined whether parents’: (a) active mediation; and (b) restrictive mediation determines whether children and adolescents have their phones within reach in bed or not. We used longitudinal data from a two-wave panel survey (NTime2 = 384) of early adolescents (10–14 years, MTime2=12.37, SD = 1.48, 46.4% girls) and one of their parents (=parent–child dyads) in Germany. A polling company collected the data in a four-month interval in 2018 and 2019, using a quota-sample procedure based on parents’ age and gender. Structural equation modelling revealed that active but not restrictive parental mediation at Time 1 (baseline) negatively predicted adolescents having their smartphones in bed at Time 2 (follow-up). We found that having a smartphone in bed increased adolescents’ daytime tiredness. Daytime tiredness was associated with decreased physical well-being over time. The findings indicate that parents should use active mediation to reduce their children’s use of their smartphones at nighttime to protect their physical well-being from tiredness.
... In contrast, positive and more organized family environments tend to ensure better sleep for adolescents. This is evidenced by studies documenting better sleep outcomes for adolescents when parents enforce bedtimes (Peltz et al., 2020;Short et al., 2011), establish pre-bedtime routines (Bartel et al., 2015), or engender more positive home environments (Cousins et al., 2007;Doane et al., 2019). ...
... Specifically, they might add another potential avenue of intervention if they can effectively address their adolescent's sleep needs. In this light, research has shown that parental enforcement of adolescents' bedtimes can result in longer sleep durations for adolescents (Peltz et al., 2020). One challenge that remains, however, is that enforcing an adolescent's bedtime can be a breeding ground for conflict, so parents and children must find ways to work collaboratively to establish effective sleep routines. ...
Full-text available
Pubertal development has been separately linked to adolescents’ sleep problems and larger family functioning, but research connecting these inter-related processes remains sparse. This study aimed to examine how pubertal status and tempo were related to early adolescents’ sleep and their family functioning. Using longitudinal data from the Adolescent Brain and Cognitive Development study, the study’s sample (N = 4682) was 49.2% female, was an average of 9.94 years old at baseline, and was 60.1% white. Analyses in the current study modeled the indirect associations between pubertal change and changes in family conflict via adolescent sleep duration and variability of duration. The results suggested that pubertal status and tempo predicted shorter adolescent sleep durations and greater variability in those durations, which predicted residual increases in family conflict. The findings highlight the role of adolescents’ pubertal changes in their sleep and how such changes can negatively affect family functioning.
... In addition, studies have found that the benefits of earlier parent-set bedtimes extend to adolescents' daytime functioning (e.g., daytime alertness, better mood; Khor et al., 2021;Gangwisch et al., 2010). Although most studies in this area are cross-sectional, a few longitudinal studies support the positive effect of parent-set bedtimes on later sleep outcomes for adolescents (Maume, 2013;Peltz et al., 2020;Tashjian et al., 2019). ...
Full-text available
This study investigated how changing or maintaining parent-set bedtimes over time relates to adolescents’ sleep timing, latency, and duration. Adolescents (n = 2509; Mage = 12.6 [0.5] years; 47% m) self-reported their sleep patterns, and whether they had parent-set bedtimes on two separate occasions in 2019 (T1; 12.6 years) and 2020 (T2; 13.7 years). We identified four groups based on parent-set bedtimes: (1) bedtime rules at both T1 and T2 (46%, n = 1155), (2) no bedtime rules at T1 nor T2 (26%, n = 656), (3) bedtime rules at T1 but not T2 (19%, n = 472), (4) no bedtime rules at T1 but a parent-set bedtime at T2 (9%, n = 226). As expected, the entire sample showed that bedtimes generally became later and sleep duration shorter across adolescence, but the change differed among the groups. Adolescents whose parents introduced bedtime rules at T2 reported earlier bedtimes and longer sleep duration (~20 min) compared with adolescents with no bedtime rules at T2. Importantly, they no longer differed from adolescents who consistently had bedtimes across T1 and T2. There was no significant interaction for sleep latency, which declined at a similar rate for all groups. These results are the first to suggest that maintaining or re-introducing a parent-set bedtime may be possible and beneficial for adolescents’ sleep.
... Poor sleep health may contribute to poor physical health, poor mental health, and behavior problems in children [4]. Specifically, insufficient sleep duration is associated with increased child body mass index and obesity [5], poor executive functioning and school performance [6,7], and increased risk for major depression [8], while regular bedtime contributes to children's nighttime sleep consolidation and adolescents' longer sleep duration and less daytime fatigue [9][10][11]. ...
Background: Environmental factors may contribute to short sleep duration and irregular bedtime in children. Neighborhood factors and children's sleep duration and bedtime regularity remain a less investigated area. The aim of this study was to investigate the national and state-level proportions of children with short sleep duration and irregular bedtime and their neighborhood predictors. Methods: A total of 67,598 children whose parents completed the National Survey of Children's Health in 2019-2020 were included in the analysis. Survey-weighted Poisson regression was used to explore the neighborhood predictors of children's short sleep duration and irregular bedtime. Results: The prevalence of short sleep duration and irregular bedtime among children in the United States (US) was 34.6% [95% confidence interval (CI) = 33.8%-35.4%] and 16.4% (95% CI = 15.6%-17.2%) in 2019-2020, respectively. Safe neighborhoods, supportive neighborhoods, and neighborhoods with amenities were found to be protective factors against children's short sleep duration, with risk ratios ranging between 0.92 and 0.94, P < 0.05. Neighborhoods with detracting elements were associated with an increased risk of short sleep duration [risk ratio (RR) = 1.06, 95% CI = 1.00-1.12] and irregular bedtime (RR = 1.15, 95% CI = 1.03-1.28). Child race/ethnicity moderated the relationship between neighborhood with amenities and short sleep duration. Conclusions: Insufficient sleep duration and irregular bedtime were highly prevalent among US children. A favorable neighborhood environment can decrease children's risk of short sleep duration and irregular bedtime. Improving the neighborhood environment has implications for children's sleep health, especially for children from minority racial/ethnic groups.
... Examples of strategies used by authoritative parents include setting limit for use upon agreeing it with adolescents and agreeing on alternative activities to distract adolescents from Internet use. Limit setting and commitment have been shown to be effective in other problematic behaviours such as unhealthy eating behaviours (Balantekin et al., 2020) and sleeping patterns (Peltz et al., 2020) and seem to also have a potential for success in the case of IA in adolescents. According to Van den Eijnden et al. (2010), parental rules regarding setting time limits on Internet use stimulate IA in adolescents, whereas monitoring Internet use content helps prevent it. ...
Full-text available
This study aimed to identify typical interactions between adolescents’ Internet addiction and family environment factors. 165 parents were surveyed about family environment and problematic Internet use for themselves and their adolescents. Three distinct clusters were identified. Cluster 1 (assertive interaction) involved non-addicted authoritative parents with adolescents at risk of addiction. These parents had occasional arguments with their adolescents. Cluster 2 (aggressive interaction) included at-risk authoritarian parents with Internet addicted adolescents. These parents often had arguments with their adolescents. Cluster 3 (lenient interaction) comprised non-addicted permissive parents with highly addicted adolescents. These parents constantly argue with their adolescents. As we have not identified a cluster where adolescents’ addiction was below minimal risk, we advocate the need to train parents along with adolescents on healthy technology use. The clusters identified can be used by professionals as a basis to produce diverse interventions that fit each of the identified family types.
... There were similar rates of co-sleeping in our preterm and post-term groups, and differences in these sleep behaviour problems between children born preterm and post-term might reflect other parental styles of managing bedtime routines. For example, increased parental concerns with preterm-born children could result in earlier bedtimes [65]. Previous studies also found that increased time was spent in bed in preterm children, irrespective of sleep duration [7], thus arbitrarily reducing sleep efficiency. ...
Full-text available
Abstract Background Both sleep quality and quantity are essential for normal brain development throughout childhood; however, the association between preterm birth and sleep problems in preschoolers is not yet clear, and the effects of gestational age across the full range from preterm to post-term have not been examined. Our study investigated the sleep outcomes of children born at very-preterm (41 weeks). Methods A national retrospective cohort study was conducted with 114,311 children aged 3–5 years old in China. Children’s daily sleep hours and pediatric sleep disorders defined by the Children’s Sleep Habits Questionnaire (CSHQ) were reported by parents. Linear regressions and logistic regression models were applied to examine gestational age at birth with the sleep outcomes of children. Results Compared with full-term children, a significantly higher CSHQ score, and hence worse sleep, was observed in very-preterm (β = 1.827), moderate-preterm (β = 1.409), late-preterm (β = 0.832), early-term (β = 0.233) and post-term (β = 0.831) children, all p41) was also seen in very-preterm (adjusted odds ratio [AOR] = 1.287 95% confidence interval [CI] (1.157, 1.433)), moderate-preterm (AOR = 1.249 95% CI (1.110, 1.405)), late-preterm (AOR = 1.111 95% CI (1.052, 1.174)) and post-term (AOR = 1.139 95% CI (1.061, 1.222)), all p
... Moreover, caregivers may play an important protective role throughout adolescence, regardless of developmental stage (Collins & Laursen, 2006;Reed et al., 1996). Parent and caregiver influence during adolescence has been observed in sleep hygiene (Peltz et al., 2020), motivation and achievement (Kriegbaum et al., 2016), substance use and peer influence (Wood et al., 2004), and changes in anxiety and depression (Butterfield et al., 2021). Maternal warmth has also been found to attenuate anxiety and depression in adolescents with high-risk for mental health (Butterfield et al., 2021). ...
The COVID-19 pandemic has touched the lives of adolescents around the world. This short-term longitudinal, observational study followed 1,334 adolescents (11–17 yo) to investigate whether social-ecological resilience relates to intra- and inter-personal resources and/or the caregiver relationship relates to changes in internalizing symptoms during five stressful weeks of COVID-19 lockdown in Perú. In this work, we contextualize social-ecological resilience in relation to culturally-relevant personal and caregiver resources that youth can use to adapt to stressful situations. We found that adolescents who reported higher levels of personal, caregiver, and overall resilience had lower levels of anxiety and depressive symptoms at week six. We also find that personal, caregiver, and overall resilience moderated the change in anxiety symptoms from week 6 to week 11 of lockdown in 2020. Our findings underscore the importance of social-ecological resilience related to both intra/interpersonal resources and the caregiver relationship for minimizing the harmful impacts of COVID-19 on adolescent internalizing symptoms.
Full-text available
Study objectives: The current study's aim was to examine the indirect effect of parent-child pre-bedtime arguing about the bedtime process on adolescents' symptoms of anxiety and depression via the mediating role of adolescents' sleep quality. In addition, this study sought to test this mediation model across different levels of both parents' and children's dysfunctional sleep-related beliefs (ie, moderated mediation). Methods: A total of 193 adolescent (mean age = 15.7 years, standard deviation [SD] = .94; 54.4% female) and parent dyads completed both baseline, online surveys, and online 7-day, twice-daily sleep diaries. Parents (mean age = 47.6 years, SD = 5.4; 80% female) reported daily for 7 days on the intensity of any conflict regarding the adolescents' bedtime process, and adolescents completed daily reports of their sleep duration and quality (morning diary) and their anxiety and depressive symptoms (evening diary). Results: Results suggested that adolescent sleep quality mediated the indirect association between parent-child pre-bedtime arguing and adolescents' anxiety and depressive symptoms. Furthermore, this mediation model was moderated by parents' dysfunctional sleep-related beliefs. Only in families with parents reporting either average or above-average (+1 SD) levels of dysfunctional beliefs did this mediation model emerge as significant. Conclusions: Results provide further evidence for the essential role of the family environment in adolescent sleep and well-being, and they suggest that parents' dysfunctional sleep-related cognitions put adolescents at risk for a negative cascade stemming from arguing over bedtime to poor-quality sleep and its negative consequences on their mental health.
Full-text available
The aim of the current study was to examine adolescents’ sleep duration and quality as potential mediators of the association between chaotic and disorganized family environments and adolescent anxiety and depressive symptoms. A total of 193 adolescent (ages 14–17; M <sub>age</sub> = 15.7 years old, SD = .94; 54.4% female; 71% White) and parent dyads completed baseline, online surveys, and adolescents also completed online 7-day, twice-daily sleep diaries. Parents ( M <sub>age</sub> = 47.6 years old, SD = 5.4; 80% female) reported on levels of family chaos, socioeconomic status (SES), and school start times, whereas adolescents completed daily reports of their sleep duration and quality (morning diary) and their anxiety and depressive symptoms (evening diary). At the within-person level, daily fluctuations in both sleep duration and quality were significantly linked to corresponding daily fluctuations in anxiety and depressive symptoms. At the between-person level, adjusting for parenting quality; adolescents’ age, gender, daytime napping, school start time; and the family’s SES, we found that adolescent sleep quality mediated the association between family chaos and disorganization and symptoms of anxiety and depression. The current study illuminates the potential influence that family chaos and disorganization play in adolescents’ sleep and mental health symptomatology and underscores the need to assess the family context to support better adolescent health and well-being.
Full-text available
Purpose: The aim of this study was to examine modifiable environmental contributors of shortened sleep duration in adolescents. Method: We assayed sleep duration over two weeks using actigraphy in a sample of 98 adolescents (ages 14-18, 51 female). Reports of adolescents setting their own bedtime and parental monitoring of bedtime were collected and, using principal components analysis, reduced to one factor representing bedtime autonomy. In a subsample of participants (n = 63) frequency of nighttime cellphone use and reports of cellphone disruption were assessed and combined into a composite score of cellphone usage. Results: Increasing age was associated with shorter total sleep duration, r(98) = -.28, p = .006. Age-related sleep duration was mediated by bedtime autonomy, abcs = -.11, 95% BC CI [-.2167, -.0370]. The effects of bedtime autonomy were moderated by nighttime cellphone use such that bedtime autonomy was most problematic for adolescents who used cellphones more frequently, B = -10.44, SE = 4.64, 95% BC CI [-21.3749, -2.8139], compared with those who used cellphones less frequently, B = -1.94, SE = 3.28, 95% BC CI [-9.8694, 3.6205]. Conclusions: Adolescence is characterized by insufficient sleep due to biological and environmental factors. Although age is frequently cited as an important element in declining sleep duration, our results suggest age may be a proxy for other co-occurring psychosocial changes during adolescence. These findings identify mechanisms by which parents and adolescents may help increase the amount of sleep adolescents achieve.
Full-text available
Objective To use theory to design and evaluate an intervention to promote sleep hygiene and health among adolescents. Methods The Theory of Planned Behavior (TPB) and the Health Action Process Approach (HAPA) were used to develop an intervention, which was then evaluated in a cluster randomized trial. Participants were high school students (N = 2,841, M age = 15.12, SD = 1.50). Adolescents in the intervention group received four face-to-face sessions providing behavior change techniques targeting the theoretical determinants of sleep hygiene. Adolescents in the control group only received educational material at the end of the study. The primary outcome was sleep hygiene measured at one and six months post intervention. A number of secondary outcomes were also measured, including beliefs about sleep, self-regulatory processes, and outcomes related to health and wellbeing. Results Sleep hygiene was improved in the intervention group as compared to the control group at both follow-up points (coefficients = 0.16 and 0.19, 95% CIs = 0.12-0.20 and 0.15-0.23 at one month and six months, respectively, for scores on the Adolescent Sleep Hygiene Scale), as were psychosocial and general aspects of health. Mediation analyses suggested that beliefs about sleep hygiene as specified by the TPB, along with self-regulatory processes from HAPA, both mediated the effect of the intervention on outcomes. In turn, the effects of the intervention on sleep hygiene mediated its impact on general health. Conclusions Healthcare practitioners might consider intervention programs based on the TPB and the HAPA to improve sleep among adolescents. Clinical Trial Registration (NCT02551913)
Full-text available
Study Objectives To investigate the short- and longer-term impact of a 45-min delay in school start time on sleep and well-being of adolescents. Methods The sample consisted of 375 students in grades 7–10 (mean age ± SD: 14.6 ± 1.15 years) from an all-girls’ secondary school in Singapore that delayed its start time from 07:30 to 08:15. Self-reports of sleep timing, sleepiness, and well-being (depressive symptoms and mood) were obtained at baseline prior to the delay, and at approximately 1 and 9 months after the delay. Total sleep time (TST) was evaluated via actigraphy. Results After 1 month, bedtimes on school nights were delayed by 9.0 min, while rise times were delayed by 31.6 min, resulting in an increase in time in bed (TIB) of 23.2 min. After 9 months, the increase in TIB was sustained, and TST increased by 10.0 min relative to baseline. Participants also reported lower levels of subjective sleepiness and improvement in well-being at both follow-ups. Notably, greater increase in sleep duration on school nights was associated with greater improvement in alertness and well-being. Conclusions Delaying school start time can result in sustained benefits on sleep duration, daytime alertness, and mental well-being even within a culture where trading sleep for academic success is widespread.
Objective: To determine whether the main reason for bedtime is associated with sleep and adaptive functioning in adolescents. Methods: Participants were 1374 adolescents (X age = 16.8 years, SD = 0.58; 33.6% male) from Helsinki, Finland. Adolescents completed a questionnaire battery including the Munich Chronotype Questionnaire, Strengths and Difficulties Questionnaire, Beck Depression Inventory, and items drawn from the School Sleep Habits Survey, and the Pittsburgh Sleep Quality Index. Results: On school nights, adolescents whose parents set their bedtime, and those who went to bed when they were tired went to bed earlier, obtained more sleep and had earlier midpoint of sleep than adolescents who went to bed when they have finished messaging/socializing or when their television show had finished. Adolescents who went to bed when they had finished their homework had sleep that fell in between these groups. On weekends, adolescents whose parents set their bedtime went to bed earlier and had an earlier midpoint of sleep. However, there were no differences between groups in terms of sleep duration once the need to rise for school in the morning was removed. Adolescents who went to bed on school nights when they were tired or once their homework was finished had better adaptive functioning. Conclusions: These results provide support for two very different approaches to help optimize the sleep of adolescents: either by implementing parental regulation of bedtimes across adolescence, or by encouraging adolescents to use their bodily cues to indicate when it is time for bed, rather than relying on an external cue.
Objectives: To examine the prevalence and correlates of excessive daytime sleepiness (EDS) among Hong Kong children and adolescents. We investigated the potential roles of sex and puberty in modulating the occurrence of EDS. Methods: A total of 10,086 students (male, 48.1%) aged 6-18 (mean ± SD: 12.3 ± 3.2) years old participated in this cross-sectional survey. EDS was defined by a total score >18 on the Pediatric Daytime Sleepiness Scale. Sociodemographic characteristics, time in bed, chronotypes, sleep problems, emotional and behavioral difficulties, mental health, and pubertal stages were assessed. Results: The overall prevalence of EDS was 29.2%, and increased from 19.8% at Tanner stage 1 (pre-puberty) to 47.2% at Tanner stage 5 (post-puberty). Female predominance emerged at Tanner stage 3 (mid-puberty). EDS was significantly associated with short weekday time in bed, both long and short weekend time in bed, eveningness chronotype, insomnia symptoms, and sleep-disordered breathing symptoms. Females were more likely to have short weekday time in bed and eveningness chronotype than males. Children and young adolescents at pre and mid-puberty were protected against EDS by morningness chronotype. EDS was independently associated with daytime napping, alcohol and energy beverage consumption, emotional and behavioral difficulties, as well as poor mental health even after adjusting for potential confounding factors. Conclusions: EDS is prevalent among children and adolescents with the emergence of female preponderance at mid-puberty and independent association with pervasive adverse emotional and behavioral problems. The mechanisms underlying the modulation effects of sex and puberty on EDS merit further investigation.
The maturation of sleep regulatory systems during adolescence in combination with psychosocial and societal pressures culminate in a "Perfect Storm" of short and ill-timed sleep and the associated consequences for many youngsters. This model, first described by Carskadon in 2011, guides our current thinking of adolescent sleep behavior. Since the original description, the field has moved forward with remarkable pace, and this review aims to summarize recent progress and describe how this new work informs our understanding of sleep regulation and sleep behavior during this developmental time frame.
Technology use has been the focus of much concern for adolescents' sleep health. However, few studies have investigated the bidirectional association between sleep duration and time spent using technology. The aim of this study was to test whether time spent using technology predicted shorter sleep duration, and/or vice versa using cross-lagged analyses over one year. Participants were 1620 high school students in the 8th and 9th grade at baseline from 17 public schools in three middle Sweden communities. Students completed questionnaires at school during the spring of 2015 and 2016. Time spent using technology was self-reported and sleep duration was calculated from reported bed-times, wake-times and sleep onset latency. Time spent using technology significantly predicted shorter subsequent sleep duration and vice versa. Public health advocates educating others about the negative impacts of technology on sleep must also be mindful of the opposite, that many young people may turn to technological devices when experiencing difficulty sleeping.