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

Abstract

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.

Full text

1
Submitted: 7 May, 2019; Revised: 28 October, 2019
© Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society.
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O A
Parents still matter: the inuence of parental
enforcement of bedtime on adolescents’ depressive
symptoms
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: jpeltz@daemen.edu.
Abstract
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.
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SLEEPJ, 2019, 1–11
doi: 10.1093/sleep/zsz287
Advance Access Publication Date: 29 November 2019
Original Article
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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].
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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
outcomes.
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.
Method
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).
Procedure
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,
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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.
Attrition
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.
Measures
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
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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.
Results
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.
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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).
Mediation
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
symptoms
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.
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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
B SE 95% CI χ2(df)PRMSEA CFI SRMR
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
LL UL
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.
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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.
Discussion
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
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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.
Funding
This investigation was supported with funding from the
National Sleep Foundation.
Conict of interest statement. None declared.
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