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Adolescent Problematic Social Networking
and School Experiences:
The Mediating Effects of Sleep Disruptions and Sleep Quality
Lynette Vernon, BA, BSc,
1
Bonnie L Barber, PhD,
2,3
and Kathryn L Modecki, PhD,
1–3
Abstract
An important developmental task for adolescents is to become increasingly responsible for their own health
behaviors. Establishing healthy sleep routines and controlling media use before bedtime are important for
adequate, quality sleep so adolescents are alert during the day and perform well at school. Despite the prevalence
of adolescent social media use and the large percentage of computers and cell phones in adolescents’ bedrooms,
no studies to date have investigated the link between problematic adolescent investment in social networking,
their sleep practices, and associated experiences at school. A sample of 1,886 students in Australia aged between
12 and 18 years of age completed self-report data on problematic social networking use, sleep disturbances, sleep
quality, and school satisfaction. Structural equation modeling (SEM) substantiated the serial mediation hy-
pothesis: for adolescents, problematic social networking use significantly increased sleep disturbances, which
adversely affected perceptions of sleep quality that, in turn, lowered adolescents’ appraisals of their school
satisfaction. This significant pattern was largely driven by the indirect effect of sleep disturbances. These findings
suggest that adolescents are vulnerable to negative consequences from social networking use. Specifically,
problematic social networking is associated with poor school experiences, which result from poor sleep habits.
Promoting better sleep routines by minimizing sleep disturbances from social media use could improve school
experiences for adolescents with enhanced emotional engagement and improved subjective well-being.
Introduction
Adolescents spend a great deal of time immersed in
technology, and for some youth, this can become
problematic.
1,2
Extensive use of technology, often accessible
in the bedroom, raises a number of issues concerning ado-
lescents’ reliance on social media to fulfil their emotional
needs. In particular, heavy media use can alter sleeping and
waking patterns,
1
and thus undermine adolescents’ perfor-
mance at school.
2
Research investigating whether such
excessive use of technology by adolescents has negative
implications is growing.
3–7
Studies have documented young
people’s growing dependency on social networking,
8
label-
ing it a behavioral addiction (see Kuss and Griffiths
9
for lit-
erature review), which is strongly correlated with other
dysfunctional Internet behaviors such as online gambling.
7
Further, problematic use of social networking has been as-
sociated with negative indicators such as depression,
10,11
low
self-esteem,
12
and suppression of empathic social skill.
4
Because computers and online media devices have been
woven into the fabric of society, it is crucial to understand
whether some young people may be vulnerable to problem-
atic social networking use and so to reduced sleep quality that
is so vital for engaging in key aspects of daily life, particu-
larly schooling.
Many parents, encouraged to develop a habitual bedtime
routine for their young child, indicate ‘‘time for bed’’ or
‘‘lights out’’ by darkening their child’s bedroom. Yet, this
may not continue to signal for adolescents the same imper-
ative to settle into a quality night’s sleep. Rather, for some
young people, social interactions with peers can occur via
communication technology, 24 hours a day, 7 days a week,
and can interfere with the ability to get a good night’s sleep.
Developmentally, adolescents require about 9 hours of sleep
per night, and inadequate sleep on a regular basis can have
adverse effects, including decreased motivation.
1,13
Time in
bed often now includes sending and receiving text messages,
posting on or perusing social networking sites (SNS), or
gaming with online ‘‘friends,’’ all of which can keep ado-
lescents up well into the night, steadily eroding their sleep.
1
School of Psychology and Exercise Science, Murdoch University, Murdoch, Western Australia.
2
School of Applied Psychology,
3
Menzies Health Institute Queensland, Griffith University, Southport, Australia.
CYBERPSYCHOLOGY,BEHAVIOR,AND SOCIAL NETWORKING
Volume 18, Number 7, 2015
ªMary Ann Liebert, Inc.
DOI: 10.1089/cyber.2015.0107
386
Research has examined sleep patterns and technology use,
and found both game playing and Internet use are negatively
related to good sleep patterns.
14
Prevalence of mobile phone
use after lights out is also linked to tiredness.
15
Indeed, the
hours devoted to media use can compete with sleep, leading
to changes in adolescents’ sleep habits and sleep time, re-
sulting in a trend toward insufficient sleep and reduced sleep
quality (see Cain and Gradisar
16
for literature review).
1,14,15
The more adolescents multitask with a variety of techno-
logical devices (texting, talking on the cell phone, using the
Internet, listening to an iPod), the fewer hours they sleep,
which increases their daytime sleepiness.
17
As a result, re-
searchers suggest multitasking could put these students at
risk for changes in their school performance.
17
Although this
has not yet been empirically tested,
17
it may be that those
adolescents who heavily invest in social networking become
tired during the day and so encounter difficulties trying to
meet the cognitive demands of study.
One of the most important tasks for adolescents is to en-
gage positively in school and achieve to the best of their
ability.
18
Considering the amount of time students spend at
school, it is important that their emotional appraisal of
their school-related experiences—that is, their school sat-
isfaction—contributes positively to their subjective well-
being.
19–21
Students who are tired and sleepy in class have
trouble performing tasks related to academic performance,
such as effective time management, and sustaining effort,
interest, and attention. Therefore, they do poorly at school,
and tired students feel less satisfied with their school expe-
rience.
13,22
Further, the use of SNS among college students
has been linked to poor academic performance,
23,24
with
social networking use likely interfering with sleep. However,
sleep deprivation has not yet been investigated as a negative
consequence of social networking use in relation to poor
experiences in high school. Further, there are no studies of
adolescents that explore the association between all three
components: social networking, sleep habits, and subsequent
school experiences.
The current study investigates the possibility of a devel-
opmental mismatch between adolescents’ need for sufficient,
uninterrupted sleep and adequate sleep quality to navigate
successfully both school and their desire to network online
socially. Identifying contexts (especially online) that hinder
connections to school is an important and understudied area.
Although research has established a link between adolescent
sleep habits and academic outcomes, and separate research
has demonstrated links between problematic adolescent
media use and sleep habits, research is lacking that investi-
gates how problematic investment in social networking re-
lates to adolescent sleep patterns and school experience. It is
hypothesized that sleep disturbances and perceived sleep
quality will play a serial mediating or explanatory role in the
association between problematic use of social networking
and school satisfaction.
Method
Participants
Data come from the Youth Activity Participation Study of
Western Australia.
25,26
Participants included 1,886 students
(59.2% female) from 32 high schools (68% metropolitan,
32% regional). The mean age of the participants was 15 years
(SD =1.41 years), and youth ranged from 12 to 18 years of
age. Of the sample, 71.4% of participants were Caucasian,
8.7% Asian, 1.9% Aboriginal or Torres Strait Islanders, and
18% other. Socioeconomic status (SES) was measured at
school level for survey schools
27
and obtained from the
Australian Curriculum Assessment and Reporting Authority
(ACARA), which computes the Index of Community Socio-
Educational Advantage (ICSEA).
28
Schools are placed on a
numerical scale that describes their comparative socioeco-
nomic advantage, and survey schools ranged between two
standard deviations above and below the state mean on the
ICSEA.
27,28
Materials and procedure
Data for this study were collected in 2010 and 2011.
During a 45-minute classroom session, participants entered
responses onto laptops, or alternatively completed a paper
and pencil version of the survey. Ethics approval to conduct
research was obtained from the university Human Research
Ethics Committee, the Education Department, and the
Catholic Education Office. Study participation required ac-
tive informed parent and student consent.
Measures
Problematic social networking use. This was a latent
construct that measured the degree to which adolescents’ use
of SNS affected their well-being. The construct consisted of
four observed indicators (see Table 1). Possible responses
ranged from 0 =‘‘no SNS profile’’ to 1 =‘‘completely dis-
agree’’ and 5 =‘‘completely agree.’’ These items were
adapted from Young’s
29
Internet Addiction Scale to reflect
emotionally problematic use of SNS.
Table 1. Latent Constructs with Model Fit Indices
Problematic social networking use
1. I use SNS as a way of making me feel good.
2. I get into arguments with other people about the
amount of time I spend on SNS.
3. I prefer to spend time on SNS rather than attend social
activities/events.
4. If I can’t access SNS, I feel moody and irritable.
v
2
(2, n=1,883)
=4.943, p=0.085, CFI =0.998,
RMSEA [90% CI] =0.028 [0.000–0.061]
Sleep disturbance
1. How often have you arrived late to class because you
overslept?
2. How often have you fallen asleep in morning class?
3. How often have you stayed up until at least 3 a.m.?
Sleep quality
1. How often have you felt satisfied with your sleep?
2. How often have you had a good night’s sleep?
v
2
(4, n=1,863)
=7.197, p=0.126, CFI =0.997,
RMSEA [90% CI] =0.021 [0.000–0.045]
School satisfaction (not identified)
1. School is interesting.
2. I enjoy school activities.
3. I look forward to going to school.
Measurement model
v
2
(48, n=1,882)
=103.271, p=0.000, CFI =0.991,
RMSEA [90% CI] =0.025 [0.018–0.031]
SOCIAL NETWORKING, SLEEP, AND SCHOOL EXPERIENCES 387
Sleep quality and sleep disturbance. These were latent
constructs measuring adolescents’ perceptions about sleep
behaviors during the previous 2 weeks (see Table 1). Re-
sponses for all sleep indicators were 1 =‘‘never,’’ 2 =‘‘once,’’
3=‘‘twice,’’ 4 =‘‘severa l tim es,’’ and 5 =‘‘every day/night.’’
These items were adapted from the School Sleep Habits
Survey.
13,22
School satisfaction. This was a latent variable measuring
adolescents’ perceptions of how satisfied they were with their
school experience (see Table 1). Responses on all indicators
ranged from 1 =‘‘not at all true for me’’ to 5 =‘‘very true for me.’’
These items were adapted from the Multidimensional Students’
Life Satisfaction Scale (MSLSS).
21
Relevant covariates were
controlled for, including sex (0 =‘‘female,’’ 1 =‘‘male’’), SES,
and age; both SES and age were mean centered.
Analysis
Covariance structure analysis, using the statistical software
package Mplus7.1,
30
analyzed the measurement models using
maximum likelihood estimation with robust standard errors
(MLR).
31
Bias-corrected (BC) bootstrap confidence intervals
were used to generate an estimate for each indirect effect
along with a 95% confidence interval to examine the signifi-
cance and strength of a particular mediator in the multiple
mediated model.
32–35
This approach adjusts for non-normality
and uses data from cases where the information is available to
obtain estimates with missing data.
31
First, a confirmatory factor analysis (CFA) was performed
to ensure that the measurement model was an appropriate fit,
overall. Each of the latent constructs representing the con-
ceptual variables for problematic SNS use, sleep disturbance,
sleep quality, and school satisfaction were simultaneously
estimated in the measurement model. The sample was also
divided into two groups based on age (age <14.62 =0), and
sex (female =0), and estimated a multiple group measure-
ment model. Chi-square difference tests determined whether
the items used to measure the latent constructs adequately
measured the underlying conceptual variables for each of the
two age groups and sex.
30,36
Next, using Multiple Indicators, Multiple Causes (MIMIC)
modeling,
37,38
the observed variables—sex, age, and SES—
were used to predict the unobserved latent variables in the
overall sample—problematic SNS use, sleep quality, sleep
disturbance, and school satisfaction. Finally, a serial media-
tion model was hypothesized,
39
whereby problematic social
networking increased sleep disturbance, which in turn de-
creased sleep quality and, in turn, reduced students’ satisfac-
tion with their school. The product of coefficients approach
was used,
32–35
calculating the indirect effects by multiplying
the path coefficients that link problematic SNS (X)toschool
satisfaction (Y) through the mediators of sleep disturbances
(M
1
) and sleep quality (M
2
) as shown in Figure 1.
Results
Descriptive data (means, standard deviations) for sex,
SES, age, the four latent variables of problematic social
networking, sleep disturbance, sleep quality, and school
satisfaction, along with correlations among the variables, are
presented in Table 2.
FIG. 1. Research model
and test results.
388 VERNON ET AL.
The measurement model demonstrated good fit—v
2
(48) =103.271, p=0.000, CFI =0.991, RMSEA [90%
CI] =.025 [0.018–0.031]. Incremental fit indices were used
to test for measurement invariance for age and sex.
30,36,40,41
Although weak factorial invariance was supported, meaning
the different sex and age groups employed the same con-
ceptual framework to answer the survey items,
36
scalar in-
variance was not achieved for either age or sex. Therefore,
both were included as predictors for the mediators, thus re-
moving any secondary influences related to the association
between the latent variables.
37,38
MIMIC modeling investigated direct and interaction effects
of covariates, age, sex, and SES, with good fit—v
2
(104) =
332.17, p<0.001, CFI =0.972, RMSEA [90% CI] =0.033
[0.029–0.038]. Further information related to the measurement
and MIMIC models is available from the author. Finally, a
structural model was estimated to examine the direct and in-
direct relations between the latent variables of interest and the
background variables for sex, age, and SES.
33–41
Sleep quality and sleep disturbances
as serial mediators
A focal question of the study was whether problematic
SNS use influenced students’ school satisfaction by adversely
affecting their sleep. This hypothesis was modeled and tested
Table 2. Correlations and Descriptive Statistics
Variables 1 2 3 4 5 6
1. Problematic social networking —
2. Sleep disturbances 0.38*** —
3. Sleep quality -0.17*** -0.20*** —
4. School satisfaction -0.16*** -0.33*** 0.33*** —
5. Sex
a
-0.08*** 0.14*** 0.13*** -0.06* —
6. Age
b
0.13*** 0.06 -0.09*** 0.00 0.00 —
7. Socioeconomic status
c
0.01 -0.15*** 0.08* 0.24*** -0.04 0.15***
M1.36 1.46 3.50 3.18
SD 0.98 0.65 0.99 0.97
Range 0–5 1–5 1–5 1–5
a
Sex: 0 =‘‘female,’’ 1 =‘‘male.’’
b
Age: mean centered.
c
Socioeconomic status: mean centered.
*p<0.05; ***p<0.001.
Table 3. Path Coefficients, Indirect Effects, and 95% Bias-Corrected Bootstrapped
Confidence Intervals Corresponding to the Three-Path Mediation Model
Consequent
M
1
(Sleep disruptions) M
2
(Sleep quality) Y (School satisfaction)
95% BC
Bootstrap CI
95% BC
Bootstrap CI
95% BC
Bootstrap CI
Antecedent Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper
Total effect c
a
-0.139 -0.185 -0.087
Direct effect c¢
a
-0.012 -0.060 0.049
X(SNS) a
1
a
0.161 0.124 0.207 a
2
a
-0.081 -0.127 -0.018
M
1
(Disruption) d
1
a
-0.310 -0.528 -0.139 b
1
a
-0.547 -0.788 -0.355
M
2
(Quality) b
2
a
0.302 0.231 0.373
C
1
(Sex) 0.147 0.091 0.203 0.266 0.018 0.366 -0.104 -0.203 -0.014
C
2
(Age) 0.014 -0.005 0.036 -0.048 -0.077 -0.013 0.009 -0.022 0.044
C
3
(SES) -0.001 -0.001 0.000 0.001 0.000 0.001 0.002 0.001 0.002
Indirect effects
a
1
b
1
-0.088 -0.129 -0.058
a
2
b
2
-0.024 -0.042 -0.006
a
1
d
1
b
2
-0.015 -0.026 -0.008
Total indirect Effect -0.128 -0.167 -0.093
a
1
b
1
–a
2
b
2
b
-0.064 -0.110 -0.028
a
1
b
1
–a
1
b
2
d
1
b
-0.073 -0.112 -0.043
a
2
b
2
–a
1
b
2
d
1
b
-0.009 -0.031 0.014
Models include covariates sex, age, SES; age and SES centered at means.
a
The coefficients for a,a
2
,b
1
,b
2
,c,c¢, and d
1
refer to the paths in Figure 1.
b
Comparison of multiplicative paths and bootstrap CIs.
SOCIAL NETWORKING, SLEEP, AND SCHOOL EXPERIENCES 389
by setting direct paths from problematic social networking to
sleep disturbances, sleep quality, and school satisfaction, as
well as a serial mediation pathway through sleep disturbances
and sleep quality to school satisfaction (see Fig. 1) while
statistically controlling for sex, age, and SES (not depicted in
Fig. 1). The data fit the model well—v
2
(75) =348.319,
p=0.000, CFI =0.971, RMSEA [90% CI] =0.044 [0.039–
0.049].
Results are presented in Figure 1 and Table 3, and indicate
a significant indirect path, such that participants with prob-
lematic social networking behavior experienced sleep dis-
turbance, which in turn was associated with a perception of
poor quality sleep and a stronger dissatisfaction with their
schooling. A bias-corrected bootstrap confidence interval for
the total indirect effect (a
1
b
1
+a
2
b
2
+a
1
d
1
b
2
=-0.128 [CI
-0.167 to -0.093]) indicated a significant effect between
problematic social networking, sleep quality, sleep distur-
bance, and school satisfaction. There was no evidence that
problematic social networking influenced school satisfac-
tion, independent of its effect on sleep disturbance and on
sleep quality (c¢= -0.012, p=0.666). Examination of the
single mediator paths compared to the serial mediation
showed problematic social networking use had a stronger
influence on school satisfaction through sleep disturbance in
isolation (a
2
b
2
–a
1
b
2
d
1
=-0.064 [CI 0.110 to -0.028]) than
through the serial mediation, which also included sleep
quality.
Discussion
The purpose of this study was to examine the role of sleep
in the association between problematic social networking
use and students’ satisfaction with schooling. A serial me-
diation model confirmed that sleep disturbances and sleep
quality mediated the association. Students reporting high
levels of problematic social networking use reported more
sleep disturbance problems, which in turn were associated
with lower sleep quality, resulting in lower school satis-
faction. Notably, sleep disturbance exerted a stronger in-
fluence on school satisfaction than did sleep quality. This
makes sense as the indicators of disturbance—staying up
until at least 3 a.m., arriving late to school because of
oversleeping, and falling asleep in morning class—impair
every aspect of the school experience. When tired students
try to engage in school activities, their resulting emotional
evaluative response toward their school satisfaction will be
low compared to students who routinely get a good night’s
sleep.
Consistent with the predictions, it was found that poor
sleep habits were an important underlying mechanism that
helped explain why students with problematic SNS use re-
ported low school satisfaction. When youth use their SNS at
appropriate times of the day, and not into the night, they are
less likely to experience sleep disturbance and so report
better sleep quality, which in turn is associated with an
evaluative response that their school experience was posi-
tively contributing to their subjective well-being.
19
This
finding is congruent with previous research, which shows
that the protective effects of a good night’s sleep improve
academic performance.
13,22,42–44
One of the factors that contributes to problematic SNS use
for adolescents included using social media as a way of
making them feel good (Table 1). So if students have a poor
experience at school, they may tend to increase social media
use to improve feelings of well-being. This overuse may then
further disturb their sleep, and lead to lower satisfaction with
their school the next day. This finding affirms the value of
good sleep habits for adolescents’ subjective sense of well-
being as they undertake their student role.
Several limitations need to be considered when inter-
preting the results of the present study. First, causal infer-
ences cannot be made from these cross-sectional data.
Studies using longitudinal data could provide further evi-
dence for the effects found in this study, particularly related
to following the trajectories of students to examine associ-
ations over time. Second, data were based on students’ ret-
rospective self-reported measures of their sleep habits.
Although previous research has shown that retrospective
self-report measures of sleep quality as collected in this study
do not contain strong bias,
45
future studies may benefit from
using technology to capture real time sleep patterns.
Despite these limitations, this research has practical im-
plications. The results suggest that parents should consider
limiting adolescents’ access to mobile technology (comput-
ers and cell phones) in the bedroom in order to reduce the
risk of poor sleep habits and associated poor school experi-
ence. Students who are identified as problematic users of
SNS have associated problems with their sleep, suggesting
they continue to use social media after bedtime or lights
out.
15
Given the fact that a positive school experience is
associated with strong academic achievement,
19,20
parents
will likely wish to maximize their child’s emotional en-
gagement in school
42–44,46
by making sure they have un-
disturbed, quality sleep. Well-rested students find school
both enjoyable and interesting.
The present findings could also inform school curriculum
and pedagogy. Illustratively, high schools could include
course content detailing positive sleep habits, and could
provide opportunities for students to develop and monitor
their own individualized sleep schedules. Students could
examine their sleep habits with an associated behavioral
analysis of their SNS practices (i.e., monitoring bedtimes,
checking for excessive use of SNS past bedtime, and feelings
when using SNS), and reporting their tiredness and related
emotional evaluation of their daytime school experiences.
When provided with information about the importance of
regular sleep patterns, adolescents will arguably be better
equipped to make healthy lifestyle choices. At a minimum,
such knowledge may facilitate students’ acceptance of the
potentially fraught job of parents in monitoring nighttime
technology use.
Acknowledgments
The Youth Activity Participation Study of Western
Australia (YAPS-WA) has been supported through three
grants under Australian Research Council’s Discovery
Projects funding scheme: DP0774125 and DP1095791 to
Bonnie Barber and Jacquelynne Eccles, and DP130104670
to Bonnie Barber, Kathryn Modecki, and Jacquelynne Ec-
cles. We would like to thank the high school principals,
their staff, and the students who participated in the YAPS-
WA study. We are also grateful to everyone in the YAPS-
WA team.
390 VERNON ET AL.
Author Disclosure Statement
No competing financial interests exist.
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Address correspondence to:
Dr. Lynette Vernon
School of Applied Psychology—Gold Coast
Gold Coast campus
Griffith University
QLD 4222, Australia
E-mail: l.vernon@murdoch.edu.au
392 VERNON ET AL.
