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Social Media Use Before Bed and Sleep Disturbance Among Young Adults in the United States: A Nationally Representative Study

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Abstract

Study Objectives Social media (SM) use has been positively associated with disturbed sleep among young adults. However, previous studies have not elucidated the specific importance of SM use immediately before bed. We aimed to determine the independent association of SM use during the 30 minutes before bed and disturbed sleep while controlling for covariates including total SM use throughout the day. Methods We assessed a nationally-representative sample of 1763 U.S. young adults ages 19-32. Participants estimated to what extent they used SM in the 30 minutes before bed. We assessed sleep disturbance using the brief Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep Disturbance measure. After testing the proportional odds assumption, we used ordered logistic regression to compute the independent association between SM use before bed and sleep disturbance controlling for covariates, including total SM use. Results Compared with those who rarely or very rarely check SM in the 30 minutes before bed, those who often or very often check SM at that time had an AOR of 1.62 (95% CI=1.31-2.34) for increased sleep disturbance. Additionally, we found a significant linear trend in the odds ratios between the frequency of checking SM in the 30 minutes before bed and increased sleep disturbance (p=0.007). Results were consistent in all sensitivity analyses. Conclusions SM use in the 30 minutes before bed is independently associated with disturbed sleep among young adults. Future work should use qualitative and experimental methods to further elucidate the directionality of—and mechanisms underlying—this association.
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pii: zsx113 http://dx.doi.org/10.1093/sleep/zsx113
SLEEP, Vol. 40, No. 9, 2017 SM Before Bed and Sleep Disturbance—Levenson et al.
ORIGINAL ARTICLE
Social Media Use Before Bed and Sleep Disturbance Among Young Adults in
the United States: ANationally RepresentativeStudy
Jessica C.Levenson, PhD1; ArielShensa, MA2,3; Jaime E.Sidani, PhD2,3; Jason B.Colditz, MEd2,3; Brian A.Primack, MD, PhD2,3
1Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA; 2Center for Research on Media, Technology, and Health, University of Pittsburgh,
Pittsburgh, PA; 3Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA
This work was performed at the University of Pittsburgh, 230 McKee Place, Suite 600, Pittsburgh, PA, USA.
Study Objectives: Social media (SM) use has been positively associated with disturbed sleep among young adults. However, previous studies have not
elucidated the specific importance of SM use immediately before bed. We aimed to determine the independent association of SM use during the 30 minutes
before bed and disturbed sleep while controlling for covariates including total SM use throughout the day.
Methods: We assessed a nationally representative sample of 1763 US young adults aged 19–32. Participants estimated to what extent they used SM in the
30 minutes before bed. We assessed sleep disturbance using the brief Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep
Disturbance measure. After testing the proportional odds assumption, we used ordered logistic regression to compute the independent association between SM
use before bed and sleep disturbance controlling for covariates, including total SM use.
Results: Compared with those who rarely or very rarely check SM in the 30 minutes before bed, those who often or very often check SM at that time had an
adjusted odds ratio of 1.62 (95% confidence interval=1.31–2.34) for increased sleep disturbance. Additionally, we found a significant linear trend in the odds
ratios between the frequency of checking SM in the 30 minutes before bed and increased sleep disturbance (p=.007). Results were consistent in all sensitivity
analyses.
Conclusions: SM use in the 30 minutes before bed is independently associated with disturbed sleep among young adults. Future work should use qualitative
and experimental methods to further elucidate the directionality of—and mechanisms underlying—this association.
Keywords: bed, social media, technology, sleep quality, PROMIS.
INTRODUCTION
Insufficient sleep is highly prevalent among young adults,1 and
it is associated with daytime sleepiness2 and a range of poor
health outcomes.3–6 Social media (SM) use, which has increased
rapidly in recent years,1,7 has been positively associated with
disturbed sleep among young adults.8–11 SM use has also been
associated with factors linked to disturbed sleep—such as
higher levels of anxiety and depression—among adolescents.12
Screen media are commonly used by youth and young adults,13
and their use has been associated with important sleep-related
outcomes such as shorter sleep duration, later sleep timing, and
poorer sleep quality.14 Because SM commonly involve inter-
active screen time, it may be more detrimental to sleep than
more passive types of screen media, such as watching televi-
sion.15 Therefore, a focus on SM, rather than screen time more
generally, has particular relevance for sleep and related health
outcomes.
Little work has examined the specific association between
the time of day of SM use and sleep outcomes. It may be that
any SM use is associated with poor sleep, regardless of time
of day. SM use commonly involves alerts interrupting thought
processes throughout the day,12 which may impede relaxation at
bedtime and potentially disturb sleep at night.12,16 Additionally,
increased SM use during the day may displace time that was
meant to be devoted to completing other tasks; this may delay
bedtime or disturb the nighttime routine if daytime tasks are
instead being completed late at night.
However, it may also be that SM use specifically near bedtime
may be particularly influential with regard to poor sleep, even
when compared with daytime use. For example, nighttime SM
use may contribute to anxiety or depression at bedtime,12 per-
haps because of social comparison or negative interactions expe-
rienced while on SM.17 Another possibility is that SM use close
to bedtime may disrupt normal sleep-wake cycles and circadian
rhythms by introducing light close to the eyes from the blue end
of the color spectrum; this type of short-wavelength-enriched
light is emitted by many devices on which individuals use SM.18,19
Determining the relative importance of SM use just before
bed—compared with total SM use (SM use across the entire
day)—will be valuable in the development of evidence-based
recommendations for young adults regarding best practices
around ubiquitous SM use. For example, if the last 30 minutes
before bed is identified as a potentially critical time, clinical
recommendations for individuals with disturbed sleep can be
appropriately focused. Therefore, the purpose of this study was
to assess the independent association of SM use during the 30
minutes before bed and poor sleep outcomes, even when con-
trolling for total SM use. Based on some prior literature noted
above,12 we hypothesized that increased use in the 30 minutes
before bed would be independently associated with poor sleep.
Statement of Significance
While total social media (SM) use has been associated with sleep disturbance, this study extends prior findings by determining that SM use in the 30
minutes before bed is independently associated with disturbed sleep. Because this study was cross-sectional, future work should focus on elucidating the
direction of this association and the mechanisms underlying it. Should future work show a causal association between these variables, it will be valuable
for individuals experiencing sleep disturbance to target reduction of SM use in the period before bed to potentially reduce disturbed sleep. This may be
more feasible and acceptable than attempting to reduce total SM use. Future work should also focus on identifying and testing feasible prevention and
intervention strategies aimed at reducing SM use before bed.
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SLEEP, Vol. 40, No. 9, 2017 SM Before Bed and Sleep Disturbance—Levenson et al.
METHODS
Participants and Procedures
Overall methodology has been described in detail in prior
reports.9 In brief, we assessed a nationally representative sam-
ple of US young adults, drawn from a large-scale web-based
research panel developed and maintained by the survey research
company Growth from Knowledge (GfK). The panel, known as
the KnowedgePanel®, was formed by GfK via both random digit
dialing—from both land lines and cell phones—and address-
based sampling.20,21 Therefore, the KnowledgePanel’s sampling
frame represents approximately 97% of US households.
From October to November 2014, the web-based survey was
sent via electronic mail to a random sample of 3048 noninsti-
tutionalized adults between the ages of 19 to 32. These adults
had consented to participate in a previous wave of a two-wave
longitudinal study assessing various health behaviors (wave
1 response rate = 54%). The data used for this analysis were
collected as part of the study 18-month follow-up (wave
2) because SM use was not assessed during the baseline sur-
vey. This study was approved by the Institutional Review Board
of the University of Pittsburgh and was granted a Certificate
of Confidentiality from the National Institutes of Health. All
participants provided informed consent before commencing
participation.
Measures
Independent Variable: Social Media Use BeforeBed
Participants were asked to estimate how frequently over the
past year they “Checked social media in the 30 minutes before
falling asleep at night?” In order to capture a full range of SM
use before bed, initial response choices were very rarely; rarely;
sometimes; often; and very often. For analysis, these response
choices were collapsed into three categories: (1) rarely and very
rarely, (2) sometimes, (3) often and very often to form more
clinically relevant results.
Dependent Variable: Sleep Disturbance
We assessed sleep disturbance with the Patient-Reported
Outcomes Measurement Information System (PROMIS®)
Sleep Disturbance scale.22,23 This instrument assesses problems
with sleep, difficulty falling asleep, whether sleep was refresh-
ing, and sleep quality over the past 7 days. Each item was
measured on a Likert-type scale, ranging from not at all (1) to
very much (5). Due to the non-normal distribution of data, we
collapsed the sleep measure into tertiles (low, medium, or high
sleep disturbance) based on the raw score distribution. Specific
cut points were consistent with those previously reported in the
literature.9
Covariates
In order to ensure that SM use before bed was not simply a
marker for total SM use, we included as a covariate total SM
use. Participants self-reported the typical total number of min-
utes per day they used SM for personal use. Based on the distri-
bution of data and on prior published literature, responses were
collapsed into quartiles (0–30 minutes; 31–60 minutes; 61–120
minutes; 121 minutes+).24–27 Participants were specifically
instructed not to report on work-related use. We also assessed
multiple sociodemographic factors that have previously been
associated with SM use and/or sleep problems, including age,
sex, race, relationship situation, living situation, household
income, and educational attainment.28 We decided a priori to
categorize age into tertiles based on the distribution of the data
and in order to achieve more interpretable results.
Analysis
We used chi-square tests to examine differences in each of the
SM use variables and covariates among each sleep disturbance
group. After confirming that the proportional odds assumption
was satisfied, we used ordered logistic regression to assess the
independent association of SM use before bed with sleep distur-
bance while controlling for all covariates, including overall SM
use. Ordered logistic regression was appropriate because the
dependent variable (sleep disturbance) was ordered categorical.
All primary analyses used survey weights that adjusted not only
for undercoverage and overcoverage but also for nonresponse.
Survey weights were calculated by GfK using post-stratifica-
tion adjustment based on sociodemographic benchmark distri-
butions. Because each control variable has been associated with
sleep disturbance in the past,28 primary analyses controlled for
all measured covariates.
To confirm the robustness of our results, we conducted three
sets of sensitivity analyses. First, we conducted all multivaria-
ble analyses only adjusting for covariates with bivariable asso-
ciations of p < .15 or stronger with the outcome to ensure that
our models had not overcontrolled. Second, we computed all
models operationalizing all variables as continuous when possi-
ble (eg, age). Finally, we conducted all analyses without survey
weights. Because all sensitivity analyses showed similar find-
ings to primary results, only results from the primary analyses
are presented here.
Statistical analyses were performed with Stata 12.1 (Stata
Corp, College Station, Texas), and two-tailed p-values < .05
were considered significant.
RESULTS
Of the 3048 individuals to whom invitations were sent, 1796
responded (response rate = 59%). A total of 1763 individuals
had complete data on our primary independent and dependent
variables. There were no differences between those who pro-
vided complete responses and were thus included in our analy-
ses (n = 1763) and those who did not (n = 1285) in terms of age
(p = .12), sex (p = .07), or race (p = .21).
Table 1 depicts demographic characteristics of the weighted
sample. Accounting for survey weights, 39.8% of our sample
was categorized into the low sleep disturbance group, 27.3%
was categorized into the medium disturbance group, and 32.9%
was in the high sleep disturbance group. Nearly one-third of
the sample reported checking SM often or very often in the 30
minutes before bed.
SM use in the 30 minutes before bed, total SM use, and
household income differed significantly among the three sleep
disturbance groups. Specifically, those in the low sleep distur-
bance group were more likely to rarely check SM (49.6%) than
to check it sometimes (22.3%) or often (28.1%), while those
in the high sleep disturbance group were more likely to check
SM often (37.5%) than to check it rarely (34.7%) or sometimes
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SLEEP, Vol. 40, No. 9, 2017 SM Before Bed and Sleep Disturbance—Levenson et al.
Table1—Whole Sample Characteristics and Bivariate Associations With Sleep Disturbance.
Independent variables Whole samplea (n=1763) Sleep disturbance p valueb
Lowa (n=701) Mediuma (n=482) Higha (n=580)
Social media use
Check 30 minutes before bed .005
Rarely 41.7 49.6 37.2 34.7
Sometimes 25.9 22.3 29.3 27.8
Often 32.4 28.1 33.6 37.5
Minutes per day .001
Q1 (0–60) 50.5 59.1 47.0 41.4
Q2 (61–120) 24.0 20.7 24.3 28.7
Q3 (121+) 25.4 20.2 28.7 29.9
Covariates
Age, years .06
19–23 33.7 37.8 34.2 27.1
24–26 24.6 25.2 21.4 26.9
27–32 41.7 37.0 44.4 46.0
Sex .05
Female 50.1 45.5 50.5 56.3
Male 49.9 54.5 49.5 43.7
Racec.06
White, non-Hispanic 57.3 63.4 53.0 52.6
Black, non-Hispanic 13.2 13.0 13.4 13.2
Hispanic 20.7 17.3 21.0 25.4
Otherd8.9 6.4 12.7 8.8
Relationship status .36
Singlee44.5 47.2 41.0 43.9
Committed relationshipf55.5 52.8 59.0 56.1
Living situation .28
Parent/guardian 34.0 36.8 30.5 33.4
Significant other 35.4 31.1 39.6 37.8
Otherg30.6 32.2 30.0 28.8
Household income .001
Low (under $30,000) 22.8 18.7 19.2 32.2
Medium ($30,000–$74,999) 38.6 39.1 38.5 37.9
High ($75,000 and above) 38.7 42.2 42.3 30.0
Education level .50
High school or less 35.6 33.6 36.2 38.1
Some college 38.6 39.4 36.0 40.0
Bachelor’s degree or higher 25.8 27.0 27.8 25.8
aColumn percentages. Values may not total 100 due to rounding.
bp value derived using Rao-Scott chi-square tests for independence; bolded values indicate a significant association between the 2 variables.
cn=1760.
dMultiracial.
eWidowed, divorced, and separated.
fEngaged, married, and in a domestic partnership.
gNot living with a parent/guardian or significant other.
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SLEEP, Vol. 40, No. 9, 2017 SM Before Bed and Sleep Disturbance—Levenson et al.
(27.8%). With regard to household income, participants in the
low and medium sleep disturbance groups were most likely
to report high levels of household income (42.2% and 42.3%,
respectively), while those in the high sleep disturbance groups
were most likely to report medium levels of household income
(37.9%).
In bivariable regression models, using SM before bed was
significantly associated with sleep disturbance. For example,
compared with those who rarely or very rarely check SM before
bed, those who often or very often check SM before bed had an
odds ratio (OR) of 1.69 (95% confidence interval [CI] = 1.24–
2.33) for having a higher level of sleep disturbance. There was
a significant linear trend in the ORs between SM use before
bed and sleep disturbance (p = .001). In bivariable analyses,
sleep disturbance was also associated with total SM, older age,
female sex, Hispanic and other race/ethnicity, and low socioec-
onomic status (Table 2).
In fully adjusted models, participants with more frequent SM
use before bed retained significantly greater odds of having
sleep disturbance, even after accounting for total SM use and
all covariates. In particular, compared with those who rarely or
very rarely check SM before bed, those who often or very often
check SM before bed had an adjusted odds ratio (AOR) of 1.62
(95% CI = 1.31–2.34) for having a higher level of sleep distur-
bance. Similarly, compared with those who rarely or very rarely
check SM in the 30 minutes before bed, those who reported
“sometimes” checking SM 30 minutes before bed had an AOR
of 1.48 (95% CI = 1.06–2.08) for having a higher level of sleep
disturbance. There was a significant overall linear trend in the
ORs between use before bed and sleep disturbance (p = .007).
Other covariates significantly and independently associated
with increased sleep disturbance included overall SM use, older
age, other race, and low socioeconomic status (Table 2).
DISCUSSION
We found that, among a nationally representative sample of
young adults, greater SM use in the 30 minutes before bed was
independently associated with greater sleep disturbance, even
when controlling for total SM use. A second major finding was
that total SM use remained independently associated with sleep
disturbance in the overall adjusted model.
These findings build on previous work demonstrating greater
total SM use to be associated with greater sleep disturbance.9,12
In particular, we extend the existing literature by identifying
that there is a specific time of day, just before bed, when SM
use may have a particularly strong association with sleep. The
current analysis does not offer information on the specific
direction of effect, limiting our ability to provide specific clin-
ical recommendations at this time. Nevertheless, should future
work show a causal association between SM use before bed and
subsequent sleep disturbance, we would then be able to suggest
that individuals experiencing sleep disturbance may choose to
target reduction of SM use in the period before bed to poten-
tially reduce disturbed sleep. Future work should also investi-
gate the period before bed in which reduction of SM use would
have the most impact on improving sleep (eg, 30 minutes vs. 1
hour). As opposed to the suggestion to reduce total SM use, a
time-specific recommendation may be more feasible for some
young adults, should future work support it. This suggestion
would be in accord with recent recommendations from the
American Academy of Pediatrics that children and adolescents
get adequate sleep, that they not sleep with devices in their bed-
rooms, and that they avoid exposure to devices/screens for 1
hour before bed.29 Our findings are also consistent with a recent
report showing that media use of various types in the 2 hours
before bed was associated with shorter total sleep time and later
bedtimes among university students.30
It is interesting to note that total SM use remained a signifi-
cant predictor of sleep disturbance in the adjusted model. This
suggests that total amount of SM use across the day also has a
meaningful association with sleep that is not explained by prox-
imity to bedtime. Therefore, while focusing on the 30 minutes
before bed may be of particular value for some young adults,
for others, SM use at other times of the day may also be valua-
ble to explore. For example, some young adults may use SM in
the afternoon as a way of avoiding school work or other tasks;
this may serve to push back the completion of this work further
into the evening or night, delaying bedtime or impacting sleep
quality. Thus, our findings serve as an important reminder of the
individual differences in SM use and its association with sleep.
As mentioned above, because of the cross-sectional nature of
our data, it is impossible to determine the direction of asso-
ciation among SM use before bed and sleep disturbance. One
possibility is that increased SM use before bed contributes to
greater sleep disturbance. For example, the blue light emitted
by many SM devices may impact self-reported sleepiness, slow
wave activity during sleep, and circadian phase, and recent work
has shown that SM use before bed can impair these sleep-related
outcomes.18,31 While preliminary research has suggested that
these effects can be mitigated by daytime bright light exposure
or blue-light shielding eyewear,32,33 using these methods may
have limited practicality for some young adults. Additionally,
because SM use may promote emotional or cognitive arousal,
SM use before bed in particular may interfere with sleep.34 This
is consistent with a prior study in which nighttime-specific SM
use and an emotional investment in SM were both associated
with poorer sleep quality, lower self-esteem, and higher levels
of anxiety and depression among adolescents.12 Thus, in some
young adults, SM use before bed may contribute to anxiety and
emotional arousal, which may contribute to sleep disturbance.
Future qualitative and experimental work would help to further
elucidate these mechanisms.
On the other hand, it is possible that the association between
SM use and sleep disturbance is in the opposite direction; that
is, those who have greater levels of sleep disturbance may be
more likely to use SM, especially in the 30 minutes before
bed.35 Some young adults who have difficulty falling asleep
may turn to SM as a way of passing the time or distracting from
the frustration of not sleeping while in bed attempting to do
so. Indeed, a recent report showed that a substantial proportion
of adults use media as a sleep aid, which was associated with
increased sleep disturbance.36 Thus, it may not be surprising
that we found an association between sleep disturbance and SM
use specifically in the 30 minutes before bed because the period
just before bed may be one of the most common times of the
day to use SM among those who already have difficulty sleep-
ing. It is also possible that unmeasured variables such as anxiety
may account for the association between SM use before bed
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Table2—Bivariable and Multivariable Associations Between Social Media Use and Sleep Disturbance.
Independent variables Sleep disturbance
OR (95% CI) paAORb (95% CI) pa
Social media use
Check 30 minutes before bed .001 .007
Rarely Ref Ref
Sometimes 1.62 (1.17–2.25) 1.48 (1.06–2.08)
Often 1.69 (1.24–2.33) 1.62 (1.31–2.34)
Minutes per day <.001 .02
Q1 (0–60) Ref Ref
Q2 (61–120) 1.72 (1.22–2.43) 1.52 (1.06–2.19)
Q3 (121+) 1.81 (1.33–2.47) 1.50 (1.06–2.15)
Covariate
Age, years .009 .008
19–23 Ref Ref
24–26 1.31 (0.93–1.85) 1.36 (0.93–1.99)
27–32 1.51 (1.11–2.06) 1.63 (1.14–2.34)
Sex
Female Ref Ref
Male 0.72 (0.55–0.94) 0.79 (0.60–1.04)
Race
White, non-Hispanic Ref Ref
Black, non-Hispanic 1.19 (0.77–1.85) 1.05 (0.64–1.71)
Hispanic 1.59 (1.10–2.29) 1.46 (0.995–2.14)
Otherc1.53 (1.03–2.27) 1.59 (1.07–2.35)
Relationship status
SingledRef Ref
Committed relationshipe1.14 (0.87–1.49) 1.04 (0.70–1.54)
Living situation
Parent/guardian Ref Ref
Significant other 1.30 (0.94–1.78) 1.22 (0.78–1.90)
Otherf1.01 (0.71–1.43) 0.93 (0.65–1.35)
Household income .001 .002
Low (under $30,000) Ref Ref
Medium ($30,000–$74,999) 0.63 (0.44–0.90) 0.64 (0.44–0.93)
High ($75,000 and above) 0.52 (0.36–0.74) 0.54 (0.37–0.79)
Education level .18 .67
High school or less Ref Ref
Some college 0.90 (0.65–1.26) 1.09 (0.77–1.54)
Bachelor’s degree or higher 0.79 (0.57–1.10) 0.88 (0.60–1.28)
AOR=adjusted odds ratio; CI=confidence interval; OR=odds ratio.
ap value indicates significance level for test of overall linear trend of each ordered categorical variable. Significant effects shown in bold.
bAdjusted for all other variables in the table.
cMultiracial.
dWidowed, divorced, and separated.
eEngaged, married, and in a domestic partnership.
fNot living with a parent/guardian or significant other.
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and sleep disturbance. Some young adults feel anxious at dis-
engaging from SM; for example, they may be concerned about
the possibility of missing an SM prompt from a friend. Indeed,
recent work has shown that emotional investment in SM sig-
nificantly predicted poorer sleep quality among adolescents.12
Should future research support this hypothesized direction of
effect, clinical recommendations may include that individuals
experiencing sleep disturbance seek evidence-based treatment
for difficulty falling asleep, rather than turning to SM as a cop-
ing strategy. For these individuals, using SM as a way of passing
the time may reinforce the association between being in bed and
being awake, which is contrary to evidence-based approaches
for treating insomnia.37 Moreover, coping in this way might
result in interactive screen time, which can be more stimulating
than other non-SM activities such as reading a book.15
One important limitation of these analyses is the fact that both
SM use and sleep disturbance were assessed via self-report.
While this methodology was necessary for this large survey,
future work should aim to include more robust, gold-standard
measures of sleep quality, such as actigraphy, sleep diary, and/
or the Pittsburgh Sleep Quality Index.38 We chose to collapse
the SM use response choices into three categories, based on
distribution of the data and difficulty in interpreting the dif-
ference between “very rarely” and “rarely” and “very often”
and “often”. Because this may have limited our understanding
of the users at the extremes, it would be interesting for future
research to obtain more nuanced responses such as, “on an aver-
age day, how many minutes out of the past 30 are you using
social media?” Similarly, it would be useful for future research
to utilize more intensive measures of SM use, such as ecolog-
ical momentary assessment. Additionally, as mentioned above,
it should be reiterated that, because the data included in these
analyses are cross-sectional, our ability to draw causal conclu-
sions about the direction of the observed associations is lim-
ited. While we have suggested that “state” anxiety related to
SM use may account for the association between SM use and
sleep disturbance, it is also possible that trait level anxiety may
also play an important role. We were unable to control for trait
anxiety in the current analyses, but future work should seek to
determine the impact of this factor. Last, participants estimated
the frequency of their SM usage before bed on average, over
the course of the year. Thus, it is not possible to know whether
participants were checking SM for just a minute or two prior
to bed on a very frequent basis or whether they were spending
the entire 30 minutes on a less frequent basis, and these two
patterns of usage may have warranted the same rating.
Despite these limitations, this nationally representative study
suggests that SM use in the last 30 minutes before bed is an
independent risk factor for disturbed sleep among young adults.
Suggestions for limiting SM use during this time, particularly
to people with sleep concerns, may be a feasible and effective
strategy that should be further studied in both prevention and
intervention efforts.
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ACKNOWLEDGMENTS
The authors wish to thank all individuals who participated in this study.
SUBMISSION & CORRESPONDENCE INFORMATION
Submitted for publication January, 2017
Submitted in final revised form April, 2017
Accepted for publication June, 2017
Address Correspondence to: Brian A. Primack, MD, PhD, 3600 Cathedral of
Learning, 4200 5th Avenue, Pittsburgh, PA 15260. Telephone: 412-624-6880;
Fax: 412-624-6885;
E-mail: bprimack@pitt.edu
Downloaded from https://academic.oup.com/sleep/article-abstract/40/9/zsx113/3926043 by guest on 22 May 2019
... They indicate a practically and statistically significant relationship between social media use and sleep patterns, particularly late sleep onset [17][18][19][20]. Most studies have investigated the effect of smartphone use at times that are close to bedtime [21,22]. To the best of our knowledge, no study has investigated the impact of using different smartphone applications at different times of the day on sleep quality, nor how a specific type requires different cognitive functions of smartphone use ("active" or "passive") influences sleep quality. ...
... Scott et al. indicated a practically and statistically significant relationship between social media use and sleep patterns, particularly late sleep onset [17]. Social media use before going to bed is independently related to disturbed sleep among young adults [21]. Bhat et al. showed that using social media in bed causes sleep and mode dysfunction in adults [22]. ...
... Other studies highlight similar results. For example, electronic device use in bed reduces sleep duration and quality in adults [103], and in-bed electronic social media use has direct associations with insomnia, daytime sleepiness, mood, and sleep duration in adults [21,22]. Both bedtime procrastination and poor self-regulation caused by smartphone addiction lead to poor sleep quality [104]. ...
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Smartphones have become an integral part of people’s everyday lives. Smartphones are used across all household locations, including in the bed at night. Smartphone screens and other displays emit blue light, and exposure to blue light can affect one’s sleep quality. Thus, smartphone use prior to bedtime could disrupt the quality of one’s sleep, but research lacks quantitative studies on how smartphone use can influence sleep. This study combines smartphone application use data from 75 participants with sleep data collected by a wearable ring. On average, the participants used their smartphones in bed for 322.8 s (5 min and 22.8 s), with an IQR of 43.7–456. Participants spent an average of 42% of their time in bed using their smartphones (IQR of 5.87–55.5%). Our findings indicate that smartphone use in bed has significant adverse effects on sleep latency, awake time, average heart rate, and HR variability. We also find that smartphone use does not decrease sleep quality when used outside of bed. Our results indicate that intense smartphone use alone does not negatively affect well-being. Since all smartphone users do not use their phones in the same way, extending the investigation to different smartphone use types might yield more information than general smartphone use. In conclusion, this paper presents the first investigation of the association between smartphone application use logs and detailed sleep metrics. Our work also validates previous research results and highlights emerging future work.
... Thus, based on the previously presented research, nighttime social media might be used to cope with the presence of other negative emotional and cognitive states that are associated with cognitive pre-sleep arousal (e.g., boredom, loneliness, stress, ruminations about the past or worries about the future) and assumed to be associated with FoMO (Exelmans & Van den Bulck, 2014;Hoyt, Maslowsky, Olson, Harvey, Deardorff & Ozer, 2018;Levenson et al., 2017;Tavernier & Willoughby, 2014), but this last hypothesis and the relationship between maladaptive repetitive thought, FoMO, nighttime social media use and sleep quality remain unexplored. ...
... Nighttime social media use predicted poor sleep quality independently from other predictors (H4), which is in line with previous literature (e.g., Scott & Woods, 2018;Levenson et al., 2017) and the theoretical frameworks considered in this study (Gross, 2015;Kardefelt-Winther, 2014). It is possible to hypothesize that despite potentially being used as a coping strategy, social media is not associated with improvements in sleeping difficulties, and rather seems to be associated to poorer sleep quality. ...
... Considering the importance of social media in the lives of university students (Levenson et al., 2017;Nasirudeen et al., 2017;Shane-Simpson & Bakken, 2022;Xu et al., 2015), their risk for developing insomnia (e.g., Adams et al., 2020), the association between nighttime social media use and negative sleep outcomes (namely, poor sleep quality) (Adams et al., 2017;Bhat et al., 2017;Levenson et al., 2017;Scott & Woods, 2018;Scott et al., 2019) and the negative consequences of poor sleep on cognitive, social and emotional functioning (e.g., Adams et al., 2017;Foulkes et al., 2019;Woods & Scott, 2016), a better comprehension of the relationship between nighttime social media use and sleep quality might contribute to the development of new and improved sleep interventions. ...
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Research shows that nighttime social media is negatively associated with sleep quality and that it might be utilized to cope with aversive psychological states related to cognitive pre-sleep arousal (i.e., transdiagnostic psychopathology variables, referring to maladaptive repetitive thought), namely Fear of Missing Out (FoMO). The use of nighttime social media to cope with other aversive cognitive states (i.e., worry/rumination), their relationship with FoMO, and these variables' association with sleep are not fully understood. This study explored the relationships between nighttime social media, sleep quality, FoMO, cognitive pre-sleep arousal, and maladaptive cognitive emotion regulation (i.e., worry/rumination). The present correlational study followed a cross-sectional design. Participants were 525 university students, ranging in age from 18 to 64 (M = 22.39, SD = 5.62). Measures of sleep quality, morningness/eveningness, cognitive pre-sleep arousal, worry, rumination, FoMO, nighttime screen, and social media use were collected online or in pencil-paper format. Multiple linear regression analyses were performed. Nighttime social media and FoMO were associated with rumination, worry (H1) and cognitive pre-sleep arousal. Both FoMO and worry predicted higher levels of cognitive pre-sleep arousal (H2) and nighttime social media use (H3). Nighttime social media use independently predicted poor sleep quality (H4). These results suggest that worry and FoMO may potentially affect sleep quality by increasing cognitive pre-sleep arousal and nighttime social media. A possible explanation for these findings is that nighttime social media might be used as a strategy to cope with aversive cognitive states. These conclusions may contribute to improving sleep intervention in this population.
... These lockdowns have substituted the normal circadian rhythm sleep with other more addictive activities such as binging countless hours on social media platforms. This has massively delayed the time at which a person goes to bed 1 . ...
... It is reported that use of social media devices has been far greater in younger adults than in older age groups and its correlation with sleep has been reported 1,3 . The use of social media especially before or near bedtime can disrupt the sleep-wake cycle far more adversely than in general day-time use 1,3 . Poor quality of sleep or a complete lack of it has also been linked to down regulation of one's immune system and hormonal imbalance 4 . ...
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This policy statement focuses on children and adolescents 5 through 18 years of age. Research suggests both benefits and risks of media use for the health of children and teenagers. Benefits include exposure to new ideas and knowledge acquisition, increased opportunities for social contact and support, and new opportunities to access health-promotion messages and information. Risks include negative health effects on weight and sleep; exposure to inaccurate, inappropriate, or unsafe content and contacts; and compromised privacy and confidentiality. Parents face challenges in monitoring their children's and their own media use and in serving as positive role models. In this new era, evidence regarding healthy media use does not support a one-size-fits-all approach. Parents and pediatricians can work together to develop a Family Media Use Plan (www.healthychildren. org/MediaUsePlan) that considers their children's developmental stages to individualize an appropriate balance for media time and consistent rules about media use, to mentor their children, to set boundaries for accessing content and displaying personal information, and to implement open family communication about media.
Article
Introduction: Many factors contribute to sleep disturbance among young adults. Social media (SM) use is increasing rapidly, and little is known regarding its association with sleep disturbance. Methods: In 2014 we assessed a nationally-representative sample of 1788U.S. young adults ages 19-32. SM volume and frequency were assessed by self-reported minutes per day spent on SM (volume) and visits per week (frequency) using items adapted from the Pew Internet Research Questionnaire. We assessed sleep disturbance using the brief Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep Disturbance measure. Analyses performed in Pittsburgh utilized chi-square tests and ordered logistic regression using sample weights in order to estimate effects for the total U.S. Population: Results: In models that adjusted for all sociodemographic covariates, participants with higher SM use volume and frequency had significantly greater odds of having sleep disturbance. For example, compared with those in the lowest quartile of SM use per day, those in the highest quartile had an AOR of 1.95 (95% CI=1.37-2.79) for sleep disturbance. Similarly, compared with those in the lowest quartile of SM use frequency per week, those in the highest quartile had an AOR of 2.92 (95% CI=1.97-4.32) for sleep disturbance. Associations all demonstrated a significant linear trend. Discussion: The strong association between SM use and sleep disturbance has important clinical implications for the health and well-being of young adults. Future work should aim to assess directionality and to better understand the influence of contextual factors associated with SM use.
Article
We investigated sleep quality and melatonin in 12 adults who wore blue-light shield or control eyewear 2 hours before sleep while using a self-luminous portable device, and assessed visual quality for the two eyewear types. Overnight melatonin secretion was significantly higher after using the blue-light shield (P < 0.05) than with the control eyewear. Sleep efficacy and sleep latency were significantly superior for wearers of the blue-light shield (P < 0.05 for both), and this group reported greater sleepiness during portable device use compared to those using the control eyewear. Participants rated the blue-light shield as providing acceptable visual quality.