British Journal of Psychology (2018)
©2018 The Authors. British Journal of Psychology published by
John Wiley & Sons Ltd on behalf of British Psychological Society
Facebook use and sleep quality: Light interacts with
socially induced alertness
Jenny Bowler and Patrick Bourke*
School of Psychology, University of Lincoln, UK
It has been demonstrated that the use of social networking sites late at night can lead to
sleep-related problems that extend into the next day. A common explanation is that the
light emitted from screens is disrupting the users’ circadian rhythms. An alternative
explanation is that the social cognition inherent in the use of social networking sites is
responsible. Here, the two factors were looked at together. Participants used Facebook
on iPad tablets before sleep. This was done on different nights with two lighting conditions
and with two levels of content. In the ‘light’ condition, blue wavelength light was
manipulated so that it was either full wavelength or blue light ﬁltered. In the ‘alertness’
condition, the personal signiﬁcance of the content was changed from personally relevant
to irrelevant. A modiﬁed version of the Pittsburgh Sleep Quality Index was used to
measure sleep-related problems. No evidence was found that simply ﬁltering blue light or
simply removing relevant content improved sleep quality. However, the two factors
interacted. The results suggest that the light emitted from screens can affect sleep quality
under some conditions but this is behaviourally irrelevant in the context of normal
There is growing evidence that using laptops, tablets, and phones before going to sleep
has a detrimental effect on sleep quality. Disruptive effects have been reported in large-
scale questionnaire surveys of sleep quality (Gradisar et al., 2013; Levenson, Shensa,
Sidani, Colditz, & Primack, 2016). One explanation is that this is due to the pre-sleep
exposure to blue wavelength light emanating from screens. This inference is drawn from
physiological studies which have demonstrated that bright light in the evening inhibits the
secretion of melatonin which in turn can delay the onset of drowsiness and sleep
(Cajochen et al., 2011; Chang, Aeschbach, Duffy, & Czeisler, 2014).
It is light with short wavelengths, especially in the blue wavelength range of around
460–480 nm that produce the most melatonin suppression. Cajochen et al. (2011) report
a suppression of the normal evening increase in melatonin levels when people are
exposed to a computer screen that emanates more blue light relative to one that produces
less. Similarly, Wood, Rea, Plitnick, and Figueiro (2013) found that when participants
viewed tablets through clear goggles with attached blue light-emitting diodes (LED),
melatonin increase was signiﬁcantly suppressed relative to when they viewed tablets
through orange-tinted glasses that block blue light. The effect seems mediated by a
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which
permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no
modiﬁcations or adaptations are made.
*Correspondence should be addressed to Patrick Bourke, School of Psychology, University of Lincoln, Sara Swift Building, Lincoln
LN6 7TS, UK (email: email@example.com).
circadian rhythm phase delay that leads to a postponement of sleep onset (Cajochen
et al., 2011). Likewise, Van der Lely et al. (2015) found that when viewing an LED
computer screen while wearing blue light-blocking glasses, melatonin levels increased
and self-perceived alertness reduced. Furthermore, Chang et al. (2014) found that light
emitted from eBooks viewed on an iPad tablet, reduced participants’ level of melatonin,
and increased their sleep latency by 10 min, compared to those participants that read a
printed book. Together, these ﬁndings show that the use of computer screens before
sleep has a direct physiological effect via the light emitted from the screen. This
suppresses the normal evening rise in melatonin; thus, it is argued, sustaining alertness,
and delaying sleep onset.
Mainstream media and device manufacturers have inferred from this that light from
screens is the cause of sleep disturbance. A corollary of this is the assumption that if
appropriate ﬁlters are used, the supposed effects on sleep quality can be mitigated ‘for
example, f.lux’ (Flux Software LLC, 2017) ‘Kids Sleep Dr’ (RMA Consulting Ltd, 2015).
However, the psychological consequences of the delay in melatonin production are far
from clear. Even the evidence for its effect on sleepiness is mixed. A recent meta-analysis
(Souman, Tinga, te Pas, van Ee, & Vlaskamp, 2018) identiﬁed a set of 28 papers that
manipulated light in the blue part of the spectrum. The results show that the relationship
with alertness is not as robust as often assumed. While some studies report strong effects,
others report no signiﬁcant difference. Nonetheless, the hypothesis that light from
screens may be the factor that causes the sleep disruption reported in the survey studies
remains plausible given the clear physiological evidence of disrupted melatonin release
and independent reports that this can lead to delays in sleepiness. It is similarly plausible
that light in the blue part of the spectrum from screens has an effect on the quality of
peoples’ sleep beyond possibly delaying its onset. Existing ﬁndings of the effect of light on
sleep do not address the range of negative effects reported in survey studies, for example,
next day sleepiness and motivation. Similarly, it is far from clear whether the use of blue
light ﬁlters has any beneﬁcial effect on sleep quality.
The content of the material viewed on computer screens before trying to sleep is a
second plausible explanation for the sleep disturbance reported in the large-scale surveys.
If the content viewed is stimulating, it will lead to alertness at a time when the alertness
system needs to be shut down (Bakoti
c & Rado
cek, 2012; Gradisar et al., 2013).
This is particularly likely in the case of Social Media use. Here, personally signiﬁcant
materials are being browsed and can lead to a high arousal happy state (Arora, Broglia,
Thomas, & Taheri, 2014) or high arousal-negative states such as jealousy (Tandoc,
Ferrucci, & Duffy, 2015) and anxiety (McCord, Rodebaugh, & Levinson, 2014). More
speciﬁcally, the use of ‘Facebook’ has been found to induce a state of high arousal and high
positive valence as measured by skin conductance (Mauri, Cipresso, Balgera, Villamira, &
Riva, 2011). Facebook use has also speciﬁcally been linked to negative emotional states in
college students, that is, envy and depression (Sagioglou & Greitemeyer, 2014; Tandoc
et al., 2015). More generally, Arora et al. (2014) identiﬁed a sleep latency increase in
social network users that they attributed to the higher alertness that comes from the
interactivity and thought required. Hence, it may be the personal engagement with the
content viewed on the device that interferes with sleep, acting via the heightened
physiological state of arousal (Mauri et al., 2011) that it produces. If this is the sole cause,
the use of light ﬁlters can be expected to be ineffectual.
Whether either of these factors do account for the reports of poor sleep quality
(Gradisar et al., 2013; Levenson et al., 2016) remains an open question. Equally, whether
they could interact to produce a poorer quality of sleep is also unknown. The previous
2Jenny Bowler and Patrick Bourke
research on other aspects of sleep such as the onset of sleep and wakefulness does identify
non-additive interactions. In that case, the interactions are between the point in a
circadian rhythm and the hours since last sleep (Borb
ely, Daan, Wirz-Justice, & Deboer,
2016). Similarly, arousal and exposure to blue wavelength light may interact to affect
perception of sleep quality. Models of sleep and alertness assume a linked network by
which circadian and social cues can inﬂuence the arousal system of the brain. For
example, in the model of Saper, Scammell, and Lu (2005), the dorsomedial hypothalamic
nucleus is identiﬁed as a site of integration of circadian, social, and other cues. More
recently, Gompf et al. (2010) report links from the anterior cingulate to the locus
coeruleus arousal centre. A possible basis for an interaction seems to exist. However, the
effect of content viewed before sleep and the effect of light have tended not to be
investigated together. An exception is Higuchi, Motohashi, Liu, and Maeda (2005). Under
laboratory conditions, they studied the effects of computer gaming under two light
intensity conditions. While game playing did affect sleep latency and time spent in REM
sleep, no effect of light was found on sleep nor did light level interact with game playing
versus the control condition. The current study evaluates the impact on quality of sleep as
experienced by the users of social media, for example, next day sleepiness. Rather than
light intensity, it investigates whether the reduction in sleep quality is due to the
wavelength of light emitted from the screen, the alerting content of what is being viewed
on the device or an interaction of these two factors.
The Pittsburgh Sleep Quality Index (PSQI) was used. This is a widely utilized measure
of how people experience the quality of their sleep and is a standardized clinical measure
(Buysse, Reynolds, Monk, Berman, & Kupfer, 1989; Mollayeva et al., 2015). Participants
answer questions about different aspects of their sleep. Sleep quality is divided into seven
narrower domains. These are as follows: sleep duration, sleep disturbance, sleep onset
latency, daytime dysfunction, sleep efﬁciency, and overall quality. A ‘medication use’
element is also included. By the addition of the component scores, a global score of sleep
quality can be produced; the higher global scores represent poorer sleep quality. The
previous studies on the effects of electronic media use have successfully employed this
scale (Mollayeva et al., 2015).
In the current study, participants took an iPad tablet home for the duration of the
study. On different nights, participants viewed their own or a mock Facebook account,
with an amber ﬁlm over the tablet screen or with no ﬁlm over the tablet screen. The next
day they reﬂected on their sleep quality by completing a modiﬁed PSQI.
Thirty undergraduate students (21 females) aged between 18 and 23 years were recruited
and accredited for their participation. Participants were selected who routinely
used social media before sleep and who reported having a normal sleep pattern.
Participants were screened to exclude those taking antidepressants, sleep medication, or
There were four conditions. In the baseline condition, the participant viewed their real
Facebook account on a tablet with the normal settings. Alternatively, they viewed a mock
Facebook use and sleep quality 3
Facebook account (described below) on the tablet with the normal settings; their real
Facebook account on the tablet with an amber ﬁlter over the screen (described below); or
a mock Facebook account on the tablet with the amber ﬁlter over the screen. The
experiment was run over four weekday nights. The order of the conditions was pseudo-
randomized across participants within the 4-day block. A written introduction and
consent form were given to participants outlining how the study would be carried out.
Instructions were also verbally delivered, and participants’ understanding of the
instructions was checked. The instructions included the speciﬁcation that depending
on condition, they were to view only their own Facebook page or the mock Facebook
page. To balance demand characteristics, for half of the participants, this included a brief
statement that ‘light level was predicted to affect the quality of sleep’, whereas for the
other half of participants, it was stated that ‘interest level was predicted to affect the
quality of sleep’. After written consent was obtained; participants received an Apple iPad
4th-generation tablet (model MD513B/A, LED-backlit screen =9.5 97.31 inches; Apple
Inc., Cupertino, CA, USA) to take home for the duration of the study (four nights).
Participants viewed Facebook either on the tablet screen with the normal settings or
with a LEE 104 deep amber ﬁlter ﬁlm cut to the size of the tablet screen and attached over
the screen by a bulldog clip in each corner of the device, to ﬁlter blue light. The LEE amber
ﬁlm ﬁltered all but 0–18% of 460–480 nm wavelength, as measured with a double-beam
scanning spectrophotometer (Lee Filters Worldwide, 2016). In the normal ‘blue light’
condition, the tablet was viewed with no ﬁlm over the screen. Brightness of the tablet
screens was measured by a luminance (lux) meter (Lutron Digital Lux meter; Lx101 series,
Lutron, Taiwan). To ensure the brightness was the same for each condition, the tablets
were set to full brightness when the amber ﬁlm was used (200 lux at screen level) and
brightness was lowered to match this when no ﬁlm was used. The luminance of each
tablet was also checked after each participant returned the tablet. Hence, blue light
wavelength transmission was altered but brightness (lux) was kept the same for each
The stimulating/arousing condition had two levels: a ‘high arousal’ level, where the
participant’s own Facebook account was viewed and a ‘low arousal’ level where the
participant viewed a mock Facebook account. The mock Facebook account was a proﬁle
set up for this study; this proﬁle contained no photographs or ‘friends’ for users to contact.
The proﬁle included 53 ‘liked’ pages of companies that were not targeted to the
participant’s age range, for example, Fisher-price toys. Most users report their screen time
exposure before sleep as either <15 min or <30 min (Moulin & Chung, 2017; Nordnes,
Storemark, Bjorvatn, & Pallesen, 2014). Each night participants were instructed to view
either their own or the mock Facebook account for 15–30 min in the hour before bed. To
minimize disruption to participants’ normal evening FaceTime viewing routine, they
were not asked to change what they did in the rest of the hour before sleep. Participants
were instructed to do the study in a dark environment with the curtains closed and no
other lights on, so the only light in the room was from the tablet screen. They were asked
to not change the brightness settings of the screen. They were asked to hold the device at a
comfortable viewing distance. To minimize disruption to their FaceTime viewing routine,
participants were trusted to follow the instructions, without further checks.
The next morning a modiﬁed version of the PSQI (described below), was completed by
the participant to assess their sleep on the previous night. Exceptions were questions
which dealt with daytime experience (questions 8, 9, 10) which were completed the
following evening. On completion of the study, participants received a written debrief,
this fully explained the true aim of the experiment, that is, to assess whether the quality of
4Jenny Bowler and Patrick Bourke
sleep may be reduced by the arousing content, the light emitted from the screen, or both.
At this session, they were also asked about how interesting they found the mock Facebook
condition and their ability to comply with the instructions.
A small modiﬁcation was made to the standard PSQI questions. This was required as
the standard PSQI measures sleep quality over the previous month, and the current study
wished to measure sleep quality after each night. So, questions were changed from asking
about sleep over the previous month to sleep over the previous night. For example,
question 6 ‘During the past month, how would you rate your quality of sleep overall?’ was
altered to ‘During the previous night how would you rate your sleep quality overall?’ This
was the only change necessary for questions 1, 2, 3, 4, 6, 9 and 10 which addressed
bedtime, time to fall asleep, time of rising, actual hours of sleep, and sleep quality. Three of
the PSQI questions (5, 7, 8) ask about frequency of occurrence over the previous month;
not occurred, less than once a week, once or twice a week, and three or more times a
week. These were changed to yes/no answer questions; not occurred, occurred. These
questions dealt with trouble sleeping, medication used, and trouble staying awake.
This study received ethical approval from the University of Lincoln, Psychology Research
The individual questions scores were used to compute standard subcomponent scores.
The components were duration, disturbance, latency, daytime dysfunction, efﬁciency,
quality, and hypnotics used. Scores could range from 0 to 3 for the seven components of
sleep tested, which totalled to produce a global modiﬁed PSQI score (range of 0–21) for
each questionnaire. The lower the global modiﬁed PSQI score, or score for any
component, the better the quality of sleep.
Participants’ modiﬁed global PSQI scores are shown in Figure 1.
At debrief, participants conﬁrmed that while they did not ﬁnd the mock Facebook page
interesting, they were able to follow the instruction of viewing for 15–30 min.
The results were analysed by a 2 92 within-subjects ANOVA. This dealt with self-
reported sleep quality for the factors ‘arousal level’ (high arousal or low arousal content)
and ‘light’ (blue light or ﬁltered blue light). The results show no signiﬁcant main effect of
arousal level, F(1, 29) =2.60, p=.118, g2
p=.082, or light, F(1, 29) =1.39, p=.247,
p=.046. However, there was a signiﬁcant interaction between arousal level and light
level, F(1, 29) =6.867, p=.014, g2
p=.191. The interaction (see Figure 1) shows that the
best sleep quality was obtained for the night condition of ‘blue-ﬁltered light’ and ‘low
arousal’ relative to the other three nights.
To further explore which components of sleep quality were the most affected, 2 92
within-subject ANOVAs were conducted on the components of the PSQI. There were no
main effects. The results show a signiﬁcant interaction of arousal level and light level for
the components of sleep duration, F(1, 29) =5.66, p<.05, g2
p=.163, sleep onset
latency, F(1, 29) =7.10, p<.05, g2
p=.197, and daytime activity dysfunction on the
Facebook use and sleep quality 5
following day, F(1, 29) =6.99, p<.05, g2
p=.192. Like the main analysis, these different
aspects of sleep quality were all best in the ‘low arousal-no blue light’ condition. In
contrast, no signiﬁcant interactions were found for the components of sleep disturbance,
F(1, 29) =0.05, p>.05, g2
p=.002, sleep efﬁciency, F(1, 29) =0.86, p>.05, g2
or sleep quality overall, F(1, 29) =0.71, p>.05, g2
p=.024. The component of
medication was not analysed as use of medication was in the participant exclusion criteria.
To test whether the instructions affected the reporting of sleep quality, instructions
was added as a between subjects factor. There was no main effect, F(1, 28) =.21, p=.65,
p=.007, nor any interaction effect; with ‘light’, F(1, 28) =.22, p=.65, g2
‘arousal level’, F(1, 28) =2.43, p=.13, g2
p=.08, or ‘light’ by ‘arousal level’,
F(1, 28) =.42, p=.52, g2
p=.015. The same pattern was found for each of the
components of sleep, there were no signiﬁcant main effects of instructions, nor did
instruction interact with the other two factors.
The effect of pre-sleep Facebook use on sleep quality was measured using a self-report of
aspects of the previous night’s sleep (the modiﬁed PSQI). Two potentially disrupting
factors were manipulated; the wavelength composition of the light emitted from the
screen and the personal interest of the content that was being viewed. The factors
interacted so that superior quality sleep was only reported when a non-personal Facebook
account was viewed in blue-ﬁltered light. At a practical level, the results suggest that the
use of blue light ﬁlters is unlikely to be effective when viewing Facebook under normal
Figure 1. Effect of arousal and blue wavelength ﬁltered light on sleep quality. In the high arousal
conditions, participants viewed their own Facebook account; in the low arousal conditions, they viewed a
mock Facebook account. Screens were either viewed with (full light) or with a blue wavelength ﬁlter.
Poor/Good sleep is as measured on the modiﬁed Pittsburgh Sleep Quality Index. Lower numbers indicate
higher quality of sleep. One standard error bars are shown.
6Jenny Bowler and Patrick Bourke
viewing conditions. The results further show that under normal lighting conditions, the
content of Facebook pages, at least as varied in this study, does not affect sleep quality.
The wavelength of light can have an effect on how people perceived the quality of the ir
sleep. Previous work had shown a signiﬁcant circadian rhythm phase delay in the release
of melatonin (Chang et al., 2014; Van der Lely et al., 2015). The present results indicate
that this physiological effect is visible at a wide behavioural level (i.e., sleep quality,
daytime activity dysfunction) but only when the content viewed is ‘low arousing’. This is
consistent with Van der Lely et al. (2015) whose participants were tested under
conditions that can be considered ‘low arousal’. These participants spent 3 hrs in front of
a bright monitor with relatively little interruption (mainly half hourly interruptions to
measure sleepiness and to collect saliva). In the condition where blue wavelength light
was blocked, a drop in vigilance was reported as was an increased sense of sleepiness.
The current results provide additional information on the length of exposure
necessary and the percentage of blue wavelength light needed to effect sleep. Expanding
on existing results (Cajochen et al., 2011; Gringras, Middleton, Skene, & Revell, 2015;
Tandoc et al., 2015; Van der Lely et al., 2015), the current study has found that even a
brief 15- to 30-min exposure can be sufﬁcient to produce a signiﬁcant difference in sleep
quality, when arousal level is low. This is considerably shorter than that typically known
from previous studies, 3 hrs (Van der Lely et al., 2015), 4 hrs (Chang et al., 2014), 5 hrs
(Cajochen et al., 2011), and 2 hrs (Wood et al., 2013). However, that effects can be seen
this quickly is indicated by Horne, Donlon, and Arendt (1991). The amount of blue light
that needs to be ﬁltered to enhance sleep quality is also indicated by the current results.
The use of a tinted LEE ﬁlter in the ‘no blue light’ level reduced the blue light emitted from
the screen to 0–18% in the 460–480 nm wavelength range . This transmission rate is higher
than that used in other studies. For example, the glasses utilized in the study by Van der
Lely et al. (2015) transmitted 1.7–2% and the glasses utilized by Wood et al. (2013)
transmitted 0% of light at 460–480 nm. The level ﬁltered in the current study was
adequate to enhance sleep quality when viewing low arousing content. However, this
does raise the question of whether the same result would be seen with a more complete
blocking of blue light.
Filtering the wavelength of light was only effective in our ‘low arousing’ condition.
This raises the question of what it is about the material or activity that makes it ‘low
arousing’? The material was designed to be unexciting and uninteresting. However, this
may not be the critical factor. Our ‘low arousing’ condition inevitably avoided social
cognition and interaction (jealousy, envy, happiness). It may be this lack of personal
signiﬁcance that makes out stimuli ‘low arousing. This view seems sensible when a
comparison with Chang et al. (2014) is made. In that study, participants supplied their
own reading material and it had to be ‘pleasure’ or ‘leisure’ reading material. They report
greater sleepiness when reading a printed book in dim light relative to reading an Ebook.
Clearly here the activity is not dull or uninteresting but nonetheless, a similar
‘wakefulness’ in normal screen light and ‘sleepiness’ in dim light is seen. It may well be
that so long as people do not have to involve themselves with the worries, concerns, and
issues of their lives (McCord et al., 2014; Tandoc et al., 2015) sleep will be undisrupted;
the stimuli will be ‘low arousal’.
The global disruption of sleep quality can be better understood by examining which
aspects of sleep were and were not disrupted. The sleep onset latency component of the
PSQI is disrupted, consistent with the studies showing a delay in melatonin release when
viewing a screen in the evening, and this delay may also explain why total sleep duration is
shorter. It is interesting to note that while participants report no awareness of any
Facebook use and sleep quality 7
difference in their quality of sleep across the conditions, they nonetheless experienced an
effect the next day of daytime dysfunction (the impact on staying awake or getting up
enough enthusiasm to get things done the next day).
There are some speciﬁc methodological issues that deserve consideration. The ﬁrst is
whether the study had sufﬁcient power to detect main effects of ‘Light’ or ‘Arousal’ if they
were there. The effect of light in the blue part of the spectrum on sleepiness is not as
robust as often assumed (Souman et al., 2018). Nonetheless, given that earlier studies
(Cajochen et al., 2011) have shown that ﬁltering blue light changes melatonin levels and
can change measures of sleepiness, the credibility of the null result of varying the light is
worth further consideration. Sample size had been chosen based on previous studies
using the PSQI (Gross, Kreitzer, Russas, & Treesak, 2004; Lai & Good, 2005; Sun, Kang,
Wang, & Zeng, 2013; Yook et al., 2008). However, to explore whether the study had
sufﬁcient power, a power analysis with the program G*Power (Faul, Erdfelder, Lang, &
Buchner, 2007) was conducted to evaluate sample sizes sufﬁcient to detect changes in
sleep quality. Given an alpha of p<.05, desired power of .90 and moderate effect sizes
(0.06) gives a power of .91 for a sample size of 30 participants. It seems likely therefore
that the current study had sufﬁcient power to detect differences had they existed.
Nonetheless, it remains possible that the effect sizes were small and would not have been
detected. This is plausible given the large proportion of studies that did not ﬁnd a
signiﬁcant effect of manipulating the blue light content of the spectrum (Souman et al.,
2018). If so then the sample sizes would have had to be considerably larger. Souman et al.
(2018) suggest a sample of n>155. This would be a more reliable sample if the effect sizes
are indeed small. Assuming moderate effect sizes, it seems reasonable to interpret the lack
of signiﬁcant difference between the ‘Light’ conditions and between ‘Arousal’ conditions
as indicating that there were no simple ‘Filtered light’ or ‘Arousal’ effects.
A second methodological issue relates to the variability allowed to participants’
viewing time. An important consideration in the design of this study was ensuring that
participants were fully engaged in the social media experience in the intended arousing
condition. For this reason, they were allowed to use any time between 15 and 30 min, that
is, they could switch off the tablet if it was no longer interesting to them. While this is likely
to have succeeded in ensuring socially induced high arousal throughout this condition, it
does allow some variation in viewing time. It is possible that participants spent the
shortest time viewing screens in the Low Arousal +Blue-Filtered-Light condition. This
could happen with the reasonable assumption that people viewed the low arousal pages
for less time (as they are boring) and that in blue-ﬁltered light, they also viewed the display
for less time (as they become drowsy). This could produce an alternative explanation.
Rather than supposing that the effect on sleep quality is due to blue light ﬁltering, it might
instead arise from differing light exposure durations, that is, 15 min versus 30 min. It is
known that full-spectrum light exposure can affect alertness (Souman et al., 2018). One
study (Cajochen, Zeitzer, Czeisler, & Dijk, 2000) has looked systematically at the effect of
duration of light exposure. They showed that the longer participants were exposed to full-
spectrum light, the greater were the effects on alertness and sleep. However, as Cajochen
et al. (2000) made recordings every 30 min whether such effects would be seen with only
a 15-min difference in light exposure is unknown from this study. No difference in
subjective alertness was seen until after two hours exposure to very bright light (3,190
lux) compared to very dull light (23 lux). Souman et al. (2018) report eight studies that
have looked at subjective alerting effects for shorter durations where full-spectrum light
was used at differing intensities. With exposures of 30 min, effects on alertness are
reported in six of the studies and no effect in the remaining two studies. However, as these
8Jenny Bowler and Patrick Bourke
studies used extreme brightness (thousands of lux), it is difﬁcult to relate them to the
current case where light intensity from a screen is low (200 lux). The one study (R€
Gordijn, Beersma, de Vries, & Daan, 2005) that used a low illumination (100 lux)
compared to very low (<10) found no effects after 4 hrs. The mixed nature of these results
leaves open the real possibility that sleepiness could be affected by 15 min shorter light
exposure. This then highlights a confound in the current study and presents a possible
alternative explanation for the current results. These factors could be separated in future
experimentation. One strategy might be to match the exposure duration to that
spontaneously used by a given participant when viewing Facebook before going to sleep.
It would also be important to monitor behaviours around bedtime to ensure that there was
no systematic variation in light exposure or activity. This could be done with a
combination of technical measures, self-report, and by the wearing of ActiGraphs or other
commercially available sleep and activity ‘watches’.
Finally, there is the issue of demand effects. Two alternative sets of instructions were
used to control this, half were led to believe that light wavelength but not content would
affect sleep, and the other half the opposite. These alternative instructions had no effect. A
more sophisticated expectation effect might be that participants guessed that dull light
and boring material would improve sleep quality. If so this may have affected how they
reported and so explain the pattern observed. However, such demand effects seem
unlikely. Participants may have adopted an equally plausible alternative. They might have
guessed that sleep quality would be worse after looking at exciting pages in full light. Most
participants would have had to have adopted the same assumption for any statistically
signiﬁcant effect to emerge. There seems no obvious reason to think they would all opt for
the ﬁrst over the second. Indeed, the possibility that the results could be due to
participants making an educated guess at the hypotheses is further weakened by the lack
of a consistent effect on the subscales. If they really thought that ﬁltered light and boring
material produced better sleep, then they would be likely to report this consistently. They
would likely report less disruption the next day, longer time asleep, quicker time to get to
sleep, less next day disruption, less sleep disturbance, more time spent sleeping when in
bed, and indeed a higher explicit rating of their sleep quality. In fact, only the ﬁrst three
showed any effect.
The practical issue of the everyday sleep quality of young adults who engage with
Facebook before sleep was addressed. Evidence was found for some diminution of sleep
quality (including next day functioning) when engaged in normal Facebook use. This did
not improve when a blue light ﬁlter was placed over the screen, suggesting that when
viewing social media, ﬁlters may not be as effective as sometimes assumed. However,
there is some indication that ﬁltering short wavelength light can have an effect on sleep
quality but this seems only to be visible when the content viewed is non-arousing. Further
work is needed particularly to understand this apparent interaction.
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Received 3 July 2017; revised version received 10 August 2018
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