The association between social media use and sleep disturbance among young adults
Open accessAbstract
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 1788 U.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.
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Written from the paper’s full text.
TL;DR
In a nationally representative sample of U.S. young adults ages 19–32, greater self-reported social media use was associated with higher odds of sleep disturbance. The association was stronger for frequency of social media visits than for total daily volume.
Key findings
- Median social media use was 61 minutes per day (IQR = 30, 135) and 30 visits per week (IQR = 8.5, 56.5).
- Weighted sleep disturbance distribution was 42.6% low, 28.0% medium, and 29.4% high.
- In fully adjusted models, compared with the lowest quartile of daily social media volume, the highest quartile had an AOR of 1.95 (95% CI = 1.37–2.79) for higher sleep disturbance.
- Compared with the lowest quartile of weekly social media frequency, the highest quartile had an AOR of 2.92 (95% CI = 1.97–4.32) for higher sleep disturbance; associations showed a significant linear trend (all P < .001).
Methods
- Cross-sectional analysis of 1,788 U.S. young adults ages 19–32 from the GfK KnowledgePanel, using data collected in the 18-month follow-up survey from October to November 2014.
- Social media use was self-reported as average daily minutes (volume) and weekly visits across 11 platforms (frequency); sleep disturbance was assessed with four PROMIS Sleep Disturbance short-form items and grouped into low, medium, and high tertiles.
- Chi-square tests and ordered logistic regression with survey weights were used, adjusting for sociodemographic covariates; sensitivity analyses used more parsimonious models, no survey weights, and continuous social media variables.
Limitations
- The paper states that the cross-sectional design prevents determining directionality or causality between social media use and sleep disturbance.
- The paper states reliance on retrospective self-report of social media use and sleep, without formal sleep studies or ecological momentary assessment, and notes that results cannot be generalized beyond ages 19–32.
- The paper states that the 4-item PROMIS short form, rather than computerized adaptive testing, may have contributed to non-normal response data and limited sleep disturbance to an ordinal-categorical variable; it also notes that future work should examine a broader set of covariates such as mental health status.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
0 of 21 cited sources checked · 0 supported · 0 flagged
- Findingown data
The relationship between social media use and sleep disturbance showed a statistically significant linear trend in both unadjusted and adjusted models (all P < .001).
“Associations all demonstrated a significant linear trend.”
- Also stated in: § Multivariable Analyses · boosted
“Post-estimate tests demonstrated linearity of the overall association between each of the independent variables and the dependent variable in both unadjusted and adjusted models (all P < .001).”
Quote in the paperSubstantial claim - Also stated in: § Multivariable Analyses · boosted
- Findingown data
The median daily volume of social media use was 61 minutes (IQR 30, 135; range 0-1447).
“Median volume was 61 minutes of SM use per day (IQR = 30, 135; range = 0-1447), while median frequency was 30 visits per week (IQR=8.5, 56.5; range=0-385).”
Quote in the paperNot a substantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Findingown data
In fully adjusted models, the highest quartile of daily social media use had an adjusted odds ratio of 1.95 (95% CI 1.37-2.79) for greater sleep disturbance compared with the lowest quartile.
“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.”
- Also stated in: § Multivariable Analyses · plain
“For example, compared with those in the lowest quartile, those in the highest quartile had an AOR of 1.95 (95% CI = 1.37 - 2.79) for having a higher level of sleep disturbance.”
Quote in the paperSubstantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Also stated in: § Multivariable Analyses · plain
- Findingown data
The median weekly frequency of social media use was 30 visits (IQR 8.5, 56.5; range 0-385).
“Median volume was 61 minutes of SM use per day (IQR = 30, 135; range = 0-1447), while median frequency was 30 visits per week (IQR=8.5, 56.5; range=0-385).”
Quote in the paperNot a substantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Findingown data
Among U.S. young adults aged 19-32 in a nationally representative sample, greater social media use volume was linked to significantly higher odds of sleep disturbance after adjustment for all sociodemographic covariates.
“In models that adjusted for all sociodemographic covariates, participants with higher SM use volume and frequency had significantly greater odds of having sleep disturbance.”
- Also stated in: § Multivariable Analyses · plain
“In fully-adjusted models (Table 2), participants with higher SM volume had significantly greater odds of having sleep disturbance.”
Quote in the paperSubstantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Also stated in: § Multivariable Analyses · plain
- Findingown data
Female sex was significantly associated with higher levels of sleep disturbance.
“Among the demographic variables, sex and level of household income were significantly associated with sleep disturbance. Females were more likely to have high levels of sleep disturbance than males, and lower levels of income were associated with higher levels of sleep disturbance.”
Quote in the paperNot a substantial claim - Findingown data
Lower household income was significantly associated with higher levels of sleep disturbance.
“Among the demographic variables, sex and level of household income were significantly associated with sleep disturbance. Females were more likely to have high levels of sleep disturbance than males, and lower levels of income were associated with higher levels of sleep disturbance.”
Quote in the paperNot a substantial claim - Findingown data
In fully adjusted models, the highest quartile of weekly social media use frequency had an adjusted odds ratio of 2.92 (95% CI 1.97-4.32) for greater sleep disturbance compared with the lowest quartile.
“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.”
- Also stated in: § Multivariable Analyses · plain
“Similarly, compared with those in the lowest quartile for frequency of use, those in the highest quartile had an AOR of 2.92 (95% CI = 1.97 - 4.32) for having a higher level of sleep disturbance.”
Quote in the paperSubstantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Also stated in: § Multivariable Analyses · plain
- Interpretationown data
The frequency of social media visits may be a better predictor of sleep difficulty than total time spent on social media.
“This may indicate that the frequency of visits is a better predictor of sleep difficulty than overall SM time.”
Quote in the paperSubstantial claim - Interpretationcitation
Social media use may directly cause disturbed sleep by displacing sleep.
“SM use may directly cause disturbed sleep in three ways.20 First, SM use may directly displace sleep; for example, if an individual stays up late posting pictures on Instagram, his or her sleep time may be reduced.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] Cain N Gradisar M Electronic media use and sleep in school-aged children and adolescents: A review Sleep Med 2010 11 735 742 20673649 source
Not checked in the free run.
- Study design and conductown data
Sensitivity analyses using more parsimonious models, no survey weights, and/or continuous independent variables yielded results consistent with the primary findings in both significance and magnitude.
“All sensitivity analyses generated results consistent with the primary results. Analyses which used more parsimonious models, no survey weights, and/or continuous independent variables did not result in differences in terms of either levels of significance or magnitude of findings.”
Quote in the paperNot a substantial claim - Interpretationcitation
Social media use may directly cause disturbed sleep by promoting emotional, cognitive, or physiological arousal.
“Second, SM use may promote emotional, cognitive, and/or physiological arousal. For example, watching a provoking video on YouTube or engaging in a contentious discussion on Facebook just before going to bed may contribute to disturbed sleep.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] Cain N Gradisar M Electronic media use and sleep in school-aged children and adolescents: A review Sleep Med 2010 11 735 742 20673649 source
Not checked in the free run.
- Study design and conductown data
The study used a nationally representative sample of U.S. young adults.
“We assessed a nationally-representative sample of U.S. young adults ages 19-32 who were participating in a longitudinal survey assessing multiple health behaviors.”
- Also stated in: § Strengths and Limitations · plain
“This study is unique in its assessment of SM use and sleep disturbance in a nationally-representative sample of young adults.”
Quote in the paperSubstantial claim - Also stated in: § Strengths and Limitations · plain
- Interpretationown data
The findings cannot be generalized to age groups outside young adults aged 19-32.
“Additionally, because this study was limited to young adults ages 19 to 32 the results cannot be generalized to other age groups.”
Quote in the paperNot a substantial claim - Interpretationown data
The volume and frequency measures of social media use may capture qualitatively different components of social media use, since both were significantly associated with sleep disturbance in multivariable models.
“Both of these measures were significantly associated with sleep disturbance in multivariable models, suggesting that these measures may reflect qualitatively different components of SM use.”
Quote in the paperSubstantial claim - Premisecitation
In that sample, frequent use of social networking sites had the strongest impact on reduced weekday sleep duration.
“Still, frequent use of social networking sites had the strongest impact on reduced weekday sleep duration among this sample.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22] Arora T Broglia E Thomas EN Taheri S Associations between specific technologies and adolescent sleep quantity, sleep quality, and parasomnias Sleep Med 2014 15 240 247 24394730 source
Not checked in the free run.
- Interpretationcitation
Bright light from social media devices may delay circadian rhythms.
“Third, bright light emitted by SM devices may delay circadian rhythms.36”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [36] Chang AM Aeschbach D Duffy JF Czeisler CA Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness Proc Natl Acad Sci U S A 2015 112 1232 1237 25535358 PMC4313820 source
Not checked in the free run.
- Premisecitation
The KnowledgePanel has been shown to be a statistically valid method for surveying and analyzing health indicators from a nationally representative sample.
“The research panel, known as the KnowledgePanel®, has been shown to be a statistically valid method for surveying and analyzing health indicators from a nationally-representative sample.25;26”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [25] Baker R Blumberg SJ Brick JM Research synthesis: AAPOR report on online panels Public Opin Q 2010 74 711 781
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [26] Wagner TH Baker LC Bundorf MK Singer S Use of the Internet for health information by the chronically ill Prev Chronic Dis 2004 1 A13 15670445 PMC1277953
Not checked in the free run.
- Premisecitation
96% of young adults aged 18-30 use some type of technology before bed.
“While nearly all research in this area has focused on children and adolescents, 96% of young adults in the 18-30 year-old range use some type of technology—such as cell phones (67%), computers (60%), and electronic music devices (43%)—before bed.4”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Gradisar M Wolfson AR Harvey AG Hale L Rosenberg R Czeisler CA The sleep and technology use of Americans: findings from the National Sleep Foundation’s 2011 Sleep in America poll J Clin Sleep Med 2013 9 1291 1299 24340291 PMC3836340 source
Not checked in the free run.
- Premisecitation
Other studies have suggested associations between social media use and sleep disturbance in Australia and China.
“Other studies have suggested associations between SM use and sleep disturbance in countries such as Australia and China,23;24”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [23] An J Sun Y Wan Y Chen J Wang X Tao F Associations between problematic internet use and adolescents’ physical and psychological symptoms: possible role of sleep quality J Addict Med 2014 8 282 287 25026104 source
Not checked in the free run.
- cites [24] Vernon L Barber BL Modecki KL Adolescent Problematic Social Networking and School Experiences: The Mediating Effects of Sleep Disruptions and Sleep Quality Cyberpsychol Behav Soc Netw 2015 18 386 392 26167837 source
Not checked in the free run.
- May combine two claims. 8
- Classified as probably not a substantial claim. 13
May combine two claims.
Findingown dataThe association with sleep disturbance was stronger for social media use frequency than for volume, with an adjusted odds ratio of about 3 versus about 2.
“It is interesting that associations with sleep disturbance were higher for the frequency variable than for the volume variable. For example, compared to those in the lowest quartile for frequency, those in the highest quartile had an AOR of about 3 for increased sleep disturbance, while for the volume variable the AOR was about 2.”
Quote in the paperSubstantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
May combine two claims.
Findingown dataIn bivariable analyses, both social media use volume and frequency were significantly related to sleep disturbance, with more use corresponding to greater disturbance.
“SM volume and frequency were significantly associated with sleep disturbance, with greater level of SM use associated with higher levels of disturbance.”
Quote in the paperSubstantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
May combine two claims.
Findingown dataIn the weighted sample, 42.6% had low sleep disturbance, 28.0% had medium sleep disturbance, and 29.4% had high sleep disturbance.
“Accounting for survey weights, 42.6% of our sample was in the low sleep disturbance group, 28.0% was in the medium disturbance group, and 29.4% was in the high sleep disturbance group.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe PROMIS Sleep Disturbance assessment provides a global measure of sleep rather than focusing on a particular sleep disorder.
“The PROMIS Sleep Disturbance assessment, in particular, reflects a global measure of sleep, rather than focusing on a particular disorder.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [31] Buysse DJ Yu L Moul DE Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments Sleep 2010 33 781 792 20550019 PMC2880437 source
Not checked in the free run.
- cites [32] Yu L Buysse DJ Germain A Development of short forms from the PROMIS Sleep Disturbance and Sleep-Related Impairment item banks Behav Sleep Med 2012 10 6 24 22250775 PMC3261577 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrevious work has described the development and psychometric testing of the PROMIS sleep disturbance measure among adults aged 18 and older.
“Previous work has described the development and psychometric testing of the PROMIS sleep disturbance measure among individuals age 18 and older. 31;32”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [31] Buysse DJ Yu L Moul DE Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments Sleep 2010 33 781 792 20550019 PMC2880437 source
Not checked in the free run.
- cites [32] Yu L Buysse DJ Germain A Development of short forms from the PROMIS Sleep Disturbance and Sleep-Related Impairment item banks Behav Sleep Med 2012 10 6 24 22250775 PMC3261577 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe decision to include all covariates in the models was made a priori based on their possible impact on sleep disturbance.
“The decision to include all covariates in the models was made a priori based on their possible impact on sleep disturbance.35”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] Grandner MA Patel NP Gehrman PR Who gets the best sleep? Ethnic and socioeconomic factors related to sleep complaints Sleep Med 2010 11 470 478 20388566 PMC2861987
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPROMIS measures are highly reliable and precise for patient-reported health, and their short forms maintain precision across a range of domains.
“PROMIS measures have the advantage of being highly reliable and precise measures of patient-reported health, utilizing short forms of its measures to lessen participant burden while maintaining precision across a range of domains.33;34”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [33] NIH About PROMIS. PROMIS website. 2015 12 22 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [34] NIH Selecting an Instrument. PROMIS website. 2015 12 22 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningMissing data accounted for between 0.4% and 1.6% depending on the analysis.
“Thus, missing data accounted for between 0.4-1.6%, depending on the analysis.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationTwo recent reviews found an inverse association between electronic media use and sleep parameters such as longer time to fall asleep, delayed bedtime, and reduced total sleep time.
“Two recent reviews in this area demonstrated an inverse association between electronic media use and sleep parameters such as longer time to fall asleep, delayed bedtime, and reduced total sleep time.20;21”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [20] Cain N Gradisar M Electronic media use and sleep in school-aged children and adolescents: A review Sleep Med 2010 11 735 742 20673649 source
Not checked in the free run.
- cites [21] Hale L Guan S Screen time and sleep among school-aged children and adolescents: a systematic literature review Sleep Med Rev 2015 21 50 58 25193149 PMC4437561 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationPeople who have difficulty sleeping may subsequently spend more time using social media.
“Alternatively, those who have difficulty sleeping may subsequently spend more time using SM.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [37] Tavernier R Willoughby T Sleep problems: predictor or outcome of media use among emerging adults at university? J Sleep Res 2014 23 389 396 24552437 source
Not checked in the free run.
- cites [38] Eggermont S Van den Bulck J Nodding off or switching off? The use of popular media as a sleep aid in secondary-school children J Paediatr Child Health 2006 42 428 433 16898880 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenPrimary analyses used survey weights calculated by GfK and applied with a post-stratification adjustment based on socio-demographic benchmark distributions.
“We conducted all primary analyses using survey weights, which were calculated by GfK and applied using a post-stratification adjustment based on socio-demographic benchmark distributions.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataDiagnostics were performed to confirm that the data met the proportional-odds assumption required for ordered logistic regression.
“We first conducted appropriate diagnostics to ensure that the data satisfied the proportional-odds assumption, which is necessary to conduct ordered logistic regression.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningBecause the data are cross-sectional, the study cannot determine whether social media use contributes to sleep disturbance, whether sleep disturbance contributes to social media use, or both.
“Because the findings presented here are cross-sectional, it is not possible to determine whether SM use contributes to sleep disturbance, sleep disturbance contributes to SM use, or both.”
- Also stated in: § Strengths and Limitations · plain
“As noted above, one important limitation of the cross-sectional nature of the data is that we cannot determine the direction of association between SM use and sleep disturbance, nor can we attribute causality to any effect.”
Quote in the paperSubstantial claim- Also stated in: § Strengths and Limitations · plain
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataOf 1796 respondents (55.2%), 1788 (54.9%) had complete sleep data and were included in the analysis.
“Responses were received from 1796 participants (55.2%), of which 1,788 participants (54.9%) had complete sleep data and were included in the analysis.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductreasoningThe study relied on retrospective self-report of social media use and sleep, and intensive validation with formal sleep studies or ecological momentary assessment was not feasible.
“It was a necessary limitation that we relied on participants’ retrospective report of their SM use and sleep in completing the survey. For example, in this large population it was not feasible to intensively validate sleep patterns using formal sleep studies or to validate SM use with ecological momentary assessment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningDifficulty sleeping may increase social media use, which may then worsen sleep problems.
“It may also be that both of these hypotheses are true—difficulty sleeping may lead to increased use of SM, which may in turn lead to more problems sleeping.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA study of 11-13 year olds in the Midlands region of the United Kingdom found that television, music, and video games were more substantially associated with sleep problems than social media.
“However, another study among 11-13 year olds in the Midlands region of the United Kingdom demonstrated that, compared with SM, use of other technologies such as television, music, and video games was more substantially associated with sleep problems.22”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22] Arora T Broglia E Thomas EN Taheri S Associations between specific technologies and adolescent sleep quantity, sleep quality, and parasomnias Sleep Med 2014 15 240 247 24394730 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
Interpretationown dataThe observed strong link between social media use and sleep disturbance carries meaningful clinical implications for young adults' health and well-being.
“The strong association between SM use and sleep disturbance has important clinical implications for the health and well-being of young adults.”
Quote in the paperNot a substantial claim- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
The paper relies on this without support.
Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
“It was a necessary limitation that we relied on participants’ retrospective report of their SM use and sleep in completing the survey.”
The paper relies on this without support.
The 11 social media platforms assessed capture the relevant social media use for this population.
“The frequency variable was adopted from a Pew Internet Research survey asking participants to indicate how frequently they visit or use each of the 11 most popular SM platforms: Facebook, YouTube, Twitter, Google Plus, Instagram, Snapchat, Reddit, Tumblr, Pinterest, Vine, and LinkedIn.”
The paper relies on this without support.
The sample remains nationally representative after excluding participants with missing data.
“Responses were received from 1796 participants (55.2%), of which 1,788 participants (54.9%) had complete sleep data and were included in the analysis.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previous studies have found a significant association between social media use and measures of sleep disturbance (including long sleep onset latency and difficulty falling asleep) when controlling for age and sex, sociodemographic measures, and sleep hygiene behaviours.
“Previous studies have found a significant association between social media use and measures of sleep disturbance (including long sleep onset latency and difficulty falling asleep) when controlling for: age and sex19; sociodemographic measures21 and sleep hygiene behaviours.15”
From Social media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study (2019) · cites this paper as [21]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredUK adolescents' social media use is generally heavier than that of young adults.
“This indicates generally heavier social media use compared with young adults21 and provides a current normative profile for UK adolescents.”
From Social media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study (2019) · cites this paper as [21]
Social network sites use is associated with poorer sleep quality among young adults.
“Numerous studies have reported a strong association between social network sites use and poorer sleep quality among young adults [20–22].”
From A serial mediation model of social media addiction and college students' academic engagement: the role of sleep quality and fatigue (2023) · cites this paper as [20]
Higher overall social media use is linked to poorer sleep outcomes, including shorter sleep duration, later bedtimes and poorer sleep quality.
“These findings have supported links between higher overall social media use and poorer sleep outcomes, including shorter sleep duration, later bedtimes and poorer sleep quality [17,26].”
From Understanding Links Between Social Media Use, Sleep and Mental Health: Recent Progress and Current Challenges (2019) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Complete-data count conflicts with social-media counts
MinorThe paper says 1,788 participants had complete data for the independent and dependent variables, but then says only 1,768 reported social-media volume and 1,781 reported social-media frequency. Both social-media measures are described as independent variables.
“There were complete data for the independent and dependent variables for 1788 participants.”
“As is noted in Table 1, 1768 participants reported SM volume (minutes per day), while 1781 reported SM frequency (visits per week).”
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“(Kaplan and Haenlein, 2010) The rapidly growing rate of SM use in recent years (Duggan et al., 2015) raises concern that SM usemay adversely affect sleep quality and may displace total amount of sleep (Zimmerman, 2008).”
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“Sleep and circadian functioning are essential to promoting good health (Buysse, 2014).”
BackgroundBehavioral and Physiological Consequences of Sleep Restriction
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Hale, L., & Guan, S. (2015) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2014.07.007 · Cited by 1,468 · cited 1× · see citing works
“This is because many forms of SM involve interactive screen time, whichmay bemore stimulating and engaging, and thus potentially detrimental to sleep – compared with more passive activities such as watching television and reading books (Hale and Guan, 2015).”
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“Third, bright light emitted by SM devices may delay circadian rhythms (Chang et al., 2015).”
BackgroundInsufficient Sleep in Adolescents and Young Adults: An Update on Causes and Consequences
Owens, J., ADOLESCENT SLEEP WORKING GROUP, COMMITTEE ON ADOLESCENCE, Au, R., Carskadon, M., Millman, R., Wolfson, A., Braverman, P. K., Adelman, W. P., Breuner, C. C., et al. (2014) · Pediatrics · doi:10.1542/peds.2014-1696 · Cited by 1,413 · cited 1× · see citing works
“This includes biological changes in the accumulation of homeostatic sleep pressure (the likelihood of falling asleep), increasing academic and vocational demands, and use of substances such as alcohol and caffeine (Moore and Meltzer, 2008; Hershner and Chervin, 2014; Owens, 2014; Millman, 2005).”
BackgroundElectronic media use and sleep in school-aged children and adolescents: A review
Cain, N., & Gradisar, M. (2010) · Sleep Medicine · doi:10.1016/j.sleep.2010.02.006 · Cited by 1,376 · see citing works
Yu, L., Buysse, D. J., Germain, A., Moul, D. E., Stover, A., Dodds, N. E., Johnston, K. L., & Pilkonis, P. A. (2012) · Behavioral Sleep Medicine · doi:10.1080/15402002.2012.636266 · Cited by 1,241 · cited 3× · see citing works
“We assessed sleep disturbance using four items from the Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep Disturbance measure (Buysse et al., 2010; Yu et al., 2012), which assessed problems with sleep, difficulty falling asleep, whether sleep was refreshing, and sleep quality over the past 7 day…”
BackgroundMethodBuysse, D. J., Yu, L., Moul, D. E., Germain, A., Stover, A., Dodds, N. E., Johnston, K. L., Shablesky-Cade, M. A., & Pilkonis, P. A. (2010) · Sleep · doi:10.1093/sleep/33.6.781 · Cited by 850 · cited 3× · see citing works
“We assessed sleep disturbance using four items from the Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep Disturbance measure (Buysse et al., 2010; Yu et al., 2012), which assessed problems with sleep, difficulty falling asleep, whether sleep was refreshing, and sleep quality over the past 7 day…”
BackgroundMethodCauses and consequences of sleepiness among college students
Hershner, S. D., & Chervin, R. D. (2014) · Nature and Science of Sleep · doi:10.2147/nss.s62907 · Cited by 818 · cited 1× · see citing works
“This includes biological changes in the accumulation of homeostatic sleep pressure (the likelihood of falling asleep), increasing academic and vocational demands, and use of substances such as alcohol and caffeine (Moore and Meltzer, 2008; Hershner and Chervin, 2014; Owens, 2014; Millman, 2005).”
BackgroundMetabolic consequences of sleep and sleep loss
Van Cauter, E., Spiegel, K., Tasali, E., & Leproult, R. (2008) · Sleep Medicine · doi:10.1016/s1389-9457(08)70013-3 · Cited by 812 · cited 1× · see citing works
“For example, insufficient sleep negatively affects cognitive performance, mood, immune function, cardiovascular risk, weight, and metabolism (Banks and Dinges, 2007; Grandner et al., 2010a; Van et al., 2008).”
BackgroundResearch synthesis: AAPOR report on online panels
Prepared for the AAPOR Executive Council by a Task Force operating under the auspices of the AAPOR Standards Committee, W. M. I., Baker, R., Blumberg, S. J., Brick, J. M., Couper, M. P., Courtright, M., Dennis, J. M., Dillman, D., Frankel, M. R., Garland, P., et al. (2010) · Public Opinion Quarterly · doi:10.1093/poq/nfq048 · Cited by 584 · cited 1× · see citing works
“The research panel, known as the KnowledgePanel®, has been shown to be a statistically valid method for surveying and analyzing health indicators from a nationally-representative sample (Baker et al., 2010; Wagner et al., 2004).”
MethodWho gets the best sleep? Ethnic and socioeconomic factors related to sleep complaints
Grandner, M. A., Patel, N. P., Gehrman, P. R., Xie, D., Sha, D., Weaver, T., & Gooneratne, N. (2010) · Sleep Medicine · doi:10.1016/j.sleep.2009.10.006 · Cited by 562 · cited 2× · see citing works
“The decision to include all covariates in themodels wasmade a priori based on their possible impact on sleep disturbance (Grandner et al., 2010b).”
BackgroundGradisar, M., Wolfson, A. R., Harvey, A. G., Hale, L., Rosenberg, R., & Czeisler, C. A. (2013) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.3272 · Cited by 498 · cited 5× · see citing works
“Among young adults ages 19–29, 67% report not getting enough sleep to function properly (Gradisar et al., 2013).”
BackgroundInfluentialGreenwood, S., Perrin, A., & Duggan, M. (2016) · Cited by 496 · see citing works
Sleep duration and health in young adults
Steptoe, A. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.16.1689 · Cited by 425 · see citing works
Excessive Sleepiness in Adolescents and Young Adults: Causes, Consequences, and Treatment Strategies
Millman, R. P., & Working Group on Sleepiness in Adolescents/Young Adults; and AAP Committee on Adolescence (2005) · Pediatrics · doi:10.1542/peds.2005-0772 · Cited by 372 · see citing works
Grandner, M. A., Patel, N. P., Gehrman, P. R., Perlis, M. L., & Pack, A. I. (2010) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2009.08.001 · Cited by 291 · see citing works
Sleep loss and daytime sleepiness in the general adult population of Japan
Liu, X., Uchiyama, M., Kim, K., Okawa, M., Shibui, K., Kudo, Y., Doi, Y., Minowa, M., & Ogihara, R. (2000) · Psychiatry Research · doi:10.1016/s0165-1781(99)00119-5 · Cited by 278 · cited 1× · see citing works
“Sleep disturbance and insufficient sleep duration are associatedwith daytime sleepiness (Jiang et al., 2011; Liu et al., 2000) and a range of poor health outcomes.”
BackgroundArora, T., Broglia, E., Thomas, G. N., & Taheri, S. (2014) · Sleep Medicine · doi:10.1016/j.sleep.2013.08.799 · Cited by 261 · cited 1× · see citing works
“However, another study among 11–13 year olds in the Midlands region of the United Kingdom demonstrated that, compared with SM, use of other technologies such as television, music, and video games was more substantially associated with sleep problems (Arora et al., 2014).”
BackgroundArnett, J. J. (2006) · doi:10.1093/acprof:oso/9780195309379.001.0001 · Cited by 254 · see citing works
The sleepy adolescent: Causes and consequences of sleepiness in teens
Moore, M., & Meltzer, L. J. (2008) · Paediatric Respiratory Reviews · doi:10.1016/j.prrv.2008.01.001 · Cited by 235 · cited 1× · see citing works
“This includes biological changes in the accumulation of homeostatic sleep pressure (the likelihood of falling asleep), increasing academic and vocational demands, and use of substances such as alcohol and caffeine (Moore and Meltzer, 2008; Hershner and Chervin, 2014; Owens, 2014; Millman, 2005).”
BackgroundNodding off or switching off? The use of popular media as a sleep aid in secondary‐school children
Eggermont, S., & Van den Bulck, J. (2006) · Journal of Paediatrics and Child Health · doi:10.1111/j.1440-1754.2006.00892.x · Cited by 196 · see citing works
Sleep problems: Predictor or outcome of media use among emerging adults at university?
Tavernier, R., & Willoughby, T. (2014) · Journal of Sleep Research · doi:10.1111/jsr.12132 · Cited by 190 · see citing works
National Health Interview Survey
&NA; (1997) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-199706001-00029 · Cited by 145 · see citing works
Use of the Internet for health information by the chronically ill.
(2004) · PubMed Central · Cited by 118 · see citing works
Sleep duration and self-rated health: The national health interview survey 2008
Shankar, A., Charumathi, S., & Kalidindi, S. (2011) · SLEEP · doi:10.5665/sleep.1232 · Cited by 108 · see citing works
Vernon, L., Barber, B. L., & Modecki, K. L. (2015) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2015.0107 · Cited by 104 · cited 2× · see citing works
“Other studies have suggested associations between SM use and sleep disturbance in countries such as Australia and China (An et al., 2014; Vernon et al., 2015), but there is a need to explore these findings among large, nationally-representative populations in the U.”
BackgroundEffect of chronic sleep restriction on sleepiness and working memory in adolescents and young adults
Jiang, F., VanDyke, R. D., Zhang, J., Li, F., Gozal, D., & Shen, X. (2011) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2011.570252 · Cited by 92 · cited 1× · see citing works
“Sleep disturbance and insufficient sleep duration are associatedwith daytime sleepiness (Jiang et al., 2011; Liu et al., 2000) and a range of poor health outcomes.”
BackgroundAn, J., Sun, Y., Wan, Y., Chen, J., Wang, X., & Tao, F. (2014) · Journal of Addiction Medicine · doi:10.1097/adm.0000000000000026 · Cited by 90 · cited 2× · see citing works
“Other studies have suggested associations between SM use and sleep disturbance in countries such as Australia and China (An et al., 2014; Vernon et al., 2015), but there is a need to explore these findings among large, nationally-representative populations in the U.”
BackgroundChildren's Media Use and Sleep Problems: Issues and Unanswered Questions. Research Brief.
Zimmerman, F. J. (2008) · Cited by 17 · see citing works
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Children and Adolescents and Digital Media
Reid Chassiakos, Y. (., Radesky, J., Christakis, D., Moreno, M. A., Cross, C., COUNCIL ON COMMUNICATIONS AND MEDIA, Hill, D., Ameenuddin, N., Hutchinson, J., Levine, A., et al. (2016) · Pediatrics · doi:10.1542/peds.2016-2593 · Cited by 1,340 · see citing works
Twenge, J. M., Martin, G. N., & Spitzberg, B. H. (2019) · Psychology of Popular Media Culture · doi:10.1037/ppm0000203 · Cited by 703 · see citing works
Media Use in School-Aged Children and Adolescents
COUNCIL ON COMMUNICATIONS AND MEDIA, Hill, D., Ameenuddin, N., Chassiakos, Y. (. R., Cross, C., Radesky, J., Hutchinson, J., Levine, A., Boyd, R., Mendelson, R., et al. (2016) · Pediatrics · doi:10.1542/peds.2016-2592 · Cited by 622 · see citing works
Social media? It's serious! Understanding the dark side of social media
Baccarella, C. V., Wagner, T. F., Kietzmann, J., & McCarthy, I. P. (2018) · European Management Journal · doi:10.1016/j.emj.2018.07.002 · Cited by 504 · see citing works
Wong, H. Y., Mo, H. Y., Potenza, M. N., Chan, M. N. M., Lau, W. M., Chui, T. K., Pakpour, A. H., & Lin, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17061879 · Cited by 512 · see citing works
“The relationship between SMA severity and sleep quality may reflect cognitive and emotional arousal when using social media before sleep, with increased arousal preventing sleep [60].”
BackgroundRecent increases in depressive symptoms among US adolescents: trends from 1991 to 2018
Keyes, K. M., Gary, D., O’Malley, P. M., Hamilton, A., & Schulenberg, J. (2019) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-019-01697-8 · Cited by 465 · see citing works
Interplay between social media use, sleep quality, and mental health in youth: a systematic review
Alonzo, R., Hussain, J., Stranges, S., & Anderson, K. K. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2020.101414 · Cited by 456 · see citing works
Kolhar, M., Kazi, R. N. A., & Alameen, A. A. M. (2021) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2021.01.010 · Cited by 397 · see citing works
“This can lead to a sedentary lifestyle and increase one’s risk of developing non-communicable diseases such as diabetes, obesity, and hypertension (Melkevik et al., 2015; Zou et al., 2019; Hu et al., 2001).”
BackgroundTwitter and Research: A Systematic Literature Review Through Text Mining
Karami, A., Lundy, M., Webb, F., & Dwivedi, Y. K. (2020) · IEEE Access · doi:10.1109/access.2020.2983656 · Cited by 286 · see citing works
“…Twitter-related issues such as investigating social media users’ preference for oral health information searching [196], analyzing the relationship between gender, personality, and Twitter addiction [197], sleep disturbance and social media use [198], and eating concerns and social media use [199].”
BackgroundYou, Y., Chen, Y., Wen, F., Li, X., Wang, R., Liu, J., & Ma, X. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1080782 · Cited by 243 · see citing works
The Negative Effects of Digital Technology Usage on Children’s Development and Health
Mustafaoğlu, R., Zirek, E., Yasacı, Z., & Özdi̇nçler, A. R. (2018) · Addicta The Turkish Journal on Addictions · doi:10.15805/addicta.2018.5.2.0051 · Cited by 246 · see citing works
Is boredom proneness related to social media overload and fatigue? A stress–strain–outcome approach
Whelan, E., Islam, A. K. M. N., & Brooks, S. L. (2020) · Internet Research · doi:10.1108/intr-03-2019-0112 · Cited by 239 · see citing works
“Fatigue can also be caused by poor sleep quality and emerging research links the extent of social media use to sleep curtailment (Levenson et al., 2016; Woods and Scott, 2016).”
BackgroundSedentary Behavior and Sleep Problems: a Systematic Review and Meta-Analysis
Yan, Y., Shin, J. C., Li, D., & An, R. (2016) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9609-0 · Cited by 228 · see citing works
Digital media use and sleep in late adolescence and young adulthood: A systematic review
Brautsch, L. A., Lund, L., Andersen, M. M., Jennum, P. J., Folker, A. P., & Andersen, S. (2023) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2022.101742 · Cited by 218 · see citing works
Tandon, A., Kaur, P., Dhir, A., & Mäntymäki, M. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106487 · Cited by 209 · see citing works
Scott, H., Biello, S. M., & Woods, H. C. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-031161 · Cited by 221 · see citing works
“15 17 21 In contrast, first establishing what constitutes typical use and then comparing outcomes for relatively higher or lower users against this reference point can support more meaningful conclusions.”
BackgroundResultThe Internet and Children’s Psychological Wellbeing
McDool, E., Powell, P., Roberts, J., & Taylor, K. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2019.102274 · Cited by 189 · see citing works
Dresp, B., & Hutt, A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19116910 · Cited by 182 · see citing works
Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2017) · Sleep · doi:10.1093/sleep/zsx113 · Cited by 177 · see citing works
Self-citationShimoga, S. V., Erlyana, E., & Rebello, V. (2019) · Journal of Medical Internet Research · doi:10.2196/14290 · Cited by 153 · see citing works
“While a high risk of poor-quality sleep or sleep disturbance is associated with frequent social media use among adolescents [11,12,14-16], there is some indication that sleeplessness precedes excessive media use [17,18].”
Effect of diurnal fasting on sleep during Ramadan: a systematic review and meta-analysis
Faris, M. E., Jahrami, H., Alhayki, F. A., Alkhawaja, N. A., Ali, A. M., Aljeeb, S. H., Abdulghani, I. H., & BaHammam, A. S. (2019) · Sleep And Breathing · doi:10.1007/s11325-019-01986-1 · Cited by 159 · see citing works
Social media addiction and burnout: The mediating roles of envy and social media use anxiety
Liu, C., & Ma, J. (2018) · Current Psychology · doi:10.1007/s12144-018-9998-0 · Cited by 152 · see citing works
Evers, K., Chen, S., Rothmann, S., Dhir, A., & Pallesen, S. (2020) · Journal of Adolescence · doi:10.1016/j.adolescence.2020.08.011 · Cited by 135 · see citing works
The dark side of social media: Stalking, online self‐disclosure and problematic sleep
Dhir, A., Talwar, S., Kaur, P., Budhiraja, S., & Islam, A. K. M. N. (2021) · International Journal of Consumer Studies · doi:10.1111/ijcs.12659 · Cited by 153 · see citing works
Family Digital Literacy Practices and Children’s Mobile Phone Use
Terras, M. M., & Ramsay, J. E. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01957 · Cited by 130 · see citing works
“Evidence indicates that the four parenting styles identified by Baumrind influence both the nature and number of internet mediation strategies used.”
Backgroundvan der Schuur, W. A., Baumgartner, S. E., & Sumter, S. R. (2018) · Health Communication · doi:10.1080/10410236.2017.1422101 · Cited by 135 · see citing works
“Whereas one study showed that SM frequency was related to more sleep disturbances, including more difficulty falling asleep and lower sleep quality (Levenson et al., 2016), other studies showed limited or no support for a negative relationship between SM use and sleep (Garett et al.”
BackgroundResultInfluentialKwok, C., Leung, P. Y., Poon, K.-C., & Fung, X. C. C. (2021) · Asian Journal of Social Health and Behavior · doi:10.4103/shb.shb_81_20 · Cited by 129 · see citing works
Loneliness in the lives of Danish adolescents: Associations with health and sleep
Eccles, A. M., Qualter, P., Madsen, K. R., & Holstein, B. E. (2020) · Scandinavian Journal of Public Health · doi:10.1177/1403494819865429 · Cited by 125 · see citing works
“Another limitation in relation to the measurement of sleep in the current study is the lack of other confounding variables – including social media and use of technology before bedtime – that are important in current discussion relating to sleep [51,52].”
BackgroundMammalian circadian systems: Organization and modern life challenges
Finger, A., & Kramer, A. (2021) · Acta Physiologica · doi:10.1111/apha.13548 · Cited by 117 · see citing works
Bhat, S., Pinto-Zipp, G., Upadhyay, H., & Polos, P. G. (2018) · Sleep Health · doi:10.1016/j.sleh.2017.12.004 · Cited by 114 · see citing works
“”(5) Similarly, there appears to be a clear association between increased ESM use and anxiety,(6) as well as depression, and frequency of texting and duration of ESM use both during the day and at bedtimehave been shown to be predictive of sleep disturbances.(8,10,11) Given its addictive nature, it is not surprising t…”
BackgroundChinoy, E. D., Duffy, J. F., & Czeisler, C. A. (2018) · Physiological Reports · doi:10.14814/phy2.13692 · Cited by 116 · see citing works
“…the evenings, as is typical among adolescents and young adults who frequently multitask using different LE-devices (Van den Bulck 2007; Foley et al. 2013; Gradisar et al. 2013; Arora et al. 2014; Gamble et al. 2014; Hale and Guan 2015; Hysing et al. 2015; Lemola et al. 2015; Levenson et al. 2016).”
BackgroundInfluentialAkdeniz, G., Kavakci, M., Gozugok, M., Yalcinkaya, S., Kucukay, A., & Sahutogullari, B. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00695 · Cited by 94 · see citing works
The relationship between social media use and sleep quality among undergraduate students
Garett, R., Liu, S., & Young, S. D. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1266374 · Cited by 111 · see citing works
Fanning, J. T., Porter, G. C., Awick, E. A., Ehlers, D. K., Roberts, S. A., Cooke, G. E., Burzynska, A. Z., Voss, M. W., Kramer, A. F., & McAuley, E. (2016) · Journal of Behavioral Medicine · doi:10.1007/s10865-016-9788-9 · Cited by 107 · see citing works
“For instance, in very recent years, a number of publications have drawn associations between greater social networking website usage, as well as higher levels of digital media usage before bedtime, with poorer sleep outcomes in young adults (Levenson et al., 2016; Orzech et al., 2016).”
Hughes, N., & Burke, J. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.03.047 · Cited by 101 · see citing works
Scott, H., & Woods, H. C. (2019) · Current Sleep Medicine Reports · doi:10.1007/s40675-019-00148-9 · Cited by 105 · see citing works
Malaeb, D. N., Salameh, P., Barbar, S., Awad, E., Haddad, C., Hallit, R. R., Sacre, H., Akel, M., Obeïd, S., & Hallit, S. R. (2020) · Perspectives In Psychiatric Care · doi:10.1111/ppc.12576 · Cited by 97 · see citing works
“84 On the other hand, some studies provided different hypotheses about the effect of the SM use on sleeping patterns.”
Marker, C., Gnambs, T., & Appel, M. (2017) · Educational Psychology Review · doi:10.1007/s10648-017-9430-6 · Cited by 94 · see citing works
Samuelsson, L. B., Bovbjerg, D. H., Roecklein, K. A., & Hall, M. H. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.10.011 · Cited by 93 · see citing works
“The average human in industrialized societies today is regularly exposed to a significant degree of LAN and chronodisruption, regardless of shift work status, particularly in relation to the recent surge in technological availability and nighttime use (Vijakkhana et al., 2015; Levenson et al., 2016) and overall countr…”
BackgroundThe dark side of mobile money: Perspectives from an emerging economy
Mogaji, E., & Nguyen, N. P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122045 · Cited by 93 · see citing works
The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review
Yu, D. J., Wing, Y. K., Li, T. M. H., & Chan, N. Y. (2024) · Current Psychiatry Reports · doi:10.1007/s11920-024-01481-9 · Cited by 140 · see citing works
Salari, N., Zarei, H., Hosseinian-Far, A., Rasoulpoor, S., Shohaimi, S., & Mohammadi, M. (2025) · Journal of Public Health · doi:10.1007/s10389-023-02012-1 · Cited by 91 · see citing works
“Sleep disorders are also associated with an increased risk of metabolic disorders such as obesity, hypertension, and diabetes (Hershner and Chervin 2014; Knutson and Cau-ter 2008; Levenson et al. 2016).”
Identifying drivers for bedtime social media use despite sleep costs: The adolescent perspective
Scott, H., Biello, S. M., & Woods, H. C. (2019) · Sleep Health · doi:10.1016/j.sleh.2019.07.006 · Cited by 95 · see citing works
Hjetland, G. J., Skogen, J. C., Hysing, M., & Sivertsen, B. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.794307 · Cited by 92 · see citing works
“These findings suggest a significant role of screen use in relation to students’ sleep quantity and quality, and are in line with previous studies on students and young adults (14, 15, 24).”
BackgroundResultAlbqoor, M. A., & Shaheen, A. M. (2020) · Sleep And Breathing · doi:10.1007/s11325-020-02188-w · Cited by 86 · see citing works
Sampasa‐Kanyinga, H., Hamilton, H. A., & Chaput, J. (2018) · Acta Paediatrica · doi:10.1111/apa.14210 · Cited by 84 · see citing works
“(20) found that heavy use of social media was significantly associated with greater odds of sleep disturbances.”
BackgroundZhuang, J., Mou, Q., Zheng, T., Gao, F., Zhong, Y., Lu, Q., Gao, Y., & Zhao, M. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-04799-5 · Cited by 77 · see citing works
Primack, B. A., & Escobar-Viera, C. G. (2017) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2016.12.007 · Cited by 71 · see citing works
“Researchers examined a nationally representative sample of young adults and found that increased SM use was independently associated with poor sleep.(74) Sleep disturbance was assessed using the brief PROMIS sleep disturbance measure.”
MethodResultSelf-citationDas‐Friebel, A., Lenneis, A., Realo, A., Sanborn, A., Tang, N. K. Y., Wolke, D., von Mühlenen, A., & Lemola, S. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13326 · Cited by 71 · see citing works
“On the other hand, it has been linked to a range of negative consequences, for example to poorer sleep (Levenson et al., 2016, 2017), lower affective wellbeing (Sagioglou & Greitemeyer, 2014; Weinstein, 2018), and poorer mental health (Keles et al., 2019).”
BackgroundDigital Technology and Media Use by Adolescents: Latent Class Analysis
Moreno, M. A., Binger, K., Zhao, Q., Eickhoff, J., Minich, M., & Uhls, Y. T. (2022) · JMIR Pediatrics and Parenting · doi:10.2196/35540 · Cited by 66 · see citing works
“Previous studies have illustrated that digital technology and media use is associated with negative outcomes such as impaired sleep [4-6], decreased physical activity [5,7,8], problematic internet use (PIU) [9-11], and risk of depression [12].”
BackgroundSocial media use of adolescents who died by suicide: Lessons from a psychological autopsy study
Balt, E., Mérelle, S., Robinson, J., Popma, A., Creemers, D., van den Brand, I., van Bergen, D., Rasing, S., Mulder, W., & Gilissen, R. (2023) · Child and Adolescent Psychiatry and Mental Health · doi:10.1186/s13034-023-00597-9 · Cited by 66 · see citing works
“International evidence reports associations between the frequency and volume of social media use and sleep disturbance [12] as well as loneliness and depression [13, 14], and highlights the risks of upward social comparison [15–17].”
BackgroundPrevalence of smartphone addiction and its correlates in a sample of Egyptian university students
Okasha, T. A., Saad, A. E., Ibrahim, I., ElHabiby, M. M., Khalil, S. A., & Morsy, M. H. A. (2021) · International Journal of Social Psychiatry · doi:10.1177/00207640211042917 · Cited by 63 · see citing works
“of 64% 57%, for sleep disturbance respectively among American university students (Becker et al., 2018; Levenson et al., 2016) and in Arab countries studies showed similar rates as in Egypt 53% (Elwasify et al.”
ResultInfluentialAzhari, A., Toms, Z., Pavlopoulou, G., Esposito, G., & Dimitriou, D. (2022) · Acta Psychologica · doi:10.1016/j.actpsy.2022.103706 · Cited by 70 · see citing works
“Beyond normative social media activities, two studies on 12-to 18-year-old adolescents and youths by Vernon et al. (2015) and Levenson et al. (2016) respectively demonstrated that excessive social media usage, in particular, aggravated the quality of sleep.”
“Leave your smartphone out of bed”: quantitative analysis of smartphone use effect on sleep quality
Kheirinejad, S., Visuri, A., Ferreira, D., & Hosio, S. J. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-022-01694-w · Cited by 67 · see citing works
Current trends in digital media: How and why teens use technology
Chassiakos, Y. R., & Stager, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817319-0.00002-5 · Cited by 57 · see citing works
Digital media use and subsequent self-harm during a 1-year follow-up of Chinese adolescents
Wang, L., Liu, X., Liu, Z., & Jia, C.-X. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.05.066 · Cited by 54 · see citing works
Kaur, P., Dhir, A., Alkhalifa, A. K., & Tandon, A. (2021) · Internet Research · doi:10.1108/intr-04-2020-0187 · Cited by 55 · see citing works
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“It has been found that PSMU is positively associated with depression, anxiety, psychological stress, sleep disorders, and cyber victimization (Byrne et al. 2018; Keles et al. 2019; Levenson et al. 2016).”
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Facebook use and sleep quality: Light interacts with socially induced alertness
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“The light emitted from these devices reinforces the message to the brain to stay active and awake (Levenson et al. 2016).”
BackgroundThe influence of parents and peers on adolescents’ problematic social media use revealed
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“Yet, research has also highlighted potential dark sides of SM, with studies linking greater time on SM to a myriad of negative outcomes, including depression [4,5]), body image concerns [6,7]), sleep disturbance [8,9], alcohol use [10,11], misperceived health behavior norms [12,13], and cyberbullying victimization [14…”
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“Although the internet, social media, and online gaming offer numerous positive opportunities for connection, an increasing number of incidents have involved excessive/pathological use that may cause stress and lead to behavioral addiction, increased nondirectional social comparisons and loneliness, low self-esteem, lo…”
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Sedentary Behavior and Lack of Physical Activity among Children in Indonesia
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“A study published in the Journal of Adolescence found that excessive use of electronic devices, especially social media, was associated with poorer sleep quality and increased symptoms of depression and anxiety in adolescents [44].”
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“However, digital technology use has also been associated with negative health outcomes including impaired sleep [3-5], decreased physical activity [4,6,7], problematic internet use [8-10], and risk for depression [11,12].”
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The impact of stress on sleep quality: a mediation analysis based on longitudinal data
Zhang, J., Xiang, S., Li, X., Tang, Y., & Hu, Q. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1431234 · Cited by 49 · see citing works
“When sleep quality deteriorates, individuals’ coping abilities may weaken, making them more prone to falling into a cycle of emotion-focused coping strategies and mobile phone dependency (Levenson et al., 2016).”
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“The share of teenagers using Facebook has decreased rapidly in the United States, with YouTube and other online social media being more commonly used [5].”
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- Authors
- Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Primack, Brian A.
- Venue
- Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 85
- Pages
- 36-41
- ISSN
- 0091-7435 (ISSN-L)
- PMC
- PMC4857587
- OpenAlex
- W2235566653
- Semantic Scholar
- 5e2c889b53c3b0bda312ee55d2aa24306f465416
- Cited by
- 447
- References
- 33
- Citations by year
- 2026: 53 · 2025: 59 · 2024: 60 · 2023: 55 · 2022: 47 · 2021: 48 · 2020: 50 · 2019: 32 · 2018: 19 · 2017: 12 · 2016: 11
- FWCI
- 35.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01-CA140150, T32-HL082610)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Keywords
- social media use, sleep disorders, young adults, social media use frequency, sleep health
- MeSH terms
- Patient Reported Outcome Measures, Adult, Female, Humans, Longitudinal Studies, Male, Surveys and Questionnaires, Sleep Wake Disorders, Time Factors, United States, Chi-Square Distribution, Logistic Models, Young Adult, Social Media
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