The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index; none of the 21 sources its claims cite is checked for support yet.
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.
Overview
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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.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Multivariable Analyses · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Social Media Use
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Social Media Use
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Bivariable Analyses
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Bivariable Analyses
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This may indicate that the frequency of visits is a better predictor of sleep difficulty than overall SM time.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
What the sources say
- Not checked yetcites [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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- It may state two separate claims as one. 8
- Probably not a substantial claim. 13
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SM volume and frequency were significantly associated with sleep disturbance, with greater level of SM use associated with higher levels of disturbance.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Bivariable Analyses
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Sleep
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The PROMIS Sleep Disturbance assessment, in particular, reflects a global measure of sleep, rather than focusing on a particular disorder.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Measures
What the sources say
- Not checked yetcites [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 yetcites [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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Previous work has described the development and psychometric testing of the PROMIS sleep disturbance measure among individuals age 18 and older. 31;32”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Measures
What the sources say
- Not checked yetcites [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 yetcites [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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The decision to include all covariates in the models was made a priori based on their possible impact on sleep disturbance.35”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Analysis
What the sources say
- Not checked yetcites [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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“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”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Measures
What the sources say
- Not matchedcites [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.
- Not matchedcites [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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, missing data accounted for between 0.4-1.6%, depending on the analysis.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Participants
Strength:plain· not yet calibratedn = 1,788Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > INTRODUCTION
What the sources say
- Not checked yetcites [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 yetcites [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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Alternatively, those who have difficulty sleeping may subsequently spend more time using SM.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
What the sources say
- Not checked yetcites [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 yetcites [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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Analysis
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We first conducted appropriate diagnostics to ensure that the data satisfied the proportional-odds assumption, which is necessary to conduct ordered logistic regression.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Analysis
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § Strengths and Limitations · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Procedures
Strength:plain· not yet calibratedn = 1,788Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION > Strengths and Limitations
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > INTRODUCTION
What the sources say
- Not checked yetcites [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
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The strong association between SM use and sleep disturbance has important clinical implications for the health and well-being of young adults.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Discussion
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- 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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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).”
Citations
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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).”
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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×
“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).”
BackgroundChang, A.-M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418490112 · Cited by 1,430 · cited 1×
“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×
“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
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×
“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×
“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…”
BackgroundMethod
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Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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