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Published article available at: Primack BA, Shensa A, Sidani JE, Whaite EO, Lin L, Rosen D,
Colditz JB, Radovic A, Miller E. Social media use and perceived social isolation among young
adults in the U.S. American Journal of Preventive Medicine. 2017;53(1):1-8. DOI:
10.1016/j.amepre.2017.01.010
Social Media Use and Social Isolation among Young Adults in the United States
Brian A. Primack, MD, PhD a,b,c
Ariel Shensa, MA a,b
Jaime E. Sidani, PhD, MPH a,b
Erin O. Whaite, BS a,d
Liu yi Lin, MD a,e
Daniel Rosen, PhD a,f
Jason Colditz, MEda,b
Ana Radovic, MD, MSc a,c
Elizabeth Miller, MD, PhD a,c
a Center for Research on Media, Technology, and Health, University of Pittsburgh, Pittsburgh, PA
b Division of General Internal Medicine, Department of Medicine, University of Pittsburgh
School of Medicine, Pittsburgh, PA
c Division of Adolescent Medicine, Department of Pediatrics, University of Pittsburgh School of
Medicine, Pittsburgh, PA
d University of Pittsburgh School of Medicine, Pittsburgh, PA
e UPMC McKeesport Family Medicine and Psychiatry Residency Program, Pittsburgh, PA
f School of Social Work, University of Pittsburgh, Pittsburgh, PA
Corresponding Author:
Brian A. Primack, M.D., Ph.D.
230 McKee Place Suite 600
Pittsburgh, PA 15213
bprimack@pitt.edu
412-586-9789 (phone); 412-692-4838 (fax)
Words in abstract: 248
Words in text: 2996
References: 35
Figures: 0
Tables: 3
Conflict of Interest Statement: This research was funded by the National Cancer Institute at the
National Institutes of Health (R01-CA140150), awarded to Dr. Primack. The funding source had
1
no role in the study design, collection, analysis, interpretation of data, writing of the manuscript,
or the decision to submit this manuscript for publication.
Financial Disclosures: No financial disclosures were reported by the authors of this manuscript.
2
ABSTRACT
Introduction. Social isolation is associated with substantial morbidity and mortality. Social
media platforms, which are commonly used by young adults, seem to offer an opportunity for
amelioration of social isolation. We aimed to assess associations between social media use
(SMU) and social isolation among U.S. young adults.
Methods. We surveyed a nationally-representative sample of U.S. young adults ages 19-32 in
October-November 2014. They were recruited using a sampling frame that represented 97% of
the U.S. population. SMU was assessed using both time and frequency of using 11 social media
platforms, including Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest,
Tumblr, Vine, Snapchat, and Reddit. Our dependent variable was SI as measured using the
Patient-Reported Outcomes Measurement Information System (PROMIS) scale. We used
ordered logistic regression to assess associations between SMU and SI while controlling for
eight covariates. Data analysis occurred in 2015.
Results. In fully-adjusted multivariable models that included survey weights, compared to those
in the lowest quartile for SMU time, participants in the highest quartile had twice the odds of
having greater SI (AOR=2.0, 95% CI=1.4-2.8). Similarly, compared with those in the lowest
quartile, those in the highest quartile of SMU frequency had more than three times the odds of
having greater SI (AOR=3.4, 95% CI=2.3-5.1). Associations were linear (P<.001 for all), and
results were robust to all sensitivity analyses.
3
Conclusions. Contrary to our hypothesis, young adults with high SMU seem to be more, and not
less, socially isolated. Future research should focus on determining directionality and elucidating
reasons for these associations.
4
INTRODUCTION
Social isolation can be defined as a state in which an individual lacks a sense of social belonging,
true engagement with others, and fulfilling relationships.1 Social isolation is associated with
increased morbidity and mortality from both physical and emotional health conditions.2 Recently,
social isolation has been compared to obesity in terms of their potential association with negative
health effects.3 Humans thrive on social interactions, and when socially isolated, physical and
emotional problems ensue. For example, social isolation is known to be associated with
unnatural increases in cortisol patterns, and these aberrant patterns can disrupt sleep, immune
function, and brain functioning.2,4 Other research suggests that social isolation affects gene
expression in such a way as to negatively impact vascular and mental health.5,6 In view of these
underlying mechanisms, it is not surprising that social isolation can substantially increase the risk
for all-cause mortality.7
Recent increases in social media use (SMU) via platforms such as Facebook, Reddit, and Tumblr
may provide opportunities for alleviation of social isolation. For example, if people are isolated
due to their physical environment and/or routine, they may be able to access supportive networks
online. Similarly, SMU may facilitate forming connections among people with similar health
needs. For example, they may help individuals with rare or stigmatizing conditions to form
valuable support systems that might otherwise be difficult to establish. SMU has increased in
particular among young adults, who are navigating critical stages of social identity formation.8
As many as 90% of young adults in the U.S. use social media, and the majority of users visit
these sites at least once a day.9
5
However, it may be that SMU in this population may counter-intuitively increase social isolation.
For example, frequent SM users may substitute SMU for face-to-face social interactions.
Similarly, frequent exposure to highly curated, unrealistic portrayals on social media may give
people the impression that others are living happier, more connected lives, which may make
people feel more socially isolated in comparison.10 In empiric studies, SMU—described both in
terms of time and frequency of use—have been associated with constructs such as depression.11
To our knowledge, though, the association between SMU and social isolation has not been
assessed in a large-scale nationally-representative study.
Therefore, we aimed to assess multivariable associations between social media use and social
isolation in a nationally-representative sample of U.S. young adults. We focused on young adults
because of the particular increase in SMU in this population.9 Additionally, social isolation can
begin during emerging adulthood, when people are naturally leaving more structured
environments such as school, military training, or home of origin.12 Because of the seeming
strength of SMU to provide social support, we hypothesized that increased SMU would be
associated with lower social isolation.
6
METHODS
Design, Participants, and Setting
We surveyed a nationally-representative sample of U.S. young adults aged 19 to 32 regarding
social media use and social isolation. We drew our sample from a research panel maintained by
Growth from Knowledge (GfK), which recruited participants via random digit dialing and
address-based sampling.13 Using this process, they maintained a sampling frame including over
97% of the U.S. population.13 GfK’s sampling strategy has been shown to be a statistically valid
method for surveying and analyzing health indicators from a nationally representative sample.14,15
From October 2014 to November 2014, our Web-based survey was sent via email to a random
sample of 3,048 non-institutionalized adults ages 19 to 32 who had consented to participate in a
previous study wave that held no criteria except that participants had to be between 18 and 30
years at baseline. The current data were collected during the 18-month follow-up of the prior
study, which assessed health behaviors among individuals ages 18 to 30 at baseline. We used
only the 18-month follow-up data for the current analysis because the social media items were
not asked at baseline. Responses were received from 1,787 participants (59%). This represented
a strong response rate, because many of the baseline respondents were likely no longer in the
GfK panel, which turns over participants every 2 years so as to prevent cohorts from becoming
fatigued by surveys. Additionally, it should be noted that survey weights accounted for non-
response and there were no demographic differences between responders and non-responders,
both of which attest to the strong external generalizability of the results.
7
GfK instituted multiple strategies to improve data quality. For example, they screened all data
sets for patterns suggesting lack of effort. GfK also instituted procedures such as minimizing
survey length, reducing the need for scrolling, and avoiding the use of long grids. If individuals
did not answer a question, they were prompted once to answer with the statement “your answer
is important to us. Please put your best guess.” However, participants were not forced to answer
any items.
The median time for survey completion was 15 minutes and participants received $15 for their
participation. This study was approved by the University of Pittsburgh Institutional Review
Board and was granted a Certificate of Confidentiality from the National Institutes of Health.
Measures
Participants completed online survey items including social isolation (dependent variable), social
media use (independent variable), and covariates.
Social Isolation. We assessed social isolation using a 4-item scale developed by the Patient-
Reported Outcomes Measurement Information System (PROMIS). PROMIS is a National
Institutes of Health Roadmap initiative whose aim is to provide precise, valid, reliable, and
standardized questionnaires measuring patient–reported outcomes across the domains of
physical, mental, and social health.16 The PROMIS social isolation scale was developed using
item response theory to promote precision and decrease respondent burden.17–19 Additionally, the
8
PROMIS social isolation scale has been correlated with and validated against other commonly
used social isolation measures.20,21 The social isolation scale assesses perceptions of being
avoided, excluded, detached, disconnected from, or unknown by, others. The specific items ask
participants how frequently in the past 7 days they had felt: “I feel left out”; “I feel that people
barely know me”; “I feel isolated from others”; and “I feel that people are around me but not
with me.” These items were scored on a 5-point Likert scale ranging from 1 to 5, corresponding
to responses of “Never,” “Rarely,” “Sometimes,” “Often” and “Always.” Thus, with four items,
each scored from 1 to 5, we calculated a raw score for social isolation that ranged from 4 to 20.
Based upon the non-normal distribution of resulting data, we collapsed the raw scores into
tertiles of “low,” “medium” and “high” for analysis. This was appropriate because one of the
specific aims of the PROMIS social isolation scale is to grade its severity instead of merely
providing a dichotomous cut-off. Similarly, because there is no established clinical cut-off for
social isolation, we divided the groups into approximate tertiles using the appropriate function in
Stata 13.1 (Stata Corp, College Station, Texas) rather than basing the categories on specific
numbers. Thus, all participants were categorized as having low, medium, or high social isolation,
which represented 39%, 31%, and 30% of the sample, respectively. Low, medium, and high
social isolation corresponded to raw scores of 4-6, 7-10 and 11 and above, respectively. The scale
exhibited excellent internal consistency reliability (Cronbach’s alpha = 0.92).
Social Media Use. We assessed participants’ social media use in two complementary ways: time
and frequency of use. First, participants were asked to estimate time spent on social media for
personal use. This item specifically instructed participants to not count any time spent on social
9
media for work. Participants provided estimates in numerical fields for hours and minutes on an
average day. Second, participants were asked to report frequency of their use of each of 11
widely used social media platforms, including Facebook, Twitter, Google+, YouTube, LinkedIn,
Instagram, Pinterest, Tumblr, Vine, Snapchat, and Reddit.9,22 Seven response choices ranged from
“I do not use this platform” to “I use this platform 5 or more times a day." We based these items
on the measures used by Pew Internet Research.9 Using weighted averages based on the
frequency responses, we computed social media site visits per week. To improve interpretability
of results, we collapsed all independent variables into quartiles for primary analyses. To ensure
robustness of results, we also conducted all analyses with independent variables as continuous.
Covariates. For analysis, we divided the sample into three age groups (19-23; 24-26; 27-32) and
race/ethnicity into five mutually exclusive categories (White, non-Hispanic; Black, non-
Hispanic; Hispanic; biracial or multiracial; or other non-Hispanic). We also assessed other
environmental and personal factors that may affect social isolation and social media use.9,23
These factors included relationship status (single or in a committed relationship), living situation
(with a parent or guardian; with a significant other; or other situation), household income (under
$30,000; $30,000-$74,999; or $75,000 or more) and education level (high school or less; some
college; or bachelor's degree or higher).
Statistical Analysis
10
We included all participants who had complete data on the PROMIS social isolation scale and
the social media items. Because only about 1% of participants had missing data for these
variables, this did not affect our results. To describe our sample, we computed percentages of the
dependent variable, the two independent variables (time and frequency of SMU), and the seven
covariates. Next, we used chi-square tests to determine bivariable associations between each of
the independent variables and covariates and the social isolation score.
After confirming that the proportional odds assumption was met, we used ordered logistic
regression to examine bivariable and multivariable associations between each social media
variable and social isolation. We decided a priori to include all covariates in our primary
multivariable models. To take advantage of the nationally-representative nature of the data, all
primary analyses were conducted using survey weights which took into account sex, age,
race/ethnicity, education, household income, census region, metropolitan area, and internet
access. We used similar regression analyses to examine whether there was an overall linear trend
between each ordered categorical independent variable and the dependent variable.
We also conducted three sets of sensitivity analyses to examine the robustness of our results.
First, we conducted all analyses with independent variables as continuous instead of ordered
categorical variables. Second, we conducted all analyses using only covariates that had a
bivariable association of P < .15 with the outcome. Third, we conducted all analyses without
survey weights. Results from all sensitivity analyses showed similar levels of significance and
magnitude to the primary analyses described here.
11
Statistical analyses were performed in 2015 with Stata 13.1 (Stata Corp, College Station, Texas),
and two-tailed P-values < .05 were considered to be significant.
12
RESULTS
Participants
A total of 1,787 participants completed the questionnaire. The weighted sample was 50.3%
female, 57.5% White, 13.0% African American, 20.6% Hispanic and 8.9% biracial/multiracial or
other. Of these, slightly more than half (55.6%) were in a committed relationship and
approximately a third (35.6%) reported living with a significant other. In terms of household
income, 22.9% were in the “low” category (under $30,000) and 38.7% were in the “high”
category ($75,000 and above). About one-third (36.0%) of participants had not attended any
college, while a quarter (25.7%) had a B.A. or higher (Table 1). There were no differences
between responders and non-responders in terms of age (P = .12), sex (P = .07), or race (P = .
21).
Social Isolation
Accounting for survey weights, 42% of respondents were classified as “low social isolation,”
31% were classified as “medium social isolation,” and 27% of participants were classified as
“high social isolation.”
13
Social Media Use
Median total time on social media was 61 minutes per day (interquartile range [IQR] = 30, 135).
Median social media site visits per week across all platforms was 30 (IQR = 9, 57). Only 58
individuals (3.2%) reported 0 site visits per week.
Bivariable Analyses
There were significant bivariable associations between social isolation and each of the primary
SMU variables. Compared with those who used social media < 30 minutes per day, those who
used social media ≥ 121 minutes per day had about double the odds for increased social isolation
(OR = 2.0, 95% CI = 1.4, 2.8) (Table 2). Similarly, compared with those who visited social
media platforms < 9 times per week, those who visited ≥ 58 times per week had about triple the
odds of increased social isolation (OR = 3.4, 95% CI = 2.3, 5.0) (Table 3).
Bivariable analyses also showed significant associations between social isolation and two
covariates: relationship status and yearly household income (Tables 1 and 2). Compared with
single individuals, married individuals had lower odds of having higher social isolation (OR =
0.6, 95% CI = 0.5, 0.8) (Table 2). Similarly, compared with those who earned less than $30,000
per year, those earning more than $75,000 had lower odds of increased social isolation (OR =
0.6, 95% CI = 0.4, 0.9) (Table 2).
14
Multivariable Analyses
In a fully-adjusted model, compared with those in the lowest quartile, participants in the highest
quartile of time of SMU had significantly greater odds of increased social isolation (AOR = 2.0,
95% CI = 1.4, 2.8) (Table 2). This association showed a linear effect (P < .001) (Table 2). The
only other variables significantly associated with social isolation in the multivariable model were
relationship status and yearly household income (Table 2).
In a second fully-adjusted model, compared with those in the lowest quartile, participants in the
highest quartile of frequency of SMU had significantly greater odds of increased social isolation
(AOR = 3.4, 95% CI = 2.3, 5.1) (Table 3). This association also showed a linear effect (P < .001)
(Table 3). Again, the only other variables significantly associated with social isolation were
relationship status and yearly household income (Table 3).
15
DISCUSSION
Among a nationally-representative cohort of individuals ages 19-32, we found robust linear
associations between increased SMU and increased social isolation, even after adjusting for a
comprehensive set of covariates. Therefore, our findings suggest that young adults with high
SMU are more, and not less, likely to be socially isolated.
This finding is not what we hypothesized. Theoretically, we expected SMU to increase one’s
circle of acquaintances and strengthen existing friendships by facilitating interaction. Consistent
with this, some empiric studies have found associations between increased SMU and increased
social capital.24,25 Our findings are, however, consistent with other studies examining more distal
outcomes related to emotional health and support. For example, Shensa et al. recently found that
increased SMU does not have an expected association with higher emotional support.10 Others
have also found increased SMU to be associated with feelings of inferiority and negative
mood.11,26–28 However, it remains noteworthy that SMU in this study was strongly and
independently associated with social isolation, even while one of the main apparent purposes of
SMU is to reduce social isolation.
Because our data were cross-sectional, the directionality of this association cannot be determined
based on these data alone. It may be that individuals who are socially isolated tend to use more
social media. Individuals with fewer “in person” social outlets may indeed turn to online
networks as a substitute. For example, individuals with mental illnesses report using social media
16
to reach out to others.29 However, it is worth noting that if this is the case, our findings suggest
that increasing SMU may not successfully reduce this initial social isolation.
Another possibility is that those who use increased amounts of social media subsequently
develop increased social isolation. While in some ways this may seem counter-intuitive, there are
several possible mechanisms. First, increased time spent on social media may displace more
authentic social experiences that might truly decrease social isolation. Second, certain
characteristics of the online milieu may facilitate feelings of being excluded. For example, an
individual may discover pictures or other evidence of events to which they were not invited.
Finally, instead of accurately representing reality, social media feeds are in fact highly curated by
their owners.30 Exposure to such highly idealized representations of peers’ lives may elicit
feelings of envy and the distorted belief that others lead happier and/or more successful lives,
which may increase social isolation.31
This study focused on self-reported overall time and frequency of SMU. However, it should be
emphasized that not all SMU is the same, and future research should examine more specific
social media exposures. For example, some users tend to passively consume social media content
while others engage in more active communication. It may be that those who are more active feel
more engaged and derive more social capital from social media interactions.32 However, it may
also be that active users are more prone to having negative experiences such as arguments or
being “unfriended,” both of which ultimately can be isolating.
17
While our overall results suggest associations between increased SMU and increased social
isolation on a population level, certain individuals or groups may derive social benefit from
SMU. For example, individuals with certain health conditions may find it useful to connect over
social media, especially if they are geographically isolated. Prior studies have demonstrated
value for these types of networks.33,34 Similarly, individuals with certain personality types (e.g.,
extroverted vs. introverted) might derive more or less benefit.
Because many socially isolated people use social media, this may be a good medium for
intervention. While this study raises potential concerns, there also may be useful ways of
leveraging social media to identify socially isolated individuals and helping them connect to
more valuable in-person networks. Understanding the relationship between SMU and social
isolation will help to ensure that these interventions are appropriately designed and provide the
support necessary.
Limitations
Due to the large sample size, we were unable to use “gold standard” measures of social media
exposure such as ecological momentary assessment or data downloaded directly from social
media sites. Additionally, our frequency measure, although it was adapted from a validated
scale,9 may not be sufficient for modern users. It is also a limitation that our data were cross-
sectional. Finally, it should be reiterated that we studied young adults ages 19-32; therefore,
these results cannot be generalized to other populations, such as older adults.
18
Conclusion
Despite these limitations, it is noteworthy that increased SMU was strongly and independently
associated with increased social isolation in a nationally representative sample of young adults.
As social media platforms continue to evolve, it will be critical that future assessments use more
fine-grained measurements. This will be useful so that recommendations about SMU and social
isolation can be appropriately targeted.
19
ACKNOWLEDGEMENTS
Funding Source: This research was funded by the National Cancer Institute at the National
Institutes of Health (R01-CA140150), awarded to Dr. Primack. The funding source had no role
in the study design, collection, analysis, interpretation of data, writing of the manuscript, or the
decision to submit this manuscript for publication.
Financial Disclosures: No financial disclosures were reported by the authors of this manuscript.
20
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25
TABLES
TABLE 1—Social Media Use and Sociodemographic Characteristics of the Whole Sample
and Different Levels of Social Isolation: U.S. Survey of Social Media Use and Emotional
Health, 2014
Independent Variables
Whole
Sample
Column
%a
Low SI
(n = 699)
Column
%a
Medium SI
(n = 549)
Column
%a
High SI
(n = 537)
Column
%aP Valueb
Social Media Use
Time, min per day .002
Quartile 1 (0-30) 29.8 35.4 28.2 22.3
Quartile 2 (31-60) 20.8 21.8 23.2 16.3
Quartile 3 (61-120) 24.0 22.8 21.0 29.6
Quartile 4 (121 and above) 25.5 20.1 27.6 31.9
Frequency, visits per weekc, d <.001
Quartile 1 (0-8) 28.3 37.7 23.8 18.2
Quartile 2 (9-30) 25.1 23.6 30.1 21.3
Quartile 3 (31-57) 24.1 22.3 26.5 24.1
Quartile 4 (58 and above) 22.5 16.4 19.6 36.4
Sociodemographic
Age, y .09
19-23 33.7 32.9 33.7 34.8
24-26 24.8 21.6 30.5 23.1
27-32 41.6 45.5 35.9 42.1
Sex .07
Female 50.3 45.7 55.0 52.2
Male 49.7 54.3 45.0 47.8
Race .06
White, non-Hispanic 57.5 58.1 56.7 57.3
Black, non-Hispanic 13.0 15.3 9.9 12.9
Hispanic 20.6 21.4 20.4 19.6
Othere8.9 5.2 13.0 10.2
Relationship Status <.001
Single/Widowed/Divorced 44.5 36.1 50.6 51.0
Married/Committed
relationship
55.6 63.9 49.4 49.0
Living Situation .003
Parent/Guardian 34.0 34.5 33.5 33.8
Significant other 35.6 41.4 27.9 35.4
Otherf30.4 24.1 38.5 30.9
Yearly Household Income, $ .003
0-30,000 22.9 18.8 20.5 32.7
30,000-74,999 38.4 40.8 41.2 31.2
≥ 75,000 38.7 40.5 38.3 36.1
26
Education Level .95
High school or less 36.0 36.7 34.6 36.3
Some college 38.3 37.0 39.8 38.8
Bachelor’s degree or higher 25.7 26.3 25.6 25.0
Note. SI = Social isolation. The sample size was n = 1 785.
a Values may not total 100 due to rounding. Column percentages are based upon survey weighted
data, therefore may not be congruent with the cell frequency proportion of total N.
b P value derived using chi-square analyses comparing proportion of users in each category.
c Includes Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine,
Snapchat, and Reddit.
d Based on weighted averages using a 7-point Likert-type response scale ranging from “I don’t use
this platform” to “5 or more times a day.”
e Includes Multiracial.
f Defined as not living with a parent/guardian or significant other.
27
TABLE 2—Odds Ratios and Adjusted Odds Ratios for Social Isolation: U.S. Survey of Social
Media Use and Emotional Health, 2014
Social Media Use
Social Isolationa
OR (95% CI) Pc
Social Isolationa
AORb (95% CI) Pc
Time, min per day <.001 <.001
Quartile 1 (0-30) ref ref
Quartile 2 (31-60) 1.2 (0.8, 1.7) 1.2 (0.9, 1.7)
Quartile 3 (61-120) 1.7 (1.2, 2.5) 1.6 (1.1, 2.4)
Quartile 4 (121 and above) 2.0 (1.4, 2.8) 2.0 (1.4, 2.8)
Age, y .37 .83
19-23 ref ref
24-26 1.1 (0.8, 1.4) 1.1 (0.8, 1.6)
27-32 0.9 (0.6, 1.2) 1.0 (0.7, 1.4)
Sex
Female ref ref
Male 0.8 (0.6, 1.02) 0.9 (0.7, 1.1)
Race
White, non-Hispanic ref ref
Black, non-Hispanic 0.8 (0.5, 1.3) 0.6 (0.4, 1.1)
Hispanic 1.0 (0.7, 1.4) 0.8 (0.5, 1.2)
Othere1.6 (1.1, 2.4) 1.4 (0.9, 2.1)
Relationship Status
Single/Widowed/Divorced ref ref
Married/Committed relationship 0.6 (0.5, 0.8) 0.6 (0.4, 0.8)
Living Situation
Parent/Guardian ref ref
Significant other 0.8 (0.6, 1.2) 1.3 (0.8, 2.0)
Otherf1.3 (0.9, 1.7) 1.2 (0.8, 1.6)
Yearly Household Income, $ .01 .01
0-30,000 ref ref
30,000-74,99 0.6 (0.4, 0.8) 0.6 (0.4, 0.8)
≥ 75,000 0.6 (0.4, 0.9) 0.6 (0.4, 0.8)
Education Level .95 .55
High school or less ref ref
Some college 1.1 (0.8, 1.5) 1.1 (0.8, 1.6)
Bachelor’s degree or higher 1.0 (0.7, 1.4) 1.1 (0.8, 1.6)
Abbreviations: OR, odds ratio; CI, confidence interval ; AOR, adjusted odds ratio.
a Social isolation is divided into low, medium, and high tertiles.
b Adjusted for age, sex, race, relationship status, living situation, household income, and education
level.
c Significance level determined by post-estimate tests for an overall linear trend of an ordered
categorical independent variable.
d Includes Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine,
Snapchat, and Reddit.
28
e Includes Multiracial.
f Defined as not living with a parent/guardian or significant other.
29
TABLE 3—Odds Ratios and Adjusted Odds Ratios for Social Isolation: U.S. Survey of Social
Media Use and Emotional Health, 2014
Social Media Use
Social Isolationa
OR (95% CI) Pc
Social Isolationa
AORb (95% CI) Pc
Frequency, visits per weekd, e <.001 <.001
Quartile 1 (less than 9) ref ref
Quartile 2 (9-30) 1.8 (1.3, 2.5) 1.8 (1.3, 2.6)
Quartile 3 (31-57) 1.9 (1.3, 2.8) 1.9 (1.3, 2.8)
Quartile 4 (58 and above) 3.4 (2.3, 5.0) 3.4 (2.3, 5.1)
Age, y .37 .63
19-23 ref ref
24-26 1.1 (0.8, 1.4) 1.2 (0.9, 1.7)
27-32 0.9 (0.6, 1.2) 1.1 (0.8, 1.6)
Sex
Female ref ref
Male 0.8 (0.6, 1.02) 0.8 (0.7, 1.1)
Race
White, non-Hispanic ref ref
Black, non-Hispanic 0.8 (0.5, 1.3) 0.7 (0.4, 1.2)
Hispanic 1.0 (0.7, 1.4) 0.8 (0.6, 1.2)
Otherf1.6 (1.1, 2.4) 1.4 (0.9, 2.1)
Relationship Status
Single/Widowed/Divorced ref ref
Married/Committed relationship 0.6 (0.5, 0.8) 0.6 (0.4, 0.8)
Living Situation
Parent/Guardian ref ref
Significant other 0.8 (0.6, 1.2) 1.2 (0.8, 1.9)
Otherg1.3 (0.9, 1.7) 1.1 (0.8, 1.6)
Yearly Household Income, $ .01 .007
0-30,000 ref ref
30,000-74,999 0.6 (0.4, 0.8) 0.6 (0.4, 0.8)
≥ 75,000 0.6 (0.4, 0.9) 0.6 (0.4, 0.8)
Education Level .95 .97
High school or less ref ref
Some college 1.1 (0.8, 1.5) 1.1 (0.8, 1.5)
Bachelor’s degree or higher 1.0 (0.7, 1.4) 1.0 (0.7, 1.4)
Abbreviations: OR, odds ratio; CI, confidence interval ; AOR, adjusted odds ratio.
a Social isolation is divided into low, medium, and high tertiles.
b Adjusted for age, sex, race, relationship status, living situation, household income, and education
level.
c Significance level determined by post-estimate tests for an overall linear trend of an ordered
categorical independent variable.
d Includes Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine,
Snapchat, and Reddit.
30
e Based on a 7-point Likert-type response scale ranging from “I don’t use this platform” to “5 or
more times a day.”
f Includes Multiracial.
g Defined as not living with a parent/guardian or significant other.
31
