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Abstract

Introduction: Perceived social isolation (PSI) is associated with substantial morbidity and mortality. Social media platforms, commonly used by young adults, may offer an opportunity to ameliorate social isolation. This study assessed associations between social media use (SMU) and PSI among U.S. young adults. Methods: Participants were a nationally representative sample of 1,787 U.S. adults aged 19-32 years. They were recruited in October-November 2014 for a cross-sectional survey using a sampling frame that represented 97% of the U.S. Population: SMU was assessed using both time and frequency associated with use of 11 social media platforms, including Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine, Snapchat, and Reddit. PSI was measured using the Patient-Reported Outcomes Measurement Information System scale. In 2015, ordered logistic regression was used to assess associations between SMU and SI while controlling for eight covariates. Results: In fully adjusted multivariable models that included survey weights, compared with those in the lowest quartile for SMU time, participants in the highest quartile had twice the odds of having greater PSI (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 PSI (AOR=3.4, 95% CI=2.3, 5.1). Associations were linear (p<0.001 for all), and results were robust to all sensitivity analyses. Conclusions: Young adults with high SMU seem to feel more socially isolated than their counterparts with lower SMU. Future research should focus on determining directionality and elucidating reasons for these associations.
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
... Humans are fundamentally a social species: it is in their nature to interact and form various types of relationships with others. Social isolation has been understood as both an objective phenomenon experienced by individuals, such as that characterised by a 'lack of social interaction' [19], 'the actual lack of social ties' [20], and 'social disconnectedness' [21]. It is also understood as a subjective experience by individuals, such as a 'lack of engagement with others' [20], 'loneliness' [22] or 'the perceived discrepancy between actual and desired social relationships' [23]. ...
... Social isolation has been understood as both an objective phenomenon experienced by individuals, such as that characterised by a 'lack of social interaction' [19], 'the actual lack of social ties' [20], and 'social disconnectedness' [21]. It is also understood as a subjective experience by individuals, such as a 'lack of engagement with others' [20], 'loneliness' [22] or 'the perceived discrepancy between actual and desired social relationships' [23]. The widespread mandated household confinement and mobility restrictions can be understood as creating objectively real physical isolation, immediately and severely reducing direct social interaction and contact with anyone outside the household. ...
... Other researchers have found contradictory evidence. A study of adults in the US, aged 19 to 32 years, found linear associations between increased social media use and an increase in perceived social isolation [20]. This suggests that there is no such simple correlation between social media use and social isolation; age seems to matter. ...
Article
Full-text available
Background “The impacts of the Coronavirus Disease 2019 (COVID-19) pandemic and the shutdown it triggered at universities across the world, led to a great degree of social isolation among university staff and students. The aim of this study was to identify the perceived consequences of this on staff and their work and on students and their studies at universities. Method The study used a variety of methods, which involved an on-line survey on the influences of social isolation using a non-probability sampling. More specifically, two techniques were used, namely a convenience sampling (i.e. involving members of the academic community, which are easy to reach by the study team), supported by a snow ball sampling (recruiting respondents among acquaintances of the participants). A total of 711 questionnaires from 41 countries were received. Descriptive statistics were deployed to analyse trends and to identify socio-demographic differences. Inferential statistics were used to assess significant differences among the geographical regions, work areas and other socio-demographic factors related to impacts of social isolation of university staff and students. Results The study reveals that 90% of the respondents have been affected by the shutdown and unable to perform normal work or studies at their institution for between 1 week to 2 months. While 70% of the respondents perceive negative impacts of COVID 19 on their work or studies, more than 60% of them value the additional time that they have had indoors with families and others. . Conclusions While the majority of the respondents agree that they suffered from the lack of social interaction and communication during the social distancing/isolation, there were significant differences in the reactions to the lockdowns between academic staff and students. There are also differences in the degree of influence of some of the problems, when compared across geographical regions. In addition to policy actions that may be deployed, further research on innovative methods of teaching and communication with students is needed in order to allow staff and students to better cope with social isolation in cases of new or recurring pandemics.
... Against this background, it is not surprising that the extant findings on the relationship between SMU and loneliness have offered inconsistent outcomes. While some empirical studies found a positive relationship between SMU and decreasing loneliness (Apaolaza et al., 2013;Hajek and Konig, 2019;Lou et al., 2012), others report the reverse (Aarts et al., 2015;Primack et al., 2017;Sutcliffe et al., 2018). Because most of these studies treated SMU as a monolithic activity, researchers have suggested a further clarification of the association between SMU and loneliness by considering the multidimensionality of SMU and its different impacts (Trifiro and Gerson, 2019). ...
... Hajek and Konig (2019) showed that daily social media users report lower loneliness scores than less frequent users or non-users. In contrast, other studies have suggested that SMU is not related to loneliness (Aarts et al., 2015;Sutcliffe et al., 2018) or else is associated with increasing loneliness (Primack et al., 2017). We would like to point out that most of these studies paid attention to younger people. ...
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Purpose The literature reports inconsistent findings about the effects of social media usage (SMU). Researchers distinguish between active and passive social media usage (ASMU and PSMU), which can generate different effects on users by social support and social comparison mechanisms, respectively. Drawing on social presence theory (SPT), this study integrates an implicit social presence mechanism with the above two mechanisms to explicate the links between SMU and seniors' loneliness. Design/methodology/approach Data were collected from a field study by interviewing seniors living in eight aging care communities in China. Loneliness, social media activities and experiences with social media in terms of online social support (OSS), upward social comparison (USC) and social presence (SP) were assessed. Factor-based structural equation modeling was used to analyze the data. Findings OSS can mediate the relationship between ASMU and seniors' loneliness. Moreover, SP mediates between ASMU, PSMU, and seniors' loneliness, and between OSS, USC and seniors' loneliness. OSS mediates the relationship between ASMU and SP, and USC mediates the relationship between PSMU and SP. Practical implications This study shows that social media can alleviate seniors' loneliness, which could help relieve the pressures faced by health and social care systems. Social presence features are suggested to help older users interact with social health technologies in socially meaningful ways. Originality/value This study not only demonstrates that SP can play a crucial role in the relationship between both ASMU and PSMU and loneliness, but also unravels the links between SP and OSS, as well as USC.
... Social isolation is defined as "a deficit of personal relationships or being excluded from social networks" (Choi and Noh, 2019, p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity (Primack et al., 2017). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities (Schinka et al., 2012). ...
... Social media usage has been associated with anxiety, loneliness, and depression (Dhir et al., 2018;Reer et al., 2019), and social isolation (Van Den Eijnden et al., 2016;Whaite et al., 2018). However, some recent studies have argued that social media use decreases social isolation (Primack et al., 2017;Meshi et al., 2020). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. ...
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... Under its umbrella, we can also place a range of psychological interface harms wrought through particular patterns of using a technology over time. Much of the contemporary research on this issue has focused on the impact social media can have on users' mental health (Primack et al. 2017), with a variety of scholars exploring how the design of platforms may cause sadness (Lovink 2019), unsettledness (Lupinacci 2020), and distraction (Williams 2018) among users. ...
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... Although the Reddit platform does not necessarily represent either the general, or the whole drug enthusiasts' population, it still appears as a large open web information source in terms of the number of posts and active users; variety of topics being discussed; and real-time discussion of trends. Hence, Reddit has been used for a range of research purposes, including both general psychiatry [21][22][23][24][25][26][27][28] and addiction/drug abuse [29][30][31][32][33][34]. Indeed, the social networks' web monitoring approach has been suggested to be helpful to better understand some clinical and psychopharmacological issues related to a range of novel psychoactive substances (NPS) [35]. ...
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... Overall, the use of multiple social media platforms, including Twitter, was associated with increased levels of depression and anxiety (44). Furthermore, the time used on social media was positively associated with a perceived social isolation score (45). ...
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... "Siento que las personas están a mi alrededor, pero no conmigo"), los Conectados reportaron mayores niveles que Focalizados y Funcionales, y los Hiperconectados niveles más altos aún. Esto podría dar cuenta del fenómeno conocido como desplazamiento social (Primack et al., 2017), que refiere a que el tiempo utilizado en Internet y redes sociales puede estar sustituyendo el tiempo que podría dedicarse a establecer interacciones cara a cara, aumentando los niveles de aislamiento social. ...
Technical Report
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... Individuals who scored 50 and below (i.e., raw scores below 13) were considered as demonstrating poor wellbeing [20]. The cut-off has been used as an index of poor wellbeing among university students [21,22]. The scale has been validated cross-culturally in both clinical and general population, with consistent reliability (Cronbach's alpha ranging from 0.88 to 0.93) [23][24][25]. ...
Article
Full-text available
Background: The COVID-19 pandemic has posed risks to public mental health worldwide. University students, who are already recognised as a vulnerable population, are at elevated risk of mental health issues given COVID-19-related disruptions to higher education. To assist universities in effectively allocating resources to the launch of targeted, population-level interventions, the current study aimed to uncover predictors of university students' psychological wellbeing during the pandemic via a data-driven approach. Methods: Data were collected from 3973 Australian university students ((median age = 22, aged from 18 to 79); 70.6% female)) at five time points during 2020. Feature selection was conducted via least absolute shrinkage and selection operator (LASSO) to identify predictors from a comprehensive set of variables. Selected variables were then entered into an ordinary least squares (OLS) model to compare coefficients and assess statistical significance. Results: Six negative predictors of university students' psychological wellbeing emerged: White/European ethnicity, restriction stress, perceived worry on mental health, dietary changes, perceived sufficiency of distancing communication, and social isolation. Physical health status, emotional support, and resilience were positively associated with students' psychological wellbeing. Social isolation has the largest effect on students' psychological wellbeing. Notably, age, gender, international status, and educational level did not emerge as predictors of wellbeing. Conclusion: To cost-effectively support student wellbeing through 2021 and beyond, universities should consider investing in internet- and tele- based interventions explicitly targeting perceived social isolation among students. Course-based online forums as well as internet- and tele-based logotherapy may be promising candidates for improving students' psychological wellbeing.
... Il y a donc une « désinhibition toxique » des individus (Wachs et Wright, 2018) accompagnée souvent par une incapacité des témoins à déterminer et exercer une sanction (Neto et al., 2017 ;Bastiaensens et al., 2015). Plusieurs travaux démontrent aussi qu'une exposition croissante à la violence des utilisateurs des réseaux sociaux, d'une part augmente le manque d'estime de soi, la solitude, l'isolement social, la dépression voire le suicide (Primack et al., 2017;Luxton et al., 2012;A Vogel et al., 2014;Bauman, 2013) et, d'autre part, pousse les utilisateurs eux-mêmes à agir de manière agressive (Hsueh, 2015;Rösner et al., 2016) tout en favorisant l'émergence d'une méfiance envers les médias (Borah, 2013 ;Anderson et al., 2016). ...
Preprint
Nous nous basons sur une étude inédite de 2.209.206 commentaires, dont 1.184.859 se trouvent dans les fils de discussion , répartis dans les espaces de commentaires de 46.090 vidéos. Ces dernières sont issues d’un panel de 57 chaînes de médias français aux catégories institutionnelles et aux positionnements différents. Cet échantillon (cf. Annexe 1) est en partie construit sur la base des travaux empiriques précédents (Marty et al., 2012 ; Cardon et al., 2019; Lyubareva et Rochelandet, 2016; Lyubareva et al., 2020a), recensant des médias de presse traditionnels, nationaux et régionaux et les pure players (par exemple, Reporterre ou Slate). Parmi les médias, cités dans ces recherches et représentatifs de leurs domaines, nous avons sélectionné 57 acteurs disposant d’une chaîne YouTube. Sans prétendre à une couverture exhaustive des médias français présents sur YouTube, cette sélection nous permet d’établir une catégorisation des chaînes en trois groupes (cf. Annexe 1) : les médias généralistes couvrant un large éventail de thématiques, qui existent en version papier, version web ou à la télévision ; les médias « partisans » de gauche et de droite marqués par un positionnement politique plus extrême, qui ont une version papier ou existent uniquement en version web; et les médias thématiques et spécialisés couvrant exclusivement certains sujets ou zones géographiques. Nous nous posons comme objectif de comprendre, d’une part, où et quand l’espace des commentaires des médias français en vient à être touché par les débats brutaux ou agressifs et, d’autre part, quels facteurs limitent ou favorisent cette agressivité. Pour atteindre cet objectif, nous employons un dispositif d’enquête inhabituel au regard des méthodes utilisées les travaux abordant le terrain YouTube en sciences sociales ; dispositif se positionnant à la lisière du traitement automatisé du langage et de l’économie politique des médias (Guibert et al., 2016). Dans une première partie, nous allons résumer certaines orientations retrouvées dans la littérature afin de préciser quels facteurs sont susceptibles de peser sur l’agressivité dans les commentaires YouTube et de comprendre sous quelles conditions l’agressivité intervient dans les espaces de commentaire de la plateforme. Dans une seconde partie, nous reviendrons sur les données et la méthodologie mises en œuvre. Nous verrons ainsi dans notre analyse que des formes de participation très différentes sont observées selon que nous considérons les commentaires sur les chaînes de médias que nous classons en “généralistes”, celles des médias que nous classons en “partisans”, ou enfin celles des médias que nous classons en “thématiques et spécialisés” Dans la troisième partie, nous parcourons les résultats de cette recherche et vérifions comment les médias en ligne, espaces de commentaires et d’agressivité sont liés par un ensemble de pratiques oscillant, selon les chaînes et les niveaux de fréquentation des espaces de commentaire des vidéos, de peu à fortement agressif.
Thesis
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There is a lack of research about the lived experiences of self-identified lesbian, gay, bisexual, transgender, queer, and otherwise-identified (LGBTQ+) young adults with disabilities who use the Internet to achieve particular social aims. Using open-ended survey questions, the researcher applied multidimensional and overlapping frameworks of intersectionality, feminist-disability theory, and social work to answer the following: What are the lived experiences of disabled, LGBTQ+ young adults who use social media for social support and identity construction? Using secondary data, fifteen (N=15) cases of LGBTQ+ disabled young adults aged 18 to 31 living in the United States were selected, and data was analyzed using a phenomenological thematic analysis. The research revealed salient themes, such as community/belonging, access to “others like me,” positive identity formation and protective mental health factors to name a few, each of which respectively facilitated or complicated participants’ motives to use social media platforms. Implications of v the research findings for social science scholars and suggestions for future research are discussed.
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Purpose: To investigate the association between social isolation and loneliness, how they relate to depression, and whether these associations are explained by genetic influences. Methods: We used data from the age-18 wave of the Environmental Risk Longitudinal Twin Study, a birth cohort of 1116 same-sex twin pairs born in England and Wales in 1994 and 1995. Participants reported on their levels of social isolation, loneliness and depressive symptoms. We conducted regression analyses to test the differential associations of isolation and loneliness with depression. Using the twin study design, we estimated the proportion of variance in each construct and their covariance that was accounted for by genetic and environmental factors. Results: Social isolation and loneliness were moderately correlated (r = 0.39), reflecting the separateness of these constructs, and both were associated with depression. When entered simultaneously in a regression analysis, loneliness was more robustly associated with depression. We observed similar degrees of genetic influence on social isolation (40 %) and loneliness (38 %), and a smaller genetic influence on depressive symptoms (29 %), with the remaining variance accounted for by the non-shared environment. Genetic correlations of 0.65 between isolation and loneliness and 0.63 between loneliness and depression indicated a strong role of genetic influences in the co-occurrence of these phenotypes. Conclusions: Socially isolated young adults do not necessarily experience loneliness. However, those who are lonely are often depressed, partly because the same genes influence loneliness and depression. Interventions should not only aim at increasing social connections but also focus on subjective feelings of loneliness.
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Background: Social media (SM) use is increasing among U.S. young adults, and its association with mental well-being remains unclear. This study assessed the association between SM use and depression in a nationally representative sample of young adults. Methods: We surveyed 1,787 adults ages 19 to 32 about SM use and depression. Participants were recruited via random digit dialing and address-based sampling. SM use was assessed by self-reported total time per day spent on SM, visits per week, and a global frequency score based on the Pew Internet Research Questionnaire. Depression was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Scale Short Form. Chi-squared tests and ordered logistic regressions were performed with sample weights. Results: The weighted sample was 50.3% female and 57.5% White. Compared to those in the lowest quartile of total time per day spent on SM, participants in the highest quartile had significantly increased odds of depression (AOR = 1.66, 95% CI = 1.14-2.42) after controlling for all covariates. Compared with those in the lowest quartile, individuals in the highest quartile of SM site visits per week and those with a higher global frequency score had significantly increased odds of depression (AOR = 2.74, 95% CI = 1.86-4.04; AOR = 3.05, 95% CI = 2.03-4.59, respectively). All associations between independent variables and depression had strong, linear, dose-response trends. Results were robust to all sensitivity analyses. Conclusions: SM use was significantly associated with increased depression. Given the proliferation of SM, identifying the mechanisms and direction of this association is critical for informing interventions that address SM use and depression.
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Low emotional support is associated with poor health outcomes. Engagement with face-to-face social networks is one way of increasing emotional support. However, it is not yet known whether engagement with proliferating electronic social networks is similarly associated with increased emotional support. Thus, the purpose of this study was to assess associations between social media use and perceived emotional support in a large, nationally-representative sample. In October 2014, we collected data from 1796 U.S. adults ages 19-32. We assessed social media use using both total time spent and frequency of visits to each of the 11 most popular social media platforms. Our dependent variable was perceived emotional support as measured by the brief Patient-Reported Outcomes Measurement Information System (PROMIS) emotional support scale. A multivariable model including all sociodemographic covariates and accounting for survey weights demonstrated that, compared with the lowest quartile of time on social media, being in the highest quartile (spending two or more hours per day) was significantly associated with decreased odds of having higher perceived emotional support (AOR 0.62, 95 % CI 0.40, 0.94). However, compared with those in the lowest quartile, being in the highest quartile regarding frequency of social media use was not significantly associated with perceived emotional support (AOR 0.70, 95 % CI 0.45, 1.09). In conclusion, while the cross-sectional nature of these data hinder inference regarding directionality, it seems that heavy users of social media may actually feel less and not more emotional support.
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Purpose: Our work as a primary research site of the Patient-Reported Outcomes Measurement Information System (PROMIS(®)), combined with support from the Patient-Centered Outcomes Research Institute, allowed us to evaluate the real-world applicability and acceptability of PROMIS measures in an addiction medicine setting. Methods: As part of a 3-month prospective observational study, 225 outpatients at a substance abuse treatment clinic completed PROMIS item banks for alcohol use (as well as 15 additional item banks from 8 other PROMIS domains, including emotional distress, sleep, and pain), with assessments at intake, 1-month follow-up, and 3-month follow-up. A subsample of therapists and their patients completed health domain importance ratings and qualitative interviews to elicit feedback regarding the content and format of the patients' assessment results. Results: The importance ratings revealed that depression, anxiety, and lack of emotional support were rated highest of the non-alcohol-related domains among both patients and clinicians. General alcohol use was considered most important by both patients and clinicians. Based on their suggestions, changes were made to item response feedback to facilitate comprehension and communication. Conclusions: Both therapists and patients agreed that their review of the graphical display of scores, as well as individual item responses, helped them to identify areas of greatest concern and was useful for treatment planning. The results of our pilot work demonstrated the value and practicality of incorporating a comprehensive health assessment within a substance abuse treatment setting.
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Actual and perceived social isolation are both associated with increased risk for early mortality. In this meta-analytic review, our objective is to establish the overall and relative magnitude of social isolation and loneliness and to examine possible moderators. We conducted a literature search of studies (January 1980 to February 2014) using MEDLINE, CINAHL, PsycINFO, Social Work Abstracts, and Google Scholar. The included studies provided quantitative data on mortality as affected by loneliness, social isolation, or living alone. Across studies in which several possible confounds were statistically controlled for, the weighted average effect sizes were as follows: social isolation odds ratio (OR) = 1.29, loneliness OR = 1.26, and living alone OR = 1.32, corresponding to an average of 29%, 26%, and 32% increased likelihood of mortality, respectively. We found no differences between measures of objective and subjective social isolation. Results remain consistent across gender, length of follow-up, and world region, but initial health status has an influence on the findings. Results also differ across participant age, with social deficits being more predictive of death in samples with an average age younger than 65 years. Overall, the influence of both objective and subjective social isolation on risk for mortality is comparable with well-established risk factors for mortality. © The Author(s) 2015.
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Since 1960 demographic trends towards longer time in education and late age to enter into marriage and of parenthood have led to the rise of a new life stage at ages 18–29 years, now widely known as emerging adulthood in developmental psychology. In this review we present some of the demographics of emerging adulthood in high-income countries with respect to the prevalence of tertiary education and the timing of parenthood. We examine the characteristics of emerging adulthood in several regions (with a focus on mental health implications) including distinctive features of emerging adulthood in the USA, unemployment in Europe, and a shift towards greater individualism in Japan.
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Young onset dementia accounts for up to 1 in 10 dementia diagnoses. Those diagnosed face premature transition into the realm of aged care services and adjustment to an illness of ageing prior to age 65. To help elicit communication of the perceived psychosocial needs of this group, provide a platform to gain peer support and advocate for increased awareness, the Young Onset Dementia Support Group was established on the social networking site, Facebook(TM) . Followers post comments, read educational or otherwise interesting news feeds, share inspirational quotes and access others living with dementia worldwide. Facebook provides a means of rapid global reach in a way that allows people with dementia to increase their communications and potentially reduce isolation. This paper was authored by the page administrators. We aim to highlight the promising utility of a social network platform just entering its stride amongst health communication initiatives.
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Background: To guide measure development, National Institutes of Health-supported Patient reported Outcomes Measurement Information System (PROMIS) investigators developed a hierarchical domain framework. The framework specifies health domains at multiple levels. The initial PROMIS domain framework specified that physical function and symptoms such as Pain and Fatigue indicate Physical Health (PH); Depression, Anxiety, and Anger indicate Mental Health (MH); and Social Role Performance and Social Satisfaction indicate Social Health (SH). We used confirmatory factor analyses to evaluate the fit of the hypothesized framework to data collected from a large sample. Methods: We used data (n=14,098) from PROMIS's wave 1 field test and estimated domain scores using the PROMIS item response theory parameters. We then used confirmatory factor analyses to test whether the domains corresponded to the PROMIS domain framework as expected. Results: A model corresponding to the domain framework did not provide ideal fit [root mean square error of approximation (RMSEA)=0.13; comparative fit index (CFI)=0.92; Tucker Lewis Index (TLI)=0.88; standardized root mean square residual (SRMR)=0.09]. On the basis of modification indices and exploratory factor analyses, we allowed Fatigue to load on both PH and MH. This model fit the data acceptably (RMSEA=0.08; CFI=0.97; TLI=0.96; SRMR=0.03). Discussion: Our findings generally support the PROMIS domain framework. Allowing Fatigue to load on both PH and MH improved fit considerably.