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Social media allows users to explore self-identity and express emotions or thoughts. Research looking into the association between social media use (SMU) and mental health outcomes, such as anxiety or depressive symptoms, have produced mixed findings. These contradictions may best be addressed by examining different patterns of SMU as they relate to depressive symptomatology. We sought to assess the independent associations between active versus passive SMU and depressive symptoms. For this, we conducted an online survey of adults 18-49 of age. Depressive symptoms were measured using the Patient-Reported Outcomes Measurement Information System brief depression scale. We measured active and passive SMU with previously developed items. Factor analysis was used to explore the underlying factor structure. Then, we used ordered logistic regression to assess associations between both passive and active SMU and depressive symptoms while controlling for sociodemographic covariates. Complete data were received from 702 participants. Active and passive SMU items loaded on separate factors. In multivariable analyses that controlled for all covariates, each one-point increase in passive SMU was associated with a 33 percent increase in depressive symptoms (adjusted odds ratio [AOR] = 1.33, 95 percent confidence interval [CI] = 1.17-1.51). However, in the same multivariable model, each one-point increase in active SMU was associated with a 15 percent decrease in depressive symptoms (AOR = 0.85, 95 percent CI = 0.75-0.96). To inform interventions, future research should determine directionality of these associations and investigate related factors.
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Passive and Active Social Media Use and Depressive
Symptoms Among United States Adults
Ce´ sar G. Escobar-Viera, PhD, MD,
1–3
Ariel Shensa, MA,
1,2
Nicholas D. Bowman, PhD,
4
Jaime E. Sidani, PhD,
1,2
Jennifer Knight, MA,
4
A. Everette James, JD, MBA,
3
and Brian A. Primack, MD, PhD
1,2,5
Abstract
Social media allows users to explore self-identity and express emotions or thoughts. Research looking into the
association between social media use (SMU) and mental health outcomes, such as anxiety or depressive symptoms,
have produced mixed findings. These contradictions may best be addressed by examining different patterns of SMU
as they relate to depressive symptomatology. We sought to assess the independent associations between active
versus passive SMU and depressive symptoms. For this, we conducted an online survey of adults 18–49 of age.
Depressive symptoms were measured using the Patient-Reported Outcomes Measurement Information System brief
depression scale. We measured active and passive SMU with previously developed items. Factor analysis was used
to explore the underlying factor structure. Then, we used ordered logistic regression to assess associations between
both passive and active SMU and depressive symptoms while controlling for sociodemographic covariates.
Complete data were received from 702 participants. Active and passive SMU items loaded on separate factors. In
multivariable analyses that controlled for all covariates, each one-point increase in passive SMU was associated
with a 33 percent increase in depressive symptoms (adjusted odds ratio [AOR] =1.33, 95 percent confidence
interval [CI] =1.17–1.51). However, in the same multivariable model, each one-point increase in active SMU was
associated with a 15 percent decrease in depressive symptoms (AOR =0.85, 95 percent CI =0.75–0.96). To inform
interventions, future research should determine directionality of these associations and investigate related factors.
Keywords: social media, active use, passive use, depressive symptoms, United States adults
Introduction
Social media (SM) encompasses websites and mobile
applications that enable users to create content and par-
ticipate in online social networking (e.g., YouTube, Tumblr,
Facebook).
1
In 2016, over 90 percent of adults 18–49 of age
reported using at least one SM platform over the last 12
months.
2
SM allows users to explore self-identity, express
emotions or thoughts. This has led researchers to look into the
association between social media use (SMU) and mental
health outcomes, such as depression.
Depression is a prevalent condition and a public health
concern
3
; in 2015, 16.1 million United States adults suffered
at least one episode of depression, which is now one of the
leading causes of disability worlwide.
4,5
Initial studies found
significant associations between time of SMU and depressive
symptomatology,
6–11
whereas others found no significant
associations.
12–17
Subsequently, other research have ad-
dressed these contradictions by examining different patterns
of SMU (other than time) as they relate to depressive
symptomatology.
18,19
For example, research has found in-
creased odds of depression and related outcomes among in-
dividuals who used a greater number of SM platforms,
20
had
a skewed distribution of their social network on SM,
21
ex-
perienced negative interactions on SM sites,
14,22,23
or ex-
hibited problematic SMU.
24,25
Nevertheless, research is limited regarding whether pas-
sive and active SMU are associated with depressive symp-
toms. Active users share life experiences; create text, audio,
or video content; and respond frequently to other users.
26,27
These activities may increase social capital from acquain-
tances or emotional support from close friends and lead to
improved well-being.
28
Conversely, passive users (or
‘‘lurkers’’) tend to observe and maintain low engagement
1
Center for Research on Media, Technology, and Health, University of Pittsburgh, Pittsburgh, Pennsylvania.
2
Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
3
Health Policy Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
4
Department of Communication Studies, West Virginia University, Morgantown, West Virginia.
5
Division of Adolescent Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
CYBERPSYCHOLOGY,BEHAVIOR,AND SOCIAL NETWORKING
Volume 21, Number 7, 2018
ªMary Ann Liebert, Inc.
DOI: 10.1089/cyber.2017.0668
437
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with other users.
27,29
Passive SMU is the dominant activity
on SM sites,
30
and it has been associated with decreased
well-being
31–33
and social anxiety.
34,35
These opposing ef-
fects underline the importance of studying both the differ-
ence between and conjoint effect of active and passive SMU
on depression symptoms.
Two previous studies suggested that increased Facebook
passive use was associated with higher depression risk (envy
mediated the association in one of them)
17,36
; a third found
that active use was negatively associated with depression, but
only among women with neurotic personality traits.
16
While
these studies focused on a single SM platform, use of multiple
platforms is prevalent and increasing. For example, use of
two or more SM platforms recently increased by 10 percent
within a single year.
37
Because of this, it would be useful to
examine associations between both passive and active SMU
and depression across SMU in general. While SM platforms
differ in purpose and functional features, previous literature
used platform-specific features to encompass constructs of
passive and active SMU. For example, both Instagram
browsing and Facebook newsfeed scrolling have been cate-
gorized as passive SMU,
31,38
and broadcasting a video on
Instagram and posting a status update on Facebook have been
categorized as active SMU.
28,38
Therefore, we surveyed United States young adults to further
assess associations between passive and active SMU across
platforms and depressive symptoms with three aims. First, we
aimed to examine the association between passive SMU and
depressive symptoms. Our second aim was to assess the asso-
ciation between active SMU and depressive symptomatology.
Finally, we examined the conjoint association between passive
and active SMU and depressive symptoms. We hypothesized
that passive SMU would be positively associated with depres-
sion (H1), while active SMU would be negatively associated
with depressive symptoms (H2). For our third aim, we made no
specific hypotheses about the directionality or magnitude of
effects.
Materials and Methods
Design, participants, and setting
We conducted an online survey with adults 18 years of age
and older between July and August 2016. Participants were
recruited from Reddit, an online community of registered
members or ‘‘redditors.’’ These tend to be more White, male,
18–49 years of age, and mid- or higher income.
39
Redditors
may post, submit links, or upload pictures. Posts are orga-
nized and aggregated by topic or subreddits (e.g., image
sharing, news, location, or community). Each subreddit be-
haves as a modular community and new or existing users who
are exploring new topics are most likely to become active in
each subreddit.
40
Redditors are able to discuss and rate posts
within each subreddit, regardless of whether they are Reddit
friends with the original poster. We chose Reddit for this
study because it allows creating anonymous and ‘‘throw-
away’’ accounts. Given the social stigma surrounding mental
health concerns, we thought the feature of anonymity would
help in attracting individuals with concerns about depression
because of the disinhibition caused by anonymity.
41
We conducted a search for ‘‘depression’’ on Reddit and
identified 39 potential subreddits. Because many subreddits
require approval from the moderator to post announcements,
we submitted requests to each subreddit for which a moder-
ator could be identified. Seven depression-related and one
local subreddit granted approval to us for posting the survey
invitation (/r/depression,/r/EOOD,/r/GFD,/r/depressionregi-
mens,/r/PostPartum_Depression,/r/trolldepression,/r/mental-
health, and/r/MorgantownWV). This was adequate because it
allowed us to recruit participants directly from the commu-
nity. However, we were not able to identify whether users in
each subreddit, or those that chose to participate in the survey,
had only a temporary interest in depression or were actually
struggling with depression symptoms. Because trustworthi-
ness and survey participation increase when researchers
maintain an online presence,
42
one of the authors was avail-
able on each subreddit to engage with and answer questions
from the community.
Our posts contained a brief invitation to participate along
with a link to a survey conducted through Qualtrics research
software.
43
Survey materials can be found at: https://osf.io/
48bsq/?view_only=bcdf31ed9b2b4b8bbb1ef8c445bcc5dd.
Participants who clicked on the link were directed to an in-
troduction explaining the study and an informed consent to
participate in a 15-minute survey. Those who consented were
entered into a drawing for a monetary incentive. A total of
848 individuals consented to participate. For this study, we
used data from participants 18–49 years of age due to high
rates of SMU and depression among this population.
2
Data
collection procedures were approved by the West Virginia
University Institutional Review Board.
Measures
Participants completed a questionnaire that asked about
depressive symptoms, SMU, and demographic variables.
Depressive symptoms. We evaluated depressive symp-
toms using the four-item Patient-Reported Outcomes Mea-
surement System (PROMIS) scale. PROMIS is a National
Institutes of Health initiative that provides standardized, val-
idated, and reliable self-reported measurement tools across
several health domains.
44
The PROMIS four-item depression
combines great screening precision while decreasing partici-
pant burden.
44
Items asked about frequency of experiencing
feelings of hopelessness, helplessness, worthlessness, and
depression over the last 7 days. Response options for each item
ranged from 1 (‘‘never’’) to 5 (‘‘always’’). Total scores ranged
from 4 to 20. Given that PROMIS scores use a t-score-based
system,
44
we followed the American Psychiatric Association
recommendations for cutoff points
45
and depressive symp-
toms were categorized as follows: ‘‘none to slight’’ (4–7),
‘‘mild’’ (8–10), ‘‘moderate’’ (11–16), and ‘‘severe’’ (17–20).
Passive and active SMU. We measured both passive
and active SMU with seven items originally developed for
general internet use
29,46
and later validated for research.
47
Participants were asked how often they engage in a number
of behaviors while using any SM site.
47
Items associated
with passive SMU included ‘‘read discussions,’’ ‘‘read
comments/reviews,’’ and ‘‘watch videos or view pictures.’
Items associated with active SMU included ‘‘like/favorite/
voting,’’ ‘‘share others’ content,’’ ‘‘comment on or respond to
someone else’s content,’’ and ‘‘post your own content.’’ Re-
sponse options were ‘‘never,’’ ‘‘less than once a week,’’ ‘‘once
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a week,’’ ‘‘2–6 times a week,’’ ‘‘once a day,’’ and ‘‘several
times a day.’’
Demographic characteristics. Demographic variables
were defined a priori and included participants’ age, gender,
race/ethnicity (Non-Hispanic White vs. Non-White), rela-
tionship status (single or in a committed relationship), living
situation (living with family/living with friends or alone), and
highest education level attained (high school or less, some
college or two-year technical degree, and bachelor’s degree or
higher). To ensure robustness of statistical analyses, catego-
rization of these variables was determined based on the dis-
tribution of each one. For example, given there were few
Black, Asian, or Hispanic participants, it was preferable to
treat the ‘race/ethnicity’ variable as dichotomous.
Data analyses
We examined the distribution of our seven SMU variables
using the Shapiro–Wilk test of normality as well as graphical
methods (e.g., histograms and Q–Q plots). Because there
were substantial departures from normality (all p-values
p0.001), we examined the pairwise correlation matrix of all
SMU variables using Spearman’s rank correlation coeffi-
cients (qranging from 0.15 to 0.65). The Kaiser–Meyer–
Olkin (KMO) test of sampling adequacy indicated that the
sample was factorable (KMO =0.75).
48–50
While previous
research informed the latent constructs of passive and active
SMU,
28,47
to examine the underlying factor structure of our
particular set of adapted items and create composite scales,
we performed factor analysis using principal component
analysis (PCA) with varimax rotation. We chose this simpler,
orthogonal rotation because there is no prior work suggesting
the factors are strongly correlated and varimax rotation
yielded meaningful item groupings and strong, unambiguous
factor loadings.
51
Items were retained if they had minimum factor loading of
0.40, with the highest factor loading greater than 0.50–0.60
and the second highest smaller than 0.20–0.30, and if the
difference between primary–secondary factor loadings is
sufficiently large (i.e., 0.30–0.40).
52
The item ‘‘like/favorite/
voting’’ loaded onto both passive (0.47) and active (0.57)
SMU, the difference between factor loadings was only 0.1
and the difference between them was not large enough (0.3–
0.4).
52
Thus, this item was removed from the analysis. Factor
loadings for the remaining six items ranged from 0.57 to 0.86
and yielded a two-factor solution explaining 65 percent of
the variance among items. Visual inspection of a scree plot
confirmed a two-factor solution. Factor I (‘‘active SMU’’)
had an eigenvalue of 3.2 and explained 34 percent of the
variance. Factor II (‘‘passive SMU’’) had an eigenvalue of
1.4 and explained 30 percent of the variance. Internal con-
sistency was good for both active (a=0.80) and passive
(a=0.72) SMU items (Table 1).
Composite scales ranged from 0 to 5. Passive SMU was
slightly skewed left with a mean of 3.9 (standard deviation
[SD]=1.1) and a median of 4.0 (interquartile range
[IQR] =3.0–5.0). Active SMU approached a normal distri-
bution with a mean of 2.6 (SD =1.3) and median of 2.5
(IQR =1.8–3.5).
We computed percentages and means to describe all our
variables. We operationalized the dependent variable as a
four-level categorical variable to correspond with the clini-
cally relevant cut-points delineated above. Next, we deter-
mined bivariable associations between the dependent
variable and each of the independent variables using chi-tests
for categorical variables and analysis of variance for con-
tinuous variables.
We used ordered logistic regression to assess bivariable
associations between depressive symptoms and passive
SMU, active SMU, and each of the covariates. Then, we
assessed the independent associations between depressive
symptoms and each independent variable while controlling
for all demographic variables (Models 1 and 2). Finally, we
tested a third model (Model 3) that included both passive and
active SMU in the same model while controlling for demo-
graphic variables. This was appropriate because, as de-
scribed, these two modes of use may not be mutually
exclusive of one another (Model 3).
Results
Univariable and bivariable analyses
We included data from 702 participants from whom we
received complete information on both dependent and in-
dependent variables. Participants’ mean age was 23.4
(SD =6.2), 56.1 percent identified as female, and 68.7 per-
cent were single. In this sample, 75 percent of participants
were White, non-Hispanic, 63.8 percent lived with either
family or friends, and 55.3 percent had some college edu-
cation (Table 2). Over one-third of participants reported no
depressive symptoms (39.5 percent), whereas 23.5 percent,
30.1 percent, and 6.9 percent reported mild, moderate, and
severe symptoms, respectively. In bivariable analyses, each
one-point increase in passive SMU was significantly associ-
ated with increased odds of severe depressive symptoms (odds
Table 1. Factor Structure and Scale Development
of Passive and Active Social Media Use
Complete item
a
Factor
loading I
‘‘Active use’
Factor
loading II
‘‘Passive
use’’
Read discussions 0.11 0.80
Read comments/reviews 0.14 0.86
Watch videos or view pictures 0.15 0.70
Share others’ content
(e.g., retweet, share
posts or status updates)
0.81 0.07
Like/favorite/voting 0.57 0.47
Comment on, or respond
to someone else’s content
0.76 0.28
Post your own content
(e.g., tweet, status update)
0.86 0.07
Cronbach’s a
b
0.80 0.72
Factor variance
c
0.34 0.30
Note:
a
Each item asked participants to indicate how often they
engage in the behavior on any social media platform; response
scales ranged from never to several times a day.
b
Measuring internal consistency of items.
c
Measuring the percentage of explained common variance.
SOCIAL MEDIA USE AND DEPRESSION ON U.S. ADULTS 439
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ratio =1.34, 95 percent confidence interval [CI] =1.18–1.52,
pp0.05). However, no significant association was found
between active SMU and depression.
Multivariable analysis
For H1, we hypothesized that passive SMU would be pos-
itivelyassociatedwithdepression.InTable3Model1,each
one-point increase in passive SMU was associated with a 33
percent increase in odds of reporting severe depressive
symptoms (adjusted odds ratio [AOR] =1.33, 95 percent,
CI =1.17–1.51). Thus, H1 was supported. Conversely, for H2
we hypothesized that active SMU would be negatively asso-
ciated with depressive symptoms. Model 2, which used active
SMU as the main independent variable, showed no significant
results. Therefore, H2 was not supported.
Aim 3 was to examine the conjoint association between
passive and active SMU and depressive symptoms. Model 3
showed that each one-point increase in passive SMU was as-
sociated with a 44 percent increase in odds of reporting severe
depressive symptoms (AOR =1.44, 95 percent CI =1.25–1.66).
Additionally, each one-point increase in active SMU was as-
sociated with a 15 percent decrease in odds of reporting severe
depressive symptoms (AOR =0.85, 95 percent CI =0.75–0.96;
Table 3).
Discussion
Among a convenience sample of adults 18–49 years of
age, PCA identified six items distinctly useful to assess
passive and active SMU constructs. While increased passive
SMU was positively associated with depressive symptoms,
active SMU showed no significant association. A composite
third model showed that—when they were included in the
same model—passive SMU was associated with increased
depression, whereas active SMU was associated with lower
depression.
Our PCA analysis showed that, except for how often par-
ticipant engaged in ‘‘like/favorite/voting’’ (i.e., agreement with
others’ post), items were brief, showed good psychometric
properties, and were useful for identifying two SMU patterns.
Although liking someone else’s posts implies directed action, it
does not necessarily imply ‘‘seeking to, and interacting with
the medium in order to manipulate or create new content,’’ a
defining characteristic of active SMU.
29
Therefore, we suggest
removing this item from future active and passive SMU scales.
Our results are consistent with previous research exam-
ining passive SMU and depression (Model 1).
17,36
It might
be that individuals with depressive symptoms use SM more
passively due to depression features, such as anhedonia,
which is the inability to find pleasure in things an individual
used to enjoy. Conversely, passive SMU may trigger depressive
Table 2. Social Media Use and Sample Characteristics with Depressive Symptoms
by Self-Reported Depressive Symptoms (N=702)
Independent variable and covariates
Whole
sample
Depressive symptoms
p-Value
a
No
(n=277)
Mild
(n=165)
Moderate
(n=211)
Severe
(n=49)
Independent variable
Passive SMU, M(SD) 3.9 (1.1)
b
3.7 (1.1) 3.8 (1.1) 4.1 (1.1) 4.2 (1.0) <0.001
c
Active SMU, M(SD) 2.6 (1.2)
b
2.6 (1.2) 2.8 (1.2) 2.7 (1.3) 2.3 (1.1) 0.06
c
Covariates
Age in years, M(SD) 23.4 (6.2)
b
23.0 (6.2) 22.5 (5.4) 24.4 (6.5) 24.0 (7.0) 0.01
c
Sex 0.37
Female 56.1 59.6 50.9 55.9 55.1
Male 43.9 40.4 49.1 44.1 44.9
Race 0.24
White, non-Hispanic 75.0 71.6 80.4 74.8 76.6
Non-White
d
25.0 28.4 19.6 25.2 23.4
Relationship status 0.01
Single
e
68.7 68.9 72.7 61.6 83.7
In a committed relationship
f
31.3 31.1 27.3 38.4 16.3
Living situation 0.61
With family or friends 63.8 65.0 65.5 60.2 67.4
Alone
g
36.2 35.0 34.5 39.8 32.6
Education level 0.38
High school or less 29.6 31.1 30.3 24.6 40.8
Some college or 2-year technical degree 55.3 54.8 53.9 58.3 49.0
Bachelor’s degree or higher 15.1 14.1 15.8 17.1 10.2
Note;
a
p-Value derived using v
2
analysis comparing proportion of users for each categorical variable.
b
Measured in mean and standard deviation.
c
p-Value derived using ANOVA analyses comparing mean scores for each continuous variable.
d
Includes multiracial.
e
Includes engaged, married, and in a domestic partnership.
f
Includes widowed.
g
Defined as not currently living with a parent/guardian or significant other.
SMU, social media use; ANOVA, analysis of variance; SD, standard deviation.
440 ESCOBAR-VIERA ET AL.
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symptomatology. For example, a person seeking social support
may perceive not getting enough of it,
53
which could contribute
to depression.
Contrary to previous research
36
and our H2, we found no
significant association between active SMU and depression
(Model 2). A few reasons could explain this discrepancy.
While Frison and Eggermont studied Facebook use among
high school adolescents (mean age 15 years), our study focused
on adults 18–49 years of age.
36
Different patterns of active
SMU between adolescents and adults could influence results.
For example, while adolescents might have a more recreational
and social approach, adults may have an information-seeking
and opinion-exchange approach.
54
Longitudinal research on
variation of SMU over time accounting for other social and
behavioral factors may help to answer these questions.
Our third model estimated the odds of depressive symptoms
with both passive and active SMU as main predictors. In this
model, passive SMU still had a positive association with de-
pression, whereas active SMU showed a significant, negative
association, even when controlling for demographic charac-
teristics. Furthermore, there was a noticeable difference in
magnitude of effect sizes between passive and active SMU. In
particular, each point on the passive SMU scale was associated
with 44 percent increased odds of depression, whereas each
point on the active SMU scale was associated with only 15
percent decreased odds of depression. Passive SMU had a
larger effect size when active SMU was added to the model.
Two different perspectives may help explain these findings.
First, some forms of active SMU may generate different
feedback.
54
For example, some active user may use SM mostly
for recreational purposes, sharing content, but not as many
opinions, eliciting less conflict and more positive feedback,
making the person feel connected and included. Conversely,
other active users may utilize SM primarily to exchange points
of view and ideas, expecting conversation or debate, which
could make users feel left out or marginalized.
On the other hand, from the statistical perspective this
might be a case of suppression effect, where the inclusion of
a third independent variable increases the effect of another
on the outcome under study.
55
It could be that the magnitude
increase of the association between passive SMU and de-
pression occurred because active SMU explained some of the
variability in passive use. Future research should disentangle
the potential of active SMU in predicting variability of other
constructs, such as passive SMU or problematic SMU.
24,25
Our findings suggest that, while it is still soon to determine
the extent to which SMU may impact depression outcomes, it
is appropriate for encouraging more active use when engaging
with SM as opposed to passive lurking. Moreover, the mag-
nitude increase of the effect of passive SMU when combined
with active in the same model speaks to the likelihood of these
SMU patterns being more intertwined than previously thought.
Table 3. Multivariable Associations Between Passive and Active Social Media Use and Depressive Symptoms
Independent variables and covariates
Depressive symptoms
a
Model 1 Model 2 Model 3
AOR
b
(95 percent CI) AOR
b
(95 percent CI) AOR
b
(95 percent CI)
Passive SMU
c
1.33 (1.17–1.51) 1.40 (1.22–1.61)
Active SMU
c
0.98 (0.88–1.10) 0.85 (0.75–0.96)
Age in years
c
1.04 (1.01–1.07) 1.04 (1.01–1.07) 1.04 (1.01–1.07)
Sex
Male (Reference)
Female 0.96 (0.72–1.27) 0.92 (0.70–1.23) 1.04 (0.78–1.38)
Race
White, non-Hispanic (Reference)
Non-White
d
0.91 (0.65–1.26) 0.83 (0.60–1.16) 0.88 (0.64–1.22)
Relationship status
Single
e
(Reference)
In a committed relationship
f
0.85 (0.61–1.99) 0.90 (0.64–1.25) 0.84 (0.60–1.18)
Living situation
With family or friends
g
(Reference)
Alone 1.11 (0.83–1.48) 1.06 (0.80–1.42) 1.12 (0.83–1.50)
Education level
High school or less (Reference)
Some college or 2-year technical degree 0.87 (0.62–1.23) 0.90 (0.64–1.27) 0.85 (0.60–1.20)
Bachelor’s degree or higher 0.71 (0.42–1.22) 0.72 (0.42–1.23) 0.68 (0.40–1.17)
Note: Bold values represent statistical significance ( pp0.05).
a
Depressive symptoms were treated as a four-level categorical variable.
b
Adjusted for all variables in the table.
c
Associated odd ratios represent the odds for each one-unit increase on the scale.
d
Includes multiracial.
e
Includes separated, divorced, and widowed.
f
Includes engaged, married, and in a domestic partnership.
g
Includes parent/guardian, significant other, and friends.
AOR, adjusted odds ratio; CI, confidence interval.
SOCIAL MEDIA USE AND DEPRESSION ON U.S. ADULTS 441
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It may be useful to educate users in healthier ways to actively
use SM, avoiding situations that could lead to users feeling
more stressed and left out after the exchange, which could
increase feelings of marginalization.
Our study had limitations. Survey participants were re-
cruited from an online community. Thus, we cannot claim
whether subreddit users or survey participants had only a
temporary interest in depression or were clinically depressed.
We partially addressed this limitation by using measures that
were developed to identify depression risk among large, oth-
erwise healthy population samples.
44
Our survey did not assess
participants’ employment status. Research has shown that
unemployed persons have more leisure compared with em-
ployed individuals.
56–59
Thus, future research should account
for this variable. Our sample had limited representativeness;
given the universality of SM, future research should consider a
sampling frame that is more diverse and informative for both
developed and developing societies.
60
It should be emphasized
that our main independent variables assessed passive and ac-
tive use in SM platforms in general. More nuanced approaches
might help to isolate the impact of specific types of SM (e.g.,
social networking vs. location-based services) on mental health
outcomes. In spite of these limitations, our results warrant
further research to determine directionality of these associa-
tions and assess why passive and active SMU are so strongly
associated with depression symptoms.
Acknowledgment
The authors acknowledge funding from The Fine Foun-
dation for the completion of this research.
Author Disclosure Statement
No competing financial interests exist.
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... increased well-being, enhanced life satisfaction) (Kim and Lee 2011;Kim and Fingerman 2022), while others link SNU to negative outcomes (e.g. decreased life satisfaction, increased depressive symptoms) (Cheng and Nguyen 2021;Escobar-Viera et al. 2018;Ho, Lee, and Liao 2016;Nguyen, Cheng, and Tran 2023). Verduyn et al. (2015) categorized SNU into active social network usage (ASNU) and passive social network usage (PSNU), highlighting distinct psychological impacts. ...
... ASNU includes activities like posting, commenting, sharing, and liking, which are linked to psychological benefits (Seabrook, Kern, and Rickard 2016;Dang 2021). In contrast, PSNU involves activities like viewing, browsing, and observing, which can result in negative outcomes for users' mental health (Cheng, Nguyen, and Nguyen 2024;Escobar-Viera et al. 2018; Thorisdottir et al. 2019;Quiroz and Mickelson 2021). Despite criticisms of being overly simplistic and vague, the active/passive dichotomy has proven valuable. ...
... 12 Passive social media use has deleterious effects on well-being. [12][13][14] This may be because many individuals post unrealistically flattering self-depictions, leading passive users to engage in more upward social comparison. 15,16 Interestingly, active use in general has not shown the same mental health risks, and in some cases, is even linked to lower depression. ...
... 15,16 Interestingly, active use in general has not shown the same mental health risks, and in some cases, is even linked to lower depression. 13 However, there can be more nuance within the realm of active use, as recent work has also found that the active process of creating and edited idealized self-representations is associated with lower body dissatisfaction 17,18 and posting self-images to social media has resulted in lower feelings of attractiveness. 19 Taken together, this indicates the importance of examining different types of use. ...
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Background Social networking sites (SNSs) have become a pervasive part of modern culture, which may also affect mental health. Objective The aim of this systematic review was to identify and summarize research examining depression and anxiety in the context of SNSs. It also aimed to identify studies that complement the assessment of mental illness with measures of well-being and examine moderators and mediators that add to the complexity of this environment. Methods A multidatabase search was performed. Papers published between January 2005 and June 2016 relevant to mental illness (depression and anxiety only) were extracted and reviewed. Results Positive interactions, social support, and social connectedness on SNSs were consistently related to lower levels of depression and anxiety, whereas negative interaction and social comparisons on SNSs were related to higher levels of depression and anxiety. SNS use related to less loneliness and greater self-esteem and life satisfaction. Findings were mixed for frequency of SNS use and number of SNS friends. Different patterns in the way individuals with depression and individuals with social anxiety engage with SNSs are beginning to emerge. Conclusions The systematic review revealed many mixed findings between depression, anxiety, and SNS use. Methodology has predominantly focused on self-report cross-sectional approaches; future research will benefit from leveraging real-time SNS data over time. The evidence suggests that SNS use correlates with mental illness and well-being; however, whether this effect is beneficial or detrimental depends at least partly on the quality of social factors in the SNS environment. Understanding these relationships will lead to better utilization of SNSs in their potential to positively influence mental health.
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Although studies have shown that Instagram use and young adults' mental health are cross-sectionally associated, longitudinal evidence is lacking. In addition, no study thus far examined this association, or the reverse, among adolescents. To address these gaps, we set up a longitudinal panel study among 12- to 19-year-old Flemish adolescents to investigate the reciprocal relationships between different types of Instagram use and depressed mood. Self-report data from 671 adolescent Instagram users (61% girls; MAge = 14.96; SD = 1.29) were used to examine our research question and test our hypotheses. Structural equation modeling showed that Instagram browsing at Time 1 was related to increases in adolescents' depressed mood at Time 2. In addition, adolescents' depressed mood at Time 1 was related to increases in Instagram posting at Time 2. These relationships were similar among boys and girls. Potential explanations for the study findings and suggestions for future research are discussed.
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This paper introduces the psychological empowerment framework from community psychology to the social media context. Intrapersonal empowerment and interactional empowerment are tested as two focal components of the empowerment process at the individual level. This paper aims to assess the connections between active and passive social media use and psychological empowerment. An online survey was conducted through an online panel (MTurk) with 371 effective responses. Results showed that active use was positively related to both intrapersonal and interactional empowerment, while only a weak association was found between passive use and interactional empowerment.
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Rationale: Depression is the leading cause of disability worldwide. The suggested association between social media use (SMU) and depression may be explained by the emerging maladaptive use pattern known as problematic social media use (PSMU), characterized by addictive components. Objective: We aimed to assess the association between PSMU and depressive symptoms-controlling for overall time and frequency of SMU-among a large sample of U.S. young adults. Methods: In October 2014, participants aged 19-32 (N = 1749) were randomly selected from a nationally-representative U.S. probability-based panel and subsequently invited to participate in an online survey. We assessed depressive symptoms using the validated Patient-Reported Outcomes Measurement Information System (PROMIS) brief depression scale. We measured PSMU using an adapted version of the Bergen Facebook Addiction Scale to encompass broader SMU. Using logistic regression models, we tested the association between PSMU and depressive symptoms, controlling for time and frequency of SMU as well as a comprehensive set of socio-demographic covariates. Results: In the multivariable model, PSMU was significantly associated with a 9% increase in odds of depressive symptoms (AOR [adjusted odds ratio] = 1.09; 95% CI [confidence interval]: 1.05, 1.13; p < 0.001.) Increased frequency of SMU was also significantly associated with increased depressive symptoms, whereas SMU time was not (AOR = 1.01; 95% CI: 1.00, 1.01; p = 0.001 and AOR = 1.00; 95% CI: 0.999-1.001; p = 0.43, respectively). Conclusion: PSMU was strongly and independently associated with increased depressive symptoms in this nationally-representative sample of young adults. PSMU largely explained the association between SMU and depressive symptom, suggesting that it may be how we use social media, not how much, that poses a risk. Intervention efforts aimed at reducing depressive symptoms, such as screenings for maladaptive SMU, may be most successful if they address addictive components and frequency-rather than time-of SMU.
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Most people use Facebook on a daily basis; few are aware of the consequences. Based on a 1-week experiment with 1,095 participants in late 2015 in Denmark, this study provides causal evidence that Facebook use affects our well-being negatively. By comparing the treatment group (participants who took a break from Facebook) with the control group (participants who kept using Facebook), it was demonstrated that taking a break from Facebook has positive effects on the two dimensions of well-being: our life satisfaction increases and our emotions become more positive. Furthermore, it was demonstrated that these effects were significantly greater for heavy Facebook users, passive Facebook users, and users who tend to envy others on Facebook.
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Online social networking sites (SNSs) such as Facebook, Twitter, and MySpace are used by billions of people every day to communicate and interact with others. There has been increasing interest in the potential impact of online social networking on wellbeing, with a broadening body of new research into factors associated with both positive and negative mental health outcomes such as depression. This systematic review of empirical studies (n = 30) adds to existing research in this field by examining current quantitative studies focused on the relationship between online social networking and symptoms of depression. The academic databases PsycINFO, Web of Science, CINAHL, MEDLINE, and EMBASE were searched systematically using terms related to online social networking and depression. Reporting quality was critically appraised and the findings discussed with reference to their wider implications. The findings suggest that the relationship between online social networking and symptoms of depression may be complex and associated with multiple psychological, social, behavioral, and individual factors. Furthermore, the impact of online social networking on wellbeing may be both positive and negative, highlighting the need for future research to determine the impact of candidate mediators and moderators underlying these heterogeneous outcomes across evolving networks.
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*Gelinas, *Pierce (co-lead authors)The use of social media as a recruitment tool for research with humans is increasing, and likely to continue to grow. Despite this, to date there has been no specific regulatory guidance and there has been little in the bioethics literature to guide investigators and institutional review boards (IRBs) faced with navigating the ethical issues such use raises. We begin to fill this gap by first defending a nonexceptionalist methodology for assessing social media recruitment; second, examining respect for privacy and investigator transparency as key norms governing social media recruitment; and, finally, analyzing three relatively novel aspects of social media recruitment: (i) the ethical significance of compliance with website “terms of use”; (ii) the ethics of recruiting from the online networks of research participants; and (iii) the ethical implications of online communication from and between participants. Two checklists aimed at guiding investigators and IRBs through the ethical issues are included as appendices.