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No More FOMO: Limiting Social Media Decreases Loneliness and Depression

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Introduction: Given the breadth of correlational research linking social media use to worse well-being, we undertook an experimental study to investigate the potential causal role that social media plays in this relationship. Method: After a week of baseline monitoring, 143 undergraduates at the University of Pennsylvania were randomly assigned to either limit Facebook, Instagram and Snapchat use to 10 minutes, per platform, per day, or to use social media as usual for three weeks. Results: The limited use group showed significant reductions in loneliness and depression over three weeks compared to the control group. Both groups showed significant decreases in anxiety and fear of missing out over baseline, suggesting a benefit of increased self-monitoring. Discussion: Our findings strongly suggest that limiting social media use to approximately 30 minutes per day may lead to significant improvement in well-being.
Content may be subject to copyright.
Journal of Social and Clinical Psychology, Vol. 37, No. 10, 2018, pp. 751-768
© 2018 Guilford Publications, Inc.
Address correspondence to Melissa G. Hunt, 425 S. University Ave., Philadelphia, PA
19104; E-mail: mhunt@psych.upenn.edu
LIMITING SOCIAL MEDIA
HUNT ET AL.
NO MORE FOMO: LIMITING SOCIAL MEDIA
DECREASES LONELINESS AND DEPRESSION
MELISSA G. HUNT, RACHEL MARX, COURTNEY LIPSON,
AND JORDYN YOUNG
University of Pennsylvania
Introduction: Given the breadth of correlational research linking social media
use to worse well-being, we undertook an experimental study to investigate the
potential causal role that social media plays in this relationship. Method: After a
week of baseline monitoring, 143 undergraduates at the University of Pennsyl-
vania were randomly assigned to either limit Facebook, Instagram and Snapchat
use to 10 minutes, per platform, per day, or to use social media as usual for three
weeks. Results: The limited use group showed signicant reductions in loneliness
and depression over three weeks compared to the control group. Both groups
showed signicant decreases in anxiety and fear of missing out over baseline,
suggesting a benet of increased self-monitoring. Discussion: Our ndings strong-
ly suggest that limiting social media use to approximately 30 minutes per day may
lead to signicant improvement in well-being.
Keywords: social media; social networking sites; Facebook; Snapchat; Instagram;
well-being; depression; loneliness
Social Networking Sites (SNS) have become a ubiquitous part
of the lives of young adults. As of March of 2018, 68% of adults
in the United States had a Facebook account, and 75% of these
people reported using Facebook on a daily basis. Moreover, 78%
of young adults (ages 18–24) used Snapchat, while 71% of young
adults used Instagram (Smith & Anderson, 2018). Widespread
adoption of social media has prompted a flurry of correlational
studies on the relationship between social media use and mental
752 HUNT ET AL.
health. Self-reported Facebook and Instagram usage have been
found to correlate positively with symptoms of depression, both
directly and indirectly (Donnelly & Kuss, 2016; Lup, Trub, &
Rosenthal, 2015; Rosen, Whaling, Rab, Carrier, & Cheever, 2013;
Tandoc, Ferrucci, & Duffy, 2015;). Higher usage of Facebook
has been found to be associated with lower self-esteem cross-
sectionally (Kalpidou, Costin, & Morris, 2011) as well as greater
loneliness (Song et al., 2014). Higher usage of Instagram is cor-
related with body image issues (Tiggemann & Slater, 2013).
In a large population based study, Twenge and colleagues
(Twenge, Joiner, Rogers, & Martin, 2017) found that time spent on
screen activities was significantly correlated with more depres-
sive symptoms and risk for suicide-related outcomes, although
the correlations with SNS use specifically were quite small, and
only significant for girls. A major limitation of that study was
that the data bases used suffered from restricted range in SNS
use, with the highest category (almost every day) being en-
dorsed by more than 85% of females in the samples (Daly, 2018).
This simply cannot capture differences in use as they occur natu-
ralistically. Checking Facebook for 5 minutes almost every day
is surely different that spending hours a day on SNS platforms.
Two studies have used prospective, naturalistic designs. Us-
ing experience sampling, Kross and colleagues (2013) found
that Facebook use predicts less satisfaction with life over time.
In a two-week diary design, Steers, Wickham, & Acitelli (2014)
found that the relationship between Facebook use and depres-
sive symptoms was mediated by social comparisons. Indeed,
several studies have demonstrated that social comparison and
peer envy often play a major role in these findings (Tandoc et al.,
2015; Verduyn et al., 2015).
Thus, there is considerable evidence that SNS use is associ-
ated with reductions in well-being. However, the vast majority
of work done in this domain has been correlational in design,
which does not allow for causal inferences. Two studies (Kross
et al., 2013 and Steers et al., 2014) used prospective longitudinal
designs, but were not experimental. It is quite possible that more
depressed or lonely individuals use SNS more in an attempt to
connect with others. Similarly, it is possible that individuals with
lower self-esteem or poorer self-image are more prone to engage
LIMITING SOCIAL MEDIA 753
in social comparison by spending time on SNS sites. Only experi-
mental studies can address the direction of causality definitively.
In our review of the literature, we were able to find only two
experimental studies, both of which examined only Facebook
use. The first study found that subjects assigned to passively
scroll through Facebook (as opposed to those assigned to ac-
tively post and comment) subsequently reported lower levels
of well-being and more envy, indicating not only that Facebook
impacts mental health but also that the way in which we engage
with Facebook matters (Verduyn et al., 2015). It is reasonable
to think that the longer one spends on social media, the more
one will be engaging with it in a passive way (as opposed to
actively posting content, commenting, etc.) In the second study,
subjects who were randomly assigned to abstain from Facebook
for a week demonstrated improved satisfaction with life and af-
fect (Tromholt, 2016). While this study was a considerable im-
provement methodologically on prior work, the ecological va-
lidity of the study is somewhat suspect. First, the intervention
lasted only one week. While it is interesting that subjects showed
measurable increases in well-being over this short time, it is un-
clear whether this would have been sustainable. Second, many
users have grown so attached to social media that a long-term
intervention requiring complete abstention would be unrealistic;
limiting SNS use seems more likely to be acceptable and sustain-
able. Third, this study relied upon self-report to measure compli-
ance with study instructions—there was no objective measure
of actual time spent on Facebook. Lastly, both of these studies
only explored the effects of Facebook usage. While Facebook is
the most widely used SNS among adults, many other sites, es-
pecially Snapchat and Instagram, attract large numbers of users
and play a major role in these users’ lives; this is most notably
true for young adults.
The current study was designed to be a rigorous, ecologically
valid, experimental study of the impact on well-being of limiting
(but not eliminating) the use of multiple SNS platforms over an
extended period of time. We improve upon prior studies in sev-
eral ways. First, the study is experimental, allowing for causal
inferences to be made. Second, we gathered objective data on
actual usage, both during a baseline phase (to account for the
754 HUNT ET AL.
effects of self-monitoring) and during the active intervention
phase. Third, we included three major SNS platforms (Facebook,
Snapchat, and Instagram). Fourth, we limited usage to 10 min-
utes per platform per day, as this seems far more realistic than
asking people to abstain from SNS use completely. Many organi-
zations, student groups, businesses, and so on rely on social me-
dia posts to communicate with members and customers about
meeting times, events, etc. It is unrealistic to expect young peo-
ple to forego this information stream entirely. Finally, we mea-
sured well-being at multiple time points, including before and
after the initial self-monitoring baseline, at multiple time points
throughout the intervention, and at one-month follow-up after
the intervention formally ended.
METHODS
PARTICIPANTS
A total of 143 subjects (108 women, 35 men) were recruited from
a pool of undergraduates at the University of Pennsylvania, and
began the study on a rolling basis. Seventy-two subjects partici-
pated in the fall semester, and 71 in the spring. The subject pool
consisted of students enrolled in psychology courses for which
they could participate in studies to earn course credit. Subjects
were required to have Facebook, Instagram, and Snapchat ac-
counts, and to own an iPhone.
MEASURES
Subjective Well-Being Survey
To measure well-being, we used a battery consisting of seven
validated scales. Given the lack of experimental research on our
topic, we decided to use a wide variety of well-being constructs
that have been found to correlate with social media usage. The
survey also included a consent form and questions regarding de-
mographic information (age, sex, and race). The scales compris-
ing the subjective well-being survey are listed below.
LIMITING SOCIAL MEDIA 755
Social Support. The Interpersonal Support and Evaluation List
(ISEL; Cohen & Hoberman, 1983) consists of 20 items scored on
a 0–3 scale (definitely false to definitely true). We modified item
8 slightly to make it specific to Philadelphia (If I wanted to go on
a trip for a day to Center City, I would have a hard time finding
someone to go with me). Items pertain to accessibility of social
support and include statements such as “When I feel lonely, there
are several people I can talk to” and “If I decide one afternoon
that I would like to go to a movie that evening, I could easily find
someone to go with me.” The ISEL has good construct validity
and good internal consistency with α = 0.77 (Cohen, Mermel-
stein, Kamarck, & Hoberman, 1985).
Fear of Missing Out. The Fear of Missing Out Scale (FoMOs;
Przybylski, Murayama, DeHaan, & Gladwell, 2013) is a validat-
ed measure of distress related to missing out on social experienc-
es (α = .87). It consists of 10 items scored on a scale of 1 (not at all
true of me) to 5 (extremely true of me); items include statements
such as “I get anxious when I don’t know what my friends are up
to,” “Sometimes, I wonder if I spend too much time keeping up
with what is going on,” and “I fear others have more rewarding
experiences than me.”
Loneliness. The UCLA Loneliness Scale (revised UCLA Loneli-
ness Scale; Russell, Peplau, & Cutrona, 1980) measures perceived
social isolation. The original version was revised to include re-
verse-scored items and consists of 20 items, scored on a scale of
1 (never) to 4 (often). Sample items include statements such as
“No one really knows me well,” “My interests and ideas are not
shared by those around me,” and “I feel in tune with the people
around me” (reverse scored). The scale has good construct valid-
ity and internal consistency with α = 0.94 (Russell et al., 1980).
Anxiety. The Spielberger State-Trait Anxiety Inventory (STAI-S;
Spielberger, Gorsuch, & Lushene, 1970) is a widely used measure
of anxiety symptoms. The inventory consists of two instruction
sets, which measure state (in-the-moment) and trait (general)
anxiety. We only used the state anxiety version, which consists
of 20 items such as “I feel worried” and “I feel calm” (reverse
scored). Subjects can respond on a scale of 1 (not at all) to 4 (ex-
tremely so).
756 HUNT ET AL.
Depression. The Beck Depression Inventory (BDI-II; Beck, Steer,
& Brown, 1996) is a standard clinical measure of depressive
symptoms. It consists of 21 items covering the vegetative, affec-
tive and cognitive symptoms of depression. Respondents can in-
dicate the severity of each symptom on a scale of 0–3 (e.g.. for the
symptom loss of pleasure, one can respond: “I get as much plea-
sure as I ever did from the things I enjoy,” “I don’t enjoy things
as much as I used to,” “I get very little pleasure from the things I
used to enjoy,” or “I can’t get any pleasure from the things I used
to enjoy”).
Self-Esteem. The Rosenberg Self-Esteem Scale (RSES; Rosen-
berg, 1979) assesses how one feels about oneself. It consists of
10 items, scored 0 (strongly disagree) to 3 (strongly agree), with
higher scores indicating more positive feelings about oneself.
Items include “I feel that I have a number of good qualities,” “I
feel I do not have much to be proud of” (reverse scored), and “I
take a positive attitude toward myself.”
Autonomy and Self-Acceptance. The Ryff Psychological Well-Be-
ing Scale (PWB; Ryff, 1989) operationalizes psychological well-
being in 6 dimensions. We selected the dimensions of autonomy
and self-acceptance, as these dimensions are most pertinent to
the potential effects of social media. We utilized the 42-item ver-
sion, selecting the 14 items belonging to these two dimensions.
Items are scored on a scale of 1 (strongly disagree) to 6 (strongly
agree), with higher scores indicating higher levels of well-being.
Examples of items from the autonomy subscale include “My
decisions are not usually influenced by what everyone else is
doing” and “I tend to worry about what other people think of
me” (reverse scored). Examples of items from the self-acceptance
subscale include “I like most aspects of my personality” and “In
many ways, I feel disappointed about my achievements in life”
(reverse scored).
Objective Measure of Social Media Usage
To track usage of social media, we had subjects email screenshots
of their iPhone battery usage at specified increments. iPhones
automatically track the total minutes each application is actively
LIMITING SOCIAL MEDIA 757
open on the screen. The battery screen allows users to display
their usage for the past 24 hours or 7 days. We provided instruc-
tions on how to get to this screen with every reminder to send in
a screenshot. In the spring semester of the study, subjects were
also asked to estimate their daily usage of Facebook, Instagram,
and Snapchat before starting the baseline self-monitoring period.
PROCEDURE
FALL SEMESTER
Subjects signed up via the online website for the University psy-
chology subject pool. Upon signing up, they were directed to
a secure Qualtrics platform where they saw the consent form,
and then completed the baseline survey of mood and well-being
measures. Subjects were then sent a welcome email describing
the study in more detail. This email informed them that, start-
ing that night, they would be sending in a screenshot of their
battery screen displaying the past 24 hours of usage in minutes.
They were told they would be doing this each night for the next
four weeks. Subjects were told to use social media as usual un-
til they received their next email. If they had signed up for the
study but had not yet completed the baseline survey, they were
sent a similar email detailing the study, with the added reminder
to complete the baseline survey. They were not told to send in
screenshots until they completed the baseline survey.
One week after completing the baseline survey, subjects were
emailed their second survey. This survey was identical to the
baseline survey, but excluded the BDI-II (it was assumed that
depression would not fluctuate much on a week-by-week basis).
Subjects were asked to send a screenshot of their battery usage,
and then received their group assignment. The control group
was instructed to continue to use social media as usual, while
the intervention group was told to limit their usage on Facebook,
Instagram and Snapchat to 10 minutes per platform per day.
Subjects continued to send in nightly screenshots for the next 3
weeks. They also continued to take the survey at the end of each
week (the surveys at the end of the 2nd and 3rd weeks did not
include the BDI-II, but the survey at the end of the 4th week—
758 HUNT ET AL.
i.e. at the end of the intervention phase—did include the BDI-II).
At the end of the fourth week, they were sent a wrap-up email
after completing their survey and sending in their screenshot.
This email explained that they would be receiving course credit
shortly and that they were essentially done with the study, with
the exception of a one-time follow-up that would be sent out
around a month later. This follow-up included the survey (with
the BDI-II), and a final screenshot of their usage for the past 24
hours.
SPRING SEMESTER
In the spring semester the procedure was essentially the same
as in the fall, albeit with two changes. First, we decided to in-
clude the BDI-II in all surveys that were sent out. We regretted
not having as much intermediate data on depression levels. Sec-
ond, instead of having subjects send in screenshots every night,
we instructed them to send in screenshots displaying the past
7 days of usage once a week. This was done for two reasons.
First, although subjects were encouraged to send in screenshots
at around the same time each night, subjects inevitably sent
screenshots in earlier or later than the time they had sent in the
screenshot the previous night. Having a screenshot sent in an
hour early compromises the quality of data in the context of a 24-
hour window much more than it does in the context of a 7-day
window. Second, because the study lasted for four weeks, night-
ly screenshots were a significant logistical commitment for both
the subjects and researchers. We expected that people would be
more likely to send in all screenshots if they were just asked to
send in 5 screenshots, as opposed to 29. In addition, it was more
manageable for researchers to promptly follow up with subjects
who did not send in a screenshot. This reduced error variance
from subjects submitting screenshots at different times, or for-
getting to send specific screenshots. Unfortunately, the battery
usage app resets each time the phone is turned off. Thus, for a
few subjects, we had to extrapolate weekly usage from fewer
LIMITING SOCIAL MEDIA 759
than seven full days of battery usage. However, given that this
is the first study to attempt to measure usage objectively (rather
than relying on retrospective self-report) we are confident that
our usage data are more reliable and valid than those of previous
studies.
RESULTS
CORRELATIONAL AND PROSPECTIVE RESULTS PRIOR TO
RANDOMIZATION
We found that baseline depression, loneliness, anxiety, perceived
social support, self-esteem, and well-being did not actually cor-
relate with baseline social media use in the week following com-
pleting the questionnaires. That is, more distressed individuals
did not use social media more prospectively. Baseline Fear of
Missing Out, however, did predict more actual social media use
prospectively (r = .20, p < .05). Similarly, actual usage during the
first week of baseline monitoring was not associated with well-
being at the end of the week, controlling for baseline well-being.
These results are somewhat at odds with prior research, which
often finds an association with estimated, self-reported social
media use and measures of well-being prospectively.
In the spring, we asked subjects to give us estimates of their
use (essentially retrospective self-report data as is used in most
correlational studies of social media use and well-being). Inter-
estingly, estimated use was significantly negatively correlated
with perceived social support (r = −.24, p < .05) and marginally
negatively correlated with both self-esteem (r = .23, p = .056)
and overall well-being (r = −.21, p = .08). Estimated use and actu-
al use were significantly, but only modestly correlated with each
other (r = .31, p = .01). Eliminating three univariate outliers from
the data (people who estimated over 900 minutes, or 15 hours
of use per week) yielded even more modest results (r = .26, p <
.05). That is, people were not very good at estimating their actual
use, and retrospective self-report bias appears to explain at least
some of the correlational findings.
760 HUNT ET AL.
EXPERIMENTAL RESULTS
MANIPULATION CHECK
First, we ensured that subjects in the experimental condition did
indeed limit their usage by conducting an independent samples
t-test at each week of the intervention. Although not every sub-
ject complied perfectly with the established time limit, on aver-
age the experimental group used significantly less social media
than the control group for week one, t(117) = 5.69, p < .001, week
two, t(119) = 6.516, p < .001, and week three, t(113) = 5.78, p <
.001, of the intervention. On average, the experimental group
also remained within the limit of 210 minutes per week at weeks
one (M = 179, SD = 140), two (M = 166, SD = 149), and three (M =
176, SD = 155). See Figure 1.
EFFECT OF CONDITION ON LONELINESS
We then ran an analysis of covariance to determine the effect of
condition on loneliness. Controlling for baseline loneliness and
actual usage, subjects in the experimental group scored signifi-
FIGURE 1. Total weekly social media use over time by condition.
LIMITING SOCIAL MEDIA 761
cantly lower on the UCLA Loneliness Scale at the end of the in-
tervention, F(1,111) = 6.896, p = .01. See Figure 2.
EFFECT OF CONDITION ON DEPRESSION
Next, we first ran a univariate analysis of variance to assess the
effect of group assignment on depression, controlling for base-
line depression, actual usage, and the interaction of baseline de-
pression and condition. There was a significant interaction be-
tween condition and baseline depression, F(1, 111) = 5.188, p <
.05. To help with interpretation of the interaction effect, we split
the sample into high and low baseline depression. Subjects were
considered low in baseline depression if they scored below the
clinical cut-off of 14 on the BDI, and high if they scored a 14 or
above. When analyzed this way, there were significant main ef-
fects of both baseline depression and condition on depressive
symptoms at week 4, for High/Low baseline, F(1,111) = 44.5, p
< .001; for Condition, F(1,111) = 4.5, p < .05. In sum, individuals
high in baseline depression in the control group saw no change
in mean BDI score over the course of the study (at baseline, mean
BDI = 22.8, at Week 4 mean BDI = 22.83). In contrast, individuals
in the experimental group saw clinically significant declines in
FIGURE 2. Loneliness at week 4 by condition.
762 HUNT ET AL.
depressive symptoms, from a mean of 23 at baseline, to a mean
of 14.5 at Week 4. Individuals low in baseline depression in the
experimental group saw a statistically, but not clinically signifi-
cant decline of a single point in mean BDI (from 5.1 at baseline to
4.1 at Week 4). Individuals low in baseline depression in the con-
trol group, on the other hand, showed neither statistically nor
clinically significant change in depressive symptoms (from 5 at
baseline to 4.67 at Week 4). See Figure 3.
EFFECT OF CONDITION ON ALL OTHER MEASURES
After running analyses of covariance on interpersonal support,
fear of missing out, anxiety, self-esteem, and psychological well-
being, we found no significant differences between the two
groups.
We did, however, see a slight, but statistically significant de-
cline from baseline to the end of the intervention in fear of miss-
ing out in both the control, t(46) = 3.278, p < .002, and experimen-
tal, t(65) = 3.568, p < .001, groups. Similarly, we observed a slight
decline in anxiety in both the control, t(46) = 3.035, p < .004, and
experimental, t(65) = 2.477, p < .016, groups.
FIGURE 3. Depressive symptoms by condition and baseline BDI.
LIMITING SOCIAL MEDIA 763
FOLLOW-UP DATA
Unfortunately, we experienced significant attrition from the
study at the final follow-up wave of data collection in both the
fall and spring semesters. In total, we were able to collect com-
plete follow-up data (including both objective use and well-be-
ing data) from only 30 individuals (21%). We deemed that sam-
ple size too small to provide reliable or meaningful results.
DISCUSSION
As hypothesized, experimentally limiting social media usage on
a mobile phone to 10 minutes per platform per day for a full
three weeks had a significant impact on well-being. Both lone-
liness and depressive symptoms declined in the experimental
group. With respect to depression, the intervention was most
impactful for those who started the study with higher levels of
depression. Subjects who started out with moderately severe de-
pressive symptoms saw declines down to the mild range by the
simple expedient of limiting social media use for three weeks.
Even subjects with lower levels of depression saw a statistically
significant improvement as the result of cutting down on so-
cial media, although a mean decline of one point in BDI score is
probably not clinically meaningful. As one subject shared with
us “Not comparing my life to the lives of others had a much
stronger impact than I expected, and I felt a lot more positive
about myself during those weeks.” Further, “I feel overall that
social media is less important and I value it less than I did prior
to the study.”
Throughout the four-week intervention, subjects in both groups
also showed a significant decline in both fear of missing out and
anxiety. We posit that this was a result of the self-monitoring in-
herent in the study. As one subject in the experimental group
said “I am much more conscious of my usage now. This was defi-
nitely a worthwhile study in which to partake.” Another noted
“It was easier than I thought to limit my usage. Afterwards I
pretty much stopped using Snapchat because I realized it wasn’t
something I missed.” Although there was no statistically signifi-
764 HUNT ET AL.
cant decline in usage in the control group, even those subjects
reported that self-monitoring impacted their awareness of their
use. For example, one said “The amount of time spent on social
media is alarming and I will be more conscientious of this in the
future.” Another reported “I was in the control group and I was
definitely more conscious that someone was monitoring my us-
age. I ended up using less and felt happier and like I could focus
on school and not (be as) interested in what everyone is up to.”
Interestingly, our subjects did not show any improvement in
social support, self-esteem, or psychological well-being. Perhaps
these measures are truly unaffected by social media. It is also
possible that the intervention was not long enough to produce
any changes in these measures. Or, it could be that the time limit
we imposed was either too restrictive or not restrictive enough
to bring about positive change in these domains.
With the exception of fear of missing out, well-being at base-
line did not predict actual social media use prospectively during
the first week of self-monitoring. FOMO, however, did predict
more usage, as might be expected. Similarly, actual use during
the first week did not predict changes in well-being over that
week controlling for baseline. Estimated use, however, was nega-
tively correlated with perceived social support, self-esteem, and
overall well-being. That is, more distressed individuals believed
that they used social media more than less distressed individu-
als, despite the fact that there were no differences in objective
use. Since ours is the first study that we know of to collect objec-
tive use data, this highlights the importance of future research
not relying on retrospective self-report or estimated use data.
LIMITATIONS
This study had several important limitations. While we did our
best to monitor and limit social media usage, we were only able
to do so on mobile phones (this was not an issue for Snapchat,
which can only be used through the mobile application). While
participants were instructed to only use Facebook, Instagram,
and Snapchat through the applications on their phones, they
still had the ability to use social media on their computers, use
LIMITING SOCIAL MEDIA 765
friends’ phones, access the websites via the internet on their
phone, etc. Furthermore, we could not actually turn someone’s
social media off if they went over 10 minutes. While most people
were compliant with the study instructions, there were individu-
als in the experimental group who used significantly more social
media than they were supposed to.
Moreover, social media does not just include Facebook, Snap-
chat, and Instagram. While we only measured and manipulated
these three platforms, participants could still opt to go on Twit-
ter, Tumblr, Pinterest, Facebook Messenger, dating sites, and so
on. Indeed, some subjects noted that they spent a lot more time
on dating apps, perhaps as the result of limiting other platforms.
In addition, our sample was a convenience sample of Univer-
sity of Pennsylvania students who had iPhones. We excluded
Android phone users only because tracking battery usage data
would have required downloading a separate app for those us-
ers. However, informal surveys suggested that the vast major-
ity of Penn students were iPhone users, so we are not unduly
worried about the sample being biased in this regard. However,
future studies should certainly include Android users.
Lastly, we suffered from significant attrition at follow-up, los-
ing 79% of our subjects, largely because we were forced to grant
the extra credit for participation prior to the follow-up data col-
lection time point. As a result, there was no incentive for subjects
to complete the lengthy battery or take the trouble to submit a
screen shot of their usage. This precluded reliable analysis of
post-intervention social media habits and well-being. Thus, we
were not able to assess maintenance of gains in well-being or to
determine whether people reverted to their old use patterns. Fu-
ture studies should build in incentives for subjects to continue to
participate so that this valuable data could be collected.
FUTURE DIRECTIONS
As our study was the first of its nature, there are many opportu-
nities for further investigation. These findings certainly bear rep-
lication with a more diverse population. The study should also
be replicated with a broader inclusion of social media platforms,
766 HUNT ET AL.
including Twitter, Pinterest, Tumblr, etc. Dating apps in particu-
lar might be a fruitful avenue of investigation especially for in-
dividuals in their late teens to late twenties. Future researchers
should also incentivize follow-up participation to decrease at-
trition. This will allow for critical analyses pertaining to habit
maintenance.
Furthermore, moderators associated with social media use
could be assessed further. These could include number of Face-
book friends, Instagram followers, length of Snapchat streaks,
and so on. These potential moderators could be analyzed in the
context of ability to comply with the restrictions, as well as the
success of the intervention.
Lastly, the length of the intervention and the length and nature
of the limits imposed on usage could be explored in more detail
going forward. It may be that there is an optimal level of use
(similar to a dose response curve) that could be determined. This
would allow for a more nuanced understanding of the amount
of social media that is adaptive for most users. Alternatively, one
could also explore the utility and impact of apps that actually
control or limit the use of other apps (such as App Detox, An-
tiSocial, and Off the Grid). Informally, however, many students
shared with us that either they (or their parents) had tried such
apps, but that they are so easy for tech savvy young adults to
circumvent that they didn’t really work. A better strategy might
be apps that increase self-monitoring and awareness of use, such
as In Moment and Space. Empirical investigation of their efficacy
and impact might well be warranted.
CONCLUSION
Most of the prior research that has been done on social media
and well-being has been correlational in nature. A few prospec-
tive and experimental studies have been done, but they have
only focused on Facebook. Our study is the first ecologically
valid, experimental investigation that examines multiple social
media platforms and tracks actual usage objectively. The results
from our experiment strongly suggest that limiting social media
usage does have a direct and positive impact on subjective well-
LIMITING SOCIAL MEDIA 767
being over time, especially with respect to decreasing loneliness
and depression. That is, ours is the first study to establish a clear
causal link between decreasing social media use, and improve-
ments in loneliness and depression. It is ironic, but perhaps not
surprising, that reducing social media, which promised to help
us connect with others, actually helps people feel less lonely and
depressed.
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