a Targeting any social media platform.
b RCT: randomized controlled trial.
c Usual social media use.
d IG: intervention group.
e CG: control group.
f All participants receive all interventions, but the order in which they receive them (the sequence) is randomized.
g N/R: not reported.
h CBT: cognitive behavioral therapy.
Sample sizes ranged from 24 to 888 individuals, with 44% (10/23) of the studies including a sample size of <100 people. Where provided, sample ages ranged from 17 to 71 years, and 61% (14/23) of the studies reported mean ages between 20 and 30 years. Of the 23 studies, 4 (17%) did not provide ages and categorized participants as undergraduates [ 22 , 46 , 54 , 55 ]. A little more than half (12/23, 52%) of the studies recruited participants via university sampling, 30% (7/23) used web advertisements, and 17% (4/23) combined these methods [ 39 , 42 , 51 , 53 ].
Nearly all studies (22/23, 96%) were given a weak global score, and no studies achieved a strong global score. Only the study by Throuvala et al [ 39 ] achieved a moderate score and this study showed a beneficial effect of the intervention on outcomes. Most of the studies were subject to selection bias, with 70% (16/23) being of moderate quality and 30% (7/23) being of weak quality on this criterion, because most of the studies (16/23, 70%) used convenience sampling from university populations. Most of the studies (21/23, 91%) did not report blinding of the researcher or participants. A little more than a third (8/23, 35%) of the studies were weak in study design because although they randomized participants, they did not report how they did this. The studies had relatively low withdrawals and dropouts, with a little more than half (12/23, 52%) reporting that ≥80% of the participants completed the studies. Less than half (9/23, 39%) of the studies fully accounted for confounding variables. A little more than half (12/23, 52%) demonstrated that the data collection tools used were reliable and valid. Scores for all sections are provided in Multimedia Appendix 3 alongside the effectiveness of the interventions.
The details of study outcomes in relation to mental well-being are provided in Table 3 . The main outcomes related to mental well-being reported in the studies were depression, life satisfaction, anxiety, fear of missing out (FOMO), mental well-being, positive affect, negative affect, loneliness, stress, self-esteem, and mindfulness. Depression was the most common outcome that was assessed (10/23, 44%), followed by life satisfaction (9/23, 39%), mental well-being (8/23, 35%), and anxiety (6/23, 26%). The least common outcomes that were assessed were self-esteem (3/23, 13%) and mindfulness (2/23, 9%). Most of the studies (18/23, 78%) investigated >1 mental well-being outcome. More than a third (9/23, 39%) of the studies demonstrated improvements in well-being–related outcomes. Almost one-third (7/23, 30%) of the studies found mixed effects across different well-being–related outcomes [ 35 , 41 , 46 , 49 - 51 , 54 ], and almost a third (7/23, 30%) found no effect [ 42 , 43 , 45 , 47 , 52 , 53 , 56 ].
Effectiveness of social media use interventions on mental well-being sorted by intervention type.
Study, year | Outcomes | Measures | Comparison measurement | Postintervention-reported values (unless labeled) | Effect size (Cohen ) and interpretation | Main finding | Direction of effect | |
| Brown and Kuss [ ], 2020 | Mean difference between before and after the intervention (SD) | ) ) | Significant improvements after the intervention | ↑ | |||
| Fioravanti et al [ ], 2020 | Calculated mean difference between IG and CG for women and men | ) ) | Significantly improved life satisfaction and positive affect for women but not for men in the IG compared with those in the CG | ↕ | |||
| Hall et al [ ], 2019 | ] | Mean (SD) | No significant difference for the IG compared with the CG across all measures | ↔ | |||
| Hanley et al [ ], 2019 | Standardized coefficients from multiple regression | No significant difference for the IG compared with the CG across all measures | ↔ | ||||
| Lambert et al [ ], 2022 | Mean (SD) | Significant improvements for the IG compared with the CG across all measures | ↑ | ||||
| Mitev et al [ ], 2021 | Mental well-being | Daily satisfaction question, self-esteem scale, and positive and negative affect scales were combined to create an overall composite score of participants’ well-being | value and partial eta–squared value | =0.11; P=.89; η =.001 | 0.06 (X) | No significant difference for the IG compared with the CG | ↔ |
| Mosquera et al [ ], 2020 | Mean difference between before and after the intervention (SD) | — | Significant improvements for the IG compared with the CG for depression but not for life satisfaction | ↕ | |||
| Przybylski et al [ ], 2021 | Mean (SD) | No significant difference for the IG compared with the CG | ↔ | ||||
| Tromholt [ ], 2016 | and 4 items from PANAS combined | Mean (SD) | Significant improvements for the IG compared with the CG across all measures | ↑ | |||
| Turel et al [ ], 2018 | Marginal means (95% CI) | Significantly reduced absolute stress in the IG but not relative stress compared with the CG | ↕ | ||||
| Vally and D’Souza [ ], 2019 | Mean (SD) | Significantly reduced life satisfaction and increased negative feelings and loneliness for the IG compared with the CG but no difference in positive affect or stress | ↕ | ||||
| Vanman et al [ ], 2018 | Mean difference between before and after the intervention (SD) | 0.54 (M) | The IG had significantly reduced life satisfaction compared with the CG but no other outcomes were significantly different | ↕ | |||
| Chen et al [ ], 2022 | Mean (SD) | Significant improvements for the IG compared with the CG across all measures | ↑ | ||||
| Hou et al [ ], 2019 | Mean (SD) | Significant improvements for the IG compared with the CG across all measures | ↑ | ||||
| O’Connell [ ], 2020 | Mean difference between before and after the intervention (N/R ) | Significant reduction in mindfulness after the intervention but no difference in FOMO, well-being, depression, or anxiety | ↕ | ||||
| Esmaeili Rad and Ahmadi [ ], 2018 | Mean rank (N/R) | Significant improvements within the IG across all measures | ↑ | ||||
| Throuvala et al [ ], 2020 | Mean (SD) | Significant improvements for the IG compared with the CG across all measures | ↑ | ||||
| Zhou et al [ ], 2021 | Life satisfaction | SWLS | Mean (SD) | 0.50 (M) | Significant improvements for the IG compared with the CG | ↑ | |
| Brailovskaia et al [ ], 2020 | Mean difference (95% CI) between the groups | No significant improvement in the IG compared with the CG across both measures | ↔ | ||||
| Graham et al [ ], 2021 | Well-being | WEMWBS | Mean (SD) | 0.01 (X) | No significant improvement in the IG compared with the CG | ↔ | |
| Hunt et al [ ], 2018 | Mean | — | Significant improvements in depression and loneliness for the IG compared with the CG; no significance for other outcomes | ↕ | |||
| Hunt et al [ ], 2021 | Depression | BDI | Mean (SD) | — | Significant improvements for IG participants who were highly depressed compared with CG and AG participants | ↑ | |
| Thai et al [ ], 2021 | Mean (SD) | No significant improvement in the IG compared with the CG across both measures | ↔ |
a FOMO: fear of missing out.
b FoMOs: Fear of Missing Out Scale.
c WEMWBS: Warwick-Edinburgh Mental Well-Being Scale.
d P<.001.
e M: medium.
f S: small.
g Beneficial effect.
h SWLS: Satisfaction With Life Scale.
i PANAS: Positive and Negative Affect Schedule.
j IG: intervention group.
k CG: control group.
l P<.05.
m L: Large.
n X: negligible.
o Mixed effects.
p SF-36: 36-item Short Form Health Survey.
q No effect.
r GAD-7: General Anxiety Disorder-7.
s OECD: Organisation for Economic Co-operation and Development.
t Not available (number in each group was not specified to calculate effect size).
u Average mean and SD reported across the 3 countries because the relationship among variables was the same across the countries. Estimates based on unadjusted means because the adjusted means were not provided.
v CES-D: Center for Epidemiological Studies Depression Scale.
w PSS: Perceived Stress Scale.
x CES-D-20: Center for Epidemiological Studies Depression Scale, 20-item version.
y ULS-8: University of California Los Angeles Loneliness Scale.
z GHQ-30: General Health Questionnaire-30.
aa MAAS: Mindful Attention Awareness Scale.
ab PWB: Psychological Well-Being Scale.
ac N/R: not reported.
ad BDI: Beck Depression Inventory.
ae Comparing the IG between before the intervention and after. The effect of the intervention between the IG and the CG was not reported and could not be calculated.
af DASS-21: Depression Anxiety Stress Scales 21.
ag AG: active group (limited use of social media at 30 minutes per day plus 1 action every 3 minutes, eg, posting and replying).
An overview of the effectiveness of the interventions by outcome is shown in Figure 2 . The most improved outcome was depression, with 70% (7/10) of the studies that measured this outcome showing a benefit of the intervention, with large or medium effect sizes being reported [ 22 , 38 , 44 , 46 , 48 , 54 , 55 ], whereas 30% (3/10) showed no effect of the intervention [ 35 , 52 , 56 ]. Anxiety was the next most improved outcome, with 50% (3/6) of the studies that assessed this outcome showing significant improvement in anxiety, with medium and large effect sizes being reported [ 38 , 39 , 44 ]; however, 50% (3/6) reported no effect on anxiety [ 35 , 54 , 56 ]. FOMO also improved in 50% (2/4) of the studies assessing this outcome, with medium and small effect sizes being reported [ 36 , 39 ], whereas 50% (2/4) found no effect on FOMO [ 35 , 54 ]. Brown and Kuss [ 36 ] also explored the effect on FOMO based on gender and identified no significant differences between men and women after the intervention. Mental well-being improved in 38% (3/8) of the studies assessing this outcome, with small, medium, and large effect sizes being reported [ 36 , 40 , 44 ], whereas 63% (5/8) found no significant improvements in mental well-being [ 35 , 42 , 45 , 53 , 54 ]. Self-esteem improved in 33% (1/3) of the studies assessing this outcome, with a medium effect size being reported [ 40 ], but 67% (2/3) found no effect of the intervention on self-esteem [ 47 , 54 ].
Summary of social media use intervention effects on mental well-being–related outcomes. FOMO: fear of missing out.
The other outcomes showed mixed and some negative results. Loneliness was reduced in 40% (2/5) of the studies that measured this outcome [ 22 , 54 ], whereas 40% (2/5) found no effect [ 42 , 51 ]; however, 1 (20%) of these 5 studies also found that the intervention increased loneliness, with a medium effect size being reported [ 50 ]. Mindfulness improved in 1 (50%) of the 2 studies that measured this outcome, with a large effect size being reported [ 39 ], but it was found to reduce in another study (1/2, 50%), with a small effect size being reported [ 35 ]. Life satisfaction improved in a third (3/9, 33%) of the studies that measured this outcome, with small, medium, and large effect sizes being reported [ 37 , 38 , 48 ], whereas another third (3/9, 33%) of the studies showed no effect [ 43 , 46 , 52 ], and 22% (2/9) found full abstinence to be harmful, reducing life satisfaction, with medium effect sizes being reported [ 50 , 51 ]. Of these 9 studies, 1 (11%) found mixed effects because a significant improvement in life satisfaction was found for women, with a large effect size being reported, but not for men [ 41 ]. For stress, 1 (25%) of the 4 studies that measured this outcome found reductions in stress, with a medium effect size being reported [ 39 ], but half (2/4, 50%) of the studies showed no effect of the intervention on stress [ 50 , 51 ], whereas 25% (1/4) showed mixed effects because absolute stress reduced but the relative stress score compared with the score at baseline did not [ 49 ]. For negative affect, 1 (20%) of the 5 studies that measured this outcome found that negative affect increased after the intervention, with a small effect size being reported [ 50 ], but the other studies (4/5, 80%) showed no effect [ 41 , 43 , 47 , 51 ]. Positive affect also did not change after the intervention in most of the studies (4/5, 80%) [ 43 , 47 , 50 , 51 ]; however, 20% (1/5) found mixed results because positive affect improved for women, with a small effect size being reported, but not for men [ 41 ].
Figure 3 shows an overview of the effectiveness of social media use interventions by intervention type. Therapy-based trials were the most effective because 83% (5/6) of the studies that assessed this intervention type found significant improvements in mental well-being outcomes [ 22 , 37 - 40 ], and only 17% (1/6) found mixed effects [ 35 ]. Full-abstinence interventions showed mixed effectiveness overall, with 42% (5/12) of them showing mixed effects [ 41 , 46 , 49 - 51 ], a third (4/12, 33%) showing no effect [ 42 , 43 , 45 , 47 ], and a quarter (3/12, 25%) showing a benefit of the intervention [ 36 , 44 , 48 ]. Social media use interventions that limited use also showed mixed effectiveness overall, with more than half (3/5, 60%) of the studies showing no effect [ 52 , 53 , 56 ] and 20% (1/5) showing an improvement [ 55 ] and mixed effects [ 54 ], respectively.
Summary of the effectiveness of the different types of social media use interventions on mental well-being–related outcomes.
This review provides some evidence for the improvement of mental well-being in adults after social media use interventions. Of the 23 studies included in our analysis, 9 (39%) demonstrated improvements in mental well-being–related outcomes, with most of them (8/9, 89%) showing medium to large effect sizes. Depression was the most improved outcome overall, with 70% (7/10) of the studies that assessed depression showing improvement. Therapy-based interventions were the most effective, with 83% (5/6) of the studies evaluating these interventions showing improvement. Most of the studies (22/23, 96%) were of low quality, with significant issues related to selection bias and blinding affecting their quality.
This review found that interventions that simply restrict social media use or impose full abstinence may not have as much benefit for mental well-being as therapy-based interventions. These interventions used established approaches such as counseling and CBT-based techniques to encourage mindfulness and reflection on how social media activity affects thoughts, emotions, and behavior. Therapy-based interventions may be more effective in causing behavior change in users than abstinence by enabling them to replace negative actions with structured goals and positive thinking and by providing motivation [ 58 ]. Therapy-based interventions may also help to reduce FOMO by encouraging individuals to reevaluate life priorities, focus on other activities, and reduce social comparison and envy [ 36 , 39 ].
We found limiting social media use to be the least effective method. One hypothesis is that users would still have been exposed to social media during the trial and may have intensified their use owing to an awareness of a time restriction. This would have offset any potential positive improvement. Adherence to limiting social media use or abstaining from social media use may also have been challenging for participants, and adherence is difficult to track and measure across devices used to access social media [ 37 ]. In 1 study, 19.4% (35/180) of the participants were excluded from analysis because they were unable to abstain from social media for >2 days, which may give an indication of compliance rates in these studies [ 42 ]. These findings overall suggest that health and care professionals, mental health charities, and public health bodies should encourage the use of therapy-based approaches to manage social media use rather than focusing on reducing time spent on social media. These interventions can also be relatively cost-effective because this review showed improvements in participants’ well-being after they used internet-based self-guided therapy-based interventions to manage problematic social media use. However, delivering therapy-based approaches to manage (problematic) social media use is currently limited owing to resource and time constraints in health and care systems.
This review found that 3 (13%) of the 23 studies showed a reduction in some mental well-being–related outcomes after the intervention, such as life satisfaction and loneliness [ 35 , 50 , 51 ]. The causes for these findings could be due to methodological reasons because the authors of 1 (33%) of these 3 studies proposed that their study participants were unaware at the time of recruitment that abstinence from social media may be required [ 50 ], which may have made participants less receptive to the intervention and eliminated the beneficial consequences of abstinence that may have arisen in other studies. Previous research also suggests that reducing or limiting social media use can reduce mental well-being by causing a loss of social connection and increasing loneliness [ 9 ]. Some individuals are reported to find social connections easier to maintain over the web, with social media enabling users to preserve their relationships [ 59 , 60 ]. Social media can also help to create and maintain social capital, fostering inclusion within web-based communities [ 61 ]; for instance, members of the lesbian, gay, bisexual, transgender, queer, and similar minority community report greater levels of social support over the web [ 62 ].
This review supports evidence from previous studies that show that the link between social media use and mental well-being is conflicting, with there being some benefits and some disadvantages of social media use related to mental well-being [ 9 , 25 ]. The variation in effectiveness across the studies could be due to individual differences [ 63 ]. Different people will have different responses to social media, and self-regulatory capabilities may be affected by factors such as gender, age, and personality traits [ 64 ]. Previous research has shown that those with neurotic or introverted tendencies have a higher risk of addiction to internet content [ 65 ]. Others may not be affected by social media use owing to elevated levels of digital resilience. Digital resilience is a person’s ability to cope with the negative consequences of being over the web, such as cyberbullying and misleading information [ 66 , 67 ]. Gender has been found to be a moderating factor in previous studies examining the relationship between social media use and mental well-being, with adolescent girls seeming to experience more negative effects from social media use than adolescent boys [ 4 , 68 , 69 ]. In this review, we found that 1 (4%) of the 23 studies showed that abstinence increased positive affect and life satisfaction for women but not for men, also suggesting that gender may affect the relationship between social media use and mental well-being [ 41 ]. Future research is needed to explore who may be most affected by problematic social media use to enable the development of more targeted interventions to improve mental well-being.
There was a large degree of heterogeneity in the studies reviewed, with several different intervention types and outcome measures used. Therefore, it was not possible to conduct a meta-analysis to provide integrated results on the outcomes of the social media use interventions [ 70 ]. A further limitation to this review is that the search strategy may not have retrieved all relevant papers owing to the inclusion of English-language publications only. Our review also did not include unpublished studies; thus, it was not possible to estimate the degree of publication bias. This review also did not explore the impact of the type of social media use, such as passive use or active use, because this was out of the scope of the review, but this could affect mental well-being. Active use denotes direct messaging, posting, or responding to content, whereas passive use corresponds to scrolling and browsing profiles. Previous literature has suggested that passive use is associated with greater declines in subjective well-being [ 71 , 72 ], but a recent review found that this was not supported across 40 survey-based studies [ 73 ]. The review suggested that future studies should explore the characteristics of the content of social media as well as its senders and receivers to understand how different uses of social media affect mental well-being [ 73 ]. Understanding this relationship could help to develop more targeted problematic social media use interventions that move beyond simply aiming to reduce time spent on social media by targeting the reduction of specific negative activities or interactions.
A major limitation of the studies included in this review is that the majority (16/23, 70%) relied largely on convenience samples of those who were likely to be interested in reducing their social media use and improving their mental health. In addition, more than half (16/23, 70%) of the studies recruited university students. Therefore, these results must be interpreted with caution because they are not generalizable to all adults and are likely to be more relevant for young adults. Furthermore, none of the studies received a strong global score for quality using the Effective Public Health Practice Project Quality Assessment Tool. The sustainability of these interventions is also difficult to establish because most of the interventions (20/23, 87%) lasted <1 month, and the outcomes were assessed immediately after the interventions. Only 2 (9%) of the 23 studies included a longer-term measure. Brailovskaia et al [ 52 ] found consistency with their short-term recorded outcomes, with no significant difference in life satisfaction and depression between groups 3 months after the intervention. By contrast, Chen et al [ 22 ] found that the significant improvements in depression and loneliness for the intervention group continued to remain at 2 months.
There is some evidence that social media use interventions are effective in improving mental well-being in adults, especially for depression and when using therapy-based interventions. Current experimental research is of low quality, with issues of selection bias making it difficult to generalize the findings. Further experimental and longitudinal research is needed with representative samples to investigate who may benefit most from social media use interventions. Health and care professionals should be aware of the growing evidence that reducing social media use alone is unlikely to benefit mental well-being. Taking a more therapy-based approach and reflecting on how and why individuals are interacting with social media and managing these behaviors could help to improve mental well-being.
The authors would like to thank the librarians at University College London for helping them to refine the search strategy. RP holds a fellowship (MH013) funded by the Three National Institute for Health and Care Research (NIHR) Research Schools Mental Health Programme. This research is also supported by the NIHR Applied Research Collaboration North Thames. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
CBT | cognitive behavioral therapy |
FOMO | fear of missing out |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
Multimedia appendix 2, multimedia appendix 3.
Conflicts of Interest: None declared.
Exploring the impact of social media on anxiety among university students in the united kingdom: qualitative study, psychometric properties of the social media addiction scale (smas) on chilean university students, digital detriments: unraveling the psychological consequences of social media, problematic social media use and its relationship with depression or anxiety: a systematic review, social media disorder, mental health, and validation of the chinese version of 27-item social media disorder scale in chinese college students, the perceptions of social media users of digital detox apps considering personality traits, problematic social networking site use-effects on mental health and the brain, social media use and its impact on adult's mental health and well-being: a scoping review., the attitudes of students toward the use of smartphones, “to evaluate the effectiveness of planned teaching program [ptp] on knowledge regarding digital detoxification among students in selected junior colleges”., 26 references, problematic social media use: results from a large-scale nationally representative adolescent sample, the relations among social media addiction, self-esteem, and life satisfaction in university students, social media use is (weakly) related to psychological distress, facebook addiction and loneliness in the post-graduate students of a university in southern india, the relationship between social networking addiction and academic performance in iranian students of medical sciences: a cross-sectional study, association between facebook dependence and poor sleep quality: a study in a sample of undergraduate students in peru, the relationship between addictive use of social media, narcissism, and self-esteem: findings from a large national survey., the social media disorder scale, internet addiction among adolescents in lebanon, facebook addiction and its relationship with mental health among thai high school students., related papers.
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