social media and mental health experimental research

• Open access
• Published: 13 March 2024

Effects of a 14-day social media abstinence on mental health and well-being: results from an experimental study

• Lea C. de Hesselle 1 &
• Christian Montag 1  

BMC Psychology volume  12 , Article number:  141 ( 2024 ) Cite this article

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Background and aim

The study investigated the effects of a 14-day social media abstinence on various mental health factors using an experimental design with follow-up assessment. Hypotheses included positive associations between problematic smartphone use (PSU) and depression, anxiety, fear of missing out (FoMO), and screentime. Decreases in screentime, PSU, depression and anxiety, and increases in body image were assumed for the abstinence group. Additionally, daily changes in FoMO and loneliness were explored.

Participants completed different questionnaires assessing PSU, FoMO, depression and anxiety, loneliness and body image and were randomized into control and social media abstinence groups. Daily questionnaires over 14 days assessed FoMO, loneliness, screentime, and depression and anxiety. 14 days after the abstinence, a follow-up questionnaire was administered. Multilevel models were used to assess changes over time.

PSU was positively associated with symptoms of depression, anxiety and FoMO, but not with screentime. Spline models identified decreased screentime and body image dissatisfaction for the intervention group. Depression and anxiety symptoms, PSU, trait and state FoMO, and loneliness, showed a decrease during the overall intervention time but no difference between the investigated groups could be observed (hence this was an overall trend). For appearance evaluation and body area satisfaction, an increase in both groups was seen. Daily changes in both loneliness and FoMO were best modelled using cubic trends, but no group differences were significant.

Results provide insights into effects of not using social media for 14 days and show that screentime and body image dissatisfaction decrease. The study also suggests areas for future studies to better understand how and why interventions show better results for some individuals.

Peer Review reports

Social media is part of everyday life with 4.76 billion users worldwide and a 3.0% annual increase [ 1 ]. The average time spent on social media is 2.5 hours, totalling 5 hours of screentime per day [ 1 ]. Simultaneously, there is a global rise in mental health issues with a 25% increase in anxiety disorders and a 28% increase in depressive symptoms, primarily affecting young adults [ 2 ]. It has already been discussed if the increase in social media use paved the way for the increase is psychopathologies, but establishing causality remains difficult [ 3 ]. Despite this, studies have linked problematic social media use to problems such as symptoms of depression and anxiety, [ 4 , 5 , 6 ] stress, negative body image and low physical activity [ 7 , 8 , 9 , 10 ].

While most studies use cross-sectional data, assessing changes over time in longitudinal data is necessary. The present study combines a longitudinal design and experimental approach to evaluate effects of a 14-day social media abstinence on several mental health factors.

Problematic smartphone use (PSU)

Smartphones enable various activities (e.g., communication, entertainment, gaming, online surfing or using social media). Excessive smartphone use which can lead to adverse consequences has been termed problematic smartphone use (PSU, [ 11 , 12 ]) This includes relying on the smartphone to regulate one’s mood, experiencing agitation in its absence, and unsuccessful attempts to reduce usage [ 11 , 12 ].

PSU has been associated with different negative life and health outcomes such as poor sleep quality, [ 13 , 14 , 15 ] impaired work and academic performance, [ 16 , 17 , 18 , 19 ] neck and shoulder pain, [ 20 , 21 ] and visual impairment [ 22 , 23 ]. Further, PSU has been positively associated with depression, anxiety, and Fear of Missing Out (FoMO) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Though simple cross-sectional associations do not allow causal interpretation, according to the Compensatory Internet Use Theory (CIUT, [ 31 ]) excessive smartphone use can be interpreted as a coping mechanism for dealing with life stressors and negative emotions. Seen this way: associations between negative affect and overuse of technology might exist due to “self-medication” principles, although such medical language needs to be further investigated regarding its fit in the realm of Internet Use Disorders [ 32 ]. Another theoretical framework which is often used in PSU research is the Interaction of Person-Affect-Cognition-Execution model (I-PACE, [ 33 , 34 ]). This model describes different core characteristics and dispositional factors (personality, history of psychopathology, genetics, etc.) which can impact on how situations are received and what the response is (cognitive biases, certain affective responses), thereby contributing to the development of PSU. It offers insights into, for example, how stressful situations might lead to heightened smartphone use (in detail: use of certain applications) as a coping mechanism for dealing with stress. Of note, the I-PACE model also presents a history of psychopathologies such as depression as a vulnerability factor within the P-variable to develop excessive online use patterns. Hence, much of the variables investigated and introduced later in this manuscript could be seen through the lens of the I-PACE model: in particular, we mention that the intervention aiming at reduction of social media use could trigger changes in cognitive and affective processes which in turn might result in lower psychopathological tendencies as recorded via several variables in the present work (e.g. depressive tendencies or body image dissatisfaction).

Though most aforementioned studies assess PSU via self-report questionnaires, some others have used objective measurements of smartphone use (screentime, screen unlocks) and found either no association between depression and screentime [ 27 ] or an inverse relationship between depression and number of screen unlocks, [ 27 , 35 ] indicating even a lower unlock frequency for depressed compared to non-depressed individuals. However, objective and subjective measures of smartphone use are only moderately associated [ 27 , 36 ].

Social media use

Social media use presents one specific form of spending (excessive) time on the smartphone as platforms enable users to share real-time pictures, videos, and other content, facilitating connections through likes, comments, and multimedia messages. A lot of daily smartphone screentime is spent on social media, potentially leading to adverse consequences. Problematic social media use (PSMU) shows itself in symptoms similar to PSU, however it applies especially to social media use. This includes maladaptive behaviours such as escalating time spent on social media and unsuccessful efforts to reduce usage, resulting in negative consequences for the user. One can see from the symptoms that an addiction framework will be used for the present work, although PSMU could also mean very different behavior such as cyberbullying online.

PSMU in terms of an addictive behavior (not officially recognized) has been linked to different health outcomes. Koc and Gulyagci [ 37 ] and Hong et al. [ 38 ] found that depressive symptoms positively predict Facebook addiction. Koc and Gulyagci [ 37 ] further identified anxiety and insomnia as positive predictors. Additionally, FoMO was identified as a strong predictor of (problematic) social media use [ 39 , 40 ] and is also linked to both higher PSMU and lower meaning in life [ 41 ]. Furthermore, associations between PSMU and depression, anxiety, stress, higher cognitive failures [ 42 ] and poor sleep quality were found [ 4 , 5 , 6 , 43 , 44 , 45 , 46 ]. Though most studies are cross-sectional, limiting causal interpretation, some longitudinal studies have been performed. One study found a bidirectional relationship between PSMU and depression and identified PSMU as predictor of insomnia, suicide related outcomes and ADHD symptoms [ 45 ]. PSMU could also lead to negative consequences such as low academic achievement, decrease in real life social participation, [ 47 , 48 ] negative work-family balance, and decreased job performance [ 49 ].

Marino et al. [ 50 ] and Montag et al. [ 51 , 52 ] showed moderate to high associations between PSU and PSMU, resulting from an overlap of both phenomena. See also other works [ 53 , 54 ], showing robust overlap between PSU and distinct forms of social media overuse. While a lot of time is spent on social media, [ 1 ] not all smartphone use can be attributed to social media use, because smartphones also serve for gaming, browsing or video watching, instead, total screentime represents all uses. In the present study, total smartphone screentime was assessed as the original intention was to focus on smartphone gaming as well (see Procedure and Sample) and the smartphone serves as the platform for both social media engagement and gaming activities. Consequently, screentime serves as a comprehensive measure, reflecting both gaming and social media usage (but also a myriad of other activities including e-mail-checking, listening to music, etc.).

Abstinence studies

Apart from assessing smartphone use and different outcomes, as mentioned above, assessing changes in outcomes due to not using the smartphone pose a possibility to infer about the causal direction of effects to answer questions such as that abstaining from smartphone and/or social media use results in less reported clinical symptoms, e.g. in the realm of depression or eating disorders. Several studies explored the effects of social media abstinence. Radtke et al. [ 55 ] found significant decreases in screen time during and after the intervention, mixed results on life satisfaction, decrease in anxiety and stress, improvement of sleep quality and mixed effects on FoMO and loneliness. However, the authors argue that different implementations of abstinence and measurements might account for the heterogeneity of findings. Another review by Fernandez et al. [ 56 ] found similar effects: increase in life satisfaction, affective well-being, decrease in perceived stress, and an increase in boredom, craving and time distortion.

Further studies – some with experimental designs – found a decrease in FoMO, increase in mental well-being and social connectedness, [ 57 ] and decreased depression and anxiety [ 58 ]. However, Vally and D’Souza [ 59 ] found a decrease in well-being, an increase in negative affect and loneliness during intervention and a nonsignificant increase in stress for the experimental group. Brailovskaia et al. [ 60 ] assessed if a full abstinence is necessary to see improvements in mental health or if a reduction of one hour per day would be enough. They found increased well-being and positive lifestyle changes in both experimental groups with stronger effects in the reduction group.

The effect sizes found in the mentioned studies are small to moderate with just few large effects.

Most of the aforementioned studies employed 7 days of abstinence with some exceptions where an abstinence of 14 days was implemented. Also, the foci of these studies were mainly on mental health variables like depression, anxiety, and FoMO. While these are key variables in the present study, another goal is to assess effects of social media abstinence on body image.

Research questions and hypotheses

This study aims to assess the effect of a 14-day social media abstinence on different mental health and well-being factors using an experimental design. A follow-up assessment 14 days after the end of the intervention was implemented to assess stability of effects. A single 14-day follow-up was chosen due to economic reasons as retaining study participants is harder, the longer a study runs. Also, previous studies [ 55 , 58 , 60 , 61 ] have realised different periods between end of intervention and follow-up (e.g. 48 hours, 4 days, 1 week, 1 month and 3 months), so using 14 days is somewhere in between, economically feasible and of the same length as the intervention period. Daily questionnaires were used to analyse changes during the intervention period.

The following hypotheses and research questions will be evaluated.

The hypotheses H1, H2.1 and H2.2 are based on baseline data collected before randomization into different groups.

H1: In the overall sample, PSU is positively associated with reported total screentime, depression and anxiety symptom severity, and FoMO, respectively.

Previous studies (but not all) showed a moderate positive association between PSU and objectively measured screentime [ 27 , 36 ]. Although participants manually input screentime (total smartphone use, not just social media), similar low to moderate associations can be expected. The present study should also be able to replicate positive associations between PSU and depression and anxiety symptoms, and FoMO.

H2.1: In the overall sample, screentime is positively but weakly associated with depression and anxiety scores, FoMO, and loneliness, respectively.

H2.2: In the overall sample, more screentime is negatively associated with body image.

Many of these associations have not been shown with screentime, but with (problematic) smartphone use [ 37 , 38 , 39 , 40 , 43 ]. This study did not assess (problematic) social media use. Instead, the variable of interest is screentime in association with different mental health outcomes. However, since a large amount of screentime also in the present participants is spent on social media, [ 1 ] it should be associated with the mentioned variables as well. Nonetheless, the correlations should be small, as Huang [ 62 ] showed in a meta-analysis that the time spent on social network sites is only weakly correlated with psychological wellbeing.

H3: Screentime decreases in the experimental group.

Since a good portion of screentime is spent on social media [ 1 ] an abstinence should reflect in overall decreased screentime. Total screentime was chosen as the measurement, because it reflects both time spent on social media and other smartphone activities.

H4: Depression and anxiety scores, and PSU scores decrease in the social media abstinence group.

Depression, anxiety, and PSU have been positively linked to problematic social media use [ 37 , 38 , 40 , 43 , 50 ]. So, reducing – or eliminating – social media should lead to decreasing symptoms. Additionally, previous abstinence studies showed decreased anxiety, stress and depression scores [ 55 , 60 ].

H5: Body image improves in the abstinence group.

Body image is negatively associated with social media use, as exposure to idealized body types and social comparison in particular on visual driven social media platforms could lead to body dissatisfaction [ 7 , 63 , 64 ]. Although social media is not the only factor contributing to a negative body image, [ 65 , 66 ] abstaining from it is likely to improve body image by reducing the exposure to social comparison.

RQ1: How does FoMO change over time?

Previous studies reported mixed results concerning changes in FoMO, [ 55 , 57 ] possibly due to different intervention durations. Potentially, FoMO increases during the first few days of social media abstinence and then decreases once participants adapt to not using social media to check up on their friends. This study aims to provide insights into the changes over time during the abstinence phase by assessing FoMO daily and comparing different trends over time.

RQ2: What is the impact of abstinence on loneliness?

Several studies assessed the effect of social media abstinence on loneliness and found mixed results [ 55 , 56 , 57 , 59 ]. Since loneliness was assessed daily, changes over the duration of abstinence can be detected and different trends can be compared.

RQ3: Are the changes observed during abstinence stable after the intervention?

Positive and negative changes due to abstinence from social media were already mentioned, but not much is known about the stability of these changes over time. Brailovskaia et al. [ 61 ] reported stable effects of changes after a 14-day gaming abstinence, however not much is known about stability after social media abstinence. Stability will be evaluated through change in scores between the end of intervention and the follow-up.

This study took place between October 2022 and February 2023. Participants were recruited via different university mailing lists, flyers posted around the university buildings, social media and eBay marketplace and underwent assessments outlined in Fig.  1 . Inclusion criteria were: legal age (18+), good knowledge of the German language, and use of smartphone and social media. This online study was conducted using the SurveyCoder website, [ 67 ] with questionnaires administered at baseline, daily, end of intervention and at follow-up. Participants received a daily link to the website via email at 4 pm. After the baseline questionnaires, participants were randomized into four groups and received intervention instructions. Since no tracking apps were used, the deinstallation of apps was not monitored. Participants were allowed to use their smartphones as normal for all other purposes and were only instructed to deinstall apps from their smartphones (other devices were not mentioned in the instruction). At the end of the intervention, participants were allowed to reinstall apps and were asked how they intend to manage their future social media consumption.

Schematic procedure. The procedure of the study is presented in the figure, the abbreviations for the included questionnaires are presented in the questionnaire section of this work

Originally, comparisons between all groups were planned, however due to data cleaning steps (see Sample), only groups 1) and 2) were used for analyses in the main body of this manuscript.

The initial sample compromised N  = 196 participants who provided combined datasets (baseline, daily, end of intervention and follow-up). After exclusion of non-users of social media or gaming apps in the experimental groups, a sample of n  = 165 participants was left. Since this sample consisted of 83.6% females and one focus of the study was the change in body image (which mainly shows effects for women), [ 68 ] only the female participants were analysed further.

From our view, this led to a too small group size for the gaming abstinence group to run robust statistics ( n  = 21). Since negative consequences due to gaming are mostly prevalent in men [ 69 ] and a small group size compared to the other groups can be problematic in analyses, this group was excluded from the main body of this manuscript (but see Supplement ). The combined abstinence group ( n  = 31) was also excluded as this would have been relevant to provide insights in particular in comparison to both the distinct gaming and social media abstinence groups. But since the gaming abstinence group was excluded, it was decided to exclude this from the manuscript as well (again, for more information see Supplement ).

Thus, the effective sample consisted of n  = 86 female participants which were randomized into control group ( n  = 35) and social media abstinence group ( n  = 51). Most participants held A-level qualifications (64.0%) or university degrees (29.1%) and were currently enrolled at university (77.9%). Groups were comparable in terms of age (m control = 23.17, s control = 6.99; m socmed =24, s socmed =4.63; t(54.233) = -0.61, p = .54), education (majority have A-levels (63% in control group; 65% in social media abstinence group); followed by university degree (28% in control group; 29% in social media abstinence group), \({\chi }^{2}\) (3) = 1.47, p = .69), and current occupational status (77% university students in control group; 78% university students in social media abstinence group; \({\chi }^{2}\) (4) = 3.33, p = .50).

Analyses including excluded groups are presented in the Supplementary Materials 1 and 5  -  10 .

Questionnaires

Fear of missing out.

Trait and online specific state FoMO was assessed using the TS FoMO scale [ 70 ] at baseline, end of the intervention and follow-up. Participants were asked to rate their agreement to 12 statements on a 5-point Likert scale (1 = “strong disagree”, 5 = “strong agree”). Mean scores for both subscales were computed and showed high internal consistency at all timepoints ( \(\alpha\)  = 0.76 – 0.83 and \(\alpha\)  = 0.77 – 0.79, respectively). The German version as provided by Wegmann et al. [ 70 ] was used in the present work.

Daily FoMO was assessed using a single item question (FoMOsf; [ 71 ]): “Do you experience FoMO (the fear of missing out)?” Riordan et al. [ 71 ] proposed this single item assessment which showed good validity. Participants rated how much this applied to them on the current day on a 5-point Likert scale (1 = “no, not true of me”, 5 = “yes, extremely true of me”).

Problematic smartphone use

PSU was assessed using the Smartphone Addiction Scale – Short Version (SAS-SV; [ 72 ]) where participants rated their agreement with different statements concerning their smartphone use. These statements include difficulty concentrating, agitation in the absence of the smartphone, persistent preoccupation with the device, exceeding intended use duration, frequent checking behaviour, experiencing physical discomfort during use, and missed work obligations due to excessive smartphone use. Agreement was provided on a 6-point Likert scale ranging from 1 = “strongly disagree” to 6 = “strongly agree” and sum scores were used in analyses (higher values = more PSU). The scale showed high internal consistencies at all time points ( \(\alpha\)  = 0.81 – 0.86). German version was used as in Haug et al. [ 73 ].

Depression and anxiety symptoms

Depression and anxiety symptoms were assessed using the 4-item Patient Health Questionnaire (PHQ-4; [ 74 ]). Participants were asked how often in the past seven days (for daily measurements: on the current day) they experienced different symptoms of depression or anxiety. Answers ranged from 0 = “no, not at all” to 3 = “nearly every day” (“nearly the whole day”) and were summed with higher values indicating more severe symptoms. The PHQ-4 demonstrated high internal consistency at all time points ( \(\alpha\)  = 0.81 - 0.87).

How often participants experienced loneliness and isolation from others was assessed using the German version of the UCLA 3-item loneliness scale [ 75 ] as in Montag et al. [ 76 ]. Answers were given on a 3-point Likert scale (1 = “hardly ever”, 3 = “often”) and summed with higher values indicating higher loneliness. The scale demonstrated good internal consistency ( \(\alpha\)  = 0.78 – 0.89).

Body Image Dissatisfaction (BID) was assessed using the BIAS-BD, [ 77 ] which presents two rows of schematic body figures ranging from 60% to 140% of the average BMI, separated for sex. Participants chose the figure best representing their actual and ideal body. Percentages were transformed into BMI equivalents and a BID score was computed as the difference between actual and ideal body size.

Further, the MBSRQ-AS [ 78 ] was used to measure different body image dimensions on 34 items: appearance evaluation (How content people are with their appearance), appearance orientation (How much attention people pay to their own appearance), body area satisfaction (How satisfied they are with different areas of their body), overweight preoccupation (How concerned they are with their weight and staying thin), and self-classified weight (How they would rate their own weight and how other people would rate their weight). Scores were summed for each dimension, and all showed good internal consistency with \(\alpha =\) 0.66 – 0.92.

Participants were asked to open the screentime feature on their smartphones and type the hours and minutes into the questionnaire. Values were converted into minutes for analysis. At baseline and follow-up, the screentime from the last seven days was averaged to represent the average daily screentime at baseline and follow-up, respectively. No differences were made between smartphone operating systems.

Fear of COVID-19

Fear of COVID-19 (FCV) was assessed at baseline using the FCV19S [ 79 , 80 ] to use as a covariate in analyses. This was due to the study being conducted amid the COVID-19 pandemic (October 2022 to February 2023), allowing for the proper consideration of various pandemic-related constraints in the analyses. The FCV19S demonstrated a good internal consistency of \(\alpha\)  = 0.83. Participants were asked to rate seven statements concerning their fear of COVID-19 on a 5-point Likert scale from 1 = “strongly disagree” to 5 = “strongly agree”. Scores were summed for analysis and higher values indicated more Fear of COVID-19. The German version used herein was by Fatfouta and Rogoza [ 80 ].

Further questionnaires

The following questionnaires were assessed but not included in the main analyses. They are included in the supplement (see Supplementary Material ): IPAQ (physical activity; [ 81 ]), PANAS – positive affect subscale, [ 82 , 83 ] Perceived Stress Scale (PSS-4; [ 84 , 85 ]) and satisfaction with life scale (SWLS; [ 86 , 87 ]). The cited German versions were used for all scales.

Data analysis

Data analysis was performed using R version 4.1.3 [ 88 ]. Apart from descriptive statistics at baseline, correlations were computed using Holm’s correction for p -values.

To model trends in outcome variables (PSU, FoMO, screentime, depression/anxiety, loneliness, body image), multilevel models were used. For the variables measured at three time points (baseline, end of intervention, follow-up), spline models were used with the knot point set to the end of intervention. This allows assessment of change between baseline values and end of intervention and between end and follow-up. Further, RQ3 can be answered using these models. For an explanation on spline models in the multilevel modelling framework, see Grimm et al. [ 89 ]. First, only the trend over time was modelled for the total sample (called model 1 for each outcome). Then, covariates were added (baseline FCV, PSU, and BID for MBSRQ-AS outcomes), and group differences were accounted for using dummy coded variables (0 = control, 1 = abstinence group; called model 2 for each outcome). Random intercepts were used for all models to allow for interindividual differences in values and where possible, random slopes were used to allow for interindividual differences in change over time. The R-packages lme4 version 1.1.28 [ 90 ] and lmerTest version 3.1.3 [ 91 ] were used.

For daily measured outcomes, several multilevel models were computed. First, a linear trend over time for all experimental groups was evaluated. This model included baseline PSU, FCV, and the respective baseline value of each outcome. Then, the group variable was added to a separate model. To further assess changes over time in FoMO and loneliness (RQ1 and RQ2), different trends over time were assumed (quadratic, cubic) and models were compared using AIC, BIC and Likelihood Ratio Tests.

Datasets and analysis scripts are available at the Open Science Framework https://osf.io/qdp8r/ .

The present study was approved by the university’s ethics committee (Ethics committee of University of Ulm) under application number 252/22.

Descriptive statistics at baseline are presented in Table 1 . Due to randomization, no group differences were expected, and t-tests were non-significant.

Correlations

Correlations at baseline are presented in Table 2 with Holm corrected p -values and 95% confidence intervals.

Multilevel models

Results from models for outcome variables measured at three time points are presented in Table 3 .

For depression and anxiety symptoms, model 1 with fixed slope showed a significant negative trend before the knot point (b = -0.60, t(160.76) = -2.52, p  = .013), but no change afterwards. Model 2 showed no significant change over time or differences between groups.

Model 1 for screentime showed nonsignificant changes across all time points. Upon incorporating covariates, no significant changes over time were observed for the control group. However, there was a significant difference in change between baseline and end of intervention between control group and abstinence group, as evidenced by one-sided testing (b = -38.905, t(159.8) = -1.685, p  = .094/2 = .047). This indicates a decrease in screentime in the abstinence group. See Fig.  2 for a graphical representation of mean values in both groups.

Plot of main results: changes in Body Image Dissatisfaction (BID) and average daily screentime

Using PSU as outcome, model 1 showed a significant negative linear trend before the knot point (b = -4.023, t(150.54) = -5.583, p  < .001) and a nonsignificant negative trend after the knot point. There was random variance for the trend over time. In model 2, only baseline FCV was added as covariate and had a significant influence on PSU. Further, the pre-knot negative trend was significant for the control group and no differences between groups were seen.

Model 1 for loneliness showed a significant negative linear change between baseline and end of intervention (b = -0.66, t(139.39) = -4.751, p  < .001), indicating an overall decrease in loneliness during the intervention. In model 2, the negative linear change between baseline and end of intervention was significant for the control group and there were no group differences.

For state FoMO, model 1 showed a significant negative trend between baseline and end of intervention (b = -0.22, t(161.11) = -3.813, p  < .001) and no change between end of intervention and follow-up. Model 2 showed no significant trend over time in the control group and no differences between groups.

There was a significant negative trend in model 1 for trait FoMO for the change between baseline and end of intervention (b = -0.52, t(157.63) = -7.30, p  < .001) and no change afterwards. Model 2 showed a significant negative pre-knot point trend for the control group. This trend did not differ between groups, however the difference in values at the knot point (end of intervention) was significantly lower for the abstinence group.

The first model assessing trend over time showed a nonsignificant decrease in BID between baseline and end of intervention and no change afterwards. Model 2 showed no significant change over time for the control group. However, the difference in change between baseline and end of intervention was significant for one-sided testing (b = -0.95, t(139.64) = -1.900, p  = .0595/2 = .029), indicating that BID values decreased more for the social media abstinence group compared to the control group. See Fig.  2 for a graphical representation of the mean values of both groups.

In model 1, appearance evaluation showed a significant increase between baseline and end of intervention (b = 0.988, t(161.02) = 2.736, p  < .001) and no change afterwards. In model 2, the positive change between baseline and end of intervention was only significant for the control group.

Overweight preoccupation showed no change over time in model 1. Upon adding covariates and a group variable, there was a significant negative trend for the change between baseline and end of intervention in the control group. No group differences were found.

Model 1 revealed an almost significant increase in body area satisfaction during the intervention (b = 0.698, t(136.24) = 1.899, p  = .0597) and no change afterwards. However, this trend was not significant in model 2 with covariates and group variable, nor was there a difference between groups.

No effect of either time or group could be identified for self-classified weight and appearance orientation.

Daily data models

For the daily data models, different trends were modelled for each variable. As covariates in all models the respective values at baseline were used as well as baseline FCV19, PSU, and BID. Results are provided in Table 4 .

For the total sample, linear (model 1) or quadratic (model 2) trend over time for screentime could not be found. Upon adding the group variable in the quadratic trend model (model 4), the interaction term for linear trend and abstinence group was almost significant (b = 8.56, t(1046.18) = 1.800, p  = .072), as well as the interaction between the quadratic trend and the abstinence group (b = -0.61, t(1045.28) = -1.727, p  = .084). This indicates a different change in screentime for the social media abstinence group than observed in the control group.

Linear (model 1) and quadratic (model 2) trends for changes in loneliness were not supported for the total sample. Model 3 assumed a cubic trend and found significant results for the linear, quadratic and cubic parts of the trend. Model comparisons between three models identified model 3 as the best fitting model (AIC = 3596.9, BIC = 3647.2, M2 vs. M3:  \({\chi }^{2}\) (1) = 6.78, p  < .01).

Models 4 (linear trend and group) and 5 (quadratic trend and group) showed no trends over time nor group differences. Model 6 – including a cubic trend – showed significant linear, quadratic and cubic trends for the control group and no differences between groups. This model fit the data best (AIC = 3597.2, BIC = 3667.6, M5 vs. M6:  \({\chi }^{2}\) (2) = 9.92, p  < .01). However, there was no significant difference between models with and without the group variable (M3 vs. M6: \({\chi }^{2}\) (4) = 7.6911, p  = .1036).

Model 1 showed no significant linear trend over time in depression and anxiety symptoms. Model 2 included the linear trend over time and the group variable and showed an almost significant negative change for the control group (b = -0.04, t(1048.51) = -1.834, p  = .067) and a significant interaction between daily change and the abstinence group (b = 0.06, t(1046.40) = 2.429, p  < .05), indicating different changes over time between both groups.

Model 1 found a significant negative linear trend with an average 0.0195 decrease per day for FoMO. Model 2 included a quadratic trend as well as the linear trend and found the linear trend to be significant (b = -0.054, t(1043) = -2.506, p  < .05). Model 3 assumed a cubic trend and found this to be significant. Model comparisons identified model 3 as best fitting (AIC = 2862.6, BIC = 2918.0, M2 vs. M3: \({\chi }^{2}\) (1) = 19.116, p  < .001).

Model 4 found a significant negative linear trend for the control group and no group differences. Model 5 (quadratic trend) found no significant linear or quadratic trend for either group. Model 6 (cubic trend) found a significant linear, quadratic and cubic trend for the control group, but no difference between the groups. Model 6 was identified as the best fitting model containing the group variable (AIC = 2866.4, BIC = 2941.9, M5 vs. M6: \({\chi }^{2}\) (2) = 20.22, p  < .001), however the fit was not significantly different from model 3 (M3 vs. M6: \({\chi }^{2}\) (4) = 4.1824, p  = .3819).

The aim of this study was to evaluate the impact of a 14-day social media abstinence on different mental health and well-being variables and body image. Results are discussed below.

Associations between variables

The study’s findings align with the expectations outlined in H1. PSU demonstrated a weak positive association with screentime, although it was not statistically significant, which is consistent with prior research [ 27 , 36 ]. This supports the notion that self-reported PSU and screentime is not necessarily the same construct and that screentime is not an appropriate measure for PSU. Furthermore, different uses and motives for smartphone use can explain why some people have high screentime but low PSU.

Motives can be evaluated using the CIUT [ 31 ]. Though literature on associations between smartphone use motives and screentime is not exhaustive, studies have found that motives like mood regulation and enjoyment are positively associated with PSU, whereas information seeking and socializing are less likely to have an influence on addictive behaviour in the realm of smartphones [ 92 , 93 ]. Additional motives for use were distress tolerance and mindfulness, [ 94 ] FoMO, [ 95 , 96 ] and boredom proneness [ 96 , 97 ].

PSU was assumed to be positively associated with depression and anxiety symptom severity (H1) and a moderate association (albeit not significant for this sample) has been found. Again, this is consistent with previous literature [ 24 , 25 , 26 , 27 ].

The hypothesized association between PSU and state FoMO was highly positive whereas the association with trait FoMO was moderately positive, supporting H1. Both can be interpreted as people experiencing more PSU symptoms also experience more FoMO. Again, these results are in accordance with previous studies identifying FoMO as a correlate of PSU [ 25 ]. Furthermore, according to the I-PACE model [ 33 , 34 ] trait FoMO can be seen as a core characteristic impacting how certain situations are received and responded to, thus, contributing to the development of PSU (please note that due to the overlap with neuroticism, it might be also seen as a trait; [ 98 ]).

Positive but weak associations were found for screentime and depression/anxiety symptom severity, FoMO, and loneliness, supporting H2.1. All associations are low (to moderate for screentime and depression/anxiety) and not significant in the present sample. This was expected, as Huang [ 62 ] reported very small associations between time spent on social network sites and mental health variables. There are different uses of smartphones that can be unproblematic but lead to high screentimes (e.g attending online meetings or using the phone to study). This should be controlled for in future studies.

Hypothesis H2.2 assumed a negative correlation between screentime and body image. This hypothesis is supported only descriptively, as no correlation is significant. Screentime showed weak negative associations with appearance evaluation and body area satisfaction, and positive associations with appearance orientation (see that also using objective screentime-measures, a recent work by Rozgonjuk et al. [ 64 ] established links between longer smartphone use and higher body dissatisfaction; in this work also patients with eating disorders were investigated). The present findings suggest that individuals who spend more time on their smartphones are a little more appearance oriented and a little less satisfied with their bodies. However, it is crucial to note that screentime and exposure to online media are not the sole factors influencing BID [ 65 , 66 ]. Studies found that the type of screentime influences development of BID, at least for TV or computers [ 99 , 100 , 101 ]. Specifically, computer use for leisure activities was positively associated with BID whereas computer use for homework showed negative associations with BID [ 99 ]. Hrafnkelsdottir et al. [ 100 ] found positive correlations between gaming, TV/DVD/internet watching and BID and low correlations between BID and online communication. This suggests that different uses of smartphones and social media might have different impacts on body image. An assessment of motives of use could provide further insight.

Changes over time

An overall decrease in screentime during the intervention, especially for the abstinence group was found, supporting hypothesis H3. Since a large portion of screentime is spent on social media, [ 1 ] abstinence from selected applications should be reflected in overall decreased screentime. These results align with previous abstinence studies which also reported decreased screentime [ 55 , 57 ]. However, on a day-to-day basis during the intervention, no significant changes in screentime were found. This could be attributed to fluctuating screentimes or compensatory behaviour, such as switching to other apps to fill the time.

Depression and anxiety scores decreased when assessing the total sample but there was no change nor difference between groups when considering the group variable. Therefore, H4 is not supported for depression and anxiety. Contrary to the hypothesis, daily models showed a decrease in the control group and an increase in the experimental group (please note that these observations are on a descriptive level only and changes were not pronounced). However, according to the CIUT, [ 31 ] smartphones and by extension social media can act as a coping mechanism and as an escape to handle negative emotions and daily hassles. If this outlet is unavailable, symptoms of depression and anxiety might increase (we are not of the opinion though that social media use should be seen as an effective way to deal with one's own problems and it is unclear how long lasting the effect around depression and anxiety would be). Motives of use are often evaluated in gaming research and escapism was identified as a strong predictor for gaming time (and gaming disorder, [ 102 ]), highlighting the tendency of dealing with negative emotions by escaping into an online world [ 103 ].

Additional analyses were conducted to examine the relationship between changes in depression and anxiety scores and baseline PSU, across groups (total sample). The results indicated a weak negative correlation, suggesting that, across groups, individuals with higher baseline PSU scores experienced more decrease in depression and anxiety scores compared to those with lower PSU scores. Since there was only a minimal difference in baseline PSU scores between the experimental groups (see Table 1 ), the association between baseline PSU values and change in depression and anxiety scores cannot be the reason for the different trends over time measured in the depression and anxiety scores.

However, when baseline depression and anxiety scores were correlated with changes in anxiety and depression scores, a moderate negative correlation emerged. The control group displayed slightly higher baseline scores than the abstinence group, although this difference was not statistically significant. This provides a possible explanation to the reduction in depression and anxiety scores in the control group compared to the abstinence group.

For PSU, an overall decrease was found during and after the intervention. However, there were no differences between groups. Possibly, the study attracted individuals seeking to change their social media habits as it was advertised as an abstinence study. Intention towards future social media use was assessed at the end of intervention and follow-up with most participants expressing a desire to reduce their social media time (end of intervention: 57% in control group, 49% in abstinence group; follow-up: 48% in control group, 66% in abstinence group). Since there was no big group difference in the number of participants with this answer, controls possibly intended to reduce their social media consumption even before their study participation and changed their behaviour, thus experiencing less PSU.

Additionally, PSU is not synonymous with social media use. Though studies found a strong positive association between PSU and PSMU, [ 50 ] PSU can develop through other smartphone uses than social media. Plus, participants were asked to abstain only from selected social media but were able to freely use their phones for other uses.

Body image was assessed using different variables. Appearance orientation and self-classified weight showed no changes over time or between groups. There was an overall increase in appearance evaluation and body area satisfaction due to the intervention, but no differences between groups. Overweight preoccupation decreased for the control group and there was no difference in changes between groups.

The BID values decreased significantly more in the abstinence group than in the control group, suggesting that taking a break from exposure on social media is helpful in decreasing BID. Overall, hypothesis H5, suggesting social media abstinence improves body image satisfaction and decreases dissatisfaction, was partially supported. However, the effect is small, as social media is not the only factor influencing body image [ 65 , 66 ]. Social comparison occurs not only on social media but in real-life interactions and through other media like TV or magazines.

Daily change in FoMO (RQ1) was best modelled using a cubic trend. Nevertheless, there were no group differences, suggesting day-to-day fluctuation in FoMO regardless of whether social media apps were used or not. Previous studies found mixed outcomes regarding intervention on FoMO, [ 55 , 57 ] but only assessed it for 7 days. Since FoMO fluctuates, assessing changes over a longer period of time offers a more comprehensive dataset for fitting appropriate models.

Trait and state FoMO decreased during intervention in both groups and remained stable afterwards. The absence of group differences can be attributed to individuals using their phones for different uses that do not necessarily influence FoMO. Elhai et al. [ 104 ] found that FoMO is more associated with non-social smartphone use like entertainment, news and relaxation compared to social smartphone use. Though PSU was positively related to both trait and state FoMO, screentime was not associated with either. This suggests that simply abstaining from social media may not lead to reduced FoMO, at least not in the here investigated time interval. Furthermore, since traits are considered relatively stable constructs, [ 105 ] it is debatable if a change in trait FoMO can be expected. Trait FoMO can be also conceptualized as dispositional factor in the I-PACE model [ 33 , 34 ] and is a stable influence on the development of PSU. Since dispositional factors are not expected to change strongly – especially not in a short time frame – the observed change was more likely an artifact in data.

A cubic trend was also the best way to model daily changes in loneliness (RQ2), though there were no significant group differences. Both groups experienced a decrease in loneliness during the intervention and no change afterwards. This suggests that overall, loneliness decreased but due to factors other than not using social media apps. This study did not assess other life events, making it challenging to explain this change fully. Furthermore, previous studies found mixed effects of abstinence on loneliness [ 55 , 56 , 57 , 59 ]. Moreover, there are different motives for social media use and not all are related to social interaction. These results can also be interpreted in the context of the uses and gratification theory [ 106 ] as the smartphone can be used to fulfil individuals needs such as representation, maintaining social networks, receiving online support, relaxing, or escaping from pressures [ 107 ]. Not all motives are related to loneliness.

The majority of spline models did not show significant changes after the intervention, indicating stability in the effects and addressing RQ3. Specifically, this applies to changes in BID and screentime, where differences between the experimental and control groups were seen. For the other variables, there were no group differences, but the changes between baseline and end of intervention measurements suggested an overall decrease (depression and anxiety, PSU, overweight preoccupation, state and trait FoMO, loneliness) or increase (appearance evaluation, body area satisfaction) and no change afterwards. However, these changes apply to both groups, meaning the control group changed as well and abstinence was not the sole reason for change, but maybe the intention to reduce consumption was.

Contribution and limitations

The present study provides novel insights into the relationships between social media use and mental health and well-being. Notably, this study used an experimental design to implement a 14-day intervention and conducted a follow-up assessment 14 days after this intervention. Furthermore, this work focussed on body image and its changes over time. This can provide information for future studies or intervention designs as it shows that body image dissatisfaction can be decreased by not using social media for 14 days. The experimental approach adds depth to the understanding of the impact of social media on body image, a topic that has primarily been explored through correlative studies. The present results can also provide a first basis for inventing and implementing interventions in the realm of eating disorders or body schema disorders, as it shows that abstaining from social media might improve body satisfaction (replication of the present findings is of importance). But: As there is currently no consensus or official diagnosis for PSU and also against the limitations mentioned below, authors refrain from proposing clinical implications based on reduced screentime during intervention at the moment.

Additionally, previous studies modelled FoMO as linear change over time and often only assessed one week of change. The present study provides more detailed insight into daily FoMO as well as daily loneliness changes and found that both can be best represented using a cubic trend.

There are several limitations. First, the original study design intended to include four groups for comparison, but due to a high percentage of women in the sample, only female participants were analysed in the main manuscript, leading to exclusion of the gaming disorder groups. Consequently, small sample sizes were used for analyses, resulting in low statistical power. While some results were directionally clear, they did not reach statistical significance in the present work. Second, PSMU was not assessed alongside PSU, which should be considered in future studies. Screentime was not objectively measured but participants manually input the information from their screentime feature (hence we have an indirect objective screentime measure, which might be prone to transfer error though but was checked for plausability). In further studies, an objective measurement could be implemented by either using tracking apps – and thus validating if participants use social media apps – or asking for screenshots of the screentime feature. Measurement of total screentime and PSU were chosen as the original intention was to include the gaming and combined abstinence groups. In that case, both screentime and PSU would be acceptable measurements for all groups, as gaming and social media use can be reflected in total screentime and can both lead to symptoms of PSU.

Aside from assessing PSMU and using an objective measure for future studies it is suggested to assess motives and uses for individual’s smartphone use because this could provide further insight into why outcomes change for some participants but not for all. This could also aid in developing more nuanced interventions that properly fit a person’s needs. Lastly, different groups with different levels of abstinence could be realized. This has previously been done by Brailovskaia et al. [ 60 ] for general smartphone use.

Using a longitudinal and experimental approach to a 14-day social media abstinence, the present study was able to show significant decreases in BID and screentime due to abstinence. Further, mental well-being factors were evaluated and showed improvement over time but did not differ between groups. Using daily assessments of FoMO and loneliness, cubic trends were identified as the best way to model fluctuation in these variables. These findings provide valuable insights into the complex dynamics of social media use and its impact on mental health and well-being and can provide information to plan future interventions addressing social media/smartphone use or body image related disorders.

Availability of data and materials

Abbreviations.

Akaike Information criterion

Bayesian information criterion

Body image dissatisfaction

Body mass index

Compensatory Internet Use Theory

Fear of Missing Out

Positive affect negative affect scale

Patient health questionnaire 4

Problematic social media use

Perceived stress scale

Research question

Smartphone Addiction Scale – short version

Satisfaction with Life scale

Trait State FoMO scale

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Dr. Montag reports no conflict of interest. However, for reasons of transparency Dr. Montag mentions that he has received (to Ulm University and earlier University of Bonn) grants from agencies such as the German Research Foundation (DFG). Dr. Montag has performed grant reviews for several agencies; has edited journal sections and articles; has given academic lectures in clinical or scientific venues or companies; and has generated books or book chapters for publishers of mental health texts. For some of these activities he received royalties, but never from gaming or social media companies. Dr. Montag mentions that he was part of a discussion circle (Digitalität und Verantwortung: https://about.fb.com/de/news/h/gespraechskreis-digitalitaet-und-verantwortung/ ) debating ethical questions linked to social media, digitalization and society/democracy at Facebook. In this context, he received no salary for his activities. Finally, he mentions that he currently functions as independent scientist on the scientific advisory board of the Nymphenburg group (Munich, Germany). This activity is financially compensated. Moreover, he is on the scientific advisory board of Applied Cognition (Redwood City, CA, USA), an activity which is also compensated.

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• Published: 07 September 2022

The mental health and well-being profile of young adults using social media

• Nina H. Di Cara 1 , 2 ,
• Lizzy Winstone 1 ,
• Luke Sloan 3 ,
• Oliver S. P. Davis 1 , 2 , 4   na1 &
• Claire M. A. Haworth 4 , 5   na1  

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• Human behaviour
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• Psychiatric disorders

The relationship between mental health and social media has received significant research and policy attention. However, there is little population-representative data about who social media users are which limits understanding of confounding factors between mental health and social media. Here we profile users of Facebook, Twitter, Instagram, Snapchat and YouTube from the Avon Longitudinal Study of Parents and Children population cohort ( N  = 4083). We provide estimates of demographics and mental health and well-being outcomes by platform. We find that users of different platforms and frequencies are not homogeneous. User groups differ primarily by sex and YouTube users are the most likely to have poorer mental health outcomes. Instagram and Snapchat users tend to have higher well-being than the other social media sites considered. Relationships between use-frequency and well-being differ depending on the specific well-being construct measured. The reproducibility of future research may be improved by stratifying by sex and being specific about the well-being constructs used.

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Introduction.

The trails of data left online by our digital footprints are increasingly being used to measure and understand our health and well-being. Data sourced from social media platforms has been of particular interest given their potential to be used as a form of ‘natural’ observational data about anything from our voting intentions to symptoms of disease. There is not a single, widely agreed definition of the term ‘social media’ 1 , but for the purposes of this study we understand it to be a broad category of internet-based platforms that allow for the exchange of user-generated content by ‘users’ of that platform 2 . Both the huge volumes of data available on such platforms, and their increasing uptake across the population 3 have led to two main fields of interest in the intersections of social media and mental health. These are the prediction of mental health and well-being from our online data 4 and, somewhat reciprocally, the influence of social media on our mental health, particularly in the case of children and young people 5 , 6 . These fields both ask fundamental questions about the mental health and well-being of social media users, to either understand the ways our mental health influences our social media behaviour, or how our social media behaviours influence our mental health.

Across both contexts a wide range of psychological outcomes have been studied, including predicting suicide at a population-level 7 and individually 8 , mapping the influences of social media platforms on disordered eating 9 and self-harm 10 , understanding the impacts of cyberbullying through social media platforms 11 , 12 , and even ethnographic research into online support networks 13 . As highlighted in a recent review which considered research on the relationship between social media use and well-being in adolescents 14 , there has tended to be an inherent assumption that social media is the cause of harm when examining the effect of social media on our health. However, recent investigations such as those by Orben and Przybylski 15 , 16 and Appel and colleagues 17 illustrate that the role of social media in causing harm may be over-estimated. It seems likely that there is some reciprocal relationship between mental health and social media, that requires longitudinal research studies to begin to understand the complexity, coupled with large representative samples to explore the heterogeneity 18 , 19 . Further, there is increasing attention on the role of within-person effects that see impact change between contexts 20 , 21 , as well as individual differences 22 . Meanwhile, attention has also been drawn to the comparative lack of investigation into the potential benefits of social media, such as access to peer support and the ability to readily connect with friends and family, or into the psychological well-being of social media users as opposed to focusing on pathology. Similarly, most psychological prediction tasks using social media focus on predicting illness rather than wellness 4 , 23 .

Regardless of the direction of interest in the relationship between social media and psychological outcomes, researchers face common challenges, with one of the primary issues being a lack of high-quality information on the characteristics of the whole population of social media users 24 . Valuable demographic information on social media users in the United States is regularly produced by the Pew Research Centre 25 , but often researchers rely on algorithmic means to make predictions about the demographics of the groups they study online if they are not recruiting a participant sample whose demographics are known and can be recorded 4 , 24 , 26 . What we do know about social media users is that they are not homogeneous. The demographic features of populations using them vary across platforms and do not tend to be consistent with the characteristics of the general population 25 , 26 , 27 , 28 . This work on the demographic context has been important in understanding the samples that can be drawn from social media platforms, but there remains a lack of information about other characteristics of social media users that are relevant to study outcomes, including mental health and well-being. Consequently, attempts to compare user well-being and mental health between platforms may be unknowingly confounded by differences in the mental health profile of each individual platform. Mellon and Prosser 28 investigated this form of selection bias with respect to differences in political opinion between Facebook and Twitter, and noted the potential for study outcomes to be biased when the outcome variable of interest is associated with the probability of being included in the sample 29 . This also has implications for our assessment of mental health and well-being classification algorithms 30 . For instance, if using Twitter data to classify depression in a random sample of users how many of these users should we expect to be depressed? Should we expect to find more depressed users on Facebook or Instagram? This bench-marking would allow the research community, who frequently face the challenge of establishing reliable ground truth in social media research, to contextualise the sensitivity and specificity of developed models 4 , 24 .

This study aimed to address the gap in the availability of high-quality descriptive data about social media users by describing social media use in a representative UK population cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC) 31 . We aimed to profile the users of the social media platforms Facebook, Instagram, Twitter, Snapchat and YouTube by considering a range of mental health and well-being measures that are regularly studied, with the objective of better characterising social media users against variables of interest to researchers. These measures included disordered eating, self-harm, suicidal thoughts, and depression as well as positive well-being outcomes which are sometimes neglected in the context of social media research 14 , 16 , 22 like subjective happiness, mental well-being and fulfilment of basic psychological needs. In answering our research questions we also sought to illustrate how cross-sectional data from a representative population cohort can provide meaningful contextual information that informs the way we interpret past and future research about social media users and their mental health. Unlike other studies using cross-sectional data 14 we had no intention of exploring causal questions, but aimed to address unanswered questions of who social media users are, and whether selection bias across platforms may have the potential to unintentionally bias outcome statistics about mental health and well-being.

Specifically, our research questions were:

Are there demographic differences in patterns of social media use (e.g. frequency)?

Are there demographic differences in the user groups of different social media platforms?

Are there differences in the mental health and well-being of those using social media sites at different frequencies?

Are there differences in the mental health and well-being of user groups of different social media platforms?

Sample description

The sample for this study is drawn from the Avon Longitudinal Study of Parents and Children (ALSPAC) 31 , 32 , 33 . Pregnant women resident in Avon, UK with expected dates of delivery from 1st April 1991 to 31st December 1992 were invited to take part in the study. The initial number of pregnancies enrolled was 14,541. Of these initial pregnancies, 13,988 children were alive at 1 year of age. When the oldest children were ~7 years of age an additional 913 children were enrolled. The total sample size for ALSPAC of children alive at one year of age is 14,901. However, since this time there has been a reduction in the sample due to withdrawals, deaths of those in the cohort and also people simply being lost to follow-up. As such the exact number of participants invited to each data collection activity changes with time. Please note that the ALSPAC study website contains details of all the data that is available through a data dictionary and variable search tool ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ). Study data were collected and managed using REDCap electronic data capture tools hosted at the University of Bristol 34 .

The analysis presented in this study is based on a sub-sample of 4083 participants who responded to a self-report questionnaire at a mean age of 24 years old in 2016/17. The survey was sent to 9211 currently enrolled and contactable participants, of whom 4345 (47%) returned it. To maintain a consistent sample throughout the following analyses we considered the 4083 observations with complete cases for questions related to self-harm, suicidal thoughts, disordered eating, and social media use, and without the respondents who said that they ‘didn’t know’ whether they had a social media account ( n  < 5); no respondents stated that they did not have a social media account. As well as the survey at age 24, we considered the responses by those in our main sample to a survey one year previously, at age 23, which collected the well-being measures and the Moods and Feelings Questionnaire, matched to their social media use responses at age 24. This resulted in a sub-sample of 2991 participants who had responded to both surveys. Table 1 gives a comparison of the demographic breakdowns across these samples.

This study considered the participants’ responses to a range of mental health and well-being measures, as well as demographic data. A brief overview of each of the measures used is given below.

Throughout this paper, we used Male and Female to refer to the participant’s assigned sex at birth. Participant ethnicity was reported by their parent/s, and is available in the data as White , Ethnic Minority Group , or Unknown , where Ethnic Minority Group was only available as one group rather than broken down into specific ethnicities. There were two variables relevant to socio-economic status. The first was whether the participant had achieved an A Level or equivalent qualification by age 20, the second was their parents’ occupation. Parental occupation was measured using the Registrar General’s Social Class schema 35 , and was collected prior to the birth of the index cohort; we took the higher occupational class of the participant’s parents where available and grouped the overall schema of six categories into those in manual work , and those in non-manual work .

Social media use was measured using three questions. These were: (1) Do you have a social media profile or account on any sites or apps? with possible responses of ‘Yes’, ‘No’ or ‘Don’t know’; (2) Given a list of social media sites, Do you have a page or profile on these sites or apps, and how often do you use them? , where the social media sites were listed and response options were ‘Daily’, ‘Weekly’, ‘Monthly’, ‘Less Than Monthly’ or ‘Never’; (3) How often do you visit any social media sites or apps, using any device? with response options being ‘More than 10 times per day’, ‘2 to 10 times per day’, ‘Once per day’ or ‘Less than once per day’. Here, the definition of ‘social media sites’ in questions (1) and (3) was left to the participant to interpret, whereas in (2) a specific list was provided. In the following analyses, we have summed responses for the use frequencies per platform from question (2) so that ‘Weekly’, ‘Monthly’ and ‘Less than monthly’ are combined to represent ‘Less than daily’.

Depressive symptoms were measured using the short Mood and Feelings Questionnaire (MFQ) 36 , a 13-item scale that has been validated for measuring depressive symptoms in adolescents 37 and in young adulthood 38 . It asks respondents to rate statements, such as I cried a lot and I thought nobody really loved me , as Not true , Sometimes or True based on how they felt over the past two weeks. Missing items were filled with the mode of the individual’s other responses, provided 50% or more of the items were completed. Scores range from 0 to 26, with a higher score indicating more severe depressive symptoms 37 . Here we applied a cut-off score of 12 or above as indicating depression 38 .

Suicidal thoughts were assessed with the question Have you ever thought of killing yourself, even if you would not really do it? with those who indicated that they had ‘within the past year’ being included. Similarly, intentional self-harm was assessed by asking if participants had hurt [themselves] on purpose in any way and we included those who said this had happened at least once within the last year.

Disordered eating was a composite variable that included participants who indicated that they had been told by a healthcare professional that they had an eating disorder (anorexia nervosa, bulimia nervosa, binge eating disorder or another unspecified eating disorder). Participants were also included if they indicated they had engaged in any of the following behaviours at least once a month over the past year with the intention of losing weight or avoiding weight gain: fasting, throwing up, taking laxatives or medication. This classification of disordered eating followed a similar methodology to that used by Micali and colleagues 39 .

Well-being was measured using seven questionnaires. The Warwick Edinburgh Mental Well-being Scale (WEMWBS) is a fourteen-item questionnaire that has been validated for measuring general well-being in the general population 40 , 41 , as well as in young people 42 , 43 . It asks respondents to rate statements such as I’ve been dealing with problems well and I’ve been feeling cheerful , on a five-point Likert-type scale. The total score is between 14 and 70. All items in the WEMWBS are positively worded, and it is focused on measuring positive mental health.

The Satisfaction with Life Scale 44 , 45 is five-item questionnaire designed to measure global cognitive judgements of satisfaction with one’s life, which includes statements such as If I could live my life over, I would change almost nothing . Each question uses a seven-point Likert-type measure and the total score is between 5 and 35. The Subjective Happiness Scale 46 is a four-item questionnaire based on seven-point Likert-type questions, with the overall score being a mean of the four questions, lying in the range of 1 to 7. Respondents answer questions such as whether they consider themselves to be more or less happy than their peers.

The Gratitude Questionnaire (GQ-6) is a six-item measure that uses a seven-point Likert-type scale to assess individual differences in proneness to experiencing gratitude in daily life 47 . This scale includes statements such as I have so much in life to be thankful for and I am grateful to a wide variety of people . Each score is summed to a total between 6 and 42. The Life Orientation Test (LOT-R) is a measure of dispositional optimism that has ten items asked on a 5-point Likert-type scale 48 , though only four of these items are ‘filler’ questions that do not contribute to the final score. The overall score is in the range of 0 to 24, and items that contribute to this include In uncertain times, I usually expect the best and I hardly ever expect things to go my way .

The Meaning in Life questionnaire has 10 items designed to measure two dimensions of meaning in life: (1) Presence of Meaning (how much respondents feel their lives have meaning), and (2) Search for Meaning (how much respondents strive to find meaning and understanding in their lives) 49 . Statements include I understand my life’s meaning in the Presence sub-scale, and I am looking for something that makes my life feel meaningful in the Search sub-scale. Respondents answered each item on a 7-point Likert-type scale, with the two sub-scales scored in total between 5 and 35.

The psychological constructs of autonomy, competence and relatedness associated with self-determination theory were measured using the Basic Psychological Needs in General (BPN) questionnaire 50 . This questionnaire has 21 seven-point Likert-style questions with the final score for each of the three sub-domains being the mean of the responses for that sub-domain. As such each of autonomy, competence and relatedness were scored overall from 1 to 7. Example items include People in my life care about me and I often do not feel very capable .

For all measures missing items were filled with the person-level average, provided that half or more of the items were completed. All of the well-being measures listed were scored in a positive direction, where higher scores indicate higher alignment with the construct being measured.

The descriptive statistics were calculated using the R programming language (v4.0.1) 51 in RStudio (v1.3), primarily using the tidyverse (v1.3.0) package 52 for data manipulation and ggplot2 (v3.3.1) 53 for visualisation. A reproducible version of the manuscript and supporting code can be found from the Code availability statement.

Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. The full list of ethical approval references for ALSPAC can be found on their website ( https://www.bristol.ac.uk/alspac/researchers/research-ethics/ ).

Demographics

We first consider the demographics of social media users across different frequencies of use, and across the five social media platforms: Facebook, Twitter, Instagram, Snapchat and YouTube. These are both taken from the main sample, as described in our ‘Methods’. Table 2 presents the frequency that participants reported using any social media sites each day, based on sex, ethnicity, education, and their parents’ occupational group.

Table 3 gives the percentage of participants from each demographic group who reported being a user of each platform with any use frequency.

The breakdown of every demographic by frequency of use on each platform is provided in full in Supplementary Table 1 . Figure 1 illustrates this breakdown for sex, which is the demographic by which all our following results are stratified due to the imbalance in our sample and the results in Tables 2 and 3 . Social media use and mental health and well-being outcomes are also known to vary according to gender 54 , 55 , 56 .

All social media users in the sample ( N   =  4083) are split by female ( N   =  2698) and male ( N   =  1385), and the frequency with which they use each social media platform given as either ‘Daily’, ‘Less than daily’ or ‘Never’. Labels on the stacked charts give the precise percentage of the group in each of the frequencies for each platform.

Mental health and well-being

First we will consider well-being and indicators of poor mental health across different use frequencies. Figure 2 shows how indicators of poor mental health vary across the three frequencies of use, which are more than 10 times a day, 2–10 times a day and once per day or less; no participants reported using no social media at all. These frequencies are contextualised by the prevalence of each outcome in all users of social media. This figure shows that the lowest category of social media use, that is once per day or less, has the highest proportions of disordered eating, self-harm and suicidal thoughts among women. As seen in Table 2 , only 7.1% of women and 12% of men used social media less than once per day, and so these measurements are subject to wider confidence intervals. Here, depression is defined as being present in those who scored above the cut-off score of 12 in the Short Mood and Feelings Questionnaire (MFQ) 38 . Additional descriptive data about mental health outcomes in the sample is also available in Supplementary Figure 1 and in Supplementary Tables 2 to 6 .

The frequency with which participants used any social media is reported as ‘more than ten times a day’, ‘between two and ten times a day’ or ‘once or less per day’, and the percentage of participants in that group who reported each mental health outcome is given in each sub-plot, with 95% confidence intervals. Disordered eating, self-harm and suicidal thoughts were assessed in the main sample alongside the social media questions ( N   =  4083) and included for those participants who reported them in the past year. Depression ( N   =  2991) was measured in the sub-sample with the Moods and Feelings questionnaire in the year prior to the social media measurement, and uses a cut off of 12 or more to indicate the presence of depression.

Similarly, each well-being construct is presented in Fig. 3 , and contextualised by the result for all users of social media, regardless of frequency. Separate outcomes are presented for the three sub-scales of the Basic Psychological Needs (BPN) scale and the two sub-scales of the Meaning in Life (MIL) scale. The Life Orientation Test measures optimism, and the Warwick Edinburgh Mental Well-being Scale (WEMWBS) measures overall positive well-being.

Each sub-graph presents each of the seven well-being measures, including the Basic Psychological Needs scale (BPN) sub-scales autonomy, relatedness and competence, and the Meaning In Life (MIL) scale’s two sub-scales of presence and search. Satisfaction With Life, the Life Orientation Test, the Gratitude Questionnaire, Subjective Happiness Scale and the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS) are also included. The mean of each scale is given for all participants ( N   =  2991) with 95% confidence intervals, split by male and female, and then for each dichotomous category of use-frequency which is one of ‘more than ten times a day’, ‘between two and ten times a day’ or ‘once or less per day’.

Next we consider the characteristics of daily users of each platform. The relative percentage of daily users against other types of users for each platform can be referred to in Fig. 1 , and versions of Figs. 4 and 5 for all users of each platform are given in Supplementary Figures 2 and 3 .

The percentage of daily users of each platform who have reported each symptom is given in each sub-graph, with 95% confidence intervals. Disordered eating, self-harm and suicidal thoughts were assessed in the main sample alongside the social media questions ( N   =  4083) and included for those participants who reported them in the past year. Depression ( N   =  2991) was measured in the sub-sample with the Moods and Feelings questionnaire in the year prior to the social media measurement, and uses a cut off of 12 or more to indicate the presence of depression. Participants can belong to the daily user group of more than one platform.

Each sub-graph presents each of the seven well-being measures, including the Basic Psychological Needs scale (BPN) sub-scales autonomy, relatedness and competence, and the Meaning In Life (MIL) scale’s two sub-scales of presence and search. Satisfaction With Life, the Life Orientation Test, the Gratitude Questionnaire, Subjective Happiness Scale and the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS) are also included. The mean of each scale is given for all daily users of each platform from the sub-sample ( N   =  2991) with 95% confidence intervals, split by male and female.

Finally Fig. 5 gives the mean well-being score across each platform for each of the seven well-being measures.

This study used data from a UK population cohort study to describe the demographics and key mental health and well-being indicators of social media users by their self-reported frequency of using social media and five different platforms used at ages 23 and 24. Overall, we saw that there were differences in demographics and mental states of users across use-patterns and platforms used. In the following sections, we detail and discuss the implications of these findings for future research across the themes of demographics, use-frequency and platform used.

In general, just over half of participants reported using social media 2–10 times per day, with more than ten times per day still being common at 39%, and only approximately one in ten participants using social media once per day or less. The results showed that those who rated their social media use at the highest frequency (more than ten times per day) were more likely to be women, more likely to be White and more likely have parents who worked in manual occupations. However, sex was the only demographic that appeared to have a statistical relationship with frequency of use, based on a Chi-squared test. Davies and colleagues 57 saw similar results from a Welsh population survey of social media use that found there was a difference in social media use across genders, but not by measures of deprivation.

Figure 1 showed that Facebook is, unsurprisingly, the most popular platform both in being used by 97% of the participants and being the most used platform on a daily basis. Instagram and YouTube showed substantial differences in use patterns across male and female users, with approximately double the percentage of women using Instagram daily as men and, conversely, approximately double the percentage of men using YouTube daily as women. Snapchat also saw higher proportions of daily and overall female users, though this difference between sexes was not as dramatic as for Instagram and YouTube. These patterns of use generally agree with the demographics of users on these sites reported for 18–29-year-old US adults by the Pew Research Center 25 , although our sample saw slightly more Twitter users than their estimated 38%, and fewer YouTube users than their estimated 91% (see Table 3 ). This difference in YouTube users may be partly explained by the fact that it is the only platform with a substantially higher proportion of men than women using it (68% of women vs 83% of men), and that men were under represented in our sample overall compared to women. This emphasises the importance of stratifying results by sex.

Previous research into the demographics of UK Twitter users also aligns with our findings that men and people from higher socio-economic backgrounds are more likely to be Twitter users than women 26 , 28 . Here, we also saw that those from ethnic minority groups are more likely to be Twitter users than White participants, though this is limited by the fact that we could not further separate out results for people with different ethnicities due to the variables available. Across our sample, Twitter was the only social media platform that had a noticeably higher proportion of both A Level educated participants and parents in non-manual occupations. Snapchat saw the reverse pattern with a higher proportion of participants who did not have A Level qualifications and a higher proportion of participants whose parents worked in manual occupations.

Overall, the sex differences between all male and female users varied across outcomes. For instance, a higher percentage of women experienced depression, disordered eating and self-harm overall, but the gap in the prevalence of suicidal thoughts between men and women was much smaller. This concurs with evidence from the last UK-wide psychiatric morbidity survey, in that ‘common mental health disorders’ are more prevalent in women than men 58 . When it came to well-being, we saw that women also displayed higher mean levels of well-being across most measures. Exceptions were the Life Orientation Test, which showed men generally had higher levels of optimism, the Subjective Happiness Scale where scores were roughly equivalent, and the WEMWBS where men’s general well-being was slightly higher. These results, apart from the WEMWBS, are consistent with findings on UK-wide well-being at the time of the survey, and that men tend to have higher optimism in general 59 , 60 . Previous research into the WEMWBS has not generally found large sex differences, but there is evidence that in younger samples there are differences that may be explained by socio-economic status 40 , 41 , 61 ; we note that higher attrition of men in our sample was likely to lead to a bias towards men who are more socio-economically privileged, which may explain why they had higher well-being.

The patterns of mental health outcomes by use frequency displayed in Fig. 2 showed some support for the so-called ‘Goldilocks theory’ of social media use that hypothesises a quadratic, rather than linear, stimulus-response relationship between social media use and mental well-being 62 . This would mean that moderate use of social media, rather than very little or excessive use, is best for well-being. However, this pattern did not consistently apply. For instance, there was an inverse relationship between social media use and percentage of women who self-harm, and in men only the group with the highest level of social media use had more severe depressive symptoms. Previous research has found that in young women higher social media use was associated with increased risk of self-harm 63 , which is in contrast to our results. Similarly, research using the Millennium Cohort Study also found an increasing relationship between objectively measured number of hours spent on social media and how many respondents had clinically relevant symptoms of depression 64 , with a greater increase for girls than boys. Our findings roughly concur with those for the boys, but in women we found that those who used social media the least had the highest rates of depression. However, these differences in findings could reflect the difference in the age of participants or the ways that social media was measured differently across studies. Here we were using use-frequency as categorised into three groups which, as we discuss further in our limitations, may be more reflective of the individual’s mental health and relationship with social media than how frequently they use it 65 .

When considering the results by well-being measure in Fig. 3 we saw that subjective happiness and optimism as measured by the Life Orientation Test both appeared relatively consistent across use categories. Relatedness presented the clearest difference across use categories, with relatedness in women being higher for the two most frequent use frequencies. However, perhaps the most notable outcome was the inconsistency between well-being scales which implies that the choice of scale could affect the interpretation of the impact of well-being on social media use. Research into the relationship between social media use and well-being has been said to suffer from what is known as the ‘jingle-jangle’ paradox where the term ‘well-being’ is used as a catch-all for anything from depression rates to life satisfaction 66 , 67 . This conflation of different well-being measures leads to comparisons of different psychological constructs which may interact differently with social media use: this is hypothesised as one of the reasons that researchers find conflicting evidence for this relationship 66 , which our results support. This also adds to the picture of researcher degrees of freedom in choosing how to measure psychological constructs, which has been shown to have a substantial impact on the outcome of analyses of social media and mental health 15 . Subjective well-being is a complex and multi-faceted psychological concept 68 , 69 , and these findings illustrate the importance of recognising that different measures of well-being could imply different relationships between social media and “well-being”.

When considering participant outcomes by daily users of each platform more consistent patterns emerge than for use-frequencies. We saw that, particularly for women, YouTube had the highest proportion of users reporting disordered eating, self-harm, suicidal thoughts and depression, with higher prevalence of depression in female users of YouTube compared to male users (Fig. 4 ). Whilst overall mental well-being across platforms, as measured by the WEMWBS in Fig. 5 , shows YouTube as being marginally but not drastically lower than other platforms, other well-being measures illustrated some key differences. For instance, YouTube users had lower life satisfaction, relatedness and, particularly for female users, levels of competence (Fig. 5 ). Conversely, daily users of Instagram, and in some cases Snapchat, appeared to have the highest subjective well-being across most measures, with this being particularly noticeable for relatedness, gratitude and happiness (Fig. 5 ). The role of self-determination theory in social media use has previously been explored for Facebook and social media in general 70 with relatedness hypothesised as a key motivating factor for social media use. Previous findings have shown that Instagram and Snapchat are used more for social interaction than Twitter and Facebook 71 , and so our results may corroborate the importance of relatedness in the use of particular platforms. Regardless of the specific measure, our results have illustrated that there is variation amongst platforms which further challenges the idea that ‘social media’ or ‘social networking sites’ are a homogeneous group, and reiterates the importance of understanding the context of research about or using social media 28 , 71 .

At face value, our results appear to directly contrast with the outcomes of the Status of Mind report published by the Royal Society for Public Health 72 , where young people rated YouTube as being the most beneficial site for their well-being and Instagram as the worst, based on health-related outcomes such as their anxiety and depression. Our findings that a higher prevalence of YouTube users suffer from poorer mental health and well-being may mean that whilst some platforms are seen as ‘worse’ for young people’s mental health, that does not equate to finding more unwell young people on those platforms. One explanation may be that those experiencing poorer mental health are more likely to use YouTube because they experience more benefits to their mental health from YouTube, such as community building and peer support 13 , than they do from spending time on sites like Instagram. However, this is certainly an interesting area for further exploration in future quantitative and qualitative research.

Whilst this research draws evidence from a robust and well-documented study and the sample being from a birth cohort means that our results are not confounded by age, there are limitations to the cohort sample that we have used. Firstly, the cohort measures a specific age group so we can only infer information about a single age group at each measurement time point. We suspect that different patterns might be found at different ages, knowing that rates of various mental health conditions such as anxiety, depression and suicidality change over the course of childhood, adolescence and adulthood 73 , and since each generation may use social media differently 74 . It is also important to note that the two data collection points used in this study were taken a year apart, and so not all measures were taken exactly at the same time. This means that although we have primarily considered the data cross-sectionally there is a potential for some longitudinal effects to have influenced the data. Secondly, as discussed in the ‘Methods’ section, there was also a limitation in that ethnicity was only available as two categories (White or Ethnic Minority Groups) and so it was not possible to look further into differences in social media by users of difference ethnicities. Additionally, the make up of the area of Bristol that ALSPAC represents is predominantly White. Given these limitations of the sample it would be valuable to conduct similar research in other cohorts that represent more diverse areas. Thirdly, ALSPAC has seen differential attrition over time and so, as seen in Table 1 , the sample for this study when the index cohort were in their early twenties has fewer men than women, and more participants from privileged socio-economic groups in terms of education and class background 31 . As well as this, typical social media use changes over time and by age 25 , and so further assessment of social media use across a variety of population-representative age groups would be the most effective way to understand differences between generations.

Another limitation of this study is a lack of specificity about the nature of social media use that participants are referring to when responding. It is possible that activities related to ‘using’ social media, such as posting content versus passive use, change depending on platform used and that there are individual preferences to account for 54 , 71 , 75 , 76 . For instance, YouTube is distinct from other platforms in this study in that its primary function is passive content consumption as opposed to social networking. Previous research has suggested a reciprocal association between passive social media use and lower subjective well-being 75 , whilst using social media for direct communication has been positively associated with perceived friend support 77 . This may better reflect the uses of platforms like Snapchat. As well as the subjective nature of ‘use’, there are also ongoing concerns about using self-reported measures of use-frequency to measure social media behaviours 78 , 79 , 80 . Emerging evidence is showing that self-reports do not align well with objective measurement due to recall bias and differences in interpreting how to include notifications or fleeting checks of social media 79 , 80 with self-reported smartphone pickups underestimating associations with mental health compared to objective measures of use 65 . It might be that different ways of measuring social media use, such as types of use, are more useful when considering associations with mental health and well-being outcomes 54 . It is worth noting that the use-frequency measures used in this study are distinct from screen-time, and equivalent use-frequency across platforms may have different time implications; someone may spend short amounts of time on Instagram or Snapchat checking notifications, but do so frequently, versus visiting YouTube once in a day but spending several hours watching content. These nuances are challenging to capture, but by reporting on mental health prevalence across the available responses in a cohort study we can add to the growing understanding of how self-reported social media use frequency is related to mental health. Statistical modelling to test the extent of the differences observed between mental health constructs, use-frequencies and platforms would be valuable future research.

In summary, our results amplify the importance of attending to complexity when measuring and analysing social media use and mental health and well-being. It is important to note that our results do not, and cannot, imply that different types of social media use cause poorer or better health outcomes in young people, but they do provide vital contextual information on user groups that can help us better understand the reasons that previous research has found conflicting results. We have provided estimates of seven well-being measures and the prevalence of four key mental health outcomes (depression, disordered eating, suicidal thoughts and self-harm) across the five platforms Facebook, Twitter, Instagram, Snapchat and YouTube, as well as across three use frequencies. Our findings have shown that the demographic and mental health foot-print of each platform is different. Primarily users differ by sex, but when it comes to platforms YouTube is particularly likely to have both male and female users with poorer mental health and well-being across a range of indicators, alongside evidence that daily Instagram users have better overall well-being than daily users of other platforms. Our findings also indicate that relationships between use-frequency and multiple mental health and well-being outcomes are often non-linear, which supports the importance of considering non-linear dose-response relationships between social media and mental health and well-being in future research. Lastly, we saw that the relationship between use-frequencies and well-being changes depending on the measure of well-being used. This means that we cannot conflate different types of well-being, and doing so will likely result in low replicability.

This research has implications for both those who conduct research on the relationship between social media and mental health, and those who study mental health prediction. We must ensure we are considering both platform-specific and outcome-specific effects rather than conflating types of social media use, social media sites and well-being as single entities. Future research should also stratify results by sex since it is unlikely that studies with differently balanced samples will replicate. Our findings on use-frequencies also suggest that we cannot assume linear relationships between social media use and mental health. Our understanding of these methodological issues would be improved by examining profiles of different user age-groups, as well as examining relationships between these variables longitudinally to understand the potential for reciprocal effects. The differences between platforms should be further considered too, as to how different content types and communication modes on different platforms may affect mental health differently.

Data availability

The datasets analysed during the current study are not publicly available as the informed consent obtained from ALSPAC participants does not allow data to be made freely available through any third party maintained public repository. However, data used for this submission can be made available on request to the ALSPAC Executive, with reference to project number B3227. The ALSPAC data management plan describes in detail the policy regarding data sharing, which is through a system of managed open access. Full instructions for applying for data access can be found here: http://www.bristol.ac.uk/alspac/researchers/access/ . The ALSPAC study website contains details of all the data that are available ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ).

Code availability

The code used to produce the results in this study can be found at https://doi.org/10.17605/OSF.IO/RKXM6 .

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Acknowledgements

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). The data used in this research was specifically funded by the NIHR (1215-20011), the Wellcome Trust (SSCM.RD1809) and the MRC (102215/2/13/2, MR/M006727/1). N.D. is supported by an MRC GW4 BioMed studentship in Data Science and AI (MR/N013794/1). C.M.A.H. is supported by a Philip Leverhulme Prize. N.H., O.S.P.D. and C.M.A.H. will serve as guarantors for the contents of this paper.

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These authors jointly supervised this work: Oliver S. P. Davis, Claire M. A. Haworth.

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Department of Population Health Science, University of Bristol, Bristol, UK

Nina H. Di Cara, Lizzy Winstone & Oliver S. P. Davis

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

Nina H. Di Cara & Oliver S. P. Davis

Cardiff University, Cardiff, Wales, UK

The Alan Turing Institute, London, UK

Oliver S. P. Davis & Claire M. A. Haworth

Department of Psychological Science, University of Bristol, Bristol, UK

Claire M. A. Haworth

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N.D. was responsible for data curation, formal analysis, investigation, methodology, visualisation and writing (original draft and reviewing and editing). L.W. was responsible for methodology, investigation and writing (reviewing and editing). L.S. was responsible for methodology, investigation, supervision and writing (reviewing and editing). O.D. and C.H. were responsible for funding acquisition, conceptualisation, methodology, investigation, supervision and writing (reviewing and editing).

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Correspondence to Nina H. Di Cara .

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Di Cara, N.H., Winstone, L., Sloan, L. et al. The mental health and well-being profile of young adults using social media. npj Mental Health Res 1 , 11 (2022). https://doi.org/10.1038/s44184-022-00011-w

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Social Media and Mental Health

95 Pages Posted: 14 Sep 2021 Last revised: 22 Aug 2023

Luca Braghieri

Bocconi University - Department of Decision Sciences

Tel Aviv University - Eitan Berglas School of Economics

Alexey Makarin

Massachusetts Institute of Technology (MIT) - Sloan School of Management; Einaudi Institute for Economics and Finance (EIEF); Centre for Economic Policy Research (CEPR)

Date Written: July 28, 2022

The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.

Keywords: social media, mental heath, Facebook, depression, social comparisons

JEL Classification: D12, D72, D90, I10, L82, L8

Suggested Citation: Suggested Citation

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Experimental longitudinal evidence for causal role of social media use and physical activity in COVID-19 burden and mental health

Affiliation.

• 1 Mental Health Research and Treatment Center, Department of Clinical Psychology and Psychotherapy, Ruhr-Universität Bochum, Massenbergstr. 9-13, 44787 Bochum, Germany.
• PMID: 36068852
• PMCID: PMC9437404
• DOI: 10.1007/s10389-022-01751-x

Aim: The COVID-19 outbreak has severely impacted people's mental health. The present experimental study investigated how to reduce this negative effect by a combination of two interventions.

Subject and methods: Participants ( N total = 642) were users of social media in Germany. For two weeks, the social media group ( N = 162) reduced its social media use (SMU) by 30 minutes daily, the physical activity group ( N = 161) increased its physical activity by 30 minutes daily, the combination group ( N = 159) followed both instructions, and the control group ( N = 160) did not get specific instructions. Online surveys assessed variables of SMU, physical activity, mental health, COVID-19 burden, and lifestyle at six measurement time points up to six months after the intervention.

Results: In the experimental groups, (addictive) SMU, depression symptoms, and COVID-19 burden decreased, while physical activity, life satisfaction, and subjective happiness increased. All effects were stronger and more stable in the combination group in the longer-term. Smoking behavior decreased in the social media group only.

Conclusion: Thus, the conscious combination of less SMU and more physical activity leads causally to more psychological resilience against negative pandemic impacts and to higher levels of mental health over six months. Prevention programs could improve their effectiveness by integrating the time- and cost-efficient interventions - separately or in combination.

Supplementary information: The online version contains supplementary material available at 10.1007/s10389-022-01751-x.

Keywords: COVID-19 burden; Experimental longitudinal study; Lifestyle; Mental health; Physical activity increase; Social media use reduction.

© The Author(s) 2022.

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Conflict of interest statement

Conflicts of interest/competing interestsThe authors state that there are no conflicts of interest or competing interests.

Results of repeated measure analyses…

Results of repeated measure analyses of variance (ANOVAs) for time and intensity of…

Results of repeated measure analyses of variance (ANOVAs) for variables of mental health…

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American Economic Review

• November 2022

Social Media and Mental Health

ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)

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Social Media Use and Mental Health: A Review of the Experimental Literature and Implications for Clinicians

• Published: 24 February 2024
• Volume 11 , pages 1–16, ( 2024 )

Cite this article

• Kaitlyn Burnell PhD 1 ,
• Kara A. Fox MA 1 ,
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Purpose of Review

Social media use is widespread. Because social media can yield both positive and negative mental health effects, it is critical for clinicians to consider how their clients use social media. The purpose of this review is to examine the extant experimental literature on the positive and negative effects of social media, with an eye towards how clinicians can (1) assess use, (2) educate on harmful use, and (3) promote skills that encourage healthier use.

Recent Findings

The existing literature suggests that active social media use that promotes positive connection, reminiscing, or warmth can be beneficial, whereas social media use that involves exposure to and production of highly idealized content, a focus on physical appearance, or a reliance on feedback can be harmful. To encourage healthier social media use, clinicians can encourage the building of intrapersonal skills, including reappraising comparison-inducing content, self-compassion, and mindfulness.

Although additional experimental work is needed to thoroughly inform treatment plans, findings suggest avenues that may be effective for clinicians when treating clients who struggle with their social media use. Changing how clients approach social media, rather than encouraging abstinence from use, may be more effective and practical in this digitally saturated age.

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Burnell, K., Fox, K.A., Maheux, A.J. et al. Social Media Use and Mental Health: A Review of the Experimental Literature and Implications for Clinicians. Curr Treat Options Psych 11 , 1–16 (2024). https://doi.org/10.1007/s40501-024-00311-2

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Science News

Social media harms teens’ mental health, mounting evidence shows. what now.

Understanding what is going on in teens’ minds is necessary for targeted policy suggestions

Most teens use social media, often for hours on end. Some social scientists are confident that such use is harming their mental health. Now they want to pinpoint what explains the link.

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By Sujata Gupta

February 20, 2024 at 7:30 am

In January, Mark Zuckerberg, CEO of Facebook’s parent company Meta, appeared at a congressional hearing to answer questions about how social media potentially harms children. Zuckerberg opened by saying: “The existing body of scientific work has not shown a causal link between using social media and young people having worse mental health.”

But many social scientists would disagree with that statement. In recent years, studies have started to show a causal link between teen social media use and reduced well-being or mood disorders, chiefly depression and anxiety.

Ironically, one of the most cited studies into this link focused on Facebook.

Researchers delved into whether the platform’s introduction across college campuses in the mid 2000s increased symptoms associated with depression and anxiety. The answer was a clear yes , says MIT economist Alexey Makarin, a coauthor of the study, which appeared in the November 2022 American Economic Review . “There is still a lot to be explored,” Makarin says, but “[to say] there is no causal evidence that social media causes mental health issues, to that I definitely object.”

The concern, and the studies, come from statistics showing that social media use in teens ages 13 to 17 is now almost ubiquitous. Two-thirds of teens report using TikTok, and some 60 percent of teens report using Instagram or Snapchat, a 2022 survey found. (Only 30 percent said they used Facebook.) Another survey showed that girls, on average, allot roughly 3.4 hours per day to TikTok, Instagram and Facebook, compared with roughly 2.1 hours among boys. At the same time, more teens are showing signs of depression than ever, especially girls ( SN: 6/30/23 ).

As more studies show a strong link between these phenomena, some researchers are starting to shift their attention to possible mechanisms. Why does social media use seem to trigger mental health problems? Why are those effects unevenly distributed among different groups, such as girls or young adults? And can the positives of social media be teased out from the negatives to provide more targeted guidance to teens, their caregivers and policymakers?

“You can’t design good public policy if you don’t know why things are happening,” says Scott Cunningham, an economist at Baylor University in Waco, Texas.

Increasing rigor

Concerns over the effects of social media use in children have been circulating for years, resulting in a massive body of scientific literature. But those mostly correlational studies could not show if teen social media use was harming mental health or if teens with mental health problems were using more social media.

Moreover, the findings from such studies were often inconclusive, or the effects on mental health so small as to be inconsequential. In one study that received considerable media attention, psychologists Amy Orben and Andrew Przybylski combined data from three surveys to see if they could find a link between technology use, including social media, and reduced well-being. The duo gauged the well-being of over 355,000 teenagers by focusing on questions around depression, suicidal thinking and self-esteem.

Digital technology use was associated with a slight decrease in adolescent well-being , Orben, now of the University of Cambridge, and Przybylski, of the University of Oxford, reported in 2019 in Nature Human Behaviour . But the duo downplayed that finding, noting that researchers have observed similar drops in adolescent well-being associated with drinking milk, going to the movies or eating potatoes.

Holes have begun to appear in that narrative thanks to newer, more rigorous studies.

In one longitudinal study, researchers — including Orben and Przybylski — used survey data on social media use and well-being from over 17,400 teens and young adults to look at how individuals’ responses to a question gauging life satisfaction changed between 2011 and 2018. And they dug into how the responses varied by gender, age and time spent on social media.

Social media use was associated with a drop in well-being among teens during certain developmental periods, chiefly puberty and young adulthood, the team reported in 2022 in Nature Communications . That translated to lower well-being scores around ages 11 to 13 for girls and ages 14 to 15 for boys. Both groups also reported a drop in well-being around age 19. Moreover, among the older teens, the team found evidence for the Goldilocks Hypothesis: the idea that both too much and too little time spent on social media can harm mental health.

“There’s hardly any effect if you look over everybody. But if you look at specific age groups, at particularly what [Orben] calls ‘windows of sensitivity’ … you see these clear effects,” says L.J. Shrum, a consumer psychologist at HEC Paris who was not involved with this research. His review of studies related to teen social media use and mental health is forthcoming in the Journal of the Association for Consumer Research.

Cause and effect

That longitudinal study hints at causation, researchers say. But one of the clearest ways to pin down cause and effect is through natural or quasi-experiments. For these in-the-wild experiments, researchers must identify situations where the rollout of a societal “treatment” is staggered across space and time. They can then compare outcomes among members of the group who received the treatment to those still in the queue — the control group.

That was the approach Makarin and his team used in their study of Facebook. The researchers homed in on the staggered rollout of Facebook across 775 college campuses from 2004 to 2006. They combined that rollout data with student responses to the National College Health Assessment, a widely used survey of college students’ mental and physical health.

The team then sought to understand if those survey questions captured diagnosable mental health problems. Specifically, they had roughly 500 undergraduate students respond to questions both in the National College Health Assessment and in validated screening tools for depression and anxiety. They found that mental health scores on the assessment predicted scores on the screenings. That suggested that a drop in well-being on the college survey was a good proxy for a corresponding increase in diagnosable mental health disorders. 

Compared with campuses that had not yet gained access to Facebook, college campuses with Facebook experienced a 2 percentage point increase in the number of students who met the diagnostic criteria for anxiety or depression, the team found.

When it comes to showing a causal link between social media use in teens and worse mental health, “that study really is the crown jewel right now,” says Cunningham, who was not involved in that research.

A need for nuance

The social media landscape today is vastly different than the landscape of 20 years ago. Facebook is now optimized for maximum addiction, Shrum says, and other newer platforms, such as Snapchat, Instagram and TikTok, have since copied and built on those features. Paired with the ubiquity of social media in general, the negative effects on mental health may well be larger now.

Moreover, social media research tends to focus on young adults — an easier cohort to study than minors. That needs to change, Cunningham says. “Most of us are worried about our high school kids and younger.” 

And so, researchers must pivot accordingly. Crucially, simple comparisons of social media users and nonusers no longer make sense. As Orben and Przybylski’s 2022 work suggested, a teen not on social media might well feel worse than one who briefly logs on. 

Researchers must also dig into why, and under what circumstances, social media use can harm mental health, Cunningham says. Explanations for this link abound. For instance, social media is thought to crowd out other activities or increase people’s likelihood of comparing themselves unfavorably with others. But big data studies, with their reliance on existing surveys and statistical analyses, cannot address those deeper questions. “These kinds of papers, there’s nothing you can really ask … to find these plausible mechanisms,” Cunningham says.

One ongoing effort to understand social media use from this more nuanced vantage point is the SMART Schools project out of the University of Birmingham in England. Pedagogical expert Victoria Goodyear and her team are comparing mental and physical health outcomes among children who attend schools that have restricted cell phone use to those attending schools without such a policy. The researchers described the protocol of that study of 30 schools and over 1,000 students in the July BMJ Open.

Goodyear and colleagues are also combining that natural experiment with qualitative research. They met with 36 five-person focus groups each consisting of all students, all parents or all educators at six of those schools. The team hopes to learn how students use their phones during the day, how usage practices make students feel, and what the various parties think of restrictions on cell phone use during the school day.

Talking to teens and those in their orbit is the best way to get at the mechanisms by which social media influences well-being — for better or worse, Goodyear says. Moving beyond big data to this more personal approach, however, takes considerable time and effort. “Social media has increased in pace and momentum very, very quickly,” she says. “And research takes a long time to catch up with that process.”

Until that catch-up occurs, though, researchers cannot dole out much advice. “What guidance could we provide to young people, parents and schools to help maintain the positives of social media use?” Goodyear asks. “There’s not concrete evidence yet.”

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• Introduction
• Conclusions
• Article Information

The dashed lines represent potential confounding. The solid line represents the main association of interest. BMI indicates body mass index.

Error bars indicate 95% CIs.

eFigure. Participant selection from the complete PATH sample into the analytic sample.

eTable 1. Items from the GAIN-SS scale used to assess internalizing and externalizing problems.

eTable 2. Unadjusted and adjusted relative risk ratios for each category of social media use in relation to internalizing and externalizing problems among U.S. youth in the PATH Study, 2013-2016, after multiple imputation with chained equations (n=7,234).

eMethods. Calculating population attributable fractions from adjusted models.

• Social Media and the Youth Mental Health Crisis JAMA Medical News & Perspectives July 3, 2023 This Medical News article discusses potential harms and benefits of social media use and what primary care physicians can do to protect children and adolescents and support families. Jennifer Abbasi
• US Surgeon General Calls for Social Media Warning Labels JAMA Medical News & Perspectives August 9, 2024 This Medical News article is an interview with US Surgeon General Vivek Murthy, MD, MBA, and JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, about his call for a warning label on social media platforms. Jennifer Abbasi; Yulin Hswen, ScD, MPH
• Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters JAMA Psychiatry Comment & Response April 1, 2020 Katherine M. Keyes, PhD; Noah Kreski, MPH
• Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters—Reply JAMA Psychiatry Comment & Response April 1, 2020 Kenneth A. Feder, PhD; Kira E. Riehm, MSc; Ramin Mojtabai, MD

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Riehm KE , Feder KA , Tormohlen KN, et al. Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. JAMA Psychiatry. 2019;76(12):1266–1273. doi:10.1001/jamapsychiatry.2019.2325

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Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth

• 1 Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
• 2 Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
• 3 Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland
• 4 Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
• 5 Division of Child and Adolescent Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, Maryland
• 6 Department of Behavioral and Community Health, University of Maryland, College Park, College Park
• 7 Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina
• 8 Washington State Department of Health, Olympia
• Medical News & Perspectives Social Media and the Youth Mental Health Crisis Jennifer Abbasi JAMA
• Medical News & Perspectives US Surgeon General Calls for Social Media Warning Labels Jennifer Abbasi; Yulin Hswen, ScD, MPH JAMA
• Comment & Response Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters Katherine M. Keyes, PhD; Noah Kreski, MPH JAMA Psychiatry
• Comment & Response Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters—Reply Kenneth A. Feder, PhD; Kira E. Riehm, MSc; Ramin Mojtabai, MD JAMA Psychiatry

Question   Is time spent using social media associated with mental health problems among adolescents?

Findings   In this cohort study of 6595 US adolescents, increased time spent using social media per day was prospectively associated with increased odds of reporting high levels of internalizing and comorbid internalizing and externalizing problems, even after adjusting for history of mental health problems.

Meaning   Adolescents who spend more than 3 hours per day on social media may be at heightened risk for mental health problems, particularly internalizing problems.

Importance   Social media use may be a risk factor for mental health problems in adolescents. However, few longitudinal studies have investigated this association, and none have quantified the proportion of mental health problems among adolescents attributable to social media use.

Objective   To assess whether time spent using social media per day is prospectively associated with internalizing and externalizing problems among adolescents.

Design, Setting, and Participants   This longitudinal cohort study of 6595 participants from waves 1 (September 12, 2013, to December 14, 2014), 2 (October 23, 2014, to October 30, 2015), and 3 (October 18, 2015, to October 23, 2016) of the Population Assessment of Tobacco and Health study, a nationally representative cohort study of US adolescents, assessed US adolescents via household interviews using audio computer-assisted self-interviewing. Data analysis was performed from January 14, 2019, to May 22, 2019.

Exposures   Self-reported time spent on social media during a typical day (none, ≤30 minutes, >30 minutes to ≤3 hours, >3 hours to ≤6 hours, and >6 hours) during wave 2.

Main Outcomes and Measure   Self-reported past-year internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems during wave 3 using the Global Appraisal of Individual Needs–Short Screener.

Results   A total of 6595 adolescents (aged 12-15 years during wave 1; 3400 [51.3%] male) were studied. In unadjusted analyses, spending more than 30 minutes of time on social media, compared with no use, was associated with increased risk of internalizing problems alone (≤30 minutes: relative risk ratio [RRR], 1.30; 95% CI, 0.94-1.78; >30 minutes to ≤3 hours: RRR, 1.89; 95% CI, 1.36-2.64; >3 to ≤6 hours: RRR, 2.47; 95% CI, 1.74-3.49; >6 hours: RRR, 2.83; 95% CI, 1.88-4.26) and comorbid internalizing and externalizing problems (≤30 minutes: RRR, 1.39; 95% CI, 1.06-1.82; >30 minutes to ≤3 hours: RRR, 2.34; 95% CI, 1.83-3.00; >3 to ≤6 hours: RRR, 3.15; 95% CI, 2.43-4.09; >6 hours: RRR, 4.29; 95% CI, 3.22-5.73); associations with externalizing problems were inconsistent. In adjusted analyses, use of social media for more than 3 hours per day compared with no use remained significantly associated with internalizing problems alone (>3 to ≤6 hours: RRR, 1.60; 95% CI, 1.11-2.31; >6 hours: RRR, 1.78; 95% CI, 1.15-2.77) and comorbid internalizing and externalizing problems (>3 to ≤6 hours: RRR, 2.01; 95% CI, 1.51-2.66; >6 hours: RRR, 2.44; 95% CI, 1.73-3.43) but not externalizing problems alone.

Conclusions and Relevance   Adolescents who spend more than 3 hours per day using social media may be at heightened risk for mental health problems, particularly internalizing problems. Future research should determine whether setting limits on daily social media use, increasing media literacy, and redesigning social media platforms are effective means of reducing the burden of mental health problems in this population.

For adolescents in the United States, social media use is ubiquitous. A 2018 Pew Research Center poll found that 97% of adolescents report using at least 1 of the 7 most popular social media platforms (YouTube, Instagram, Snapchat, Facebook, Twitter, Tumblr, and Reddit). Moreover, digital media use by adolescents is common: 95% report owning or having access to a smartphone, and almost 90% report they are online at least several times a day. 1

Social media offers numerous potential benefits to users, including exposure to current events, interpersonal connection, and enhancement of social support networks. 2 However, concerns are increasingly raised about potential harms of social media use. 2 One-quarter of adolescents think social media has a mostly negative influence on people their age, pointing to reasons like rumor spreading, lack of in-person contact, unrealistic views of others’ lives, peer pressure, and mental health issues. 1

An increasing body of literature suggests that social media use is associated with mental health problems in adolescence. Numerous cross-sectional studies and a limited number of longitudinal studies suggest that high levels of social media use are associated with internalizing problems, including depressive and anxiety symptoms, 3 - 6 although results are not entirely consistent. 7 Some studies also suggest an association between social media use and externalizing problems, such as bullying and attention problems. 8 , 9 Furthermore, a previous study 4 produced mixed results regarding the possible moderating effect of sex.

The prevalence of major depressive disorder and depressive symptoms has increased among adolescents in the United States, 10 , 11 and adolescent suicide death and attempt rates have increased sharply during the past 2 decades. 12 , 13 Some authors 14 have postulated that increases in depression may be attributable to rapid increases in social media use. However, evidence of this association in nationally representative samples is scarce, and little is known about whether reducing time spent on social media might influence the prevalence of mental health problems at a national level.

In this article, we build on existing literature by examining the prospective association of time spent on social media with internalizing and externalizing problems in a representative sample of US adolescents. We used data from the Population Assessment of Tobacco and Health (PATH) study, which is a nationally representative, longitudinal cohort of adolescents. 15 Unlike a prior study, 16 we adjusted for mental health problems measured before the exposure, which is critical for reducing the influence of reverse causality. We hypothesized that greater time spent on social media would prospectively be associated with internalizing and externalizing problems alone, as well as comorbid problems at 1-year follow-up. On the basis of past research, 5 we also examined whether these associations differed between males and females.

In this longitudinal cohort study, participants were drawn from the public-use data files of waves 1 (September 12, 2013, to December 14, 2014), 2 (October 23, 2014, to October 30, 2015), and 3 (October 18, 2015, to October 23, 2016) of the PATH study. 15 The methods of the PATH study have been previously described. 15 In brief, the target population for this survey was the civilian household population in the United States. Data were collected in 1-year intervals, starting with wave 1 from September 12, 2013, to December 14, 2014. Multistage-stratified sampling was used to obtain a sample of households from which up to 2 individuals aged 12 to 17 years were randomly selected to be interviewed. Data analysis was performed from January 14, 2019, to May 22, 2019. After oral parent permission and adolescent assent were obtained, adolescents were interviewed using audio computer-assisted self-interviewing. The current analyses were considered exempt from human subjects research according to Johns Hopkins institutional review board policy because the data were publicly available and deidentified.

The weighted response rate for adolescents during wave 1 was 78.4%, and the weighted retention rate during wave 3 was 83.3%. 17 A total of 7595 adolescents (aged 12-15 years during wave 1, aged 13-16 years during wave 2, and aged 14-17 years during wave 3) completed all 3 PATH survey waves. Of these, 1000 adolescents (13.2%) were excluded because they were missing data on at least 1 variable required for this analysis; the remaining 6595 adolescents comprised the analytic sample (eFigure in the Supplement ).

Past-year mental health problems, the outcome of interest, were assessed during wave 3 using the Global Appraisal of Individual Needs–Short Screener (GAIN-SS). 18 The GAIN-SS is a screening measure intended to identify a probable mental health disorder and assess symptom severity; it has been validated in adolescents 19 and includes internalizing and externalizing subscales (eTable 1 in the Supplement ). Each item measures 1 symptom; for this study, symptoms were considered to be present if the respondent selected in the past month or 2 to 12 months from the response options that indicated the last time they had experienced that symptom. Symptom counts were generated for each subscale. Adolescents were classified as reporting low to moderate (0-3 symptoms) or high (≥4 symptoms) internalizing and externalizing problems. These cut points have been validated for use when making treatment decisions 18 and have previously been used with the PATH sample. 20 , 21 We combined these subscales to create a single outcome variable with 4 mutually exclusive categories: no or low internalizing and externalizing problems, internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems. Comorbid problems were defined as having all 4 internalizing and 4 or more externalizing symptoms.

The exposure of interest was time spent using social media per day during wave 2. Adolescents who reported that they ever went online were asked, “Sometimes people use the internet to connect with other people online through social networks like Facebook, Google Plus, YouTube, MySpace, Linkedin, Twitter, Tumblr, Instagram, Pinterest, or Snapchat. This is often called ‘social media.’ Do you have a social media account?” Adolescents who reported that they had a social media account that they visited were asked, “On a typical day, about how much total time do you spend on social media sites?” The response options were up to 30 minutes; more than 30 minutes, up to 3 hours; more than 3 hours, up to 6 hours; and more than 6 hours. We retained these categories for our exposure variable, with an additional category of none for adolescents who reported not going online, not having a social media account, or never visiting their social media account.

Potential confounders, including demographic characteristics (ie, sex, age, race, and parental educational level), body mass index (based on parent-reported weight and height), self-reported lifetime marijuana use and alcohol use, and scale scores for lifetime internalizing and externalizing problems, were adjusted for in the analyses. To ensure that we did not improperly adjust for mediating variables, 22 we used covariates measured at wave 1 instead of wave 2. The full study design is displayed in Figure 1 .

Multinomial logistic regression was used to estimate the associations between time spent on social media per day with internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems (reference group: no or low internalizing and externalizing problems). Both unadjusted and adjusted analyses were conducted. Regression coefficients were exponentiated for interpretation as relative risk ratios (RRRs). In addition, we used the adjusted model to generate and plot predicted probabilities of high internalizing and externalizing problems for each level of social media use for an otherwise average study participant.

We tested for the presence of a linear trend in the coefficients for social media use in their relation to each category of mental health problems by converting the social media use variable to an ordinal variable and reestimating the adjusted model (ie, a Mantel test for trend 23 ). A linear trend would suggest that more time spent on social media is associated with a proportionally greater likelihood of reporting mental health problems.

We tested whether any observed association of social media use with mental health problems differed between males and females by testing an interaction term between social media use and sex in our adjusted model.

In addition, we estimated the respective proportions of high internalizing and high externalizing problem cases that would be potentially prevented if adolescents spent less time using social media (ie, the population-attributable fraction [PAF] for social media use). We did this for 4 counterfactual scenarios that represented increasingly greater population reductions in social media use. In scenario 1, adolescents who actually used social media more than 6 hours per day would instead use social media more than 3 hours to 6 hours or less per day; in scenario 2, adolescents who actually used social media more than 3 hours per day would instead use social media more than 30 minutes to 3 hours or less per day; in scenario 3, adolescents who actually used social media more than 30 minutes per day would instead use social media 30 minutes or less per day; and in scenario 4, adolescents who actually spent any amount of time on social media per day would instead not spend any time on social media.

We estimated each scenario by generating a counterfactual population from our adjusted model using the approach to calculate PAFs described by Greenland and Drescher 24 and Rückinger et al. 25 See the eMethods in the Supplement for a detailed description.

To test whether our results were sensitive to missing data, we repeated analyses using multiply imputed data. We performed multiple imputation using chained equations and recomputed the unadjusted, adjusted, and sex-interaction models. We stratified by sex and generated 10 imputed data sets to account for the hypothesized interaction between sex and social media use. 26

Data for analyses were weighted to be representative of 12- to 15-year-old adolescents living in the United States in 2013 to 2014. Standard errors were estimated using the wave 3 all-waves replicate weights constructed using balanced repeated replication (the Fay method) provided in the PATH data set. Statistical significance was assessed at a 2-sided P  < .05 level. All analyses were conducted using Stata, version 14 (StataCorp).

A total of 6595 adolescents (aged 12-15 years during wave 1; 3400 [51.3%] male) were included in the analysis. During wave 3, of the sample of 6595 adolescents, 611 (9.1%) reported internalizing problems alone, 885 (14.0%) reported externalizing problems alone, 1169 (17.7%) reported comorbid internalizing and externalizing problems, and the remaining 3930 (59.3%) reported no or low problems. During wave 2, a total of 1125 adolescents (16.8%) reported no social media use, 2082 (31.8%) reported 30 minutes or less, 2000 (30.7%) reported more than 30 minutes to 3 hours or more, 817 (12.3%) reported more than 3 hours to 6 hours or less, and 571 (8.4%) reported more than 6 hours of use per day. Sample characteristics are given in Table 1 .

Compared with adolescents who did not use social media, the use of social media for more than 30 minutes per day was associated with greater risk of internalizing problems alone (≤30 minutes: RRR, 1.30; 95% CI, 0.94-1.78; >30 minutes to ≤3 hours: RRR, 1.89; 95% CI, 1.36-2.64; >3 to ≤6 hours: RRR, 2.47; 95% CI, 1.74-3.49; >6 hours: RRR, 2.83; 95% CI, 1.88-4.26) and comorbid internalizing and externalizing problems (≤30 minutes: RRR, 1.39; 95% CI, 1.06-1.82; >30 minutes to ≤3 hours: RRR, 2.34; 95% CI, 1.83-3.00; >3 to ≤6 hours: RRR, 3.15; 95% CI, 2.43-4.09; >6 hours: RRR, 4.29; 95% CI, 3.22-5.73) ( Table 2 ). In the adjusted model, the associations for the 2 highest categories of social media use persisted for internalizing problems alone (>3 to ≤6 hours: RRR, 1.60; 95% CI, 1.11-2.31; >6 hours: RRR, 1.78; 95% CI, 1.15-2.77), and the associations for the 3 highest categories of social media use persisted for comorbid internalizing and externalizing problems (>30 minutes to ≤3 hours: RRR, 1.59; 95% CI, 1.23-2.05; >3 to ≤6 hours: RRR, 2.01; 95% CI, 1.51-2.66; >6 hours: RRR, 2.44; 95% CI, 1.73-3.43). In contrast, in unadjusted analyses, the association of social media use with externalizing problems was inconsistent (≤30 minutes: RRR, 1.28; 95% CI, 0.98-1.67; >30 minutes to ≤3 hours: RRR, 1.60; 95% CI, 1.16-2.21; >3 to ≤6 hours: RRR, 1.36; 95% CI, 0.97-1.90; >6 hours: RRR, 1.59; 95% CI, 1.07-2.37) and not significant in the adjusted analysis (≤30 minutes: RRR, 1.18; 95% CI, 0.89-1.56; >30 minutes to ≤3 hours: RRR, 1.37; 95% CI, 0.98-1.92; >3 to ≤6 hours: RRR, 1.22; 95% CI, 0.86-1.72; >6 hours: RRR, 1.40; 95% CI, 0.90-2.19) ( Table 2 ). The predicted probabilities of high internalizing, externalizing, and comorbid problems for each level of social media use, with all other covariates set to their mean, are displayed in Figure 2 .

We observed a significant linear trend in the coefficients for both internalizing ( F 1,99  = 8.86, P  = .004) and comorbid problems ( F 1,99  = 35.16, P  < .001); as time on social media increased, the odds of these outcomes increased proportionately. In contrast, we observed no association for externalizing problems ( F 1,99  = 2.25, P  = .14).

We observed no statistically significant interaction between social media use and sex for internalizing ( F 4,96  = 0.84, P  = .50), externalizing ( F 4,96  = 0.32, P  = .86), or comorbid problems ( F 4,96  = 0.73, P  = .57).

All PAF estimates are given in Table 3 . On the basis of our adjusted model assuming no confounding, 0.8% to 18.9% of internalizing problems and 0.8% to 15.3% of externalizing problems could be prevented if participants had instead used less social media.

Results of analyses using multiple imputation methods did not differ appreciably from the main analyses (eTable 2 in the Supplement ).

Consistent with a prior study, 4 we found that adolescent social media use was prospectively associated with increased risk of comorbid internalizing and externalizing problems as well as internalizing problems alone. This association remained significant after adjusting for demographics, past alcohol and marijuana use, and, most importantly, a history of mental health problems, which mitigates the possibility that reverse causality explains these findings. In contrast, we did not find an association of social media use with externalizing problems alone. This finding suggests that the association of social media use with comorbid problems occurs primarily because of the association of social media with internalizing problems and the high comorbidity of internalizing and externalizing problems. Unlike a prior study, 4 we found no evidence of moderation by sex, perhaps because of the simplicity of our social media use variable, which could not capture the nature of interactions on social media that may differ by sex.

Numerous mechanisms could account for the association between social media use and internalizing problems. Adolescents who engage in high levels of social media use may experience poorer quality sleep, which may be a mediator on the pathway to internalizing problems. 27 Time spent on social media may increase the risk of experiencing cyberbullying, which has a strong association with depressive symptoms. 28 Social media may also expose adolescents to idealized self-presentations that negatively influence body image and encourage social comparisons. 4 Poor emotion regulation and lack of social interaction may also be associated with social media use and contribute to symptoms of anxiety and depression. 29

These mechanisms are potentially consistent with the notion that spending less time on social media may contribute to mental health. In fact, the PAFs obtained in our study suggest that if adolescents using social media for more than 30 minutes per day had instead used it for 30 minutes or less, there would have been 9.4% fewer high internalizing problem cases and 7.3% fewer high externalizing problem cases. Of importance, this is not meant to imply that reductions in mental health problems would definitively happen if social media use were reduced or that all social media use is harmful. Instead, these PAFs suggest the potential influence of our findings on the population at a national level assuming a causal effect of social media use and no confounding—both strong assumptions. Future research could improve on our PAF estimates by using data from randomized clinical trials (RCTs).

Our findings must be balanced with the potential benefits of social media use, which include exposure to current events, communication over geographic barriers, and social inclusion for those who may be otherwise excluded in their day-to-day lives (eg, lesbian, bisexual, transgender, queer, and questioning youth). 2 A limitation of our study is that we measured overall time spent on social media; prior studies 30 - 32 have found that social media use may be positively or negatively associated with mental health depending on which platforms are used and how. Nevertheless, a number of interventions could lead to a reduction in time spent on social media by adolescents, while still allowing for the benefits of such use. The American Academy of Pediatrics has developed a Family Media Use Plan, which can be tailored to specific developmental phases and help parents set reasonable rules for digital media use. 2 Pediatricians and teachers are essential for promoting these plans, as well as helping parents identify problematic social media use in their children. 33 There is also evidence that interventions that promote media literacy, defined as “specific knowledge and skills that can help critical understanding and usage of the media,” 34 (p 455) counteract the harmful association of media use with behavioral health. 34 Also, there is an increasing movement to improve the design of social media platforms; a notable recent example is not displaying the number of “likes” that an Instagram post receives. 35 We believe that technology companies and regulators responsible for social media platforms should consider how these platforms can be designed to minimize risk of mental health problems.

Some researchers have raised concerns that studies on technology use and well-being are limited by publication bias. 36 We believe that this is a legitimate concern given that many studies on this topic, including the present study, are secondary analyses of data not collected for the purpose of studying social media. 36 There appears to be an urgent need for experimental research, specifically a priori registered RCTs that examine interventions designed to reduce social media use. Our study findings suggest a population-level association between social media use and mental health problems, and evidence from RCTs could build on this by examining changes in mental health as a result of changes in social media use. The existing observational study findings and at least 1 RCT in college students 37 appear to be sufficient to justify investment in these trials. In addition, RCTs may be valuable for developing clinical guidelines and informing regulatory policy for social media design.

Some limitations of this study should be noted. First, adolescents self-reported the exposure and outcome, which may inflate the observed associations. Second, we measured mental health problems with a self-report questionnaire rather than a diagnostic interview. Third, the validity of self-reported time spent on social media in the PATH study is unknown. Some research suggests that self-reported time on social media may exceed actual use 38 ; future studies should consider the use of digital trace data to capture actual time spent using social media. 39 Fourth, social media use continues to change rapidly over time; although our data were collected relatively recently, they may not reflect current trends. Fifth, although our study design mitigates the possibility of reverse causality, some residual confounding from imprecise measurement of prior mental health problems may have been present. Sixth, it remains possible that mental health problems are prospectively associated with social media use, but we could not examine this in the present study because of data limitations. Seventh, it is possible that the observed associations were an artifact of unmeasured confounding. Although we controlled for a number of potential confounders, there may be others, such as physical activity, that we were unable to include because of data limitations.

This study suggests that increased time spent on social media may be a risk factor for internalizing problems in adolescents. Future research should determine whether setting limits on daily social media use, increasing media literacy, and redesigning social media platforms are effective means of reducing the burden of mental health problems in this population.

Accepted for Publication: June 14, 2019.

Corresponding Author: Kira E. Riehm, MS, Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 N Broadway, Baltimore, MD 21205 ( [email protected] ).

Published Online: September 11, 2019. doi:10.1001/jamapsychiatry.2019.2325

Author Contributions: Ms Riehm had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Riehm, Feder, Crum, Green, La Flair, Mojtabai.

Acquisition, analysis, or interpretation of data: Riehm, Feder, Tormohlen, Young, Green, Pacek, La Flair.

Drafting of the manuscript: Riehm, Feder, Pacek.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Riehm, Feder, Green, Pacek.

Administrative, technical, or material support: Green.

Supervision: Crum, Green, Mojtabai.

Conflict of Interest Disclosures: Dr Young reported receiving grants from the National Institute on Drug Abuse and the Brain and Behavior Research Foundation during the conduct of the study, receiving grants from Supernus Pharmaceuticals and Psychnostics LLC outside the submitted work, and receiving personal fees from University of Montana's American Indian/Alaska Native Clinical Translational Program. Dr Pacek reported receiving grants from the National Institute on Drug Abuse during the conduct of the study. No other disclosures were reported.

Funding/Support: Ms Riehm was supported by grant 5T32MH014592-39 from the National Institute of Mental Health Psychiatric Epidemiology Training Program (Peter Zandi, principal investigator) and by a doctoral foreign study award from the Canadian Institutes of Health Research. Dr Feder was supported by National Research and Service Award F31DA044699 from the National Institute on Drug Abuse. Ms Tormohlen was supported by grant T32DA007292 (Renee M. Johnson, principal investigator), Dr Young was supported by grant K23DA044288, and Dr Pacek was supported by grant K01DA043413 from the National Institute on Drug Abuse.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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Social media use increases depression and loneliness

In the first experimental study of facebook, snapchat, and instagram use, psychologist melissa g. hunt showed a causal link between time spent on the platforms and decreased well-being..

The link between the social-media use, depression, and loneliness has been talked about for years, but a causal connection had never been proven. For the first time, Penn research based on experimental data connects Facebook, Snapchat, and Instagram use to decreased well-being. Psychologist Melissa G. Hunt published her findings in the December Journal of Social and Clinical Psychology . 

Few prior studies have attempted to show that social media use harms users’ well-being, and those that have either put participants in unrealistic situations or were limited in scope, asking them to completely forego Facebook and relying on self-report data, for example, or conducting the work in a lab in as little time as an hour. 

“We set out to do a much more comprehensive, rigorous study that was also more ecologically valid,” says Hunt, associate director of clinical training in Penn’s Psychology Department .  

To that end, the research team, which included recent alumni Rachel Marx and Courtney Lipson and Penn senior Jordyn Young, designed their experiment to include the three platforms most popular with a cohort of undergraduates and then collected objective usage data automatically tracked by iPhones for active apps, not those running the background.

Each of 143 participants completed a survey to determine mood and well-being at the study’s start, plus shared shots of their iPhone battery screens to offer a week’s worth of baseline social-media data. Participants were then randomly assigned to a control group, which had users maintain their typical social-media behavior, or an experimental group that limited time on Facebook, Snapchat, and Instagram to 10 minutes per platform per day. 

For the next three weeks, participants shared iPhone battery screenshots to give the researchers weekly tallies for each individual. With those data in hand, Hunt then looked at seven outcome measures including fear of missing out, anxiety, depression, and loneliness. 

“Here’s the bottom line,” she says. “Using less social media than you normally would leads to significant decreases in both depression and loneliness. These effects are particularly pronounced for folks who were more depressed when they came into the study.” 

Hunt stresses that the findings do not suggest that 18- to 22-year-olds should stop using social media altogether. In fact, she built the study as she did to stay away from what she considers an unrealistic goal. The work does, however, speak to the idea of limiting screen time on these apps. 

“It is a little ironic that reducing your use of social media actually makes you feel less lonely,” she says. But when she digs a little deeper, the findings make sense. “Some of the existing literature on social media suggests there’s an enormous amount of social comparison that happens. When you look at other people’s lives, particularly on Instagram, it’s easy to conclude that everyone else’s life is cooler or better than yours.” 

Because this particular work only looked at Facebook, Instagram, and Snapchat, it’s not clear whether it applies broadly to other social media platforms. Hunt also hesitates to say that these findings would replicate for other age groups or in different settings. Those are questions she still hopes to answer, including in an upcoming study about the use of dating apps by college students. 

Despite those caveats, and although the study didn’t determine the optimal time users should spend on these platforms or the best way to use them, Hunt says the findings do offer two related conclusions it couldn’t hurt any social-media user to follow. 

For one, reduce opportunities for social comparison, she says. “When you’re not busy getting sucked into clickbait social media, you’re actually spending more time on things that are more likely to make you feel better about your life.” Secondly, she adds, because these tools are here to stay, it’s incumbent on society to figure out how to use them in a way that limits damaging effects. “In general, I would say, put your phone down and be with the people in your life.” 

Melissa G. Hunt is the associate director of clinical training in the Department of Psychology in the School of Arts and Sciences at Penn. 

Rachel Marx and Courtney Lipson graduated from Penn in 2018. 

Jordyn Young is a member of the Class of 2019 at Penn.

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Social Media Use and Mental Health: A Global Analysis

1 Department of Public Health & Prevention Science, Baldwin Wallace University, Berea, OH 44017, USA; ude.wb@61htimsj

Ajlina Karamehic-Muratovic

2 Department of Sociology and Anthropology, St Louis University, St. Louis, MO 63108, USA

Mahdi Baghbanzadeh

3 Department of Business Development, Ofogh Kourosh Chain Stores, Tehran 1433894961, Iran; [email protected]

Ateka Bashir

4 Department of Public Health, Amherst College, Amherst, MA 01002, USA; moc.liamg@68aaketa

Jacob Smith

Ubydul haque.

5 Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, North Texas, Fort Worth, TX 76107, USA; [email protected]

Associated Data

Data will be shared based upon request through the corresponding author.

Research indicates that excessive use of social media can be related to depression and anxiety. This study conducted a systematic review of social media and mental health, focusing on Facebook, Twitter, and Instagram. Based on inclusion criteria from the systematic review, a meta-analysis was conducted to explore and summarize studies from the empirical literature on the relationship between social media and mental health. Using PRISMA guidelines on PubMed and Google Scholar, a literature search from January 2010 to June 2020 was conducted to identify studies addressing the relationship between social media sites and mental health. Of the 39 studies identified, 20 were included in the meta-analysis. Results indicate that while social media can create a sense of community for the user, excessive and increased use of social media, particularly among those who are vulnerable, is correlated with depression and other mental health disorders.

1. Introduction

Mental health is defined as emotional, psychological, and social well-being [ 1 ]. It plays a role in nearly every aspect of one’s life and can determine how we think, feel, act, respond to stress, relate to others, and even make choices [ 1 ]. According to the DSM-5, mental health disorders are “characterized by clinically significant disturbance in an individual’s cognition, emotion regulation, or behavior that reflects a dysfunction in the psychological, biological, or developmental processes underlying mental functioning.” Mental health disorders are common and their etiology ranges from biological factors, such as genes or brain chemistry, to life experiences, such as trauma or a history of abuse [ 1 ]. Approximately one in five American adults have some mental health issue, one in ten young people experience a period of major depression, and one in twenty-five Americans report living with a serious mental illness, such as schizophrenia, bipolar disorder, or major depression [ 1 ].

Furthermore, mental health disorders are influenced by and affect our daily social interactions [ 1 ]. Many of our social interactions occur via social media, with individuals spending a significant amount of time on popular social media sites such as Facebook, Twitter, and Instagram, among others. As of December 2019, Facebook reported 2.5 billion monthly active users, Twitter reported 330 million monthly active users, and as of January 2020, Instagram had over 1 billion active monthly users worldwide.

Social media platforms are a great tool for individuals to interact, connect, and support one another [ 2 ]. Moreover, many individuals with mental health problems turn to social media platforms to seek support networks and aid others [ 2 ]. Social media can further promote a sense of community and assist in keeping relationships not otherwise maintained, which could improve mental health outcomes if correct information and advice are obtained [ 3 ]. At the same time, however, increased use of social media may also lead to a constant desire to be connected and can promote negative experiences, which in turn can affect the mental health of the users [ 3 ]. Negative effects of increased social media use are especially pronounced for youth; the literature suggests, for instance, that social media use has the potential to amplify the risk of alcohol and drug use among youth [ 4 , 5 ].

In this paper, a meta-analysis was performed to explore the relationship between social media use (Facebook, Instagram, and Twitter) and mental health, using a systematic review of studies from January 2010 to June 2020. The paper additionally assessed the strength of the evidence presented regarding social media and mental health and sought to determine whether a positive effect exists between social media use and mental health.

A literature search using PRISMA guidelines was conducted to explore the relationship between social media site (Facebook, Instagram, and Twitter) usage and mental health ( Figure 1 ). A multi-database search identified studies published between January 2010 and June 2020. Articles from PubMed and Google Scholar were selected to investigate the relationship of each type of social media site and mental health. While Google Scholar has wide coverage in terms of interdisciplinary scientific studies, it was supplemented and complemented by PubMed due to PubMed’s widely accessible resources and because the database has a provision of MEDLINE and other National Library of Medicine (NLM) resources. Search terms were chosen to broadly capture the various ways social media and mental health have been defined and explored in the existing literature. See Box 1 for a summary of the search strategy and selection process for the systematic review.

Literature search related to social media and mental health.

• Period search: January 2010 to June 2020.
• Source: PubMed and Google Scholar.
• Search terms: (‘mental health’) AND (‘Social Media’) AND (‘Twitter’ OR ‘Facebook’ OR ‘Instagram’) AND (‘COVID-19′).
• Inclusion criteria: Only articles that displayed use of Social Media, Twitter, Instagram, and Facebook.
• Articles found = 71; Articles included = 39.
• All age groups, male/female, publication date last ten years, all countries and origins.
• Exclusion criteria: Any articles that do not focus on either Twitter, Instagram, or Facebook.
• Articles retained for evaluation = 39.

PRISMA 2009 flowchart showing research of records.

This study followed Warton et al.’s (2011) recommendation to use GLMMs for the meta-analysis with the logit transformation in meta-analysis problems with single proportions [ 6 ]. All the statistical analyses were performed in R studio (version 3.6.1, The R Foundation, Vienna, Austria), using the meta3 and metaphor4 packages [ 6 , 7 , 8 , 9 , 10 ].

Subgroup Analyses

To identify differences among studies with different scopes, a subgroup analysis with five groups was conducted. Studies were clustered into groups depending on the social media site specified as being the focus of the study. Thus, five groups were created, including only Facebook (labeled F, n = 10), only Twitter (labeled T, n = 2), only Instagram (labeled I, n = 2), all three social media platforms (labeled FTI, n = 1), and unknown (social media platform not specified) (labeled U, n = 5).

In the subgroup analysis, the sample sizes and the year of publication of the study were also considered ( Table S1: Supplementary Materials ). For the sample size, we considered the median value of the sample sizes (600) for the cut-off, and for the year of publication studies were categorized as before 2018 and after 2018 (including 2018). To test for the existence of publication bias, we used a funnel plot.

A total of 71 articles were identified through the Google Scholar and PubMed database search. After duplicates were removed, 39 articles were retained for evaluation.

Of the 39 studies included, 14 studies focused on Facebook only [ 3 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].

Two studies focused on both Facebook and Twitter [ 2 , 24 ], and twelve focused on exclusively Twitter [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].

Three studies included all three social media sites [ 37 , 38 , 39 ]. Furthermore, three studies focused on Instagram only [ 40 , 41 , 42 ].

Finally, in five studies, the social media platform included was unknown/not specified [ 43 , 44 , 45 , 46 , 47 ].

The literature spanned vast geographical ranges including Italy [ 3 ], Thailand [ 15 ], Poland [ 16 ], The United States [ 11 , 14 , 17 , 20 , 22 , 28 , 30 , 33 , 38 , 40 , 42 , 43 ], Germany [ 18 , 41 ], Australia [ 20 , 26 ], Korea [ 13 , 29 ]. The United Kingdom [ 21 , 24 , 35 , 36 ], Japan [ 32 ], China [ 44 , 45 ], Iraq [ 39 ], India [ 46 ], and Pakistan [ 47 ]. Of the studies included, Twitter was the social media platform most used due to its ease of collecting data by extrapolating large numbers of tweets simultaneously. Some studies were only based on Twitter posts and other studies were based on users of Facebook, Twitter, and Instagram.

The studies included in our meta-analysis employed various analysis methodologies including a study of the association of factors on social media, risk assessment, repeated-measures ANOVA, logistic regression, Poisson multilevel regression, bivariate and multivariate analysis, correlation analysis, advance sentiment analysis, multistage clustering techniques, a sample test of proportional, and statistical inference.

3.1. Facebook

Of the 39 included studies, Facebook emerged as one of the main social media sites in 14 of the studies where the relationship between social media and mental health was examined. At least seven of the studies reviewed provided support for a positive relationship between social media use and mental health. For instance, in a survey study conducted in Germany [ 18 ], it was found that Facebook users had higher values of certain reported personality traits and positive variables protecting mental health than did non-users. Similarly, while assessing mental health issues such as depression, anxiety, and PTSD, Masedu et al. (2014) reported that Facebook use among adults 25–54 years old had a positive impact on mental health and quality of life outcomes in the years following a disaster [ 3 ]. Naslund et al. (2018) found Facebook to be promising for supporting health behavior change among people with serious mental illness [ 11 ].

Three of the studies included in the analysis found a negative relationship between social media use and mental health. For example, Hanprathet et al. (2015) illustrated some risks of Facebook usage that affected the mental health status of Thai adolescents in their cross-sectional study [ 15 ]. Blachnio et al. (2015) found additional evidence that daily internet use time in minutes, gender, and age were predictive of Facebook intrusion [ 16 ].

Therefore, studies included in our analysis that focused on Facebook only indicate evidence for both a positive and negative relationship between social media usage and mental health, with slightly more studies evidencing a positive relationship.

3.2. Twitter

Of the 12 studies focusing exclusively on Twitter, it was clear that Twitter has been used to raise awareness about many different mental health issues and to help individuals connect and feel that they are not alone [ 25 , 26 ]. For instance, Cavazos-Rehg et al. (2016) reported supportive and knowledge-based awareness tweets about fighting depression to be most common, making up 40% of the tweets reported [ 27 ]. Cavazos-Rehg et al. (2016) suggest that health professionals can use Twitter to tailor and target prevention and awareness about mental health [ 27 ]. Twitter data have also been found to be useful in providing insight for mental health surveillance before and after traumatic events such as natural disasters [ 28 ].

Furthermore, Twitter has been useful in the detection and anticipation of mental health issues [ 28 ]. For example, Reece et al. (2017) built models to predict the emergence of depression and PTSD by using learning algorithms analyzing the linguistic patterns in Tweets of the sample months before a clinical diagnosis of depression [ 48 ]. The results of their study indicated that despite the limitation of 180 characters per tweet, people who were depressed showed signs of depression in their tweets significantly before the actual diagnosis, resulting in the viable option to use Twitter as a predictive depression evaluation tool for clinicians. Similarly, Berry et al. (2017) conducted a study using Text mining methods for Twitter to collect and organize tweets from the hashtag #WhyWeTweetMH [ 25 ]. Four overarching themes were derived from the tweets collected: (1) A sense of community; (2) raising awareness and combatting stigma; (3) a safe space for expression; and (4) coping and empowerment [ 25 ]. Therefore, evidence from studies focusing on Twitter seems to suggest a positive relationship between social media and mental health.

3.3. Instagram

Three of the studies included in the analysis focused on Instagram. Across these studies, the general trend was that Instagram may be a contributing factor in causing body image and self-harm issues in young people. Of the three studies focusing exclusively on Instagram, one study found a relationship between consistent Instagram usage and negative body image and self-harm [ 40 ]. This study focused on content posted on Instagram between 18 June 2014, and 30 June 2014, to evaluate the meaning, popularity, and content advisory warnings related to ambiguous non-suicidal self-injury (NSSI) hashtags on Instagram. The sample of 201 Instagram posts led to the identification of 10 ambiguous NSSI hashtags, with some common terms including #selfinjuryy and #MySecretFamily. “#MySecretFamily” was a popular term that described the broader community of NSSI and mental illness. The term #MySecretFamily had approximately 900,000 search results at the time. Content Advisory warnings were only generated by one-third of the relevant hashtags [ 40 ]. Another study discussed how image-based social media such as Instagram may become a source of mental health-related information and a tool for health communication [ 42 ]. Brown et al. (2019) pointed out in their study how although most of the study participants (80%) had come across expressions of active suicidal thoughts, activity and language use on Instagram did not predict acute suicidality [ 41 ].

It is important to add that Instagram is the newest platform of the three social media platforms included in this paper, so its lack of history makes it difficult to draw specific conclusions of mental health issues about its long-term use. Nevertheless, based on the inclusion of a limited number of studies, one can conclude that there appears to be a correlation between consistent Instagram usage and the effect on negative body image and self-harm.

3.4. Facebook, Twitter, Instagram

Analysis of three studies focusing on all three social media platforms generally indicates that social media use has the potential to influence people’s mental health and psychological well-being. For example, Lis et al. (2015) researched the opinions of psychiatrists on whether social media had adverse effects on psychosis [ 37 ]. The study found that 37% of participants believed there was an association between psychopathology and social media sites [ 37 ].

In a subsequent study, Lin et al. (2016) assessed depression and social media use across multiple social media platforms in a large and nationally representative sample of young adults [ 38 ]. It was found that social media use was significantly associated with increased depression [ 38 ]. Most recently, a quantitative survey study by Ahmad et al. (2020) obtained data from the Kurdish social media and found a statistically significant positive correlation between self-reported social media use and the spread of panic related to COVID-19 (R = 0.8701) Results from this study also showed that majority of youth aged 18–35 years are facing psychological anxiety [ 39 ]. Therefore, though the number of studies focusing on all three social media platforms included in this analysis is limited, the results of studies included show a negative relationship between social media usage and mental health.

3.5. Unknown/Not Specified

Five of the studies included in the analysis did not specify a social media platform analyzed in their respective study. These studies were more recent in terms of their respective publication date and focus on the relationship between social media use and mental health, primarily during COVID-19. For example, in Hill et al.’s (2019) study, medical students from one US allopathic medical school were asked to complete a 12-item questionnaire [ 43 ]. Questions were designed to assess students’ ability to identify, address, and counsel patients on the association between social media and mental health. Results indicated that most of the students believed there could be both a positive and negative effect of social media on mental health [ 43 ].

Gao et al. (2020) investigated the prevalence of depression, anxiety, and a combination of depression and anxiety (CDA) during the COVID-19 outbreak in Wuhan, China, by using multivariable logistic regression to identify associations between social media exposure with mental health problems after controlling for covariates [ 44 ]. They found that more than 80% of participants reported frequent exposure to social media [ 44 ]. Findings showed that there was a high prevalence of mental health problems, which were in turn positively associated with frequent social media exposure during the COVID-19 outbreak [ 44 ].

Another study conducted by Ni et al. (2020) examined risk factors, including the use of social media, for probable anxiety and depression in the community and among health professionals also in Wuhan, China [ 45 ]. A multivariable logistic regression analysis was used to examine these factors [ 45 ]. Of the 1577 community-based adults, about one-fifth of respondents reported probable anxiety and depression [ 45 ]. Similarly, of the 214 health professionals, about one-fifth of surveyed health professionals reported probable anxiety or depression [ 45 ]. Interestingly, social support was associated with less probable anxiety and depression in both health professionals and community-based adults [ 45 ]. The results of this study suggest that online platforms can be leveraged to survey community-based adults and health professionals during an epidemic and lockdown [ 45 ].

Roy et al. (2020) attempted to assess knowledge, attitude, anxiety experience, and perceived mental healthcare need among the adult Indian population during the COVID-19 pandemic [ 46 ]. An online survey was conducted using a semi-structured questionnaire using a non-probability snowball sampling technique [ 46 ]. The respondents had a moderate level of knowledge about the COVID-19 infection and adequate knowledge about its preventive aspects [ 46 ]. In addition to distress-related social media and sleep difficulties, paranoia about acquiring COVID-19 infection was also reported [ 46 ]. The perceived mental healthcare need was seen in more than 80% of participants [ 46 ]. The authors suggest that there is a need to intensify the awareness and address the mental health issues of people during this COVID-19 pandemic [ 46 ].

In Balkhi et al.’s (2020) study, a structured, self-administered questionnaire was constructed, assessing the psychological impact and behavioral changes about COVID-19 [ 47 ]. This research examined data from 400 participants residing in Karachi, Pakistan [ 47 ]. The responses were compared based on gender, age, and level of education, to find possible statistical correlations using the chi-square test [ 47 ]. The study found increased levels of anxiety due to the use of social media among people below 35 years resulted in avoidance behaviors ( p = 0.04) [ 47 ].

In sum, the five studies included in the analysis that did not specify a social media platform suggest not only that the COVID-19 pandemic has exacerbated mental health issues among social media users, but that many have used social media during the pandemic to seek social support for their mental health issues.

4. Meta-Analysis Results

Of the twenty studies, nine reported a proportion lower than 50% for a positive effect ( Figure 1 ). These results are based on the random-effects model. Confidence intervals are based on the Clopper–Pearson interval (exact binomial interval). Here, Q is distributed as a chi-square statistic with k (number of studies) minus 1. It indicates a wide range of values in the outcomes of the studies, and according to I2 = 100%, it was estimated that approximately all of the variance was due to heterogeneity ( Figure 1 ). The forest plots for the studies are found in Figure 2 . Considering the scope of the studies (Facebook, Twitter, Instagram, all three, or unknown/not specified), the year of the publication (before 2018 and after 2018), and the sample size of the studies (below 600 and above 600), a subgroup analysis was used to determine the effect of this variation on the pooled results, and the results of these analyses are reported in Table S1, Supplementary Materials . The forest plots for each scenario are illustrated in Figure 3 , Figure 4 and Figure 5 , respectively.

Forest plot of the studies.

Forest plot of the studies. Grouped by social media platforms.

Forest plot of the studies. Grouped by sample size.

Forest plot of the studies. Grouped by year of publication.

The subgroup analysis of 10 studies (those which focused only on Facebook) showed an identical pooled proportion of 0.67 (95% CI: 0.38–0.86) with a homogenous characteristic ( p -value for heterogeneity = 0.09, I2 = 100%). Two studies were sub-grouped based on only Twitter (proportion of 0.59 CI (95% CI: 0.22–0.88)) and two studies focused on Instagram (proportion 0.29 (95% CI: 0.16–0.47)). Among the studies, the studies focused on Instagram both reported a proportion lower than 50% ( Table 1 ). One study was grouped as all three platforms (proportion 0.44 (95% CI: 0.42–0.46)). Five studies were grouped as unknown (proportion 0.62 (95% CI: 0.38–0.81)). According to the groups’ Q (Qb = 7.92, df = 4, p -value = 0.09), there was no significant difference found between groups at level α = 0.05 ( Table 1 ). Furthermore, there is no significant difference between studies with sample sizes below and above 600 ( Figure 3 and Table 1 ) and no significant difference between studies before and after 2018 ( Figure 4 and Table 1 ). The funnel plot does not show any clear asymmetrical pattern in publications ( Figure 6 ).

Funnel plot for publication bias.

*: Heterogeneity of between groups.

We also conducted Egger’s regression test. The results of the test (z = −0.19, p -value = 0.85) show that there is no significant evidence of publication bias in our study ( Table 1 ).

5. Discussion

Social media sites play an important role in individuals’ mental health. In a rapidly evolving world where people experience less face-to-face interaction, understanding the relationship between social media and mental health is essential for the utilization of digital platforms to promote mental health and create a healthier world. The findings of our meta-analysis are mixed and show that social media can both support and hinder one’s mental health. The variations observed depended on the social media platform used as well as whether the study was conducted before or after the COVID-19 pandemic. In general, studies that focused on Twitter and Instagram social media platforms described the worst mental-health expression for the population that was considered in the respective study.

Facebook, the largest and the most used social media site worldwide, connects people from all over the world and enables individuals and communities to easily band together and create movement. Its’ ability for the global exchange of information is unparalleled, as it can bridge people of multiple faiths, nationalities, and orientations in one platform to pursue common goals and raise movements of reform. Facebook can also be used to bridge the worlds of numerous people in a relatively small location through the promotion of health campaigns and community activity to encourage wellness and social interaction.

In terms of mental health, our study shows that Facebook can be and is used to promote mental health through the connection to other users, mental health professionals, and organizations. Our analysis also shows that Facebook can promote mental health among its users by giving them the ability to connect and share their stories with other people who may have the same mental health challenges, making them feel less alone. Using Facebook as a social media awareness platform is an important way to promote mental health through social media. Facebook has “groups” and “pages” that can be used exclusively for mental health awareness. It can also be used to educate individuals and communities about prevention, which could be effective, provided the pages can guarantee anonymity. Facebook’s global reach is quite vast; therefore, any type of mental health intervention employed has the potential of reaching and affecting many individuals.

Likewise, our study shows that there are mental health risks associated with Facebook overuse. One study that stands out in this finding is by Park et al. (2013), which investigated overall life satisfaction before and after Facebook [ 13 ]. Results from this study indicated decreased levels of contentment with the self and life after excessive Facebook usage [ 13 ]. Therefore, the relationship between social media usage and mental health when only Facebook is considered varies, and the amount and quality of time spent on Facebook might be an important variable to consider in future studies.

Twitter is a large platform for people to engage in conversation. It has a strong and loyal audience. Introducing a hashtag and having many people retweet it creates a strong story or interest in the topic it is following. In terms of mental health, Twitter in many ways can serve as a window into users’ mental health. For example, any positive phrases or words related to mental health could be followed by a #mentallove or #mentalhealthlove, and the tweet will be placed in these categories so that any person can search the tweets that are potentially helping others.

Our study indicates that Twitter can be a useful social media platform to combat mental health issues by observing tweets that contain suggestions of depression and then targeting ads or certain pages to respective individuals where they can express their emotions or obtain the necessary help (i.e., nearby medical facilities). Mental health professionals can read and evaluate the tweets to determine if a post shows signs of a mental health issue. People can then be guided to the needed mental health service. With these interventions, professionals can use Twitter to improve the mental health outcomes of many of its users. Policymakers, as well as public health professionals, can use tweets about depression, or other mental health issues, to help find the root cause. They can also reach out to the people who tweet about depression and obtain their feedback on how they can spread awareness.

Since a survey of studies examined in our study suggests a positive relationship between its use and mental health, it is fair to conclude that Twitter may be particularly helpful in promoting an aspect of realness that is fleeting on social media as time goes on. This sense of realness in a virtual community such as Twitter can help minimize skewed mental images, blurring the lines of reality and facade. A true sense of community occurs when role models promote awareness and relate to others as well, so celebrities, policymakers, and athletes tweeting about a mental health issue can also have positive results.

Instagram is a photo-based platform that emphasizes photo and video sharing via its mobile app with over 700 million users worldwide. Our analysis of existing studies focusing on Twitter as a social media platform shows that if Twitter is not used responsibly, it has the potential to negatively influence young people’s body image and self-esteem, such as the evidence from the MacMillan et al.’s (2017) study [ 49 ] indicates.

Though the number of studies focusing on Twitter that are included in our analysis is limited to only three studies, it was clear that young women were the largest group of people that were found to be affected by the negative impact of Instagram, and mostly in terms of their mental health. Matt Kreacher, the author of the #StatusofMind report, suggests that “Instagram draws young women to compare themselves against unrealistic, largely curated, filtered and Photoshopped versions of reality.” All of this is in the palm of their hands for viewing any time of the day or night thus potentially creating a development of body image issues. Because of Instagram and the high level of mental health issues it has been associated with within the literature, the Royal Society of Mental Health proposed social media platforms place a warning on images that have been digitally enhanced or altered photos to reduce feelings of inadequacy [ 50 ]. Non-Suicidal Self Injury (NSSI) continues to be a growing and concerning trend on the social media picture-sharing app Instagram, particularly during middle school or early high school years with an estimated prevalence of approximately 7–24% [ 40 ].

Other mental health issues that have arisen with the increase in Instagram usage are anxiety, depression, bullying, fear of missing out, and disruptive sleep patterns. Studies have shown that young people who spend more than two hours a day on social media are more likely to report psychological distress [ 51 ]. The #StatusofMind report claims that Instagram users may develop a ‘compare and despair’ attitude if they spend too much time on Instagram or other social media platforms.

Other conclusions that can be drawn from our analysis are that studies examined within our parameters that focus on all three social media platforms support the powerful effect that social media has on one’s mental health. Though the number of studies included in our study to arrive at this conclusion is limited, the results of these studies are consistent in showing that increased social media usage equals lower mental health.

Moreover, the COVID-19 pandemic and social distancing have created an unprecedented setting for examining the relationship between social media usage and mental health. Studies included in our analysis, most of which did not specify a social media platform of focus, inevitably show that while social media usage increased and was rewarding to many users looking for support when the COVID-19 pandemic hit, excessive use also led to mental health issues such as depression and anxiety. Therefore, it can be argued that social media usage during the COVID-19 pandemic specifically is much like a double-edged sword; it can promote mental health, but its overuse can likewise hinder one’s mental health. Mental health consequences of the COVID-19 pandemic will likely be studied well into the future including among ethnic minorities [ 52 , 53 ].

6. Implications

Mental health professionals and others promoting psychological health can benefit from learning more about social media and its relationship with mental health. Social media campaigns can likewise promote more knowledge and awareness of specific mental health conditions. A successful advertising campaign can bring awareness to complex mental health issues such as depression and anxiety. Such a campaign has the potential to lead to policymakers flagging or possibly deactivating accounts that promote negative mental health issues. In addition to deactivating negativity, social media sites can promote and advertise positive mental health messages, which would allow the self-help promoting information to reach a wider audience. Using certain hashtags could connect people who are suffering from these issues and give them a needed virtual support group they likely would not have attended in person due to stigma. As an example, the Royal Society for Mental Health is recommending that social media platforms create a “heavy usage” notification to pop up after too much time has been spent online. Social media is not going away, so developing a safe relationship and constructively using social media may not only decrease the negative impact of social media on one’s health but may have a positive impact instead.

7. Strengths and Limitations

A major strength of this review is that it analyzes studies from areas all over the globe, including the United States, the Middle East, Asia, and several European countries. Additionally, the relationship between social media usage and mental health is a particularly important and timely topic to consider. In March of 2020, the WHO declared the COVID-19 outbreak a global pandemic. Global lockdowns required citizens to start spending more time at home, and as a result, social media usage has both increased and changed. More than ever, individuals have turned to social media for socialization, interaction, entertainment, and social support for their mental health.

A limitation of this meta-analysis is the number of databases used to conduct a systematic review, as well as limitations inherent in specifically using Google Scholar and PubMed as databases to identify highly relevant research studies. Each database is limited in its focus and scope, and neither is optimal for topical research. Ideally, multiple databases would provide the optimal and most comprehensive systematic review. Utilizing databases such as PsycINFO, MEDLINE (Ovid), Scopus, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and other educational resources should be considered in future studies. Furthermore, it is well known that most users of social media tend to be adolescents [ 40 ], limiting the generalizability of the findings to a wider and older audience. Additionally, only a few of the studies included in the review focused on Instagram only and all three platforms, limiting the conclusions that can be drawn about the relationships between Instagram specifically and mental health.

8. Conclusions

Our study shows that individuals suffering from mental health issues use social media as an outlet, and we should continue to use social media to promote wellness. Although these platforms can be a distorted reality for some, they ultimately still serve as platforms where individuals can express themselves. Such expression can be therapeutic for those experiencing mental health issues. Our analysis further shows that Facebook and Twitter have generally been used to both benefit mental health by bringing people of similar mental health situations together and creating a supportive environment. We must continue to strengthen the communities within social networks so that people will be more connected, which will in turn potentially improve their mental health.

The most important finding of this analysis, however, is that there is an untapped potential for early detection using social media platforms. Providing education and tools to navigate social media constructively in schools is a good way to promote self-esteem and mental health. The greatest suggestion to emerge from this study as we move forward into the digital age is to create forums on these social media sites used to benefit the health of the community. Finally, the way people use technology has important implications for healthcare professionals. Social media use should be closely examined from a clinical and public health perspective.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/epidemiologia3010002/s1 , Table S1: Social media use and mental health: A global analysis.

Author Contributions

Conceptualization, O.U., U.H. and J.S.; methodology, O.U., A.K.-M., U.H. and M.B.; software, M.B.; validation, O.U., A.K.-M. and J.S.; formal analysis, U.H.; investigation, A.B., J.S. and O.U.; resources, A.K.-M.; data curation, A.B.; writing—original draft preparation, O.U., A.B., A.K.-M. and J.S.; writing—review and editing, O.U., A.B., M.B., J.S. and A.K.-M.; visualization, J.S., M.B. and A.B.; supervision, U.H.; project administration, O.U. and A.K.-M. All authors have read and agreed to the published version of the manuscript.

No external funding was available for this study. U.H. was supported by the Research Council of Norway (grant # 281077).

Institutional Review Board Statement

Not applicable since we used publicly available data.

Informed Consent Statement

Patient consent was waived since we used de-identified delinked publicly available data.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The top 10 journal articles from 2023 examined the effects of social media, CBT for substance use, and the psychology of gig work

APA’s 89 journals published more than 5,500 articles in 2023. Here are the top 10 most read

Vol. 55 No. 1 Print version: page 22

1. Looking through a filtered lens: Negative social comparison on social media and suicidal ideation among young adults.

Spitzer, E. G., et al.

Young adults who engage in comparisons to others on social media and thus feel bad about themselves are more likely to think about suicide, this research in Psychology of Popular Media (Vol. 12, No. 1) suggests. Researchers surveyed 456 college students about their frequency of social media use and used scales to assess participants’ tendency to engage in negative social comparison on Instagram and Facebook, suicidal ideation, and thwarted belongingness (i.e., feeling as if lacking connections or meaningful relationships with others). Results indicated that participants who engaged in negative social comparisons were more likely to report suicidal ideation than those who did not. Specifically, on Instagram, those who negatively compared themselves to others the most also showed the highest levels of association between thwarted belongingness and suicidal ideation. These findings suggest the need for limits on social media use and education around its mental health effects. DOI: 10.1037/ppm0000380

2. Self-compassion and women’s experience of social media content portraying body positivity and appearance ideals.

Rutter, H., et al.

The type of social media content women view can affect their self-compassion—how kind to themselves and accepting of their flaws they are—suggests this study in Psychology of Popular Media (advance online publication). In two experiments, a total of 247 women viewed content consistent with appearance ideals (fitspiration body photos; faces with makeup), appearance-neutral content (landscapes), or body-positive content (body-positive body photos, body-positive quotes, faces without makeup). In both experiments, women who viewed content consistent with appearance ideals reported a state of worse self-compassion and worse thoughts about themselves than those who viewed body-positive or appearance-neutral content. Women who already had daily low self-compassion or high disordered-eating symptoms were the most affected by viewing content consistent with appearance ideals. On the contrary, viewing body-positive content increased the state of self-compassion relative to viewing appearance-neutral content. DOI: 10.1037/ppm0000453

3. Reducing social media use improves appearance and weight esteem in youth with emotional distress.

Thai, H., et al.

Reducing smartphone social media use to 1 hour per day might improve body image and weight esteem in youth with emotional distress who are heavy social media users, this study in Psychology of Popular Media (advance online publication) suggests. The researchers randomly assigned 220 participants (ages 17 to 25 who used social media at least 2 hours per day) to either a 4-week intervention in which they limited their social media use to 1 hour per day or to a control condition with unrestricted access to social media. After the 4-week intervention, the group with restricted social media use felt better about their appearance and weight relative to before the intervention, whereas the other group showed no changes. Thus, reducing smartphone social media use appears to be a good method to improve how youths feel about their appearance and weight and could become a component in the prevention and treatment of body image-related disturbances. DOI: 10.1037/ppm0000460

4. Interventions to reduce the negative impact of online highly visual social networking site use on mental health outcomes: A scoping review.

Herriman, Z., et al.

In this review, published in Psychology of Popular Media (advance online publication), researchers identified 39 studies published between 2011 and 2022 that examined how interventions designed to reduce the negative impact of online highly visual social networking site (e.g., Facebook, Instagram) use impact mental health. Most of the studies were conducted on Western adults younger than age 35 and varied widely in terms of the variables assessed, making it difficult to highlight overall conclusions. Nevertheless, results indicate that interventions focused on reducing the exposure to highly visual social media platforms benefited well-being but may also reduce social connectedness. Interventions focused on social media literacy programs may reduce addiction and improve body image. Other interventions that adopted varied psychological approaches did not appear to lead to significant results. The researchers also highlighted the gaps in research that should be addressed to improve the efficacy of such interventions, including a need for interventions that are more guided by psychological theories and assessments of these interventions that are rigorous and include diverse populations. DOI: 10.1037/ppm0000455

5. On the outside looking in: Social media intensity, social connection, and user well-being: The moderating role of passive social media use.

Roberts, J. A., & David, M. E.

According to this study in the Canadian Journal of Behavioural Science (Vol. 55, No. 3) , heavy passive social media use may be linked with a weaker sense of social connection and well-being. In two survey-based studies with 226 participants in the United States, researchers found that passive engagement with social media (viewing social media but not regularly posting or interacting through the platform) was associated with less social connection, lower well-being, and higher stress. In a third, experimental study, with 160 participants, the researchers asked participants to use social media heavily (10 minutes) or lightly (5 minutes) and engage with it actively or passively. The results indicated that heavy social media use had a negative impact on feelings of social connection when used passively but a positive effect when used actively. DOI: 10.1037/cbs0000323

6. Social media usage is associated with lower knowledge about anxiety and indiscriminate use of anxiety coping strategies.

Wolenski, R., & Pettit, J. W.

Social media might not be the best source to learn about anxiety and how to reduce it, this study in Psychology of Popular Media (advance online publication) suggests. Young adults (N=250) responded to an online survey in which they reported their sources of information about anxiety, the strategies they use to cope with anxiety, and their anxiety symptoms and severity. The researchers also tested participants’ knowledge about anxiety. Participants rated the internet (e.g., Wikipedia, medical websites) as their most used information source, followed by friends and family, therapy, and social media. Participants with an anxiety diagnosis or severe symptoms sought information on social media more frequently than the other participants. Across all participants, those who sought information on social media more frequently showed a lower knowledge about anxiety and were more likely to report using both adaptive and maladaptive strategies to reduce anxiety. On the contrary, using the internet was associated with more knowledge about anxiety. These findings suggest the need to promote the dissemination of accurate information about anxiety on social media. DOI: 10.1037/ppm0000456

7. The psychological scaffolding of arithmetic

Grice, M., et al.

In this article in Psychological Review (advance online publication), the authors propose that arithmetic has a biological origin, rather than philosophical, logical, or cognitive basis. This assertion rests on four principles of perceptual organization—monotonicity, convexity, continuity, and isomorphism—that shape how humans and other animals experience the world. According to the authors, these principles exclude all possibilities except the existence of arithmetic. Monotonicity is the idea that things change in the same direction, so that approaching objects appear to expand, while retreating objects appear to shrink. Convexity deals with betweenness, such that the four corners of a soccer pitch define the playing field even without boundary lines connecting them. Continuity describes the smoothness with which objects appear to move in time and space. Isomorphism is the idea of analogy, allowing people to recognize that cats are more similar to dogs than rocks. The authors’ analysis suggests that arithmetic is not necessarily an immutable truth of the universe but rather follows as a natural consequence of our perceptual system. DOI: 10.1037/rev0000431

8. An evaluation of cognitive behavioral therapy for substance use disorders: A systematic review and application of the society of clinical psychology criteria for empirically supported treatments.

Boness C. L., et al.

Cognitive behavioral therapy (CBT) is an empirically supported treatment for substance use disorder (SUD), is the conclusion of this review in Clinical Psychology: Science and Practice (Vol. 30, No. 2) . The researchers reviewed five meta-analyses of the effect of CBT on SUD, but only one had sufficient quality for inclusion to evaluate the size of the effects of CBT. This meta-analysis found that CBT produced small to moderate effects on SUD when compared with minimal treatment (e.g., waitlist, brief psychoeducation) and nonspecific treatment (e.g., treatment as usual, drug counseling). These effects were smaller in magnitude when compared with other active treatments (e.g., motivational interviewing, contingency management). The effects of CBT on SUD tended to diminish over time (i.e., CBT was most effective at early follow-up of 1 to 6 months posttreatment compared with late follow-up of at least 8 months posttreatment). The researchers recommend CBT to be used as an evidence-based approach to SUD but highlight the need for more research to identify patient characteristics that might moderate response to CBT and the best deployment of CBT (e.g., as a standalone or an adjunct intervention). DOI: 10.1037/cps0000131

9. A network approach to understanding parenting: Linking coparenting, parenting styles, and parental involvement in rearing adolescents in different age groups.

Liu, S., et al.

Mothers’ and fathers’ behaviors that promote a sense of family integrity (i.e., coparenting integrity), warmth, and emotional involvement are central components of the parenting network in two-parent families, according to this study in Developmental Psychology (Vol. 59, No. 4) . Researchers used network analysis to explore different facets of maternal and paternal coparenting (e.g., integrity, conflict), parenting styles (e.g., rejection, warmth), and parental involvement (e.g., emotional support, discipline) in two-parent families in China with a total of 4,852 adolescents at different stages of adolescence. They found that maternal and paternal coparenting integrity, warm parenting style, and emotional involvement were key to the parenting network, as indicated by the central spot they occupied in the network analysis. They also found that the expected influence of these characteristics varied for adolescents in different developmental stages—maternal integrity, warmth, and emotional involvement were important throughout adolescence, but paternal integrity, warmth, and emotional involvement were particularly important in early adolescence. The results suggest that supportive parenting might be a prime target for enhancing parenting systems. DOI: 10.1037/dev0001470

10. Seeking connection, autonomy, and emotional feedback: A self-determination theory of self-regulation in attention-deficit hyperactivity disorder.

Champ, R.E., et al.

In this article in Psychological Review (Vol. 130, No. 3) , the authors propose a new framework on the basis of self-determination theory (SDT) for understanding attention-deficit hyperactivity disorder (ADHD) and developing treatment approaches. The researchers suggest that using SDT, which proposes that humans have a natural tendency toward growth and self-actualization, supporting intrinsic motivation and self-organization, can offer a new positive understanding of ADHD and its symptoms. This approach counters the negative characterizations of ADHD; moves beyond symptom reduction and the focus on how ADHD presents motivation, engagement, and self-regulation issues; and instead focuses on potential positive aspects of ADHD and well-being. In addition, the framework highlights the need to help individuals with ADHD better understand how they function, tell the difference between biological and individual needs, and develop self-autonomy and self-regulation skills. According to this SDT approach, treatments that are autonomy supportive and increase self-determination could improve the functioning of individuals with ADHD. DOI: 10.1037/rev0000398

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• Research Note
• Open access
• Published: 22 August 2024

The relationship between social media dependency and psychological distress due to misunderstanding and fear of COVID-19 in medical students

• Parmida Vaezpour 1 ,
• Mohamad Ali Jahani 2 ,
• Zeinab Gholamnia-Shirvani 2 ,
• Hossein-Ali Nikbakht 2 ,
• Romina Hamzehpour 3 ,
• Amir Pakpour 4 &
• Arman Mirzaie 1  

BMC Research Notes volume  17 , Article number:  232 ( 2024 ) Cite this article

Metrics details

Improper use of social media during the COVID-19 outbreak, leading to fear and misunderstanding, can contribute to psychological disorders in vulnerable populations. This descriptive and analytical cross-sectional study was conducted in 2023 on 511 medical students of Babol University of Medical Sciences. Data were collected using demographic, psychological distress, fear and misunderstanding questionnaires related to COVID-19 and social media dependency. A total of 511 medical students, with an average age of Mean and S.D; 23.57 ± 3.03 participated in the study. The average psychological distress score was 23.82 ± 7.73 (out of 54), the average score of social media dependency was 17.53 ± 3.09 (out of 30), for the fear of COVID-19 was12.63 ± 2.56 (out of 35), and for the misperception of COVID-19 was 0.53 ± 0.09 (out of 18). Path analysis results) showed that direct path from improper use of social media to psychological distress is significant (P < 0.001, B = 0.19) but this relationship is not significant through fear and misperception related to COVID-19. Improper use of social media, identified as the strongest predictor, can directly increase psychological distress in medical students, without mediation through fear and misperception related to COVID-19. These findings should be taken into consideration when designing and evaluating interventions aimed at promoting mental health and fostering appropriate use of social media among students during disease outbreaks.

Peer Review reports

Introduction

From December 2019 until now, countries worldwide have been grappling with the COVID-19 virus [ 1 ]. The global outbreak of this disease was so rapid that it was recognized as the most significant public health threat in 2020 [ 2 ]. According to the World Health Organization, over 690 million COVID-19 cases and more than 6.8 million deaths were reported globally by September 2023. More than 7.5 million cases of COVID-19 were reported just in Iran [ 3 ]. Initially, many governments devoted special attention and effort to control COVID-19 infections, implementing national policies to minimize the virus’s spread [ 4 , 5 , 6 ]. These measures included controlling borders and travel, mandating citizens to stay at home [ 7 ] and prohibiting outdoor activities [ 8 ]. While these measures effectively controlled the spread of COVID-19 [ 9 , 10 ], they also resulted in the emergence of psychological symptoms among the population [ 11 , 12 ]. University students were susceptible to psychological symptoms during the COVID-19 outbreak [ 13 , 14 ]. The prolonged stay-at-home mandates compelled people to transition from social activities to home-based activities, engaging in sedentary behaviors such as increased Internet and social media usage instead of normal social behaviors [ 15 ].

The use of smartphones has witnessed a significant increase among various age groups during the coronavirus epidemic [ 16 , 17 ]. Individuals within these groups were exposed to a barrage of misinformation, potentially stemming from increased access to communication technologies and messaging platforms [ 18 ]. Moreover, those subjected to home quarantine have exhibited noteworthy psychological challenges [ 19 ].

Fear, as the primary adaptive mechanism for organism defense, represents a preparatory response vital for survival. Prolonged and persistent fear can serve as a fundamental element in the development of various mental disorders [ 20 ]. Psychological distress has been reported among populations undergoing quarantine [ 2 , 21 , 22 ]. The fear of COVID-19 also stands out as a significant psychological issue [ 23 ] capable of exerting adverse effects on individuals' health [ 4 , 24 ]. Indeed, fear, depression, anxiety and distress are psychological issues associated with the COVID-19 disease [ 25 ]. Information sourced from the Internet or social media is not always accurate. Studies on people's knowledge and perceptions of COVID-19 reveal instances where individuals believe in misinformation and false news disseminated through social media [ 26 ]. Instances such as death and poisoning resulting from the excessive consumption of alcohol as a purported treatment for COVID-19 exemplify the prevalence of such misconceptions on social media. This particular misconception originated in Iran, where false information circulated suggesting that consuming alcohol could disinfect the body and prevent or cure COVID-19. Such dangerous myths highlight the critical need for accurate public health communication, especially during pandemics [ 27 ].

The spread of the coronavirus has led to several consequences, including the generation of social panic and rapid changes in people's lifestyles through social media [ 28 ]. Research has identified a significant relationship between the use of virtual social networks and heightened levels of anxiety and insomnia [ 29 ]. Furthermore, studies indicate that the adverse changes in community during the COVID-19 outbreak are directly related to an escalation in psychological distress, necessitating prompt preventive and therapeutic interventions [ 30 ]. Moreover, the addictive use of smartphones has shown a significant association with the manifestation of symptoms of psychological distress [ 31 ]. This disease has imposed an overwhelming mental burden on individuals at various levels [ 15 , 32 , 33 , 34 ]. Given the elevated prevalence of COVID-19 and its repercussions, along with the imperative to explore the relationship between social media dependency, fear, and misperception within the context of COVID-19, and psychological distress—particularly in vulnerable and pivotal demographic groups such as medical students functioning as community health advocates—the groundwork for this research was provided by Lin et al.'s model. Their model played an effective role in explaining the relationships between these variables. Study revealed that during the COVID-19 outbreak, individuals underwent heightened psychological distress and insomnia due to the improper use and dependency on social media. In response, healthcare providers are advised to devise campaigns aimed at mitigating fear and anxiety related to COVID-19 [ 30 ]. Examining the aforementioned relationship, along with the factors influencing psychological distress and social media dependency, holds the potential to inform educational intervention planning. This study was undertaken to assess the correlation between social media dependency and psychological distress through misperception and fear of COVID-19 among medical students of University of Medical Sciences.

Hypothesis: There is a significant relationship between social media dependency and psychological distress among medical students, mediated by the misunderstanding and fear of COVID-19.

Participants

The participants were selected through an available sampling method. Analytics Calculators, an online software tool, was utilized for path analysis.

In path analysis studies using structural equation modeling (SEM), the sample size is considered to be at least 5–15 people per item [ 35 ], But the more realistic minimum required sample size is estimated based on the number of hidden variables and the number of measured variables and to identify the standard effect size of the expected coefficients and based on the confidence level and power of the test. Analytic Calculator software was used in this sample size design. The sample size required to detect the expected effect size is 0.18 [ 30 ] With 4 hidden variables and 24 measured variables in the SEM model, 95% confidence level and 90% test power are estimated to be about 479 cases [ 36 ] that Including 5% drop, at least 503 samples are required for this study. The chosen parameters for path analysis encompassed an anticipated effect size of 0.18 [ 30 ], Ultimately, the study included 511 general medicine students spanning various academic levels, including basic sciences, clinical preparation, internship, and clerkship, enrolled in both public university and self-governing campuses between the years 2015 and 2021. Exclusion criteria included unwillingness to participate and history of mental disorder.

Research design This research is a cross-sectional study, conducted in 2023, employing a descriptive-analytical approach on 511 medical students affiliated with University of Medical Sciences.

Informed Consent The Ethics Committee of Babol University of Medical Sciences approved the study protocol at 03.06.2022. Informed consent was obtained from the participants. Consent to participate was written. This research followed the ethical guidelines of the Declaration of Helsinki. Subsequently, the authors compiled a set of assessment instruments, which are fully described in the Instruments section, then the researchers administered these assessment instruments to the study participants. In order to ensure the confidentiality of the participants' answers and to clarify the objectives of the study, comprehensive explanations were provided to the participants along with the distribution of informed consent forms.

Data Analysis Data description utilized mean (standard deviation), mean difference (standard), median (interquartile range), percentage, and frequency. Pattern path analysis was conducted using AMOS22 software explore the factors influencing psychological distress. Univar ate and multivariate regression analyses were employed to examine the relationship between demographic variables and psychological distress, as well as social media dependency. Pearson’s correlation coefficient was utilized to assess the correlation among model variables. Statistical analysis was performed using SPSS22 software at a significance level of P < 0.05.

The data collection tool in this research includes demographic questionnaire and scales of psychological distress, social media dependency, fear and anxiety related to COVID-19. The Brief Symptom Inventory (BSI), created by Derogatis in 1975, stands as a crucial standardized screening tool, facilitating the quantitative evaluation of psychological distress and psychiatric disorders [ 37 , 38 ]. Comprising 18 items, the BSI offers response options on a four-point Likert scale (0–3). The questionnaire’s scoring spans from 0 to 54, with higher scores indicating a more severe level of disorder. Notably, the Persian version of the BSI has demonstrated satisfactory psychometric properties in prior studies [ 39 ].

Social media dependency  Dependency to social media was assessed using the Bergen Social Media Addiction Scale (BSMAS), initially developed and psychometrically evaluated by Andreassen et al. in 2016 [ 40 ], The Persian version of this tool has undergone psychometric evaluation in Iran by Lin et al. in 2017 [ 41 ]. This questionnaire is a six-item self-report scale with response options on a five-point Likert scale (1–5), yielding a score range of 6 to 30. A score higher than 19 indicates a higher level of risk of social media addiction. The psychometric properties of the Persian BSMAS version (including construct validity, concurrent validity, stability and internal consistency) have been satisfactory in previous researches [ 30 , 41 ].

Fear of COVID-19  This variable was assessed using the Fear of COVID-19 Scale (FCV-19S), initially designed and psychometrically evaluated by Pakpour et al. in Iran in 2020 [ 23 ]. The tool has undergone validity and reliability testing in various countries [ 42 , 43 , 44 ]. Comprising 7 items with response options on a five-point Likert scale (1–5), the questionnaire has a score range of 7 to 35. A higher score indicates a greater fear of COVID-19. The Persian version of the Fear of COVID-19 Scale has demonstrated satisfactory psychometric properties in previous research, encompassing construct validity, concurrent validity, stability, and internal consistency [ 23 ].

Misperception about COVID-19  This variable was evaluated using a valid and reliable 18-item questionnaire developed by the researcher, inspired by the work of Lin et al. [ 30 ]. The questionnaire, demonstrating high internal consistency with a Cronbach's alpha of 0.9, assesses an individual's understanding of information related to COVID-19. Response options are categorized as correct (1) or incorrect (0). Scores range from 0 to 18, with higher scores indicating a greater misperception of information regarding COVID-19.

A total of 511 medical students from University of Medical Sciences participated in the study, with an average age of 23.57 ± 3.03 years, Among them, 211 (41.29%) were male, and 349 (68.30%) were single. 332 individuals (64.97%), resided in the dormitory. Additionally, 392 (76.71%) were native. In terms of academic progression, 173 students (33.86%) were at the clinical preparatory stage. 366 subjects (71.62%), had a history of COVID-19, and 505 participants (98.83%), reported using social media (Table 1 ).

The average psychological distress score was 23.82 ± 7.73 (out of 54), social media dependency score was 17.53 ± 3.09 (out of 30), fear of COVID-19, was 12.63 ± 2.56 (out of 35) and misperception of COVID-19 was 0.53 ± 0.09 (out of 18) (Table  2 ).

The path analysis results regarding factors influencing psychological distress revealed that the variable of social media dependency had the most substantial impact on psychological distress, serving as the most robust explanatory factor (P < 0.001, B = 0.19). Notably, a direct and significant relationship was observed between social media dependency and psychological distress. However, this relationship did not reach significance for fear of COVID-19 and misperception of COVID-19. Similarly, the relationships between fear and misperception of COVID-19 with psychological distress were not found to be significant. The model under investigation demonstrated a good fit (RMSEA = 0.000, CFI = 1.000, Chi-square = 0.344, P = 0.577) among medical students at University of Medical Sciences. (Fig.  1 ).

Path analysis of the pattern of psychological distress in Babol medical students according to standard path coefficients

The findings showed that, in the univariate regression analysis, father's education and place of residence exhibited a statistically significant relationship with the use of social media. However, in the multivariate regression analysis, no significant relationship was observed (Table  3 ).

The results indicate that marriage, mother’s education, place of residence, grade of education, and use of social media exhibit a statistically significant relationship with symptoms of psychological distress in both univariate and multivariate regression analyses. However, father's education demonstrates a statistically significant relationship only in the univariate analysis (Table  4 ).

The results of the present study indicate that the inappropriate use of social media and dependence on it, being the strongest predictor, can directly and, without the mediation of fear and misperception of COVID-19, affect the increase of psychological distress in medical students. Variables such as father's education and place of residence were associated with social media dependency, as well as mother's education, place of residence, level of education and use of social media with psychological distress.

According to our results, the average score of psychological distress in participants was 23.82% ± 7.73. A study conducted in Iran focusing on medical students revealed that 57.97% of students had encountered psychological distress during the COVID-19 outbreak [ 45 ]. Notably, medical students engaged in direct contact with COVID-19 patients showed more severe symptoms of psychological distress, with anxiety and stress playing pivotal roles. The heightened risk of infection during the outbreaks of serious infectious diseases places medical students under significant psychological pressure [ 46 ].

As indicated by the results of the current study, the average fear of COVID-19 among medical students was 12.63 ± 2.56. In a study by Quadros et al. the level of fear of COVID-19 among Indian students was reported as 45.2% [ 47 ]. Another study by Patelarou conducted in Italy on nursing students demonstrated that after vaccination, the fear of COVID-19 decreased [ 48 ]. The variance in findings could be attributed to the timing of the studies, as the earlier ones were conducted at the peak of the COVID-19 pandemic when fear levels were naturally higher. In contrast, the present study was conducted after three rounds of vaccination, and as COVID-19 transitioned from a pandemic to an endemic state, which may have contributed to reduced fear levels among the participants. In the present study, the average score of misperceptions of COVID-19 was 0.53 ± 0.09, indicating a low level among students. However, in a study by Pakpour et al. which focused on investigating the mediating effects of fear of COVID-19 and misperception of COVID-19 on the relationship between the problematic use of social media, psychological distress and insomnia, high level of misperception of COVID-19 was reported [ 30 ]. This disparity could be attributed to the heightened knowledge of medical students about COVID-19 and related issues. Regarding social media dependency, the current study found a score was 17.53 ± 3.09 (out of 30) among medical students, with 27% exhibiting inappropriate use of social media. In a systematic study and meta-analysis by Zang et al. on medical students in China, it was reported that 30% of students were addicted to the Internet [ 49 ].

The findings of the current study indicate that social media dependency directly predicts psychological distress, and fear and anxiety about COVID-19 do not mediate this relationship. This aligns with Bányai et al.’s study on problematic smartphone use, which found that excessive smartphone use was associated with increased symptoms of psychological distress [ 50 ]. Similarly, Lei et al.’s investigation into the relationship between distress symptoms and smartphone addiction in medical students revealed a significant positive correlation between smartphone addiction and students' mental health issues [ 51 ]. Chen et al.’s study in Taiwan suggested that one of the early signs of problems related to smartphone addiction is a disorder of psychological distress [ 52 ]. Pakpour et al.’s study also supported the significant relationship between psychological distress and improper use of social media, both directly and indirectly through fear and misperception of COVID-19 [ 30 ]. The absence of this mediating relationship in the current study may be explained by the lower levels of fear and anxiety about COVID-19 among medical students.

To mitigate psychological distress and inappropriate social media use among medical students, educational institutions should implement social media literacy programs, provide accessible mental health counseling, and launch awareness campaigns about misinformation. Additionally, promoting healthy digital habits and offering stress management workshops can further support students' mental well-being.

The present study revealed that variables such as the father’s education and place of residence were correlated with social media dependency Additionally, the mother's education, place of residence, level of education, and use of social media were associated with psychological distress. This is consistent with Bashirian et al.’s study, which explored the link between the level of education and parents’ occupation with internet addiction, depression, and anxiety among students in Sanandaj city [ 53 ]. The explanation may lie in the encouragement provided by educated parents for their children to engage in healthy activities such as studying and exercising in their free time. Khazaei et al.’s research also highlighted the connection between demographic variables and psychiatric disorders in young individuals [ 54 ]. However, Ghahfarokhi et al.’s study yielded different results, indicating that demographic factors such as gender, type of university, place of residence, and field of study were not influential in students’ dependence on mobile phones [ 55 ].

Limitations of the study and future directions

This research was faced with several limitations, firstly, the data of this research was collected with a limited volume and a more accurate judgment needs to consider students with a larger volume and in a longer time frame and in different disciplines. Also, cross-sectional studies are not able to discover the cause and effect relationship, therefore, it is recommended to conduct longitudinal studies to discover the connections, and this study was conducted in the form of self-reporting, which may cause bias in the answers, of course, by saying it confidentially. Keeping the information to the sample of the subjects, this limitation has been removed to some extent. For future research, it is suggested to explore the following areas: the role of specific social media platforms in psychological distress; the impact of social media use in different educational settings or professional fields; the effects of interventions aimed at reducing social media dependency; and the potential moderating effects of social support, coping strategies, and personality traits on psychological outcomes. These areas of investigation can provide deeper insights into the complex relationship between social media use and mental health.

Conclusions

The study identified inappropriate use of social media as the strongest predictor directly influencing the increase in psychological distress among medical students, independent of the mediation of fear and misperceptions of COVID-19. While acknowledging limitations such as self-report scales, convenience sampling, the cross-sectional design, and the indeterminacy of causal relationships, these findings offer valuable insights. They underscore the need for developing, implementing, and evaluating educational interventions aimed at promoting mental health and fostering responsible social media use among medical students during disease outbreaks. It is recommended that comprehensive educational programs be designed, including workshops, psychological counseling, and guidance on time management and social media usage. These initiatives can better leverage the study's findings to enhance the mental well-being of medical students in crisis situations.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors express their sincere gratitude to the Research and Technology Vice-Chancellor of University of Medical Sciences for providing spiritual support for this project (Number: 724133925). The authors also extend their appreciation to the participating students for their valuable contributions to the study.

There was no financial support in the design of the study, data collection, analysis and interpretation of the results and writing of this article.

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MA.J, P.V, HA.N and Z.Gh were the principal investigators and designed the study. Z.Gh searched literature. MA.J and P.V supported the interview development. A.M and R.H collected data and prepared data for qualitative analyses. MA.J and A.B supervised data collection. HA.N analyzed data. M.A drafted the manuscript and both MA.J and A.P supported drafting the manuscript. All authors have provided comments and critical revisions to the manuscript. All authors approved the final manuscript prior to submission.

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Vaezpour, P., Jahani, M.A., Gholamnia-Shirvani, Z. et al. The relationship between social media dependency and psychological distress due to misunderstanding and fear of COVID-19 in medical students. BMC Res Notes 17 , 232 (2024). https://doi.org/10.1186/s13104-024-06895-5

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Seven Insights From Teens About Social Media and Mental Health

For better or for worse, social media has become the go-to for hot takes and heated debate, whether it’s about the election, movies, or pizza. But for parents and others who care about kids, the most important discussion at the moment is about the existence of social media itself, and how it affects the mental health of children, teens, and young adults.

For many, Instagram, TikTok, X/Twitter, Snapchat, YouTube, and so on are at best a time suck, and at worst a virtual-world cesspool bubbling with bullies , braggarts, and bad ideas. It seems logical, then, to think that these platforms—which zoomed to prominence only within the last two decades—are somehow to blame for the concurrent rise in the youth mental health crisis .

And, indeed, lawmakers and public health officials are seeking measures to restrict social media’s hold on a population that still has a lot of growing up to do. Earlier this year, the U.S. surgeon general, in an opinion piece in the New York Times , even recommended warning labels on social media, alerting users to the platforms’ association with mental health problems in teens.

But other experts point out that there haven’t been enough studies to prove causation, only correlation, and they feel that efforts to limit young people’s access to these platforms are misdirected. What’s more, they say, oversimplifying the problem distracts us from finding the true culprits behind the rising anxiety, depression, self-harm, and suicidal thoughts among young people.

Now, a new survey , funded by Hopelab with support from Common Sense Media , offers a slightly different perspective. Aiming to deepen our understanding of social media among teens and young adults, the researchers reached out to young people themselves—instead of their parents, teachers, or doctors. Their results illuminate just how complex the relationship between young adults and social media actually is.

The youth POV

The survey’s title describes it best: A Double-Edged Sword: How Diverse Communities of Young People Think About the Multifaceted Relationship Between Social Media and Mental Health . Distinguishing itself from other recent surveys on teens and social media (such as those from Gallup and the Institute for Family Studies , and the ">Pew Research Center ), this one is a collaboration of sorts between the researchers and young people across the country aged 14 to 22.

Teens (age 14 to 17, in this study) and young adults (age 18 to 22) not only helped to design and survey, they also assisted in interpreting the nationally representative data once it was collected. In all, a racially diverse group of 1,274 young people participated. Some of the questions were open-ended, which allowed respondents to share their personal experiences. The outcome? Findings that feel richly nuanced and strikingly authentic. A few highlights:

Young people rely on social media for a range of needs. Almost a quarter said they are on social media almost constantly throughout the day (about the same as in 2020). More than half said social media is important for seeking support or advice, and that they use social media to feel less alone.

The vast majority said that social media is important for fun and entertainment (89%), communicating with friends (85%), and unwinding when stressed (83%). It’s also a creative outlet, as Amy Green, head of research at Hopelab, points out. For instance, some said they create mood boards on Pinterest, which they turn to when they need a mental boost.

In fact, almost 40% of teens and young adults who use social media said that it cheers them up when they’re feeling sad, stressed, or anxious—the same number as those who reported feeling neither good nor bad. Only 8% said social media makes them feel worse, and 13% said they experience both negative and positive feelings.

They are aware of social media’s downsides and try to control their use. Almost three-quarters admitted they reach for it when they’re bored; almost half said they use it more than they intend to or can’t control their use; 46% acknowledge that it has taken time away from activities they care about.

The good news, says Green, is that many young people also took action to limit and shape their social media use so that it felt healthier to them. Across all age groups, more than three-quarters had, in the previous 12 months, taken measures to control what they see—curating their feed to get rid of posts they prefer not to see (67%); or taking a temporary (63%) or permanent (41%) break from a social media account.

Older adolescents (age 18 to 22) were likelier than the younger group (14 to 17) to identify the downsides of social media and then take action. For instance, half of young adults admitted that social media gets in the way of sleep, compared to 34% of teens. And 51% of young adults believe social media has chipped away at their attention span and ability to concentrate, compared to 39% of teens. This suggests that perhaps teens can get better at navigating social media as they get older.

Negative interactions occur, but so do positive ones. More than half of young people who reported using social media said they often or sometimes encountered body shaming, sexist remarks, transphobia, homophobia, and racism, directed at themselves or others. White respondents were more likely to report coming across hurtful comments than their non-white peers, perhaps because they’re also less likely to curate their feeds to avoid potential hurtful comments (more on that below).

At the same time, young people also reported seeing positive comments—including those that celebrated a range of body shapes, sizes, and capabilities (68%); affirmed different racial and ethnic backgrounds (63%); and affirmed lesbian, gay, bisexual, transgender, and queer (LGBTQ+) identities (63%).

More on Teen Mental Health

Discover five ways to help prevent teen depression .

Learn how we can help young brains fight off anxiety .

Explore whether smartphones are bad for teen mental health .

Find out how to help teens feel good about themselves .

LGBTQ+ youths report experiencing support and identity affirmation with social media, but also exposure to harassment and stress. Almost three-quarters of these social media users say the platforms are important in helping them feel less alone, compared to only about half of self-described cisgender, heterosexual teens and young adults. Yet almost the same number of LGBTQ+ youths say posting to public accounts would open themselves up to harassment. More of these youths, compared to their non-LGBTQ+ peers, also reported bearing the brunt of the negative aspects of social media, including sleeping less and spending less time doing other activities they care about, such as exercising and spending time with friends.

These youths were also more likely than others to proactively minimize the hurtful comments. A whopping 89% said they’ve tried to avoid content they don’t like (compared to 74% of non-LGBTQ+ peers) or tinkered with their feed to tailor it to their needs. Still, a little over half said they prefer connecting over social media than in-person—that’s significantly more than the non-LGBTQ+ respondents who said they felt the same way (38%). When asked why, many LGBTQ+ users explained that they actually felt safer online.

More Black teens and young adults than non-Black youths cite social media as an important tool for specific tasks. For instance: keeping up with the news (80% of Blacks vs. 65% of whites); learning about professional or academic opportunities (80% of Blacks vs. 63% of Latinos vs. 53% of whites); and keeping up with influencers or creators (63% of Blacks vs. 52% for both whites and Latinos). (The sample sizes for Asian and Asian Pacific Islander young people were too small to be able to conduct significant testing, even though those individuals are included in the overall sample.)

Green says that, like other marginalized groups, Black youths saw social media as a way to connect with people who look like them or share their culture. It’s also a way to access resources they don’t necessarily have in their own community, such as information on universities, scholarships, and applications.

“We think of social media as being social,” says Green. “But it’s also a resource, and that’s something that often gets left out of the conversation.”

Black and Latino young people are more likely to quit a social media platform. Black (42%) and Latino (40%) teens and young adults are about twice as likely as white (21%) youth to report taking a permanent break from a social media account because of harassment or other negative experiences online. Black and Latino young people are also about one-and-a-half times more likely to take a temporary break.

In interviews, the youths explained that the racism they’ve had to deal with offline lowers tolerance for online harassment and discrimination. “The online world is sometimes safer and easier than day-to-day for them,” says Green, “because they can just block the negative comments or take a break from the platform.”

More teens and young adults with depressive symptoms experience negative feelings from social media—but they’re also more likely to use it as a source for support. Almost two-thirds of those with moderate to severe depressive symptoms and more than half of those with milder symptoms said that when they use social media, they felt that other people’s lives were better than theirs. Only 38% of non-depressed users said the same. In this report, we see similar discrepancies when it comes to feeling stressed about the bad news seen on social media, as well as feeling bad about their body or appearance.

Nevertheless, those who reported elevated depressive symptoms were also more likely to say that “social media is important for cheering them up” (78%) than those reporting no symptoms of depression at all. Those with elevated depressive symptoms were also more likely to find social media to be a good creative outlet and helpful for feeling less alone.

How adults can think about teens and social media

As Hopelab’s survey makes clear, the effects of social media on young people can’t be described with a blanket statement, and there probably isn’t a one-size-fits-all solution to the problems it has created.

“Social media can be harmful when used in some ways for some people,” says Green. “But used in a different way, it can also be a helpful resource.”

Most experts would agree with her—the impact of social media on an individual level depends on the person and the circumstances. What they don’t agree on is how, exactly, social media affects mental health on a population level and how to address that.

Candice Odgers , associate dean and professor of psychology and informatics at University of California, Irvine, points out that the science to date “does not support the widespread panic around social media and mental health.” She notes that, in addition to findings from multiple large-scale meta-analyses and reviews, an expert committee , convened by the National Academies of Sciences, reported in 2023 that the available research on social media and kids’ health and well-being shows only “small effects and weak associations.”

Efforts to limit social media use, then, seem hasty and, for some kids, even harmful. She fears that calling normal behavior shameful or dangerous can have bad consequences, leading to “conflict within families and may result in young people being shut out of spaces where they find community, support, and often help that they cannot otherwise access in their lives,” she explains in a written statement. Blaming social media also distracts from other possible reasons behind the adolescent mental health crisis, such as family- or school-related stressors.

Zach Rausch—the lead researcher on Jonathan Haidt’s recent bestselling book The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness —has a different perspective. He points out that some of social media’s benefits can simply be found online; for instance, mental health information can already be searched for and discovered on, say, a mental health website. As he puts it: “How much did hyper-viral social media platforms add to those benefits?”

He also adds there is more harm than good when it comes to social media. “You have hundreds (if not thousands) of kids who are harmed by TikTok challenges, pervasive and anonymous cyberbullying, sextortion, online predation, widespread sexual harassment—with all of these things, there is causality of harm there,” says Rausch, who is also an associate research scientist at New York University Stern School of Business.

“It’s happening on these platforms, through these platforms, and would not have happened without them. With any other consumer product designed for kids that is doing this on this scale, we would immediately take it off the shelves and fix it.”

Until that happens, most experts believe that parents should take a balanced approach when helping kids navigate social media. “Social media is more complicated than things like nicotine, which you should abstain from,” says Amy Green. “It’s about helping you recognize and work to minimize some of the more negative and challenging parts that might not be good for you—but still allow you to develop deeper connections with friends and find resources.”

About the Author

Joanne Chen

Joanne Chen is a writer and editor in New York City. Her articles on children and parenting have appeared in The Bump , Parents Magazine , and the New York Times .

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• All News & Insights

New USC study sheds light on adolescent mental health crisis in the United States

Results emphasize the interconnectedness of mental health, attendance and school grades—a necessary reality for schools to grapple with.

Key Findings

This study suggests:

• Teen girls and pre-teen boys exhibit distress differently, with pre-teen boys struggling with externalizing behaviors and hyperactivity, while teen girls are experiencing symptoms of anxiety and depression.
• Students who are on track to be chronically absent or who are earning Cs are three or more times as likely to face mental health challenges as those with fewer absences or As and Bs.
• Black and lower-income families report fewer school mental health services, but are more likely to utilize them when available.
• Nearly 20 percent of families without access to mental health services would enroll their children if offered.

The mental health of children in the United States has reached a critical juncture, with rising rates of teen suicides, emergency room visits and anxiety and depression among youth. Contributing factors include the social isolation of the pandemic, academic disruptions, family challenges, economic impacts and social media’s inescapable influence.

Today, researchers with USC Dornsife College of Letters, Arts and Sciences and USC Rossier School of Education released a new report titled “A Nation’s Children at Risk: Insights on Children’s Mental Health from the Understanding America Study” that examined the current state of adolescent mental health in the United States.

In a nationally representative sample of U.S. families, this new report examines adolescent mental health through the lens of their school experiences and parental perspectives. The study delved into mental health scores across multiple demographic groups and explored the correlation between scores, school attendance and course grades. Importantly, the study also investigated the availability of mental health resources in schools to support students in need.

Study co-authors Amie Rapaport , Morgan Polikoff , Anna Saavedra and Daniel Silver presented the finding’s implications and offered recommendations in their report.

“Our data supports the interconnected nature of student needs; to improve academic outcomes, schools must need to also prioritize mental health and attendance,” said Rapaport, co-director with the Center for Applied Research in Education (CARE) and research scientist with the Center for Economic and Social Research (CESR) both with USC Dornsife.

The study suggests that when students receive mental health support in school, 75 percent of parents report that these services are beneficial, with 72 percent expressing satisfaction. However, disparities in service availability exist, with service availability more than 20 percentage points greater in schools serving more White and higher-income households. This despite the fact that lower-income families are more than 5 times as likely as higher-income families to take up the services in schools when offered.

“While there is a growing awareness of the mental health struggles faced by adolescents, our study underscores that different student groups are experiencing different struggles–clearly, a one-size-fits-all solution to this problem will not work,” said Polikoff, USC Rossier professor of education and co-faculty director of the USC EdPolicy Hub .

Among the study’s implications:

• While the mental health struggles of our nation’s adolescents often are in the headlines, the report sheds light on the unique challenges faced by different subgroups of children.
• The study recommends a need for targeted allocation of resources to address mental health needs in schools.
• The correlation between mental health struggles and academic outcomes-including the approximately threefold increase in mental health warning flags among students chronically absent or with lower grades - underscores the importance of comprehensive support systems for students.

“The study shows that there is substantial unmet need for mental health services in schools, especially for the most disadvantaged students–states and the federal government need to step in and provide resources and guidance to address this crisis,” said Saavedra, a research scientist with CESR and co-director at CARE.

The study was supported by the Peter G. Peterson Foundation Pandemic Policy Research Fund at the USC Schaeffer Center for Health Policy & Economics .

Morgan Polikoff

• Professor of Education

USC EdPolicy Hub

Article Type

Article topics.

• K–12 education policy

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• Supporting Student Mental Health: Key…

Supporting Student Mental Health: Key Takeaways From School and District Staff

Date posted:.

Mental health concerns among youth remain prevalent following the COVID-19 pandemic. Percentages of youth experiencing symptoms of depression, anxiety or traumatic stress are particularly troublesome. In fact, the National Center for Education Statistics reported that almost 60% of public schools noted increases in youth requesting school-based mental health services last school year.

Given these rising mental health concerns, K-12 schools are an important setting to help address youth mental health needs. In particular, schools are well-suited to provide mental health prevention programs to support the well-being of all students, which are known as Tier I services, as well as to provide targeted interventions to those at risk, which are Tier II services.

Researchers from PolicyLab have worked closely with local school districts on several studies of Tier I and Tier II mental health programs and other initiatives. This connection provided an opportunity to build on these relationships to work together on the Children’s Hospital of Philadelphia (CHOP) Tri-County School Mental Health Consortium (SMHC) . In SMHC, our team of CHOP researchers is partnering with the Chester, Delaware and Montgomery County Intermediate Units to support public schools’ Tier I and Tier II mental health efforts. In Pennsylvania, Intermediate Units are regional education agencies that provide a range of services to schools and districts. 

During the first phase of this project, we wanted to learn from district and school leaders, teachers, and other student support staff who are on the front lines of supporting student mental health and well-being.

We surveyed school district leaders and conducted qualitative interviews with each of these groups of school professionals. Our goals were to learn about the current landscape of Tier I and Tier II mental health programming in this region. We also wanted to understand what programs are in place, what is going well, and what school and district staff see as key needs and priorities to help support students.

Through this phase of our research, we identified several key takeaways:

1. Regardless of their roles, school and district staff care deeply about student mental health and well-being.

Many educators we spoke with recognized that student mental health and well-being are closely linked with academic engagement. Although they shared that it can be challenging to support student mental health within the context of limited resources, competing demands, and schools' primary focus on academics, they also shared their dedication to doing so.

2. Schools and districts face continued challenges regarding unmet youth mental health needs.

Many of the school professionals we spoke with shared that they have seen increased mental health needs among students in recent years. Many educators expressed particular concern about anxiety among students. They frequently attributed this and related concerns to structural factors, including disruptions due to the COVID-19 pandemic, stressors related to social media use, and academic demands.

One district leader emphasized the link between youth anxiety and increased school demands: “I think anxiety is probably at the top of the list, and I think there’s a variety of reasons. I think there’s a lot of pressure around students being exceptional all the time.”

3. Schools and districts are utilizing innovative and thoughtful approaches to support implementation of Tier I and Tier II programming .

School and district staff described approaches they use to garner buy-in from key parties in many roles, including students and caregivers. They also described the importance of using a “common language” within the school building and aligning programming to school and district structures and priorities.

4. Supporting youth mental health takes a team effort.

Educators spoke about the importance of partnerships: they highlighted the value of their partnerships with Intermediate Units and academic partners, and how much they benefit from collaborating with and learning from other schools and districts.

As one school principal shared, “I think communication is critical, and just the partnerships between the different agencies…and make sure that the stakeholders that need to be at that table talking about this are at the table.” Other educators highlighted the critical importance of being able to connect students to mental health resources in the community, including higher levels of care when necessary.

We were grateful to have the opportunity to learn from school and district staff, as these individuals have important perspectives about youth mental health needs and priorities for school-based programming.

Our learnings from this phase of the research highlight the importance of mental health prevention programming in schools, particularly given the level of mental health needs that school and district staff see during their day-to-day work. Our learnings also highlight the excellent work that schools and districts are already doing to support student mental health and well-being. We plan to disseminate these findings over the coming year, through presentations and publications aimed at both school and research audiences.

We hope that the next phase of the SMHC project , which includes learning collaboratives to inform and support Tier I and Tier II programs, can build upon these strengths to provide another layer of support for schools and districts in helping their students thrive. 

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• Mental Health

6 Red Flags About the Mental-Health Content You’re Being Bombarded With on Social Media

T he classic vision of therapy revolved around a person on a couch, supine, tapping into their deepest and darkest hopes and fears. A modern-day remix might look like this: a person still on a couch, but at home, scrolling through a constantly refreshing selection of mental-health content on social-media platforms like TikTok and Instagram.

Though it may feel therapeutic, experts advise proceeding with caution. As an increasing number of psychologists step into the role of mental-health influencer, opening the door to fame and financial incentives , their posts—on attachment styles or unresolved trauma or whatever else might be the disorder du jour—are reaching millions of people. 

There are certainly benefits: “We’re coming out of a time when mental health was very highly stigmatized, and it kept people from seeking treatment,” says Evelyn Hunter, a counseling psychologist in Auburn, Ala. “Social media has removed that in some ways, and normalized the fact that sometimes we struggle.”

It can be difficult, however, to suss out which so-called experts are credible and whether their information is trustworthy—which can lead to misinformation and harmful misunderstandings. “The way information is so rapidly spread on social media can make it difficult to figure out what’s accurate, what’s professional, and what’s expertise-driven,” Hunter says. She stresses that mental-health professionals who are active on social media should demonstrate three qualities: competence, good-faith interpretation of evidence, and integrity.

With that in mind, if your algorithm is feeding you mental-health content, keep an eye out for these red flags.

1. The person running the account doesn’t share their credentials.

Most credible mental-health influencers will be transparent about their training, licensure, and areas of expertise. The American Psychological Association (APA) Guidelines for the Optimal Use of Social Media in Professional Psychological Practice encourage psychologists to routinely update their personal and professional websites, and to monitor the information others have posted about them online and verify its accuracy.

For those of us looking to vet professionals, Victoria Riordan, an Ohio-based licensed professional clinical counselor with the counseling practice Thriveworks, suggests starting by checking the account’s bio. That’s where many practitioners specify whether they are, for example, a psychiatrist, social worker, or something else that perhaps doesn’t require specific training or regulation, like a life coach. They’ll also likely include their area of expertise and link to a place where you can read more about them.

If you’re not seeing much information, search the person’s name online. If they’re legitimate, “they’re going to come up on Psychology Today, LinkedIn, or their own private practice website,” Riordan says. “You’re going to find them via several different sources, not just social media.” You can also check to see if they’re currently licensed, which is a vote of confidence in support of their education, experience, and ethical standards. Start with state licensing board websites or a resource like the Association of State and Provincial Psychology Boards .

2. They’re trying to sell you something.

It’s natural for psychologists to utilize their social-media platforms to promote things like online courses and books they’ve written, notes Genesis Games, a psychotherapist in Miami. “But if all their content leads you back to their storefront, that means they’re more concerned about making you their customer than providing quality education,” she says. “There’s a difference between saying, ‘These things are resources,’” and posting exclusively to hawk products.

The APA Ethics Code stresses that psychologists must avoid conflicts of interest. But these can still pop up online when people promote products—say, a supplement that allegedly alleviates anxiety—without disclosing a business relationship. If something feels off to you, trust your gut and do additional research, like Googling the person’s name and the product and seeing what that search yields, Games suggests. Another telltale sign of a business relationship is if the company is routinely tagging the expert in its own social media posts.

In these situations, it’s ultimately up to each consumer to do what feels best, Games says. As a first step, she recommends asking for clarification about the person’s connection to a product, or simply unfollowing them. But maybe you’ve stumbled upon an unfathomable claim or something that really bothers you. “If you think it’s ill-intended or is putting others in severe danger,” you could report the practitioner to the licensing board or college in the state where they practice, she says.

3. Posts are jargon-heavy.

Therapy speak is infiltrating everyday language. Talk about boundaries, repression, inner-child work , attachment styles, trauma, triggers—and much more—is tossed around frequently and casually. This overuse is concerning for a number of reasons, experts say. For one, people who don’t fully understand the terms but see them on their social media feeds might be more likely to weaponize them in relationships to create a power dynamic. For another, in some cases latching onto and incorrectly using certain words “dilutes their meaning,” says Mollie Spiesman, a licensed clinical social worker in New York.

She cautions social media users to keep an eye out for accounts that turn these types of therapy terms into buzzwords. Trust-worthy practitioners “typically aren’t using therapy terms and therapy jargon, because they want to make therapy and mental health more approachable and digestible,” she says. “If someone is trying to seem smarter than they are, or they’re using all these terms that you don’t understand, that’s a red flag.” 

4. Practitioners promote self-diagnosis or labels.

A rule of thumb: Never diagnose yourself or others, Spiesman cautions. Social media is often fixated on labels: If you do these five things, you’re depressed—or you’re a narcissist or have ADHD or are on the autism spectrum. That can run counter to the APA’s social media guidelines , which recommend psychologists avoid offering diagnoses, giving advice, “or otherwise behaving as if they were conducting treatment.”

“What I notice with clients and friends is that people internalize these messages so deeply, and they do diagnose themselves,” Spiesman says. “They’ll see something on TikTok and be like, ‘Oh, I have this issue, or my partner has that.’” She now spends time in sessions asking clients to take a step back and reflect on why a specific post resonated with them so much. 

5. They interact with clients on social media.

When Jeff Guenther, a licensed professional counselor based in Oregon, started to crave a creative outlet during the pandemic, he launched a TikTok account. Now, about two years later, he’s perhaps best known as his online alter ego—Therapy Jeff—and has accumulated nearly 3 million followers on TikTok and more than 850,000 on Instagram .

Guenther describes social media as the Wild West. He navigates it as carefully as possible by setting rules for himself—like not responding to existing clients who comment on his posts. Doing so could break confidentiality and blur the boundaries of the professional relationship, as the APA points out in its guidelines . “You have to tell your clients that you will ignore them, just like you would if you ran into them at the grocery store,” he says. So if you see a practitioner engaging with someone they’ve clearly worked with? Unfollow—it’s a red flag.

6. They tout one modality as superior to all others.

Therapists are typically trained in a number of different modalities, such as cognitive behavioral therapy, eye movement desensitization and reprocessing (EMDR), gestalt therapy, somatic therapy, and art therapy. They might specialize in and even prefer one, but they shouldn’t be touting it online as the end-all, be-all, Games says. “Not one single therapeutic modality works for everyone,” she stresses. “It’s not one-size-fits-all.”

Games sometimes notices newer practitioners, in particular, emphasizing a certain treatment as though it’s a panacea. “Let’s say I keep getting content around EMDR, and I think, ‘I need to go find an EMDR therapist,’” she says. “So I try it, and it doesn’t feel good to me. Instead of thinking it’s just not the right treatment for me, I might think I’m the problem, and there’s something wrong with me, because it’s supposed to work and it’s not working.” 

That drives home one of practitioners’ primary points about mental-health content on social media: Not every piece of information will apply to every person, so take it with a grain of salt. Therapists on social media are just that: therapists. They’re not your therapist.

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