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Four of the largest school boards in Canada are suing the companies behind popular social media apps Instagram and Facebook, Snapchat and TikTok. According to the Ontario boards, students are experiencing an attention, learning and mental health crisis because of prolific and compulsive use of social media products.

The school boards are collectively seeking over $4 billion in damages. Boards say theyre facing financial strain due to providing increased mental health supports for students as well as diverting resources to monitor social media related to threats or harassment.

Some observers have suggested it should be the responsibility of parents and teachers to control childrens social media use. But the problem is that for too long we have been trying to individualize solutions to a collective problem.

How social media negatively impacts kids mental health has been meticulously outlined in a new book by social psychologist Jonathan Haidt of New York University, The Anxious Generation: How the Great Rewiring of Childhood is Causing and Epidemic of Mental Illness. In the book, Haidt discusses four ways social media is harming children:

Social deprivation, whereby the time children spend on social media has displaced opportunities to form more authentic personal connections;

Sleep deprivation, as social media use has been tied to reduced sleep duration and poorer sleep quality;

Attention fragmentation, as students are continually bombarded by messages and notifications, compromising their ability to focus.

Finally, addiction: tech companies are intentionally designing their social media apps in ways that exploit the vulnerabilities of children.

Haidt documents how internal documents revealed by former Facebook employee and whistleblower Frances Haugen show an employee presentation about why teens and young adults choose Instagram (owned by Facebook):

Teens decisions and behaviour are mainly driven by emotion, the intrigue of novelty and reward. While these all seem positive, they make teens very vulnerable at the elevated levels they operate on. Especially in the absence of a mature frontal cortex to help impose limits on the indulgence of these.

In Haidts analysis, its no mystery why we are seeing such sharp declines in youth mental health.

Read more: Excessive social media use during the COVID-19 pandemic exacerbated adolescent mental health challenges

According to the 2021 Ontario Student Drug Use and Health Survey, the proportion of students reporting poor or fair mental health and the proportion of students experiencing serious psychological distress have both more than doubled since 2013.

As claimed by the Canadian school boards, it has largely fallen on schools to address these issues. To their credit, schools have tried to provide students with access to psychologists, social workers, youth workers and mental health specialists, but there is only so much they can do given their resource constraints.

According to data from the Annual Ontario School Survey, 95 per cent of schools report needing additional resources to support the mental health and well-being of students.

Boards allege the conduct of social media companies has been negligent and they are unfairly bearing the brunt of the learning and mental health epidemic caused by their apps.

Phones and social media use are also clearly having a detrimental impact on student learning: The most recent results of the OECDs PISA study show that math, reading and science scores have been plummeting over the last decade in Canada and other developed countries, due in large part to technology used for leisure rather than instruction, such as mobile phones.

This corresponds with a 2023 study led by researchers from the University of Michigan that tracked the phone use of 200 children (ages 11 to 17) over the course of a week.

It found that during the school day, the devices were used for educational purposes less than two per cent of the time. Rather, the most common uses of phones during school hours were social media (32 per cent), YouTube (26 per cent) and gaming (17 per cent).

Ontario Premier Doug Ford has expressed surprise at the lawsuit, stating: We banned cellphones in the classroom, so I dont know what the kids are using.

However, the reality is that Ontarios ban has been mostly symbolic. The reason for this is twofold. First is the way the ban was constructed: it allowed an exception for when the phones were being used for educational purposes.

Second, many students are unable or unwilling to comply with restrictions on their use something hardly surprising since social media apps are designed to be as addictive as possible. That means it has been left up to individual teachers to enforce restrictions in their classrooms, and resistant students arent provided with clear and consistent expectations. Meanwhile, some parents say their children need their devices.

While some say its up to individual children to fight these temptations, individual parents to better monitor their kids and individual teachers to get control of their classrooms, we must remember that the companies behind popular social media platforms are among the wealthiest on the planet. They use their enormous resources to render attempts at individual willpower futile.

Read more: 'Never-ending pressure': Mothers need support managing kids' technology use

Change may come from the courts or through the court of public opinion. Apart from whether companies are legally held responsible, reversing the harms being inflicted on our children by social media is going to require collective action among educators, parents and policymakers.

See the original post here:
School board social media lawsuits: For too long we've sought individual solutions to a collective problem - The Conversation Indonesia

Donald Trumps social media platform, Truth Social, went public on Tuesday March 26. Shares in parent company Trump Media & Technology Group surged 15% after its first day of trading on the Nasdaq stock exchange, adding US$1.1 billion (876 million) to the companys value.

Trump wrote I LOVE TRUTH SOCIAL on the platform, echoing the sentiment of I just like the stock from the GameStop share rally that occurred in January 2021. For those who do not remember the GameStop case, shares in the Texan computer games retail chain experienced an unprecedented surge in prices following the activity of retail investors on the social media platform, Reddit.

Millions of investors from Reddits WallStreetBets community pushed GameStop shares from US$20 to US$480 during the January short squeeze, in which they drove some hedge funds into heavy losses after forcing them to liquidate massive bets against the stock. The power of small, amateur investors to outplay Wall Street giants was celebrated all over the internet, and even inspired the 2023 film Dumb Money.

It appears that the Trump Media stock is yet another example of a so-called meme stock, whose popularity is driven by social media activities and memes posted on various platforms, such as Truth Social.

However, while similarities with GameStop are apparent, the Trump Media movement looks unlikely to be as successful. On Monday April 1, less than a week after it began trading, shares of Trump Media fell by more than 20%.

The social media hype around GameStop originated from within a community of retail investors that took a David v Goliath mentality. The firm behind Truth Social has tried to cultivate a similar sentiment of small guys resisting Big Tech censorship.

Devin Nunes, the CEO of Trump Media, stated: As a public company, we will passionately pursue our vision to build a movement to reclaim the internet from big tech censors.

However, Trump Media stocks are directly linked to the exceptionally famous persona of Donald Trump, who owns 58% of the shares. Thus, parallels could be drawn with PR campaigns that have been launched for crypto assets, such as NFTs (non-fungible tokens), where celebrities are often used to attract investors to the projects.

Read more: From GameStop to crypto: how to protect yourself from meme stock mania

The Trump media stock is undoubtedly appealing to his loyal supporters, who appear to have fuelled the surge in price. But meme stocks may attract a broader range of investors due to the social media hype.

That is why it is important to understand that investing in any meme stock or meme coin is a risky endeavour. Surges in price that cannot be explained by any company fundamentals are called asset price bubbles. Speculative bubbles are quite common in financial and cryptocurrency markets and offer opportunities to generate abnormal returns in a short period of time.

However, they can be risky for investors as they have a tendency to burst. Participating in such speculative behaviour is typically considered irrational since it is extremely hard to justify the growth of the price.

More importantly, it is nearly impossible to predict exactly when the bubble will burst. Retail investors should be cautious and definitely should not make decisions based solely on social media announcements regarding public figures.

The share price of Trump Media has now dipped to almost pre-surge levels. For many retail investors, it is fair to assume that it is yet again too late to get abnormal returns on this surge. This is because the price of a meme stock tends to simply fluctuate after the initial surge.

Trump Media stock plummets within a week of going public

Only the long-term growth potential of an asset should be assessed to generate somewhat stable returns in the future. However, the long-term stock performance is rarely assessed when it comes to meme stocks, as investors in these stocks tend to have a very short investment horizon.

Following the cryptocurrency market crash in 2022, many retail investors lost their savings as the bubble burst. Many of the collapsed crypto assets were meme coins that had been promoted by celebrities. Dogecoin, for example, was promoted by Elon Musk.

Celebrities have immense power to influence the public. But when it comes to financial decisions, the ethical implications of those campaigns are often not considered.

At the time of writing, there is yet another bullish trend in cryptocurrency prices, particularly in Bitcoin. Yet there is still no clarity in regulation or consumer protection, and there has been no regulatory response to concerns about the environmental impacts of Bitcoin mining.

Some experts who invest in cryptocurrency and have direct financial benefit from surges in prices would, of course, argue that the rally is not a bubble and prices will keep growing. However, it might be unethical to expose consumers to unjustified risks.

According to some studies, awareness of the green critiques associated with cryptocurrency markets is growing. But recent research that I conducted with my colleagues shows that retail investors generally do not care. Understanding that crypto is unsustainable and somewhat unethical does not decrease the odds of investing in crypto assets among retail investors.

The movement in Trump Medias share price will have been backed by Trump supporters. But it will also have attracted some investors who simply wanted to partake in this share rally, even if they do not share Trumps political views. The desire to make money quickly is one of the main driving factors of meme stock investments, and social media campaigns are great fuel for this sentiment.

Excerpt from:
Truth Social: why Donald Trump's social media 'meme stock' surged and fell by over US$1 billion within a week - The Conversation Indonesia

Things could be going better for X, the social network formerly known as Twitter. Elon Musk's platform is facing a further decline in user interest. In fact, according to an American study, X lost 30% of its users between 2023 and 2024.

Internet users no longer seem to have as much interest in using X, the social platform formerly known as Twitter, according to the latest Edison Research "Infinite Dial" report on digital media usage, conducted in January 2024 among 1,086 Americans aged 12 and over.

Indeed, the platform is experiencing a decline in usage. According to the report, 19% of Americans surveyed said they currently use the social network X (formerly Twitter) in 2024, compared with 27% in 2023. This is a significant drop, since according to Edison Research, this would translate into an estimated loss of 22 million users. The report counts 77 million users of the platform in 2023, compared to 55 million in 2024.

While X may be floundering, this is not the case for all social networks starting with Facebook, which, despite its less than stellar reputation, remains the most widely used social media in the United States. Around two-thirds of Americans over the age of 12 (63%) report using the platform. This figure has remained stable over the past two years. Instagram comes second with 44%, followed by TikTok (35%). The X platform is in seventh place, behind Pinterest, LinkedIn and Snapchat.

At generational level, 12- to 34 year-olds largely favour Instagram, followed by TikTok and Facebook. The older generation is more drawn to Facebook, which is still very present in the daily lives of 35- to 54-year-olds and 55+-year-olds.

X, on the other hand, has a harder time finding a place in the lives of users, across all generations. The platform lags far behind among 12- to 34 year-olds and 55+-year-olds, and comes in fifth place among 35- to 54-year-olds.

Other studies have already reported a slowdown for social network X. Apptopia, for example, reported a 13% drop in daily active users since Elon Musk's takeover. Meanwhile, SimilarWeb revealed a 14% drop in the social network's web traffic.

According to the Edison Research report, 82% of the US population aged 12 and over or around 235 million people currently ever use social media in 2024. AFP Relaxnews

More:
Is social network X falling out of favour with Americans? - The Star Online

Thursday 04 April 2024 5:05 am

By: Matthew Lesh

Matthew Lesh is Director of Public Policy and Communications at the Institute of Economic Affairs

Social media is an easy target for societys woes, but there is little hard evidence for its link with bad mental health, writes Matthew Lesh

A new book has ignited debate on social media this week. It is perhaps appropriate that the book, The Anxious Generation, is itself about social media.

Author Jonathan Haidt, a high-profile social psychologist, argues that childhood development has been disturbed by replacing play and in-person socialising with screen time, driving a youth mental health crisis. This book will undoubtedly bolster the campaign from those aiming to ban social media use for under-16s. Theres just one big problem: the evidence does not support Haidts apocalyptic claims.

In a review of The Anxious Generation in Nature magazine, psychology professor Candice Odgers warns that assertions about social media driving an epidemic of mental illness are not supported by science. Odgers says that research consistently finds a mix of no, small and mixed associations. Many studies find correlation rather than proven causation. Its quite possible that young people who use social media in an unhealthy manner already have mental health problems.

Haidt has responded to this criticism by listing the number of studies linking youth mental health issues with social media. But this is hardly exemplary of the scientific method. Science is not a democratic process, with whoever publishes the most papers winning the argument. Rather, its necessary to weigh the strength of each individual paper.

One such study that sought to review the reviews (that is, analyse metastudies in the field) found that the claimed links between social media and mental health are weak or inconsistent. One such review, from Amy Orben of the University of Cambridge, found links in both directions and claimed negative associations are at best very small. One study, for example, found that wearing glasses negatively impacted youth mental health more than screen time.

If the internet has a big negative impact, we expect to see worsening mental health globally. But thats not the case. The most reliable statistic to assess is teen suicide, as it addresses variations in self-reporting of mental health issues across time and place. On this front, there has been a clear increase in teen suicide over the last decade in the United States, but elsewhere, including the United Kingdom, teen suicide rates remain low or stable.

But even when you look at self-reported survey findings, the impact of social media is still far from clear. Matti Vuorre and Professor Andrew K Przybylski of Oxford University examined life satisfaction and internet uptake among 2m people in 168 countries over two decades. Looking at this broader data set and cross-national measures, unlike many narrower studies that claim negative effects, they find minor and inconsistent shifts in global mental health.

Its important not to oversimplify in this debate. Social media and screen time may have been harmful for some children. For many, however, technology is used to connect with friends and family, explore new ideas and build communities. One study by Andrew K Przybylski and Netta Weinstein, which analysed social media among English adolescents, found that moderate use may be good for mental health, while high levels had a measurable, small negative impact.

Technology is a tool that enriches our lives when used properly. The challenge for parents and schools is to ensure teens understand the risks and encourage positive behaviour. Broad generalisations and unrealistic knee-jerk bans will achieve little and could do much harm to healthy childhood development.

Read more here:
Let's be honest, social media isn't driving a teen mental health crisis - City A.M.

Description of studies

Of 17077 studies screened, 688 full text studies were assessed, with 126 included (73 in the meta-analysis; fig 2). The final sample included 1431534 adolescents (mean age of 15.0 years). Most included studies were cross-sectional (n=99; 79%) and investigated high income countries (n=113; 90%),73 with 44 studies (35%) investigating US adolescents. Appendix 11 shows the geographical distribution of included study populations. Included and excluded study characteristics are presented in appendix 11 and 12.

PRISMA flow diagram. APA=American Psychological Association.*One study92 was not included in the synthesis without meta-analysis (SWiM) as this resulted in counting of study participants twice; we were able to include estimates from this study in meta-analyses stratified by outcome where this issue did not occur

For 122 included cross-sectional and cohort studies, 57 (47%) of studies were graded high risk of bias, 31 (25%) were moderate, and 34 (28%) were low. Of the four randomised controlled trials included, two were graded with some concerns and two as low risk of bias (appendix 13). Reviewer risk of bias agreement was strong (=0.91).79

Within included studies, many social media exposure measures were reported, with most investigating multiple measures (appendix 14). All were incorporated in our exploration of how social media use is measured, therefore, the number of datapoints reported differs across syntheses.

In total, 253 social media measures were reported: 135 (53%) assessed frequency, 61 (24%) assessed exposure to content displaying health risk behaviour, 45 (18%) assessed time spent, and 12 (5%) other social media activities. Despite our broad definition of social media, most included studies assessed a narrow range of social media categories (or adopted a broad definition). Social networking sites was the most common category investigated (56%; n=141). Of those social media measures investigating a specific platform (n=86), Facebook was most investigated (n=40), followed by Twitter (n=10).

Of those 61 measures assessing exposure to content displaying a health risk behaviour, 36 (59%) assessed marketer generated content, 16 (26%) assessed user generated content, and nine (15%) assessed both types of content. In total, 134 (53%) of the 253 social media measures provided sufficient information to differentiate between use that was active (eg, positing and commenting on posts; n=90) or passive (eg, observing others, content, or watching videos; n=44). Exposure ascertainment primarily used unvalidated adolescent self-report surveys (n=221) with a minority using data-driven codes, validated adolescent self-report questionnaires and/or clinical records (n=32).

Alcohol use was the most extensively studied outcome (appendix 15). For time spent, 15/16 studies (93.8%) reported harmful associations (95% confidence interval 71.7% to 98.9%; n=100354; sign test P<0.001), 16/17 studies (94.1%) for frequency (73.0% to 99.0%; n=390843; sign test P<0.001), and 11/12 studies (91.7%) for exposure to content displaying health risk behaviour (64.6% to 98.5%; n=24247; sign test P=0.006). The category other social media activities was investigated by one study (ie, participants had a Facebook account) that reported a harmful association (95% confidence interval 20.7% to 100%; n=4485; fig 3 for effect direction plot).

Effect direction plot for studies of the association between social media use and adolescent alcohol use, by social media exposure. Arrow size indicates sample size; arrow colour indicates study risk of bias. Sample size is represented by the size of the arrow, measured on a log scale. Outcome measure is number of outcome measures synthesised within each study. Studies organised by risk of bias grade, study design, and year of publication. Repeat cross-sectional studies, multiple study populations from different countries, and age subsets originating from the same study reported as separate studies. ESP=Spain; FIN=Finland; KOR=South Korea; NOS=assessed via adapted Newcastle Ottawa Scale; RCS=repeat cross-sectional study; SM=social media

In meta-analyses, frequent or daily (v infrequent or non-daily) social media use was associated with increased alcohol consumption (odds ratio 1.48 (95% confidence interval 1.35 to 1.62); I2=39.3%; n=383068; fig 4A). In stratified analyses (appendix 16, p162-167), effect sizes were larger for adolescents 16 years or older compared with participants who were younger than 16 years (1.80 (1.46 to 2.22) v 1.34 (1.26 to 1.44); P<0.01 for test of differences). Social networking sites were associated with increased alcohol consumption, while microblogging or media sharing sites had an unclear association (P=0.03).

Forest plots for association between frequency of social media use and A) alcohol use, B) drug use, and C) tobacco use. (A) Binary exposure (frequent or daily v infrequent or non-daily) and binary or continuous alcohol use outcome meta-analysis, with OR used as common metric (N=383068). (B) Binary exposure (frequent/daily v infrequent/non-daily) and binary or continuous drug use outcome meta-analysis, with OR used as common metric (N=117645). (C) Binary exposure (frequent v infrequent) and binary or continuous tobacco use outcome meta-analysis, with OR used as common metric (N=424326). Hard drugs were defined by the cited papers as prescription drugs without a doctors prescription (eg, OxyContin), cocaine crack, methamphetamine, ecstasy, heroin, or opioids. CI=confidence interval; ESP=Spain; FIN=Finland; KOR=South Korea; OR=odds ratio; RoB=Risk of bias; SM=social media; SNS=Social networking sites

Social media use for 2 h or more (v <2 h per day) was associated with increased alcohol consumption (odds ratio 2.12 (95% confidence interval 1.53 to 2.95); I2=82.0%; n=12390), as was exposure (v no exposure) to content displaying health risk behaviours (2.43 (1.25 to 4.71); I2=98.0%; n=14731; appendix 16, p168). Stratified analyses for time spent and exposure to health risk behaviour content generally did not show important differences by age and social media category (appendix 16, p169-171). Associations were slightly stronger for exposure to health risk behaviour content in user generated (3.21 (2.37 to 4.33)) versus marketer generated content (2.35 (1.30 to 4.22); P=0.28; appendix 16, p172). Meta-analyses for frequency of use, time spent on social media, and exposure to content displaying health risk behaviour (assessed on a continuous scale) showed similar findings (appendix 16, p173-174). On stratification (appendix 16, p175-179), for exposure to content displaying health risk behaviour, associations were larger for adolescents 16 years or older versus younger than 16 years (Std.Beta 0.35 (0.29 to 0.42) v 0.09 (0.05 to 0.13); P<0.001). The results indicated that for every one standard deviation increase in exposure to content displaying health risk behaviour, alcohol consumption increased by 0.35 standard deviation for older adolescents compared with 0.09 standard deviation for younger adolescents.

For drug use, across all exposures investigated, 86.6% of studies (n=13/15; 53.3% low/moderate risk of bias) reported harmful associations (appendix 16, p180). The pooled odds ratio for frequent or daily use (v infrequent or non-daily) was 1.28 ((95% confidence interval 1.05 to 1.56), I2=73.2%; n=117645) (fig 4B). Stratification showed no clear differences (appendix 16, p182-184). Few studies (n=3) assessed time spent on social media with estimates suggestive of harm (odds ratio 1.45 (95% confidence interval 0.80 to 2.64); I2=87.4%; n=7357 for 1 h v >1 h/day) (appendix 16, p185).

For tobacco use, 88.9% (n=16/18; 50.0% low risk of bias) studies reported harmful associations of social media use (appendix 16, p 186). Frequent (v infrequent) use was associated with increased tobacco use (odds ratio 1.85 (95% confidence interval 1.49 to 2.30); I2=95.7%; n=424326) (fig 4C), as was exposure (v no exposure) to content displaying health risk behaviours (specifically, marketer generated content) (1.79 (1.63 to 1.96); I2=0.00%; n=22882) (appendix 16, p188). In stratified analyses (appendix 16, p189-193) for frequency of use, stronger associations were observed for low and middle income countries versus for high income countries (2.47 (1.56 to 3.91) v 1.72 (1.35 to 2.19); P=0.17), and for use of social networking sites versus for general social media (2.09 (1.72 to 2.53) v 1.48 (1.01 to 2.18; P=0.29).

Across all exposures investigated, 88.9% of studies (n=8/9; 77.8% low/moderate risk of bias) reported harmful associations on electronic nicotine delivery system use (appendix 16, p194). Exposure to content displaying health risk behaviour (specifically marketer generated content) (v no exposure) was associated with increased electronic nicotine delivery system use (odds ratio 1.73 (95% confidence interval 1.34 to 2.23); I2=63.4%; n=721322) (appendix 16, p195). No clear differences were identified on stratification (appendix 16, p196-197).

After excluding one study with inconsistent findings, across all exposures investigated 90.3% (n=28/31; 67.7% high risk of bias) reported harmful associations for sexual risk behaviours (appendix 16, p 198). Frequent or at all use (v infrequent or not at all) was associated with increased sexual risk behaviours (eg, sending a so-called sext, transactional sex, and inconsistent condom use) (odds ratio 1.77 (95% confidence interval 1.48 to 2.12); I2=78.1%; n=47280) (fig 5A). Meta-regression (coefficient 0.37 (0.70 to 0.05); P=0.03) (appendix 16, p276) and stratified analyses (appendix 16, p200-206) suggested stronger associations for younger versus older adolescents (<16 years v 16 years), but no moderation effects were by social media category (P=0.13) or study setting (P=0.49). Few studies assessed associations for time spent on social media (appendix 16, p207).

Forest plots for association between frequency of social media use and A) sexual risk behaviour, B) gambling, C) anti-social behaviour, and D) multiple risk behaviours. (A) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous sexual risk behaviour outcome meta-analysis, with OR used as common metric. N=47280. (B) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous gambling outcome meta-analysis, with OR used as common metric. N=26537. (C) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous anti-social behaviour outcome meta-analysis, with OR used as common metric. N=54993. (D) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous multiple risk behaviours outcome meta-analysis, with OR used as common metric. N=43571. CI=confidence interval; n=Number of study participants; OR=odds ratio; RoB=Risk of bias; SM=Social media; SNS=Social networking sites

After excluding one study that had inconsistent findings, across all exposures investigated, all six studies investigating gambling reported harmful associations (appendix 16, p208). Frequent or at all use (v infrequent or not at all) was associated with increased gambling (not via social media) (odds ratio 2.84 (95% confidence interval 2.04 to 3.97); I2=85.6%; n=26537) (fig 5B). On differentiation by social media category, a relatively large association was found for online gambling via social media (3.22 (2.32 to 4.49)), however, associations were not present for social networking sites and general social media (appendix 16, p211).

Across all exposures investigated, all 16 studies (43.8% low/moderate risk of bias) that investigated anti-social behaviour showed harmful associations (appendix 16, p212). Frequent or at all use (v infrequent or not at all) was associated with increased anti-social behaviour (eg, bullying, physical assault, and aggressive/delinquent behaviour) (odds ratio 1.73 (1.44 to 2.06); I2=93.3%; n=54993) (fig 5C), with time spent similarly associated with increased risk (appendix 16, p214). No subgroup differences were noted (appendix 16, p215-217).

For inadequate physical activity, after excluding three studies with inconsistent findings, 36.4% of studies (n=4/11; 72.7% low/moderate risk of bias) reported harmful associations across all exposures investigated (appendix 16, p218). No association between time spent on social media (assessed on a continuous scale) and adolescent engagement in physical activity was seen (Std.Beta 0.00 (95% confidence interval 0.02 to 0.01); I2=59.8%; n=37417) (appendix 16, p219), with no important differences across subgroups (appendix 16, p220-222).

Across all exposures investigated, all 13 studies (including four randomised controlled trials: two rated low risk of bias and two some concerns) that investigated unhealthy dietary behaviour showed harmful associations, with most at low risk of bias (61.5%) (appendix 16, p223). Exposure to health risk behaviour content (specifically marketer generated content) was associated with increased consumption of unhealthy food (odds ratio 2.48 (95% confidence interval 2.08 to 2.97); I2=0.00%; n=7892) when compared with adolescents who had no exposure (appendix 16, p224-225).

For multiple risk behaviours, all nine studies showed harmful associations across all exposures investigated (appendix 16, p226). The pooled odds ratio for frequent and at all social media use (v infrequent and not at all) was 1.75 ((95% confidence interval 1.30 to 2.35); I2=97.9%; n=43571) (fig 5D), but the few studies precluded stratification.

For electronic nicotine delivery system use, associations were stronger for cohort study datapoints (odds ratio 2.13 (95% confidence interval 1.72 to 2.64) v 1.43 (1.20 to 1.69) for cross-sectional datapoints; P=0.004) (appendix 16, p228) but no clear differences were seen for other outcomes (appendix 16, p229-240). Although based on few studies, for unhealthy dietary behaviour a stronger association was found for the randomised controlled trial datapoint versus for the cross-sectional datapoints (3.21 (1.63 to 6.30) v 2.48 (2.08 to 2.97); P=0.44) (appendix 16, p241).

When stratifying by adjustment for critical confounding domains, no clear differences were identified (appendix 16, p242-253), with some exceptions. Associations were stronger for unadjusted versus adjusted datapoints for exposure to content displaying health risk behaviour and alcohol use (Std.Beta 0.28 (0.14 to 0.43) v 0.07 (0.03 to 0.12); P=0.008) and for frequent (v infrequent) social media use and alcohol use (odds ratio 1.54 (95% confidence interval 1.36 to 1.78) v 1.34 (1.24 to 1.44); P=0.06) (appendix 16, p254-255).

For alcohol use, effect sizes were generally stronger for moderate and high risk of bias datapoints (v low) (appendix 16, p256-257), excluding time spent (2 v <2 h per day) and exposure to health risk behaviour content (v no exposure) where low (compared with moderate and high) risk of bias datapoints displayed stronger associations (appendix 16, p258-259). For drug use and sexual risk and anti-social behaviour, no differences were detectable or low/moderate risk of bias datapoints showed stronger associations (compared with high) (appendix 16, p260-264). For tobacco use and gambling, stronger associations were found for high risk of bias datapoints or no clear differences were identified (appendix 16, p265-267). No clear differences by risk of bias were observed for the remaining outcomes (appendix 16, p268-269).

When we excluded datapoints that overlapped the age range of 10-19 years, a marginal reduction in effect size (appendix 16, p270) or no important differences were noted (appendix 16, p271-274).

Funnel plots and Eggers test results suggested some publication bias in the meta-analysis investigating frequent or at all social media use (v infrequent or not at all) and sexual risk behaviours (P=0.04; bias towards the null) (appendix 17). Insufficient data precluded investigation of other outcomes.

As frequency was the most investigated exposure, and continuous and binary exposures reported similar effects, we focused the GRADE assessment on the binary exposure of frequency of use. We report harmful effects on alcohol use with low certainty, and with drug, tobacco, electronic nicotine delivery system use, sexual risk behaviours, gambling, and multiple risk behaviours with very low certainty.

We conducted a post-hoc GRADE assessment for exposure to content displaying health risk behaviour (v no exposure) and unhealthy dietary behaviour because of the substantial difference in quality of evidence observed (four randomised controlled trials); we report moderate GRADE certainty (table 1, appendix 18).59

Condensed summary of findings and certainty of evidence (as per GRADE)

Read the original:
Social media use and health risk behaviours in young people: systematic review and meta-analysis - The BMJ