Mediaboss Marketing

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

GLASGOW, Scotland A new study highlights a concerning link between adolescents frequently using social media and risky decision-making that could put their lives in danger. With millions of young people scrolling through Instagram, Facebook, TikTok and other platforms on a daily basis, the effects of these platforms are far-reaching.

Social media, a vibrant mix of content sharing, social networking, and blogging, has become a cornerstone of modern communication, especially among teenagers. Its not just about staying connected; it offers a sense of freedom and belonging. The World Health Organization even recognizes its power in promoting health, noting its role in fostering healthy lifestyles, accessibility to health information, and emotional support.

However, its not all positive. Researchers from the University of Glasgow found a strong association between regular social media use and various risky health behaviors among young children and teens. These include increased underage drinking, drug use, and smoking, as well as antisocial behavior, such as unsafe sexual activity and gambling.

But how does this happen? The study points to several factors:

The study, which analyzed data from 1.4 million adolescents between the ages of 10 and 19 from 1997 to 2022, found that exposure to social media content promoting risky activities, such as alcohol advertisements, showed the most substantial evidence of harm. This was particularly evident in the cases of alcohol consumption and unhealthy eating habits.

Key findings revealed that spending a minimum of two hours daily on social media doubled the likelihood of alcohol consumption compared to those who used it for less than two hours. Published in The BMJ, the study also highlighted that frequent or daily social media usage increased the probability of alcohol consumption by 48 percent, drug use by 28 percent, and tobacco use by 85 percent, compared to those who used social media infrequently or not on a daily basis.

Additionally, regular social media engagement was linked to a 77-percent rise in risky sexual behaviors, such as sexting, transactional sex, and inconsistent condom use, as well as a 73-percent increase in antisocial activities like bullying, physical assault, and aggressive or delinquent behavior. The study also noted that frequent social media users were almost three times more likely to engage in gambling compared to their peers who used social media infrequently or not every day.

Experimental and risk-taking behaviors are an inherent part of adolescence, the study authors write in a media release. However, as safeguards for a digital world are still evolving, precaution across academic, governmental, health and educational sectors may be warranted before the risks of adolescents use of social media is fully understood.

The study underlines the urgent need for more targeted research, particularly in low and middle-income countries. It also calls for a multi-pronged approach to safeguarding young people online, including better digital literacy education and more robust online safety policies.

South West News Service writer Isobel Williams contributed to this report.

Follow this link:
Kids who use social media more prone to making dangerous decisions - Study Finds

Study design and setting

This is an institution-based, descriptive, cross-sectional study. It was conducted at the Faculty of Medicine, University of Khartoum, Khartoum state, Republic of Sudan in January, February, and March 2021.

We included all undergraduate medical students at the faculty of medicine, university of Khartoum (the total population was 2016 students from 6 classes, both males and females). We excluded students unwilling to participate.

With a total population (N) of 2016 and a level of precision (d) of 0.05; a sample size (n) of 333 students was calculated using the formula:(n=N/1+(Ntimes d^2 )). The sample was selected using proportionate simple random sampling.

The total population was divided into 6 classes that correspond to the academic years. The size of the student sample from each class was proportionate to the size of the class (56 from 1st year class, 54 from 2nd year class, 55 from 3rd year class, 59 from 4th year class, 57 from 5th year class, 52 from 6th year class). Students from each class were selected by simple random sampling.

Data was collected using an online, self-administered structured questionnaire (Google form) which consisted of sociodemographic data, social networking intensity (SNI) scale, and fear of missing out (FOMO) scale. Names were not included to ensure confidentiality.

The questionnaire (Supplementary 1) was developed for this study and composed of 23 items, divided into 3 sections. The socio-demographic characteristics section was composed of 6 variables (AgeSexBatchPlace of residenceMarital statusMonthly income) (Table 1). Participants were asked about the way they access the internet through most of the time (smartphones or laptops), and how they connect to the internet (Table 1). In social networking intensity section, a scale of 5 items was used to assess the level of social networking intensity (SNI) of each participant, each item used a 5-point Likert scale (1=Not at all true for me, 2=Slightly true for me, 3=Moderately true for me, 4=Very true for me, and 5=Extremely true for me), SNI score for each individual was calculated by summation of the five items. Scores ranged between 5 and 25 (5 represents low SNI and 25 represents the highest level of SNI). According to Salehan and Negahban, this scale has a good internal consistency, with a Cronbach alpha coefficient of 0.88 [8]. In our study, the Cronbach alpha coefficient for the SNI scale was 0.84 which suggests a good internal consistency and reliability for the scale regarding our sample (Table 2). SNI scores were classified into three grades: low (scores 510), moderate (scores 1119), and high (scores 2025).

In fear of missing out section, a scale of 10 items was used to assess FOMO among the participants, each item used a 5-point Likert scale (1=Not at all true for me, 2=Slightly true for me, 3=Moderately true for me, 4=Very true for me, and 5=Extremely true for me), FOMO score for each participant was calculated by summation of the ten items. Scores ranged between 10 and 50 (10 represents low FOMO and 50 represents the highest level of FOMO). According to Przybylski, Murayama, DeHann, and GladWell (2013), this scale has a good internal consistency, with a Cronbach alpha coefficient of 0.89 [9]. In our study; we had a Cronbarch alpha coefficient of 0.88 which suggests reliable results with good internal consistency for the FOMO scale regarding our sample (Table 3). FOMO scores were classified into three grades: low (scores 1020), moderate (scores 2139) and high (scores 4050).

Statistical Package for Social Science 26 (SPSS-26) software was used for data entry and analysis. Simple descriptive statistics were used to determine the frequencies and percentages of the different variables. Cronbach's alpha coefficient was calculated for the two scales to determine their internal consistency. Pearson correlation coefficient was used to assess the association between SNI & FOMO. Linear regression analysis was used to describe the relation between FOMO and SNI.

Independent t-test and one-way ANOVA were used to examine the associations and differences related to the socio-demographic groups.

See the rest here:
Social networking and fear of missing out (FOMO) among medical ... - BMC Psychology