Factors Associated With Prevalence and Pattern of Social Media Addiction Among Medical Students – A Systematic Review

Factors Associated With Prevalence and Pattern of Social Media Addiction Among Medical Students – A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Factors Associated With Prevalence and Pattern of Social Media Addiction Among Medical Students – A Systematic Review Ayesha Manzoor, Natalie Quinn-Walker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6165651/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : With the advancement of technology, social media use/overuse is becoming a vital part of human life. Due to the high prevalence of social media users, researchers found that there is a risk of developing social media addiction (SMA) among medical students. Some risk factors are associated with SMA, which significantly impacts students' behaviours, habits, and, in fact, their entire lives with adverse consequences. Aim : The primary objective of this review is to investigate the factors associated with the prevalence and pattern of social media addiction among medical students. Methodology : For this systematic review, articles were searched using three databases: PubMed, MEDLINE, and Scopus. The PIO guidelines and PRISMA flow diagram were used to retrieve relevant studies. Ten studies were identified for this review, all cross-sectional. All English language studies were included with a time range of 2010-2022. The quality of studies was assessed using JBI's quality assessment tool. The data was analysed using comparative analysis. Results : A high prevalence of SMA in almost all study participants is associated with varied risk factors. However, there is a significant association between the intense use of social media and mental distress, anxiety, depression and loneliness, which are considered the primary risk factors of SMA in almost all the studies. However, age, gender, personal behaviours and habits such as the habit of chewing khat, alcohol & junk food consumption, low self-esteem, poor sleep quality and use of anti-psychotic drugs were also considered as the risk factors associated with the prevalence and pattern of social media addiction among medical students. Conclusion : Social media overuse linked to psychological problems was more prevalent among social media-addicted students. There is a significant need to make the general public well aware of this problem, and high self-awareness is required to prevent this issue. However, psychological treatments and rehabilitation centres need to be made accessible for the treatment of people with an addiction suffering from this public health issue. Social Media Addiction Medical Students Psychological Social Media Mental Health Figures Figure 1 Introduction 1.1. Social Media Addiction Research on social media addiction has increased dramatically since the 1990s with the development of internet-based technology, especially when more and more cases of addiction have been reported among students, which have been detected by university healthcare professionals ( 1 , 3 ). The term social media addiction is a combination of three different words, which include social media and addiction. The term social refers to society or socialisation. According to Rhee ( 60 ), the term social relates to activities in which people meet, communicate and spend time with other people, which happens during their free time, while media, which is a plural of medium, refers to the communication channels through which we disseminate news, music, movies, education, promotional messages and other public or private content/ data. There are different types of media, such as print media, broadcast media, and internet media ( 60 ). Internet media includes social media through which people communicate, interact and socialise with family, friends, relatives, professional community and others ( 62 ). Hopkins ( 30 ) defined social media as an umbrella term that refers to electronic communication and interaction among people, allowing people to quickly create and share ideas, personal messages, and other content using different forms of media, such as social networking websites and applications. Hence, social media is a collective term that refers to a group of internet-based applications like Facebook, Instagram, Twitter, Skype, WhatsApp, YouTube, Snapchat, TikTok, online gaming, virtual worlds, Blogs, etc. and websites (web 2.0 & 3.0 sites) that allows the creation and exchange of user-generated contents ( 74 ). Hence, social media is a broader term that runs on internet-based applications and websites using various electronic devices such as smartphones, tablets, laptops, and computers. Addiction, by WHO, is defined as dependence, as the continuous use of something for the sake of relief, comfort, or stimulation, which often causes cravings when it is absent (35; 48). There are two significant categories of addiction. The first one is substance addiction, e.g. drug or alcohol addiction. In contrast, the other one is behavioural addiction, such as mobile phone or smartphone addiction, social media addiction, and internet addiction ( 63 ). Hence, from the definition of addiction, Uyaroğlu et al. ( 76 ) defined Social Media Addiction (SMA) as a behavioural addiction which refers to paying excessive attention to social media activities, often to the neglect of all other activities, and uncontrollable use to the extent that it interferes with other vital areas of life including psychological health, interpersonal relationships, emotional consequences, academic performance, and occupation to the detriment of the individual. Research on social media addiction has dramatically increased since the launch of smartphones, different internet-based applications, and the modernisation of information technology (71;41). Social media is an umbrella term consisting of social networking sites and messenger applications; while discussing this broader term, researchers also consider different media for its use, overuse or addiction, such as the internet, smartphones, internet-based applications and websites or social networking sites (SNS). In addition to social media addiction, internet addiction, or smartphone addiction, terminologies like problematic social media use, social media overuse, social media dependency, smartphone addiction/overuse, problematic smartphone use, internet addiction/overuse, pathological internet addiction, internet dependency, internet addiction disorder, social networking sites overuse, online networking sites, are also used in the research area to describe social media addiction. The linkage between social media addiction and overall health is not new; in the late 1990s, clinicians and scholars began claiming that excessive internet use was an increasing problem that should be recognised as an addiction (34; 55). The psychiatrist Ivan Goldberg then introduced the term' Internet addiction disorder' (IAD) in 1995, and the concept of Internet addiction was being taken more seriously by 1996. It was then proposed to be a clinical disorder ( 49 ) because the statistics of social media usage are increasing significantly, and the frequency of SM use/ addiction varies in different geographical locations and different eras of time. Research has shown different statistics in various eras for the prevalence of social media usage ( 12 ). For instance, a study states that in 2017, there were about 2.46 billion, which is approximately two and a half of the world's population, actively using social media, and according to this study, the number of active social media users was expected to increase by 3.196 billion globally by 2021 ( 81 ). However, the world has reached this number by 2018. In 2018, the number of internet users was 4.021 billion, and 3.196 billion people were active users of social networking sites regularly worldwide (7;3). In July 2020, there were over four billion active social media users around the globe, and half of them were Facebook users. In 2021, we have over four billion population using social media regularly ( 2 ), Which means that the greater the exposure to the internet, the greater the addiction rate, and there will be a vast number of people experiencing adverse consequences of social media addiction (64; 43). Masthi et al. ( 47 ) claim that 3.77 billion people are internet users through modern gadgets such as smartphones, tablets and computers. This includes 81% of the developed world population and 41% of the developing world population ( 47 ). Similarly, different countries have different statistics on social media usage; findings of a study in the UK state that 18% of young people are internet addicted ( 78 ), while another study in China states that 12% of males and 5% of females students are internet addicted ( 44 ). Internet addiction is not only restricted to university or college students; a high prevalence of internet addiction has been found in school students. A study in Hong Kong states that 26.7% of high school students are internet addicted (79; 80). According to the Iranian Center of Statistics, the use of social media has tripled over the past three years, and more than 47 million Iranians are using social networks ( 31 ). The time spent on the internet has a great significance in distinguishing between the addicted and non-addicted status of a person. Research has shown that students who spend 8.5 hours per week to 21.2 hours per week are indicated as social media-addicted students, which means that the more significant the amount of time spent online, the greater the symptoms of internet addiction ( 13 ). Studies found that students who are social media addicted were found to use more data packages on monthly expenditure, and they tend to spend more hours on the internet as compared to social media addicts. Goel et al. ( 25 ) state that addicted students spend 38.5 hours per week as compared to non-addicts, who spend only 4.9 hours per week on computers. These students reportedly spent Rs. 300/- monthly on social media. However, the frequency of YouTube use was 100%, followed by Facebook (91.4%) and Twitter (70.4%). The rate of addiction varies according to the criteria used: 47.2% were found to be YouTube addicted, 14.2% were found to be Facebook addicted, and 33.3% were Twitter addicted. However, the rate of addiction to YouTube, Facebook and Twitter was reduced to 13.8%, 6.3%, and 12.8% when work-related activity was taken into account ( 49 ). Furthermore, this institute reports that people between the age group of 25–34 years use SM at higher rates, while the rate is higher between the age group of 18–24 years on a second number. Therefore, university students are an essential group for discussion. Despite this vast number of social media addicts facing the adverse consequences of social media addiction around the globe and in different countries individually, social media addiction (SMA) is not yet considered a disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) or the International Classification of Diseases 11th Revision (ICD-11) ( 29 ). However, many countries have declared SMA as a health concern individually, like the Chinese government declared internet addiction a "public health hazard" ( 42 ). In contrast, the South Korean government declared internet addiction a public health concern (Koo et al., 2011). Similarly, several countries, including China, South Korea, Japan, the United Kingdom, the Netherlands, and the United States, have established various clinics to treat internet addiction ( 8 ). For example, the Centre for Gaming and Internet Addiction is the first and only specialist service on the NHS, established after the World Health Organization (WHO) declared gaming disorder as a mental health disorder. At the same time, psychiatrists and clinical psychologists treat patients (children and adults) aged between 13–25 whose lives are being wrecked by severe or complex behavioural issues associated with gaming, gambling and social media. Similarly, different rehabilitation centres have been established for treating SMA all over the UK, costing NHS billions of pounds (59; 58). 1.2. Causes of social media addiction With the revolutionisation of internet-based technology and the increasing use of smartphones, social media has become an integral part of human life, particularly among students. They use it either for learning activities or entertainment purposes. They also use social media for socialisation; however, the primary reason is to stay connected with family and old friends or make new friends. It has been emergingly used to play a significant role in human lives ( 81 ). Different studies explain the reasons for SMA in terms of various theories and models, claiming that according to dynamic psychological theory, people use social media in response to psychological shocks, emotional deficiencies in childhood or personality traits ( 4 ). However, social control theory states that addiction varies with age, gender, socioeconomic status and geographical location, which means that people/ students of certain groups of societies can be more addicted as compared to others. The biomedical explanation theory claims the presence of certain chemicals (chromosomes or hormones) that trigger the brain activity for addiction. When people log on and start engaging with social media, their brain releases a pleasure hormone named dopamine; the level of this pleasure chemical increases each time they use social media because the brain considers it as a rewarding activity, which triggers the brain to use it more and more to feel the same sense of pleasure again and again. It will cause them to increase their screen time, and they become addicted naturally in a continuous cycle ( 11 ). However, Turel and Qahri-Saremi ( 75 ) provide the reason for SMA in terms of three different models; according to the Cognitive-behavioral model, unfamiliar and awkward situations trigger the use of social media, while lack of presentation skills and low self-esteem increase the use of social media for virtual communication is another reason of SMA according to social skill model. However, people's behaviour towards receiving the positive outcome that is likes, comments, and tagging their photos on social media is another reason for social media use by people, according to a socio-cognitive model, which is the most common form of SMA among students. Constant social media thinking, social media anxiety and unhappiness, and non-self-control behaviours are defined as social media cognitive disorders; it is possible to define behavioural disorders such as not being able to control the duration of social media usage, refusing to socialise when experiencing a problem, and failing to fulfil responsibilities in social, academic, familial and professional relations ( 26 ). Findings of an independent study conducted by Zenebe et al. ( 82 ) show the most frequent reasons for internet use among Wollo University undergraduate students. 93.6% of the respondents use the internet for courses/assignments, 76.6% for reading/posting news, 47.6% for chat rooms and 49.8% for e-mail ( reading, writing). In comparison, 85.6% use social networks (Facebook, etc.), 66.6% use them for getting into relationships online, 44.5% for playing mobile games, 65.7% for downloading music or videos, and 57.8% for watching videos. While a minimum number of students, which is 22.8%, use it for retrieving sexual information. However, AlFaris et al. ( 5 ) claim that 97% of students use social media. The frequency of the most used applications was 87.8% for WhatsApp, 60.8% for YouTube and 51.8% for Twitter, while the frequency of YouTube for learning purposes was 83.5%, 35.5% for WhatsApp and 35.3% for Twitter. The time spent was around 71% visited SM > 4 times/day, and 55% paid − 4 hours/day. The significant reasons for SM use were entertainment (95.8%), staying up-to-date with news (88.3%), and socialising (85.5%) for academic studies (40%). However, the study could not establish any significant association between Grade Point Average (GPA) and the frequency of daily SM use or use during lectures. The fear of missing out (FOMO) can also be a significant reason for frequent social media use, regardless of the time of day, at the expense of other activities (14; 24). Similarly, literature on Turkish university students states that 41.8% read the news, 71.9% accessed information, 52.8% used the internet for entertainment, 44.3% viewed videos or shared photographs, 59.8% used the internet to spend leisure time, 62.3% utilised social networking sites, and 50.3% performed online shopping. It was also determined that 43.4% of the participants used the internet for 4–5 hours daily (Uyaroğlu et al., 2022). Liu et al. ( 45 ) say that social media is designed to make us addicted because while using it, no one can feel its harmful impacts; people think it is a fun activity instead. Further, Liu et al. ( 45 ) explain the psychological dependence of SMA, which states that people use social media when they feel lonely, stressed or unsatisfied with their lives or depressed. 1.3. Impacts of social media addiction Social media has many benefits in our daily lives but also negatively impacts overall health. Students with excessive social media are at risk of developing poor mental health, depression, anxiety, loneliness and low esteem. It can also cause learning difficulties, psychological problems, daily communication difficulties, and long-term impacts on self-presentation skills, intellectual growth, and concentration abilities. Furthermore, Masters ( 49 ) says that the characteristics of social media addiction are similar to those of internet addiction. Excessive mental preoccupation and repetitive thoughts to limit internet use and repetitive failure provoke the use of social media despite its significant impact on daily life. Considering the psychological profile of students, studies revealed that there is a significant correlation between social media addiction and depression, locus of control, loneliness, and low esteemed anxiety ( 52 ), which can be diagnosed as a specific mental disorder, Generalised Anxiety Disorder (GAD), panic disorder, social anxiety disorder or Obsessive Compulsive Disorder (OCD) ( 14 , 15 ). Furthermore, Al-Menayes ( 1 ) states that the level of loneliness and depression was higher among internet addicts. Internet students as compared to non-addicts; it also states that poor mental health is also a consequence of internet addiction among university students in Kuwait. Masthi (2018) reports that the frequency of SMA was found to be 36.9% among both genders of universities, and as a consequence of this high rate of SMA, they suffer from several health consequences, and the most common health problems identified were a strain on eyes, anger and sleep disturbance. Furthermore, according to statistics, the strain frequency on the eyes was recorded at 38.4%, anger at 25.5%, and sleep disturbance at 26.1%. However, the habit of smoking, alcohol and tobacco, consumption of junk food, having ringxiety - a term ringxiety was coined by David Laramie in 2006, who was a doctoral student at the California School of Professional Psychology and who was studying the effect of psychology on behaviour, is a blend of Ring + anxiety, which is a new mental disorder in which a person thinks or feels or hear like their phone is ringing or vibrating when this is not happening in reality ( 65 ) and selfitis ( a new kind of mental disorder the obsessive-compulsive desire to take photos of one's self and post them on social media as a way to make up for the lack of self-esteem and to fill an intimacy gap ( 77 ) were found to be significant risk factors for social media addiction among students. Despite the significant advantages of social media, it has become a serious life threat in today's society and has been proven to be a serious public health hazard. Social networking site usage has significantly increased in the last decade, particularly among students and young people. With the advancement of technology, social networking sites have made our lives easier. Social media sites such as Facebook, Instagram, and Twitter have become integral to human life ( 53 ). Research has established that 2.32 billion people use Facebook regularly, which has increased to about 11% around the globe. Social media is not something traditional, but it provides an opportunity for its users to create, share and upload information according to the account settings, and it also allows them to respond to, share or tag others' content (54; 66). With the increasing use of smartphones, sharing has become a more common and essential part of life. The availability of this sharing feature provides young people with unprecedented access to private information and a ready platform to use this information, knowledge and personal content against others ( 73 ). Although the advantages of social media can never be neglected, the habitually unconscious and uncontrollable use of internet technology can cause danger to security. The most significant negative impact of social networking sites is violent behaviours such as harassment, threats, and insults made through the internet and mobile communication tools. Cyberbullying has become a big problem as a consequence of excessive use of social media. Cyberbullying is the use of information and communication technology to harass and do harm in a conscious, repetitive and hostile way ( 46 ). Excessive use of social media causes aggressive behaviour. Cyberbullying allows people to use social media in such a way that they can create fake profiles and share user-generated content, rumours, and personal and private information of others using fake profiles and identities to annoy, intimidate and bully others. These actions can hurt the mental and behavioural health of people, resulting in social isolation and exclusion. Therefore, cyberbullying, which is increasing day by day all around the globe, with the connection of social media, has become a serious public health concern worldwide. Hence, the excessive or abnormal use of social media has increased the risk of people adopting poor eating and sleeping habits, loss of interest in leisure activities, and impaired social interaction, which means people avoid socialise face-to-face and think that the time spent without Internet or social media is an idle time they had nothing done in that time. They find the most helpful time of their life is the time they spend on mobile phones or social media. It has highly affected the quality of life among students, such as mental instability, mood swings, anxiety, and decreased academic success. It can also cause physical violence against themselves and can make a person drug and alcohol addicted to hide from reality ( 27 ). It has also increased the risk of depression, self-isolation and suicide. Furthermore, many legends, hosts, models, actors or actresses, and scholars have become victims of cyberbullying, which leads them to attempt suicide and death. ( 16 ). However, emotional loneliness is both a cause and a consequence of social media addiction. A recent study conducted by Uyaroğlu et al. ( 76 ) stated that students living with their families have low rates of smartphone or social media addiction; however, students living alone showed higher rates of social media addiction. It is also reported that loneliness is found to be higher in those students who have worse economic status and poor academic performance. Therefore, they are more prone to addiction to SNS, particularly problematic use of Facebook, which is due to social, family or romantic loneliness. Contrary to this, students with good socioeconomic status enjoy social networking sites 5hours per day or more, have reduced emotional loneliness, and perform better in school. It is further reported in another study that 14% of problematic Facebook use was explained by family loneliness and 5% by romantic loneliness ( 17 ). Jaspal and Breakwell ( 32 ) reported that higher social media addiction was associated with worse mental health. It increases the risk of self-isolation; hence, the study established that lonely individuals use the internet more frequently. In a recent survey of Norwegian college and university students, the frequency of insomnia was found to be 34.2% among female students and 22.2% among male students. While poor sleep deteriorates their mental and physical health, poor sleep quality and daytime sleepiness can also impair students' academic performance. Research has established a negative correlation between sleep quality and screen quality ( 29 ). Furthermore, Hjetland et al. ( 29 ) report that using screen-based devices is associated with pre-bedtime arousal, meaning that 76% of university students between the ages of 20–24 use their mobile phones after bed. A large study on over 7000 American university students states that sleep problems are more prevalent among students; only one-third of students sleep for more than 7 hours each night, while the rest fall short of the recommended 7–8 hours of sleep every night ( 67 ). With the above discussion, the significant need for a current systematic review of the prevalence of social media addiction, particularly among medical students, is known in general. Because there is no sufficient evidence to prove social media addiction as a disorder itself, however, previous studies have confirmed its association with different risk factors and health concerns. The frequency and consequences of SMA on students' health have been studied in prior research. Even though there is a considerable number of students using social media or internet-based devices for whatever reason and facing its consequences in almost every country of the world, it is still unclear why SMA has not been recognised as a health disorder according to DSM-5 and ICD ( 29 ), but still a risk factor or a consequence of various physical, psychological, mental and behavioural health issues ( 80 ). However, different countries have declared SMA a public health concern individually ( 36 ) and are making significant government responses to reduce the problem's incidence or rehabilitate the population from SMA ( 59 ). The prevalence of social networking site addiction has also been explored in previous research in different countries of the world. However, some studies have not established a significant prevalence of SMA and its associated factors among medical students. In contrast, other studies remain successful in establishing a substantial prevalence of SMA and its related factors among medical students. This review is to address the information gap. Shaibani ( 68 ) states that SMA may be beneficial for students; however, it is essential to maintain a balance of social networking use by students so it may not affect them negatively. Taha et al. ( 72 ) claim that internet addiction is more prevalent among medical students at the rate of 12.4% associated with stress and psychological issues, while 57.9% are at risk of becoming internet addicted and are at risk of developing serious physical and mental health issues. Although these studies remain successful in showing a significant prevalence of SMA among students, the reason for social media use and the demographic profile of students will remain unclear. All the previous research states a standard limitation of the inability to generalise the results to the whole population due to a small number of the study population or a restricted geographical location. However, the Royal Society of Public Health (RSPH) ( 58 ) states that social media addiction is becoming an integral part of young people all over the UK. Ninety-one per cent of the people aged between 16–24 years use the internet and social networking sites and are more prone to internet or social media addiction, followed by 85% of the people between the age group of 25–34 years. Social media is more addictive as compared to cigarettes and alcohol and is linked to the rates of anxiety, depression, poor sleep, poor dietary habits and low self-esteem among prevalent age groups of social media use ( 58 ). A systematic review conducted by Cheng et al. ( 10 ) ensures that there is sufficient data to show the incidence of social media addiction while establishing the significant incidence rate of SMA among populations of 32 nations; the systematic review is unclear to address the gaps of cultural differences and associated risk factors of SMA among different countries. Furthermore, Cheng et al. ( 10 ) have some limitations, such as an imbalanced gender ratio, limited age range and the inclusion of non-clinical structured protocols for the assessment of social media addicts. The current review is aimed to address the literature gap. Two key research papers were also identified, informing the researcher about the research questions, which are internet addiction and its determinants among medical students and prevalence and factors associated with social networking addiction among Saudi university students: a cross-sectional survey; both were the cross-sectional analytical studies, although these studies were comprehensive but were conducted within a limited geographical location, including specific age group with a narrow range of students. Also, the results of independent studies are not generalised. To conclude, although there are several independent studies conducted at the national level to show the rate of social media addiction among medical students, there are still many gaps about the prevalence of social media addiction and its associated factors among medical students due to limited age group selected in the studies, limited geographical location and insufficient data on the cultural regions of students. For example, a systematic review conducted by Cheng et al. ( 10 ) on the prevalence of social media addiction across 32 nations stated that the age range should be broad in further studies. Kolaib et al. ( 40 ) state that the study was conducted in a single university at one location, affecting the results' generalizability. According to NIH (National Institute of Health) and TRACE (Tennessee Research and Creative Exchange), several theories are applied to the social media addiction factor, such as the Social Identity Model of Deindividuation which effects, the Interpersonal Impact Hypothesis, the Differential Impact Hypothesis, which Uses and Gratifications Theory, Cognitive Behaviour Therapy, and Media Dependency Theory. Therefore, a complete, in-depth and comprehensive systematic review with a broader age range and no restriction to geographical location is required. The results of this study will help public health policymakers develop interventional programs such as regulations on marketing, inform time and money spent on digital media/ games, notify parents of the use of information, and restrict advertisements. However, providing education and awareness through media campaigns and conducting university seminars for the students would help reduce the incidence of the problem. Methodology A systematic review is "a review of the evidence on a formulated question that uses systematic and explicit methods to identify, select and critically appraise relevant primary research, and to extract and analyse data from the studies included in the review ( 20 ). The methods used must be reproducible and transparent. Systematic review is a form in which the scientific method is used to systematically identify, evaluate and synthesise the existing evidence to form an unbiased conclusion concerning a particular review question ( 37 ). Due to the availability of a large and continuously increasing number of research papers and study material, it is hard for decision-makers to study a vast number of primary research papers and make the most appropriate healthcare decision beneficial for the public. Therefore, the current systematic review is written to provide an up-to-date summary of more reliable findings of the existing research knowledge that informs the decisions about the limited use of social media among students ( 19 ). Systematic review methodology states that a systematic review should have the following characteristics: it should be highly organised, have a specific research question with a clear objective of the review, a clear eligibility criteria of the included studies, an in-depth evaluation of the quality of the included studies and a comprehensive analysis and synthesis of the included studies. It must be transparent, unbiased, and have a reproducible approach to conclude. However, conducting a thorough systematic review is challenging and time-consuming, and it has several barriers, which include lack of awareness, lack of evidence or limited access to evidence, and lack of knowledge ( 21 ). 2.1. Rationale of conducting a quantitative systematic review A systematic review can be conducted using three methodologies: qualitative, quantitative, or mixed qualitative and quantitative. Qualitative research focuses on an in-depth understanding of the individual's experiences, opinions and thoughts. In contrast, quantitative research is the methodology which involves the process of collecting and analysing numerical data to describe, predict, or control variables of interest to test the relationship between variables, to make predictions and to generalise the results to a broader population ( 6 ). Furthermore, qualitative data is time-consuming and less able to be generalised. However, quantitative data is more efficient but may miss contextual details. As in this systematic review, data is collected to explore the factors and frequency of social media addiction among medical students; therefore, to suit the purpose of this systematic review, the quantitative approach is the most suitable methodology to collect, analyse, synthesise, and interpret the numerical information. 2.2. Philosophical underpinning of the review question The paradigm is a branch of philosophy considered a conceptual framework that symbolises the researcher's philosophical approach to proposing and conducting his research ( 39 ). There are different aspects of the paradigm. However, it is considered that the positivistic paradigm or positivism focuses on the real world by using quantitative analysis to interfere with the generalisation for recognition ( 33 ). Positivism focuses on the quantitative methodology approach and uses numerical data for statistical analysis; therefore, it represents valid and reliable data (in terms of internal and external validity) ( 61 ). This review aims to make a statistical and quantifiable analysis of the factors associated with the prevalence and pattern of SMA to create a trustworthy conclusion using the positivistic approach. Also, the cross-sectional study design of this review is in alignment with the positivistic approach, as positivist studies adopt the deductive approach, which is best suited to test the association of factors with the prevalence and pattern of SMA among medical students ( 57 ). To conclude, the qualitative approach is subjective and is used to analyse people's feelings and experiences; however, quantitative research is objective and represents the reliability of data to investigate the research question. Therefore, the quantitative methodology, underpinned by the positivistic paradigm approach, best suits this systematic review. 2.3. Review question of systematic review This systematic review is written to critically appraise and formally synthesise the best existing evidence to make a conclusive statement to answer a specific review question. The review question for this study is: what are the factors associated with the prevalence and patterns of social media addiction among medical students? Several frameworks can be used to structure a systematic review question. The most helpful framework is PIO, which is used to develop a focused research question for a quantitative systematic review. Cochranes PIO's components are called Population, Issue, and Outcome. They are shown in Table 1 , which is used to identify the components of evidence for the systematic review of the existing evidence-based data. Furthermore, the PIO helps to formulate a review question more precisely ( 23 ). This is a quantitative review, and the question stated in terms of PIO is shown in Table 1 . Table 1 Review question in terms of the PIO Framework Population (P) Medical students (undergraduate & postgraduate university or medical college students) Issue (I) Social media addiction (SMA) Outcome (O) Risk factors of SMA linked to its prevalence and pattern of use among medical students The PIO framework is used in evidence-based medical studies, can be used to develop a searchable query in public health, and can critically appraise the significance of the literature to be identified (Huang et al., 2006). 2.4. Aims and objectives The primary aim of the current study is to investigate the factors associated with the prevalence/frequency and pattern of social media addiction among medical students. The objectives of this study are: To review the factors associated with the incidence of social media addiction among medical students. To assess the pattern of social media addiction among medical students 2.5. Search strategy A comprehensive literature search was carried out using a range of databases to identify the relevant and primary research papers, as many as possible, on the prevalence of social media addiction among medical students. The databases provide quantitative data on the proposed review question. Usually, it is acknowledged that a minimum of two reviewers are required to write a systematic review to increase trustworthiness and minimise personal errors ( 69 ). However, this systematic review was conducted by a research student to fulfil a master's degree in public health. Therefore, a PRISMA flow diagram was used to maximise the transparency and minimise the biases. Furthermore, the search strategy and implementation were carried out under the guidance of "The Cochrane Handbook for Systematic Reviews of Interventions" ( 38 ). A systematic review search should be conducted in a wide range to increase the likelihood of relevant research papers and reduce the possibility of biases. It is not possible and reliable to look for a single database covering all the information, and it is hard to access all the databases and information to answer a specific review question ( 28 ). Different databases have been developed to identify the type of research; for example, AMED is used for alternative and allied therapy research, CINAHL is used in nursing and allied health research, and PsycINFO is used in psychology, psychiatry and social sciences research. Similarly, PubMed and MEDLINE databases are also searched while undertaking healthcare research. However, the author must search the relevant platform for the chosen topic and review the questions ( 69 ). Electronic databases The search for this systematic review was carried out using three electronic databases, which include MEDLINE, PubMed, and Science Direct. Searching beyond a single database is essential to minimise publication and language bias. The selected databases retrieve data and information in health care and medical searches. Therefore, considering the review question and the scope of this systematic review, these databases are relevant and suitable, as they provide up-to-date information and the latest research papers pertinent to the research question. It is easy to find relevant research papers on electronic databases, as they are regularly updated. Also, the feature to apply filters to get the most advanced and relevant primary research paper has made it more convenient to use. According to Cooper et al. ( 18 ), publication limits should be applied to limit the focus searches and to prevent biases in the available evidence. The lack of access to translation and the need for funding to pay the cost of translation has restricted the author from setting a language limit ( 51 ). Therefore, research papers in English are included only; any paper other than English is not included when the translation is unavailable to save time and cost. Due to the versatility of the database and the massive number of information available, the librarian was consulted to minimise the risk of missing essential papers, along with searching the key resources and adopting the appropriate method to find the relevant and vital evidence available ( 70 ). Available evidence and information to address the specific review question can be searched in different ways, including published or non-published literature. Published literature includes bibliographic databases, academic books, journals, peer-reviewed papers, scholarly articles, and reports. At the same time, the unpublished literature consists of grey literature, which provides for conference proceedings, thesis and dissertations, government documents, news, and magazines. Commercial publishers do not monitor this literature but provide additional information on the evidence available to enhance the study's quality ( 69 ). Furthermore, a manual search (hand search) was also carried out to retrieve all the literature, BCU library resources, hand-searched journals, and research papers studied. Also, Grey literature was searched, including conference papers and grey literature reports. In addition to this, organisational websites were also investigated. In addition, a list of included studies was also searched to retrieve data from the ongoing studies to assess possible inclusion. 2.6. Search key terms, synonyms and search strategy After identifying bibliographic databases, the search terms for the literature search were developed, including the key and relevant information to the review question. Furthermore, Smith et al. ( 69 ) state that the author should pay significant attention to the scope of the search terms. Search terms should be broad enough to capture all the relevant data but also narrow enough to minimise the capture of irrelevant literature, which helps save time and effort spent assessing irrelevant articles. Initially, the search terms and the synonyms, known as free-text words or keywords within the literature, were identified using the PIO tool to find as many relevant research papers as possible, as shown in Table 2 . A comprehensive search strategy comprises keywords, free text words, index terms, or medical subject headings (MeSH). Major bibliographic databases used these terms to describe the components of each published article using a controlled vocabulary. For example, MeSH was used to retrieve the data from PubMed; however, to retrieve information from MEDLINE, two retrieval approaches were used: the first is based on text words in the abstract and titles, while the other is MeSH. Furthermore, different spellings, terminologies, and synonyms were also identified to retrieve more pertinent literature. Truncation such as an asterisk (*) with appropriate Boolean Operators AND, OR, NOT were also used ( 50 ) as shown in Table 3 . A session with an expert librarian was also attended to conduct the searches on the available and relevant literature to reduce personal biases. A reference list of identified reviews was also studied to check that various elements of the search strategy have been considered. Key search terms were developed using the PIO framework. Key searched terms and their synonyms are shown in Table 2 below. Table 2 Key search terms using the PIO tool Population (P) Issue (I) Outcome (O) Medical students (20-34years) Social media addiction/overuse Factors, associated factors, risk factors or determinants Under-grad* students Internet addict* Prevalence of SMA Post-grad* students Social Networking sites (SNS) addiction/overuse Frequency or Incidence of social media addiction Medical/ university students Smartphone addiction/ overuse Rate of use of Social networking sites Medical college students Frequency and pattern of Internet use To investigate the benefits of the PIO tool, the author used identical search terms, a mixture of medical subject headings and keywords, combined using Boolean Operators. Table 3 shows the search terms of each database. Table 3 Searched terms for each database Search terms PIO tool P I O PubMed (((("social media addiction")) AND (medical students) OR (university students)) OR (undergrad*)) OR (postgrad*)) OR (students)))) (((("social media addiction")) OR ("social media addict*")) OR (SMA overuse)) OR ("internet addict*"))) OR ("problematic internet use")) OR ("smartphone addict*")) ((((((((factors) OR (risk factors)) AND (association)) OR (link)) OR (relationship)) AND (frequency of SMA) ) OR (rate of SMA)) OR (incidence of SMA) ) AND (pattern of SMA) MEDLINE TX "social media addiction" AND TX students OR TX "medical students" OR TX "university students" OR TX "postgrad* OR TX undergrad* TX "social media addiction" OR TX "social media overuse" OR TX "problematic internet use" OR TX "smartphone addiction" TX factors of SMA OR TX risk factors of SMA AND TX incidence OR TX prevalence OR TX rate AND TX pattern of SMA Scopus ALL ( "social media addiction" AND "medical students" OR "university students") ALL ( "social media addiction" , "internet addiction" , OR "smartphone addiction" ) ALL ( "factors associated", AND "prevalence", OR "frequency", OR "incidence", OR "occurrence", OR "rate" AND "pattern"). The synonyms of each variable in the same column were combined with the Boolean Operator “OR,” while the variables in different columns but in the same row were combined with “AND” ( 50 ). For example the search terms for PubMed were written as: ((((((((((((((medical students) OR (university students)) OR (undergrad*)) OR (postgrad*)) OR (students)) AND ("social media addiction")) OR ("social media addict*")) OR (SMA overuse)) OR ("internet addict*"))) OR ("problematic internet use")) OR ("smartphone addict*")) AND ((((factors) OR (risk factors)) AND (association)) OR (link)) OR (relationship)) AND (frequency of SMA) ) OR (rate of SMA)) OR (incidence of SMA)) AND (pattern of SMA) and for MEDLINE as: TX "social media addiction" OR TX "social media overuse" OR TX "problematic internet use" OR TX "smartphone addiction" AND TX students OR TX "medical students" OR TX "university students" OR TX "postgrad* OR TX undergrad* AND TX factors of SMA OR TX risk factors of SMA AND TX incidence OR TX prevalence OR TX rate AND TX pattern of SMA, while for Scopus as: ALL ( "social media addiction", OR "internet addiction", OR "smartphone addiction" AND "medical students" OR "university students", AND "factors associated", OR "prevalence", OR "frequency", OR "incidence", OR "occurrence", OR "rate" AND "pattern"). The author determined the relevance of the included studies by screening the title and the abstracts. Then, the identified papers were thoroughly studied, and the duplicates were removed ( 69 ). 2.7. Selection of studies The selection procedure for included studies was carried out in different steps. The results were synthesised by the studies that met the inclusion criteria (Smith et al., 2011). The selection process is as follows: Identification: First, studies relevant to the research question/key terms will be identified using the search strategy. Screening: Screening was done in two phases. First, irrelevant studies were filtered based on abstract and title. The results from electronic search, hand search, and grey literature were combined to remove duplication. The second phase was screening the full text of selected/ scanned studies. Also, a study was excluded if the full text could not be found. Included: All studies that meet the criteria will be included. 2.7.1. Types of participants (P) Medical students between 18–34 years are the participants of this study. As literature states that, the people between the age group of 25–34 years use SM at higher rates primarily while the rate is higher between the age group of 18–24 years on a second number (Turkish Statistical Institute, 2021; 76). Therefore, the current review is to investigate the prevalence of social media addiction among medical students between 18–24 years and the 25–34 years, due to high prevalence of social media use among this age group (Turkish Statistical Institute, 2021). Children and adults aged between 13–25 years are a vulnerable population to social media addiction, gaming and gambling (59; 58). However, as this study's primary population is medical students, children below the age of 18 were excluded. However, older adults over 34 years were excluded due to lack of information and literature based evidence. 2.7.2. Issue (I) Due to high prevalence of SMA among students and vast number of studies conducted on students, in almost every part of the world, SMA is declared a serious public health issue, however, the DSM-5 and ICD has not declared it a universal mental health disorder (29; Li, et. al., 2019). In order to address the literature gap this systematic review will be written on the Social Media Addiction (SMA), among medical students. 2.7.3. Types of outcomes (O) . The primary outcome to be studied in this review was the factors associated with the frequency and pattern of SMA among medical students. All those studies which assessed the risk factors linked to the prevalence and pattern of SMA among students were included. However, the prevalence, pattern of social media use and mental health issues like depression, anxiety, loneliness, low self-esteem, reduced academic performance, poor sleep and dietary patterns associated with SMA among students were also considered. 2.7.4. Types of studies There are two types of cross-sectional studies: descriptive and analytical ( 6 ). Descriptive cross-sectional studies are used to analyse the prevalence of health outcomes in a specific population. However, in analytical cross-sectional studies, data on the prevalence of health outcomes and the exposure is collected and observed to determine the difference between the exposed and unexposed population. Therefore, depending upon the aim of the current systematic review, in order to analyze the findings of studies on the prevalence and pattern of SMA among medical students, cross-sectional studies were included, both analytical and descriptive. 2.7.5. Regions, languages and published dates Depending upon the broad scope of the review question, the research papers were not restricted to the specific geographical location. All those papers relevant to the review questions were included from all over the world. However, non-English papers were excluded as it is impossible to get the translation of all those studies published in languages other than English due to limited time and to save the cost ( 51 ). In order to avoid selection bias, no publication date was included; however, in order to avoid outdated studies, studies before 2010 were excluded Cooper et al., ( 18 ). Further information on the inclusion and exclusion criteria of the selected studies can be found in Table 4 . Table 4 Inclusion and Exclusion Criteria PIO Framework Inclusion Criteria Exclusion criteria Type of Population (P) All students including under & post-graduate students of both genders of universities and medical colleges - aged between 20–34 years All students of schools and colleges and outside of the selected age group. Type of Issue (I) All students having active internet connection, social media accounts or social networking applications and internet based devices such as smartphones, tablets, laptops and computers etc. Students who do not have internet access, social media accounts and smartphones or internet-based devices Outcome measures (O) All studies investigating the factors associated with the prevalence and pattern of SMA among medical students All studies, not investigating the factors associated with the prevalence and pattern of SMA among medical students Type of studies All cross-sectional analytical quantitative studies will be included. All quantitative, RCTs, quasi- experimental etc. studies except cross-sectional analytical studies Geographical location All conducted on social media, internet, social networking sites addiction among medical students with no specific geographical location No restriction Language All studies published in English language will be included. All those studies published in languages other than English will be excluded. Year of publication Studies from 2015–2022 will be included. Studies before 2015 will be excluded. 2.8. Quality assessment selected studies The quality and strength of the information presented in the reviews influence the conclusions made in the systematic reviews, as these are aimed at providing reliable information to decision-makers. Therefore, the strength and ability of reviews to provide reliable information depends on the inclusion of reviews which meet the minimum standard of quality ( 69 ). The quality of published papers varies widely. Critical appraisal and analysis of studies is a significant process to determine the methodological quality of the studies and to evaluate the extent to which studies have explained the possible bias in their design, conduct and analysis. It is used to determine the trustworthiness and credibility of studies and their outcomes. ). A range of quality appraisal tools can be used to assess the quality of studies in systematic reviews, depending on the suitability of the review question and the type of studies included. Different quality assessment tools can be used to assess different studies. For example, the Critical Appraisal Skills Programme (CASP) checklist, Assessing the Methodology Quality of Systematic Reviews (AMSTAR), Scottish Intercollegiate Guidelines Network (SIGN), LEGEND Evidence Evaluation Tools, and Joanna Briggs Institute (JBI) (Critical Appraisal Checklist for Systematic Reviews and Research Synthesis can be used. These tools have different questions/checklists used to assess other parts of included studies ( 69 ). As the included studies were mostly cross-sectional, the quality of studies will be evaluated with the help of the Joanna Briggs Institute (JBI) appraisal tool in this review. The JBI appraisal tool is a worldwide collective supportive evidence-based exercise widely used in allied health fields. It is commonly used in all quantitative study assessment procedures; it is quick and straightforward to use and contains eight questions. It is a qualitative checklist and does not contain any scoring system for evidence; however, it effectively determines the validity of studies comprehensively ( 56 ). High-quality studies that met the inclusion criteria were included in this review. 2.9. Method of data collection Data from relevant studies was extracted using Microsoft Excel Sheet in three different ways. Firstly, data was extracted on the summary of characteristics of included studies, which provided information about the author, year of study, study design, study population, study settings and countries, primary aim and main findings of the study. This data was collected on an MS Excel spreadsheet. Secondly, data on baseline characteristics of the study population was collected on another MS Excel sheet, which included information about the author and years of study, sample size of the study population, socio-demographic characteristics of participants including age in years, gender, duration of SM use, and health indicators or risk factors of SMA. Finally, for narrative synthesis, data was analyzed and collected on the author and year of study, arms of study, models used to analyze data, outcome measures or results on the frequency of social media use, and its associated risk factors. 2.10. Method of data analysis As, the outcome measures of each study varied therefore the author was unable to perform meta-analysis, however, narrative synthesis was conducted. Each study was describes with the help of comparative quantitative analysis and results were synthesized accordingly. Results The search was conducted according to the principles of Systematic Reviews. The searched databases were: PubMed (2010–2022), MEDLINE (2010–2022), and Science Direct (2010–2022). The key terms searched were social media addiction; internet addiction; smartphone addiction; problematic social media, internet, or smartphone use/overuse; prevalence and students or medical students. Terms must include: social media addiction with prevalence among students. However, terms may include: purpose of social media, internet or gadgets used, duration of internet use, outcome measures or results on the frequency of social media use, and students’ health in response to the frequency of internet use. Using these key terms, researchers found total 86 studies from PubMed, 158 studies from MEDLINE and 51 studies from Science Direct. Among these, researchers removed 81 duplicates. After removing duplicates, 214 studies were screened on the basis of abstract and tittle. After this, researcher separated the relevant and irrelevant data after full text screening, out of 63 articles 53 papers were excluded. Finally, 10 relevant and open access studies were extracted/shortlisted for synthesis, from the searched data which is shown in the form of PRISMA flow diagram, or a flow chart is extensively used now a days by the reviewers, in order to improve the quality of systematic review. PRISMA consists of 4-phase flow diagram containing a checklist, which is used in this review to elaborate the study selection procedure (Fig. 1 ). 3.1. Summary of characteristics of included studies The included studies were cross-sectional, conducted in different parts of the globe, which includes three studies from different states of Saudi Arabia, three studies from different states of India, and one from each state of Iran, Turkey, Ethiopia, and China. A summary of characteristics of included studies shown in Table 5 . Table 5 Summary of characteristics of included studies Study ID Study Design Location/ Setting Duration of study Study Population Primary Aim Main Findings Barman et al., (2018) Cross-Sectional Kolkata, West Bengal June–August 2017 Medical College, undergraduate students To determine the pattern of SNS use, the prevalence of anxiety and depression, and the association between SNS and anxiety and depression. There was significant prevalence of SNS use, which was significantly associated with anxiety and depression. Dharmadhikari et al., (2019) Cross-Sectional Western Maharashtra, India November 2016 - January 2017 Government Medical College students To determine the rate of smartphone addiciton (SA) and its corelation with sleep quality and stress. Findings revealed that there is a high prevalence of SA among students which is significantly associated with poor sleep quality and stress. Kolaib et al., (2020) Cross-Sectional Madinah, KSA May, 2019 Taibah University Students To determine the prevalence of IA and the factors associated with it's addiction. Results found high rate of IA and the time spent more than 10 h/d was the primary risk factor of IA. Masthi et. al., (2018) Cross-Sectional Urban Bengaluru city, Karnataka, India July - December 2016 Government and Private Pre-University (PU) college students To investigate the preevalence of SMA, to determine the health issues related to SMA and to investigate the risk factors associated with SMA. Siginificant prevalence of IA was found with health problems like eye strains, anger and sleep disturbances. However, smoking, alcohol, and tobacco, junk food concumption, ringxiety and selfitis were the significant risk factors for SMA. Rahiminia et al., (2021) Cross-Sectional Tehran, Iran. 23rd Aug, 2020–29th Oct, 2020 Shahid Beheshti University of Medical Sciences students To measure the prevalence of IA and its related factors. Results revealed high prevalence of IA, while age and nerve medicine use were considered the risk factors of IA, as there is a significant association between age and IA, and nerve medicine used Shaibani (2020) Cross-Sectional Saudi Arabia April, 2019 Taif University students To assess the prevalence of SNA and its association with demographic variables. The duration of SM use, the frequency of SM use during lectures and students' perception of benefits of SM use were the indicators of SMA. Taha et al., (2019) Ccross-Sectional Buraydah, Saudi Arabia December 2017 and April 2018 Qasim University Students To assess te prevalence of IA and its association with gender, academic performance and health. Found very high prevalence of IA, associated with pooer sleep-pattern and psychological well-being, affecting academic performance of students. Females were more vulnerable to IA. Uyaroglu et al., (2022) Cross-Sectional Central Anatolian, Turkey 15th January − 30th March, 2021 Students of Health Services School - Foundational University To assess the relationship between SMA and social and emotional loneliness. Findings revealed a possitive and significant associateion between SMA and loneliness, hence, loneliness was a significant risk factors of SMA. Zenebe et al., (2021) Cross-Sectional Northeastern Ethiopia April 10 - May 10, 2019 Wollo University students, Dessie campus To assess the prevalence and associated factors associated with IA. Findings revealed that there was a high prevalence of IA and the associated factors were spending more time, having mental distress, online gaming, khat chewing, and alcohol use. Zhao et al., (2022) Cross-Sectional China April - June 2021 Medical College students of a state university (with non-medical history) To investigate whether the demographic factors (age, gender), impulsivity, self-esteem, emotions, and attentional bias were risk factors associated with SMA. Findings revealed that impulsivity, low levels of self-esteem, anxiety, social anxiety, and ANI were risk factors for SMA. 3.2. Baseline characteristics of study participants Most of the studies have shown a link between SMA and demographic variables of students, which includes age, gender, duration of SM use, sleep pattern and individual habit of smoking, tobacco, alcohol, and junk food consumption. Also, most of the studies reported association in groups depending upon age or gender or other variables; however, other studies reported as a whole. Therefore, data on demographic characteristics of participants was collected, as shown below in Table 6 . Table 6 Baseline characteristics of included Participants. Study ID Age (Years) sample size (n) Gender n (%) sociodemographic Characteristics Range Mean (SD) enrolled in study completed the study Male Female Duration of use h/d (%) Pattern of use Mean (SD) or n% Health indicators/ risk factors of SMA (%) Mean (SD) Barman et al., (2018) 21–23 21.6 (± 1.8) 189 200 − (51.0) − (49.0) always open (29.0) late night & early morning 18% depression 24 anxiety 68.5 Dharmadhikari et al., (2019) 17–27 20.23 (± 1.63) 240 195 96(49.23) 99 (50.77) No data to be reported right before sleep 87 (98.8) sleep disturbance 46.67% 6.37 (± 4.47) stress 17.70 (± 6.14) Kolaib et al., (2020) 19–26 22.1 (± 1.7) 426 426 154(36.2) 272(63.8) 2–7 (40.8) No data to be reported No data to be reported No data to be reported No data to be reported Masthi et. al., (2018) 18–25 21.2 (± 1.6) 1870 1870 921(66.4) 468 (33.6) 14 h/w No data to be reported No data to be reported junk food 87.50% Tobacco, alcohol, smoking 10.1% Rahiminia et al., (2021) 20 and older 22.81 (± 4.66) 1000 1000 342(34.2) 658(65.8) No data to be reported No data to be reported No data to be reported Antipsy-chotics 2.3( 23 ) Shaibani (2020 21–30 25.48 ± 3.39 996 697 193 (27.7) 504 (72.3) ≤ 10 ( 75 ) Lectures 2.3 (1.0) No data to be reported To forget problems 2.95 (1.31) Percieved advantages 3.31 (0.75) Taha et al., (2019) 18–26 No data to be reported 216 209 121(57.9) 88(42.1) longer than intended late night use 70.80 Depression 59.70% loss of sleep 70.80% Uyaroglu et al., (2022) No data to be reported 30.68 ± 11.45 817 555 84(15.1) 471(84.9) more than 5h 44.34 ± 14.15 No data to be reported No data to be reported social loneliness 12.21 ± 6.49 emotional loneliness 30.68 ± 11.45 Zenebe et al., (2021) 18–30 21.4 (± 1.8) 603 548 291(53.1) 257(46.9) 5 (91.4) online gaming 44.5 chewing khat 104( 19 ) mental distress 106(19.3) alcohol 139(25.4) Zhao et al., (2022) 16–23 19.68 (± 1.07) 532 520 243( 47 ) 277( 53 ) No data to be reported No data to be reported No data to be reported risk factors (impulsivity, low self-esteem, anxiety, social anxiety and ANI 38% 3.3. Quality assessment of included studies Quality assessment of included papers was done using JBI appraisal checklist consisting of 8 simple questions. The list of questions shown in Appendix 1. However, the list of answers determining the quality of each papers can be seen in Table 7 . Table 7 Critical appraisal checklist Study ID Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Barman et al., (2018) Yes Yes Yes Yes No No Yes Yes Dharmadhikari et al., (2019) Yes Yes Yes Yes No No Yes Yes Kolaib et al., (2020) Yes Yes Yes Yes Unclear Unclear Yes Yes Masthi et. al., (2018) Yes Yes Yes Yes Unclear Unclear Yes Yes Rahiminia et al., (2021) Yes Yes Yes Yes No No Yes Yes Shaibani (2020) Yes Yes Yes Yes No No Yes Yes Taha et al., (2019) Yes Yes Yes Yes Unclear Unclear Yes Yes Uyaroglu et al., (2022) Yes Yes Yes Yes No No Yes Yes Zenebe et al., (2021) Yes Yes Yes Yes Unclear Unclear Yes Yes Zhao et al., (2022) Yes Yes Yes Yes No No Yes Yes 3.5. Narrative synthesis This study included 10 primary research papers with total population of 6376. Results of these studies identified the risk factors associated with prevalence and pattern of social media addiction among medical students. Summary of results is shown below in Table 8 . Table 8 Summary of Results for Narrative Synthesis Study ID Study Arms Models/ Theories/ Tests Measures Results Barman et al., (2018) SNS Self-Structured Questionnaire Frequency & Percentages 90% Anxiety State Trait Anxiety Inventory Scale (STAI-S) Frequency & Percentages 24% Depression Becks Depression Inventory (BDI) 68.5% Association of SNS & BDI Mann–Whitney-U (MW-U) test Mean and Ranges (MW-U = 3636.0; P = 0.001) Association of SNS & STAI-S (MW-U = 3785.0; P = 0.004) Dharmadhikari et al., (2019) Smartphone addiciton Smartphone Addiction Scale-Short Version (SAS-SV) Mean and Standard Deviation (SD) 31.59 (± 9.89) Stress Cohen’s Perceived Stress Scale (PSS-10) Mean and Ranges 17.70 (± 6.14) Sleep quality Pittsburgh Sleep Quality Index (PSQI) 6.37 (± 4.47). Corelation of SA and PSQI Multiple Regression Model and Pearson’s correlation test Regression and Paerson's coefficient (r = 0.31, P < 0.001) corelation of SA and PSS (r = 0.40, P < 0.001) Kolaib et al., (2020) Internet Addiction Internet Addiction Test (IAT) Mean (SD) and Ranges 51.2 (± 16.3) 20 to 100 Association between IAT and internet use ANOVA test & T-test - (P < 0.001) Masthi et. al., (2018) SMA IAT Frequency & Percentage 27.40% Association between SMA and individual variables Univariate Logistic Regression Adjusted odds ratio (95%CI) SMA & Physical Symptoms (2.21 [1.77–2.76], P < 0.0001) SMA & Psychological changes (1.96 [1.57–2.44], P < 0.0001) SMA & Behavioral changes (2.63 [2.06–3.35], P < 0.0001) Rahiminia et al., (2021) SNA IAT Frequency & Percentage 62 (44%) Association between SNA, age and nerve medicine Logistic Regression with Stata software version 14 - SNA and age (p = 0.001) SNA and using nerve medicine (p = 0.0001) Shaibani (2020) SNA Self-Structured Questionnaire and Bergen Facebook Addiction Scale (BFAS) Mean (SD), Frequency & Percentages 50.83 ± 13.00 70% (254) Association between SMA and factors associated Pearson’s correlation test Mean Frequency Rating SMA and perceived advantages of SM (p < .010) Perceived SMA and hours of SMA use (p < .010). Taha et al., (2019) Internet Addiction IAT Frequency and percentage addicts 12.4% potential addicts 57.9% Relationship between Internet use and gender. Chi-Square Test Mean Females were more vulnerable to IA ( w = 0.006) Uyaroglu et al., (2022) SMA Social Media Addiction Scale Mean and Standard Deviation (SD) 61.29 ± 17.53 Social and Emotional Loneliness Social and Emotional Loneliness Scale for Adults (SELSA-S) 42.89 ± 15.17 Association between SMA and Social & Emotional Loneliness Spearman correlation analysis Corelation coefficient (r = 0.196 p = 0.000) (p < 0.05) Zenebe et al., (2021) IA Young’s Internet Addiction Test (YIAT) Frequency & Percentage 466(85) Association between IA and Mental Distress Binary Logistic Regression method Adjusted odds ratio, 95% CI (AOR = 2.69, 95% CI 1.02–7.06) Association between IA and duration of use (AOR = 10.13, 95% CI 1.33–77.00) Association of IA to Online gaming (AOR = 2.40, 95% CI 1.38–4.18) Association of IA to chewing khat and Alcohol (AOR = 3.34, 95% CI 1.14–9.83) and (AOR = 2.32, 95% CI 1.09–4.92) Zhao et al., (2022) Total Link between IA and its Associated factors Hierarchical Regression Analysis Frequency & Percentages 38% Impulsivity (Brief Barratt Impulsivity Scale) (β = 0.34, t = 8.50, p < 0.001) (Self-Esteem (Rosenberg Self-Esteem Scale) (β = −0.20, t = −4.38, p < 0.001) Anxiety (Self-Rating Anxiety Scale & Interaction Anxiety Scale) (β = 0.24, t = 4.43, p < 0.001), social anxiety (β = 0.25, t = 5.79, p < 0.001) Attention to Positive and Negative Inventory (β = 0.31, t = 8.01, p < 0.001) Gender (β = −0.21, t = − 4.88, p < 0.001) 3.6. Interpretation of outcomes More than 90% of the students used more than one SNS, of which 97.9% used WhatsApp, 91.4% used Facebook, and 30.5% used Instagram ( 9 ). While 59.0% used SNS for communication with friends and families, 43.1% used it for entertainment and 31.4% for academic and professional activities. Furthermore, the study reported that 29.0% of the students stayed active on SNS the whole day, while 80% accessed SNS for at least 4 hours or more daily. Also, 18.0% of the students woke up early in the morning and slept late at night to spend more time on the Internet, while 23% could not spend a day without the Internet. Moreover, the study significantly revealed that 24% had depression, out of which 4.0% had severe depression and 68.5% had a state of anxiety, out of which 59.0% had moderate, while 9.5% had severe anxiety. There was a significant association between the time spent on SNS and depression and anxietyBDI scores (MW-U = 3636.0; P = 0.001) and STAI-S scores (MW-U = 3785.0; P = 0.004) were higher among those who wake up early in the morning and sleep late at night to spend more time on SNS. Dharmadhikari et al. ( 22 ) found that 90 (46.15%) students were smartphone-addicted, while 105(53.18% ) students were non-addicts. Of 195 students, 45.45% were females, and 47.87% were males, with almost an equal gender ratio. Furthermore, 33.85% ( 66 ) of the students used WhatsApp, 12.31% used Instagram, and 5.13% used Facebook. The rate of smartphone use by the students for text messaging was 57.14%, and for internet browsing, it was 10.86%. However, 87(98.86%) students use smartphones right before sleeping. Furthermore, the study revealed that the factors associated with smartphone addiction were stress and poor sleep patterns; the average rate of stress was 17.70 (± 6.14) as per PSS, while the average score of impaired sleep was 6.37 (± 4.47) as per PSQI scores, which means that 46.67% (91) students had impaired sleep. The study further investigated the weak positive linear correlation between the SAS-SV and PSQI scores ( r = 0.31, P < 0.001) and a moderate positive linear correlation between the SAS-SV and PSS-10 scores ( r = 0.40, P < 0.001) using the Pearson's correlation test. Kolaib et al. ( 40 ) found that 6% of the participants were internet addicts while 52% were non-addicts or average users, and 42% had occasional problems. The mean (SD) ITA score was 51.2 (16.3), ranged 20 to 100. The pattern of internet use was 5–7 hours/ day in 40.8% of respondents. Moreover, 88.5% use it for social networking and 58.7% for downloading media files, while 67.6% had a history of Internet use for more than 8 years. However, the rate of IA was lower among those with a high GPA 46.9 ± 15.6 as compared to those with a low GPA (52.0 ± 16.1) and (52.1 ± 17.5), ( P = 0.004). Also, IA scores were higher among 71.6% of those who had internet access at college ( P = 0.033), 95.8% of those who had mobile internet access ( P = 0.003), and 97.2% of those who had internet access at home ( P = 0.043) and among those who used the Internet for more than 10 hours per day ( P < 0.001). However, easy access to the Internet and time spent on the Internet were the risk factors of IA among students. Masthi et al. ( 47 ) conducted a cross-sectional study on 1870 students of government and private universities, and out of 1870 students, 74.2% (1389) were Social Media Users, while 25.8% (481) were non-users, and 66.4% (921) were males while 33.6% (468) were females among the SM users. The findings of the study revealed that overall, 27.4% of the students were social media addicts, out of which 24.0% in government and 30.8% in private colleges were SM addicts. Furthermore, 38.9% of the people with an addiction used Facebook, 31% were addicted to internet gaming, and 41.2% used WhatsApp. However, 87.5% (1216) consumed junk food, 10.1% of the people with addiction were habitual to smoking, alcohol, and chewable tobacco, 339 (66%) of social media addicts had Ringxiety, and 38.7% had selfies. Therefore, the habit of smoking, alcohol, and tobacco, consumption of junk food, and having Ringxiety and selfies were considered to be significant risk factors for social media addiction among males. Therefore, these habits made males more prone to social media addiction. The results on considerable health issues identified stated that 38.4% had a strain on the eyes, 30.7% had neck pain, 25.% suffered from anger, and 26.1% had sleep disturbances. Rahiminia et al. ( 61 ) investigated that 44% (462) students were addicted to social networks, out of which 90.04% (449) were slightly while 9.96% ( 13 ) were severely addicted. Furthermore, the students' mean (SD) age was 22.81 (4.66) years. The study further revealed that 11.8% (118) students were smokers, 2.3% ( 23 ) used to take antipsychotics, out of which 47.81% took benzodiazepine, 30.43% selective serotonin reuptake inhibitor (SSRI), 13.04% tricyclic ani depressant (TCA), 4.35% serotonin-norepinephrine reuptake inhibitor (SNRI: Duloxetine), and 4.35% took sleeping pills. Hence, the findings revealed that there is a significant association between age (p = 0.001), use of nerve medicine (p = 0.0001) and social network' addiction; hence, age and use of nerve medicines were the primary risk factors of SMA. However, no significant relationship was established between SNA and gender (sig = 0.47), marital status (sig = 0.06), level of education (sig > 0.05) and smoking (sig = 0.18). Shaibani ( 68 ) found that the mean social networking addiction (SNA) was 50.83 ± 13.00, which was at a moderate level among 70% (254) students. The mean SNA was higher among males (52.65 ± 11.50) as compared to female students (49.35 ± 13.96), out of which 72.3% used social networks during lectures 2.3 (1.0) mean (SD), and they spent 7.4 (5.5) mean (SD) hours per day on average, while 75% used social media for ≤ 10 hours per days. However, the top factors associated with SMA among students in terms of perceived advantages were "using social media to help them in their studies" (mean rating = 3.75/5, RII = 75.1%), feeling more informed than others because they used social media (mean rating = 3.69/5, RII = 73.7%) "perception of solving problems with the help of social media (mean rating = 3.51/5, RII = 70.2%) "an urge to use social media more and more (mean frequency rating = 3.2/5, RII = 64.0%) and "using social media to forget about their problems" (mean frequency rating = 2.95/5, RII = 59.1%), were the main risk factors of SMA among students. However, a significant association was established between SMA and perceived advantages of SM use (p < .010) and SMA and duration of use (p < .010), indicating essential risk factors of SMA. Taha et al. ( 72 ) found that according to IAT, the rate of IA was 12.4%, while 57.9% had the potential risk of addiction among students. Females were more frequent Internet users than males ( w = 0.006). Moreover, the % of students frequently staying longer than intended was 82.3%, while the impact on academic performance was reported by 62.2%. Out of 96.8% of the response rate, 70.8% lost sleep due to late-night Internet use, 58.9% felt depressed, moody, or nervous when they were offline, and 80.4% lived with their families. Due to frequent internet use, health issues like headaches, backache, weight gain, neck pain and other psychological were also reported by respondents. A significant difference was established between the total IAT score and neck pain ( P = 0.03) and a state of sleeplessness because of staying online ( P = 0.006). In contrast, the relationship between BMI and IAT scores was not established. However, IAT scores and weight loss/ gain established a significant association ( P = 0.002). Hence, the duration of Internet use and sleeplessness among students were the primary risk factors for IA. Uyaroglu et al. ( 76 ) found that the mean rate of SMA among Turkish students was 61.29 ± 17.53 mean (SD). 43.4% of the students used the Internet for 4–5 hours daily. The mean (SD) Social loneliness score was 12.21 ± 6.49, while the mean (SD) emotional loneliness score was 30.68 ± 11.45. However, the Total loneliness score was 42.89 ± 15.17 mean (SD), respectively. Hence, a significant positive relationship between the high prevalence of social media addiction and emotional and social loneliness, and loneliness was found to be a primary risk factor for social media addiction among students ( r = 0.196 p = 0.000) ( p < 0.05). Zenebe et al. ( 82 ) revealed that the total prevalence of internet addiction (IA) was 85% (n = 466); however, the frequency was distributed in three categories, mild, moderate and severe. Results showed that out of 85% (n = 466) IA students, the rate of mild, moderate and severe IA was 55.6% (305), 27.9% (153) and 1.5% ( 8 ) respectively. In comparison, the remaining 15% ( 82 ) were non-IA. The rate of IA among students who use the Internet frequently and permanently was 92.2%, which is higher than those who do not log in often, which is 83.1%. Furthermore, the findings on the pattern of use revealed that 93.6% of the respondents use the Internet for courses/assignments, 76.6% for reading/posting news, 47.6% for chat rooms and 49.8% for e-mail (reading, writing). In comparison, 85.6% use social networks (Facebook, etc.), 66.6% use them for getting into relationships online, 44.5% for playing mobile games, 65.7% for downloading music or videos, and 57.8% for watching videos. At the same time, 22.8% use it for retrieving sexual information. However, more time spent on the Internet (AOR = 10.13, 95% CI 1.33–77.00), mental distress (AOR = 2.69, 95% CI 1.02–7.06), online gaming (AOR = 2.40, 95% CI 1.38–4.18), khat chewing (AOR = 3.34, 95% CI 1.14–9.83) and alcohol use (AOR = 2.32, 95% CI 1.09–4.92) were associated with internet addiction. However, no association was established between IA and smoking. Zhao et al. ( 81 ) investigated the rate of significant risk factors associated with SMA at 38%. The study comprised 53% (277) females and 27% (243) males. The findings of the study clearly stated that the females were more prone to SMA (β = −0.21, t = − 4.88, p < 0.001) compared to males. Furthermore, the study investigated the rate of each risk factor associated with SMA with the help of regression analysis. The identified risk factors were impulsivity (β = 0.34, t = 8.50, p < 0.001), self-esteem (β = −0.20, t = − 4.38, p < 0.001), anxiety (β = 0.24, t = 4.43, p < 0.001), social anxiety (β = 0.25, t = 5.79, p < 0.001), and negative attentional biases (β = 0.31, t = 8.01, p < 0.001), which showed a significant association with SM use. The greater the rate of depression, anxiety, and low self-esteem, the greater will be the rate of SMA. Hence, impulsivity, low levels of self-esteem, anxiety, social anxiety, and attention to negative information (ANI) were found to be risk factors for SMA; however, no link was found between IA and depression and loneliness, while gender and IA were related as females were found to be more addicted to the internet (β = −0.21, t = − 4.88, p < 0.001). Discussion More than 90% of the students used more than one SNS, of which 97.9% used WhatsApp, 91.4% used Facebook, and 30.5% used Instagram ( 9 ). While 59.0% used SNS for communication with friends and families, 43.1% used it for entertainment and 31.4% for academic and professional activities. Furthermore, the study reported that 29.0% of the students stayed active on SNS the whole day, while 80% accessed SNS for at least 4 hours or more daily. Also, 18.0% of the students woke up early in the morning and slept late at night to spend more time on the internet, while 23% could not spend a day without the internet. Moreover, the study significantly revealed that 24% had depression, out of which 4.0% had severe depression and 68.5% had a state of anxiety, out of which 59.0% had moderate, while 9.5% had severe anxiety. There was a significant association between the time spent on SNS and depression and anxietyBDI scores (MW-U = 3636.0; P = 0.001) and STAI-S scores (MW-U = 3785.0; P = 0.004) were higher among those who wake up early in the morning and sleep late at night to spend more time on SNS. Dharmadhikari et al. ( 22 ) found that 90 (46.15%) students were smartphone-addicted, while 105(53.18% ) students were non-addicts. Of 195 students, 45.45% were females, and 47.87% were males, with almost an equal gender ratio. Furthermore, 33.85% ( 66 ) of the students used WhatsApp, 12.31% used Instagram, and 5.13% used Facebook. The rate of smartphone use by the students for text messaging was 57.14%, and for internet browsing, it was 10.86%. However, 87(98.86%) students use smartphones right before sleeping. Furthermore, the study revealed that the factors associated with smartphone addiction were stress and poor sleep patterns; the average rate of stress was 17.70 (± 6.14) as per PSS, while the average score of impaired sleep was 6.37 (± 4.47) as per PSQI scores, which means that 46.67% (91) students had impaired sleep. The study further investigated the weak positive linear correlation between the SAS-SV and PSQI scores ( r = 0.31, P < 0.001) and a moderate positive linear correlation between the SAS-SV and PSS-10 scores ( r = 0.40, P < 0.001) using the Pearson's correlation test. Kolaib et al. ( 40 ) found that 6% of the participants were internet addicts while 52% were non-addicts or average users, and 42% had occasional problems. The mean (SD) ITA score was 51.2 (16.3), ranged 20 to 100. The pattern of internet use was 5–7 hours/ day in 40.8% of respondents. Moreover, 88.5% use it for social networking and 58.7% for downloading media files, while 67.6% had a history of Internet use for more than 8 years. However, the rate of IA was lower among those with a high GPA 46.9 ± 15.6 as compared to those with a low GPA (52.0 ± 16.1) and (52.1 ± 17.5), ( P = 0.004). Also, IA scores were higher among 71.6% of those who had internet access at college ( P = 0.033), 95.8% of those who had mobile internet access ( P = 0.003), and 97.2% of those who had internet access at home ( P = 0.043) and among those who used the internet for more than 10 hours per day ( P < 0.001). However, easy access to the internet and time spent on the internet were the risk factors of IA among students. Masthi et al. ( 47 ) conducted a cross-sectional study on 1870 students of government and private universities, and out of 1870 students, 74.2% (1389) were Social Media Users, while 25.8% (481) were non-users, and 66.4% (921) were males while 33.6% (468) were females among the SM users. The findings of the study revealed that overall, 27.4% of the students were social media addicts, out of which 24.0% in government and 30.8% in private colleges were SM addicts. Furthermore, 38.9% of the people with an addiction used Facebook, 31% were addicted to internet gaming, and 41.2% used WhatsApp. However, 87.5% (1216) consumed junk food, 10.1% of the people with addiction were habitual to smoking, alcohol, and chewable tobacco, 339 (66%) of social media addicts had Ringxiety, and 38.7% had selfies. Therefore, the habit of smoking, alcohol, and tobacco, consumption of junk food, and having Ringxiety and selfies were considered to be significant risk factors for social media addiction among males. Therefore, these habits made males more prone to social media addiction. The results on considerable health issues identified stated that 38.4% had a strain on the eyes, 30.7% had neck pain, 25.% suffered from anger, and 26.1% had sleep disturbances. Rahiminia et al. ( 61 ) investigated that 44% (462) students were addicted to social networks, out of which 90.04% (449) were slightly while 9.96% ( 13 ) were severely addicted. Furthermore, the students' mean (SD) age was 22.81 (4.66) years. The study further revealed that 11.8% (118) students were smokers, 2.3% ( 23 ) used to take antipsychotics, out of which 47.81% took benzodiazepine, 30.43% selective serotonin reuptake inhibitor (SSRI), 13.04% tricyclic ani depressant (TCA), 4.35% serotonin-norepinephrine reuptake inhibitor (SNRI: Duloxetine), and 4.35% took sleeping pills. Hence, the findings revealed that there is a significant association between age (p = 0.001), use of nerve medicine (p = 0.0001) and social network' addiction; hence, age and use of nerve medicines were the primary risk factors of SMA. However, no significant relationship was established between SNA and gender (sig = 0.47), marital status (sig = 0.06), level of education (sig > 0.05) and smoking (sig = 0.18). Shaibani ( 68 ) found that the mean social networking addiction (SNA) was 50.83 ± 13.00, which was at a moderate level among 70% (254) students. The mean SNA was higher among males (52.65 ± 11.50) as compared to female students (49.35 ± 13.96), out of which 72.3% used social networks during lectures 2.3 (1.0) mean (SD), and they spent 7.4 (5.5) mean (SD) hours per day on average, while 75% used social media for ≤ 10 hours per days. However, the top factors associated with SMA among students in terms of perceived advantages were "using social media to help them in their studies" (mean rating = 3.75/5, RII = 75.1%), feeling more informed than others because they used social media (mean rating = 3.69/5, RII = 73.7%) "perception of solving problems with the help of social media (mean rating = 3.51/5, RII = 70.2%) "an urge to use social media more and more (mean frequency rating = 3.2/5, RII = 64.0%) and "using social media to forget about their problems" (mean frequency rating = 2.95/5, RII = 59.1%), were the main risk factors of SMA among students. However, a significant association was established between SMA and perceived advantages of SM use (p < .010) and SMA and duration of use (p < .010), indicating essential risk factors of SMA. Taha et al. ( 72 ) found that according to IAT, the rate of IA was 12.4%, while 57.9% had the potential risk of addiction among students. Females were more frequent Internet users than males ( w = 0.006). Moreover, the % of students frequently staying longer than intended was 82.3%, while the impact on academic performance was reported by 62.2%. Out of 96.8% of the response rate, 70.8% lost sleep due to late-night Internet use, 58.9% felt depressed, moody, or nervous offline, and 80.4% lived with their families. Due to frequent internet use, health issues like headaches, backache, weight gain, neck pain and other psychological were also reported by respondents. A significant difference was established between the total IAT score and neck pain ( P = 0.03) and a state of sleeplessness because of staying online ( P = 0.006). In contrast, the relationship between BMI and IAT scores was not established. However, IAT scores and weight loss/ gain established a significant association ( P = 0.002). Hence, the duration of Internet use and sleeplessness among students were the primary risk factors for IA. Uyaroglu et al. ( 76 ) found that the mean rate of SMA among Turkish students was 61.29 ± 17.53 mean (SD). 43.4% of the students used the internet for 4–5 hours daily. The mean (SD) Social loneliness score was 12.21 ± 6.49, while the mean (SD) emotional loneliness score was 30.68 ± 11.45. However, the Total loneliness score was 42.89 ± 15.17 mean (SD), respectively. Hence, a significant positive relationship between the high prevalence of social media addiction and emotional and social loneliness, and loneliness was found to be a primary risk factor for social media addiction among students ( r = 0.196 p = 0.000) ( p < 0.05). Zenebe et al. ( 82 ) revealed that the total prevalence of internet addiction (IA) was 85% (n = 466); however, the frequency was distributed in three categories: mild, moderate and severe. Results showed that out of 85% (n = 466) IA students, the rate of mild, moderate and severe IA was 55.6% (305), 27.9% (153) and 1.5% ( 8 ) respectively. In comparison, the remaining 15% ( 82 ) were non-IA. The rate of IA among students who use the internet frequently and permanently was 92.2%, which is higher than those who do not log in often, which is 83.1%. Furthermore, the findings on the pattern of use revealed that 93.6% of the respondents use the internet for courses/assignments, 76.6% for reading/posting news, 47.6% for chat rooms and 49.8% for e-mail (reading, writing). In comparison, 85.6% use social networks (Facebook, etc.), 66.6% use them for getting into relationships online, 44.5% for playing mobile games, 65.7% for downloading music or videos, and 57.8% for watching videos. At the same time, 22.8% use it for retrieving sexual information. However, more time spent on the Internet (AOR = 10.13, 95% CI 1.33–77.00), mental distress (AOR = 2.69, 95% CI 1.02–7.06), online gaming (AOR = 2.40, 95% CI 1.38–4.18), khat chewing (AOR = 3.34, 95% CI 1.14–9.83) and alcohol use (AOR = 2.32, 95% CI 1.09–4.92) were associated with internet addiction. However, no association was established between IA and smoking. Zhao et al. ( 81 ) investigated the rate of significant risk factors associated with SMA at 38%. The study comprised 53% (277) females and 27% (243) males. The study's findings clearly stated that the females were more prone to SMA (β = −0.21, t = − 4.88, p < 0.001) compared to males. Furthermore, the study investigated the rate of each risk factor associated with SMA with the help of regression analysis. The identified risk factors were impulsivity (β = 0.34, t = 8.50, p < 0.001), self-esteem (β = −0.20, t = − 4.38, p < 0.001), anxiety (β = 0.24, t = 4.43, p < 0.001), social anxiety (β = 0.25, t = 5.79, p < 0.001), and negative attentional biases (β = 0.31, t = 8.01, p < 0.001), which showed a significant association with SM use. The greater the rate of depression, anxiety, and low self-esteem, the greater will be the rate of SMA. 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Sultan Qaboos Univ Med J. 2015;15(3):e357. Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res. 2014;14(1):1–10. Morrison A, Polisena J, Husereau D, Moulton K, Clark M, Fiander M, Mierzwinski-Urban M, Clifford T, Hutton B, Rabb D. The effect of English-language restriction on systematic review-based meta-analyses: a systematic review of empirical studies. Int J Technol Assess Health Care. 2012;28(2):138–44. Nguyen TH, Lin KH, Rahman FF, Ou JP, Wong WK. (2020). Study of depression, anxiety, and social media addiction among undergraduate students. J Manage Inform Decis Sci, 23 (4). O’brien M, Moore K, McNicholas F. (2020). Social media spread during Covid-19: the pros and cons of likes and shares. Ir Med J , 113 (4), p.52. Oxford, Analytica. (2018). Facebook seeks new users and markets as troubles mount. Emerald Expert Briefings, (oxan-db). Pantic I. Online social networking and mental health. Cyberpsychology Behav Social Netw. 2014;17(10):652–7. Porritt K, Gomersall J, Lockwood C. JBI's systematic reviews: study selection and critical appraisal. AJN Am J Nurs. 2014;114(6):47–52. Park YS, Konge L, Artino AR. The positivism paradigm of research. Acad Med. 2020;95(5):690–4. RSPH. (2017,). Social media and young people's mental health and wellbeing. From Royal Society For Public Health: https://www.rsph.org.uk/static/uploaded/d125b27c-0b62-41c5-a2c0155a8887cd01.pdf REHAB U. (2021). Internet Addiction . From UK REHAB: https://www.uk-rehab.com/behavioural-addictions/internet/ Rhee L, Bayer JB, Lee DS, Kuru O. 2021. Social by definition: How users define social platforms and why it matters. Telematics and Informatics , 59 , p.101538. Rahiminia H, Soori H, Jafari M, Khodakarim S. (2021). 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Sleep problems in university students–an intervention. Neuropsychiatric disease and treatment , 13 , p.1989. Shaibani MHA. Prevalence and Factors Associated with Social Networking Addiction among Saudi University Students: A Cross-Sectional Survey. Psycho-Educational Res Reviews. 2020;9(2):87–99. Smith V, Devane D, Begley CM, Clarke M. (2011). Methodology in conducting a systematic review of systematic reviews of healthcare interventions. BMC medical research methodology , 11 (1), pp.1–6. Spencer AJ, Eldredge JD. (2018). Roles for librarians in systematic reviews: a scoping review. Journal of the Medical Library Association: JMLA , 106 (1), p.46. Sharma MK, John N, Sahu M. Influence of social media on mental health: a systematic review. Curr Opin Psychiatry. 2020;33(5):467–75. Taha MH, Shehzad K, Alamro AS, Wadi M. Internet use and addiction among medical students in Qassim University, Saudi Arabia. Sultan Qaboos Univ Med J. 2019;19(2):e142. Tysowski PK, Zhao P, Naik K. (2011), July. Peer to peer content sharing on ad hoc networks of smartphones. In 2011 7th International Wireless Communications and Mobile Computing Conference (pp. 1445–1450). IEEE. Tutgun-Ünal A. Social Media Addiction of New Media and Journalism Students. Turkish Online J Educational Technology-TOJET. 2020;19(2):1–12. Turel O, Qahri-Saremi H. Explaining unplanned online media behaviors: Dual system theory models of impulsive use and swearing on social networking sites. New Media Soc. 2018;20(8):3050–67. Uyaroğlu AK, Ergin E, Tosun AS, Erdem Ö. (2022). A cross-sectional study of social media addiction and social and emotional loneliness in university students in Turkey. Perspectives in Psychiatric Care . Vandali V, Biradar R. Selfie Syndrome–A Mental Disorder. Int J Nurs Educ Res. 2018;6(3):287–9. Wallace P. Internet addiction disorder and youth: There are growing concerns about compulsive online activity and that this could impede students' performance and social lives. EMBO Rep. 2014;15(1):12–6. Yu L, Shek DTL. Internet addiction in Hong Kong adolescents: a three-year longitudinal study. J Pediatr Adolesc Gynecol. 2013;26(3):S10–7. Yu S, Wu AMS, Pesigan IJA. Cognitive and psychosocial health risk factors of social networking addiction. Int J mental health Addict. 2016;14(4):550–64. Zhao L. (2021). The impact of social media use types and social media addiction on subjective well-being of college students: A comparative analysis of addicted and non-addicted students. Computers in Human Behavior Reports , 4 , p.100122. Zenebe Y, Kunno K, Mekonnen M, Bewuket A, Birkie M, Necho M, Seid M, Tsegaw M, Akele B. Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: a community university-based cross-sectional study. BMC Psychol. 2021;9(1):1–10. Appendix Appendix 1 is not available with this version. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6165651","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":441564748,"identity":"2ee9931d-5dc6-49e8-99c1-6c6ee99c6e09","order_by":0,"name":"Ayesha Manzoor","email":"","orcid":"","institution":"Birmingham City University","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Manzoor","suffix":""},{"id":441564750,"identity":"2e55044b-6cee-49fd-b8b1-9cb9d8cc1d2b","order_by":1,"name":"Natalie Quinn-Walker","email":"data:image/png;base64,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","orcid":"","institution":"Birmingham City University","correspondingAuthor":true,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Quinn-Walker","suffix":""}],"badges":[],"createdAt":"2025-03-05 22:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6165651/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6165651/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82078699,"identity":"9a68d23a-9fc0-40d8-8478-e878130f6a52","added_by":"auto","created_at":"2025-05-06 14:08:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe PRISMA flow Diagram. This a visual aid to showcase the screening process for this systematic review.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6165651/v1/5313dcdfc579dc2d4c2af856.png"},{"id":82229189,"identity":"a165ee3b-0eb5-4afd-9a7f-c86f300b1a81","added_by":"auto","created_at":"2025-05-08 05:31:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666981,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6165651/v1/7acfe818-1422-4d1e-b67d-51fb87d5c10c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFactors Associated With Prevalence and Pattern of Social Media Addiction Among Medical Students – A Systematic Review\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Social Media Addiction\u003c/h2\u003e \u003cp\u003eResearch on social media addiction has increased dramatically since the 1990s with the development of internet-based technology, especially when more and more cases of addiction have been reported among students, which have been detected by university healthcare professionals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The term social media addiction is a combination of three different words, which include social media and addiction. The term social refers to society or socialisation. According to Rhee (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), the term social relates to activities in which people meet, communicate and spend time with other people, which happens during their free time, while media, which is a plural of medium, refers to the communication channels through which we disseminate news, music, movies, education, promotional messages and other public or private content/ data. There are different types of media, such as print media, broadcast media, and internet media (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Internet media includes social media through which people communicate, interact and socialise with family, friends, relatives, professional community and others (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Hopkins (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) defined social media as an umbrella term that refers to electronic communication and interaction among people, allowing people to quickly create and share ideas, personal messages, and other content using different forms of media, such as social networking websites and applications. Hence, social media is a collective term that refers to a group of internet-based applications like Facebook, Instagram, Twitter, Skype, WhatsApp, YouTube, Snapchat, TikTok, online gaming, virtual worlds, Blogs, etc. and websites (web 2.0 \u0026amp; 3.0 sites) that allows the creation and exchange of user-generated contents (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Hence, social media is a broader term that runs on internet-based applications and websites using various electronic devices such as smartphones, tablets, laptops, and computers. Addiction, by WHO, is defined as dependence, as the continuous use of something for the sake of relief, comfort, or stimulation, which often causes cravings when it is absent (35; 48). There are two significant categories of addiction. The first one is substance addiction, e.g. drug or alcohol addiction. In contrast, the other one is behavioural addiction, such as mobile phone or smartphone addiction, social media addiction, and internet addiction (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Hence, from the definition of addiction, Uyaroğlu et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) defined Social Media Addiction (SMA) as a behavioural addiction which refers to paying excessive attention to social media activities, often to the neglect of all other activities, and uncontrollable use to the extent that it interferes with other vital areas of life including psychological health, interpersonal relationships, emotional consequences, academic performance, and occupation to the detriment of the individual. Research on social media addiction has dramatically increased since the launch of smartphones, different internet-based applications, and the modernisation of information technology (71;41). Social media is an umbrella term consisting of social networking sites and messenger applications; while discussing this broader term, researchers also consider different media for its use, overuse or addiction, such as the internet, smartphones, internet-based applications and websites or social networking sites (SNS). In addition to social media addiction, internet addiction, or smartphone addiction, terminologies like problematic social media use, social media overuse, social media dependency, smartphone addiction/overuse, problematic smartphone use, internet addiction/overuse, pathological internet addiction, internet dependency, internet addiction disorder, social networking sites overuse, online networking sites, are also used in the research area to describe social media addiction.\u003c/p\u003e \u003cp\u003eThe linkage between social media addiction and overall health is not new; in the late 1990s, clinicians and scholars began claiming that excessive internet use was an increasing problem that should be recognised as an addiction (34; 55). The psychiatrist Ivan Goldberg then introduced the term' Internet addiction disorder' (IAD) in 1995, and the concept of Internet addiction was being taken more seriously by 1996. It was then proposed to be a clinical disorder (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) because the statistics of social media usage are increasing significantly, and the frequency of SM use/ addiction varies in different geographical locations and different eras of time. Research has shown different statistics in various eras for the prevalence of social media usage (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For instance, a study states that in 2017, there were about 2.46\u0026nbsp;billion, which is approximately two and a half of the world's population, actively using social media, and according to this study, the number of active social media users was expected to increase by 3.196\u0026nbsp;billion globally by 2021 (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). However, the world has reached this number by 2018. In 2018, the number of internet users was 4.021\u0026nbsp;billion, and 3.196\u0026nbsp;billion people were active users of social networking sites regularly worldwide (7;3). In July 2020, there were over four billion active social media users around the globe, and half of them were Facebook users. In 2021, we have over four billion population using social media regularly (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), Which means that the greater the exposure to the internet, the greater the addiction rate, and there will be a vast number of people experiencing adverse consequences of social media addiction (64; 43). Masthi et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) claim that 3.77\u0026nbsp;billion people are internet users through modern gadgets such as smartphones, tablets and computers. This includes 81% of the developed world population and 41% of the developing world population (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Similarly, different countries have different statistics on social media usage; findings of a study in the UK state that 18% of young people are internet addicted (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), while another study in China states that 12% of males and 5% of females students are internet addicted (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Internet addiction is not only restricted to university or college students; a high prevalence of internet addiction has been found in school students. A study in Hong Kong states that 26.7% of high school students are internet addicted (79; 80). According to the Iranian Center of Statistics, the use of social media has tripled over the past three years, and more than 47\u0026nbsp;million Iranians are using social networks (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe time spent on the internet has a great significance in distinguishing between the addicted and non-addicted status of a person. Research has shown that students who spend 8.5 hours per week to 21.2 hours per week are indicated as social media-addicted students, which means that the more significant the amount of time spent online, the greater the symptoms of internet addiction (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Studies found that students who are social media addicted were found to use more data packages on monthly expenditure, and they tend to spend more hours on the internet as compared to social media addicts. Goel et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) state that addicted students spend 38.5 hours per week as compared to non-addicts, who spend only 4.9 hours per week on computers. These students reportedly spent Rs. 300/- monthly on social media. However, the frequency of YouTube use was 100%, followed by Facebook (91.4%) and Twitter (70.4%). The rate of addiction varies according to the criteria used: 47.2% were found to be YouTube addicted, 14.2% were found to be Facebook addicted, and 33.3% were Twitter addicted. However, the rate of addiction to YouTube, Facebook and Twitter was reduced to 13.8%, 6.3%, and 12.8% when work-related activity was taken into account (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, this institute reports that people between the age group of 25\u0026ndash;34 years use SM at higher rates, while the rate is higher between the age group of 18\u0026ndash;24 years on a second number. Therefore, university students are an essential group for discussion. Despite this vast number of social media addicts facing the adverse consequences of social media addiction around the globe and in different countries individually, social media addiction (SMA) is not yet considered a disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) or the International Classification of Diseases 11th Revision (ICD-11) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, many countries have declared SMA as a health concern individually, like the Chinese government declared internet addiction a \"public health hazard\" (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). In contrast, the South Korean government declared internet addiction a public health concern (Koo et al., 2011). Similarly, several countries, including China, South Korea, Japan, the United Kingdom, the Netherlands, and the United States, have established various clinics to treat internet addiction (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). For example, the Centre for Gaming and Internet Addiction is the first and only specialist service on the NHS, established after the World Health Organization (WHO) declared gaming disorder as a mental health disorder. At the same time, psychiatrists and clinical psychologists treat patients (children and adults) aged between 13\u0026ndash;25 whose lives are being wrecked by severe or complex behavioural issues associated with gaming, gambling and social media. Similarly, different rehabilitation centres have been established for treating SMA all over the UK, costing NHS billions of pounds (59; 58).\u003c/p\u003e \u003cp\u003e1.2. Causes of social media addiction\u003c/p\u003e \u003cp\u003eWith the revolutionisation of internet-based technology and the increasing use of smartphones, social media has become an integral part of human life, particularly among students. They use it either for learning activities or entertainment purposes. They also use social media for socialisation; however, the primary reason is to stay connected with family and old friends or make new friends. It has been emergingly used to play a significant role in human lives (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Different studies explain the reasons for SMA in terms of various theories and models, claiming that according to dynamic psychological theory, people use social media in response to psychological shocks, emotional deficiencies in childhood or personality traits (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, social control theory states that addiction varies with age, gender, socioeconomic status and geographical location, which means that people/ students of certain groups of societies can be more addicted as compared to others. The biomedical explanation theory claims the presence of certain chemicals (chromosomes or hormones) that trigger the brain activity for addiction. When people log on and start engaging with social media, their brain releases a pleasure hormone named dopamine; the level of this pleasure chemical increases each time they use social media because the brain considers it as a rewarding activity, which triggers the brain to use it more and more to feel the same sense of pleasure again and again. It will cause them to increase their screen time, and they become addicted naturally in a continuous cycle (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, Turel and Qahri-Saremi (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e) provide the reason for SMA in terms of three different models; according to the Cognitive-behavioral model, unfamiliar and awkward situations trigger the use of social media, while lack of presentation skills and low self-esteem increase the use of social media for virtual communication is another reason of SMA according to social skill model. However, people's behaviour towards receiving the positive outcome that is likes, comments, and tagging their photos on social media is another reason for social media use by people, according to a socio-cognitive model, which is the most common form of SMA among students. Constant social media thinking, social media anxiety and unhappiness, and non-self-control behaviours are defined as social media cognitive disorders; it is possible to define behavioural disorders such as not being able to control the duration of social media usage, refusing to socialise when experiencing a problem, and failing to fulfil responsibilities in social, academic, familial and professional relations (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFindings of an independent study conducted by Zenebe et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e) show the most frequent reasons for internet use among Wollo University undergraduate students. 93.6% of the respondents use the internet for courses/assignments, 76.6% for reading/posting news, 47.6% for chat rooms and 49.8% for e-mail ( reading, writing). In comparison, 85.6% use social networks (Facebook, etc.), 66.6% use them for getting into relationships online, 44.5% for playing mobile games, 65.7% for downloading music or videos, and 57.8% for watching videos. While a minimum number of students, which is 22.8%, use it for retrieving sexual information. However, AlFaris et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) claim that 97% of students use social media. The frequency of the most used applications was 87.8% for WhatsApp, 60.8% for YouTube and 51.8% for Twitter, while the frequency of YouTube for learning purposes was 83.5%, 35.5% for WhatsApp and 35.3% for Twitter. The time spent was around 71% visited SM\u0026thinsp;\u0026gt;\u0026thinsp;4 times/day, and 55% paid \u0026minus;\u0026thinsp;4 hours/day. The significant reasons for SM use were entertainment (95.8%), staying up-to-date with news (88.3%), and socialising (85.5%) for academic studies (40%). However, the study could not establish any significant association between Grade Point Average (GPA) and the frequency of daily SM use or use during lectures. The fear of missing out (FOMO) can also be a significant reason for frequent social media use, regardless of the time of day, at the expense of other activities (14; 24). Similarly, literature on Turkish university students states that 41.8% read the news, 71.9% accessed information, 52.8% used the internet for entertainment, 44.3% viewed videos or shared photographs, 59.8% used the internet to spend leisure time, 62.3% utilised social networking sites, and 50.3% performed online shopping. It was also determined that 43.4% of the participants used the internet for 4\u0026ndash;5 hours daily (Uyaroğlu et al., 2022). Liu et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) say that social media is designed to make us addicted because while using it, no one can feel its harmful impacts; people think it is a fun activity instead. Further, Liu et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) explain the psychological dependence of SMA, which states that people use social media when they feel lonely, stressed or unsatisfied with their lives or depressed.\u003c/p\u003e \u003cp\u003e1.3. Impacts of social media addiction\u003c/p\u003e \u003cp\u003eSocial media has many benefits in our daily lives but also negatively impacts overall health. Students with excessive social media are at risk of developing poor mental health, depression, anxiety, loneliness and low esteem. It can also cause learning difficulties, psychological problems, daily communication difficulties, and long-term impacts on self-presentation skills, intellectual growth, and concentration abilities. Furthermore, Masters (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) says that the characteristics of social media addiction are similar to those of internet addiction. Excessive mental preoccupation and repetitive thoughts to limit internet use and repetitive failure provoke the use of social media despite its significant impact on daily life. Considering the psychological profile of students, studies revealed that there is a significant correlation between social media addiction and depression, locus of control, loneliness, and low esteemed anxiety (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), which can be diagnosed as a specific mental disorder, Generalised Anxiety Disorder (GAD), panic disorder, social anxiety disorder or Obsessive Compulsive Disorder (OCD) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, Al-Menayes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) states that the level of loneliness and depression was higher among internet addicts. Internet students as compared to non-addicts; it also states that poor mental health is also a consequence of internet addiction among university students in Kuwait. Masthi (2018) reports that the frequency of SMA was found to be 36.9% among both genders of universities, and as a consequence of this high rate of SMA, they suffer from several health consequences, and the most common health problems identified were a strain on eyes, anger and sleep disturbance. Furthermore, according to statistics, the strain frequency on the eyes was recorded at 38.4%, anger at 25.5%, and sleep disturbance at 26.1%. However, the habit of smoking, alcohol and tobacco, consumption of junk food, having ringxiety - a term ringxiety was coined by David Laramie in 2006, who was a doctoral student at the California School of Professional Psychology and who was studying the effect of psychology on behaviour, is a blend of Ring\u0026thinsp;+\u0026thinsp;anxiety, which is a new mental disorder in which a person thinks or feels or hear like their phone is ringing or vibrating when this is not happening in reality (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) and selfitis ( a new kind of mental disorder the obsessive-compulsive desire to take photos of one's self and post them on social media as a way to make up for the lack of self-esteem and to fill an intimacy gap (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e) were found to be significant risk factors for social media addiction among students.\u003c/p\u003e \u003cp\u003eDespite the significant advantages of social media, it has become a serious life threat in today's society and has been proven to be a serious public health hazard. Social networking site usage has significantly increased in the last decade, particularly among students and young people. With the advancement of technology, social networking sites have made our lives easier. Social media sites such as Facebook, Instagram, and Twitter have become integral to human life (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Research has established that 2.32\u0026nbsp;billion people use Facebook regularly, which has increased to about 11% around the globe. Social media is not something traditional, but it provides an opportunity for its users to create, share and upload information according to the account settings, and it also allows them to respond to, share or tag others' content (54; 66). With the increasing use of smartphones, sharing has become a more common and essential part of life. The availability of this sharing feature provides young people with unprecedented access to private information and a ready platform to use this information, knowledge and personal content against others (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the advantages of social media can never be neglected, the habitually unconscious and uncontrollable use of internet technology can cause danger to security. The most significant negative impact of social networking sites is violent behaviours such as harassment, threats, and insults made through the internet and mobile communication tools. Cyberbullying has become a big problem as a consequence of excessive use of social media. Cyberbullying is the use of information and communication technology to harass and do harm in a conscious, repetitive and hostile way (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Excessive use of social media causes aggressive behaviour. Cyberbullying allows people to use social media in such a way that they can create fake profiles and share user-generated content, rumours, and personal and private information of others using fake profiles and identities to annoy, intimidate and bully others. These actions can hurt the mental and behavioural health of people, resulting in social isolation and exclusion. Therefore, cyberbullying, which is increasing day by day all around the globe, with the connection of social media, has become a serious public health concern worldwide. Hence, the excessive or abnormal use of social media has increased the risk of people adopting poor eating and sleeping habits, loss of interest in leisure activities, and impaired social interaction, which means people avoid socialise face-to-face and think that the time spent without Internet or social media is an idle time they had nothing done in that time. They find the most helpful time of their life is the time they spend on mobile phones or social media. It has highly affected the quality of life among students, such as mental instability, mood swings, anxiety, and decreased academic success. It can also cause physical violence against themselves and can make a person drug and alcohol addicted to hide from reality (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). It has also increased the risk of depression, self-isolation and suicide. Furthermore, many legends, hosts, models, actors or actresses, and scholars have become victims of cyberbullying, which leads them to attempt suicide and death. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, emotional loneliness is both a cause and a consequence of social media addiction. A recent study conducted by Uyaroğlu et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) stated that students living with their families have low rates of smartphone or social media addiction; however, students living alone showed higher rates of social media addiction. It is also reported that loneliness is found to be higher in those students who have worse economic status and poor academic performance. Therefore, they are more prone to addiction to SNS, particularly problematic use of Facebook, which is due to social, family or romantic loneliness. Contrary to this, students with good socioeconomic status enjoy social networking sites 5hours per day or more, have reduced emotional loneliness, and perform better in school. It is further reported in another study that 14% of problematic Facebook use was explained by family loneliness and 5% by romantic loneliness (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eJaspal and Breakwell (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) reported that higher social media addiction was associated with worse mental health. It increases the risk of self-isolation; hence, the study established that lonely individuals use the internet more frequently. In a recent survey of Norwegian college and university students, the frequency of insomnia was found to be 34.2% among female students and 22.2% among male students. While poor sleep deteriorates their mental and physical health, poor sleep quality and daytime sleepiness can also impair students' academic performance. Research has established a negative correlation between sleep quality and screen quality (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, Hjetland et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) report that using screen-based devices is associated with pre-bedtime arousal, meaning that 76% of university students between the ages of 20\u0026ndash;24 use their mobile phones after bed. A large study on over 7000 American university students states that sleep problems are more prevalent among students; only one-third of students sleep for more than 7 hours each night, while the rest fall short of the recommended 7\u0026ndash;8 hours of sleep every night (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the above discussion, the significant need for a current systematic review of the prevalence of social media addiction, particularly among medical students, is known in general. Because there is no sufficient evidence to prove social media addiction as a disorder itself, however, previous studies have confirmed its association with different risk factors and health concerns. The frequency and consequences of SMA on students' health have been studied in prior research. Even though there is a considerable number of students using social media or internet-based devices for whatever reason and facing its consequences in almost every country of the world, it is still unclear why SMA has not been recognised as a health disorder according to DSM-5 and ICD (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), but still a risk factor or a consequence of various physical, psychological, mental and behavioural health issues (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). However, different countries have declared SMA a public health concern individually (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and are making significant government responses to reduce the problem's incidence or rehabilitate the population from SMA (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). The prevalence of social networking site addiction has also been explored in previous research in different countries of the world. However, some studies have not established a significant prevalence of SMA and its associated factors among medical students. In contrast, other studies remain successful in establishing a substantial prevalence of SMA and its related factors among medical students. This review is to address the information gap.\u003c/p\u003e \u003cp\u003eShaibani (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) states that SMA may be beneficial for students; however, it is essential to maintain a balance of social networking use by students so it may not affect them negatively. Taha et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) claim that internet addiction is more prevalent among medical students at the rate of 12.4% associated with stress and psychological issues, while 57.9% are at risk of becoming internet addicted and are at risk of developing serious physical and mental health issues. Although these studies remain successful in showing a significant prevalence of SMA among students, the reason for social media use and the demographic profile of students will remain unclear. All the previous research states a standard limitation of the inability to generalise the results to the whole population due to a small number of the study population or a restricted geographical location.\u003c/p\u003e \u003cp\u003eHowever, the Royal Society of Public Health (RSPH) (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) states that social media addiction is becoming an integral part of young people all over the UK. Ninety-one per cent of the people aged between 16\u0026ndash;24 years use the internet and social networking sites and are more prone to internet or social media addiction, followed by 85% of the people between the age group of 25\u0026ndash;34 years. Social media is more addictive as compared to cigarettes and alcohol and is linked to the rates of anxiety, depression, poor sleep, poor dietary habits and low self-esteem among prevalent age groups of social media use (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). A systematic review conducted by Cheng et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) ensures that there is sufficient data to show the incidence of social media addiction while establishing the significant incidence rate of SMA among populations of 32 nations; the systematic review is unclear to address the gaps of cultural differences and associated risk factors of SMA among different countries. Furthermore, Cheng et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) have some limitations, such as an imbalanced gender ratio, limited age range and the inclusion of non-clinical structured protocols for the assessment of social media addicts. The current review is aimed to address the literature gap.\u003c/p\u003e \u003cp\u003eTwo key research papers were also identified, informing the researcher about the research questions, which are internet addiction and its determinants among medical students and prevalence and factors associated with social networking addiction among Saudi university students: a cross-sectional survey; both were the cross-sectional analytical studies, although these studies were comprehensive but were conducted within a limited geographical location, including specific age group with a narrow range of students. Also, the results of independent studies are not generalised.\u003c/p\u003e \u003cp\u003eTo conclude, although there are several independent studies conducted at the national level to show the rate of social media addiction among medical students, there are still many gaps about the prevalence of social media addiction and its associated factors among medical students due to limited age group selected in the studies, limited geographical location and insufficient data on the cultural regions of students. For example, a systematic review conducted by Cheng et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) on the prevalence of social media addiction across 32 nations stated that the age range should be broad in further studies. Kolaib et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) state that the study was conducted in a single university at one location, affecting the results' generalizability. According to NIH (National Institute of Health) and TRACE (Tennessee Research and Creative Exchange), several theories are applied to the social media addiction factor, such as the Social Identity Model of Deindividuation which effects, the Interpersonal Impact Hypothesis, the Differential Impact Hypothesis, which Uses and Gratifications Theory, Cognitive Behaviour Therapy, and Media Dependency Theory. Therefore, a complete, in-depth and comprehensive systematic review with a broader age range and no restriction to geographical location is required. The results of this study will help public health policymakers develop interventional programs such as regulations on marketing, inform time and money spent on digital media/ games, notify parents of the use of information, and restrict advertisements. However, providing education and awareness through media campaigns and conducting university seminars for the students would help reduce the incidence of the problem.\u003c/p\u003e \u003c/div\u003e "},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003eA systematic review is \"a review of the evidence on a formulated question that uses systematic and explicit methods to identify, select and critically appraise relevant primary research, and to extract and analyse data from the studies included in the review (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The methods used must be reproducible and transparent. Systematic review is a form in which the scientific method is used to systematically identify, evaluate and synthesise the existing evidence to form an unbiased conclusion concerning a particular review question (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Due to the availability of a large and continuously increasing number of research papers and study material, it is hard for decision-makers to study a vast number of primary research papers and make the most appropriate healthcare decision beneficial for the public. Therefore, the current systematic review is written to provide an up-to-date summary of more reliable findings of the existing research knowledge that informs the decisions about the limited use of social media among students (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Systematic review methodology states that a systematic review should have the following characteristics: it should be highly organised, have a specific research question with a clear objective of the review, a clear eligibility criteria of the included studies, an in-depth evaluation of the quality of the included studies and a comprehensive analysis and synthesis of the included studies. It must be transparent, unbiased, and have a reproducible approach to conclude. However, conducting a thorough systematic review is challenging and time-consuming, and it has several barriers, which include lack of awareness, lack of evidence or limited access to evidence, and lack of knowledge (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e2.1. Rationale of conducting a quantitative systematic review\u003c/p\u003e \u003cp\u003eA systematic review can be conducted using three methodologies: qualitative, quantitative, or mixed qualitative and quantitative. Qualitative research focuses on an in-depth understanding of the individual's experiences, opinions and thoughts. In contrast, quantitative research is the methodology which involves the process of collecting and analysing numerical data to describe, predict, or control variables of interest to test the relationship between variables, to make predictions and to generalise the results to a broader population (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Furthermore, qualitative data is time-consuming and less able to be generalised. However, quantitative data is more efficient but may miss contextual details. As in this systematic review, data is collected to explore the factors and frequency of social media addiction among medical students; therefore, to suit the purpose of this systematic review, the quantitative approach is the most suitable methodology to collect, analyse, synthesise, and interpret the numerical information.\u003c/p\u003e \u003cp\u003e2.2. Philosophical underpinning of the review question\u003c/p\u003e \u003cp\u003eThe paradigm is a branch of philosophy considered a conceptual framework that symbolises the researcher's philosophical approach to proposing and conducting his research (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). There are different aspects of the paradigm. However, it is considered that the positivistic paradigm or positivism focuses on the real world by using quantitative analysis to interfere with the generalisation for recognition (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Positivism focuses on the quantitative methodology approach and uses numerical data for statistical analysis; therefore, it represents valid and reliable data (in terms of internal and external validity) (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). This review aims to make a statistical and quantifiable analysis of the factors associated with the prevalence and pattern of SMA to create a trustworthy conclusion using the positivistic approach. Also, the cross-sectional study design of this review is in alignment with the positivistic approach, as positivist studies adopt the deductive approach, which is best suited to test the association of factors with the prevalence and pattern of SMA among medical students (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). To conclude, the qualitative approach is subjective and is used to analyse people's feelings and experiences; however, quantitative research is objective and represents the reliability of data to investigate the research question. Therefore, the quantitative methodology, underpinned by the positivistic paradigm approach, best suits this systematic review.\u003c/p\u003e \u003cp\u003e2.3. Review question of systematic review\u003c/p\u003e \u003cp\u003eThis systematic review is written to critically appraise and formally synthesise the best existing evidence to make a conclusive statement to answer a specific review question. The review question for this study is: what are the factors associated with the prevalence and patterns of social media addiction among medical students?\u003c/p\u003e \u003cp\u003eSeveral frameworks can be used to structure a systematic review question. The most helpful framework is PIO, which is used to develop a focused research question for a quantitative systematic review. Cochranes PIO's components are called Population, Issue, and Outcome. They are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which is used to identify the components of evidence for the systematic review of the existing evidence-based data. Furthermore, the PIO helps to formulate a review question more precisely (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This is a quantitative review, and the question stated in terms of PIO is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReview question in terms of the PIO Framework\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation (P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical students (undergraduate \u0026amp; postgraduate university or medical college students)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIssue (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial media addiction (SMA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome (O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk factors of SMA linked to its prevalence and pattern of use among medical students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PIO framework is used in evidence-based medical studies, can be used to develop a searchable query in public health, and can critically appraise the significance of the literature to be identified (Huang et al., 2006).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.4. Aims and objectives\u003c/h3\u003e\n\u003cp\u003eThe primary aim of the current study is to investigate the factors associated with the prevalence/frequency and pattern of social media addiction among medical students.\u003c/p\u003e \u003cp\u003eThe objectives of this study are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo review the factors associated with the incidence of social media addiction among medical students.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo assess the pattern of social media addiction among medical students\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003e2.5. Search strategy\u003c/h3\u003e\n\u003cp\u003eA comprehensive literature search was carried out using a range of databases to identify the relevant and primary research papers, as many as possible, on the prevalence of social media addiction among medical students. The databases provide quantitative data on the proposed review question. Usually, it is acknowledged that a minimum of two reviewers are required to write a systematic review to increase trustworthiness and minimise personal errors (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). However, this systematic review was conducted by a research student to fulfil a master's degree in public health. Therefore, a PRISMA flow diagram was used to maximise the transparency and minimise the biases. Furthermore, the search strategy and implementation were carried out under the guidance of \"The Cochrane Handbook for Systematic Reviews of Interventions\" (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA systematic review search should be conducted in a wide range to increase the likelihood of relevant research papers and reduce the possibility of biases. It is not possible and reliable to look for a single database covering all the information, and it is hard to access all the databases and information to answer a specific review question (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Different databases have been developed to identify the type of research; for example, AMED is used for alternative and allied therapy research, CINAHL is used in nursing and allied health research, and PsycINFO is used in psychology, psychiatry and social sciences research. Similarly, PubMed and MEDLINE databases are also searched while undertaking healthcare research. However, the author must search the relevant platform for the chosen topic and review the questions (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eElectronic databases\u003c/strong\u003e \u003cp\u003eThe search for this systematic review was carried out using three electronic databases, which include MEDLINE, PubMed, and Science Direct. Searching beyond a single database is essential to minimise publication and language bias. The selected databases retrieve data and information in health care and medical searches. Therefore, considering the review question and the scope of this systematic review, these databases are relevant and suitable, as they provide up-to-date information and the latest research papers pertinent to the research question. It is easy to find relevant research papers on electronic databases, as they are regularly updated. Also, the feature to apply filters to get the most advanced and relevant primary research paper has made it more convenient to use.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAccording to Cooper et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), publication limits should be applied to limit the focus searches and to prevent biases in the available evidence. The lack of access to translation and the need for funding to pay the cost of translation has restricted the author from setting a language limit (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Therefore, research papers in English are included only; any paper other than English is not included when the translation is unavailable to save time and cost. Due to the versatility of the database and the massive number of information available, the librarian was consulted to minimise the risk of missing essential papers, along with searching the key resources and adopting the appropriate method to find the relevant and vital evidence available (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAvailable evidence and information to address the specific review question can be searched in different ways, including published or non-published literature. Published literature includes bibliographic databases, academic books, journals, peer-reviewed papers, scholarly articles, and reports. At the same time, the unpublished literature consists of grey literature, which provides for conference proceedings, thesis and dissertations, government documents, news, and magazines. Commercial publishers do not monitor this literature but provide additional information on the evidence available to enhance the study's quality (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Furthermore, a manual search (hand search) was also carried out to retrieve all the literature, BCU library resources, hand-searched journals, and research papers studied. Also, Grey literature was searched, including conference papers and grey literature reports. In addition to this, organisational websites were also investigated. In addition, a list of included studies was also searched to retrieve data from the ongoing studies to assess possible inclusion.\u003c/p\u003e\n\u003ch3\u003e2.6. Search key terms, synonyms and search strategy\u003c/h3\u003e\n\u003cp\u003eAfter identifying bibliographic databases, the search terms for the literature search were developed, including the key and relevant information to the review question. Furthermore, Smith et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) state that the author should pay significant attention to the scope of the search terms. Search terms should be broad enough to capture all the relevant data but also narrow enough to minimise the capture of irrelevant literature, which helps save time and effort spent assessing irrelevant articles.\u003c/p\u003e \u003cp\u003eInitially, the search terms and the synonyms, known as free-text words or keywords within the literature, were identified using the PIO tool to find as many relevant research papers as possible, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A comprehensive search strategy comprises keywords, free text words, index terms, or medical subject headings (MeSH). Major bibliographic databases used these terms to describe the components of each published article using a controlled vocabulary. For example, MeSH was used to retrieve the data from PubMed; however, to retrieve information from MEDLINE, two retrieval approaches were used: the first is based on text words in the abstract and titles, while the other is MeSH. Furthermore, different spellings, terminologies, and synonyms were also identified to retrieve more pertinent literature. Truncation such as an asterisk (*) with appropriate Boolean Operators AND, OR, NOT were also used (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A session with an expert librarian was also attended to conduct the searches on the available and relevant literature to reduce personal biases. A reference list of identified reviews was also studied to check that various elements of the search strategy have been considered. Key search terms were developed using the PIO framework. Key searched terms and their synonyms are shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey search terms using the PIO tool\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation (P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIssue (I)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutcome (O)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical students\u003c/p\u003e \u003cp\u003e(20-34years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial media addiction/overuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactors, associated factors, risk factors or determinants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder-grad* students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet addict*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence of SMA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-grad* students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Networking sites (SNS) addiction/overuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency or Incidence of social media addiction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical/ university students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmartphone addiction/ overuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRate of use of Social networking sites\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical college students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency and pattern of Internet use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo investigate the benefits of the PIO tool, the author used identical search terms, a mixture of medical subject headings and keywords, combined using Boolean Operators. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the search terms of each database.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSearched terms for each database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSearch terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePIO tool\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubMed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e((((\"social media addiction\")) AND (medical students) OR (university students)) OR (undergrad*)) OR (postgrad*)) OR (students))))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e((((\"social media addiction\")) OR (\"social media addict*\")) OR (SMA overuse)) OR (\"internet addict*\"))) OR (\"problematic internet use\")) OR (\"smartphone addict*\"))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e((((((((factors) OR (risk factors)) AND (association)) OR (link)) OR (relationship)) AND (frequency of SMA) ) OR (rate of SMA)) OR (incidence of SMA) ) AND (pattern of SMA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEDLINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX \"social media addiction\" AND TX students OR TX \"medical students\" OR TX \"university students\" OR TX \"postgrad* OR TX undergrad*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX \"social media addiction\" OR TX \"social media overuse\" OR TX \"problematic internet use\" OR TX \"smartphone addiction\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTX factors of SMA OR TX risk factors of SMA AND TX incidence OR TX prevalence OR TX rate AND TX pattern\u0026nbsp;of SMA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScopus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u0026nbsp;(\u0026nbsp;\"social media addiction\"\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;AND\u0026nbsp;\u0026nbsp;\"medical students\"\u0026nbsp;\u0026nbsp;OR\u0026nbsp;\u0026nbsp;\"university students\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALL\u0026nbsp;(\u0026nbsp;\"social media addiction\"\u0026nbsp;\u0026nbsp;,\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\"internet addiction\"\u0026nbsp;\u0026nbsp;,\u0026nbsp;\u0026nbsp;OR\u0026nbsp;\u0026nbsp;\"smartphone addiction\"\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALL\u0026nbsp;(\u0026nbsp;\"factors associated\", AND \"prevalence\", OR \"frequency\", OR \"incidence\", OR \"occurrence\", OR \"rate\" AND \"pattern\").\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe synonyms of each variable in the same column were combined with the Boolean Operator \u0026ldquo;OR,\u0026rdquo; while the variables in different columns but in the same row were combined with \u0026ldquo;AND\u0026rdquo; (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). For example the search terms for PubMed were written as: ((((((((((((((medical students) OR (university students)) OR (undergrad*)) OR (postgrad*)) OR (students)) AND (\"social media addiction\")) OR (\"social media addict*\")) OR (SMA overuse)) OR (\"internet addict*\"))) OR (\"problematic internet use\")) OR (\"smartphone addict*\")) AND ((((factors) OR (risk factors)) AND (association)) OR (link)) OR (relationship)) AND (frequency of SMA) ) OR (rate of SMA)) OR (incidence of SMA)) AND (pattern of SMA) and for MEDLINE as: TX \"social media addiction\" OR TX \"social media overuse\" OR TX \"problematic internet use\" OR TX \"smartphone addiction\" AND TX students OR TX \"medical students\" OR TX \"university students\" OR TX \"postgrad* OR TX undergrad* AND TX factors of SMA OR TX risk factors of SMA AND TX incidence OR TX prevalence OR TX rate AND TX pattern of SMA, while for Scopus as: ALL ( \"social media addiction\", OR \"internet addiction\", OR \"smartphone addiction\" AND \"medical students\" OR \"university students\", AND \"factors associated\", OR \"prevalence\", OR \"frequency\", OR \"incidence\", OR \"occurrence\", OR \"rate\" AND \"pattern\"). The author determined the relevance of the included studies by screening the title and the abstracts. Then, the identified papers were thoroughly studied, and the duplicates were removed (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e2.7. Selection of studies\u003c/h3\u003e\n\u003cp\u003eThe selection procedure for included studies was carried out in different steps. The results were synthesised by the studies that met the inclusion criteria (Smith et al., 2011). The selection process is as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIdentification: First, studies relevant to the research question/key terms will be identified using the search strategy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eScreening: Screening was done in two phases. First, irrelevant studies were filtered based on abstract and title. The results from electronic search, hand search, and grey literature were combined to remove duplication. The second phase was screening the full text of selected/ scanned studies. Also, a study was excluded if the full text could not be found.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIncluded: All studies that meet the criteria will be included.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7.1. Types of participants (P)\u003c/h2\u003e \u003cp\u003eMedical students between 18\u0026ndash;34 years are the participants of this study. As literature states that, the people between the age group of 25\u0026ndash;34 years use SM at higher rates primarily while the rate is higher between the age group of 18\u0026ndash;24 years on a second number (Turkish Statistical Institute, 2021; 76). Therefore, the current review is to investigate the prevalence of social media addiction among medical students between 18\u0026ndash;24 years and the 25\u0026ndash;34 years, due to high prevalence of social media use among this age group (Turkish Statistical Institute, 2021). Children and adults aged between 13\u0026ndash;25 years are a vulnerable population to social media addiction, gaming and gambling (59; 58). However, as this study's primary population is medical students, children below the age of 18 were excluded. However, older adults over 34 years were excluded due to lack of information and literature based evidence.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.7.2. Issue (I)\u003c/h3\u003e\n\u003cp\u003eDue to high prevalence of SMA among students and vast number of studies conducted on students, in almost every part of the world, SMA is declared a serious public health issue, however, the DSM-5 and ICD has not declared it a universal mental health disorder (29; Li, et. al., 2019). In order to address the literature gap this systematic review will be written on the Social Media Addiction (SMA), among medical students.\u003c/p\u003e\n\u003ch3\u003e2.7.3. Types of outcomes (O)\u003c/h3\u003e\n\u003cp\u003e. The primary outcome to be studied in this review was the factors associated with the frequency and pattern of SMA among medical students. All those studies which assessed the risk factors linked to the prevalence and pattern of SMA among students were included. However, the prevalence, pattern of social media use and mental health issues like depression, anxiety, loneliness, low self-esteem, reduced academic performance, poor sleep and dietary patterns associated with SMA among students were also considered.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7.4. Types of studies\u003c/h2\u003e \u003cp\u003eThere are two types of cross-sectional studies: descriptive and analytical (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Descriptive cross-sectional studies are used to analyse the prevalence of health outcomes in a specific population. However, in analytical cross-sectional studies, data on the prevalence of health outcomes and the exposure is collected and observed to determine the difference between the exposed and unexposed population. Therefore, depending upon the aim of the current systematic review, in order to analyze the findings of studies on the prevalence and pattern of SMA among medical students, cross-sectional studies were included, both analytical and descriptive. 2.7.5. Regions, languages and published dates\u003c/p\u003e \u003cp\u003eDepending upon the broad scope of the review question, the research papers were not restricted to the specific geographical location. All those papers relevant to the review questions were included from all over the world. However, non-English papers were excluded as it is impossible to get the translation of all those studies published in languages other than English due to limited time and to save the cost (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In order to avoid selection bias, no publication date was included; however, in order to avoid outdated studies, studies before 2010 were excluded Cooper et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Further information on the inclusion and exclusion criteria of the selected studies can be found in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInclusion and Exclusion Criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIO Framework\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion Criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Population (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll students including under \u0026amp; post-graduate students of both genders of universities and medical colleges - aged between 20\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll students of schools and colleges and outside of the selected age group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Issue (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll students having active internet connection, social media accounts or social networking applications and internet based devices such as smartphones, tablets, laptops and computers etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudents who do not have internet access, social media accounts and smartphones or internet-based devices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome measures (O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll studies investigating the factors associated with the prevalence and pattern of SMA among medical students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll studies, not investigating the factors associated with the prevalence and pattern of SMA among medical students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll cross-sectional analytical quantitative studies will be included.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll quantitative, RCTs, quasi- experimental etc. studies except cross-sectional analytical studies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographical location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll conducted on social media, internet, social networking sites addiction among medical students with no specific geographical location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo restriction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll studies published in English language will be included.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll those studies published in languages other than English will be excluded.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of publication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies from 2015\u0026ndash;2022 will be included.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies before 2015 will be excluded.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Quality assessment selected studies\u003c/h2\u003e \u003cp\u003eThe quality and strength of the information presented in the reviews influence the conclusions made in the systematic reviews, as these are aimed at providing reliable information to decision-makers. Therefore, the strength and ability of reviews to provide reliable information depends on the inclusion of reviews which meet the minimum standard of quality (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). The quality of published papers varies widely. Critical appraisal and analysis of studies is a significant process to determine the methodological quality of the studies and to evaluate the extent to which studies have explained the possible bias in their design, conduct and analysis. It is used to determine the trustworthiness and credibility of studies and their outcomes. ). A range of quality appraisal tools can be used to assess the quality of studies in systematic reviews, depending on the suitability of the review question and the type of studies included. Different quality assessment tools can be used to assess different studies. For example, the Critical Appraisal Skills Programme (CASP) checklist, Assessing the Methodology Quality of Systematic Reviews (AMSTAR), Scottish Intercollegiate Guidelines Network (SIGN), LEGEND Evidence Evaluation Tools, and Joanna Briggs Institute (JBI) (Critical Appraisal Checklist for Systematic Reviews and Research Synthesis can be used. These tools have different questions/checklists used to assess other parts of included studies (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). As the included studies were mostly cross-sectional, the quality of studies will be evaluated with the help of the Joanna Briggs Institute (JBI) appraisal tool in this review. The JBI appraisal tool is a worldwide collective supportive evidence-based exercise widely used in allied health fields. It is commonly used in all quantitative study assessment procedures; it is quick and straightforward to use and contains eight questions. It is a qualitative checklist and does not contain any scoring system for evidence; however, it effectively determines the validity of studies comprehensively (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). High-quality studies that met the inclusion criteria were included in this review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Method of data collection\u003c/h2\u003e \u003cp\u003eData from relevant studies was extracted using Microsoft Excel Sheet in three different ways.\u003c/p\u003e \u003cp\u003eFirstly, data was extracted on the summary of characteristics of included studies, which provided information about the author, year of study, study design, study population, study settings and countries, primary aim and main findings of the study. This data was collected on an MS Excel spreadsheet. Secondly, data on baseline characteristics of the study population was collected on another MS Excel sheet, which included information about the author and years of study, sample size of the study population, socio-demographic characteristics of participants including age in years, gender, duration of SM use, and health indicators or risk factors of SMA. Finally, for narrative synthesis, data was analyzed and collected on the author and year of study, arms of study, models used to analyze data, outcome measures or results on the frequency of social media use, and its associated risk factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Method of data analysis\u003c/h2\u003e \u003cp\u003eAs, the outcome measures of each study varied therefore the author was unable to perform meta-analysis, however, narrative synthesis was conducted. Each study was describes with the help of comparative quantitative analysis and results were synthesized accordingly.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe search was conducted according to the principles of Systematic Reviews. The searched databases were: PubMed (2010\u0026ndash;2022), MEDLINE (2010\u0026ndash;2022), and Science Direct (2010\u0026ndash;2022). The key terms searched were social media addiction; internet addiction; smartphone addiction; problematic social media, internet, or smartphone use/overuse; prevalence and students or medical students. Terms must include: social media addiction with prevalence among students. However, terms may include: purpose of social media, internet or gadgets used, duration of internet use, outcome measures or results on the frequency of social media use, and students\u0026rsquo; health in response to the frequency of internet use. Using these key terms, researchers found total 86 studies from PubMed, 158 studies from MEDLINE and 51 studies from Science Direct. Among these, researchers removed 81 duplicates. After removing duplicates, 214 studies were screened on the basis of abstract and tittle. After this, researcher separated the relevant and irrelevant data after full text screening, out of 63 articles 53 papers were excluded. Finally, 10 relevant and open access studies were extracted/shortlisted for synthesis, from the searched data which is shown in the form of PRISMA flow diagram, or a flow chart is extensively used now a days by the reviewers, in order to improve the quality of systematic review. PRISMA consists of 4-phase flow diagram containing a checklist, which is used in this review to elaborate the study selection procedure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Summary of characteristics of included studies\u003c/h2\u003e \u003cp\u003eThe included studies were cross-sectional, conducted in different parts of the globe, which includes three studies from different states of Saudi Arabia, three studies from different states of India, and one from each state of Iran, Turkey, Ethiopia, and China. A summary of characteristics of included studies shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of characteristics of included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy Design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation/ Setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration of study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStudy Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimary Aim\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMain Findings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBarman et al., (2018)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKolkata, West Bengal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJune\u0026ndash;August 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedical College, undergraduate students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo determine the pattern of SNS use, the prevalence of anxiety and depression, and the association between SNS and anxiety and depression.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThere was significant prevalence of SNS use, which was significantly associated with anxiety and depression.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDharmadhikari et al., (2019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWestern Maharashtra, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNovember 2016 - January 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGovernment Medical College students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo determine the rate of smartphone addiciton (SA) and its corelation with sleep quality and stress.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFindings revealed that there is a high prevalence of SA among students which is significantly associated with poor sleep quality and stress.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKolaib et al., (2020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMadinah, KSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMay, 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTaibah University Students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo determine the prevalence of IA and the factors associated with it's addiction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResults found high rate of IA and the time spent more than 10 h/d was the primary risk factor of IA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMasthi et. al., (2018)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban Bengaluru city, Karnataka, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJuly - December 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGovernment and Private Pre-University (PU) college students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo investigate the preevalence of SMA, to determine the health issues related to SMA and to investigate the risk factors associated with SMA.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSiginificant prevalence of IA was found with health problems like eye strains, anger and sleep disturbances. However, smoking, alcohol, and tobacco, junk food concumption, ringxiety and selfitis were the significant risk factors for SMA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRahiminia et al., (2021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTehran, Iran.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23rd Aug, 2020\u0026ndash;29th Oct, 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShahid Beheshti University of Medical Sciences students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo measure the prevalence of IA and its related factors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResults revealed high prevalence of IA, while age and nerve medicine use were considered the risk factors of IA, as there is a significant association between age and IA, and nerve medicine used\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShaibani (2020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApril, 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTaif University students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo assess the prevalence of SNA and its association with demographic variables.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe duration of SM use, the frequency of SM use during lectures and students' perception of benefits of SM use were the indicators of SMA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTaha et al., (2019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCcross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuraydah, Saudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecember 2017 and April 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQasim University Students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo assess te prevalence of IA and its association with gender, academic performance and health.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFound very high prevalence of IA, associated with pooer sleep-pattern and psychological well-being, affecting academic performance of students. Females were more vulnerable to IA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUyaroglu et al., (2022)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral Anatolian, Turkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15th January \u0026minus;\u0026thinsp;30th March, 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStudents of Health Services School - Foundational University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo assess the relationship between SMA and social and emotional loneliness.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFindings revealed a possitive and significant associateion between SMA and loneliness, hence, loneliness was a significant risk factors of SMA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZenebe et al., (2021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNortheastern Ethiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApril 10 - May 10, 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWollo University students, Dessie campus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo assess the prevalence and associated factors associated with IA.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFindings revealed that there was a high prevalence of IA and the associated factors were spending more time, having mental distress, online gaming, khat chewing, and alcohol use.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZhao et al., (2022)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApril - June 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedical College students of a state university (with non-medical history)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTo investigate whether the demographic factors (age, gender), impulsivity, self-esteem, emotions, and attentional bias were risk factors associated with SMA.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFindings revealed that impulsivity, low levels of self-esteem, anxiety, social anxiety, and ANI were risk factors for SMA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Baseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eMost of the studies have shown a link between SMA and demographic variables of students, which includes age, gender, duration of SM use, sleep pattern and individual habit of smoking, tobacco, alcohol, and junk food consumption. Also, most of the studies reported association in groups depending upon age or gender or other variables; however, other studies reported as a whole. Therefore, data on demographic characteristics of participants was collected, as shown below in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of included Participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003esample size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGender n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003esociodemographic Characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eenrolled in study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecompleted the study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDuration of use h/d (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ePattern of use Mean (SD) or n%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eHealth indicators/ risk factors of SMA (%) Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eBarman et al., (2018)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e21.6 (\u0026plusmn;\u0026thinsp;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;(51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;(49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ealways open (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elate night \u0026amp; early morning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003edepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eanxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDharmadhikari et al., (2019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e20.23 (\u0026plusmn;\u0026thinsp;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e96(49.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e99 (50.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eright before sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e87 (98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003esleep disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e46.67% 6.37 (\u0026plusmn;\u0026thinsp;4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003estress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e17.70 (\u0026plusmn;\u0026thinsp;6.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKolaib et al., (2020)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1 (\u0026plusmn;\u0026thinsp;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e154(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e272(63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u0026ndash;7 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMasthi et. al., (2018)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e21.2 (\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e921(66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e468 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14 h/w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ejunk food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e87.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTobacco, alcohol, smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRahiminia et al., (2021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 and older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.81 (\u0026plusmn;\u0026thinsp;4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e342(34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e658(65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAntipsy-chotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.3(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eShaibani (2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e21\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e25.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e193 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e504 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026le; 10 (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLectures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.3 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c12\" namest=\"c11\" rowspan=\"3\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTo forget problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.95 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePercieved advantages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.31 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTaha et al., (2019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e18\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e121(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e88(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elonger than intended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elate night use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e70.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e59.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eloss of sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e70.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eUyaroglu et al., (2022)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e30.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e84(15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e471(84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emore than 5h 44.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003esocial loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eemotional loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e30.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eZenebe et al., (2021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e21.4 (\u0026plusmn;\u0026thinsp;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e291(53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e257(46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eonline gaming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003echewing khat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e104(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emental distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e106(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ealcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e139(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZhao et al., (2022)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.68 (\u0026plusmn;\u0026thinsp;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e277(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo data to be reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003erisk factors (impulsivity, low self-esteem, anxiety, social anxiety and ANI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Quality assessment of included studies\u003c/h2\u003e \u003cp\u003eQuality assessment of included papers was done using JBI appraisal checklist consisting of 8 simple questions. The list of questions shown in Appendix 1. However, the list of answers determining the quality of each papers can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCritical appraisal checklist\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarman et al., (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDharmadhikari et al., (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKolaib et al., (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasthi et. al., (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRahiminia et al., (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShaibani (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaha et al., (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUyaroglu et al., (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZenebe et al., (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao et al., (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Narrative synthesis\u003c/h2\u003e \u003cp\u003eThis study included 10 primary research papers with total population of 6376. Results of these studies identified the risk factors associated with prevalence and pattern of social media addiction among medical students. Summary of results is shown below in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Results for Narrative Synthesis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy Arms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModels/ Theories/ Tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBarman et al., (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-Structured Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency \u0026amp; Percentages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eState Trait Anxiety Inventory Scale (STAI-S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrequency \u0026amp; Percentages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBecks Depression Inventory (BDI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation of SNS \u0026amp; BDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney-U (MW-U) test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean and Ranges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(MW-U\u0026thinsp;=\u0026thinsp;3636.0;\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026nbsp;= 0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation of SNS \u0026amp; STAI-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(MW-U\u0026thinsp;=\u0026thinsp;3785.0;\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026nbsp;= 0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDharmadhikari et al., (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmartphone addiciton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmartphone Addiction Scale-Short Version (SAS-SV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean and Standard Deviation (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.59 (\u0026plusmn;\u0026thinsp;9.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohen\u0026rsquo;s Perceived Stress Scale (PSS-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean and Ranges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.70 (\u0026plusmn;\u0026thinsp;6.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index (PSQI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.37 (\u0026plusmn;\u0026thinsp;4.47).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorelation of SA and PSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMultiple Regression Model and Pearson\u0026rsquo;s correlation test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegression and Paerson's coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(r\u0026thinsp;=\u0026thinsp;0.31, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecorelation of SA and PSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(r\u0026thinsp;=\u0026thinsp;0.40, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKolaib et al., (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet Addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternet Addiction Test (IAT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD) and Ranges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.2 (\u0026plusmn;\u0026thinsp;16.3)\u003c/p\u003e \u003cp\u003e20 to 100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation between IAT and internet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANOVA test \u0026amp;\u003c/p\u003e \u003cp\u003eT-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMasthi et. al., (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency \u0026amp; Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAssociation between SMA and individual variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUnivariate Logistic Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAdjusted odds ratio (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMA \u0026amp; Physical Symptoms\u003c/p\u003e \u003cp\u003e(2.21 [1.77\u0026ndash;2.76],\u0026nbsp;P\u0026nbsp;\u0026lt; 0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMA \u0026amp; Psychological changes\u003c/p\u003e \u003cp\u003e(1.96 [1.57\u0026ndash;2.44],\u0026nbsp;P\u0026nbsp;\u0026lt; 0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMA \u0026amp; Behavioral changes (2.63 [2.06\u0026ndash;3.35],\u0026nbsp;P\u0026nbsp;\u0026lt; 0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRahiminia et al., (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency \u0026amp; Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (44%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAssociation between SNA, age and nerve medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLogistic Regression with Stata software version 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNA and age\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNA and using nerve medicine (p\u0026thinsp;=\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShaibani (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-Structured Questionnaire and Bergen Facebook Addiction Scale (BFAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD), Frequency \u0026amp; Percentages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.00\u003c/p\u003e \u003cp\u003e70% (254)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAssociation between SMA and factors associated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePearson\u0026rsquo;s correlation test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean Frequency Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMA and perceived advantages of SM (p\u0026thinsp;\u0026lt;\u0026thinsp;.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerceived SMA and hours of SMA use\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;.010).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTaha et al., (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInternet Addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrequency and percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaddicts 12.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epotential addicts 57.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelationship between Internet use and gender.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-Square Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemales were more vulnerable to IA\u003c/p\u003e \u003cp\u003e(\u003cem\u003ew\u003c/em\u003e\u0026nbsp;= 0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUyaroglu et al., (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial Media Addiction Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean and Standard Deviation (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial and Emotional Loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial and Emotional Loneliness Scale for Adults (SELSA-S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.89\u0026thinsp;\u0026plusmn;\u0026thinsp;15.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation between SMA and Social \u0026amp; Emotional Loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpearman correlation analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorelation coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(r\u0026thinsp;=\u0026thinsp;0.196\u0026nbsp;p\u0026thinsp;=\u0026thinsp;0.000) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eZenebe et al., (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYoung\u0026rsquo;s Internet Addiction Test (YIAT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency \u0026amp; Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e466(85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation between IA and Mental Distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBinary Logistic Regression method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAdjusted odds ratio, 95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(AOR\u0026thinsp;=\u0026thinsp;2.69, 95% CI 1.02\u0026ndash;7.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation between IA and duration of use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(AOR\u0026thinsp;=\u0026thinsp;10.13, 95% CI 1.33\u0026ndash;77.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation of IA to Online gaming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(AOR\u0026thinsp;=\u0026thinsp;2.40, 95% CI 1.38\u0026ndash;4.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation of IA to chewing khat and Alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(AOR\u0026thinsp;=\u0026thinsp;3.34, 95% CI 1.14\u0026ndash;9.83) and\u003c/p\u003e \u003cp\u003e(AOR\u0026thinsp;=\u0026thinsp;2.32, 95% CI 1.09\u0026ndash;4.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eZhao et al., (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eTotal Link between IA and its Associated factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHierarchical\u003c/p\u003e \u003cp\u003eRegression Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency \u0026amp; Percentages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImpulsivity (Brief Barratt Impulsivity Scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(β\u0026thinsp;=\u0026thinsp;0.34,\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;= 8.50,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Self-Esteem (Rosenberg Self-Esteem Scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(β = \u0026minus;0.20,\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;= \u0026minus;4.38,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety (Self-Rating Anxiety Scale \u0026amp; Interaction Anxiety Scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(β\u0026thinsp;=\u0026thinsp;0.24,\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;= 4.43,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001),\u003c/p\u003e \u003cp\u003esocial anxiety (β\u0026thinsp;=\u0026thinsp;0.25,\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;= 5.79,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAttention to Positive and Negative Inventory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(β\u0026thinsp;=\u0026thinsp;0.31,\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;= 8.01,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(β = \u0026minus;0.21, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Interpretation of outcomes\u003c/h2\u003e \u003cp\u003eMore than 90% of the students used more than one SNS, of which 97.9% used WhatsApp, 91.4% used Facebook, and 30.5% used Instagram (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). While 59.0% used SNS for communication with friends and families, 43.1% used it for entertainment and 31.4% for academic and professional activities. Furthermore, the study reported that 29.0% of the students stayed active on SNS the whole day, while 80% accessed SNS for at least 4 hours or more daily. Also, 18.0% of the students woke up early in the morning and slept late at night to spend more time on the Internet, while 23% could not spend a day without the Internet. Moreover, the study significantly revealed that 24% had depression, out of which 4.0% had severe depression and 68.5% had a state of anxiety, out of which 59.0% had moderate, while 9.5% had severe anxiety. There was a significant association between the time spent on SNS and depression and anxietyBDI scores (MW-U\u0026thinsp;=\u0026thinsp;3636.0; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and STAI-S scores (MW-U\u0026thinsp;=\u0026thinsp;3785.0; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) were higher among those who wake up early in the morning and sleep late at night to spend more time on SNS.\u003c/p\u003e \u003cp\u003eDharmadhikari et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) found that 90 (46.15%) students were smartphone-addicted, while 105(53.18% ) students were non-addicts. Of 195 students, 45.45% were females, and 47.87% were males, with almost an equal gender ratio. Furthermore, 33.85% (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) of the students used WhatsApp, 12.31% used Instagram, and 5.13% used Facebook. The rate of smartphone use by the students for text messaging was 57.14%, and for internet browsing, it was 10.86%. However, 87(98.86%) students use smartphones right before sleeping. Furthermore, the study revealed that the factors associated with smartphone addiction were stress and poor sleep patterns; the average rate of stress was 17.70 (\u0026plusmn;\u0026thinsp;6.14) as per PSS, while the average score of impaired sleep was 6.37 (\u0026plusmn;\u0026thinsp;4.47) as per PSQI scores, which means that 46.67% (91) students had impaired sleep. The study further investigated the weak positive linear correlation between the SAS-SV and PSQI scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a moderate positive linear correlation between the SAS-SV and PSS-10 scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) using the Pearson's correlation test.\u003c/p\u003e \u003cp\u003eKolaib et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) found that 6% of the participants were internet addicts while 52% were non-addicts or average users, and 42% had occasional problems. The mean (SD) ITA score was 51.2 (16.3), ranged 20 to 100. The pattern of internet use was 5\u0026ndash;7 hours/ day in 40.8% of respondents. Moreover, 88.5% use it for social networking and 58.7% for downloading media files, while 67.6% had a history of Internet use for more than 8 years. However, the rate of IA was lower among those with a high GPA 46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6 as compared to those with a low GPA (52.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1) and (52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5), (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Also, IA scores were higher among 71.6% of those who had internet access at college (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), 95.8% of those who had mobile internet access (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and 97.2% of those who had internet access at home (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) and among those who used the Internet for more than 10 hours per day (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, easy access to the Internet and time spent on the Internet were the risk factors of IA among students.\u003c/p\u003e \u003cp\u003eMasthi et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) conducted a cross-sectional study on 1870 students of government and private universities, and out of 1870 students, 74.2% (1389) were Social Media Users, while 25.8% (481) were non-users, and 66.4% (921) were males while 33.6% (468) were females among the SM users. The findings of the study revealed that overall, 27.4% of the students were social media addicts, out of which 24.0% in government and 30.8% in private colleges were SM addicts. Furthermore, 38.9% of the people with an addiction used Facebook, 31% were addicted to internet gaming, and 41.2% used WhatsApp. However, 87.5% (1216) consumed junk food, 10.1% of the people with addiction were habitual to smoking, alcohol, and chewable tobacco, 339 (66%) of social media addicts had Ringxiety, and 38.7% had selfies. Therefore, the habit of smoking, alcohol, and tobacco, consumption of junk food, and having Ringxiety and selfies were considered to be significant risk factors for social media addiction among males. Therefore, these habits made males more prone to social media addiction. The results on considerable health issues identified stated that 38.4% had a strain on the eyes, 30.7% had neck pain, 25.% suffered from anger, and 26.1% had sleep disturbances.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRahiminia\u003c/b\u003e et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e) investigated that 44% (462) students were addicted to social networks, out of which 90.04% (449) were slightly while 9.96% (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) were severely addicted. Furthermore, the students' mean (SD) age was 22.81 (4.66) years. The study further revealed that 11.8% (118) students were smokers, 2.3% (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) used to take antipsychotics, out of which 47.81% took benzodiazepine, 30.43% selective serotonin reuptake inhibitor (SSRI), 13.04% tricyclic ani depressant (TCA), 4.35% serotonin-norepinephrine reuptake inhibitor (SNRI: Duloxetine), and 4.35% took sleeping pills. Hence, the findings revealed that there is a significant association between age (p\u0026thinsp;=\u0026thinsp;0.001), use of nerve medicine (p\u0026thinsp;=\u0026thinsp;0.0001) and social network' addiction; hence, age and use of nerve medicines were the primary risk factors of SMA. However, no significant relationship was established between SNA and gender (sig\u0026thinsp;=\u0026thinsp;0.47), marital status (sig\u0026thinsp;=\u0026thinsp;0.06), level of education (sig\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and smoking (sig\u0026thinsp;=\u0026thinsp;0.18).\u003c/p\u003e \u003cp\u003eShaibani (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) found that the mean social networking addiction (SNA) was 50.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.00, which was at a moderate level among 70% (254) students. The mean SNA was higher among males (52.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.50) as compared to female students (49.35\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96), out of which 72.3% used social networks during lectures 2.3 (1.0) mean (SD), and they spent 7.4 (5.5) mean (SD) hours per day on average, while 75% used social media for \u0026le;\u0026thinsp;10 hours per days. However, the top factors associated with SMA among students in terms of perceived advantages were \"using social media to help them in their studies\" (mean rating\u0026thinsp;=\u0026thinsp;3.75/5, RII\u0026thinsp;=\u0026thinsp;75.1%), feeling more informed than others because they used social media (mean rating\u0026thinsp;=\u0026thinsp;3.69/5, RII\u0026thinsp;=\u0026thinsp;73.7%) \"perception of solving problems with the help of social media (mean rating\u0026thinsp;=\u0026thinsp;3.51/5, RII\u0026thinsp;=\u0026thinsp;70.2%) \"an urge to use social media more and more (mean frequency rating\u0026thinsp;=\u0026thinsp;3.2/5, RII\u0026thinsp;=\u0026thinsp;64.0%) and \"using social media to forget about their problems\" (mean frequency rating\u0026thinsp;=\u0026thinsp;2.95/5, RII\u0026thinsp;=\u0026thinsp;59.1%), were the main risk factors of SMA among students. However, a significant association was established between SMA and perceived advantages of SM use (p\u0026thinsp;\u0026lt;\u0026thinsp;.010) and SMA and duration of use (p\u0026thinsp;\u0026lt;\u0026thinsp;.010), indicating essential risk factors of SMA.\u003c/p\u003e \u003cp\u003eTaha et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) found that according to IAT, the rate of IA was 12.4%, while 57.9% had the potential risk of addiction among students. Females were more frequent Internet users than males (\u003cem\u003ew\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Moreover, the % of students frequently staying longer than intended was 82.3%, while the impact on academic performance was reported by 62.2%. Out of 96.8% of the response rate, 70.8% lost sleep due to late-night Internet use, 58.9% felt depressed, moody, or nervous when they were offline, and 80.4% lived with their families. Due to frequent internet use, health issues like headaches, backache, weight gain, neck pain and other psychological were also reported by respondents. A significant difference was established between the total IAT score and neck pain (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and a state of sleeplessness because of staying online (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). In contrast, the relationship between BMI and IAT scores was not established. However, IAT scores and weight loss/ gain established a significant association (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Hence, the duration of Internet use and sleeplessness among students were the primary risk factors for IA.\u003c/p\u003e \u003cp\u003eUyaroglu et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) found that the mean rate of SMA among Turkish students was 61.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53 mean (SD). 43.4% of the students used the Internet for 4\u0026ndash;5 hours daily. The mean (SD) Social loneliness score was 12.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49, while the mean (SD) emotional loneliness score was 30.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.45. However, the Total loneliness score was 42.89\u0026thinsp;\u0026plusmn;\u0026thinsp;15.17 mean (SD), respectively. Hence, a significant positive relationship between the high prevalence of social media addiction and emotional and social loneliness, and loneliness was found to be a primary risk factor for social media addiction among students (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.196 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eZenebe et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e) revealed that the total prevalence of internet addiction (IA) was 85% (n\u0026thinsp;=\u0026thinsp;466); however, the frequency was distributed in three categories, mild, moderate and severe. Results showed that out of 85% (n\u0026thinsp;=\u0026thinsp;466) IA students, the rate of mild, moderate and severe IA was 55.6% (305), 27.9% (153) and 1.5% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) respectively. In comparison, the remaining 15% (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e) were non-IA. The rate of IA among students who use the Internet frequently and permanently was 92.2%, which is higher than those who do not log in often, which is 83.1%. Furthermore, the findings on the pattern of use revealed that 93.6% of the respondents use the Internet for courses/assignments, 76.6% for reading/posting news, 47.6% for chat rooms and 49.8% for e-mail (reading, writing). In comparison, 85.6% use social networks (Facebook, etc.), 66.6% use them for getting into relationships online, 44.5% for playing mobile games, 65.7% for downloading music or videos, and 57.8% for watching videos. At the same time, 22.8% use it for retrieving sexual information. However, more time spent on the Internet (AOR\u0026thinsp;=\u0026thinsp;10.13, 95% CI 1.33\u0026ndash;77.00), mental distress (AOR\u0026thinsp;=\u0026thinsp;2.69, 95% CI 1.02\u0026ndash;7.06), online gaming (AOR\u0026thinsp;=\u0026thinsp;2.40, 95% CI 1.38\u0026ndash;4.18), khat chewing (AOR\u0026thinsp;=\u0026thinsp;3.34, 95% CI 1.14\u0026ndash;9.83) and alcohol use (AOR\u0026thinsp;=\u0026thinsp;2.32, 95% CI 1.09\u0026ndash;4.92) were associated with internet addiction. However, no association was established between IA and smoking.\u003c/p\u003e \u003cp\u003eZhao et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e) investigated the rate of significant risk factors associated with SMA at 38%. The study comprised 53% (277) females and 27% (243) males. The findings of the study clearly stated that the females were more prone to SMA (β = \u0026minus;0.21, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to males. Furthermore, the study investigated the rate of each risk factor associated with SMA with the help of regression analysis. The identified risk factors were impulsivity (β\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), self-esteem (β = \u0026minus;0.20, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety (β\u0026thinsp;=\u0026thinsp;0.24, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), social anxiety (β\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and negative attentional biases (β\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which showed a significant association with SM use. The greater the rate of depression, anxiety, and low self-esteem, the greater will be the rate of SMA. Hence, impulsivity, low levels of self-esteem, anxiety, social anxiety, and attention to negative information (ANI) were found to be risk factors for SMA; however, no link was found between IA and depression and loneliness, while gender and IA were related as females were found to be more addicted to the internet (β = \u0026minus;0.21, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMore than 90% of the students used more than one SNS, of which 97.9% used WhatsApp, 91.4% used Facebook, and 30.5% used Instagram (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). While 59.0% used SNS for communication with friends and families, 43.1% used it for entertainment and 31.4% for academic and professional activities. Furthermore, the study reported that 29.0% of the students stayed active on SNS the whole day, while 80% accessed SNS for at least 4 hours or more daily. Also, 18.0% of the students woke up early in the morning and slept late at night to spend more time on the internet, while 23% could not spend a day without the internet. Moreover, the study significantly revealed that 24% had depression, out of which 4.0% had severe depression and 68.5% had a state of anxiety, out of which 59.0% had moderate, while 9.5% had severe anxiety. There was a significant association between the time spent on SNS and depression and anxietyBDI scores (MW-U\u0026thinsp;=\u0026thinsp;3636.0; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and STAI-S scores (MW-U\u0026thinsp;=\u0026thinsp;3785.0; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) were higher among those who wake up early in the morning and sleep late at night to spend more time on SNS.\u003c/p\u003e \u003cp\u003eDharmadhikari et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) found that 90 (46.15%) students were smartphone-addicted, while 105(53.18% ) students were non-addicts. Of 195 students, 45.45% were females, and 47.87% were males, with almost an equal gender ratio. Furthermore, 33.85% (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) of the students used WhatsApp, 12.31% used Instagram, and 5.13% used Facebook. The rate of smartphone use by the students for text messaging was 57.14%, and for internet browsing, it was 10.86%. However, 87(98.86%) students use smartphones right before sleeping. Furthermore, the study revealed that the factors associated with smartphone addiction were stress and poor sleep patterns; the average rate of stress was 17.70 (\u0026plusmn;\u0026thinsp;6.14) as per PSS, while the average score of impaired sleep was 6.37 (\u0026plusmn;\u0026thinsp;4.47) as per PSQI scores, which means that 46.67% (91) students had impaired sleep. The study further investigated the weak positive linear correlation between the SAS-SV and PSQI scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a moderate positive linear correlation between the SAS-SV and PSS-10 scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) using the Pearson's correlation test.\u003c/p\u003e \u003cp\u003eKolaib et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) found that 6% of the participants were internet addicts while 52% were non-addicts or average users, and 42% had occasional problems. The mean (SD) ITA score was 51.2 (16.3), ranged 20 to 100. The pattern of internet use was 5\u0026ndash;7 hours/ day in 40.8% of respondents. Moreover, 88.5% use it for social networking and 58.7% for downloading media files, while 67.6% had a history of Internet use for more than 8 years. However, the rate of IA was lower among those with a high GPA 46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6 as compared to those with a low GPA (52.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1) and (52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5), (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Also, IA scores were higher among 71.6% of those who had internet access at college (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), 95.8% of those who had mobile internet access (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and 97.2% of those who had internet access at home (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) and among those who used the internet for more than 10 hours per day (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, easy access to the internet and time spent on the internet were the risk factors of IA among students.\u003c/p\u003e \u003cp\u003eMasthi et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) conducted a cross-sectional study on 1870 students of government and private universities, and out of 1870 students, 74.2% (1389) were Social Media Users, while 25.8% (481) were non-users, and 66.4% (921) were males while 33.6% (468) were females among the SM users. The findings of the study revealed that overall, 27.4% of the students were social media addicts, out of which 24.0% in government and 30.8% in private colleges were SM addicts. Furthermore, 38.9% of the people with an addiction used Facebook, 31% were addicted to internet gaming, and 41.2% used WhatsApp. However, 87.5% (1216) consumed junk food, 10.1% of the people with addiction were habitual to smoking, alcohol, and chewable tobacco, 339 (66%) of social media addicts had Ringxiety, and 38.7% had selfies. Therefore, the habit of smoking, alcohol, and tobacco, consumption of junk food, and having Ringxiety and selfies were considered to be significant risk factors for social media addiction among males. Therefore, these habits made males more prone to social media addiction. The results on considerable health issues identified stated that 38.4% had a strain on the eyes, 30.7% had neck pain, 25.% suffered from anger, and 26.1% had sleep disturbances.\u003c/p\u003e \u003cp\u003eRahiminia et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e) investigated that 44% (462) students were addicted to social networks, out of which 90.04% (449) were slightly while 9.96% (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) were severely addicted. Furthermore, the students' mean (SD) age was 22.81 (4.66) years. The study further revealed that 11.8% (118) students were smokers, 2.3% (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) used to take antipsychotics, out of which 47.81% took benzodiazepine, 30.43% selective serotonin reuptake inhibitor (SSRI), 13.04% tricyclic ani depressant (TCA), 4.35% serotonin-norepinephrine reuptake inhibitor (SNRI: Duloxetine), and 4.35% took sleeping pills. Hence, the findings revealed that there is a significant association between age (p\u0026thinsp;=\u0026thinsp;0.001), use of nerve medicine (p\u0026thinsp;=\u0026thinsp;0.0001) and social network' addiction; hence, age and use of nerve medicines were the primary risk factors of SMA. However, no significant relationship was established between SNA and gender (sig\u0026thinsp;=\u0026thinsp;0.47), marital status (sig\u0026thinsp;=\u0026thinsp;0.06), level of education (sig\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and smoking (sig\u0026thinsp;=\u0026thinsp;0.18).\u003c/p\u003e \u003cp\u003eShaibani (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) found that the mean social networking addiction (SNA) was 50.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.00, which was at a moderate level among 70% (254) students. The mean SNA was higher among males (52.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.50) as compared to female students (49.35\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96), out of which 72.3% used social networks during lectures 2.3 (1.0) mean (SD), and they spent 7.4 (5.5) mean (SD) hours per day on average, while 75% used social media for \u0026le;\u0026thinsp;10 hours per days. However, the top factors associated with SMA among students in terms of perceived advantages were \"using social media to help them in their studies\" (mean rating\u0026thinsp;=\u0026thinsp;3.75/5, RII\u0026thinsp;=\u0026thinsp;75.1%), feeling more informed than others because they used social media (mean rating\u0026thinsp;=\u0026thinsp;3.69/5, RII\u0026thinsp;=\u0026thinsp;73.7%) \"perception of solving problems with the help of social media (mean rating\u0026thinsp;=\u0026thinsp;3.51/5, RII\u0026thinsp;=\u0026thinsp;70.2%) \"an urge to use social media more and more (mean frequency rating\u0026thinsp;=\u0026thinsp;3.2/5, RII\u0026thinsp;=\u0026thinsp;64.0%) and \"using social media to forget about their problems\" (mean frequency rating\u0026thinsp;=\u0026thinsp;2.95/5, RII\u0026thinsp;=\u0026thinsp;59.1%), were the main risk factors of SMA among students. However, a significant association was established between SMA and perceived advantages of SM use (p\u0026thinsp;\u0026lt;\u0026thinsp;.010) and SMA and duration of use (p\u0026thinsp;\u0026lt;\u0026thinsp;.010), indicating essential risk factors of SMA.\u003c/p\u003e \u003cp\u003eTaha et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) found that according to IAT, the rate of IA was 12.4%, while 57.9% had the potential risk of addiction among students. Females were more frequent Internet users than males (\u003cem\u003ew\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Moreover, the % of students frequently staying longer than intended was 82.3%, while the impact on academic performance was reported by 62.2%. Out of 96.8% of the response rate, 70.8% lost sleep due to late-night Internet use, 58.9% felt depressed, moody, or nervous offline, and 80.4% lived with their families. Due to frequent internet use, health issues like headaches, backache, weight gain, neck pain and other psychological were also reported by respondents. A significant difference was established between the total IAT score and neck pain (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and a state of sleeplessness because of staying online (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e \u003cp\u003eIn contrast, the relationship between BMI and IAT scores was not established. However, IAT scores and weight loss/ gain established a significant association (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Hence, the duration of Internet use and sleeplessness among students were the primary risk factors for IA.\u003c/p\u003e \u003cp\u003eUyaroglu et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) found that the mean rate of SMA among Turkish students was 61.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53 mean (SD). 43.4% of the students used the internet for 4\u0026ndash;5 hours daily. The mean (SD) Social loneliness score was 12.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49, while the mean (SD) emotional loneliness score was 30.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.45. However, the Total loneliness score was 42.89\u0026thinsp;\u0026plusmn;\u0026thinsp;15.17 mean (SD), respectively. Hence, a significant positive relationship between the high prevalence of social media addiction and emotional and social loneliness, and loneliness was found to be a primary risk factor for social media addiction among students (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.196 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eZenebe et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e) revealed that the total prevalence of internet addiction (IA) was 85% (n\u0026thinsp;=\u0026thinsp;466); however, the frequency was distributed in three categories: mild, moderate and severe. Results showed that out of 85% (n\u0026thinsp;=\u0026thinsp;466) IA students, the rate of mild, moderate and severe IA was 55.6% (305), 27.9% (153) and 1.5% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) respectively. In comparison, the remaining 15% (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e) were non-IA. The rate of IA among students who use the internet frequently and permanently was 92.2%, which is higher than those who do not log in often, which is 83.1%. Furthermore, the findings on the pattern of use revealed that 93.6% of the respondents use the internet for courses/assignments, 76.6% for reading/posting news, 47.6% for chat rooms and 49.8% for e-mail (reading, writing). In comparison, 85.6% use social networks (Facebook, etc.), 66.6% use them for getting into relationships online, 44.5% for playing mobile games, 65.7% for downloading music or videos, and 57.8% for watching videos. At the same time, 22.8% use it for retrieving sexual information. However, more time spent on the Internet (AOR\u0026thinsp;=\u0026thinsp;10.13, 95% CI 1.33\u0026ndash;77.00), mental distress (AOR\u0026thinsp;=\u0026thinsp;2.69, 95% CI 1.02\u0026ndash;7.06), online gaming (AOR\u0026thinsp;=\u0026thinsp;2.40, 95% CI 1.38\u0026ndash;4.18), khat chewing (AOR\u0026thinsp;=\u0026thinsp;3.34, 95% CI 1.14\u0026ndash;9.83) and alcohol use (AOR\u0026thinsp;=\u0026thinsp;2.32, 95% CI 1.09\u0026ndash;4.92) were associated with internet addiction. However, no association was established between IA and smoking.\u003c/p\u003e \u003cp\u003eZhao et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e) investigated the rate of significant risk factors associated with SMA at 38%. The study comprised 53% (277) females and 27% (243) males. The study's findings clearly stated that the females were more prone to SMA (β = \u0026minus;0.21, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to males. Furthermore, the study investigated the rate of each risk factor associated with SMA with the help of regression analysis. The identified risk factors were impulsivity (β\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), self-esteem (β = \u0026minus;0.20, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety (β\u0026thinsp;=\u0026thinsp;0.24, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), social anxiety (β\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and negative attentional biases (β\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which showed a significant association with SM use. The greater the rate of depression, anxiety, and low self-esteem, the greater will be the rate of SMA. Hence, impulsivity, low levels of self-esteem, anxiety, social anxiety, and attention to negative information (ANI) were found to be risk factors for SMA; however, no link was found between IA and depression and loneliness, while gender and IA were related as females were found to be more addicted to the internet (β = \u0026minus;0.21, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis research is a systematic review. No participants were used for this research, a secondary piece of work, and no funding was sourced. \u0026nbsp;Clinical trial number: not applicable. Human Ethics and Consent to Participate declarations: not applicable\u0026rsquo;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.M. is the core author, N.Q.W assisted in the development and structure. All authors have reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Menayes JJ. Dimensions of social media addiction among university students in Kuwait. Psychol Behav Sci. 2015;4(1):23\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzizi SM, Soroush A, Khatony A. The relationship between social networking addiction and academic performance in Iranian students of medical sciences: a cross-sectional study. BMC Psychol. 2019;7(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsare-Donkoh F. (2018). 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BMC Psychol. 2021;9(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Appendix","content":"\u003cp\u003eAppendix 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social Media Addiction, Medical Students, Psychological, Social Media, Mental Health","lastPublishedDoi":"10.21203/rs.3.rs-6165651/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6165651/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: With the advancement of technology, social media use/overuse is becoming a vital part of human life. Due to the high prevalence of social media users, researchers found that there is a risk of developing social media addiction (SMA) among medical students. Some risk factors are associated with SMA, which significantly impacts students' behaviours, habits, and, in fact, their entire lives with adverse consequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e: The primary objective of this review is to investigate the factors associated with the prevalence and pattern of social media addiction among medical students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e: For this systematic review, articles were searched using three databases: PubMed, MEDLINE, and Scopus. The PIO guidelines and PRISMA flow diagram were used to retrieve relevant studies. Ten studies were identified for this review, all cross-sectional. All English language studies were included with a time range of 2010-2022. The quality of studies was assessed using JBI's quality assessment tool. The data was analysed using comparative analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A high prevalence of SMA in almost all study participants is associated with varied risk factors. However, there is a significant association between the intense use of social media and mental distress, anxiety, depression and loneliness, which are considered the primary risk factors of SMA in almost all the studies. However, age, gender, personal behaviours and habits such as the habit of chewing khat, alcohol \u0026amp; junk food consumption, low self-esteem, poor sleep quality and use of anti-psychotic drugs were also considered as the risk factors associated with the prevalence and pattern of social media addiction among medical students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Social media overuse linked to psychological problems was more prevalent among social media-addicted students. There is a significant need to make the general public well aware of this problem, and high self-awareness is required to prevent this issue. However, psychological treatments and rehabilitation centres need to be made accessible for the treatment of people with an addiction suffering from this public health issue.\u003c/p\u003e","manuscriptTitle":"Factors Associated With Prevalence and Pattern of Social Media Addiction Among Medical Students – A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 14:00:52","doi":"10.21203/rs.3.rs-6165651/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"358d6e58-7ca4-4cae-b6e7-1a5fe8035b7a","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-08T05:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 14:00:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6165651","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6165651","identity":"rs-6165651","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}