{"paper_id":"39d9e1d4-bfeb-4331-8a39-c0e04122e01e","body_text":"Social Disconnection in a Hyperconnected World: Loneliness, Social Isolation, and Problematic Technology Use Across 35 Nations | 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 Research Article Social Disconnection in a Hyperconnected World: Loneliness, Social Isolation, and Problematic Technology Use Across 35 Nations Justin Thomas, Yasmin Al-Jedawi, Melisa Valle This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8624119/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 There is growing international concern that both loneliness, a subjective experience, and social isolation, an objectively measurable condition, are increasing. The public health consequences of these forms of social disconnection are well documented. However, evidence regarding the relationship between digital technologies and social disconnection remains mixed. As part of Sync’s global digital wellbeing research program, we surveyed loneliness levels and online behaviours (e.g., video game play and social media use) across 35 nations and 35,000 adult participants. This report reviews relevant literature on loneliness, social isolation, and technology use, and also details the results of the cross-national survey. Loneliness was a widespread issue across all participating countries, with Japan experiencing particularly high rates. There was a clear link between problematic technology use and social disconnection across the whole sample and within each nation. The report elaborates on these findings, offering recommendations and suggestions for future research. Health Policy Psychology Loneliness Social Connection Gaming Social Media Cross-Cultural Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction In the 8th century, in a small town just outside Baghdad, a seemingly lonely and perhaps socially isolated individual wrote the following melancholic message on a wall: “May God water the days of togetherness with His rain and return every stranger to his home. There is no good in this world without togetherness and no joy in life without a loved one”. Loneliness and social isolation know no borders; these are timeless human experiences. However, there is a perception that both loneliness, a subjective experience, and social isolation, an objectively quantifiable state, are on the rise. The public health implications of both these forms of social disconnection are well known. What is less well understood is how personal digital technologies (devices/services) might contribute to the onset, worsening, or alleviation of these states. Differentiating loneliness and social isolation Loneliness is an unpleasant feeling that accompanies the perception of unmet social needs (Hawkley & Cacioppo, 2010 ). It is not the same as “being alone”. Many individuals experience, and perhaps even enjoy, extended periods of solitude without feeling lonely (Hawkley & Cacioppo, 2010 ). Furthermore, a person may lead a seemingly active social life, yet still feel lonely. This reality is eloquently articulated in a quote attributed to the Swiss Psychologist, Carl Jung: “Loneliness does not come from having no people about one, but from being unable to communicate the things that seem important to oneself”. At its core, loneliness arises from a perceived disparity between desired and actual levels of social connection. Loneliness is a subjective reaction to perceived deficiencies in one's social world. Social isolation, on the other hand, is the objective reality of having relatively few social roles, relationships, and social interactions (World Health Organization, 2025 ) Despite these differences, loneliness and social isolation correlate (Ge et al., 2017 ; Taylor et al., 2023 ). Individuals with fewer relationships, social roles and limited human connections or social support (the socially isolated) tend to experience loneliness. Likewise, those who feel lonely may be inclined to withdraw from others, leading to relative social isolation(National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), 2024 ). Loneliness and social isolation can also be enduring, persisting over long periods. In such cases, loneliness is viewed as a trait (van Roekel et al., 2018 ), and might be described as chronic or long-term loneliness. At precisely which time point does transient or short-term loneliness become chronic loneliness? This remains a matter of debate, with suggested durations spanning anywhere from 1 to 6 years (Wolska & Creaven, 2023 ). A similar distinction is made between transient (short-lived) and long-term social isolation. Health implications of loneliness and social isolation The WHO’s constitution proposes that health is “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”. Social wellbeing is at the very heart of what it means to live a healthy life. Although social wellbeing has been relatively overshadowed, the health implications of poor social wellbeing (loneliness and social isolation) have become increasingly apparent in recent decades. According to the US Centres for Disease Control and Prevention, both loneliness and social isolation (when chronic) are independently associated with an increased risk of experiencing mental and physical health problems, including heart disease, stroke, depression, and anxiety (National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), 2024 ). Research aimed at quantifying the mortality risk of chronic loneliness and social isolation suggests that they are comparable to obesity, physical Inactivity, alcohol consumption, and cigarette smoking (National Academies of Sciences et al., 2020). The World Health Organisation estimates that almost one in six people is affected by loneliness globally, and that loneliness and social isolation contribute to around 871,000 deaths annually (World Health Organization, 2025 ) During the COVID-19 pandemic, levels of loneliness and social isolation rose significantly due to the social distancing measures (Allen et al., 2022 ). However, the growing public health concern around these issues certainly pre-dates the pandemic. In 2018, for instance, the UK government appointed Tracey Crouch as the minister for loneliness. This first-of-its-kind appointment followed an influential government report on loneliness and social isolation — the Jo Cox Commission on Loneliness . Among other statistics, the report suggests that around 200,000 older people in the UK hadn’t conversed with a friend or relative in more than a month. The report described the impact of loneliness and social isolation as being twice as harmful as obesity and comparable to smoking 15 cigarettes per day.(Jopling, 2017 ) The UK report also talked about elevated rates of loneliness among young people. University students who feel like they don’t fit in—spending days in relative isolation with nothing but college deadlines and digital devices for companionship and support(Jopling, 2017 ). Given the extensive public health implications of loneliness and social isolation, numerous national and international initiatives have emerged to address what is often referred to as \"the loneliness epidemic.\" For instance, in the UK, there is the Campaign to End Loneliness; in Australia, Ending Loneliness Together; and in the United States, the Foundation for Social Connection, along with the World Health Organisation’s Social Isolation and Loneliness initiatives, and the Global Initiative on Loneliness and Connection (Taylor et al., 2023 ). Measuring loneliness and social isolation Most attempts to measure loneliness have focused on it as a persisting trait — an enduring pattern of experience — rather than a short-lived, transient state(Oughli & Lee, 2024 ). These measures of trait loneliness can be grouped into two categories: those that treat loneliness as unidimensional (a unitary, global experience) and those that treat it as multidimensional. Within the unidimensional view, loneliness varies in intensity or frequency from low to high. Multidimensional models, however, are more nuanced and, as a result, perhaps more contested. For instance, one might obtain a low score for “alienation”, as a proposed component of loneliness, while reporting relatively high levels of estrangement. However, a criticism of the multidimensional models is that the theoretical conceptualisation of the proposed components or types of loneliness lacks clarity and consensus. Perhaps due to their brevity and ease of interpretation, unidimensional measures of trait loneliness have been the most frequently employed. The University of California, Los Angeles (UCLA) Loneliness scale is currently the most widely used among such metrics. Developed by researchers at UCLA, this scale was designed to be psychometrically adequate (valid and reliable) and easily administered. The scale has undergone several revisions, and there are now long (20-item) and short (3-item) forms. The UCLA-LS is a self-report measure where individuals are asked to indicate how frequently they felt, for example, “a lack of companionship” or “isolated from others”. Depending on the version of the scale - long or short form - items are scored from 1 to 4, where one equates to “never” having the experience and four reflects “often” feeling this way (Russell et al., 1978 ) Standard methods of validation for both multi- and unidimensional measures of loneliness involve comparing populations known to experience higher levels of loneliness, so-called “at-risk” groups, with healthy controls. Such comparisons have been made between healthy college students and, for example, (a) patients experiencing depression, (b) people attending a remedial social skills workshop, and (c) divorcees. In each instance, the at-risk groups report significantly higher levels of loneliness. In addition to comparisons with known groups, measures of loneliness are also frequently validated against self-labelled and peer-reported loneliness. In measuring social isolation, several potential objective indicators include marital or relationship status, living alone, and living with others. Additionally, self-report measures are used to quantify the size and closeness of a person’s social network by assessing the level of support they receive from family and friends (Veazie et al., 2019 ). Examples of such measures include the Social Network Scale and the Social Support Scale. These scales typically seek to quantify the levels of social support or isolation. The Social Network Scale, for example, has three items that ask respondents to quantify social interaction, such as, “How many relatives/friends do you see or hear from at least once a month?” Similarly, the six-item Social Support Scale asks respondents to report the number of people they can count on in response to questions such as: “Who can you count on when you need help?” and “Who can you count on to console you when you are very upset?” Epidemiological studies of loneliness and social isolation In 2024, the American Psychiatric Association suggested that 1 in 3 Americans are lonely; that is, 30% of US adults said they have experienced feelings of loneliness at least once a week over the past year(American Psychiatric Association, 2024 ). Similarly, in the same year, Gallup’s World Poll suggested that “Over 1 in 5 (23%) people worldwide feel lonely a lot”.(Dugan, 2024 ) Work by the WHO’s Commission on Social Connection puts the global rate at around 16%. Whatever way we slice it, there are a lot of lonely people out there. The percentage of respondents reporting loneliness in the World Poll study varied significantly by territory, ranging from 45% in Comoros to 6% in Vietnam.(Dugan, 2024 ) The authors acknowledge that at least some of the international variation may stem from individuals in certain countries reporting that they spend parts of their day physically alone, rather than emotionally alone. Loneliness is arguably conceived and experienced differently across diverse cultural and linguistic groups, complicating the determination of global prevalence, especially when using single-item measures. Establishing a global prevalence for problem loneliness is premature. This is primarily due to data scarcity, particularly in low-income countries. Additionally, across countries, Gallup found that reports of loneliness are consistently higher in web surveys than in traditional (in-person) modes of interviewing. (Dugan, 2024 ). Add to this the diverse ways of measuring and conceptualising problematic loneliness (methodological heterogeneity), and we begin to appreciate the challenge of establishing even a national, let alone International, prevalence trends for problematic loneliness. Globally, adolescents (20.9%) and young adults (17.4%) appear to experience loneliness most, whereas social isolation is more common in older age groups, 25 to 34% (World Health Organization, 2025 ). For example, data from the Programme for International Student Assessment (PISA) suggests that across most of the 37 participating countries, there was an increase in the rates of loneliness at school (15 and 16-year-olds) between 2012 and 2018 (Twenge et al., 2021 ). It is unclear whether societal levels of loneliness are increasing globally over time. Currently, there is insufficient evidence to confirm a rise in loneliness, although in some countries (e.g., the USA) and among certain age groups (18–29 years), this appears to be the case. Nevertheless, even if the rates remain relatively stable, loneliness continues to be a significant public health issue that has been underappreciated for too long. There are several hard indicators that social isolation has risen: perhaps the most common is an increase in the proportion of the population living alone. In many nations, more people are living alone than at any point in recorded history (see Fig. 1). Figure 1 Percentages of single-person occupancy households across time Source (World Health Organization, 2025 ) It is evident that to better understand the direction of travel, loneliness and social isolation should be incorporated into general health surveillance. Such initiatives need to have a broad geographical coverage, including low and middle-income countries and those without reliable access to the online world. This also calls for the consistent use of standardised and well-validated measurement tools (Dugan, 2024 ) Digital Media Use and Loneliness Although there is insufficient evidence to draw firm conclusions about a global rise in loneliness, there is a widespread perception that it is the case. Arguments about the causes of the posited increase in loneliness and social isolation tend to centre on the “lost community hypothesis”. Rooted in the work of the sociologist Ferdinand Tönnies, the lost community hypothesis proposes that the forces of modernity, such as urbanisation, industrialisation, and the rise of individualism, erode social connections. Digital technology is also viewed as one of the forces contributing to “community loss”. From the personal stereo and personal computer to the iPod, iPhone, and now personalised AI, tech typically nudges users towards hyper-individualism. Unsurprisingly, the relationship between digital media use and loneliness has received substantial research attention. For example, several studies have examined time series data, such as the Programme for International Student Assessment (PISA) and US national youth wellbeing surveys. These data frequently, but not invariably, report increases in loneliness and decreases in well-being that correspond with the popularisation of the smartphone (Heffer et al., 2019 ; Twenge, 2025 ; Twenge et al., 2021 ). Much of this research depends on the rather crude metric known as screentime. Originating in early cinema, the term broadly refers to the amount of time spent viewing screen-based digital media. Current attempts to measure screentime may consider various types of screen use (television, social media, video games, general computer use) and may also try to differentiate dimensions such as active (chatting/commenting) versus passive (scrolling/viewing), and leisure versus occupational screentime. Studies examining the link between screentime and loneliness report mixed results (MacDonald et al., 2022 ; Tang et al., 2021 ). These inconclusive findings also extend more generally to the relationship between screentime and mental health (Santos et al., 2024 ; Tang et al., 2022 ). Much of the research in this field is correlational, so drawing causal conclusions remains premature. More detailed investigations of the motivations for screen use and the type of content consumed/produced might shed further light on the nature of the relationship between digital media use and loneliness. Ultimately, longitudinal and experimental studies are required to illuminate the possible mechanisms underlying any causal relationships. Problematic Digital Media Use and Loneliness We shape our tools, and thereafter, our tools shape us. [Frequently attributed to Marshall McLuhan] There is no doubt that individuals can develop problematic relationships with digital media. However, the debate revolves around the best way to conceptualise these issues: behavioural addiction, compulsion, masked depression or maladaptive coping strategy? Clinical concern and published research interest in problematic digital media use and technology-related disorders emerged alongside the popularisation of the World Wide Web in the mid-1990s. In the earliest scholarly work on this topic, Griffiths draws insights from existing research on pathological gambling and describes technology-related disorders as non-chemical (behavioural) addictions involving human-machine interaction. Echoing Griffiths, Young proposed the inclusion of Internet Addiction Disorder [IAD] within revisions to the 4th edition of the American Psychiatric Association’s Diagnostic and Statistical Manual [DSM-IV-TR] (American Psychiatric Association, 2022 ). Young’s conceptualisation of IAD was broad, encompassing numerous subtypes that reflected different facets of problematic internet use, such as cyberrelationship [social media] addiction, cybersexual [pornography] addiction, gaming addiction, and more. Ultimately, IAD was not included in the DSM-IV-TR. However, gaming addiction, renamed internet gaming disorder [IGD], was incorporated into the revised manual. IGD was recognised as a condition warranting further research, a status it still holds in DSM-5-TR. The proposed DSM-5 criteria for IGD include preoccupation/obsession, withdrawal, tolerance, loss of control, anhedonia, continued overuse, deception, mood repair, and social/occupational impairment. It is stipulated that at least 5 of the 9 symptoms must be present for at least 12 months to meet the criteria for the proposed diagnosis. Other research teams have adapted the same criteria for “social media disorder.”(van den Eijnden et al., 2016 ) Going beyond the American Psychiatric Association (APA), the World Health Organization [WHO] has officially recognised gaming disorder as a diagnostic entity within its classification system 25. In 2018, the WHO included gaming disorder in the behavioural addictions section of the International Classification of Diseases, 11th Revision [ICD-11]. Other internet-related issues, such as problematic social media use, can also be diagnosed under the section’s residual categories: “disorders due to addictive behaviours, unspecified,” and “other specified disorders due to addictive behaviours”(Brand et al., 2020 ; Lindenberg et al., 2022 ). Extending beyond Europe and North America, many Chinese clinicians utilise the Chinese Classification of Mental Disorders, the CCMD-3, which is the most widely used psychiatric diagnostic system in China (Tejeiro et al., 2016 ; Zou et al., 2008 ). The CCMD-3 permits the diagnosis of gaming disorder within the section on habit and impulse disorders (code 61), alongside pathological gambling (Tejeiro et al., 2016 ). Despite these relatively recent developments, attempting to integrate problematic technology use within medical/psychiatric frameworks, the diagnostic utility of concepts such as gaming disorder and internet addiction disorder remains widely contested (Musetti et al., 2016 ). Similarly, as new digital technologies emerge and social norms evolve, what constitutes problematic will require conceptual re-evaluation (Ellis, 2019 ). Measuring Problematic Digital Media Use I often used social media to escape from negative feelings. Drawing on models of behavioural addiction, rooted in earlier work on pathological gambling, most measures of problematic digital media use attempt to assess “addiction” symptoms such as preoccupation, tolerance, withdrawal, persistence, mood modification, deception, displacement, and conflict. Typically, such symptoms (five or more under DSM criteria) have persisted for at least 12 months. Table 1 Symptom description of problematic social media use as a behavioural addiction Symptoms Social Media Context Preoccupation Constantly thinking about social media when not using it. Mood regulation Using social media to alleviate an unpleasant mood, to bring about a mood shift Tolerance Increasing amounts of time spent on social media Withdrawal Unpleasant feelings (anxiety, irritability, boredom) when social media use is stopped or somehow prevented Conflict and dysfunction Familial arguments and interpersonal conflicts related to social media use. Continuing to use social media despite an awareness that it is negatively impacting relationships and school/workplace performance Deception Becoming defensive and deceptive, lying about the amount of time one spends on social media Craving Heightened anticipation and a strong desire for the next social media session. Relapse/Control Repeated unsuccessful efforts to reduce or abstain from social media use. Table adapted from A critical review of “Internet addiction” criteria with suggestions for the future (Van Rooij & Prause, 2014 ) Measuring these problematic behaviours typically involves self-report inventories that assess the presence, severity or frequency of such symptoms. The inventories most often used to screen individuals for problematic social media use typically factor in most, if not all, of the symptoms mentioned above. One of the most widely used screening instruments for PSMU is the 9-item Social Media Disorder Scale (SMD9). The SMD9 short form uses a (Yes/No) response scale. Drawing on the APA’s proposed criteria for internet gaming disorder, the presence of 5 out of 9 symptoms is taken as the screening cut-off. Respondents are asked about their behaviour over the past 12 months, for example, “I often used social media to escape from negative feelings” (mood regulation) and “I tried to spend less time on social media but failed” (relapse/control). It is crucial to distinguish between frequent use and problematic use; simply spending a lot of time on social media (screen time) does not necessarily indicate problematic usage. Many people use social media very often, for long periods, without any negative effects. Such individuals do not show any signs of behavioural addiction. They are not regularly using social media to escape from an unpleasant mood. They do not become unduly angry or distressed when they cannot access their preferred platform. Most importantly, their use does not negatively affect their social (relationships) or occupational responsibilities. These individuals might be described as intense or heavy users, but their usage is not inherently problematic. Have you jeopardized or lost an important relationship, job or an educational or career opportunity because of your gaming activity? An Item from the Internet Gaming Disorder Scale 34 A nearly identical method is used to assess problematic gaming or gaming disorder. For example, the internet gaming disorder scale includes nine items that reflect symptoms of behavioural addiction related to gaming. It also asks about gaming activities over the past 12 months and is based on the APA’s proposed criteria for internet gaming disorder. The IGDS includes questions such as “Have you continued your gaming activity despite knowing it was causing problems between you and other people?” and “Have you jeopardised or lost an important relationship, job, or educational or career opportunity because of your gaming activity?” The nine proposed criteria for internet gaming disorder are listed below. Preoccupation with gaming. Withdrawal symptoms when gaming is taken away or not possible (sadness, anxiety, irritability). Tolerance, the need to spend more time gaming to satisfy the urge. Inability to reduce playing, unsuccessful attempts to quit gaming. Giving up other activities, loss of interest in previously enjoyed activities due to gaming. Continuing to game despite problems. Deceiving family members or others about the amount of time spent on gaming. The use of gaming to relieve negative moods, such as guilt or hopelessness. Risk, having jeopardized or lost a job or relationship due to gaming. Proposed criteria for Internet Gaming Disorder as listed in the American Psychiatric Association’s diagnostic manual, DSM-5-TR Beyond the presence of at least 5 of the nine symptoms, the APA also propose that these symptoms must cause \"significant impairment or distress\" in several aspects of a person's life. Epidemiology: Prevalence of Problematic Media Use Like research on loneliness, studies of problematic media use face similar issues of conceptual and measurement heterogeneity. Even the names proposed for problem use vary widely, from smartphone addiction to social media disorder. Despite conceptual diversity, attempts have been made to quantify problem prevalence (variously conceived) and to explore sociodemographic risk factors. One meta-analysis combining 62 studies, including 34,798 respondents, reported a prevalence of 5% for problematic social media use (PSMU)(Cheng et al., 2021 ). This figure was based on the most stringent screening criteria, counting only those classified as experiencing “very severe symptoms”. Using more relaxed criteria, the prevalence rose to 13%. Notably, in this analysis, the highest rates of PSMU were recorded among the youngest age group (adolescents). This meta-analysis also spanned 32 countries. Nations were grouped based on the degree to which each society emphasised individualistic versus collectivist values (Hofstede, 2001 ). Participants in collectivist nations (e.g., Japan, KSA, Taiwan) reported significantly higher rates of PSMU than their relatively individualistic counterparts (e.g., USA, UK, Australia). This is an area for future research. However, it might be that the obligations associated with interdependence, such as compliance with social norms (fitting in) and maintaining close kinship connections, drive greater social media use in collectivist societies, perhaps leading to higher rates of PSMU. With gender in focus, another meta-analysis explored 51 independent studies including both problematic social media use and gaming disorder. The authors suggest that females are more likely to report PSMU, whereas males are more likely to meet the proposed screening criteria for gaming disorder (Su et al., 2020 ). Meta-analytic studies specifically examining gaming disorder typically suggest a rate of around 3% (Chiang et al., 2022 ; Kim et al., 2022 ). One study spanning 17 countries, including close to a quarter of a million participants, reported a prevalence of 3.05%, with the issue more common in males at a ratio of about 3:1 (Stevens et al., 2021 ). However, the gaming industry has been actively working to encourage more females into gaming. Market research suggests that female gamers are increasing, particularly mobile phone gamers (Newzoo, 2020 ); the male-female ratio may shift in the coming years. Similarly, gaming disorder is currently associated with younger age groups (children and adolescents). However, this may also be an aspect of the phenomenon that evolves with sociocultural changes. Problematic Digital Media Use & Relationship with Loneliness As mentioned earlier, the link between digital media use (screen time) and loneliness remains unclear (mixed findings). However, there is much greater clarity about the connection between problematic digital media use and loneliness. Furthermore, longitudinal studies indicate a two-way relationship, where problematic technology use can precede loneliness, and loneliness can also precede problematic use. One meta-analytic study examining longitudinal investigations of problematic digital media use and loneliness reviewed 26 studies —19 of which used the UCLA-LS (Zhang et al., 2023 ). The authors concluded that adults and adolescents experiencing loneliness are at a greater risk of developing problematic internet use, and similarly, that those with problematic internet use are at a higher risk of later experiencing loneliness. These findings are explained through Reinforcement theory: tech use alleviates loneliness (negative reinforcement), causing lonely individuals to increase their internet use to preserve social benefits (entertainment, online connections), eventually leading to problematic internet use. An alternative, and complementary, explanation is the Internet displacement hypothesis: prolonged PIU results in degraded in-person social connections, leading to loneliness. It is plausible that these proposed initial mechanisms converge, creating a vicious cycle (mutual exacerbation), in which PIU displaces social connections, causing loneliness, which in turn fuels further PIU as an attempt to escape or avoid this unpleasant state. Another systematic review focused on loneliness, social anxiety and social media included 52 previously published studies (O’Day & Heimberg, 2021 ). The review concluded that Individuals with high levels of social anxiety (excessive/problematic shyness) and loneliness are more prone to PSMU. The review's authors suggest that this link is due to the socially anxious seeking social support on social media, perhaps to make up for the lack of in-person support. Furthermore, loneliness appears to be a risk factor for PSMU. Several studies have examined loneliness and PSMU over time, finding that loneliness at time one predicts increases in social media use at a later time point. Research in this area has also highlighted differences between active (interacting, commenting, posting) and passive social media use, such as aimless scrolling through the timeline. Unsurprisingly, passive use is most reliably preceded by feelings of loneliness (Verduyn et al., 2015 ) One might expect different findings in collectivist societies where families are larger and more closely connected. However, a study exploring this issue among adults from Saudi Arabia and Kuwait reported that problematic internet and social media use was significantly linked to increased feelings of loneliness in these societies (Alheneidi et al., 2021 ). There was also a dose–response relationship; that is, greater loneliness predicted higher problematic internet use. A study conducted in Lebanon reported a similar link between PSMU and loneliness (Youssef et al., 2020 ). Correlation, of course, is not causation. However, given the impact of loneliness on health and well-being, such correlational evidence signals concern and the need for further research. This study utilises data from Sync’s global digital well-being survey to investigate the relationship between loneliness and problematic technology use, specifically problematic social media use and internet gaming disorder, as outlined in DSM-5. This study explores these relationships across 35 countries spanning seven world regions aiming to further examine these posited links across diverse cultures and populations. Method Sample description across 35 countries and seven world regions The data reported here are from Sync’s global digital wellbeing survey. These data comprise 35,000 adult respondents, with 1,000 respondents per territory. Based on pre-existing panels, the sample broadly represents the Internet-using adults in each participating nation. Table 1 presents the raw count and percentage of participants identified as lonely based on the UCLA three-item loneliness scale. The study also explored gaming disorder symptoms and problematic social media using reliable, widely used and well validated scales. All these measures are detailed below. The UCLA Loneliness Scale (UCLA-LS-3) The University of California, Los Angeles (UCLA) Loneliness scale is currently the most widely used measure of Loneliness. This scale was designed to be psychometrically adequate (valid and reliable) and easily administered (Russell et al., 1978 ). In the present study we used the short (3-item) form of the scale. The UCLA-LS-3 is a self-report measure which includes three items: (1) I lack companionship, (2) I feel left out, and (3) I feel isolated from others. Respondents answered these items in terms of frequency: hardly ever, some of the time, and often, scored 1, 2, and 3, respectively. In the present study, we use the recommended cut-off, that is, loneliness as scores of 6 or higher. The scale demonstrated good internal reliability (α = 0.79). The Gaming Disorder Scale (Short Form) The short form of the Gaming disorder scale (IGDS9-SF) is a nine-item measure where each statement reflects one of the DSM-5’s proposed criteria for Internet Gaming Disorder (IGD). Respondents are asked to consider their gaming experience over the past twelve months and then respond to items (symptom descriptions) such as \"played in order to temporarily escape or relieve a negative mood” and “continued your gaming activity despite knowing it was causing problems between you and other people?”. The scale invites Yes/No responses, and endorsing 5 of 9 symptoms takes the respondent above the proposed screening cut-off, representing possible gaming disorder. The IGDS9-SF has been widely used and well validated(Feng et al., 2017 ; Pontes & Griffiths, 2015 ). Its reliability in the current study was good (α = 0.803). The Social Media Disorder Scale (Short Form) The short form (nine items) of the Social Media Disorder Scale (SMD9-SF) is derived from the original 27-item version (van den Eijnden et al., 2016 ). It is extrapolated from the DSM-5’s proposed criteria for Internet Gaming Disorder(American Psychiatric Association, 2013 ). Respondents are asked about their social media use over the past 12 months, example items include “…often used social media to escape from negative feelings?” and “tried to spend less time on social media but failed”. The SMD9-SF uses a Yes/No response scale. Endorsing 5 out of 9 symptoms takes the respondent above the proposed screening cut-off and is deemed to represent problematic social media use. The SMD9-SF has good convergent and criterion validity along with sufficient sensitivity, specificity, and test-retest reliability(van den Eijnden et al., 2016 ). In the current study, internal reliability was also good, α = 0.895 Data collection procedure PSB Insights, a global analytics consultancy with extensive experience in multinational polling services, managed the data collection for the 30-nation digital wellbeing survey (DWS). Materials were translated and back-translated from English into the majority language of each participating nation. The survey was undertaken online. Based on existing participant banks (panels), the survey obtained nationally representative samples of the adult internet-using population in each participating territory. Participants were pre-registered survey panelists in their respective countries. Potential participants received invitations via email. The survey response rate was 19.35%. Automated data quality checks ensured that those who failed to complete the survey were excluded from the analysis, as were those who completed the materials with an overly stereotyped response pattern (e.g., answering yes to everything). Similarly, automated data quality checks removed those who completed the survey too quickly (speeding). The mean exclusion rate was 17%; however, oversampling ensured that each nation had 1000 valid participants. The final sample ( N = 35,000) comprised a thousand respondents from each country. All data were collected between July 12th and July 26th, 2023. The study was reviewed and approved by the research ethics committee of King Abdulaziz Centre for World Culture (IRS 202371). Results Overall rates of loneliness Across all participating territories, there were individuals for whom loneliness was a significant issue. The highest rates for individuals scoring above the UCLA loneliness scale cut-off (scores of six or more). were observed in Pakistan and Bangladesh, where more than half of respondents reported loneliness (58%). The lowest rates were reported for China, however, even here around 23% of respondents scored above the cut-off. Across the entire sample, 39.45% were classified as lonely according to the recommended cut-off. Even when the cut-off was raised, and set to the maximum score of nine, 5.03% of respondents were identified as lonely using this stringent threshold. Breakdown of loneliness by demographics In general, more females scored above the loneliness scale’s cut-off, with younger, less educated, unemployed, childless individuals also more frequently categorised as lonely. Those categorized as problematic gamers or social media users were also more likely to score above the UCLA loneliness scale’s cut-off. Table 2 Frequency of loneliness by demographic and behavioural categories Variable Frequency (%) Above UCLA Cut-off Gender Female Male 6955 (41.54) 6860 (37.53) Age group (Median) Over 35 yrs. 35 yrs. and under 5845 (32.85) 7970 (46.26) Older Adults Over 64 yrs. 64 yrs. and under 1023 (23.84) 12792 (41.63) Completed College No Yes 7193 (41.56) 6622 (37.38) Jobseeker (unemployed) No Yes 12441 (38.41) 1374 (52.26) Parent (child under 18) No Yes 7305 (46.59) 6510 (33.66) Problematic Gaming No Yes 1473 (38.50) 2353 (61.50) Problematic Social Media Use No Yes 1262 (37.77) 2079 (62.22) Item-level analysis The loneliness scale includes three items: (1) I lack companionship, (2) I feel left out, and (3) I feel isolated from others. Respondents reported the frequency of experiencing such feelings, hardly ever, some of the time, and often, scored 1, 2, and 3, respectively. Lacking companionship (Item 1) was the most frequently endorsed (see Table 3 ) Table 3 Mean scores and item endorsement frequency for the UCLA-LS Item Item 1 Item 2 Item 3 Mean (SD) 1.73 (0.72) 1.61 (0.68) 1.67 (0.70) Percentage reporting \"often\" 16.41% 11.82% 13.64% The heatmap (Fig. 3), \"I lack companionship,\" is the most strongly endorsed in 31 of the 35 countries. Figure 3 Mean Endorsement of loneliness scale (UCLA-LS) Items by Nation Item1 = I lack companionship, Item2 = I feel left out, Item3 = I feel isolated from others. All Items scored 1 to 3 Nation-level Analysis of Loneliness Exploring the scores by nation, we find that, after controlling for age and gender, Japan reported the highest levels of loneliness. Australia, Malaysia, Pakistan, Bangladesh, and Sweden also report relatively high rates of loneliness. Figure 4 Mean scores for loneliness scale (UCLA-LS) by nation, controlling for demographic covariates For additional analyses of loneliness, please see Appendix 1 Indicators of social isolation Although social Isolation was not assessed explicitly, we did explore known risk factors. Based on previous research, we combined four demographic variables to arrive at a high-risk profile for socially Isolated Individuals. The indicators included not having children, not having a strong connection to a religion, not being employed or in education/training, and not having completed college. Data such as marital status and household occupancy were not available. The percentage of people in each of the four risk groupings was as follows. No strong connection to a religion 65.98% Not in employment, education, or training 7.50% Never completed college. 49.41 No children 44.77% The percentage of people with all four risk Indicators (high risk for social Isolation) was 1.84. Figure 5 The Percentage of participants with all four social isolation risk factors Social Isolation risk scores were correlated with UCLA Loneliness. Scores. They were also associated with age, with older Individuals tending towards higher social Isolation risk scores. Overall rates of Problematic Social Media Use Of those who used social media over the past 12 months, 9.54% reported five or more symptoms, scoring above the symptom cut-off on the social media disorder scale (SMD). Figure 6 Overall percentage of problematic social media use Looking at the nine symptoms that make up the social media disorder construct, one symptom stands out above all others across all nations. That is symptom 8, also referred to as escape, experiential avoidance, or mood repair, that is: \"I used social media to escape from negative feelings?\" More than a quarter (29%) of social media users endorsed item 8. Figure 7 Heat map showing mean scores on the social media disorder scale by item by nation Nation-level Analysis of Problematic Social Media Use (Social Media Disorder) Exploring the scores by nation we find that, after controlling for age and gender, India reported the highest levels of problematic social media use, followed by Pakistan and Saudi Arabia. The top 10 highest scorers for problematic social media use are all Asian or African Nations. The highest-scoring Western nation was Australia, with Estonia reporting the lowest levels of PSMU. Figure 8 Mean scores for problematic social media use (SMD-9) by nation, controlling for demographic covariates Adjusted marginal means of social media disorder symptom count, controlling for covariates age, gender, employment and educational status Overall rates of Gaming disorder Of those who played video games over the past 12 months, 10.92% reported five or more symptoms, scoring above the symptom cut-off on the IGD-9-SF. In line with the analysis of problematic social media use, one gaming disorder symptom also stands out above all others across all nations. Again, this is symptom 8, also referred to as escape, experiential avoidance or mood repair: \" played in order to temporarily escape or relieve a negative mood (e.g., helplessness, guilt, anxiety)\". More than half (58%) of gamers endorsed this symptom. Figure 10 Heat map showing mean scores on the internet gaming disorder scale by item by nation Nation-level Analysis of Gaming Disorder Symptoms Exploring the gaming disorder symptom scores by nation, we find that, after controlling for age and gender, India reported the highest levels of problematic social media use, followed by Pakistan, Bangladesh, Kuwait and Egypt. As with social media, the top 10 highest scorers, this time for gaming disorder, were either Asian or African nations, with four Arabic-speaking nations among them. The highest-scoring Western nation was Australia, while Germany reported the lowest levels of gaming disorder symptoms. Figure 11 Mean scores for internet gaming disorder (IGD-9) by nation, controlling for demographic covariates Note Mean symptom scores while controlling for age and gender Relationship between loneliness, social isolation risk, and problematic technology use Problematic technology use was most strongly correlated with loneliness (medium effect size). It was also associated with risk factors for social Isolation, but to a lesser degree (small effect size). Table 3 details the correlations between the key study variables. Table 3 Correlations between social isolation risk scores and problematic technology use SIR PSMU IGD Loneliness 0.159* 0.316* 0.321* SIR 0.168* 0.092* PSMU 0.509* Notes: SIR = social Isolation risk, PSMU = problematic social media use, IGD = Internet Gaming Disorder symptoms * p < .001 Overall, loneliness was most strongly correlated with gaming disorder symptoms and, to a slightly lesser degree, with problematic social media use. The graphs below visualize these relationships. As loneliness scores Increase, so too do scores for gaming disorder symptoms ( r = 0.321), with the same pattern observed between loneliness and social media disorder symptoms ( r = 0.316). Figure 12 Correlation plots depicting the positive association between loneliness and problematic social media use (left), and loneliness and gaming disorder symptoms (right) Relationship between loneliness and problematic technology use by nation The most strongly correlated variable with loneliness is gaming disorder. This pattern holds true for most countries, with the strongest positive relationship for Egypt ( r = 0.57), with a relatively weak relationship observed between IGD symptoms and loneliness observed for Japan ( r = .04). These patterns are almost Identical for problematic social media use, again with the strongest association between social media disorder symptoms and loneliness observed for Egypt ( r = .50) and the weakest again observed in Japan ( r = .15) Exploring the predictors of Loneliness Using more sophisticated analysis (bivariate logistic regression), we can explore all the predictors of loneliness while controlling for demographic factors. This can give us an idea about which factors have the strongest association with loneliness. In this study, we find that symptoms of gaming disorder and social media disorder are most strongly linked to loneliness, even after controlling for all other variables. The details of this analysis are represented in Fig. 13 and further detailed in Table 4 Figure 13 A forest plot showing the adjusted odds ratios for the risk of loneliness Table 4 Bivariate (OR) and multivariate (AOR) logistic regression predicting UCLA-3 loneliness scores above the recommended cut-off. Above-Threshold Loneliness Scale Odds Ratio Adjusted Odds Ratio N N (%) Gender Male 18277 6860 (37.53%) - - Female 16741 6955 (41.54%) 1.183 (1.133–1.235) 1.242 (1.169–13.19) Age 35 and over 17790 5845 (32.85%) - - Under 35 17227 7970 (46.26%) 1.769 (1.664–1.915) 1.186 (1.109–1.267) Completed College Yes 17713 6622 (37.38%) - - No 17305 7193 (41.56%) 1.191 (1.141–1.243) 1.156 (1.088–1.228) Jobseeker No 32389 12441 (38.41%) - - Yes 2629 1374 (52.26%) 1.776 (1.557–2.070) 1.489 (1.332–1.663) Parent Yes 19340 35 (33.66%) - - No 15678 572 (46.59%) 1.845 (1.709–2.008) 1.503 (1.408–1.605) Problematic Gaming No 17918 6259 (36.71%) - - Yes 3826 2353 (61.50%) 2.976 (2.769–3.198) 2.478 (2.281–2.692) Problematic Social Media Use No 25328 9299 (36.71%) - - Yes 3341 2079 (62.22%) 2.840 (2.636–3.059) 1.880 (1.706–2.070) Note: AOR model included all variables listed above. All ORs and AORs are significant, p values < 0.001 Discussion Loneliness was a concern across all nations in the study. Even in the nations reporting the lowest levels of loneliness, around 1 in 5 people were lonely based on the UCLA-LS scores. In some nations (Japan, Pakistan, Bangladesh, Malaysia, Ghana, and India), rates were as high as 1 in 2. The well-established and extensively documented links between loneliness and an increased risk of physical and mental health problems underscore the public health implications of social disconnection (WHO, 2025). One idea proposed to explain the perceived rise in loneliness is the “lost community” hypothesis. Within this formulation, increasing urbanisation and individualism are implicated in the erosion of social connections and the rise of loneliness. However, in the present study, nations reporting the highest levels of loneliness would traditionally be considered to have relatively collectivist national cultural values (Hofstede, 2001 ). It might be that urbanisation, industrialisation, and the creeping influence of individualistic values are most strongly associated with the erosion of social connections during the transitional phase, as people migrate from rural lifestyles to urban living and from traditional values to mindsets shaped by globalisation and information technology, ushering in a period of cultural dissonance. Problematic social media use and gaming disorder symptoms were also observed across all nations. Even if the rate of 10% is a significant overestimate, the popularity of social media and gaming renders problem use worthy of further research attention and preventative intervention. Both problematic social media use and gaming disorder symptoms were correlated (medium effect size) with loneliness. This was the case across all 35 nations, although the strength of the relationship varied widely, from large effects (strong positive correlations) for Egypt and Kuwait to small effects (weak positive correlations for Japan. Even after controlling for all other demographic correlates such as age, gender and education level, gaming disorder symptoms, and problematic social media use were the strongest predictors of loneliness. Numerous previous studies report similar associations between problematic technology use and loneliness, with several longitudinal studies reporting bidirectional relationships, that is, loneliness at time one predicts problematic technology use at time two, and vice versa (Zhang et al., 2023 ). This suggests that initiatives targeting loneliness may also reduce the risk of problematic technology use, and initiatives targeting problematic technology use may attenuate the risk of loneliness. At least one intervention study (controlled trial) reports such an effect (Thomas et al. In Prep). Although the present study did not directly assess social isolation, known risk factors associated with social isolation were quantified (e.g., not in employment, training or education; not a parent). These individuals, those with fewer social roles and opportunities for connection, were present across all nations, however to a far lesser degree than loneliness. Unsurprisingly social isolation risk was correlated with loneliness as has previously been documented (Ge et al., 2017 ; Taylor et al., 2023 ). Similarly, social isolation risk was also associated with problematic technology use, but to a far lesser extent than loneliness showing small effect sizes (weak positive correlations). The present study has the usual limitations associated with cross-sectional and correlational survey research. The correlational nature of the study means we cannot ascribe problematic technology use a causal role in the onset, maintenance or worsening of loneliness. However, a clear strength of the current study was the ability to identify the existence of the problematic technology use – loneliness relationship across each of the 35 participating countries using large representative samples within each territory. While correlation is not causation, it is a cause for concern especially when the WHO estimate that loneliness and social isolation contribute to close to a million deaths annually (World Health Organization, 2025 ). Successful attempts to reduce loneliness and social isolation will contribute greatly to the overall mental, physical, and social health of society. This study represents a modest contribution towards a better understanding of loneliness and social isolation and the possible role that problematic technology use might play. Key Recommendations Better measures and routine surveillance There is a need for better metrics for social disconnection (loneliness and social isolation) and for more robust longitudinal surveillance (regular data collection). For example, loneliness and social isolation should be incorporated into general health surveillance - an annual social disconnection census. Such initiatives, however, need to include low and middle-income countries and populations without reliable access to the online world. Even more critical, in terms of measurement, is the need to develop positively framed metrics of social connection that explore and quantify how people relate to and interact with one another. Such measures and routine surveillance will allow us to better understand trends and to evaluate the effectiveness of population-level interventions. Social flourishing is more than the absence of social disconnection. Research Our research exploring the possible digital determinants of social disconnection needs to move beyond correlational studies. For the evidence to mature, we need open science and well-designed experimental studies that aim to identify possible mechanisms underlying the relationship between problematic technology use and social disconnection. Public awareness There is a need for innovative and engaging public awareness initiatives exploring social connection/disconnection. Additionally, public awareness campaigns focused on preventing problematic technology should articulate what we presently know about the bidirectional relationship between problematic technology use and social disconnection. It is important to distinguish between technology use and problematic technology use. Policy Here we echo the World Health Organization ( 2025 ) in their call to make social connection/disconnection a global policy priority, engaging all sectors of society to work together to share ideas toward creating policy that supports social connection. Additionally, we propose that the same applies to digital well-being and the prioritisation of policies that support people to thrive online, ensuring that platforms offer safety as a default and refrain from deploying addictive design features. References Alheneidi, H., AlSumait, L., AlSumait, D., & Smith, A. P. (2021). Loneliness and Problematic Internet Use during COVID-19 Lock-Down. Behav Sci (Basel) , 11 (1). https://doi.org/10.3390/bs11010005 Allen, J., Darlington, O., Hughes, K., & Bellis, M. A. (2022). The public health impact of loneliness during the COVID-19 pandemic. BMC Public Health , 22 (1), 1654. https://doi.org/10.1186/s12889-022-14055-2 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders : DSM-5 . American Psychiatric Association. American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders : DSM-5-TR . American Psychiatric Association. American Psychiatric Association. (2024, 4/Jan/2024). New APA Poll: One in Three Americans Feels Lonely Every Week Psychiatry.org - New APA Poll: One in Three Americans Feels Lonely Every Week Brand, M., Rumpf, H. J., Demetrovics, Z., MÜller, A., Stark, R., King, D. L.,…Potenza, M. N. (2020). Which conditions should be considered as disorders in the International Classification of Diseases (ICD-11) designation of \"other specified disorders due to addictive behaviors\"? J Behav Addict , 11 (2), 150-159. https://doi.org/10.1556/2006.2020.00035 Cheng, C., Lau, Y.-c., Chan, L., & Luk, J. W. (2021). Prevalence of social media addiction across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values. Addictive Behaviors , 117 , 106845. https://doi.org/https://doi.org/10.1016/j.addbeh.2021.106845 Chiang, C. L. L., Zhang, M. W. B., & Ho, R. C. M. (2022). Prevalence of Internet Gaming Disorder in Medical Students: A Meta-Analysis [Review]. Frontiers in Psychiatry , 12 . Dugan, A. (2024). Over 1 in 5 People Worldwide Feel Lonely a Lot . Gallup. https://news.gallup.com/poll/646718/people-worldwide-feel-lonely-lot.asp Ellis, D. A. (2019). Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors. Computers in Human Behavior , 97 , 60-66. https://doi.org/10.1016/j.chb.2019.03.006 Feng, W., Ramo, D., Chan, S., & Bourgeois, J. (2017). Internet gaming disorder: trends in prevalence 1998–2016. Addictive behaviors , 75 , 17. Ge, L., Yap, C. W., Ong, R., & Heng, B. H. (2017). Social isolation, loneliness and their relationships with depressive symptoms: A population-based study. PLoS One , 12 (8), e0182145. https://doi.org/10.1371/journal.pone.0182145 Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Ann Behav Med , 40 (2), 218-227. https://doi.org/10.1007/s12160-010-9210-8 Heffer, T., Good, M., Daly, O., MacDonell, E., & Willoughby, T. (2019). The Longitudinal Association Between Social-Media Use and Depressive Symptoms Among Adolescents and Young Adults: An Empirical Reply to Twenge et al. (2018). Clinical Psychological Science , 7 (3), 462-470. https://doi.org/10.1177/2167702618812727 Hofstede, G. (2001). Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations (2nd ed.). Sage Publications. Jopling, k. (2017). Jo Cox Commission on Loneliness . https://www.ageuk.org.uk/globalassets/age-uk/documents/reports-and-publications/reports-and-briefings/active-communities/rb_dec17_jocox_commission_finalreport.pdf Kim, H. S., Son, G., Roh, E. B., Ahn, W. Y., Kim, J., Shin, S. H.,…Choi, K. H. (2022). Prevalence of gaming disorder: A meta-analysis. Addict Behav , 126 , 107183. https://doi.org/10.1016/j.addbeh.2021.107183 Lindenberg, K., Kindt, S., & Szász-Janocha, C. (2022). Effectiveness of Cognitive Behavioral Therapy-Based Intervention in Preventing Gaming Disorder and Unspecified Internet Use Disorder in Adolescents: A Cluster Randomized Clinical Trial. JAMA Netw Open , 5 (2), e2148995. https://doi.org/10.1001/jamanetworkopen.2021.48995 MacDonald, K. B., Patte, K. A., Leatherdale, S. T., & Schermer, J. A. (2022). Loneliness and screen time usage over a year. J Adolesc , 94 (3), 318-332. https://doi.org/10.1002/jad.12024 Musetti, A., Cattivelli, R., Giacobbi, M., Zuglian, P., Ceccarini, M., Capelli, F.,…Castelnuovo, G. (2016). Challenges in Internet Addiction Disorder: Is a Diagnosis Feasible or Not? Front Psychol , 7 , 842. https://doi.org/10.3389/fpsyg.2016.00842 National Academies of Sciences, E. a. M., Division of Behavioral and Social Sciences and, E., Health and Medicine, D., Board on Behavioral, C. a. S. S., Board on Health Sciences, P., & Committee on the Health and Medical Dimensions of Social Isolation and Loneliness in Older, A. (2020). In Social Isolation and Loneliness in Older Adults: Opportunities for the Health Care System . National Academies Press (US) Copyright 2020 by the National Academy of Sciences. All rights reserved. https://doi.org/10.17226/25663 National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP). (2024). Health Effects of Social Isolation and Loneliness . Retrieved May/10/2025 from https://www.cdc.gov/social-connectedness/risk-factors/index.html Newzoo. (2020). Global Games Market Report https://newzoo.com/insights/articles/the-global-games-market-will-generate-152-1-billion-in-2019-as-the-u-s-overtakes-china-as-the-biggest-market/ . Oughli, H. A., & Lee, E. E. (2024). Lonely for Life? Differences Between Chronic and Transient Loneliness and Their Impact on Depression in Older Adults. The American Journal of Geriatric Psychiatry , 32 (4), 424-426. https://doi.org/10.1016/j.jagp.2023.12.012 O’Day, E. B., & Heimberg, R. G. (2021). Social media use, social anxiety, and loneliness: A systematic review. Computers in Human Behavior Reports , 3 , 100070. https://doi.org/https://doi.org/10.1016/j.chbr.2021.100070 Pontes, H. M., & Griffiths, M. D. (2015). Measuring DSM-5 Internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior , 45 , 137-143. Russell, D., Peplau, L. A., & Ferguson, M. L. (1978). Developing a measure of loneliness. J Pers Assess , 42 (3), 290-294. https://doi.org/10.1207/s15327752jpa4203_11 Santos, R. M. S., Ventura, S. d. A., Nogueira, Y. J. d. A., Mendes, C. G., Paula, J. J. d., Miranda, D. M., & Romano-Silva, M. A. (2024). The Associations Between Screen Time and Mental Health in Adults: A Systematic Review. Stevens, M. W., Dorstyn, D., Delfabbro, P. H., & King, D. L. (2021). Global prevalence of gaming disorder: A systematic review and meta-analysis. Aust N Z J Psychiatry , 55 (6), 553-568. https://doi.org/10.1177/0004867420962851 Su, W., Han, X., Yu, H., Wu, Y., & Potenza, M. N. (2020). Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. Computers in Human Behavior , 113 , 106480. https://doi.org/https://doi.org/10.1016/j.chb.2020.106480 Tang, S., Werner-Seidler, A., Torok, M., Mackinnon, A. J., & Christensen, H. (2021). The relationship between screen time and mental health in young people: A systematic review of longitudinal studies. Clinical Psychology Review , 86 , 102021. https://doi.org/https://doi.org/10.1016/j.cpr.2021.102021 Tang, W. Y., Reer, F., & Quandt, T. (2022). The interplay of the Dark Triad and social media use motives to social media disorder. Personality and Individual Differences , 187 , 111402. https://doi.org/https://doi.org/10.1016/j.paid.2021.111402 Taylor, H. O., Cudjoe, T. K. M., Bu, F., & Lim, M. H. (2023). The state of loneliness and social isolation research: current knowledge and future directions. BMC Public Health , 23 (1), 1049. https://doi.org/10.1186/s12889-023-15967-3 Tejeiro, R., Chen, A., & L. Gómez-Vallecillo, J. (2016). Measuring Internet Gaming Disorder in Chinese International Students in the United Kingdom. Journal of Education, Society and Behavioural Science , 17 (1), 1-11. https://doi.org/10.9734/BJESBS/2016/27855 Twenge, J. M. (2025). International Declines in Academic Performance and Increases in Loneliness Are Linked to Electronic Devices. J Adolesc . https://doi.org/10.1002/jad.70058 Twenge, J. M., Haidt, J., Blake, A. B., McAllister, C., Lemon, H., & Le Roy, A. (2021). Worldwide increases in adolescent loneliness. J Adolesc , 93 , 257-269. https://doi.org/10.1016/j.adolescence.2021.06.006 van den Eijnden, R. J. J. M., Lemmens, J. S., & Valkenburg, P. M. (2016). The Social Media Disorder Scale. Computers in Human Behavior , 61 , 478-487. https://doi.org/https://doi.org/10.1016/j.chb.2016.03.038 van Roekel, E., Verhagen, M., Engels, R., Scholte, R. H. J., Cacioppo, S., & Cacioppo, J. T. (2018). Trait and State Levels of Loneliness in Early and Late Adolescents: Examining the Differential Reactivity Hypothesis. J Clin Child Adolesc Psychol , 47 (6), 888-899. https://doi.org/10.1080/15374416.2016.1146993 Van Rooij, A. J., & Prause, N. (2014). A critical review of \"Internet addiction\" criteria with suggestions for the future. Journal of behavioral addictions , 3 (4), 203-213. https://doi.org/10.1556/JBA.3.2014.4.1 Veazie, S., Gilbert, J., Winchell, K., Paynter, R., & Guise, J. M. (2019). AHRQ Rapid Evidence Product Reports. In Addressing Social Isolation To Improve the Health of Older Adults: A Rapid Review . Agency for Healthcare Research and Quality (US). Verduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J.,…Kross, E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. J Exp Psychol Gen , 144 (2), 480-488. https://doi.org/10.1037/xge0000057 Wolska, K., & Creaven, A.-M. (2023). Associations between transient and chronic loneliness, and depression, in the understanding society study. British Journal of Clinical Psychology , 62 (1), 112-128. https://doi.org/https://doi.org/10.1111/bjc.12397 World Health Organization. (2025). From loneliness to social connection - charting a path to healthier societies: report of the WHO Commission on Social Connection. Youssef, L., Hallit, R., Kheir, N., Obeid, S., & Hallit, S. (2020). Social media use disorder and loneliness: any association between the two? Results of a cross-sectional study among Lebanese adults. BMC psychology , 8 (1), 56-56. https://doi.org/10.1186/s40359-020-00421-5 Zhang, Y., Li, J., Zhang, M., Ai, B., & Jia, F. (2023). Bidirectional Associations between Loneliness and Problematic Internet Use: A Meta-analytic Review of Longitudinal Studies. Addictive Behaviors , 107916. https://doi.org/10.1016/j.addbeh.2023.107916 Zou, Y. Z., Cui, J. F., Han, B., Ma, A. L., Li, M. Y., & Fan, H. Z. (2008). Chinese psychiatrists views on global features of CCMD-III, ICD-10 and DSM-IV. Asian J Psychiatr , 1 (2), 56-59. https://doi.org/10.1016/j.ajp.2008.09.007 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8624119\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":575952717,\"identity\":\"db29a98d-c44c-48d9-9384-2c08035d87ad\",\"order_by\":0,\"name\":\"Justin Thomas\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBAC9gYgkcDwn4efnbEBKgShDHBpAapjbEhgYJaRbIZpYSZGC1CZjcFhmBBBLTPSnz94uIONx/gwc5vExz135PiZGRg//GA4bIxbS45hQ+IZHh6zw4xtkjOePTOWbGZgluxhOGyGRwtjQ2KbBEhLszHPgcOJG4AulGZgOGyDx2EPgVoMeIybgVr+QLQw/8anRXBGAtBhbQk8BsyMjY8ZIFrYQLbgdJg0zxvDGYltB3gkDjM2Puw5APILY5tlj0E6Tu/zsac/+Piz7YA9f3v7gwM/DgBDjL358I0fFdaGDbj0oIEDDOC4wh0r2LWMglEwCkbBKEAFALSCVfY+70NJAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"King Abdulaziz Center for World Culture (Ithra), Saudi Arabia\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Justin\",\"middleName\":\"\",\"lastName\":\"Thomas\",\"suffix\":\"\"},{\"id\":575952718,\"identity\":\"a36a0e99-33f6-4ae5-aee6-c8008d6202e4\",\"order_by\":1,\"name\":\"Yasmin Al-Jedawi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"King Abdulaziz Center for World Culture (Ithra), Saudi Arabia\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yasmin\",\"middleName\":\"\",\"lastName\":\"Al-Jedawi\",\"suffix\":\"\"},{\"id\":575952719,\"identity\":\"67289f68-bc2b-4874-8b62-da3eaefcf242\",\"order_by\":2,\"name\":\"Melisa Valle\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Melisa\",\"middleName\":\"\",\"lastName\":\"Valle\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-17 07:18:13\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-8624119/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8624119/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":100751321,\"identity\":\"149f5e20-ffba-441a-8bf4-b635d4d6ecf7\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:32\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":1932103,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"LonelinessintheDigitalAgeWPformatDec31.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/92e4fb8d16fe6e85a9053071.docx\"},{\"id\":100857742,\"identity\":\"95eb129b-a732-48db-a222-853c3679240c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-22 07:21:28\",\"extension\":\"json\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":342,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"rs8624119.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/ac9f107a6f6f9b28344d4ced.json\"},{\"id\":100751316,\"identity\":\"ea354fa7-7452-4971-84c8-10d8de78781d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:29\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":152945,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"rs86241190enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/8bbf30a4b258ee8a5ff0e397.xml\"},{\"id\":100751251,\"identity\":\"5bec333b-926c-452c-8cd6-241a17f7e91f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:05\",\"extension\":\"eps\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":428,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"drawingimage1.eps\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/1612983938aa0d8c10ca55fd.eps\"},{\"id\":100751317,\"identity\":\"242b33ed-f397-4232-9beb-0de1b772652c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:29\",\"extension\":\"png\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":74713,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/34f7893152bb7c2eecd5f20a.png\"},{\"id\":100751338,\"identity\":\"c1fb2a8f-2e17-41c3-8f33-4d1e9f533059\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:39\",\"extension\":\"jpeg\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":227721,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage10.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/e195f460b1f8b90b5d33c48d.jpeg\"},{\"id\":100751305,\"identity\":\"324da8c4-ac31-4955-b823-46baed43a74c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:26\",\"extension\":\"jpeg\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":155324,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage11.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/c28f897b3583b50361214fa5.jpeg\"},{\"id\":100751283,\"identity\":\"b11d4063-4d44-4dda-9805-3403157c7ed4\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:15\",\"extension\":\"jpeg\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":49562,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage12.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/a21584b49b2b482e0b5f086e.jpeg\"},{\"id\":100751294,\"identity\":\"416c2e3f-a2f6-4ce0-8503-1ac842cccfe3\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:21\",\"extension\":\"jpeg\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":48503,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage13.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/9344efdabcabab6a45293d66.jpeg\"},{\"id\":100751303,\"identity\":\"56e45580-5a68-4511-b406-6ac9975bdc97\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:26\",\"extension\":\"jpeg\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":425014,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage14.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/5c19289c8763e1981ac9eece.jpeg\"},{\"id\":100751332,\"identity\":\"dc15e9f8-af6b-499a-9712-5c4b3aaa87a0\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:34\",\"extension\":\"jpeg\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":223447,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage15.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/edda422c8e26f24a7ccbfa8b.jpeg\"},{\"id\":100751326,\"identity\":\"3921dee0-ff7d-4601-b7dd-153a6299d472\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:33\",\"extension\":\"jpeg\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":375593,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage16.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/929e569042a9bdfd774a3687.jpeg\"},{\"id\":100751314,\"identity\":\"fefcf61e-c681-4d9b-b691-666add1f1124\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:28\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":55580,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage17.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/ea3d98237e23dd514a96710e.png\"},{\"id\":100751308,\"identity\":\"c6d813f0-e8a4-4d4a-ac3a-ef52c312b00f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:27\",\"extension\":\"jpeg\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":384952,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage18.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/3025eb7c39e75498c330f76d.jpeg\"},{\"id\":100751289,\"identity\":\"65f62335-9f10-4745-bb6b-2f35b66094f3\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:19\",\"extension\":\"jpeg\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":22053,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/14972bb0a38b2bd119cafb65.jpeg\"},{\"id\":100751284,\"identity\":\"51fd5aa9-67b6-49b7-8e1f-90bc28f972d7\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:15\",\"extension\":\"jpeg\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":85780,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/c1971ebad6f203baf2024302.jpeg\"},{\"id\":100751298,\"identity\":\"bd87986f-70e1-46bc-b628-f3403785bc29\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:22\",\"extension\":\"png\",\"order_by\":16,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":87695,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/4120c97abf458f83b5abf907.png\"},{\"id\":100751286,\"identity\":\"b3006137-f5e1-47f8-b3d2-8776b3b126c9\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:17\",\"extension\":\"jpeg\",\"order_by\":17,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":18518,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/0a8a2c7f2927f5a217f23a16.jpeg\"},{\"id\":100751343,\"identity\":\"a573b0c3-7d6f-4b47-b358-7e92779cf877\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:42\",\"extension\":\"jpeg\",\"order_by\":18,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":18264,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage6.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/8be1be2022ce1cc07135ceb9.jpeg\"},{\"id\":100751253,\"identity\":\"39dab72d-f33a-48e0-83f5-ca4bffc0767b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:08\",\"extension\":\"jpeg\",\"order_by\":19,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":230376,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage7.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/5e683dd5c444b5fd143c9a30.jpeg\"},{\"id\":100751300,\"identity\":\"e6de5d8f-04e0-44b7-90ff-b5606df6c726\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:25\",\"extension\":\"jpeg\",\"order_by\":20,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":132330,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage8.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/9e20b4965097abbc93522830.jpeg\"},{\"id\":100751345,\"identity\":\"90e20359-8e3e-40b9-af4c-b5693b253b21\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:43\",\"extension\":\"jpeg\",\"order_by\":21,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":17274,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage9.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/df338ee81a22e037fe588661.jpeg\"},{\"id\":100751307,\"identity\":\"9fa2609c-9f7b-4957-a36c-053c9a350131\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:27\",\"extension\":\"png\",\"order_by\":22,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":21898,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/c9c47c0f26894f2f9f4c446b.png\"},{\"id\":100751323,\"identity\":\"20c20384-ff28-4d49-8c69-22852703ec5b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:32\",\"extension\":\"png\",\"order_by\":23,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":58730,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/be2c80986d60e0ee64418672.png\"},{\"id\":100751310,\"identity\":\"02dfb586-07a4-443d-8805-07732c1e90c2\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:27\",\"extension\":\"png\",\"order_by\":24,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":24165,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/7a5528c560d63dc9bd7a0051.png\"},{\"id\":100751336,\"identity\":\"e643f72f-a4de-47f2-bad3-7cd4670acda4\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:37\",\"extension\":\"png\",\"order_by\":25,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":19668,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage12.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/8c354f2dc88e5a76e4714d65.png\"},{\"id\":100751257,\"identity\":\"df98fa99-804f-43a2-9c3a-8d6c4d330011\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:11\",\"extension\":\"png\",\"order_by\":26,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":19991,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage13.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/fb288aabb41d966b11f6bee3.png\"},{\"id\":100751327,\"identity\":\"3bb54877-21fe-4cc0-a1a2-6c8655d0a8d9\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:33\",\"extension\":\"png\",\"order_by\":27,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":232707,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage14.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/010e607d01097a8ddad64854.png\"},{\"id\":100751306,\"identity\":\"eadfd0a7-753f-46fa-83fb-508e7b4b76a1\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:26\",\"extension\":\"png\",\"order_by\":28,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":37760,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage15.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/691584df642d3dda508cab6d.png\"},{\"id\":100751304,\"identity\":\"33879ad7-1b4b-48cd-b056-a2a171cedc75\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:26\",\"extension\":\"png\",\"order_by\":29,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":56977,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage16.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/bb091485fabbd42286c79ebc.png\"},{\"id\":100751248,\"identity\":\"fffeac35-e2aa-42d6-aa1c-fad1ca4acde4\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:04\",\"extension\":\"png\",\"order_by\":30,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":52412,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage17.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/4b60ad7524db879593f329d0.png\"},{\"id\":100751322,\"identity\":\"1d234686-f79c-4a9c-a515-b08ee43aec58\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:32\",\"extension\":\"png\",\"order_by\":31,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":50694,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage18.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/12b1939eb51ee9a073c8d60c.png\"},{\"id\":100751302,\"identity\":\"6c1d39a4-8361-467b-a147-729ec98e860b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:25\",\"extension\":\"png\",\"order_by\":32,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":8022,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/01ab3e607a833818f0af91c6.png\"},{\"id\":100751325,\"identity\":\"6c103433-8951-4c23-baff-9459c004d605\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:33\",\"extension\":\"png\",\"order_by\":33,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":28042,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/2650448f93b5678e3a634635.png\"},{\"id\":100751256,\"identity\":\"5aaceb7c-c620-41a6-8ca7-c02f53b528c2\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:11\",\"extension\":\"png\",\"order_by\":34,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":82548,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/e369dd822876087c944d1e95.png\"},{\"id\":100751296,\"identity\":\"73b53a8c-9762-489e-a68a-944870990acd\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:21\",\"extension\":\"png\",\"order_by\":35,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":5122,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/789fa107120fa2fd14e5d688.png\"},{\"id\":100751312,\"identity\":\"40368cf9-186a-4152-9264-4650abdfee40\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:28\",\"extension\":\"png\",\"order_by\":36,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":5676,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/11ce4b09ebd33bc61523b0e1.png\"},{\"id\":100751334,\"identity\":\"66f7b7b8-0eb1-46a8-97dd-4263b68159b3\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:35\",\"extension\":\"png\",\"order_by\":37,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":57304,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/6065d8211173be9c952f93c8.png\"},{\"id\":100751288,\"identity\":\"5b51802c-8320-4c1a-8949-b61bb22fca4e\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:18\",\"extension\":\"png\",\"order_by\":38,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":21417,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/7a6d54ed04bcecbc9e561828.png\"},{\"id\":100751291,\"identity\":\"bec8a1d5-e605-4177-96ed-ddee61df42df\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:20\",\"extension\":\"png\",\"order_by\":39,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":5219,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/a3f5afd1c9af8cfef431706c.png\"},{\"id\":100751331,\"identity\":\"7edc81a2-f6cc-4b64-a4af-09b20c3c95d0\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:34\",\"extension\":\"xml\",\"order_by\":40,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":149481,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"rs86241190structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/a01ecb7a36885beacc56c335.xml\"},{\"id\":100751330,\"identity\":\"8a065b5c-3083-46fd-abf4-54ddc9cd99ff\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:34\",\"extension\":\"html\",\"order_by\":41,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":163853,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/2e1236c5778050cdf6a134bf.html\"},{\"id\":100751315,\"identity\":\"7d0a8bea-4f02-4a08-88c8-15d43e239d7c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:29\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":86506,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003ePercentages of single-person occupancy households across time\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSource (World Health Organization, 2025)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/fc959d887dfa22a5d7ae65b1.jpg\"},{\"id\":100751287,\"identity\":\"2f009b44-f3c7-45c5-931f-eebe5eee5301\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:17\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":12308,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eRate of Loneliness: % of scores above UCLA-LS3 cut-off\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/5de85f635f7ebd82bc0e294c.jpg\"},{\"id\":100751290,\"identity\":\"d75ca398-8922-4177-a251-a58a9705b5b8\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:20\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":81320,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eMean Endorsement of loneliness scale (UCLA-LS) Items by Nation\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/6a9216eda6b6cd5a1deeb4a9.jpg\"},{\"id\":100751292,\"identity\":\"bfef84b8-eaf6-4db4-8f04-b5bfe8d928c9\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:20\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":93225,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eMean scores for loneliness scale (UCLA-LS) by nation, controlling for demographic covariates\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/d954036a50ff667945b48fcb.jpg\"},{\"id\":100751318,\"identity\":\"d4a182f2-877b-4562-8fc3-70faa47b42b9\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:31\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":10014,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eThe Percentage of participants with all four social isolation risk factors\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/9e54bd2c61aa0ac460ad1af2.jpg\"},{\"id\":100751295,\"identity\":\"957237fa-c6bb-4df2-8766-3964c27a21bd\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:21\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":11000,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eOverall percentage of problematic social media use\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/a40ff40df19c6c27a9eb4453.jpg\"},{\"id\":100751285,\"identity\":\"32445d73-81d8-42aa-8a1c-11398eaf1168\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:15\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":185494,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eHeat map showing mean scores on the social media disorder scale by item by nation\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/bea54cdd1bef1097d9240e03.jpg\"},{\"id\":100751282,\"identity\":\"81ffef00-bff9-4728-a78c-f781740d72c8\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:14\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":106566,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eMean scores for problematic social media use (SMD-9) by nation, controlling for demographic covariates\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/7a824869d4d5acdc97b74bcb.jpg\"},{\"id\":100751319,\"identity\":\"eae94cf5-1f22-4c84-8369-3d44563142a6\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:31\",\"extension\":\"jpg\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":9727,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRate of gaming disorder across the whole sample\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/00f4ef76af9f934fc7fd83ae.jpg\"},{\"id\":100751299,\"identity\":\"779c1388-988f-49a4-9a98-cc0bbba12ff6\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:23\",\"extension\":\"jpg\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":185410,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eHeat map showing mean scores on the internet gaming disorder scale by item by nation\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/8f321c44160a877607c09e23.jpg\"},{\"id\":100751165,\"identity\":\"d40317fd-fc49-4392-8d01-8ac9a66b782f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:44:52\",\"extension\":\"jpg\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":115419,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eMean scores for internet gaming disorder (IGD-9) by nation, controlling for demographic covariates\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"11.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/93842507bf2e4b1f285701a1.jpg\"},{\"id\":100751254,\"identity\":\"9c0c42fa-6d1b-441d-9a7e-7154ebec7124\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:08\",\"extension\":\"jpg\",\"order_by\":12,\"title\":\"Figure 12\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":68400,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eCorrelation plots depicting the positive association between loneliness and problematic social media use (left), and loneliness and gaming disorder symptoms (right)\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"12.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/d30de2d61fe91a5e3e6e8c26.jpg\"},{\"id\":100751328,\"identity\":\"c5f7e294-5862-4fe7-ad31-5ae98774be61\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:34\",\"extension\":\"jpg\",\"order_by\":13,\"title\":\"Figure 13\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":213107,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eHeatmap of the correlation coefficient values between loneliness and the key demographic and behavioural study variables by nation\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"13.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/06d2fe1936ed554836655b83.jpg\"},{\"id\":100751329,\"identity\":\"882c700b-d47f-4fdb-90e6-4cef676ddfc6\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:34\",\"extension\":\"jpg\",\"order_by\":14,\"title\":\"Figure 14\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":51736,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFigure 13 \\u003cem\\u003eA forest plot showing the adjusted odds ratios for the risk of loneliness\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"14.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/b502c5a67985c0bba9b12599.jpg\"},{\"id\":100859737,\"identity\":\"23b11653-2517-47ee-af31-f394c3114624\",\"added_by\":\"auto\",\"created_at\":\"2026-01-22 07:32:38\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2398416,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/cb724cf1-3936-4a5d-b259-0bb395541620.pdf\"},{\"id\":100751320,\"identity\":\"f41442f7-9232-4442-944d-9879aa076f6b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-21 04:45:32\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":183869,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Appendix.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8624119/v1/1c03abcce82edbff0cb67eca.docx\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eSocial Disconnection in a Hyperconnected World: Loneliness, Social Isolation, and Problematic Technology Use Across 35 Nations\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eIn the 8th century, in a small town just outside Baghdad, a seemingly lonely and perhaps socially isolated individual wrote the following melancholic message on a wall:\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003e\\u0026ldquo;May God water the days of togetherness with His rain and return every stranger to his home. There is no good in this world without togetherness and no joy in life without a loved one\\u0026rdquo;.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eLoneliness and social isolation know no borders; these are timeless human experiences. However, there is a perception that both loneliness, a subjective experience, and social isolation, an objectively quantifiable state, are on the rise. The public health implications of both these forms of social disconnection are well known. What is less well understood is how personal digital technologies (devices/services) might contribute to the onset, worsening, or alleviation of these states.\\u003c/p\\u003e \\u003cp\\u003eDifferentiating loneliness and social isolation\\u003c/p\\u003e \\u003cp\\u003eLoneliness is an unpleasant feeling that accompanies the perception of unmet social needs (Hawkley \\u0026amp; Cacioppo, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). It is not the same as \\u0026ldquo;being alone\\u0026rdquo;. Many individuals experience, and perhaps even enjoy, extended periods of solitude without feeling lonely (Hawkley \\u0026amp; Cacioppo, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Furthermore, a person may lead a seemingly active social life, yet still feel lonely. This reality is eloquently articulated in a quote attributed to the Swiss Psychologist, Carl Jung:\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003e\\u0026ldquo;Loneliness does not come from having no people about one, but from being unable to communicate the things that seem important to oneself\\u0026rdquo;.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt its core, loneliness arises from a perceived disparity between desired and actual levels of social connection. Loneliness is a subjective reaction to perceived deficiencies in one's social world. Social isolation, on the other hand, is the objective reality of having relatively few social roles, relationships, and social interactions (World Health Organization, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eDespite these differences, loneliness and social isolation correlate (Ge et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Taylor et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Individuals with fewer relationships, social roles and limited human connections or social support (the socially isolated) tend to experience loneliness. Likewise, those who feel lonely may be inclined to withdraw from others, leading to relative social isolation(National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Loneliness and social isolation can also be enduring, persisting over long periods. In such cases, loneliness is viewed as a trait (van Roekel et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), and might be described as chronic or long-term loneliness. At precisely which time point does transient or short-term loneliness become chronic loneliness? This remains a matter of debate, with suggested durations spanning anywhere from 1 to 6 years (Wolska \\u0026amp; Creaven, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). A similar distinction is made between transient (short-lived) and long-term social isolation.\\u003c/p\\u003e \\u003cp\\u003eHealth implications of loneliness and social isolation\\u003c/p\\u003e \\u003cp\\u003eThe WHO\\u0026rsquo;s constitution proposes that health is \\u0026ldquo;a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity\\u0026rdquo;. Social wellbeing is at the very heart of what it means to live a healthy life. Although social wellbeing has been relatively overshadowed, the health implications of poor social wellbeing (loneliness and social isolation) have become increasingly apparent in recent decades.\\u003c/p\\u003e \\u003cp\\u003eAccording to the US Centres for Disease Control and Prevention, both loneliness and social isolation (when chronic) are independently associated with an increased risk of experiencing mental and physical health problems, including heart disease, stroke, depression, and anxiety (National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Research aimed at quantifying the mortality risk of chronic loneliness and social isolation suggests that they are comparable to obesity, physical Inactivity, alcohol consumption, and cigarette smoking (National Academies of Sciences et al., 2020). The World Health Organisation estimates that almost one in six people is affected by loneliness globally, and that loneliness and social isolation contribute to around 871,000 deaths annually (World Health Organization, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eDuring the COVID-19 pandemic, levels of loneliness and social isolation rose significantly due to the social distancing measures (Allen et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). However, the growing public health concern around these issues certainly pre-dates the pandemic. In 2018, for instance, the UK government appointed Tracey Crouch as the minister for loneliness. This first-of-its-kind appointment followed an influential government report on loneliness and social isolation \\u0026mdash; the \\u003cem\\u003eJo Cox Commission on Loneliness\\u003c/em\\u003e. Among other statistics, the report suggests that around 200,000 older people in the UK hadn\\u0026rsquo;t conversed with a friend or relative in more than a month. The report described the impact of loneliness and social isolation as being twice as harmful as obesity and comparable to smoking 15 cigarettes per day.(Jopling, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) The UK report also talked about elevated rates of loneliness among young people. University students who feel like they don\\u0026rsquo;t fit in\\u0026mdash;spending days in relative isolation with nothing but college deadlines and digital devices for companionship and support(Jopling, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eGiven the extensive public health implications of loneliness and social isolation, numerous national and international initiatives have emerged to address what is often referred to as \\\"the loneliness epidemic.\\\" For instance, in the UK, there is the Campaign to End Loneliness; in Australia, Ending Loneliness Together; and in the United States, the Foundation for Social Connection, along with the World Health Organisation\\u0026rsquo;s Social Isolation and Loneliness initiatives, and the Global Initiative on Loneliness and Connection (Taylor et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMeasuring loneliness and social isolation\\u003c/p\\u003e \\u003cp\\u003eMost attempts to measure loneliness have focused on it as a persisting trait \\u0026mdash; an enduring pattern of experience \\u0026mdash; rather than a short-lived, transient state(Oughli \\u0026amp; Lee, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). These measures of trait loneliness can be grouped into two categories: those that treat loneliness as unidimensional (a unitary, global experience) and those that treat it as multidimensional. Within the unidimensional view, loneliness varies in intensity or frequency from low to high. Multidimensional models, however, are more nuanced and, as a result, perhaps more contested. For instance, one might obtain a low score for \\u0026ldquo;alienation\\u0026rdquo;, as a proposed component of loneliness, while reporting relatively high levels of estrangement. However, a criticism of the multidimensional models is that the theoretical conceptualisation of the proposed components or types of loneliness lacks clarity and consensus.\\u003c/p\\u003e \\u003cp\\u003ePerhaps due to their brevity and ease of interpretation, unidimensional measures of trait loneliness have been the most frequently employed. The University of California, Los Angeles (UCLA) Loneliness scale is currently the most widely used among such metrics. Developed by researchers at UCLA, this scale was designed to be psychometrically adequate (valid and reliable) and easily administered. The scale has undergone several revisions, and there are now long (20-item) and short (3-item) forms. The UCLA-LS is a self-report measure where individuals are asked to indicate how frequently they felt, for example, \\u0026ldquo;a lack of companionship\\u0026rdquo; or \\u0026ldquo;isolated from others\\u0026rdquo;. Depending on the version of the scale - long or short form - items are scored from 1 to 4, where one equates to \\u0026ldquo;never\\u0026rdquo; having the experience and four reflects \\u0026ldquo;often\\u0026rdquo; feeling this way (Russell et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e1978\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eStandard methods of validation for both multi- and unidimensional measures of loneliness involve comparing populations known to experience higher levels of loneliness, so-called \\u0026ldquo;at-risk\\u0026rdquo; groups, with healthy controls. Such comparisons have been made between healthy college students and, for example, (a) patients experiencing depression, (b) people attending a remedial social skills workshop, and (c) divorcees. In each instance, the at-risk groups report significantly higher levels of loneliness. In addition to comparisons with known groups, measures of loneliness are also frequently validated against self-labelled and peer-reported loneliness.\\u003c/p\\u003e \\u003cp\\u003eIn measuring social isolation, several potential objective indicators include marital or relationship status, living alone, and living with others. Additionally, self-report measures are used to quantify the size and closeness of a person\\u0026rsquo;s social network by assessing the level of support they receive from family and friends (Veazie et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Examples of such measures include the Social Network Scale and the Social Support Scale. These scales typically seek to quantify the levels of social support or isolation. The Social Network Scale, for example, has three items that ask respondents to quantify social interaction, such as, \\u0026ldquo;How many relatives/friends do you see or hear from at least once a month?\\u0026rdquo; Similarly, the six-item Social Support Scale asks respondents to report the number of people they can count on in response to questions such as: \\u0026ldquo;Who can you count on when you need help?\\u0026rdquo; and \\u0026ldquo;Who can you count on to console you when you are very upset?\\u0026rdquo;\\u003c/p\\u003e \\u003cp\\u003eEpidemiological studies of loneliness and social isolation\\u003c/p\\u003e \\u003cp\\u003eIn 2024, the American Psychiatric Association suggested that 1 in 3 Americans are lonely; that is, 30% of US adults said they have experienced feelings of loneliness at least once a week over the past year(American Psychiatric Association, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Similarly, in the same year, Gallup\\u0026rsquo;s World Poll suggested that \\u0026ldquo;Over 1 in 5 (23%) people worldwide feel lonely a lot\\u0026rdquo;.(Dugan, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) Work by the WHO\\u0026rsquo;s Commission on Social Connection puts the global rate at around 16%. Whatever way we slice it, there are a lot of lonely people out there.\\u003c/p\\u003e \\u003cp\\u003eThe percentage of respondents reporting loneliness in the World Poll study varied significantly by territory, ranging from 45% in Comoros to 6% in Vietnam.(Dugan, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) The authors acknowledge that at least some of the international variation may stem from individuals in certain countries reporting that they spend parts of their day physically alone, rather than emotionally alone. Loneliness is arguably conceived and experienced differently across diverse cultural and linguistic groups, complicating the determination of global prevalence, especially when using single-item measures.\\u003c/p\\u003e \\u003cp\\u003eEstablishing a global prevalence for problem loneliness is premature. This is primarily due to data scarcity, particularly in low-income countries. Additionally, across countries, Gallup found that reports of loneliness are consistently higher in web surveys than in traditional (in-person) modes of interviewing. (Dugan, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Add to this the diverse ways of measuring and conceptualising problematic loneliness (methodological heterogeneity), and we begin to appreciate the challenge of establishing even a national, let alone International, prevalence trends for problematic loneliness.\\u003c/p\\u003e \\u003cp\\u003eGlobally, adolescents (20.9%) and young adults (17.4%) appear to experience loneliness most, whereas social isolation is more common in older age groups, 25 to 34% (World Health Organization, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). For example, data from the Programme for International Student Assessment (PISA) suggests that across most of the 37 participating countries, there was an increase in the rates of loneliness at school (15 and 16-year-olds) between 2012 and 2018 (Twenge et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIt is unclear whether societal levels of loneliness are increasing globally over time. Currently, there is insufficient evidence to confirm a rise in loneliness, although in some countries (e.g., the USA) and among certain age groups (18\\u0026ndash;29 years), this appears to be the case. Nevertheless, even if the rates remain relatively stable, loneliness continues to be a significant public health issue that has been underappreciated for too long.\\u003c/p\\u003e \\u003cp\\u003eThere are several hard indicators that social isolation has risen: perhaps the most common is an increase in the proportion of the population living alone. In many nations, more people are living alone than at any point in recorded history (see Fig.\\u0026nbsp;1).\\u003c/p\\u003e \\u003cp\\u003eFigure 1\\u003c/p\\u003e\\n\\u003ch3\\u003ePercentages of single-person occupancy households across time\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSource (World Health Organization, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eIt is evident that to better understand the direction of travel, loneliness and social isolation should be incorporated into general health surveillance. Such initiatives need to have a broad geographical coverage, including low and middle-income countries and those without reliable access to the online world. This also calls for the consistent use of standardised and well-validated measurement tools (Dugan, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eDigital Media Use and Loneliness\\u003c/p\\u003e \\u003cp\\u003eAlthough there is insufficient evidence to draw firm conclusions about a global rise in loneliness, there is a widespread perception that it is the case. Arguments about the causes of the posited increase in loneliness and social isolation tend to centre on the \\u0026ldquo;lost community hypothesis\\u0026rdquo;. Rooted in the work of the sociologist Ferdinand T\\u0026ouml;nnies, the lost community hypothesis proposes that the forces of modernity, such as urbanisation, industrialisation, and the rise of individualism, erode social connections. Digital technology is also viewed as one of the forces contributing to \\u0026ldquo;community loss\\u0026rdquo;. From the personal stereo and personal computer to the iPod, iPhone, and now personalised AI, tech typically nudges users towards hyper-individualism. Unsurprisingly, the relationship between digital media use and loneliness has received substantial research attention. For example, several studies have examined time series data, such as the Programme for International Student Assessment (PISA) and US national youth wellbeing surveys. These data frequently, but not invariably, report increases in loneliness and decreases in well-being that correspond with the popularisation of the smartphone (Heffer et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Twenge, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Twenge et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMuch of this research depends on the rather crude metric known as screentime. Originating in early cinema, the term broadly refers to the amount of time spent viewing screen-based digital media. Current attempts to measure screentime may consider various types of screen use (television, social media, video games, general computer use) and may also try to differentiate dimensions such as active (chatting/commenting) versus passive (scrolling/viewing), and leisure versus occupational screentime. Studies examining the link between screentime and loneliness report mixed results (MacDonald et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Tang et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). These inconclusive findings also extend more generally to the relationship between screentime and mental health (Santos et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Tang et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Much of the research in this field is correlational, so drawing causal conclusions remains premature. More detailed investigations of the motivations for screen use and the type of content consumed/produced might shed further light on the nature of the relationship between digital media use and loneliness. Ultimately, longitudinal and experimental studies are required to illuminate the possible mechanisms underlying any causal relationships.\\u003c/p\\u003e \\u003cp\\u003eProblematic Digital Media Use and Loneliness\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eWe shape our tools, and thereafter, our tools shape us.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003e[Frequently attributed to Marshall McLuhan]\\u003c/p\\u003e \\u003cp\\u003eThere is no doubt that individuals can develop problematic relationships with digital media. However, the debate revolves around the best way to conceptualise these issues: behavioural addiction, compulsion, masked depression or maladaptive coping strategy? Clinical concern and published research interest in problematic digital media use and technology-related disorders emerged alongside the popularisation of the World Wide Web in the mid-1990s. In the earliest scholarly work on this topic, Griffiths draws insights from existing research on pathological gambling and describes technology-related disorders as non-chemical (behavioural) addictions involving human-machine interaction. Echoing Griffiths, Young proposed the inclusion of Internet Addiction Disorder [IAD] within revisions to the 4th edition of the American Psychiatric Association\\u0026rsquo;s Diagnostic and Statistical Manual [DSM-IV-TR] (American Psychiatric Association, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Young\\u0026rsquo;s conceptualisation of IAD was broad, encompassing numerous subtypes that reflected different facets of problematic internet use, such as cyberrelationship [social media] addiction, cybersexual [pornography] addiction, gaming addiction, and more. Ultimately, IAD was not included in the DSM-IV-TR. However, gaming addiction, renamed internet gaming disorder [IGD], was incorporated into the revised manual. IGD was recognised as a condition warranting further research, a status it still holds in DSM-5-TR. The proposed DSM-5 criteria for IGD include preoccupation/obsession, withdrawal, tolerance, loss of control, anhedonia, continued overuse, deception, mood repair, and social/occupational impairment. It is stipulated that at least 5 of the 9 symptoms must be present for at least 12 months to meet the criteria for the proposed diagnosis. Other research teams have adapted the same criteria for \\u0026ldquo;social media disorder.\\u0026rdquo;(van den Eijnden et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eGoing beyond the American Psychiatric Association (APA), the World Health Organization [WHO] has officially recognised gaming disorder as a diagnostic entity within its classification system 25. In 2018, the WHO included gaming disorder in the behavioural addictions section of the International Classification of Diseases, 11th Revision [ICD-11]. Other internet-related issues, such as problematic social media use, can also be diagnosed under the section\\u0026rsquo;s residual categories: \\u0026ldquo;disorders due to addictive behaviours, unspecified,\\u0026rdquo; and \\u0026ldquo;other specified disorders due to addictive behaviours\\u0026rdquo;(Brand et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Lindenberg et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Extending beyond Europe and North America, many Chinese clinicians utilise the Chinese Classification of Mental Disorders, the CCMD-3, which is the most widely used psychiatric diagnostic system in China (Tejeiro et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Zou et al., \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). The CCMD-3 permits the diagnosis of gaming disorder within the section on habit and impulse disorders (code 61), alongside pathological gambling (Tejeiro et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite these relatively recent developments, attempting to integrate problematic technology use within medical/psychiatric frameworks, the diagnostic utility of concepts such as gaming disorder and internet addiction disorder remains widely contested (Musetti et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Similarly, as new digital technologies emerge and social norms evolve, what constitutes problematic will require conceptual re-evaluation (Ellis, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMeasuring Problematic Digital Media Use\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eI often used social media to escape from negative feelings.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eDrawing on models of behavioural addiction, rooted in earlier work on pathological gambling, most measures of problematic digital media use attempt to assess \\u0026ldquo;addiction\\u0026rdquo; symptoms such as preoccupation, tolerance, withdrawal, persistence, mood modification, deception, displacement, and conflict. Typically, such symptoms (five or more under DSM criteria) have persisted for at least 12 months.\\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\\u003e\\u003cem\\u003eSymptom description of problematic social media use as a behavioural addiction\\u003c/em\\u003e\\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\\u003eSymptoms\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSocial Media Context\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePreoccupation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eConstantly thinking about social media when not using it.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMood regulation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUsing social media to alleviate an unpleasant mood, to bring about a mood shift\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTolerance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIncreasing amounts of time spent on social media\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWithdrawal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnpleasant feelings (anxiety, irritability, boredom) when social media use is stopped or somehow prevented\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eConflict and dysfunction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFamilial arguments and interpersonal conflicts related to social media use. Continuing to use social media despite an awareness that it is negatively impacting relationships and school/workplace performance\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDeception\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBecoming defensive and deceptive, lying about the amount of time one spends on social media\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCraving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHeightened anticipation and a strong desire for the next social media session.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRelapse/Control\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRepeated unsuccessful efforts to reduce or abstain from social media 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\\u003eTable adapted from A critical review of \\u0026ldquo;Internet addiction\\u0026rdquo; criteria with suggestions for the future (Van Rooij \\u0026amp; Prause, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eMeasuring these problematic behaviours typically involves self-report inventories that assess the presence, severity or frequency of such symptoms. The inventories most often used to screen individuals for problematic social media use typically factor in most, if not all, of the symptoms mentioned above. One of the most widely used screening instruments for PSMU is the 9-item Social Media Disorder Scale (SMD9). The SMD9 short form uses a (Yes/No) response scale. Drawing on the APA\\u0026rsquo;s proposed criteria for internet gaming disorder, the presence of 5 out of 9 symptoms is taken as the screening cut-off. Respondents are asked about their behaviour over the past 12 months, for example, \\u0026ldquo;I often used social media to escape from negative feelings\\u0026rdquo; (mood regulation) and \\u0026ldquo;I tried to spend less time on social media but failed\\u0026rdquo; (relapse/control).\\u003c/p\\u003e \\u003cp\\u003eIt is crucial to distinguish between frequent use and problematic use; simply spending a lot of time on social media (screen time) does not necessarily indicate problematic usage. Many people use social media very often, for long periods, without any negative effects. Such individuals do not show any signs of behavioural addiction. They are not regularly using social media to escape from an unpleasant mood. They do not become unduly angry or distressed when they cannot access their preferred platform. Most importantly, their use does not negatively affect their social (relationships) or occupational responsibilities. These individuals might be described as intense or heavy users, but their usage is not inherently problematic.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eHave you jeopardized or lost an important relationship, job or an educational or career opportunity because of your gaming activity?\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eAn Item from the Internet Gaming Disorder Scale 34\\u003c/p\\u003e \\u003cp\\u003eA nearly identical method is used to assess problematic gaming or gaming disorder. For example, the internet gaming disorder scale includes nine items that reflect symptoms of behavioural addiction related to gaming. It also asks about gaming activities over the past 12 months and is based on the APA\\u0026rsquo;s proposed criteria for internet gaming disorder. The IGDS includes questions such as \\u0026ldquo;Have you continued your gaming activity despite knowing it was causing problems between you and other people?\\u0026rdquo; and \\u0026ldquo;Have you jeopardised or lost an important relationship, job, or educational or career opportunity because of your gaming activity?\\u0026rdquo; The nine proposed criteria for internet gaming disorder are listed below.\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003ePreoccupation with gaming.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eWithdrawal symptoms when gaming is taken away or not possible (sadness, anxiety, irritability).\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eTolerance, the need to spend more time gaming to satisfy the urge.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eInability to reduce playing, unsuccessful attempts to quit gaming.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eGiving up other activities, loss of interest in previously enjoyed activities due to gaming.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eContinuing to game despite problems.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eDeceiving family members or others about the amount of time spent on gaming.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eThe use of gaming to relieve negative moods, such as guilt or hopelessness.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eRisk, having jeopardized or lost a job or relationship due to gaming.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eProposed criteria for Internet Gaming Disorder as listed in the American Psychiatric Association\\u0026rsquo;s diagnostic manual, \\u003cem\\u003eDSM-5-TR\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003eBeyond the presence of at least 5 of the nine symptoms, the APA also propose that these symptoms must cause \\\"significant impairment or distress\\\" in several aspects of a person's life.\\u003c/p\\u003e \\u003cp\\u003eEpidemiology: Prevalence of Problematic Media Use\\u003c/p\\u003e \\u003cp\\u003eLike research on loneliness, studies of problematic media use face similar issues of conceptual and measurement heterogeneity. Even the names proposed for problem use vary widely, from smartphone addiction to social media disorder. Despite conceptual diversity, attempts have been made to quantify problem prevalence (variously conceived) and to explore sociodemographic risk factors. One meta-analysis combining 62 studies, including 34,798 respondents, reported a prevalence of 5% for problematic social media use (PSMU)(Cheng et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This figure was based on the most stringent screening criteria, counting only those classified as experiencing \\u0026ldquo;very severe symptoms\\u0026rdquo;. Using more relaxed criteria, the prevalence rose to 13%. Notably, in this analysis, the highest rates of PSMU were recorded among the youngest age group (adolescents).\\u003c/p\\u003e \\u003cp\\u003eThis meta-analysis also spanned 32 countries. Nations were grouped based on the degree to which each society emphasised individualistic versus collectivist values (Hofstede, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e). Participants in collectivist nations (e.g., Japan, KSA, Taiwan) reported significantly higher rates of PSMU than their relatively individualistic counterparts (e.g., USA, UK, Australia). This is an area for future research. However, it might be that the obligations associated with interdependence, such as compliance with social norms (fitting in) and maintaining close kinship connections, drive greater social media use in collectivist societies, perhaps leading to higher rates of PSMU. With gender in focus, another meta-analysis explored 51 independent studies including both problematic social media use and gaming disorder. The authors suggest that females are more likely to report PSMU, whereas males are more likely to meet the proposed screening criteria for gaming disorder (Su et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMeta-analytic studies specifically examining gaming disorder typically suggest a rate of around 3% (Chiang et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Kim et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). One study spanning 17 countries, including close to a quarter of a million participants, reported a prevalence of 3.05%, with the issue more common in males at a ratio of about 3:1 (Stevens et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, the gaming industry has been actively working to encourage more females into gaming. Market research suggests that female gamers are increasing, particularly mobile phone gamers (Newzoo, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e); the male-female ratio may shift in the coming years. Similarly, gaming disorder is currently associated with younger age groups (children and adolescents). However, this may also be an aspect of the phenomenon that evolves with sociocultural changes.\\u003c/p\\u003e \\u003cp\\u003eProblematic Digital Media Use \\u0026amp; Relationship with Loneliness\\u003c/p\\u003e \\u003cp\\u003eAs mentioned earlier, the link between digital media use (screen time) and loneliness remains unclear (mixed findings). However, there is much greater clarity about the connection between problematic digital media use and loneliness. Furthermore, longitudinal studies indicate a two-way relationship, where problematic technology use can precede loneliness, and loneliness can also precede problematic use.\\u003c/p\\u003e \\u003cp\\u003eOne meta-analytic study examining longitudinal investigations of problematic digital media use and loneliness reviewed 26 studies \\u0026mdash;19 of which used the UCLA-LS (Zhang et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The authors concluded that adults and adolescents experiencing loneliness are at a greater risk of developing problematic internet use, and similarly, that those with problematic internet use are at a higher risk of later experiencing loneliness. These findings are explained through Reinforcement theory: tech use alleviates loneliness (negative reinforcement), causing lonely individuals to increase their internet use to preserve social benefits (entertainment, online connections), eventually leading to problematic internet use. An alternative, and complementary, explanation is the Internet displacement hypothesis: prolonged PIU results in degraded in-person social connections, leading to loneliness. It is plausible that these proposed initial mechanisms converge, creating a vicious cycle (mutual exacerbation), in which PIU displaces social connections, causing loneliness, which in turn fuels further PIU as an attempt to escape or avoid this unpleasant state.\\u003c/p\\u003e \\u003cp\\u003eAnother systematic review focused on loneliness, social anxiety and social media included 52 previously published studies (O\\u0026rsquo;Day \\u0026amp; Heimberg, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The review concluded that Individuals with high levels of social anxiety (excessive/problematic shyness) and loneliness are more prone to PSMU. The review's authors suggest that this link is due to the socially anxious seeking social support on social media, perhaps to make up for the lack of in-person support. Furthermore, loneliness appears to be a risk factor for PSMU. Several studies have examined loneliness and PSMU over time, finding that loneliness at time one predicts increases in social media use at a later time point. Research in this area has also highlighted differences between active (interacting, commenting, posting) and passive social media use, such as aimless scrolling through the timeline. Unsurprisingly, passive use is most reliably preceded by feelings of loneliness (Verduyn et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eOne might expect different findings in collectivist societies where families are larger and more closely connected. However, a study exploring this issue among adults from Saudi Arabia and Kuwait reported that problematic internet and social media use was significantly linked to increased feelings of loneliness in these societies (Alheneidi et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). There was also a dose\\u0026ndash;response relationship; that is, greater loneliness predicted higher problematic internet use. A study conducted in Lebanon reported a similar link between PSMU and loneliness (Youssef et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCorrelation, of course, is not causation. However, given the impact of loneliness on health and well-being, such correlational evidence signals concern and the need for further research. This study utilises data from Sync\\u0026rsquo;s global digital well-being survey to investigate the relationship between loneliness and problematic technology use, specifically problematic social media use and internet gaming disorder, as outlined in DSM-5. This study explores these relationships across 35 countries spanning seven world regions aiming to further examine these posited links across diverse cultures and populations.\\u003c/p\\u003e\"},{\"header\":\"Method\",\"content\":\"\\u003cp\\u003eSample description across 35 countries and seven world regions\\u003c/p\\u003e \\u003cp\\u003eThe data reported here are from Sync\\u0026rsquo;s global digital wellbeing survey. These data comprise 35,000 adult respondents, with 1,000 respondents per territory. Based on pre-existing panels, the sample broadly represents the Internet-using adults in each participating nation. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents the raw count and percentage of participants identified as lonely based on the UCLA three-item loneliness scale. The study also explored gaming disorder symptoms and problematic social media using reliable, widely used and well validated scales. All these measures are detailed below.\\u003c/p\\u003e \\u003cp\\u003eThe UCLA Loneliness Scale (UCLA-LS-3)\\u003c/p\\u003e \\u003cp\\u003eThe University of California, Los Angeles (UCLA) Loneliness scale is currently the most widely used measure of Loneliness. This scale was designed to be psychometrically adequate (valid and reliable) and easily administered (Russell et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e1978\\u003c/span\\u003e). In the present study we used the short (3-item) form of the scale. The UCLA-LS-3 is a self-report measure which includes three items: (1) I lack companionship, (2) I feel left out, and (3) I feel isolated from others. Respondents answered these items in terms of frequency: hardly ever, some of the time, and often, scored 1, 2, and 3, respectively. In the present study, we use the recommended cut-off, that is, loneliness as scores of 6 or higher. The scale demonstrated good internal reliability (α\\u0026thinsp;=\\u0026thinsp;0.79).\\u003c/p\\u003e \\u003cp\\u003eThe Gaming Disorder Scale (Short Form)\\u003c/p\\u003e \\u003cp\\u003eThe short form of the Gaming disorder scale (IGDS9-SF) is a nine-item measure where each statement reflects one of the DSM-5\\u0026rsquo;s proposed criteria for Internet Gaming Disorder (IGD). Respondents are asked to consider their gaming experience over the past twelve months and then respond to items (symptom descriptions) such as \\\"played in order to temporarily escape or relieve a negative mood\\u0026rdquo; and \\u0026ldquo;continued your gaming activity despite knowing it was causing problems between you and other people?\\u0026rdquo;. The scale invites Yes/No responses, and endorsing 5 of 9 symptoms takes the respondent above the proposed screening cut-off, representing possible gaming disorder. The IGDS9-SF has been widely used and well validated(Feng et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Pontes \\u0026amp; Griffiths, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Its reliability in the current study was good (α\\u0026thinsp;=\\u0026thinsp;0.803).\\u003c/p\\u003e \\u003cp\\u003eThe Social Media Disorder Scale (Short Form)\\u003c/p\\u003e \\u003cp\\u003eThe short form (nine items) of the Social Media Disorder Scale (SMD9-SF) is derived from the original 27-item version (van den Eijnden et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). It is extrapolated from the DSM-5\\u0026rsquo;s proposed criteria for Internet Gaming Disorder(American Psychiatric Association, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Respondents are asked about their social media use over the past 12 months, example items include \\u0026ldquo;\\u0026hellip;often used social media to escape from negative feelings?\\u0026rdquo; and \\u0026ldquo;tried to spend less time on social media but failed\\u0026rdquo;. The SMD9-SF uses a Yes/No response scale. Endorsing 5 out of 9 symptoms takes the respondent above the proposed screening cut-off and is deemed to represent problematic social media use. The SMD9-SF has good convergent and criterion validity along with sufficient sensitivity, specificity, and test-retest reliability(van den Eijnden et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In the current study, internal reliability was also good, α\\u0026thinsp;=\\u0026thinsp;0.895\\u003c/p\\u003e \\u003cp\\u003eData collection procedure\\u003c/p\\u003e \\u003cp\\u003ePSB Insights, a global analytics consultancy with extensive experience in multinational polling services, managed the data collection for the 30-nation digital wellbeing survey (DWS). Materials were translated and back-translated from English into the majority language of each participating nation. The survey was undertaken online. Based on existing participant banks (panels), the survey obtained nationally representative samples of the adult internet-using population in each participating territory. Participants were pre-registered survey panelists in their respective countries. Potential participants received invitations via email. The survey response rate was 19.35%. Automated data quality checks ensured that those who failed to complete the survey were excluded from the analysis, as were those who completed the materials with an overly stereotyped response pattern (e.g., answering yes to everything). Similarly, automated data quality checks removed those who completed the survey too quickly (speeding). The mean exclusion rate was 17%; however, oversampling ensured that each nation had 1000 valid participants. The final sample (\\u003cem\\u003eN\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;35,000) comprised a thousand respondents from each country. All data were collected between July 12th and July 26th, 2023. The study was reviewed and approved by the research ethics committee of King Abdulaziz Centre for World Culture (IRS 202371).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eOverall rates of loneliness\\u003c/p\\u003e \\u003cp\\u003eAcross all participating territories, there were individuals for whom loneliness was a significant issue. The highest rates for individuals scoring above the UCLA loneliness scale cut-off (scores of six or more). were observed in Pakistan and Bangladesh, where more than half of respondents reported loneliness (58%). The lowest rates were reported for China, however, even here around 23% of respondents scored above the cut-off. Across the entire sample, 39.45% were classified as lonely according to the recommended cut-off. Even when the cut-off was raised, and set to the maximum score of nine, 5.03% of respondents were identified as lonely using this stringent threshold.\\u003c/p\\u003e\\n \\u003ch3\\u003eBreakdown of loneliness by demographics\\u003c/h3\\u003e \\u003cp\\u003eIn general, more females scored above the loneliness scale\\u0026rsquo;s cut-off, with younger, less educated, unemployed, childless individuals also more frequently categorised as lonely. Those categorized as problematic gamers or social media users were also more likely to score above the UCLA loneliness scale\\u0026rsquo;s cut-off.\\u003c/p\\u003e \\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2\\u0026nbsp;\\u003c/strong\\u003e\\u003cem\\u003eFrequency of loneliness by demographic and behavioural categories\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" align=\\\"\\\" width=\\\"534\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003eFrequency (%)\\u003c/p\\u003e\\n \\u003cp\\u003eAbove UCLA Cut-off\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eGender \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e6955 (41.54)\\u003c/p\\u003e\\n \\u003cp\\u003e6860 (37.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eAge group (Median)\\u003c/p\\u003e\\n \\u003cp\\u003eOver 35 yrs.\\u003c/p\\u003e\\n \\u003cp\\u003e35 yrs. and under\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e5845 (32.85)\\u003c/p\\u003e\\n \\u003cp\\u003e7970 (46.26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eOlder Adults\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eOver 64 yrs.\\u003c/p\\u003e\\n \\u003cp\\u003e64 yrs. and under\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e1023 (23.84)\\u003c/p\\u003e\\n \\u003cp\\u003e12792 (41.63)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eCompleted College\\u003c/p\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;Yes\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e7193 (41.56)\\u003c/p\\u003e\\n \\u003cp\\u003e6622 (37.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eJobseeker (unemployed)\\u003c/p\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e12441\\u0026nbsp;(38.41)\\u003c/p\\u003e\\n \\u003cp\\u003e1374\\u0026nbsp;(52.26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eParent (child under 18)\\u003c/p\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e7305\\u0026nbsp;(46.59)\\u003c/p\\u003e\\n \\u003cp\\u003e6510\\u0026nbsp;(33.66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eProblematic Gaming\\u003c/p\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e1473\\u0026nbsp;(38.50)\\u003c/p\\u003e\\n \\u003cp\\u003e2353\\u0026nbsp;(61.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 264px;\\\"\\u003e\\n \\u003cp\\u003eProblematic Social Media Use\\u003c/p\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 270px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e1262\\u0026nbsp;(37.77)\\u003c/p\\u003e\\n \\u003cp\\u003e2079 (62.22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n \\u003cp\\u003eItem-level analysis\\u003c/p\\u003e \\u003cp\\u003eThe loneliness scale includes three items: (1) I lack companionship, (2) I feel left out, and (3) I feel isolated from others. Respondents reported the frequency of experiencing such feelings, hardly ever, some of the time, and often, scored 1, 2, and 3, respectively. Lacking companionship (Item 1) was the most frequently endorsed (see Table \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e)\\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 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMean scores and item endorsement frequency for the UCLA-LS\\u003c/em\\u003e\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eItem\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eItem 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eItem 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eItem 3\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.73 (0.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.61 (0.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.67 (0.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePercentage reporting \\\"often\\\"\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16.41%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.82%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.64%\\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 heatmap (Fig.\\u0026nbsp;3), \\\"I lack companionship,\\\" is the most strongly endorsed in 31 of the 35 countries.\\u003c/p\\u003e \\u003cp\\u003eFigure 3\\u003c/p\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMean Endorsement of loneliness scale (UCLA-LS) Items by Nation\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eItem1\\u0026thinsp;=\\u0026thinsp;I lack companionship, Item2\\u0026thinsp;=\\u0026thinsp;I feel left out, Item3\\u0026thinsp;=\\u0026thinsp;I feel isolated from others. All Items scored 1 to 3\\u003c/p\\u003e \\u003cp\\u003eNation-level Analysis of Loneliness\\u003c/p\\u003e \\u003cp\\u003eExploring the scores by nation, we find that, after controlling for age and gender, Japan reported the highest levels of loneliness. Australia, Malaysia, Pakistan, Bangladesh, and Sweden also report relatively high rates of loneliness.\\u003c/p\\u003e \\u003cp\\u003eFigure 4\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eMean scores for loneliness scale (UCLA-LS) by nation, controlling for demographic covariates\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor additional analyses of loneliness, please see Appendix 1\\u003c/p\\u003e \\u003cp\\u003eIndicators of social isolation\\u003c/p\\u003e \\u003cp\\u003eAlthough social Isolation was not assessed explicitly, we did explore known risk factors. Based on previous research, we combined four demographic variables to arrive at a high-risk profile for socially Isolated Individuals. The indicators included not having children, not having a strong connection to a religion, not being employed or in education/training, and not having completed college. Data such as marital status and household occupancy were not available.\\u003c/p\\u003e \\u003cp\\u003eThe percentage of people in each of the four risk groupings was as follows.\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eNo strong connection to a religion 65.98%\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eNot in employment, education, or training 7.50%\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eNever completed college. 49.41\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eNo children 44.77%\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe percentage of people with all four risk Indicators (high risk for social Isolation) was 1.84.\\u003c/p\\u003e \\u003cp\\u003eFigure 5\\u003c/p\\u003e\\n\\u003ch3\\u003eThe Percentage of participants with all four social isolation risk factors\\u003c/h3\\u003e\\n\\u003cp\\u003eSocial Isolation risk scores were correlated with UCLA Loneliness. Scores. They were also associated with age, with older Individuals tending towards higher social Isolation risk scores.\\u003c/p\\u003e \\u003cp\\u003eOverall rates of Problematic Social Media Use\\u003c/p\\u003e \\u003cp\\u003eOf those who used social media over the past 12 months, 9.54% reported five or more symptoms, scoring above the symptom cut-off on the social media disorder scale (SMD).\\u003c/p\\u003e \\u003cp\\u003eFigure 6\\u003c/p\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOverall percentage of problematic social media use\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eLooking at the nine symptoms that make up the social media disorder construct, one symptom stands out above all others across all nations. That is symptom 8, also referred to as escape, experiential avoidance, or mood repair, that is: \\\"I used social media to escape from negative feelings?\\\" More than a quarter (29%) of social media users endorsed item 8.\\u003c/p\\u003e \\u003cp\\u003eFigure 7\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eHeat map showing mean scores on the social media disorder scale by item by nation\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNation-level Analysis of Problematic Social Media Use (Social Media Disorder)\\u003c/p\\u003e \\u003cp\\u003eExploring the scores by nation we find that, after controlling for age and gender, India reported the highest levels of problematic social media use, followed by Pakistan and Saudi Arabia. The top 10 highest scorers for problematic social media use are all Asian or African Nations. The highest-scoring Western nation was Australia, with Estonia reporting the lowest levels of PSMU.\\u003c/p\\u003e \\u003cp\\u003eFigure 8\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMean scores for problematic social media use (SMD-9) by nation, controlling for demographic covariates\\u003c/h2\\u003e \\u003cp\\u003e Adjusted marginal means of social media disorder symptom count, controlling for covariates age, gender, employment and educational status\\u003c/p\\u003e \\u003cp\\u003eOverall rates of Gaming disorder\\u003c/p\\u003e \\u003cp\\u003eOf those who played video games over the past 12 months, 10.92% reported five or more symptoms, scoring above the symptom cut-off on the IGD-9-SF.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn line with the analysis of problematic social media use, one gaming disorder symptom also stands out above all others across all nations. Again, this is symptom 8, also referred to as escape, experiential avoidance or mood repair: \\\" played in order to temporarily escape or relieve a negative mood (e.g., helplessness, guilt, anxiety)\\\". More than half (58%) of gamers endorsed this symptom.\\u003c/p\\u003e \\u003cp\\u003eFigure 10\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eHeat map showing mean scores on the internet gaming disorder scale by item by nation\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNation-level Analysis of Gaming Disorder Symptoms\\u003c/p\\u003e \\u003cp\\u003eExploring the gaming disorder symptom scores by nation, we find that, after controlling for age and gender, India reported the highest levels of problematic social media use, followed by Pakistan, Bangladesh, Kuwait and Egypt. As with social media, the top 10 highest scorers, this time for gaming disorder, were either Asian or African nations, with four Arabic-speaking nations among them. The highest-scoring Western nation was Australia, while Germany reported the lowest levels of gaming disorder symptoms.\\u003c/p\\u003e \\u003cp\\u003eFigure 11\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMean scores for internet gaming disorder (IGD-9) by nation, controlling for demographic covariates\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eNote\\u003c/strong\\u003e \\u003cp\\u003eMean symptom scores while controlling for age and gender\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003eRelationship between loneliness, social isolation risk, and problematic technology use\\u003c/p\\u003e \\u003cp\\u003eProblematic technology use was most strongly correlated with loneliness (medium effect size). It was also associated with risk factors for social Isolation, but to a lesser degree (small effect size). Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e details the correlations between the key study variables.\\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\\u003e\\u003cem\\u003eCorrelations between social isolation risk scores and problematic technology use\\u003c/em\\u003e\\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\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSIR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ePSMU\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIGD\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLoneliness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e0.159*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.316*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.321*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.168*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.092*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePSMU\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.509*\\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\\u003eNotes: SIR\\u0026thinsp;=\\u0026thinsp;social Isolation risk, PSMU\\u0026thinsp;=\\u0026thinsp;problematic social media use, IGD\\u0026thinsp;=\\u0026thinsp;Internet Gaming Disorder symptoms\\u003c/p\\u003e \\u003cp\\u003e* \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003cp\\u003eOverall, loneliness was most strongly correlated with gaming disorder symptoms and, to a slightly lesser degree, with problematic social media use. The graphs below visualize these relationships. As loneliness scores Increase, so too do scores for gaming disorder symptoms (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.321), with the same pattern observed between loneliness and social media disorder symptoms (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.316).\\u003c/p\\u003e \\u003cp\\u003eFigure 12\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eCorrelation plots depicting the positive association between loneliness and problematic social media use (left), and loneliness and gaming disorder symptoms (right)\\u003c/em\\u003e \\u003c/p\\u003e\\u003cp\\u003eRelationship between loneliness and problematic technology use by nation\\u003c/p\\u003e \\u003cp\\u003eThe most strongly correlated variable with loneliness is gaming disorder. This pattern holds true for most countries, with the strongest positive relationship for Egypt (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.57), with a relatively weak relationship observed between IGD symptoms and loneliness observed for Japan (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.04). These patterns are almost Identical for problematic social media use, again with the strongest association between social media disorder symptoms and loneliness observed for Egypt (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.50) and the weakest again observed in Japan (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.15)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eExploring the predictors of Loneliness\\u003c/p\\u003e \\u003cp\\u003eUsing more sophisticated analysis (bivariate logistic regression), we can explore all the predictors of loneliness while controlling for demographic factors. This can give us an idea about which factors have the strongest association with loneliness. In this study, we find that symptoms of gaming disorder and social media disorder are most strongly linked to loneliness, even after controlling for all other variables. The details of this analysis are represented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e13\\u003c/span\\u003e and further detailed in Table \\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eA forest plot showing the adjusted odds ratios for the risk of loneliness\\u003c/h2\\u003e \\u003cp\\u003eTable 4 Bivariate (OR) and multivariate (AOR) logistic regression predicting UCLA-3 loneliness scores above the recommended cut-off.\\u003c/p\\u003e\\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Tabd\\\" border=\\\"1\\\"\\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\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAbove-Threshold Loneliness Scale\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOdds Ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eAdjusted Odds Ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGender\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18277\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6860 (37.53%)\\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-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16741\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6955 (41.54%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.183 (1.133\\u0026ndash;1.235)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.242 (1.169\\u0026ndash;13.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35 and over\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17790\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5845 (32.85%)\\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-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnder 35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17227\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7970 (46.26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.769 (1.664\\u0026ndash;1.915)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.186 (1.109\\u0026ndash;1.267)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCompleted College\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17713\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6622 (37.38%)\\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-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17305\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7193 (41.56%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.191 (1.141\\u0026ndash;1.243)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.156 (1.088\\u0026ndash;1.228)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eJobseeker\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32389\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12441 (38.41%)\\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-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2629\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1374 (52.26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.776 (1.557\\u0026ndash;2.070)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.489 (1.332\\u0026ndash;1.663)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eParent\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e19340\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35 (33.66%)\\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-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15678\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e572 (46.59%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.845 (1.709\\u0026ndash;2.008)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.503 (1.408\\u0026ndash;1.605)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eProblematic Gaming\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17918\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6259 (36.71%)\\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-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3826\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2353 (61.50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.976 (2.769\\u0026ndash;3.198)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.478 (2.281\\u0026ndash;2.692)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eProblematic Social Media Use\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25328\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9299 (36.71%)\\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-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3341\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2079 (62.22%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.840 (2.636\\u0026ndash;3.059)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.880 (1.706\\u0026ndash;2.070)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eNote: AOR model included all variables listed above. All ORs and AORs are significant, \\u003cem\\u003ep\\u003c/em\\u003e values\\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\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eLoneliness was a concern across all nations in the study. Even in the nations reporting the lowest levels of loneliness, around 1 in 5 people were lonely based on the UCLA-LS scores. In some nations (Japan, Pakistan, Bangladesh, Malaysia, Ghana, and India), rates were as high as 1 in 2. The well-established and extensively documented links between loneliness and an increased risk of physical and mental health problems underscore the public health implications of social disconnection (WHO, 2025).\\u003c/p\\u003e \\u003cp\\u003eOne idea proposed to explain the perceived rise in loneliness is the \\u0026ldquo;lost community\\u0026rdquo; hypothesis. Within this formulation, increasing urbanisation and individualism are implicated in the erosion of social connections and the rise of loneliness. However, in the present study, nations reporting the highest levels of loneliness would traditionally be considered to have relatively collectivist national cultural values (Hofstede, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e). It might be that urbanisation, industrialisation, and the creeping influence of individualistic values are most strongly associated with the erosion of social connections during the transitional phase, as people migrate from rural lifestyles to urban living and from traditional values to mindsets shaped by globalisation and information technology, ushering in a period of cultural dissonance.\\u003c/p\\u003e \\u003cp\\u003eProblematic social media use and gaming disorder symptoms were also observed across all nations. Even if the rate of 10% is a significant overestimate, the popularity of social media and gaming renders problem use worthy of further research attention and preventative intervention. Both problematic social media use and gaming disorder symptoms were correlated (medium effect size) with loneliness. This was the case across all 35 nations, although the strength of the relationship varied widely, from large effects (strong positive correlations) for Egypt and Kuwait to small effects (weak positive correlations for Japan. Even after controlling for all other demographic correlates such as age, gender and education level, gaming disorder symptoms, and problematic social media use were the strongest predictors of loneliness.\\u003c/p\\u003e \\u003cp\\u003eNumerous previous studies report similar associations between problematic technology use and loneliness, with several longitudinal studies reporting bidirectional relationships, that is, loneliness at time one predicts problematic technology use at time two, and vice versa (Zhang et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). This suggests that initiatives targeting loneliness may also reduce the risk of problematic technology use, and initiatives targeting problematic technology use may attenuate the risk of loneliness. At least one intervention study (controlled trial) reports such an effect (Thomas et al. In Prep).\\u003c/p\\u003e \\u003cp\\u003eAlthough the present study did not directly assess social isolation, known risk factors associated with social isolation were quantified (e.g., not in employment, training or education; not a parent). These individuals, those with fewer social roles and opportunities for connection, were present across all nations, however to a far lesser degree than loneliness. Unsurprisingly social isolation risk was correlated with loneliness as has previously been documented (Ge et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Taylor et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Similarly, social isolation risk was also associated with problematic technology use, but to a far lesser extent than loneliness showing small effect sizes (weak positive correlations).\\u003c/p\\u003e \\u003cp\\u003eThe present study has the usual limitations associated with cross-sectional and correlational survey research. The correlational nature of the study means we cannot ascribe problematic technology use a causal role in the onset, maintenance or worsening of loneliness. However, a clear strength of the current study was the ability to identify the existence of the problematic technology use \\u0026ndash; loneliness relationship across each of the 35 participating countries using large representative samples within each territory.\\u003c/p\\u003e \\u003cp\\u003eWhile correlation is not causation, it is a cause for concern especially when the WHO estimate that loneliness and social isolation contribute to close to a million deaths annually (World Health Organization, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Successful attempts to reduce loneliness and social isolation will contribute greatly to the overall mental, physical, and social health of society. This study represents a modest contribution towards a better understanding of loneliness and social isolation and the possible role that problematic technology use might play.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eKey Recommendations\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eBetter measures and routine surveillance\\u003c/h2\\u003e \\u003cp\\u003eThere is a need for better metrics for social disconnection (loneliness and social isolation) and for more robust longitudinal surveillance (regular data collection). For example, loneliness and social isolation should be incorporated into general health surveillance - an annual social disconnection census. Such initiatives, however, need to include low and middle-income countries and populations without reliable access to the online world.\\u003c/p\\u003e \\u003cp\\u003eEven more critical, in terms of measurement, is the need to develop positively framed metrics of social connection that explore and quantify how people relate to and interact with one another. Such measures and routine surveillance will allow us to better understand trends and to evaluate the effectiveness of population-level interventions. Social flourishing is more than the absence of social disconnection.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eResearch\\u003c/h2\\u003e \\u003cp\\u003eOur research exploring the possible digital determinants of social disconnection needs to move beyond correlational studies. For the evidence to mature, we need open science and well-designed experimental studies that aim to identify possible mechanisms underlying the relationship between problematic technology use and social disconnection.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePublic awareness\\u003c/h2\\u003e \\u003cp\\u003eThere is a need for innovative and engaging public awareness initiatives exploring social connection/disconnection. Additionally, public awareness campaigns focused on preventing problematic technology should articulate what we presently know about the bidirectional relationship between problematic technology use and social disconnection. It is important to distinguish between technology use and problematic technology use.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003ePolicy\\u003c/h2\\u003e \\u003cp\\u003eHere we echo the World Health Organization (\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) in their call to make social connection/disconnection a global policy priority, engaging all sectors of society to work together to share ideas toward creating policy that supports social connection. Additionally, we propose that the same applies to digital well-being and the prioritisation of policies that support people to thrive online, ensuring that platforms offer safety as a default and refrain from deploying addictive design features.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003cp\\u003eAlheneidi, H., AlSumait, L., AlSumait, D., \\u0026amp; Smith, A. P. (2021). Loneliness and Problematic Internet Use during COVID-19 Lock-Down. \\u003cem\\u003eBehav Sci (Basel)\\u003c/em\\u003e,\\u003cem\\u003e 11\\u003c/em\\u003e(1). https://doi.org/10.3390/bs11010005 \\u003c/p\\u003e\\n\\u003cp\\u003eAllen, J., Darlington, O., Hughes, K., \\u0026amp; Bellis, M. A. (2022). The public health impact of loneliness during the COVID-19 pandemic. \\u003cem\\u003eBMC Public Health\\u003c/em\\u003e,\\u003cem\\u003e 22\\u003c/em\\u003e(1), 1654. https://doi.org/10.1186/s12889-022-14055-2 \\u003c/p\\u003e\\n\\u003cp\\u003eAmerican Psychiatric Association. (2013). \\u003cem\\u003eDiagnostic and statistical manual of mental disorders : DSM-5\\u003c/em\\u003e. American Psychiatric Association. \\u003c/p\\u003e\\n\\u003cp\\u003eAmerican Psychiatric Association. (2022). \\u003cem\\u003eDiagnostic and statistical manual of mental disorders : DSM-5-TR\\u003c/em\\u003e. American Psychiatric Association. \\u003c/p\\u003e\\n\\u003cp\\u003eAmerican Psychiatric Association. (2024, 4/Jan/2024). \\u003cem\\u003eNew APA Poll: One in Three Americans Feels Lonely Every Week\\u003c/em\\u003e Psychiatry.org - New APA Poll: One in Three Americans Feels Lonely Every Week\\u003c/p\\u003e\\n\\u003cp\\u003eBrand, M., Rumpf, H. J., Demetrovics, Z., MÜller, A., Stark, R., King, D. L.,…Potenza, M. N. (2020). Which conditions should be considered as disorders in the International Classification of Diseases (ICD-11) designation of \\\"other specified disorders due to addictive behaviors\\\"? \\u003cem\\u003eJ Behav Addict\\u003c/em\\u003e,\\u003cem\\u003e 11\\u003c/em\\u003e(2), 150-159. https://doi.org/10.1556/2006.2020.00035 \\u003c/p\\u003e\\n\\u003cp\\u003eCheng, C., Lau, Y.-c., Chan, L., \\u0026amp; Luk, J. W. (2021). Prevalence of social media addiction across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values. \\u003cem\\u003eAddictive Behaviors\\u003c/em\\u003e,\\u003cem\\u003e 117\\u003c/em\\u003e, 106845. https://doi.org/https://doi.org/10.1016/j.addbeh.2021.106845 \\u003c/p\\u003e\\n\\u003cp\\u003eChiang, C. L. L., Zhang, M. W. B., \\u0026amp; Ho, R. C. M. (2022). Prevalence of Internet Gaming Disorder in Medical Students: A Meta-Analysis [Review]. \\u003cem\\u003eFrontiers in Psychiatry\\u003c/em\\u003e,\\u003cem\\u003e 12\\u003c/em\\u003e. \\u003c/p\\u003e\\n\\u003cp\\u003eDugan, A. (2024). \\u003cem\\u003eOver 1 in 5 People Worldwide Feel Lonely a Lot\\u003c/em\\u003e. Gallup. https://news.gallup.com/poll/646718/people-worldwide-feel-lonely-lot.asp\\u003c/p\\u003e\\n\\u003cp\\u003eEllis, D. A. (2019). Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors. \\u003cem\\u003eComputers in Human Behavior\\u003c/em\\u003e,\\u003cem\\u003e 97\\u003c/em\\u003e, 60-66. https://doi.org/10.1016/j.chb.2019.03.006 \\u003c/p\\u003e\\n\\u003cp\\u003eFeng, W., Ramo, D., Chan, S., \\u0026amp; Bourgeois, J. (2017). Internet gaming disorder: trends in prevalence 1998–2016. \\u003cem\\u003eAddictive behaviors\\u003c/em\\u003e,\\u003cem\\u003e 75\\u003c/em\\u003e, 17. \\u003c/p\\u003e\\n\\u003cp\\u003eGe, L., Yap, C. W., Ong, R., \\u0026amp; Heng, B. H. (2017). Social isolation, loneliness and their relationships with depressive symptoms: A population-based study. \\u003cem\\u003ePLoS One\\u003c/em\\u003e,\\u003cem\\u003e 12\\u003c/em\\u003e(8), e0182145. https://doi.org/10.1371/journal.pone.0182145 \\u003c/p\\u003e\\n\\u003cp\\u003eHawkley, L. C., \\u0026amp; Cacioppo, J. T. (2010). Loneliness matters: a theoretical and empirical review of consequences and mechanisms. \\u003cem\\u003eAnn Behav Med\\u003c/em\\u003e,\\u003cem\\u003e 40\\u003c/em\\u003e(2), 218-227. https://doi.org/10.1007/s12160-010-9210-8 \\u003c/p\\u003e\\n\\u003cp\\u003eHeffer, T., Good, M., Daly, O., MacDonell, E., \\u0026amp; Willoughby, T. (2019). The Longitudinal Association Between Social-Media Use and Depressive Symptoms Among Adolescents and Young Adults: An Empirical Reply to Twenge et al. (2018). \\u003cem\\u003eClinical Psychological Science\\u003c/em\\u003e,\\u003cem\\u003e 7\\u003c/em\\u003e(3), 462-470. https://doi.org/10.1177/2167702618812727 \\u003c/p\\u003e\\n\\u003cp\\u003eHofstede, G. (2001). \\u003cem\\u003eCulture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations\\u003c/em\\u003e (2nd ed.). Sage Publications. \\u003c/p\\u003e\\n\\u003cp\\u003eJopling, k. (2017). \\u003cem\\u003eJo Cox Commission on Loneliness\\u003c/em\\u003e. https://www.ageuk.org.uk/globalassets/age-uk/documents/reports-and-publications/reports-and-briefings/active-communities/rb_dec17_jocox_commission_finalreport.pdf\\u003c/p\\u003e\\n\\u003cp\\u003eKim, H. S., Son, G., Roh, E. B., Ahn, W. Y., Kim, J., Shin, S. H.,…Choi, K. H. (2022). Prevalence of gaming disorder: A meta-analysis. \\u003cem\\u003eAddict Behav\\u003c/em\\u003e,\\u003cem\\u003e 126\\u003c/em\\u003e, 107183. https://doi.org/10.1016/j.addbeh.2021.107183 \\u003c/p\\u003e\\n\\u003cp\\u003eLindenberg, K., Kindt, S., \\u0026amp; Szász-Janocha, C. (2022). Effectiveness of Cognitive Behavioral Therapy-Based Intervention in Preventing Gaming Disorder and Unspecified Internet Use Disorder in Adolescents: A Cluster Randomized Clinical Trial. \\u003cem\\u003eJAMA Netw Open\\u003c/em\\u003e,\\u003cem\\u003e 5\\u003c/em\\u003e(2), e2148995. https://doi.org/10.1001/jamanetworkopen.2021.48995 \\u003c/p\\u003e\\n\\u003cp\\u003eMacDonald, K. B., Patte, K. A., Leatherdale, S. T., \\u0026amp; Schermer, J. A. (2022). Loneliness and screen time usage over a year. \\u003cem\\u003eJ Adolesc\\u003c/em\\u003e,\\u003cem\\u003e 94\\u003c/em\\u003e(3), 318-332. https://doi.org/10.1002/jad.12024 \\u003c/p\\u003e\\n\\u003cp\\u003eMusetti, A., Cattivelli, R., Giacobbi, M., Zuglian, P., Ceccarini, M., Capelli, F.,…Castelnuovo, G. (2016). Challenges in Internet Addiction Disorder: Is a Diagnosis Feasible or Not? \\u003cem\\u003eFront Psychol\\u003c/em\\u003e,\\u003cem\\u003e 7\\u003c/em\\u003e, 842. https://doi.org/10.3389/fpsyg.2016.00842 \\u003c/p\\u003e\\n\\u003cp\\u003eNational Academies of Sciences, E. a. M., Division of Behavioral and Social Sciences and, E., Health and Medicine, D., Board on Behavioral, C. a. S. S., Board on Health Sciences, P., \\u0026amp; Committee on the Health and Medical Dimensions of Social Isolation and Loneliness in Older, A. (2020). In \\u003cem\\u003eSocial Isolation and Loneliness in Older Adults: Opportunities for the Health Care System\\u003c/em\\u003e. National Academies Press (US)\\u003c/p\\u003e\\n\\u003cp\\u003eCopyright 2020 by the National Academy of Sciences. All rights reserved. https://doi.org/10.17226/25663 \\u003c/p\\u003e\\n\\u003cp\\u003eNational Center for Chronic Disease Prevention and Health Promotion (NCCDPHP). (2024). \\u003cem\\u003eHealth Effects of Social Isolation and Loneliness\\u003c/em\\u003e. Retrieved May/10/2025 from https://www.cdc.gov/social-connectedness/risk-factors/index.html\\u003c/p\\u003e\\n\\u003cp\\u003eNewzoo. (2020). \\u003cem\\u003eGlobal Games Market Report \\u003c/em\\u003ehttps://newzoo.com/insights/articles/the-global-games-market-will-generate-152-1-billion-in-2019-as-the-u-s-overtakes-china-as-the-biggest-market/ .\\u003c/p\\u003e\\n\\u003cp\\u003eOughli, H. A., \\u0026amp; Lee, E. E. (2024). Lonely for Life? Differences Between Chronic and Transient Loneliness and Their Impact on Depression in Older Adults. \\u003cem\\u003eThe American Journal of Geriatric Psychiatry\\u003c/em\\u003e,\\u003cem\\u003e 32\\u003c/em\\u003e(4), 424-426. https://doi.org/10.1016/j.jagp.2023.12.012 \\u003c/p\\u003e\\n\\u003cp\\u003eO’Day, E. B., \\u0026amp; Heimberg, R. G. (2021). Social media use, social anxiety, and loneliness: A systematic review. \\u003cem\\u003eComputers in Human Behavior Reports\\u003c/em\\u003e,\\u003cem\\u003e 3\\u003c/em\\u003e, 100070. https://doi.org/https://doi.org/10.1016/j.chbr.2021.100070 \\u003c/p\\u003e\\n\\u003cp\\u003ePontes, H. M., \\u0026amp; Griffiths, M. D. (2015). Measuring DSM-5 Internet gaming disorder: Development and validation of a short psychometric scale. \\u003cem\\u003eComputers in Human Behavior\\u003c/em\\u003e,\\u003cem\\u003e 45\\u003c/em\\u003e, 137-143. \\u003c/p\\u003e\\n\\u003cp\\u003eRussell, D., Peplau, L. A., \\u0026amp; Ferguson, M. L. (1978). Developing a measure of loneliness. \\u003cem\\u003eJ Pers Assess\\u003c/em\\u003e,\\u003cem\\u003e 42\\u003c/em\\u003e(3), 290-294. https://doi.org/10.1207/s15327752jpa4203_11 \\u003c/p\\u003e\\n\\u003cp\\u003eSantos, R. M. S., Ventura, S. d. A., Nogueira, Y. J. d. A., Mendes, C. G., Paula, J. J. d., Miranda, D. M., \\u0026amp; Romano-Silva, M. A. (2024). The Associations Between Screen Time and Mental Health in Adults: A Systematic Review. \\u003c/p\\u003e\\n\\u003cp\\u003eStevens, M. W., Dorstyn, D., Delfabbro, P. H., \\u0026amp; King, D. L. (2021). Global prevalence of gaming disorder: A systematic review and meta-analysis. \\u003cem\\u003eAust N Z J Psychiatry\\u003c/em\\u003e,\\u003cem\\u003e 55\\u003c/em\\u003e(6), 553-568. https://doi.org/10.1177/0004867420962851 \\u003c/p\\u003e\\n\\u003cp\\u003eSu, W., Han, X., Yu, H., Wu, Y., \\u0026amp; Potenza, M. N. (2020). Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. \\u003cem\\u003eComputers in Human Behavior\\u003c/em\\u003e,\\u003cem\\u003e 113\\u003c/em\\u003e, 106480. https://doi.org/https://doi.org/10.1016/j.chb.2020.106480 \\u003c/p\\u003e\\n\\u003cp\\u003eTang, S., Werner-Seidler, A., Torok, M., Mackinnon, A. J., \\u0026amp; Christensen, H. (2021). The relationship between screen time and mental health in young people: A systematic review of longitudinal studies. \\u003cem\\u003eClinical Psychology Review\\u003c/em\\u003e,\\u003cem\\u003e 86\\u003c/em\\u003e, 102021. https://doi.org/https://doi.org/10.1016/j.cpr.2021.102021 \\u003c/p\\u003e\\n\\u003cp\\u003eTang, W. Y., Reer, F., \\u0026amp; Quandt, T. (2022). The interplay of the Dark Triad and social media use motives to social media disorder. \\u003cem\\u003ePersonality and Individual Differences\\u003c/em\\u003e,\\u003cem\\u003e 187\\u003c/em\\u003e, 111402. https://doi.org/https://doi.org/10.1016/j.paid.2021.111402 \\u003c/p\\u003e\\n\\u003cp\\u003eTaylor, H. O., Cudjoe, T. K. M., Bu, F., \\u0026amp; Lim, M. H. (2023). The state of loneliness and social isolation research: current knowledge and future directions. \\u003cem\\u003eBMC Public Health\\u003c/em\\u003e,\\u003cem\\u003e 23\\u003c/em\\u003e(1), 1049. https://doi.org/10.1186/s12889-023-15967-3 \\u003c/p\\u003e\\n\\u003cp\\u003eTejeiro, R., Chen, A., \\u0026amp; L. Gómez-Vallecillo, J. (2016). Measuring Internet Gaming Disorder in Chinese International Students in the United Kingdom. \\u003cem\\u003eJournal of Education, Society and Behavioural Science\\u003c/em\\u003e,\\u003cem\\u003e 17\\u003c/em\\u003e(1), 1-11. https://doi.org/10.9734/BJESBS/2016/27855 \\u003c/p\\u003e\\n\\u003cp\\u003eTwenge, J. M. (2025). International Declines in Academic Performance and Increases in Loneliness Are Linked to Electronic Devices. \\u003cem\\u003eJ Adolesc\\u003c/em\\u003e. https://doi.org/10.1002/jad.70058 \\u003c/p\\u003e\\n\\u003cp\\u003eTwenge, J. M., Haidt, J., Blake, A. B., McAllister, C., Lemon, H., \\u0026amp; Le Roy, A. (2021). Worldwide increases in adolescent loneliness. \\u003cem\\u003eJ Adolesc\\u003c/em\\u003e,\\u003cem\\u003e 93\\u003c/em\\u003e, 257-269. https://doi.org/10.1016/j.adolescence.2021.06.006 \\u003c/p\\u003e\\n\\u003cp\\u003evan den Eijnden, R. J. J. M., Lemmens, J. S., \\u0026amp; Valkenburg, P. M. (2016). The Social Media Disorder Scale. \\u003cem\\u003eComputers in Human Behavior\\u003c/em\\u003e,\\u003cem\\u003e 61\\u003c/em\\u003e, 478-487. https://doi.org/https://doi.org/10.1016/j.chb.2016.03.038 \\u003c/p\\u003e\\n\\u003cp\\u003evan Roekel, E., Verhagen, M., Engels, R., Scholte, R. H. J., Cacioppo, S., \\u0026amp; Cacioppo, J. T. (2018). Trait and State Levels of Loneliness in Early and Late Adolescents: Examining the Differential Reactivity Hypothesis. \\u003cem\\u003eJ Clin Child Adolesc Psychol\\u003c/em\\u003e,\\u003cem\\u003e 47\\u003c/em\\u003e(6), 888-899. https://doi.org/10.1080/15374416.2016.1146993 \\u003c/p\\u003e\\n\\u003cp\\u003eVan Rooij, A. J., \\u0026amp; Prause, N. (2014). A critical review of \\\"Internet addiction\\\" criteria with suggestions for the future. \\u003cem\\u003eJournal of behavioral addictions\\u003c/em\\u003e,\\u003cem\\u003e 3\\u003c/em\\u003e(4), 203-213. https://doi.org/10.1556/JBA.3.2014.4.1 \\u003c/p\\u003e\\n\\u003cp\\u003eVeazie, S., Gilbert, J., Winchell, K., Paynter, R., \\u0026amp; Guise, J. M. (2019). AHRQ Rapid Evidence Product Reports. In \\u003cem\\u003eAddressing Social Isolation To Improve the Health of Older Adults: A Rapid Review\\u003c/em\\u003e. Agency for Healthcare Research and Quality (US). \\u003c/p\\u003e\\n\\u003cp\\u003eVerduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J.,…Kross, E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. \\u003cem\\u003eJ Exp Psychol Gen\\u003c/em\\u003e,\\u003cem\\u003e 144\\u003c/em\\u003e(2), 480-488. https://doi.org/10.1037/xge0000057 \\u003c/p\\u003e\\n\\u003cp\\u003eWolska, K., \\u0026amp; Creaven, A.-M. (2023). Associations between transient and chronic loneliness, and depression, in the understanding society study. \\u003cem\\u003eBritish Journal of Clinical Psychology\\u003c/em\\u003e,\\u003cem\\u003e 62\\u003c/em\\u003e(1), 112-128. https://doi.org/https://doi.org/10.1111/bjc.12397 \\u003c/p\\u003e\\n\\u003cp\\u003eWorld Health Organization. (2025). \\u003cem\\u003eFrom loneliness to social connection - charting a path to healthier societies: report of the WHO Commission on Social Connection.\\u003c/em\\u003e \\u003c/p\\u003e\\n\\u003cp\\u003eYoussef, L., Hallit, R., Kheir, N., Obeid, S., \\u0026amp; Hallit, S. (2020). Social media use disorder and loneliness: any association between the two? Results of a cross-sectional study among Lebanese adults. \\u003cem\\u003eBMC psychology\\u003c/em\\u003e,\\u003cem\\u003e 8\\u003c/em\\u003e(1), 56-56. https://doi.org/10.1186/s40359-020-00421-5 \\u003c/p\\u003e\\n\\u003cp\\u003eZhang, Y., Li, J., Zhang, M., Ai, B., \\u0026amp; Jia, F. (2023). Bidirectional Associations between Loneliness and Problematic Internet Use: A Meta-analytic Review of Longitudinal Studies. \\u003cem\\u003eAddictive Behaviors\\u003c/em\\u003e, 107916. https://doi.org/10.1016/j.addbeh.2023.107916 \\u003c/p\\u003e\\n\\u003cp\\u003eZou, Y. Z., Cui, J. F., Han, B., Ma, A. L., Li, M. Y., \\u0026amp; Fan, H. Z. (2008). Chinese psychiatrists views on global features of CCMD-III, ICD-10 and DSM-IV. \\u003cem\\u003eAsian J Psychiatr\\u003c/em\\u003e,\\u003cem\\u003e 1\\u003c/em\\u003e(2), 56-59. https://doi.org/10.1016/j.ajp.2008.09.007 \\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"The King Abdulaziz Center for World Culture\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Loneliness, Social Connection, Gaming, Social Media, Cross-Cultural\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8624119/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8624119/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThere is growing international concern that both loneliness, a subjective experience, and social isolation, an objectively measurable condition, are increasing. The public health consequences of these forms of social disconnection are well documented. However, evidence regarding the relationship between digital technologies and social disconnection remains mixed.\\u003c/p\\u003e \\u003cp\\u003eAs part of Sync\\u0026rsquo;s global digital wellbeing research program, we surveyed loneliness levels and online behaviours (e.g., video game play and social media use) across 35 nations and 35,000 adult participants. This report reviews relevant literature on loneliness, social isolation, and technology use, and also details the results of the cross-national survey.\\u003c/p\\u003e \\u003cp\\u003eLoneliness was a widespread issue across all participating countries, with Japan experiencing particularly high rates. There was a clear link between problematic technology use and social disconnection across the whole sample and within each nation. The report elaborates on these findings, offering recommendations and suggestions for future research.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Social Disconnection in a Hyperconnected World: Loneliness, Social Isolation, and Problematic Technology Use Across 35 Nations\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-21 04:39:15\",\"doi\":\"10.21203/rs.3.rs-8624119/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"11d6c192-58df-46c5-84b0-53e6e94a258a\",\"owner\":[],\"postedDate\":\"January 21st, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":61284268,\"name\":\"Health Policy\"},{\"id\":61284269,\"name\":\"Psychology\"}],\"tags\":[],\"updatedAt\":\"2026-01-21T04:39:15+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-21 04:39:15\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8624119\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8624119\",\"identity\":\"rs-8624119\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}