Association between social media addiction and mental health among Greek young adults

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Aim: To examine the relationship between social media addiction and mental health among Greek young adults. Methods: We conducted an online cross-sectional study during July 2024 in Greece. We used the Bergen Social Media Addiction Scale (BSMAS) to measure levels of social media addiction in our participants. We used the Patient Health Questionnaire-4 (PHQ-4) to evaluate levels of anxiety and depression in our participants. We measured gender, age, the number of hours that participants usually sleep when next day they have to go for their work/class, and how sleepy they felt at work/class. Results: The majority of our participants were females (83.1%, n=300), while 16.9% were males (n=61). Mean age was 23.3 years. Our correlation analysis showed that social media addiction was correlated with increased anxiety and depression (p-value < 0.01 in both cases). Moreover, reduced age and reduced duration of night sleep was associated with increased social media addiction (p-value < 0.01 in both cases). Additionally, social media addiction was correlated with increased sleepiness in work/class (p-value < 0.01). Conclusions: We found a positive correlation between social media addiction, anxiety and depression among young adults in Greece. Moreover, we found that reduced age and reduced duration of night sleep was associated with increased social media addiction. Additionally, social media addiction was correlated with increased sleepiness in work/class. Therefore, social media addiction is an important issue among young adults and policy makers should develop appropriate interventions to reduce this phenomenon. Psychology Psychiatry Publishing/Media social media addiction anxiety depression young adults Introduction Social media usage is an ever-growing phenomenon, with active users exceeding 5 billion people worldwide at this moment (Statista, 2024). However, increased usage can lead to addiction as well, and it seems that social media addiction (in platforms like Facebook, Instagram, TikTok and YouTube) has emerged as a problem of global concern. In fact, according to a meta-analysis covering 64 countries, social media addiction accounted for more than 17% of the total studied population, as a subtype of digital addiction (Meng et al, 2022 ). Thus, various scales have been developed to measure this phenomenon (Varona et al, 2022 ); BSMAS (Bergen Social Media Addiction Scale), SMAS (Social Media Addiction Scale), BFAS (Bergen Facebook Addiction Scale), TTAS (TikTok Addiction Scale) (Galanis et al, 2024a , Galanis et al, 2024b ) being some of them. These instruments are based on factors and behaviours such as salience, mood modification, tolerance, withdrawal etc. (Varona, 2022). Remarkably, scholars in the last years pay special attention into TikTok use and its impact on users’ health (Galanis et al, 2024c , Katsiroumpa et al, 2024 ). Meanwhile, incidence and prevalence of both depression and anxiety seem to increase among adolescents and young adults over the last years (Xiang et al, 2024 ). Youth unemployment, little or no social mobility, decreasing affordabilty are significant factors (among others) that can contribute to the occurence of these psychological disorders (Lakasing et al, 2020). This phenomenon may intensified during the COVID-19 pandemic and the consecutive lockdowns (Brunette et al, 2023 ), but it emerged far before the 2019 outbreak (Barker et al, 2019 ), (Thapar et al, 2022). Meanwhile, in addition to the other causes mentioned above, social media addiction seems to be another risk factor for the occurrence of depressive and anxious symptoms. In this sense, various studies have indicated that excessive social media usage can lead to mental health issues, like depression, anxiety and body image acceptance (Bozzola et al, 2022 ). Another negative emotion that seems to be associated with social media addiction is FoMO (Fear of Missing Out), which in fact affects negatively the levels of life satisfaction of social media users (Bakioğlu et al, 2022 ). Decreased quality of life due to depressive symptoms derived from social media addiction is mentioned in other studies as well (Vu Anh Trong Dam et al, 2023 ), highlighting the significance of the identification of this phenomenon and its treatment. In this context, we conducted a study to examine the relationship between social media addiction and anxiety and depression among Greek young adults. Methods Study design and participants We conducted a cross-sectional study during July 2024 in Greece. We developed an online version of the study questionnaire through google forms. Then, we disseminated the online questionnaire through social media. Adults with at least one profile in social media platforms could participate in our study. We considered as young adults those with an age between 18 and 35 years of age. Personal data from participants were not collected. Our study was approved by the Ethics Committee of the Faculty of Nursing, National and Kapodistrian University of Athens (approval number; 510, June 2024). Moreover, we followed the guidelines of the Declaration of Helsinki (World Medical Association, 2013 ). Measurements We used the Bergen Social Media Addiction Scale (BSMAS) (Andreassen et al., 2016 ) to measure levels of social media addiction in our participants. The BSMAS measures six cores of the social media addiction, i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. The BSMAS includes six items, and answers are on a five-point Likert Scale very rarely (1), rarely (2), sometimes (3), often (4), and very often (5). Total BSMAS score ranges from 6 to 30. Higher scores indicate higher levels of social media addiction. We used the Greek version of the BSMAS (Dadiotis et al., 2021 ). In our study, Cronbach’s alpha for the BSMAS was 0.83. We used the Patient Health Questionnaire-4 (PHQ-4) (Kroenke et al., 2009 ) to evaluate levels of anxiety and depression in our participants. The PHQ-4 includes four items: two items measuring anxiety, and two other items measuring depression. Answers are on a four-point Likert scale from 0 (not at all) to 3 (nearly every day). Anxiety and depression scores range from 0 to 6. Higher scores indicate higher levels of anxiety and depression. We used the Greek version of the PHQ-4 (Karekla et al., 2012 ). In our study, Cronbach’s alpha for the anxiety scale was 0.79, and for the depression scale was 0.73. We measured gender, age, the number of hours that participants usually sleep when next day they have to go for their work/class, and how sleepy they felt at work/class. We measured sleepiness in at work/class on a five-point Likert scale; not at all (1), a little (2), moderate (3), a lot (4), very much (5). Statistical analysis We present categorical variables with numbers and percentages. Also, we present continuous variables with mean, standard deviation, median and range. We used Pearson’s correlation coefficient to estimate the correlation between BSMAS and anxiety, depression, and age. Also, we used Spearman’s correlation coefficient to estimate the correlation between BSMAS and the number of hours that participants usually sleep when next day they have to go for their work/class, and sleepiness. We used the independent samples t-test to identify differences between males and females regarding the BSMAS. P-values less than 0.05 were considered as statistically significant. We used the IBM SPSS 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.) for the analysis. Results The majority of our participants were females (83.1%, n = 300), while 16.9% were males (n = 61). Mean age was 23.3 years, standard deviation was 4, median was 22, and range was 17. Among our participants, 37.1% (n = 134) reported that they feel moderately sleepy at work/class, 29.9% (n = 108) reported that they feel a little sleepy, 26.3% (n = 95) reported that they feel quite sleepy, 3.9% (n = 14) reported that they do not feel sleepy at work/class, and 2.8% (n = 10) reported that they feel very sleepy. Mean duration of night sleep was 6.4 hours, standard deviation was 1, median was 6, and range was 6. Descriptive statistics for the BSMAS are shown in Table 1 . Mean BSMAS score was 16.1 (standard deviation; 5.1). Mean tolerance score was 3.1, mean withdrawal symptoms score was 2.8, mean conflict score was 2.7, mean salience score was 2.6, mean mood modification score was 2.5, and mean relapse score was 2.4. Table 1 Descriptive statistics for the Bergen Social Media Addiction Scale (BSMAS). Scale Mean Standard deviation Median Range BSMAS 16.1 5.1 16 23 Salience 2.6 1.2 3 4 Mood modification 2.5 1.1 3 4 Tolerance 3.1 1.1 3 4 Withdrawal symptoms 2.8 1.2 3 4 Conflict 2.7 1.2 3 4 Relapse 2.4 1.2 2 4 Descriptive statistics for the PHQ-4 are shown in Table 2 . Mean anxiety score was 2.8 (standard deviation; 1.6), while mean depression score was 2.6 (standard deviation; 1.2). Table 2 Descriptive statistics for the PHQ-4. Scale Mean Standard deviation Median Range Anxiety 2.8 1.6 3 6 Depression 2.6 1.2 3 4 Table 3 presents correlations between BSMAS and anxiety, depression, age, duration of night sleep, and sleepiness in work/class. Our correlation analysis showed that social media addiction was correlated with increased anxiety and depression (p-value < 0.01 in both cases). Moreover, reduced age and reduced duration of night sleep was associated with increased social media addiction (p-value < 0.01 in both cases). Additionally, social media addiction was correlated with increased sleepiness in work/class (p-value < 0.01). Table 3 Correlations between Bergen Social Media Addiction Scale (BSMAS) and anxiety, depression, age, duration of night sleep, and sleepiness in work/class. Variable Anxiety Depression Age Duration of night sleep Sleepiness in work/class BSMAS 0.32 ** 0.43 ** -0.16 ** -0.14 ** 0.21 ** Salience 0.19 ** 0.29 ** -0.14 ** -0.07 0.10 Mood modification 0.28 ** 0.24 ** -0.08 ** -0.13 * 0.11 * Tolerance 0.25 ** 0.37 ** -0.09 -0.10 0.29 ** Withdrawal symptoms 0.28 ** 0.41 ** 0.01 -0.06 0.13 * Conflict 0.13 * 0.16 * -0.08 -0.03 0.02 Relapse 0.28 ** 0.42 ** -0.24 ** -0.19 ** 0.25 ** * p-value < 0.05 ** p-value < 0.01 Discussion This study evaluated the relation between social media addiction (SMA) and anxiety and depression among Greek young adults. The results indicated that the problematic and excessive use of social media can lead to negative effects on mental and physical health, since symptoms of anxiety, depression and sleepiness were reported. In fact, the vast majority of our participants (approximately 96%) mentioned a degree of sleepiness at work / class due to SMA. Regarding mental health, a positive correlation was found between SMA and anxiety and depression, a result which aligns with previous literature (Şentürk et al, 2021 , Galanis et al. 2023 ). The correlation coefficients between SMA (measured by the Bergen Social Media Addiction Scale) and anxiety and between SMA and depression were 0.32 and 0.43 respectively, implying statistical significance (p < 0,01 in both cases). Another study suggests that similar addictions e.g. mobile phone addiction are positive indicators of anxiety and depression, and negative indicators of sleep quality (Ying et al, 2020 ). Moreover, it is worth noting that the components of the BSMAS were salience, mood modification, tolerance, withdrawal symptoms, conflict and relapse. Among those descriptive statistics, mood modification, withdrawal and relapse were found to have the highest positive association with anxiety (p < 0,01 in all cases). Likewise, the ones with the highest positive association with depression were tolerance, withdrawal and relapse (p < 0,01 in all cases). This finding is consistent with a similar finding of a study which indicated that mood modification, relapse, withdrawal, and conflict were the four components of BSMAS which were positively associated with several measures of psychopathological symptoms of the participants (Fournier et al, 2023 ). Therefore, individuals who are engaged with intense and problematic social media use are particularly at risk of lower well - being (Boer et al, 2020 ), as mentioned in the Introduction. It should be noted that both mean anxiety and mean depression scoring were considered positive among the participants in our study, according to PHQ − 4 screening tool (Löwe et al, 2010 ). SMA is a phenomenon which affects mainly adolescents and young adults, as shown in relative literature and worldwide statistics (Statista, 2019), (Abbasi et al, 2019), (Kocak et al, 2021). Among other sociodemographic factors, age plays a key role in the occurrence of SMA. Our study indicated that reduced age of the participants was associated with increased SMA (p < 0,01), thus our results agree with those of previous research in the field. Specifically, relapse (i.e. failed attempts to control social media usage) was the one variable that showed the strongest negative relationship with age, among the participants. According to Lafontaine-Poissant et al., intense and problematic social media use is associated with worse sleep health (Lafontaine - Poissant et al, 2024). Literature suggests that the blue light of the devices, the emotional content / nature of social media and “fear of missing out” seem to be the causes of poor quality of sleep among adolescents and young adults. The findings of the present study highlight that increased SMA was associated with reduced duration of night sleep as well as with increased sleepiness at class or work (p < 0,01 in both cases). Especially tolerance (i.e. more social media use in order to attain the same degree of satisfaction over time) was the variable that displayed the strongest positive correlation with sleepiness at work/class, among the others. Reduced duration of sleep may be due to delayed bedtime, difficulty falling asleep or desynchronisation of circadian rhythm during sleeptime (Zhu et al, 2023 ). Meanwhile, another study that involved university students has shown that internet addiction is a risk factor for increased sleepiness at daytime and specifically it increased the risk of daytime sleepiness approximately one time (Demir et al, 2020). However, further research is required in the Greek case so as to examine this phenomenon. Our findings agree with those of relative literature, meaning that a positive correlation between social media addiction and depressive and anxiety symptoms was found. Since this phenomenon has only occurred in recent years and there are no data for the Greek case so far, the importance of the present study is evident. However, future research to further study this correlation is essential; for instance, it is recommended to seek to what extent these negative emotions and reactions are influenced by educational, professional and economic status, gender, age group of the subjects etc. The research of the relation between Fear of Missing out (FOMO) and SMA is also estimated to illuminate certain perspectives of social media addiction. Regarding sleep quality, further research exploring the relationships between sleep aspects (daytime naps, insomnia) and SMA should be conducted. BSMAS and PHQ-4 which were selected are certified tools that measure the levels of addiction and negative symptoms respectively, reinforcing the reliability of the results. Our study had several limitations. We employed a convenience sample of young adults in Greece. Thus, our sample was not representative of young adults in Greece. Moreover, we performed a cross-sectional study, and, thus, we cannot extract causal relationships between social media addiction, anxiety and depression. Additionally, we used self-reported tools to measure social media addiction, anxiety and depression. Therefore, information bias is probable in our study. In conclusion, we found a positive correlation between social media addiction, anxiety and depression among young adults in Greece. Moreover, we found that reduced age and reduced duration of night sleep was associated with increased social media addiction. Additionally, social media addiction was correlated with increased sleepiness in work/class. Therefore, social media addiction is an important issue among young adults and policy makers should develop appropriate interventions to reduce this phenomenon. Declarations Conflicts of interest: None. Authors have no interests, affiliations, or associations that might be perceived to influence the results and/or discussion reported in this preprint submission References Abbasi, I. (2019). Social media addiction in romantic relationships: Does user's age influence vulnerability to social media infidelity? Personality and Individual Differences, 139 : 277-280. https://doi.org/10.1016/j.paid.2018.10.038. Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160 Bakioğlu, F., Deniz, M., Griffiths, M., Pakpour, A.(2022). Adaptation and validation of the Online-Fear of Missing Out Inventory into Turkish and the association with social media addiction, smartphone addiction, and life satisfaction. BMC Psychology. 10(1):154. https://doi.org/10.1186/s40359-022-00856-y. Barker, M., Beresford, B., Martin, B., Fraser, L. (2019). Prevalence and Incidence of Anxiety and Depression Among Children, Adolescents, and Young Adults With Life-Limiting Conditions. JAMA Pediatrics. 173(9):835–844. https://doi.org/10.1001/jamapediatrics.2019.1712. Boer, M., Eijnden, R., Boniel-Nissim, M., et al. (2020). Adolescents' Intense and Problematic Social Media Use and Their Well-Being in 29 Countries. Journal of Adolescent Health. 66(6S):S89-S99. https://doi.org/ 10.1016/j.jadohealth.2020.02.014. Bozzola, E., Spina, G., Agostiniani, R. et al. (2022). The Use of Social Media in Children and Adolescents: Scoping Review on the Potential Risks. International Journal of Environmental Research and Public Health. 19(16):9960. https://doi.org/ 10.3390/ijerph19169960. Brunette, M.F., Erlich, M.D., Edwards, M.L. (2023). Addressing the Increasing Mental Health Distress and Mental Illness Among Young Adults in the United States. The Journal of Nervous and Mental Disease 211(12):961-967. https://doi.org/10.1097/NMD.0000000000001734. Dadiotis, A., Bacopoulou, F., Kokka, I., Vlachakis, D., Chrousos, G. P., Darviri, C., & Roussos, P. (2021). Validation of the Greek version of the Bergen Social Media Addiction Scale in Undergraduate Students. EMBnet.Journal, 26(1), e975. https://doi.org/10.14806/ej.26.1.975. Dam, V., Dao, N., Nguyen, D. et al. (2023). Quality of life and mental health of adolescents: Relationships with social media addiction, Fear of Missing out, and stress associated with neglect and negative reactions by online peers. PLOS One. 18(6). https://doi.org/ 10.1371/journal.pone.0286766. Demir, G., Arslan, S., Kocoglu-Tanyer, D. (2023). Daytime Sleepiness in University Students and Internet Addiction as the Determinant. Journal of Addictions Nursing. 31(3):153-160. https://doi.org/10.1097/JAN.0000000000000346 Fournier, L., Schimmenti, A., Musetti, A. et al. (2023). Deconstructing the components model of addiction: an illustration through “addictive” use of social media. Addictive Behaviors. 143:107694. https://doi.org/10.1016/j.addbeh.2023.107694. Galanis, P., Katsiroumpa, Z., Katsiroumpa, A., Tsakalaki A, Vasilopoulos S, (2023). Relationship between Sexting and Self-Esteem, Depression, Anxiety, and Stress among Young People. International Journal of Caring Sciences. 16(1):62-71. Galanis, P., Katsiroumpa, A., Moisoglou, I., Konstantakopoulou, O. (2024a). The TikTok Addiction Scale: Development and validation. AIMS Public Health, 11(4):1172-1197. https://doi:10.3934/publichealth.2024061. Galanis, P., Katsiroumpa, A., Moisoglou, I., Konstantakopoulou, O. (2024b). Determining an optimal cut-off point for TikTok addiction using the TikTok Addiction Scale. Research Square. https://doi.org/10.21203/rs.3.rs-4782800/v1 Galanis, P.; Katsiroumpa, A.; Katsiroumpa, Z.; Mangoulia, P.; Gallos, P.; Moisoglou, I.; Koukia, E. (2024c). Impact of Problematic TikTok Use on Mental Health: A Systematic Review and Meta-Analysis. Preprints. 2024110266. https://doi.org/10.20944/preprints202411.0266.v1 Karekla, M., Pilipenko, N., & Feldman, J. (2012). Patient Health Questionnaire: Greek language validation and subscale factor structure. Comprehensive Psychiatry, 53(8), 1217–1226. https://doi.org/10.1016/j.comppsych.2012.05.008. Katsiroumpa, A.; Moisoglou, I.; Gallos, P.; Katsiroumpa, Z.; Konstantakopoulou, O.; Tsiachri, M.; Galanis, P. (2024). Problematic TikTok Use and Its Association with Poor Sleep: A Cross-Sectional Study Among Greek Young Adults. Preprints. 2024122088. https://doi.org/10.20944/preprints202412.2088.v1 Kocak, O., Arslan, H. (2021). Social Media use Across Generations: from Addiction to Engagement. European Integration Studies. 15(1):63-77. https://doi.org/10.5755/j01.eis.1.15.29080. Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Lowe, B. (2009). An Ultra-Brief Screening Scale for Anxiety and Depression: The PHQ-4. Psychosomatics, 50(6), 613–621. https://doi.org/10.1176/appi.psy.50.6.613. Lafontaine-Poissant, F., Lang, J., , McKinnon, B. et al. (2024). Social media use and sleep health among adolescents in Canada. Health Promotion and Chronic Disease Prevention in Canada. 44(7-8):338-346. https://doi.org.10.24095/hpcdp.44.7/8.05.da. Lakazing, E., Mirza, Z. Anxiety and depression in young adults and adolescents (2020). British Journal of General Practice 70(691):56–57. https://doi.org/ 10.3399/bjgp20X707765. Löwe, B., Wahl, I., Rose, M., el al. (2010). A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. Journal of Affective Disorders. 122(1-2):86-95. https://doi.org.10.1016/j.jad.2009.06.019. Meng, S., Cheng, J., Li, Y., et al. (2022). Global prevalence of digital addiction in general population: A systematic review and meta-analysis. Clinical Psychology Review (92),102128 https://doi.org/10.1016/j.cpr.2022.102128. Şentürk, E., Geniş, B., Coşar, B. (2021). Social Media Addiction in Young Adult Patients with Anxiety Disorders and Depression. Alpha Psychiatry. 22(5):257-262. https://doi.org/10.1530/alphapsychiatry.2021.21242. Statista. Share of online users in the United States who report being addicted to social media as of April 2019, by age group. Available from : https://www.statista.com/statistics/1081292/social-media-addiction-by-age-usa/. Last accessed at Jan 18 2025. Statista. Global digital population as of October 2024. Available from : https://www.statista.com/statistics/617136/digital-population-worldwide/. Last accessed on Dec 28 2024. Varona, M.N., Muela, A., Machimbarrena, J.M., (2022). Problematic use or addiction? A scoping review on conceptual and operational definitions of negative social networking sites use in adolescents. Addictive Behaviors (134),107400 https:// doi.org/10.1016/j.addbeh.2022.107400. World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA, 310(20), 2191. https://doi.org/10.1001/jama.2013.281053. Xiang, A.H., Martinez, M.P., Chow, T. et al (2024). Depression and Anxiety Among US Children and Young Adults. JAMA Network, 7(10):e2436906. https:// doi.org/10.1001/jamanetworkopen.2024.36906. Ying, L., Li, G., Liu, L., Wu, H. (2020). Correlations between mobile phone addiction and anxiety, depression, impulsivity, and poor sleep quality among college students: A systematic review and meta-analysis. Journal of Behavioral Addictions. 9(3):551-571. https://doi.org/10.1556/2006.2020.00057. Zhu, X., Zheng, T.,1, Ding, L. (2023). Exploring associations between social media addiction, social media fatigue, fear of missing out and sleep quality among university students: A cross-section study. PLOS One. 18(10):e0292429. https://doi.org/10.1371/journal.pone.0292429. Additional Declarations The authors declare no competing interests. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5874582","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405188024,"identity":"87d88f04-1b84-47d1-8c9f-c25fa164ee4b","order_by":0,"name":"Aglaia Katsiroumpa","email":"","orcid":"","institution":"National and Kapodistrian University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Aglaia","middleName":"","lastName":"Katsiroumpa","suffix":""},{"id":405188025,"identity":"fe77ffd3-835b-41c7-b68e-35e4504917ab","order_by":1,"name":"Ioannis Moisoglou","email":"","orcid":"","institution":"University of Thessaly","correspondingAuthor":false,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Moisoglou","suffix":""},{"id":405188026,"identity":"34db9370-f2f2-4d63-802b-40aacbe99470","order_by":2,"name":"Athanasios Mitropoulos","email":"","orcid":"","institution":"National and Kapodistrian University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Athanasios","middleName":"","lastName":"Mitropoulos","suffix":""},{"id":405188027,"identity":"c5ebd8be-da4a-49d4-a8af-d2a5ad233507","order_by":3,"name":"Antonia Paschali","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Antonia","middleName":"","lastName":"Paschali","suffix":""},{"id":405188028,"identity":"8b3c9df5-f623-49ad-ba49-87bdc73b023d","order_by":4,"name":"Olympia Konstantakopoulou","email":"","orcid":"","institution":"National and Kapodistrian University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Olympia","middleName":"","lastName":"Konstantakopoulou","suffix":""},{"id":405188029,"identity":"4160fbb1-2118-4956-a6a4-fb55def810f8","order_by":5,"name":"Zoe Katsiroumpa","email":"","orcid":"","institution":"National and Kapodistrian University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"","lastName":"Katsiroumpa","suffix":""},{"id":405187403,"identity":"834429d0-cb56-4365-b9da-9ac8de0bb083","order_by":6,"name":"Petros Galanis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYLCCBwwMciRqSWBgMCZdS2ID0arN2dsvfkiouZO+XSL38QfGNht5Bv61D/Bqsew5UyyRcOxZ7s4Z6WYSjG1phg0Szw3wajG4kZMgkcB2OHfDjTQ2Bsa2w4wNEsfwO8zg/pvkHwn/Dqcb3EhjBjrssD1hLTfYj0kkth1OAGphADrscGIDfxt+LZY9OWwWiX2HDTececYmkXAuLblNgg2/FnP2449vfPh2WN7gONBhH8psbPv5CTmMgQcpfBKAGGgXIS3sD9CE+A/g1zIKRsEoGAUjDgAALx5Ip67Yv/YAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1308-5782","institution":"National and Kapodistrian University of Athens","correspondingAuthor":true,"prefix":"","firstName":"Petros","middleName":"","lastName":"Galanis","suffix":""}],"badges":[],"createdAt":"2025-01-21 15:40:58","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5874582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5874582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74406192,"identity":"ca08504d-e9f2-4aa9-a4a8-9fd3aa43ebf3","added_by":"auto","created_at":"2025-01-22 04:27:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":501080,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5874582/v1/213230b6-f498-4184-9a97-5860298c0ee6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssociation between social media addiction and mental health among Greek young adults\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSocial media usage is an ever-growing phenomenon, with active users exceeding 5\u0026nbsp;billion people worldwide at this moment (Statista, 2024). However, increased usage can lead to addiction as well, and it seems that social media addiction (in platforms like Facebook, Instagram, TikTok and YouTube) has emerged as a problem of global concern. In fact, according to a meta-analysis covering 64 countries, social media addiction accounted for more than 17% of the total studied population, as a subtype of digital addiction (Meng et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, various scales have been developed to measure this phenomenon (Varona et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); BSMAS (Bergen Social Media Addiction Scale), SMAS (Social Media Addiction Scale), BFAS (Bergen Facebook Addiction Scale), TTAS (TikTok Addiction Scale) (Galanis et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, Galanis et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) being some of them. These instruments are based on factors and behaviours such as salience, mood modification, tolerance, withdrawal etc. (Varona, 2022). Remarkably, scholars in the last years pay special attention into TikTok use and its impact on users\u0026rsquo; health (Galanis et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e, Katsiroumpa et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeanwhile, incidence and prevalence of both depression and anxiety seem to increase among adolescents and young adults over the last years (Xiang et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Youth unemployment, little or no social mobility, decreasing affordabilty are significant factors (among others) that can contribute to the occurence of these psychological disorders (Lakasing et al, 2020). This phenomenon may intensified during the COVID-19 pandemic and the consecutive lockdowns (Brunette et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but it emerged far before the 2019 outbreak (Barker et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), (Thapar et al, 2022). Meanwhile, in addition to the other causes mentioned above, social media addiction seems to be another risk factor for the occurrence of depressive and anxious symptoms.\u003c/p\u003e \u003cp\u003eIn this sense, various studies have indicated that excessive social media usage can lead to mental health issues, like depression, anxiety and body image acceptance (Bozzola et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another negative emotion that seems to be associated with social media addiction is FoMO (Fear of Missing Out), which in fact affects negatively the levels of life satisfaction of social media users (Bakioğlu et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Decreased quality of life due to depressive symptoms derived from social media addiction is mentioned in other studies as well (Vu Anh Trong Dam et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), highlighting the significance of the identification of this phenomenon and its treatment.\u003c/p\u003e \u003cp\u003eIn this context, we conducted a study to examine the relationship between social media addiction and anxiety and depression among Greek young adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional study during July 2024 in Greece. We developed an online version of the study questionnaire through google forms. Then, we disseminated the online questionnaire through social media. Adults with at least one profile in social media platforms could participate in our study. We considered as young adults those with an age between 18 and 35 years of age.\u003c/p\u003e \u003cp\u003ePersonal data from participants were not collected. Our study was approved by the Ethics Committee of the Faculty of Nursing, National and Kapodistrian University of Athens (approval number; 510, June 2024). Moreover, we followed the guidelines of the Declaration of Helsinki (World Medical Association, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eWe used the Bergen Social Media Addiction Scale (BSMAS) (Andreassen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to measure levels of social media addiction in our participants. The BSMAS measures six cores of the social media addiction, i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. The BSMAS includes six items, and answers are on a five-point Likert Scale very rarely (1), rarely (2), sometimes (3), often (4), and very often (5). Total BSMAS score ranges from 6 to 30. Higher scores indicate higher levels of social media addiction. We used the Greek version of the BSMAS (Dadiotis et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, Cronbach\u0026rsquo;s alpha for the BSMAS was 0.83.\u003c/p\u003e \u003cp\u003eWe used the Patient Health Questionnaire-4 (PHQ-4) (Kroenke et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to evaluate levels of anxiety and depression in our participants. The PHQ-4 includes four items: two items measuring anxiety, and two other items measuring depression. Answers are on a four-point Likert scale from 0 (not at all) to 3 (nearly every day). Anxiety and depression scores range from 0 to 6. Higher scores indicate higher levels of anxiety and depression. We used the Greek version of the PHQ-4 (Karekla et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In our study, Cronbach\u0026rsquo;s alpha for the anxiety scale was 0.79, and for the depression scale was 0.73.\u003c/p\u003e \u003cp\u003eWe measured gender, age, the number of hours that participants usually sleep when next day they have to go for their work/class, and how sleepy they felt at work/class. We measured sleepiness in at work/class on a five-point Likert scale; not at all (1), a little (2), moderate (3), a lot (4), very much (5).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe present categorical variables with numbers and percentages. Also, we present continuous variables with mean, standard deviation, median and range. We used Pearson\u0026rsquo;s correlation coefficient to estimate the correlation between BSMAS and anxiety, depression, and age. Also, we used Spearman\u0026rsquo;s correlation coefficient to estimate the correlation between BSMAS and the number of hours that participants usually sleep when next day they have to go for their work/class, and sleepiness. We used the independent samples t-test to identify differences between males and females regarding the BSMAS. P-values less than 0.05 were considered as statistically significant. We used the IBM SPSS 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.) for the analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe majority of our participants were females (83.1%, n\u0026thinsp;=\u0026thinsp;300), while 16.9% were males (n\u0026thinsp;=\u0026thinsp;61). Mean age was 23.3 years, standard deviation was 4, median was 22, and range was 17.\u003c/p\u003e \u003cp\u003eAmong our participants, 37.1% (n\u0026thinsp;=\u0026thinsp;134) reported that they feel moderately sleepy at work/class, 29.9% (n\u0026thinsp;=\u0026thinsp;108) reported that they feel a little sleepy, 26.3% (n\u0026thinsp;=\u0026thinsp;95) reported that they feel quite sleepy, 3.9% (n\u0026thinsp;=\u0026thinsp;14) reported that they do not feel sleepy at work/class, and 2.8% (n\u0026thinsp;=\u0026thinsp;10) reported that they feel very sleepy. Mean duration of night sleep was 6.4 hours, standard deviation was 1, median was 6, and range was 6.\u003c/p\u003e \u003cp\u003eDescriptive statistics for the BSMAS are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mean BSMAS score was 16.1 (standard deviation; 5.1). Mean tolerance score was 3.1, mean withdrawal symptoms score was 2.8, mean conflict score was 2.7, mean salience score was 2.6, mean mood modification score was 2.5, and mean relapse score was 2.4.\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\u003eDescriptive statistics for the Bergen Social Media Addiction Scale (BSMAS).\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSMAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMood modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithdrawal symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\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\u003eDescriptive statistics for the PHQ-4 are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Mean anxiety score was 2.8 (standard deviation; 1.6), while mean depression score was 2.6 (standard deviation; 1.2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for the PHQ-4.\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\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\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents correlations between BSMAS and anxiety, depression, age, duration of night sleep, and sleepiness in work/class. Our correlation analysis showed that social media addiction was correlated with increased anxiety and depression (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in both cases). Moreover, reduced age and reduced duration of night sleep was associated with increased social media addiction (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in both cases). Additionally, social media addiction was correlated with increased sleepiness in work/class (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\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\u003eCorrelations between Bergen Social Media Addiction Scale (BSMAS) and anxiety, depression, age, duration of night sleep, and sleepiness in work/class.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDuration of\u003c/p\u003e \u003cp\u003enight sleep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSleepiness in\u003c/p\u003e \u003cp\u003ework/class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSMAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMood modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003csup\u003e*\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithdrawal symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e**\u003c/sup\u003e p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the relation between social media addiction (SMA) and anxiety and depression among Greek young adults. The results indicated that the problematic and excessive use of social media can lead to negative effects on mental and physical health, since symptoms of anxiety, depression and sleepiness were reported. In fact, the vast majority of our participants (approximately 96%) mentioned a degree of sleepiness at work / class due to SMA. Regarding mental health, a positive correlation was found between SMA and anxiety and depression, a result which aligns with previous literature (Şent\u0026uuml;rk et al, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Galanis et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The correlation coefficients between SMA (measured by the Bergen Social Media Addiction Scale) and anxiety and between SMA and depression were 0.32 and 0.43 respectively, implying statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0,01 in both cases). Another study suggests that similar addictions e.g. mobile phone addiction are positive indicators of anxiety and depression, and negative indicators of sleep quality (Ying et al, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, it is worth noting that the components of the BSMAS were salience, mood modification, tolerance, withdrawal symptoms, conflict and relapse. Among those descriptive statistics, mood modification, withdrawal and relapse were found to have the highest positive association with anxiety (p\u0026thinsp;\u0026lt;\u0026thinsp;0,01 in all cases). Likewise, the ones with the highest positive association with depression were tolerance, withdrawal and relapse (p\u0026thinsp;\u0026lt;\u0026thinsp;0,01 in all cases). This finding is consistent with a similar finding of a study which indicated that mood modification, relapse, withdrawal, and conflict were the four components of BSMAS which were positively associated with several measures of psychopathological symptoms of the participants (Fournier et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, individuals who are engaged with intense and problematic social media use are particularly at risk of lower well - being (Boer et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as mentioned in the Introduction. It should be noted that both mean anxiety and mean depression scoring were considered positive among the participants in our study, according to PHQ \u0026minus;\u0026thinsp;4 screening tool (L\u0026ouml;we et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSMA is a phenomenon which affects mainly adolescents and young adults, as shown in relative literature and worldwide statistics (Statista, 2019), (Abbasi et al, 2019), (Kocak et al, 2021). Among other sociodemographic factors, age plays a key role in the occurrence of SMA. Our study indicated that reduced age of the participants was associated with increased SMA (p\u0026thinsp;\u0026lt;\u0026thinsp;0,01), thus our results agree with those of previous research in the field. Specifically, relapse (i.e. failed attempts to control social media usage) was the one variable that showed the strongest negative relationship with age, among the participants.\u003c/p\u003e \u003cp\u003eAccording to Lafontaine-Poissant et al., intense and problematic social media use is associated with worse sleep health (Lafontaine - Poissant et al, 2024). Literature suggests that the blue light of the devices, the emotional content / nature of social media and \u0026ldquo;fear of missing out\u0026rdquo; seem to be the causes of poor quality of sleep among adolescents and young adults. The findings of the present study highlight that increased SMA was associated with reduced duration of night sleep as well as with increased sleepiness at class or work (p\u0026thinsp;\u0026lt;\u0026thinsp;0,01 in both cases). Especially tolerance (i.e. more social media use in order to attain the same degree of satisfaction over time) was the variable that displayed the strongest positive correlation with sleepiness at work/class, among the others. Reduced duration of sleep may be due to delayed bedtime, difficulty falling asleep or desynchronisation of circadian rhythm during sleeptime (Zhu et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meanwhile, another study that involved university students has shown that internet addiction is a risk factor for increased sleepiness at daytime and specifically it increased the risk of daytime sleepiness approximately one time (Demir et al, 2020). However, further research is required in the Greek case so as to examine this phenomenon.\u003c/p\u003e \u003cp\u003eOur findings agree with those of relative literature, meaning that a positive correlation between social media addiction and depressive and anxiety symptoms was found. Since this phenomenon has only occurred in recent years and there are no data for the Greek case so far, the importance of the present study is evident. However, future research to further study this correlation is essential; for instance, it is recommended to seek to what extent these negative emotions and reactions are influenced by educational, professional and economic status, gender, age group of the subjects etc. The research of the relation between Fear of Missing out (FOMO) and SMA is also estimated to illuminate certain perspectives of social media addiction. Regarding sleep quality, further research exploring the relationships between sleep aspects (daytime naps, insomnia) and SMA should be conducted. BSMAS and PHQ-4 which were selected are certified tools that measure the levels of addiction and negative symptoms respectively, reinforcing the reliability of the results.\u003c/p\u003e \u003cp\u003eOur study had several limitations. We employed a convenience sample of young adults in Greece. Thus, our sample was not representative of young adults in Greece. Moreover, we performed a cross-sectional study, and, thus, we cannot extract causal relationships between social media addiction, anxiety and depression. Additionally, we used self-reported tools to measure social media addiction, anxiety and depression. Therefore, information bias is probable in our study.\u003c/p\u003e \u003cp\u003eIn conclusion, we found a positive correlation between social media addiction, anxiety and depression among young adults in Greece. Moreover, we found that reduced age and reduced duration of night sleep was associated with increased social media addiction. Additionally, social media addiction was correlated with increased sleepiness in work/class. Therefore, social media addiction is an important issue among young adults and policy makers should develop appropriate interventions to reduce this phenomenon.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflicts of interest: None. Authors have no interests, affiliations, or associations that might be perceived to influence the results and/or discussion reported in this preprint submission\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbasi, I. (2019). Social media addiction in romantic relationships: Does user\u0026apos;s age influence vulnerability to social media infidelity? Personality and Individual Differences, 139 : 277-280. https://doi.org/10.1016/j.paid.2018.10.038.\u003c/li\u003e\n \u003cli\u003eAndreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., \u0026amp; Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252\u0026ndash;262. https://doi.org/10.1037/adb0000160\u003c/li\u003e\n \u003cli\u003eBakioğlu, F., Deniz, M., Griffiths, M., Pakpour, A.(2022). Adaptation and validation of the Online-Fear of Missing Out Inventory into Turkish and the association with social media addiction, smartphone addiction, and life satisfaction. BMC Psychology. 10(1):154. https://doi.org/10.1186/s40359-022-00856-y.\u003c/li\u003e\n \u003cli\u003eBarker, M., Beresford, B., Martin, B., Fraser, L. (2019). Prevalence and Incidence of Anxiety and Depression Among Children, Adolescents, and Young Adults With Life-Limiting Conditions. JAMA Pediatrics. 173(9):835\u0026ndash;844. https://doi.org/10.1001/jamapediatrics.2019.1712.\u003c/li\u003e\n \u003cli\u003eBoer, M., Eijnden, R., Boniel-Nissim, M., et al. (2020). Adolescents\u0026apos; Intense and Problematic Social Media Use and Their Well-Being in 29 Countries. Journal of Adolescent Health. 66(6S):S89-S99. https://doi.org/ 10.1016/j.jadohealth.2020.02.014.\u003c/li\u003e\n \u003cli\u003eBozzola, E., Spina, G., Agostiniani, R. et al. (2022). The Use of Social Media in Children and Adolescents: Scoping Review on the Potential Risks. International Journal of Environmental Research and Public Health. 19(16):9960. https://doi.org/ 10.3390/ijerph19169960.\u003c/li\u003e\n \u003cli\u003eBrunette, M.F., Erlich, M.D., Edwards, M.L. (2023). Addressing the Increasing Mental Health Distress and Mental Illness Among Young Adults in the United States. The Journal of Nervous and Mental Disease 211(12):961-967. https://doi.org/10.1097/NMD.0000000000001734.\u003c/li\u003e\n \u003cli\u003eDadiotis, A., Bacopoulou, F., Kokka, I., Vlachakis, D., Chrousos, G. P., Darviri, C., \u0026amp; Roussos, P. (2021). Validation of the Greek version of the Bergen Social Media Addiction Scale in Undergraduate Students. EMBnet.Journal, 26(1), e975. https://doi.org/10.14806/ej.26.1.975.\u003c/li\u003e\n \u003cli\u003eDam, V., Dao, N., Nguyen, D. et al. (2023). Quality of life and mental health of adolescents: Relationships with social media addiction, Fear of Missing out, and stress associated with neglect and negative reactions by online peers. PLOS One. 18(6). https://doi.org/ 10.1371/journal.pone.0286766.\u003c/li\u003e\n \u003cli\u003eDemir, G., Arslan, S., Kocoglu-Tanyer, D. (2023). Daytime Sleepiness in University Students and Internet Addiction as the Determinant. Journal of Addictions Nursing. 31(3):153-160. https://doi.org/10.1097/JAN.0000000000000346\u003c/li\u003e\n \u003cli\u003eFournier, L., Schimmenti, A., Musetti, A. et al. (2023). Deconstructing the components model of addiction: an illustration through \u0026ldquo;addictive\u0026rdquo; use of social media. Addictive Behaviors. 143:107694. https://doi.org/10.1016/j.addbeh.2023.107694.\u003c/li\u003e\n \u003cli\u003eGalanis, P., Katsiroumpa, Z., Katsiroumpa, A., Tsakalaki A, Vasilopoulos S, (2023). Relationship between Sexting and Self-Esteem, Depression, Anxiety, and Stress among Young People. International Journal of Caring Sciences. 16(1):62-71.\u003c/li\u003e\n \u003cli\u003eGalanis, P., Katsiroumpa, A., Moisoglou, I., Konstantakopoulou, O. (2024a). The TikTok Addiction Scale: Development and validation. AIMS Public Health, 11(4):1172-1197. https://doi:10.3934/publichealth.2024061.\u003c/li\u003e\n \u003cli\u003eGalanis, P., Katsiroumpa, A., Moisoglou, I., Konstantakopoulou, O. (2024b). Determining an optimal cut-off point for TikTok addiction using the TikTok Addiction Scale. Research Square. https://doi.org/10.21203/rs.3.rs-4782800/v1\u003c/li\u003e\n \u003cli\u003eGalanis, P.; Katsiroumpa, A.; Katsiroumpa, Z.; Mangoulia, P.; Gallos, P.; Moisoglou, I.; Koukia, E. (2024c). Impact of Problematic TikTok Use on Mental Health: A Systematic Review and Meta-Analysis. Preprints. 2024110266. https://doi.org/10.20944/preprints202411.0266.v1\u003c/li\u003e\n \u003cli\u003eKarekla, M., Pilipenko, N., \u0026amp; Feldman, J. (2012). Patient Health Questionnaire: Greek language validation and subscale factor structure. Comprehensive Psychiatry, 53(8), 1217\u0026ndash;1226. https://doi.org/10.1016/j.comppsych.2012.05.008.\u003c/li\u003e\n \u003cli\u003eKatsiroumpa, A.; Moisoglou, I.; Gallos, P.; Katsiroumpa, Z.; Konstantakopoulou, O.; Tsiachri, M.; Galanis, P. (2024). Problematic TikTok Use and Its Association with Poor Sleep: A Cross-Sectional Study Among Greek Young Adults. Preprints. 2024122088. https://doi.org/10.20944/preprints202412.2088.v1\u003c/li\u003e\n \u003cli\u003eKocak, O., Arslan, H. (2021). Social Media use Across Generations: from Addiction to Engagement. European Integration Studies. 15(1):63-77. https://doi.org/10.5755/j01.eis.1.15.29080.\u003c/li\u003e\n \u003cli\u003eKroenke, K., Spitzer, R. L., Williams, J. B. W., \u0026amp; Lowe, B. (2009). An Ultra-Brief Screening Scale for Anxiety and Depression: The PHQ-4. Psychosomatics, 50(6), 613\u0026ndash;621. https://doi.org/10.1176/appi.psy.50.6.613.\u003c/li\u003e\n \u003cli\u003eLafontaine-Poissant, F., Lang, J., , McKinnon, B. et al. (2024). Social media use and sleep health among adolescents in Canada. Health Promotion and Chronic Disease Prevention in Canada. 44(7-8):338-346. https://doi.org.10.24095/hpcdp.44.7/8.05.da.\u003c/li\u003e\n \u003cli\u003eLakazing, E., Mirza, Z. Anxiety and depression in young adults and adolescents (2020). British Journal of General Practice 70(691):56\u0026ndash;57. https://doi.org/ 10.3399/bjgp20X707765.\u003c/li\u003e\n \u003cli\u003eL\u0026ouml;we, B., Wahl, I., Rose, M., el al. (2010). A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. Journal of Affective Disorders. 122(1-2):86-95. https://doi.org.10.1016/j.jad.2009.06.019.\u003c/li\u003e\n \u003cli\u003eMeng, S., Cheng, J., Li, Y., et al. (2022). Global prevalence of digital addiction in general population: A systematic review and meta-analysis. Clinical Psychology Review (92),102128 https://doi.org/10.1016/j.cpr.2022.102128.\u003c/li\u003e\n \u003cli\u003eŞent\u0026uuml;rk, E., Geniş, B., Coşar, B. (2021). Social Media Addiction in Young Adult Patients with Anxiety Disorders and Depression. Alpha Psychiatry. 22(5):257-262. https://doi.org/10.1530/alphapsychiatry.2021.21242.\u003c/li\u003e\n \u003cli\u003eStatista. Share of online users in the United States who report being addicted to social media as of April 2019, by age group. Available from : https://www.statista.com/statistics/1081292/social-media-addiction-by-age-usa/. Last accessed at Jan 18 2025.\u003c/li\u003e\n \u003cli\u003eStatista. Global digital population as of October 2024. Available from : https://www.statista.com/statistics/617136/digital-population-worldwide/. Last accessed on Dec 28 2024.\u003c/li\u003e\n \u003cli\u003eVarona, M.N., Muela, A., Machimbarrena, J.M., (2022). Problematic use or addiction? A scoping review on conceptual and operational definitions of negative social networking sites use in adolescents. Addictive Behaviors (134),107400 https:// doi.org/10.1016/j.addbeh.2022.107400.\u003c/li\u003e\n \u003cli\u003eWorld Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA, 310(20), 2191. https://doi.org/10.1001/jama.2013.281053.\u003c/li\u003e\n \u003cli\u003eXiang, A.H., Martinez, M.P., Chow, T. et al (2024). Depression and Anxiety Among US Children and Young Adults. JAMA Network, 7(10):e2436906. https:// doi.org/10.1001/jamanetworkopen.2024.36906.\u003c/li\u003e\n \u003cli\u003eYing, L., Li, G., Liu, L., Wu, H. (2020). Correlations between mobile phone addiction and anxiety, depression, impulsivity, and poor sleep quality among college students: A systematic review and meta-analysis. Journal of Behavioral Addictions. 9(3):551-571. https://doi.org/10.1556/2006.2020.00057.\u003c/li\u003e\n \u003cli\u003eZhu, X., Zheng, T.,1, Ding, L. (2023). Exploring associations between social media addiction, social media fatigue, fear of missing out and sleep quality among university students: A cross-section study. PLOS One. 18(10):e0292429. https://doi.org/10.1371/journal.pone.0292429.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National and Kapodistrian University of Athens","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"social media, addiction, anxiety, depression, young adults","lastPublishedDoi":"10.21203/rs.3.rs-5874582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5874582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The use of social media is constantly increasing, but it also has several negative consequences, especially for young people.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e To examine the relationship between social media addiction and mental health among Greek young adults.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted an online cross-sectional study during July 2024 in Greece. We used the Bergen Social Media Addiction Scale (BSMAS) to measure levels of social media addiction in our participants. We used the Patient Health Questionnaire-4 (PHQ-4) to evaluate levels of anxiety and depression in our participants. We measured gender, age, the number of hours that participants usually sleep when next day they have to go for their work/class, and how sleepy they felt at work/class.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The majority of our participants were females (83.1%, n=300), while 16.9% were males (n=61). Mean age was 23.3 years. Our correlation analysis showed that social media addiction was correlated with increased anxiety and depression (p-value \u0026lt; 0.01 in both cases). Moreover, reduced age and reduced duration of night sleep was associated with increased social media addiction (p-value \u0026lt; 0.01 in both cases). Additionally, social media addiction was correlated with increased sleepiness in work/class (p-value \u0026lt; 0.01).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e We found a positive correlation between social media addiction, anxiety and depression among young adults in Greece. Moreover, we found that reduced age and reduced duration of night sleep was associated with increased social media addiction. Additionally, social media addiction was correlated with increased sleepiness in work/class. Therefore, social media addiction is an important issue among young adults and policy makers should develop appropriate interventions to reduce this phenomenon.\u003c/p\u003e","manuscriptTitle":"Association between social media addiction and mental health among Greek young adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 04:11:16","doi":"10.21203/rs.3.rs-5874582/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70d5482e-4a59-46b0-b3b2-0c2d0cbd3c8d","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43201171,"name":"Psychology"},{"id":43201172,"name":"Psychiatry"},{"id":43201173,"name":"Publishing/Media"}],"tags":[],"updatedAt":"2025-01-22T04:11:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-22 04:11:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5874582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5874582","identity":"rs-5874582","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}