Prävalenz von Internetsucht vor und während der COVID-19 Pandemie unter Studierenden der Johannes Gutenberg-Universität Mainz

Zusammenfassung

Ziel der Studie Internetsucht ist eine Verhaltensstörung, von welcher v. a. Jugendliche und junge Erwachsene, unter ihnen auch Studierende an Hochschulen betroffen sind. Die COVID-19 Pandemie führte aufgrund der Maßnahmen zu ihrer Eindämmung zu starken Einbußen sozialer Beziehungen, Studium und Freizeitaktivitäten der Studierenden. Diese Studie untersuchte, wie hoch die Prävalenz der Internetsucht unter Studierenden vor und während der COVID-19 Pandemie ausfällt und welche soziodemografischen (Geschlecht, Fachgruppe) und Gesundheitsfaktoren (Depressionssymptome, Einsamkeit, Ängste, Impulsivität) mit Internetsuchtsymptomen assoziiert sind.

Methodik In der vorliegenden Studie nahmen 2 Stichproben der Studierenden der Johannes Gutenberg-Universität Mainz im Sommer 2019 (N=4351) vor und im Sommer 2020 (N=3066) während der COVID-19 Pandemie im Rahmen des Modellvorhabens „Healthy Campus Mainz-gesund studieren“ zur Prävention und Gesundheitsförderung zu verschiedenen Gesundheitsthemen und Studienbedingungen an Online-Umfragen teil. Es wurde die Prävalenz von Internetsucht erhoben und mittels logistischer Regression Zusammenhänge mit Geschlecht, Depressionssymptomen, Einsamkeit, Ängsten und Impulsivität analysiert.

Ergebnisse Die Prävalenz der Internetsucht lag 2019 bei 3,9% und lag 2020 signifikant höher bei 7,8%. Während 2019 männliches Geschlecht noch mit Internetsucht assoziiert war (OR₂₀₁₉=0,685, p<0,05), konnten 2020 keine Geschlechtsunterschiede mehr festgestellt werden. Sowohl vor als auch während der Pandemie gingen Depressionssymptome (OR₂₀₁₉=1,121, p<0,001; OR₂₀₂₀=1,175, p<0,001) und Einsamkeit (OR₂₀₁₉=1,121, p<0,001; OR₂₀₂₀=1,071, p<0,05) mit Internetsucht einher, während der Pandemie auch Angstgefühle (OR₂₀₂₀=1,156, p<0,05).

Schlussfolgerung Studierende stellen eine gefährdete Gruppe für Internetsucht dar. Während der COVID-19 Pandemie trat die Symptomatik deutlich häufiger auf als noch ein Jahr zuvor. Es müssen unbedingt geeignete Präventions- und Interventionsangebote für Studierende implementiert werden, die sowohl Internetsucht, aber auch damit einhergehende Probleme wie Depression und Einsamkeit in den Blick nehmen.

Abstract

Purpose Internet addiction is a behavioral disorder that primarily affects adolescents and young adults, including college students. The COVID-19 pandemic resulted in severe changes in students' daily lives due to the pandemic containment measures. Therefore, the current study addressed the question of the prevalence of internet addiction among college students before and during the COVID-19 pandemic. Furthermore, sociodemographic (gender, subject group) and health factors (depression symptoms, loneliness, anxiety, and impulsivity) were considered in terms of how they are associated with internet addiction.

Methods In the present study, two samples of students at Johannes Gutenberg University (JGU) Mainz participated in online surveys on various health topics and their present study situation. The data collection took place in both summer 2019 (N=4,351) and summer 2020 (N=3,066) during the COVID-19 pandemic as part of the JGU’s health project “Healthy Campus Mainz-gesund studieren” on prevention and health promotion among students. Frequency measures for prevalence were calculated and associations with gender, depression symptoms, loneliness, anxiety, and impulsivity were analyzed using logistic regression.

Results The prevalence of internet addiction symptoms was 3.9% in 2019, while it was significantly higher with 7.8% during the pandemic. While male gender was still associated with internet addiction in 2019 (OR₂₀₁₉=0.685, p<0.05), no gender discrepancy was found in 2020. Both before and during the pandemic, depression symptoms (OR₂₀₁₉=1.121, p<0.001; OR₂₀₂₀=1.175, p<0.001) and loneliness (OR₂₀₁₉=1.121, p<0.001; OR₂₀₂₀=1.071, p<0.05) were associated with internet addiction. Additionally, anxiety also played a role in the association with internet addiction during the pandemic (OR₂₀₂₀=1.156, p<0.05).

Conclusion Students represent a highly vulnerable group for internet addiction. During the COVID-19 pandemic, the symptomatology occurred significantly more often than in the year before. It is highly needed to implement appropriate prevention and intervention services for students that address both internet addiction as well as associated problems such as depression and loneliness.

Schlüsselwörter

Internetsucht - stoffungebundene Sucht - Prävalenz - Studierende - COVID-19

Keywords

Internet addiction - non-substance addiction - prevalence - students - COVID-19

Publication History

Article published online:
03 November 2021

• Literatur

• 1 Brand M, Wegmann E, Stark R. et al The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience and biobehavioral reviews 2019; 104: 1-10
• 2 Müller KW. Internetsucht. Wiesbaden: Springer Fachmedien; 2017.
• 3 Rumpf HJ, Vermulst AA, Bischof A. et al Occurence of internet addiction in a general population sample: a latent class analysis. Eur Addict Res 2014; 20: 159-166
• 4 Müller KW, Glaesmer H, Brähler E. et al Prevalence of internet addiction in the general population: results from a German population-based survey. Behaviour & Information Technology 2013; 33: 757-766
• 5 Shen Y, Meng F, Xu H. et al Internet addiction among college students in a Chinese population: Prevalence, correlates, and its relationship with suicide attempts. Depress Anxiety 2020; 37: 812-821
• 6 Shen Y, Wang L, Huang C. et al Sex differences in prevalence, risk factors and clinical correlates of internet addiction among chinese college students. J Affect Disord 2021; 279: 680-686
• 7 Al Asqah MI, Al Orainey AI, Shukr MA. et al The prevalence of internet gaming disorder among medical students at King Saud University, Riyadh, Saudi Arabia. A cross-sectional study. Saudi Med J 2020; 41: 1359-1363
• 8 Scherer L, Mader L, Beutel M. et al Geschlechtsspezifische Aspekte der Internetsucht (Kongressbeitrag). Suchttherapie 2019; 20: S01. doi: 10.1055/s-0039-1696222
• 9 Müller KW, Beutel ME, Egloff B. et al Investigating risk factors for Internet gaming disorder: a comparison of patients with addictive gaming, pathological gamblers and healthy controls regarding the big five personality traits. Eur Addict Res 2014; 20: 129-136
• 10 Marzilli E, Cerniglia L, Ballarotto G. et al Internet Addiction among Young Adult University Students: The Complex Interplay between Family Functioning, Impulsivity, Depression, and Anxiety. Int J Environ Res Public Health 2020; 17: 8231. doi: 10.3390/ijerph17218231
• 11 Lane HY, Chang CJ, Huang CL. et al An Investigation into Smartphone Addiction with Personality and Sleep Quality among University Students. Int J Environ Res Public Health 2021; 18: 7588. doi: 10.3390/ijerph18147588
• 12 Guo W, Tao Y, Li X. et al Associations of Internet Addiction Severity With Psychopathology, Serious Mental Illness, and Suicidality: Large-Sample Cross-Sectional Study. J Med Internet Res 2020; 22: e17560
• 13 Li Y, Li G, Liu L. et al Correlations between mobile phone addiction and anxiety, depression, impulsivity, and poor sleep quality among college students: A systematic review and meta-analysis. J Behav Addict 2020; 9: 551-571
• 14 Kohls E, Baldofski S, Moeller R. et al Mental Health, Social and Emotional Well-Being, and Perceived Burdens of University Students During COVID-19 Pandemic Lockdown in Germany. Front Psychiatry 2021; 12: 643957
• 15 Werner AM, Tibubos AN, Mülder LM. et al The impact of lockdown stress and loneliness during the COVID-19 pandemic on mental health among university students in Germany. Preprint 2021.
• 16 Fu W, Yan S, Zong Q. et al Mental health of college students during the COVID-19 epidemic in China. J Affect Disord 2021; 280: 7-10
• 17 Wang X, Hegde S, Son C. et al Investigating Mental Health of US College Students During the COVID-19 Pandemic: Cross-Sectional Survey Study. J Med Internet Res 2020; 22: e22817
• 18 King DL, Delfabbro PH, Billieux J. et al Problematic online gaming and the COVID-19 pandemic. J Behav Addict 2020; 9: 184-186
• 19 Ko CH, Yen JY. Impact of COVID-19 on gaming disorder: Monitoring and prevention. J Behav Addict 2020; 9: 187-189
• 20 Reichel JL, Rigotti T, Tibubos AN. et al Challenge Accepted! a Critical Reflection on How to Perform a Health Survey Among University Students-An Example of the Healthy Campus Mainz Project. Front Public Health 2021; 9: 616437
• 21 Wölfling K, Müller KW, Beutel M. Reliability and validity of the Scale for the Assessment of Pathological Computer-Gaming (CSV-S). Psychother Psychosom Med Psychol 2011; 61: 216-224
• 22 Löwe B, Kroenke K, Herzog W. et al Measuring depression outcome with a brief self-report instrument: sensitivity to change of the Patient Health Questionnaire (PHQ-9). J Affect Disord 2004; 81: 61-66
• 23 Martin A, Rief W, Klaiberg A. et al Validity of the Brief Patient Health Questionnaire Mood Scale (PHQ-9) in the general population. Gen Hosp Psychiatry 2006; 28: 71-77
• 24 Löwe B, Wahl I, Rose M. et al A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J Affect Disord 2010; 122: 86-95
• 25 Kroenke K, Spitzer RL, Williams JBW. et al An Ultra-Brief Screening Scale for Anxiety and Depression: The PHQ–4. Psychosomatics 2009; 50: 613-621
• 26 Löwe B, Wahl I, Rose M. et al A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J Affect Disord 2010; 122: 86-95
• 27 Gierk B, Kohlmann S, Kroenke K. et al The somatic symptom scale-8 (SSS-8): a brief measure of somatic symptom burden. JAMA internal medicine 2014; 174: 399-407
• 28 Hughes ME, Waite LJ, Hawkley LC. et al A Short Scale for Measuring Loneliness in Large Surveys: Results From Two Population-Based Studies. Res Aging 2004; 26: 655-672
• 29 Luhmann M, Hawkley LC. Age differences in loneliness from late adolescence to oldest old age. Dev Psychol 2016; 52: 943-959
• 30 Klein EM, Zenger M, Tibubos AN. et al Loneliness and its relation to mental health in the general population: Validation and norm values of a brief measure. J Affect Disord Rep 2021; 4: 100120. doi: 10.1016/j.jadr.2021.100120
• 31 Cohen J. A power primer. Quant Method Psychol 1992; 112: 155-159