Problematic use of the internet, smartphones, and social media among medical students and relationship with depression: An exploratory study

Students in sub-Saharan African countries experienced online classes for the first time during the COVID-19 pandemic. For some individuals, greater online engagement can lead to online dependency, which can be associated with depression. The present study explored the association between problematic...

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Veröffentlicht in:PloS one 2023-05, Vol.18 (5), p.e0286424
Hauptverfasser: Sserunkuuma, Jonathan, Kaggwa, Mark Mohan, Muwanguzi, Moses, Najjuka, Sarah Maria, Murungi, Nathan, Kajjimu, Jonathan, Mulungi, Jonathan, Kihumuro, Raymond Bernard, Mamun, Mohammed A, Griffiths, Mark D, Ashaba, Scholastic
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Sprache:eng
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Zusammenfassung:Students in sub-Saharan African countries experienced online classes for the first time during the COVID-19 pandemic. For some individuals, greater online engagement can lead to online dependency, which can be associated with depression. The present study explored the association between problematic use of the internet, social media, and smartphones with depression symptoms among Ugandan medical students. A pilot study was conducted among 269 medical students at a Ugandan public university. Using a survey, data were collected regarding socio-demographic factors, lifestyle, online use behaviors, smartphone addiction, social media addiction, and internet addiction. Hierarchical linear regression models were performed to explore the associations of different forms of online addiction with depression symptom severity. The findings indicated that 16.73% of the medical students had moderate to severe depression symptoms. The prevalence of being at risk of (i) smartphone addiction was 45.72%, (ii) social media addiction was 74.34%, and (iii) internet addiction use was 8.55%. Online use behaviors (e.g., average hours spent online, types of social media platforms used, the purpose for internet use) and online-related addictions (to smartphones, social media, and the internet) predicted approximately 8% and 10% of the severity of depression symptoms, respectively. However, over the past two weeks, life stressors had the highest predictability for depression (35.9%). The final model predicted a total of 51.9% variance for depression symptoms. In the final model, romantic relationship problems (ß = 2.30, S.E = 0.58; p
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0286424