Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda.
Department of Psychiatry, Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda.
PLoS One. 2023 May 26;18(5):e0286424. doi: 10.1371/journal.pone.0286424. eCollection 2023.
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<0.01) and academic performance problems (ß = 1.76, S.E = 0.60; p<0.01) over the past two weeks; and increased internet addiction severity (ß = 0.05, S.E = 0.02; p<0.01) was associated with significantly increased depression symptom severity, whereas Twitter use was associated with reduced depression symptom severity (ß = 1.88, S.E = 0.57; p<0.05).
Despite life stressors being the largest predictor of depression symptom score severity, problematic online use also contributed significantly. Therefore, it is recommended that medical students' mental health care services consider digital wellbeing and its relationship with problematic online use as part of a more holistic depression prevention and resilience program.
撒哈拉以南非洲国家的学生在 COVID-19 大流行期间首次体验在线课程。对于某些人来说，更多的在线参与可能导致网络依赖，这可能与抑郁有关。本研究探讨了乌干达医学生中网络使用问题、社交媒体和智能手机与抑郁症状之间的关联。
在乌干达一所公立大学的 269 名医学生中进行了一项试点研究。使用问卷调查收集了社会人口因素、生活方式、在线使用行为、智能手机成瘾、社交媒体成瘾和网络成瘾的数据。采用分层线性回归模型探讨了不同形式的网络成瘾与抑郁症状严重程度的关系。
研究结果表明，16.73%的医学生有中度至重度抑郁症状。存在（i）智能手机成瘾风险的比例为 45.72%，（ii）社交媒体成瘾风险的比例为 74.34%，（iii）网络成瘾风险的比例为 8.55%。在线使用行为（例如，在线平均时长、使用的社交媒体平台类型、上网目的）和与在线相关的成瘾（对智能手机、社交媒体和互联网的成瘾）分别预测了大约 8%和 10%的抑郁症状严重程度。然而，在过去两周内，生活压力源对抑郁的预测能力最高（35.9%）。最终模型总共预测了 51.9%的抑郁症状变异。在最终模型中，过去两周的恋爱关系问题（ß=2.30，S.E=0.58；p<0.01）和学业成绩问题（ß=1.76，S.E=0.60；p<0.01）以及网络成瘾严重程度的增加（ß=0.05，S.E=0.02；p<0.01）与抑郁症状严重程度显著增加有关，而 Twitter 使用与抑郁症状严重程度降低有关（ß=1.88，S.E=0.57；p<0.05）。
尽管生活压力源是抑郁症状严重程度的最大预测因素，但网络使用问题也有显著贡献。因此，建议医学生的心理健康护理服务将数字健康和其与网络使用问题的关系作为更全面的预防抑郁和提高韧性计划的一部分。
