Grüneberg Catharina, Bäuerle Alexander, Karunakaran Sophia, Darici Dogus, Dörrie Nora, Teufel Martin, Benson Sven, Robitzsch Anita
Clinic for Psychosomatic Medicine and Psychotherapy, LVR-University Hospital, University of Duisburg-Essen, Virchowstraße 174, Essen, 45147, Germany, 49 201438755212.
Center for Translational Neuro- and Behavioral Sciences, University of Duisburg-Essen, Essen, Germany.
JMIR Med Educ. 2025 Jan 24;11:e58183. doi: 10.2196/58183.
Despite the high prevalence of mental health problems among medical students and physicians, help-seeking remains low. Digital mental health approaches offer beneficial opportunities to increase well-being, for example, via mobile apps.
This study aimed to assess the acceptance, and its underlying predictors, of tailored e-mental health apps among medical students by focusing on stress management and the promotion of personal skills.
From November 2022 to July 2023, a cross-sectional study was conducted with 245 medical students at the University of Duisburg-Essen, Germany. Sociodemographic, mental health, and eHealth-related data were assessed. The Unified Theory of Acceptance and Use of Technology (UTAUT) was applied. Differences in acceptance were examined and a multiple hierarchical regression analysis was conducted.
The general acceptance of tailored e-mental health apps among medical students was high (mean 3.72, SD 0.92). Students with a job besides medical school reported higher acceptance (t107.3=-2.16; P=.03; Padj=.027; Cohen d=4.13) as well as students with higher loads of anxiety symptoms (t92.4=2.36; P=.02; Padj=.03; Cohen d=0.35). The t values were estimated using a 2-tailed t test. Regression analysis revealed that acceptance was significantly predicted by anxiety symptoms (β=.11; P=.045), depressive symptoms (β=-.11; P=.05), internet anxiety (β=-.12; P=.01), digital overload (β=.1; P=.03), and the 3 UTAUT core predictors-performance expectancy (β=.24; P<.001), effort expectancy (β=.26; P<.001), and social influence (β=.43; P<.001).
The high acceptance of e-mental health apps among medical students and its predictors lay a valuable basis for the development and implementation of tailored e-mental health apps within medical education to foster their mental health. More research using validated measures is needed to replicate our findings and to further investigate medical students' specific needs and demands regarding the framework of tailored e-mental health apps.
尽管医学生和医生中心理健康问题的患病率很高，但寻求帮助的比例仍然很低。数字心理健康方法提供了有益的机会来提高幸福感，例如通过移动应用程序。
本研究旨在通过关注压力管理和个人技能提升，评估医学生对量身定制的电子心理健康应用程序的接受度及其潜在预测因素。
2022年11月至2023年7月，对德国杜伊斯堡-埃森大学的245名医学生进行了一项横断面研究。评估了社会人口统计学、心理健康和电子健康相关数据。应用了技术接受与使用统一理论（UTAUT）。检查了接受度的差异，并进行了多元层次回归分析。
医学生对量身定制的电子心理健康应用程序的总体接受度较高（平均3.72，标准差0.92）。除医学院学习外还有工作的学生报告的接受度更高（t107.3 = -2.16；P = 0.03；校正P = 0.027；科恩d = 4.13），焦虑症状负荷较高的学生也是如此（t92.4 = 2.36；P = 0.02；校正P = 0.03；科恩d = 0.35）。t值使用双尾t检验进行估计。回归分析显示，焦虑症状（β = 0.11；P = 0.045）、抑郁症状（β = -0.11；P = 0.05）、网络焦虑（β = -0.12；P = 0.01）、数字过载（β = 0.1；P = 0.03）以及UTAUT的三个核心预测因素——绩效期望（β = 0.24；P < 0.001）、努力期望（β = 0.26；P < 0.001）和社会影响（β = 0.43；P < 0.001），对接受度有显著预测作用。
医学生对电子心理健康应用程序的高接受度及其预测因素为在医学教育中开发和实施量身定制电子心理健康应用程序以促进其心理健康奠定了宝贵基础。需要更多使用经过验证的测量方法的研究来复制我们的发现，并进一步调查医学生对量身定制电子心理健康应用程序框架的具体需求。
