TY  - JOUR
AU  - Grüneberg, Catharina
AU  - Bäuerle, Alexander
AU  - Karunakaran, Sophia
AU  - Darici, Dogus
AU  - Dörrie, Nora
AU  - Teufel, Martin
AU  - Benson, Sven
AU  - Robitzsch, Anita
PY  - 2025
DA  - 2025/1/24
TI  - Medical Students’ Acceptance of Tailored e–Mental Health Apps to Foster Their Mental Health: Cross-Sectional Study
JO  - JMIR Med Educ
SP  - e58183
VL  - 11
KW  - eHealth
KW  - medical education
KW  - medical students
KW  - tailored interventions
KW  - UTAUT
KW  - intention to use
KW  - e–mental health apps
KW  - app
KW  - foster
KW  - cross-sectional study
KW  - mental health problems
KW  - physician
KW  - well-being
KW  - mobile apps
KW  - acceptance
KW  - assessment
KW  - mental health apps
AB  - Background: 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. Objective: 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. Methods: 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. Results: 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). Conclusions: 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. 
SN  - 2369-3762
UR  - https://mededu.jmir.org/2025/1/e58183
UR  - https://doi.org/10.2196/58183
DO  - 10.2196/58183
ID  - info:doi/10.2196/58183
ER  -