Prevention of mental health issues in the young: A randomised controlled evaluation of an e-mental health application for young adults to enhance mental health literacy

预防青少年心理健康问题:一项针对年轻人的电子心理健康应用程序的随机对照评估,旨在提高他们的心理健康素养

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Abstract

BACKGROUND: The mental health of young adults is deteriorating. Reasons for this are manifold, ranging from biological factors (e.g. entering a vulnerable developmental phase) to crisis-related external events (e.g. COVID-19 pandemic). Accordingly, easily accessible and universal prevention for the young is needed. Mobile Health (mHealth) interventions are on the rise and especially promising for this age group, due to numerous benefits, such as low threshold, temporal and local flexibility and high scalability. However, the effectiveness and acceptance of mHealth interventions as prevention measures are missing empirical evidence. METHOD: In a two-arm randomised controlled trial design, this study aimed to evaluate the effectiveness of a mental health app, the 'Mental Health Guide', primarily on mental health literacy as well as secondary mental health outcomes. N = 322 Participants (81.99 % female, M = 25.55 years, SD = 9.63 years, age range: 15 to 59 years) were either assigned to the intervention group (n = 158), using the Mental Health Guide for 12 weeks, or the wait-list control group (n = 164). RESULTS: The results show a significant intervention effect on mental health literacy for the intervention group in the post assessment (p = .047, d = 0.20), but no at later follow-up time points. Further variables related to mental health indicate various effects, such as improved problematic (p = .018, d = 0.20) and prosocial behaviour (p = .008, d = 0.23) in the intervention group and improved emotion regulation capacities for both groups (p < .001, d = 0.20). Overall, there was a high drop-out rate in the study (up to 80 %), especially in the intervention group. CONCLUSION: This study contributes valuable insights into the potential effectiveness of mHealth prevention in young adults and gives insights on how such applications are used under very naturalistic settings, laying a foundation for future research in this field. However, generalisability is limited due to selective sample characteristics and a rather high drop-out rate over time.

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