Accepted for/Published in: JMIR Research Protocols
Date Submitted: Feb 18, 2023
Date Accepted: Aug 22, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Micro Randomized Trial
ABSTRACT
Background:
Many university students experience mental health (MH) problems such as anxiety and depression. A transdiagnostic mobile intervention teaching emotion regulation (ER) techniques and incorporating different therapeutic approaches has been developed to support students' MH.
Objective:
This study aims to investigate the usability and proximal effects of the intervention and its components to optimize the uptake and effectiveness of the tool.
Methods:
A sequential explanatory mixed-method design combining a Micro Randomized Trial (MRT) and semi-structured interviews will be utilized. During the MRT, students (N= 200) will be prompted via the mobile app twice a day for 3 weeks to evaluate their emotional states and complete a randomly assigned intervention (i.e. exercises teaching ER skills) or a control intervention (i.e., health snippet). A subsample (n=21) of participants will participate in interviews exploring their user experience with the app and the completed interventions. Primary outcomes will be changes in emotional states and user experience. Secondary outcomes will include distal outcomes of the intervention (i.e., ER skills, distress symptoms). Contextual moderators of intervention effectiveness will also be explored (i.e., time of the day, and momentary emotional states).
Results:
n/a
Conclusions:
This study will inform how the intervention functions and what needs to be improved and will inform the design of a randomized control trial to test its full effectiveness. The results and data gathered will also be used to design and train a recommendation algorithm that will be integrated into the app linking students to relevant content. Clinical Trial: NCT05576883, https://clinicaltrials.gov/ct2/show/NCT05576883
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.