Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: The Vibe Up Study
Authors: Huckvale Kit , Hoon Leonard , Stech Eileen , Newby Jill , Zheng Wu-Yi , Han Jin , Vasa Rajesh , Gupta Sunil , Barnett Scott , Senadeera Manisha , Cameron Stuart , Kurniawan Stefanus , Agarwal Akash , Kupper Joost Funke , Asbury Joshua , Willie David , Grant Alasdair , Cutler Henry , Parkinson Bonny , Ahumada-Canale Antonio , Beames Joanne R , Logothetis Rena , Bautista Marya , Rosenberg Jodie , Shvetcov Artur , Quinn Thomas , Mackinnon Andrew , Rana Santu , Tran Truyen , Rosenbaum Simon , Mouzakis Kon , Werner-Seidler Aliza , Whitton Alexis , Venkatesh Svetha , Christensen Helen 
Published: December 2022

Introduction

Meta-analytic evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI) driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multi-arm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity.

Methods and analysis

The Vibe Up study is a pragmatically-oriented, decentralised AI-adaptive group sequential randomised controlled trial (RCT) comparing the effectiveness of one of three brief, two week digital self-guided interventions (mindfulness, physical activity, or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (DASS-21 total score) from pre-intervention to post-intervention. Secondary outcomes include change in depression, anxiety, and stress (measured by DASS-21 subscales) from pre-intervention to post-intervention. Planned contrasts will compare the four groups (i.e., the three intervention and control) using self-reported psychological distress at pre-specified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions.

Ethics and dissemination

Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice [1] was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP).

Registration details

The trial is registered with the Australian New Zealand Clinical Trials Registry (AC-TRN12621001223820).

STRENGTHS AND LIMITATIONS OF THIS STUDY

Strengths

The study addresses an important clinical question using novel, advanced methods

The trial uses short-duration interventions designed to improve coping responses to transient stressors, which addresses the most common needs of university students

A value of information analysis is included to compare the value of the new trial methods with traditionalapproaches

Digital phenotyping is used to explore smartphone sensor information with clinical outcomes

Weaknesses

More than 12 mini-trials might be required to determine the ranking for the interventions

The interventions may prove to be of the same level of effectiveness for each level of severity

Interventions other than those examined in this study, such as CBT, may be more effective and remain untested

The methodology assumes that the three digital interventions are configured to deliver similar doses and/or have approximate fidelity with standard methods