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Huckvale K, Hoon L, Stech E, Newby JM, Zheng WY, Han J, Vasa R, Gupta S, Barnett S, Senadeera M, Cameron S, Kurniawan S, Agarwal A, Kupper JF, Asbury J, Willie D, Grant A, Cutler H, Parkinson B, Ahumada-Canale A, Beames JR, Logothetis R, Bautista M, Rosenberg J, Shvetcov A, Quinn T, Mackinnon A, Rana S, Tran T, Rosenbaum S, Mouzakis K, Werner-Seidler A, Whitton A, Venkatesh S, Christensen H. 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. BMJ Open 2023; 13:e066249. [PMID: 37116996 PMCID: PMC10151864 DOI: 10.1136/bmjopen-2022-066249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Abstract
INTRODUCTION Meta-analytical 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 multiarm 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 comparing the effectiveness of one of three brief, 2-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 (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified 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 was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). TRIAL REGISTRATION NUMBER ACTRN12621001223820.
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Affiliation(s)
- Kit Huckvale
- Centre for Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Leonard Hoon
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Eileen Stech
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jill M Newby
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
- School of Psychology, UNSW Sydney, Sydney, New South Wales, Australia
| | - Wu Yi Zheng
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jin Han
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Sunil Gupta
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Scott Barnett
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Manisha Senadeera
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Stuart Cameron
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Stefanus Kurniawan
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Akash Agarwal
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Joost Funke Kupper
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Joshua Asbury
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - David Willie
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Alasdair Grant
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Henry Cutler
- Centre for the Health Economy, Macquarie University, Sydney, New South Wales, Australia
| | - Bonny Parkinson
- Centre for the Health Economy, Macquarie University, Sydney, New South Wales, Australia
| | | | - Joanne R Beames
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Rena Logothetis
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Marya Bautista
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jodie Rosenberg
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Artur Shvetcov
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Thomas Quinn
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Andrew Mackinnon
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Santu Rana
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Truyen Tran
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Simon Rosenbaum
- School of Psychiatry, UNSW Sydney, Sydney, New South Wales, Australia
| | - Kon Mouzakis
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | | | - Alexis Whitton
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Svetha Venkatesh
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Helen Christensen
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
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