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Using Artificial Intelligence-informed Experience-Based Co-Design (AI-EBCD) to create a virtual reality-based mindfulness application to reduce diabetes distress: protocol for a mixed-methods feasibility study

Angeliki Bogosian, Glenn Robert, Siobhan O'Connor, Jackie Sturt, Emma Stanmore, David Woodcock, Nicola Milne, Womba Mubita, Shraboni Ghosal, Mengying Zhang

BMJ Open December 15, 2024 DOI: 10.1136/bmjopen-2024-088576 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Mixed-methods pilot study protocol Peer reviewed
Population Adults with type 2 diabetes
Topics Meditation
Key points Proposes a co-design process to develop a bespoke VR mindfulness app for adults with type 2 diabetes, with planned evaluation to determine if it can reduce diabetes distress and improve quality of life.

Abstract

Introduction People with type 2 diabetes can experience diabetes distress which can negatively affect health outcomes. Non-pharmacological interventions such as mindfulness can help address diabetes distress. However, face-to-face programmes can be constrained by cost, poor accessibility and lack of availability. Mobile apps for mindfulness may overcome these issues but evidence of their effectiveness is limited, and some have poor interface design with basic visualisations and feedback.Methods and analysis Our study will explore using virtual reality (VR) as an immersive and interactive technology that could support mindfulness practice to help reduce diabetes distress. We will use a mixed-methods design to pilot a new co-design process called Artificial Intelligence-informed Experience-Based Co-Design. Phase 1 will identify and evaluate existing VR mindfulness apps, followed by interviews with mindfulness experts to gain their perspectives on practising mindfulness in virtual settings. This will be followed by a participatory design phase with a series of five co-design workshops where adults with type 2 diabetes will (1) discuss diabetes distress and learn about mindfulness, (2) evaluate commercially available VR mindfulness apps, (3) employ artistic methods to produce a personalised mindfulness experience, (4) create digital content for a virtual mindfulness experience via generative artificial intelligence tools and (5) prioritise key design features, functionality and content for a tailored VR mindfulness app. The final phase will focus on developing a bespoke VR mindfulness app and evaluating it with adults with type 2 diabetes using interviews, questionnaires and VR app analytics to determine if the new digital mental health intervention can help reduce diabetes distress and improve quality of life.Ethics and dissemination We received ethical approval from The University of Manchester (2024-18262-32710 and 2024-21170-37093). Written informed consent will be obtained from all participants. Dissemination will include scientific publications and presentations, social media, knowledge translation events and educational resources for teaching students.

Comparable studies

Other non-randomized and open-label trials on meditation, most cited first.

Study Year Design Participants
Mindfulness-Based Stress Reduction in Relation to Quality of Life, Mood, Symptoms of Stress, and Immune Parameters in Breast and Prostate Cancer Outpatients Early-stage breast and prostate cancer patients 2003 Pre-post intervention study n = 59
Exploring self‐compassion and empathy in the context of mindfulness‐based stress reduction (MBSR) Community sample 2010 Pre-post intervention study
Mindfulness-Based Stress Reduction Lowers Psychological Distress In Medical Students Second-year medical students 2003 Prospective nonrandomized cohort-controlled study n = 302
Intensive meditation training, immune cell telomerase activity, and psychological mediators. Retreat participants and matched controls 2011 Controlled trial with wait-list control group n = 60
The Effectiveness of Mindfulness Training for Children with ADHD and Mindful Parenting for their Parents Children aged 8-12 with ADHD and their parents 2011 Within-group waitlist controlled trial n = 22

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