Acceptability and Tolerability of Extended Reality Relaxation Training with and without Wearable Neurofeedback in Pediatric Migraine.
Mark A ConnellyMadeline BoorigieKlanci McCabePublished in: Children (Basel, Switzerland) (2023)
Objective. To determine the acceptability of using extended reality (XR) relaxation training as a preventive treatment for pediatric migraine. Methods. Youths aged 10-17 years old with migraine were recruited from a specialty headache clinic and completed baseline measures evaluating their vestibular symptoms and attitudes about technology. The patients were then instructed in three XR-based relaxation training conditions (fully immersive virtual reality with and without neurofeedback, and augmented reality with neurofeedback), in counterbalanced order, and completed acceptability and side effect questionnaires after each. The patients also took XR equipment home for one week to use for relaxation practice and again completed the measures about their experience. The acceptability and side effect data were compared against predetermined acceptable thresholds and were evaluated for their association with the participant characteristics. Results. The aggregate acceptability questionnaire scores exceeded our minimum threshold of 3.5/5, with the two fully immersive virtual reality conditions preferred over augmented reality for relaxation training ( z = -3.02, p = 0.003, and z = -2.31, p = 0.02). The endorsed side effects were rated by all but one participant as mild, with vertigo being the most common. The acceptability ratings were not reliably associated with age, sex, typical hours per day of technology use, or technology attitudes, but were inversely related to the side effect scores. Conclusions . The preliminary data on acceptability and tolerability of immersive XR technology for relaxation training among youths with migraine supports further intervention development work.
Keyphrases
- virtual reality
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- single molecule
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- healthcare
- newly diagnosed
- primary care
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- mental health
- open label
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- patient reported
- artificial intelligence
- combination therapy
- deep learning
- double blind
- quality improvement