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Background: Sleep disturbance is a common complaint reported by critically ill patients, which may in turn prolong the length of intensive care unit (ICU) stay, and increase the risks of delirium and mortality. Environmental factors, such as noise and light exposures contribute to the development of sleep disturbances in ICU. Hypnotics is the most prescribed treatment for managing ICU sleep; however, it only improves light sleep but not deep sleep, and could not deal with sleep disturbances caused by noise or light exposure.
Purposes: To examine the effects of guided virtual reality autogenic meditation on sleep quality and quantity in critically ill adults as well as the possible mechanism through which they provide this alleviation. We hypothesize that critically ill adults undergoing guided virtual reality autogenic meditation (VR) will experience greater alleviations in sleep disturbances in comparison with participants in the eye masks and usual care control group (UC).
Methods: The three-year, single-blinded randomized controlled trial will employ a three-arm parallel-group design. A total of 120 critical ill adults will be randomly allocated to the VR, Eye masks, or UC groups in a 1:1:1 ratio (40 participants in each group). For the VR group, all participants will experience 30-min, voice-guided autogenic meditation through head-mounted display device at 10 pm for 2 nights (ICU day 2 to day 4). For the Eye masks group, participants will wear eye mask from 10 pm to 7 am for 3 days. For the UC group, they will receive sleep promotion strategies, including reduced light exposure at night, decreased noise, and cluster nursing care during the study period. Primary outcomes are sleep parameters measured by the Chinese version of Richards-Campbell Sleep Questionnaire, Chinese version of Pittsburgh sleep quality index, and fitbit with one-lead electroencephalography sensor. Secondary outcomes consist of delirium, moods, and quality of life assessed using the Confusion Assessment Method for the Intensive Care Unit, visual analogue scale for anxiety, pain, stress, EuroQoL-5D, and cognitive function respectively. Measurement time points are the first day of ICU admission, pre-and post-treatment, and the day of 30 and 180 days after ICU discharge. A generalized estimating equation will be used to test research hypotheses.
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Sleep disturbance has affected up to 59% of patients who are admitted into intensive care units (ICUs). Such disturbance has been associated with prolonged ICU length of stay, the occurrence of delirium, increased medical costs. The risk factors related to poor sleep in ICU patients include bright light, artificial/augmented sound, postoperative pain, prior illness status, higher intensity of care, and equipment applications.
To date, pharmacological and nonpharmacological therapies have been widely used to relieve poor sleep in ICU. Sedative-hypnotic is the most prescribed treatment for critically ill patients with sleep problems. However, related adverse events reduce their usefulness.
According to the 2018 PADIS guideline of the Society of Critical Care Medicine, sleep disturbance has been listed as one of the critical symptoms that must be recognized in a critically clinical setting. Recently, technology-assisted interventions have been increasingly integrated into critical care. Virtual reality (VR) mediation is a promising technology for free from time and space that provides various contents or games to users. To achieve the goal of precision health, more researchers and health care providers have incorporated high technology into medical and nursing care systems. Thus far, only one randomized controlled trial involving 52 ICU patients investigated the effect of VR meditation (natural scenes and sounds) on poor sleep quality and found that the program significantly decreased wake after sleep onset and increased deep sleep time. Therefore, we aim to develop a new VR-assisted program and to examine its effects on sleep quality and quantity of adults requiring intensive care.
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15 participants in 3 patient groups
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Hsiao-Yean Chiu, PhD
Data sourced from clinicaltrials.gov
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