Impact of a white noise app on sleep quality among critically ill patients

Abstract Background Sleep disturbance negatively affects recovery and survival of patients in intensive care units (ICUs). Aims and Objectives This study aimed to measure the noise levels and evaluate the impact of a white noise app on the sleep quality of critically ill patients. Design A quasi‐experimental time series pre‐test–post‐test control group design with repeated measures was adopted. Methods The study was conducted in the high dependency unit (HDU) of a selected tertiary care hospital in Mangalore, Karnataka State, India. Conscious oriented patients with systolic blood pressure ranging from 100/70 to 140/90 mm Hg and hearing acuity of at most 20 dB in both ears were included in the study. Noise levels in the HDU were measured using calibrated sound level meter on weekdays/weekends in three different shifts and an average of 24 readings was obtained per shift. A 4‐point Likert scale was used to assess the sleep quality. The intervention included administration of white noise app twice a day, for three consecutive days by using different masking sounds such as white noise on day 1 which resembles to a humming AC conditioner, pink noise on day 2 which resembles to the sound of ocean waves and brown noise on day 3 which resembles a steady rainfall. The app was used with the help of JBL earphones C10SI an excellent noise cancellation and noise isolating earphone. Results Among the 54 subjects, the mean age of the patients was 40.28 years, majority 34 (63%) were males. The noise level in the ICU was more than 60 dB. There was a significant difference in the sleep quality after the application of the white noise app in the experimental group on Day 1 (Z = −3.996; P = .001), Day 2 (Z = −3.302; P = .001), and Day 3 (−2.822; P = .005) compared to the control group. Conclusion Adoption of technology driven noise reduction applications would enhance the quality of sleep among the ICU patients. Relevance to clinical practice The use of a technology‐driven application helps control noise levels which promotes improved sleep quality among critically ill patients in the intensive care units.