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

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.