Development of a new night-time noise index: Integration of neurophysiological theory and epidemiological findings

Abstract

Background: The effects of environmental noise on sleep are of great interest to public health. Numerous studies have been conducted to investigate these effects; however, these previous studies applied existing sound-level statistics that were not based on neurophysiology.

Aims: This study aimed to develop a new night-time noise index based on neurophysiology and epidemiology.

Methods: First, we derived a formula for predicting the noise effects on sleep based on a neurophysiological model of brainstem sleep regulation, where awakening was associated with greater electrical potentials in the brainstem. Second, we investigated the noise effects on sleep using the results of an epidemiological study conducted in the vicinity of the Kadena military airfield in Okinawa, Japan. Thirty volunteers participated in the study. Vibrations of whole-body movements were recorded using sheet-shaped sleep monitors for 26 consecutive nights. The onset of motility, which was defined by monitor vibrations, was used to index awakening reactions.

Results: Our statistical model could properly predict the fluctuating risk of motility onset. The new index, which is the mean of the sound level above 60 dB, can be successfully used, irrespective of the duration of noise exposure. Additionally, it out-performed existing event-related noise indices.

Conclusions: We derived a new night-time noise index for evaluating the noise effects on sleep. To our knowledge, this is the first study to explain the noise effects on sleep with the consideration of neurophysiology and epidemiology.

Keywords: Noise-induced sleep disturbance; epidemiology; motility; neurophysiology; sleep science.

Figure 1

An example of the vibration measured by the SSSM. The lower figure shows the first 10 seconds of the upper figure. There were periodic bodily vibrations due to respiration (around 4-seconds cycle), tiny vibrations due to heartbeat (around 0.8-second cycle) and strong vibrations due to motility (around at 21 seconds in the upper figure)

Figure 2

An example of the estimated respiratory rate (solid line) and the REM sleep (thick lines) vs. the elapsed sleep time

Figure 3

A scatter plot of the maximum sound level and duration above 60 dB of the observed noise events

Figure 4

Probability of onset of motility in each time period in a time window of 90 seconds after the beginning of the noise events. Error bars show 95% CIs with the assumption that all data were independent

Figure 5

Relationship between the time constant of the first-order lag system in the mathematical model (log scale) and the log likelihood of the survival analysis

Figure 6

Relationship between sound level and hazard ratio obtained from the survival analysis. A solid line and shading indicate the hazard ratio and its 95% CI with a threshold of 60 dB. Black circles and error bars show the results obtained by categorising the sound levels (<60 dB, 60–70 dB, 70–80 dB and >80 dB)

Figure 7

Correlation between the predicted and observed probabilities of the onset of motility. The 1-second probability is shown in the left figure, in which the observed probability and its CI were obtained by categorising the predicted probability (<0.2%, 0.2–0.4%, 0.4–0.6%, 0.6–0.8%, >0.8%). The cumulative probability is shown in the right figure, in which the observed probability and its CI were obtained by categorising the predicted probability (<10%, 10–20%, 20–30%, 30–40%, >40%). Equal-probability lines are also shown

Figure 8

An example of the fluctuations of the sound level (upper figure), the 1-second probability of the onset of motility (middle figure), and the cumulative probability (lower figure). Each of the probabilities can be divided into the spontaneous and noise-induced (shading) probabilities. The observation of the onset of motility is also shown (step line)