Cognitive Measures and the Acceptable Noise Level

Neural Mechanisms of the Acceptable Noise Level

PURPOSE

The present brain-behavior study examined whether sensory registration or neural inhibition processes explained variability in the behavioral most comfortable level (MCL) and background noise level (BNL) components of the acceptable noise level (ANL) measure.

METHOD

A traditional auditory gating paradigm was used to evoke neural responses to pairs of pure-tone stimuli in 32 adult listeners with normal hearing. Relationships between behavioral ANL, MCL, and BNL components and cortical responses to each of the paired stimuli were analyzed using linear mixed-effects regression analyses.

RESULTS

Neural responses elicited by Stimulus 2 in the gating paradigm significantly predicted the computed ANL response. The MCL component was significantly associated with responses elicited by Stimulus 1 of the pair. The BNL component of the ANL was significantly associated with neural responses to both Stimulus 1 and Stimulus 2.

CONCLUSIONS

The results suggest neural processes related to neural inhibition support the ANL and BNL component while neural stimulus registration properties are associated with the MCL a listener chooses. These findings suggest that differential neural mechanisms underlie the separate MCL and BNL components of the ANL response.

[Correlation between Mandarin acceptable noise level and cortical auditory evoked potential in young normal-hearing listeners]

Objective:To investigate the correlation between Mandarin acceptable noise level （M-ANL） and cortical auditory evoked potential （CAEP）, and to explore the possible mechanism leading to individual differences in M-ANL values. Methods:Thirty listeners aged 22-33 years with normal hearing were selected as the study subjects, and the M-ANL test and CAEP test were performed respectively. The most comfortable level （MCL）, maximum background noise level （BNL）, M-ANL and CAEP values of each subject were recorded. The latency of each wave of P1, N1, P2, N2, P300 and the amplitude of P1-N1, P2-N2, P300 in CAEP were recorded for each subject. SPSS 25.0 was used for statistical analysis to explore the correlation between the MCL value, BNL value and M-ANL values and the latency of P1, N1, P2, N2, P300 and P1-N1, P2-N2, P300 amplitudes of CAEP. Results:①The MCL value and M-ANL value were positively correlated with the P2 latency of CAEP, and the correlation coefficients were 0.404 and 0.400, respectively, and the differences were statistically significant （P<0.05）. There was no correlation with P1, N1, N2, and P300 latencies of CAEP （P>0.05）. ②The MCL value, BNL value and M-ANL value had no significant difference with the CAEP wave amplitudes of P1-N1, P2-N2, and P300 （P>0.05）. Conclusion:There was a certain correlation between M-ANL and CAEP in young adults with normal hearing, suggesting that the central auditory cortex might play a potential regulatory role in the background noise tolerance. Individuals with a greater background noise acceptance might have stronger central efferent mechanisms and/or less active central afferent mechanisms.

A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

Background

Hospital noise can adversely impact nurses’ health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and annoyance, on the quality of patient care.

Methods

This descriptive and cross-sectional study was carried out among nurses in an Iranian hospital. To determine nurses’ noise exposure level, the noise was measured in 1510 locations across the hospital in accordance with ISO 9612 standards using KIMO DB 300/2 sound level meter and analyzer. An online survey was used to collect nurses’ individual data. Study questionnaires included demographics, Weinstein noise sensitivity scale, noise annoyance scale, and quality of patient care scale. Finally, to analyze the data, Bayesian Networks (BNs), as probabilistic and graphical models, were used.

Results

For the high noise exposure state, high noise sensitivity, and high annoyance, with the probability of 100%, the probability of delivering a desirable quality of patient care decreased by 21, 14, and 23%, respectively. Moreover, at the concurrently high noise exposure and high noise sensitivity with the probability of 100%, the desirable quality of patient care decreased by 26%. The Bayesian most influence value was related to the association of noise exposure and annoyance (0.636). Moreover, annoyance had the highest association with the physical aspect of quality of care (0.400) and sensitivity had the greatest association with the communication aspect (0.283).

Conclusion

Annoyance induced from environmental noise and personal sensitivity affected the quality of patient care adversely. Moreover, noise and sensitivity had a separate direct adverse effect upon the quality of patient care, and their co-occurrence reduced the potential for delivering quality patient care.