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Articles

Acoustic comfort depends on the psychological state of the individual

ORCID Icon, , , , &
Pages 1485-1501 | Received 16 Mar 2020, Accepted 24 Jul 2020, Published online: 21 Aug 2020
 

Abstract

Recent studies have shown that comfort can be influenced more by psychological processes than from the characteristics of environmental stimulation. This is relevant for different industrial sectors, where comfort is defined only as a function of the intensity of external stimuli. In the present study, we measured physiological and psychological comfort during the exposure to four levels of acoustic noise [from 45 to 55 dB(A)] corresponding to different comfort classes inside a full-scale mock-up of a cruise ship cabin. We found an increase of psychological and physiological discomfort for higher noise intensities, but not for all the intensities defining the comfort classes. Furthermore, we found that negative psychological states determine a lower physiological sensitivity to acoustic noise variations compared to positive states. Our results show that, at normal/low intensities, psychological processes have a greater role in determining acoustic comfort when compared to the stimulus intensity.

Practitioner Summary: This study shows that psychological factors can be more relevant in determining acoustic comfort inside a ship cabin than the intensity of acoustic stimulus itself. This finding suggests that the cruise industry should consider not only the engineering measurements when evaluating comfort on board, but also the passenger’ psychological state.

Abbreviations: AIC: akaike information criterion; CCT: colour correlated temperature; cd/m2: candela/square meters; df: degrees of freedom; F-test: Fisher's test; HF: high frequency; HR: heart rate; HRV: heart rate variability; HSV: hue saturation value; K: kelvin; LF: low frequency; LF/HF: low frequency to high frequency ratio; lme: linear mixed effects; ms: milliseconds; nu: normalized unit; p: p value; pNN50: percentage of adjacent pairs of normal to normal RR intervals differing by more than 50 milliseconds; r2: coefficient of determination; rc: concordance correlation coefficient; RMSSD: square root of the mean normal to normal RR interval; SD: standard deviation; SDNN: standard deviation of normal to normal RR intervals; SEM: standard error of the mean; t-test: student's tests; χ2: chi-square test

Acknowledgments

The authors would like to acknowledge Daniel Celotti, Nicola Bassan, and Paolo Guglia from Fincantieri S.p.A. and Erina Ferro from ISTI (Istituto di Scienza e Tecnologie dell’Informazione)-National Research Council (CNR) for the support provided for the project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is part of Project AGORA’ (E-Cabin), a Research and Innovation project in the naval sector coordinated by Fincantieri S.p.A. with the participation of the National Research Council (CNR) and the University of Trieste; the project received grants from the Italian Ministry of Infrastructures and Transport (MIT).

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