Cognitive Measures and the Acceptable Noise Level

Hedrick, Mark; Stigers, Abbey; Grayless, Brittany; Plyler, Patrick N.; Bolden, Jenn; Springer, Cary

doi:10.1044/2021_aja-20-00162

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Moreover, high noise level has a significant and negative effect on quality of patient care, nurses’ mental workload and anxiety levels [ 11 , 12 ]. Cognitive functions such as memory, attention, concentration and problem solving are also influenced by exposure to noises with various characteristics such as frequency, content, and intensity [ 13 , 14 ]. These adverse health effects induced by chronic exposure to noise levels above recommended guidelines have a synergistic effect, disturbing nurses’ performance and quality of patient care.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Moreover, it is documented that high noise level has signi cant effect on quality of patient's care, mental workload and anxiety of nurses [9,10]. Cognitive functions such as memory, attention, concentration and problem solving are also in uenced by exposure to noises with various frequency and content [11,12]. These adverse health effects induced by exposure to the noise levels higher than standard interact with each other to disturb nurses' performance and quality of patient's care.…”

mentioning

confidence: 99%

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

Abbasi

Yazdanirad

Zokaei

et al. 2022

Preprint

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Background Hospital noise can threaten nurses' health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. The aim of this study was to predict the role hospital noise, annoyance, and sensitivity in 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 the 1510 points in the hospital in accordance with ISO 9612 using KIMO DB 300/2 sound level meter and analyzer. Then, an online survey was conducted to collect individual data. The applied questionnaires were a demographic questionnaire, Weinstein noise sensitivity scale, noise annoyance scale, and quality patient care scale. Finally, to analyze data, Bayesian Networks (BNs), as probabilistic and graphical models were used. Results For the high noise exposure, high noise sensitivity, and high annoyance with the probability of 100%, the probability of desirable quality of patient care decreased by 21, 14, and 23 percent, 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 most effective mean values belonged to the association of noise exposure and annoyance (0.636). Moreover, the annoyance had highest association with the physical aspect (0.400) and the sensitivity had greatest association with the communication aspect (0.283). Conclusion Annoyance induced from noise and 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 coincidence occurrence reduced the quality of patient care potentially.

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Neural Mechanisms of the Acceptable Noise Level

Miller

Anderson²,

Montou

et al. 2023

J Speech Lang Hear Res

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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.

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Cognitive Measures and the Acceptable Noise Level

Cited by 3 publications

References 40 publications

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

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

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

Neural Mechanisms of the Acceptable Noise Level

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