Abstract
Objective:
This study aimed to analyse the correlation between the degree of noise sensitivity and medical experience/psychological burden in adult patients attending fever clinics.
Methods:
A retrospective analysis was performed on 200 adult patient records extracted from the electronic medical record system and patient experience survey database of the Xiaoshan Hospital’s fever clinic between January 2023 and December 2024. The dataset encompassed general information and completed assessments using the Schutte Noise Sensitivity Questionnaire (NoiSeQ), the Patient Experience-Based Evaluation Scale for Medical Service Quality (PEES-50) and the Depression, Anxiety and Stress Scale (DASS-21). Statistical analyses included Pearson correlation, multiple linear regression and Bootstrap mediation effect analysis to examine the collected data.
Results:
Noise sensitivity assessment in the 200 fever clinic patients revealed scores of 66–144 points, with a mean value of 107.68 ± 16.78 points. Initial univariate analysis demonstrated statistically significant variations in total NoiSeQ scores according to waiting duration and the period of consultation (all P < 0.05). Subsequent multiple linear regression modelling confirmed that extended waiting times and nighttime visits remained significantly associated with elevated noise sensitivity (all P < 0.05). Correlation analysis revealed consistent inverse relationships between NoiSeQ subscale and total scores and all PEES-50 dimensions (r < 0, P < 0.05). Conversely, direct positive associations were observed with depression, anxiety, stress components and overall DASS-21 score (r > 0, P < 0.05). Further examination identified psychological burden as a serving as a partial mediator between noise sensitivity and medical experience, explaining 52.03% of the total variance (P < 0.001).
Conclusion:
This study demonstrates that noise sensitivity in fever clinic patients is associated with waiting time and time of visit, suggesting that acoustic environment modifications and psychological support measures represent potential strategies for enhancing the quality of medical services.
Keywords: medical experience, noise, psychological burden
KEY MESSAGES
-
(1)
The noise sensitivity of patients in hot clinics is related to waiting time and time of visit.
-
(2)
Reducing noise interference in the clinic may help improve the quality of medical services.
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(2)
Psychological intervention for patients in fever clinics may help improve the quality of medical services.
Introduction
Patients visiting fever clinics frequently experience physiological discomforts, such as fever and fatigue, rendering them in a vulnerable physiological and psychological state characterised by reduced tolerance to environmental noise. [1] Noise can activate the human stress response, promote cortisol fluctuations and lead to elevated blood pressure and increased heart rate. [2] Psychologically, the febrile state may potentiate the impact of noise on patients. Heightened noise sensitivity can impair the brain’s ability to filter auditory signals, making it difficult for patients in fever clinics to ignore irrelevant noise disturbances. It can subsequently trigger or exacerbate anxiety, increase psychological burden and intensify negative patient experiences. [3] The fever clinic represents a distinct zone within healthcare facilities with a relatively high-stress environment, often resulting in a suboptimal patient experience. However, noise in most fever clinics has not received sufficient attention, and systematic management strategies are often lacking. Consequently, patients are exposed to a noisy environment characterised by persistent medical equipment alarms, ventilation system sounds, rapid communication amongst healthcare staff and conversations amongst gathered patients without effective control over the duration and intensity of such noise. This exposure contributes to increased psychological distress and ultimately diminishes overall satisfaction with the medical experience. [4] Previous studies primarily focused on noise issues in general hospital settings without specifically targeting the unique context of fever clinics. Furthermore, a gap exists in understanding the relationship amongst noise sensitivity, psychological burden and patient experience in this specific environment. [5] On this rationale, the present study analysed these relationships to provide a foundation for optimising noise management protocols in fever clinics and thereby enhance patient experience.
MATERIALS AND METHODS
Study Design
This investigation was approved by the Ethics Committee of Xiaoshan Hospital affiliated to Hangzhou Normal University (Approval No: 202500142). A retrospective analysis was conducted on 212 adult patient records from the fever clinic’s electronic medical record system and patient experience survey database between January 2023 and December 2024. The database contained standardised questionnaires completed by patients during waiting or observation periods.
The inclusion criteria comprised the following: (1) aged 18–65 years presenting to the fever clinic; (2) acute febrile illness with tympanic temperature ≥37.7 °C; (3) first-time visit to the fever clinic of our institution and (4) as a retrospective analysis, all included patients had previously signed an ‘Informed Consent for Medical Information Use’ during their clinical visit, agreeing to the use of their medical records and questionnaire data for medical research.
The exclusion criteria were as follows: (1) missing or incomplete scale data affecting the assessment of core metrics (noise sensitivity, medical experience and psychological burden); (2) documented medical history or self-reported diagnosis of psychiatric disorders; (3) hearing impairment or use of hearing aids; (4) pregnancy or lactation; (5) history of major trauma or surgery within 1 month prior to the visit; (6) long-term use of psychotropic medications (e.g., sedatives or anxiolytics) and (7) exposure to high-intensity noise environments (workplace noise >85 dB or daily continuous exposure exceeding 8 hours) within 6 months preceding the visit. Noise exposure was assessed through patient self-report and an occupational history questionnaire, with cross-verification based on occupational categories involving frequent noise exposure (e.g., industrial manufacturing, construction).
Five cases were excluded because of age ineligibility, and seven were excluded because of missing or incomplete scale data, resulting in a final cohort of 200 patients.
General Information
Sociodemographic characteristics are the gender (male/female), age (<45 years/≥45 years), educational attainment (high school or below/college or above), residential location (urban/rural), employment status (employed/unemployed) and marital status (unmarried/married). Clinical characteristics are the presence of underlying comorbidities (yes/no), waiting time for medical consultation (≤ 60 minutes/> 60 minutes), body temperature at presentation (≤ 38.5 °C/> 38.5 °C) and aetiology of fever (respiratory tract infection/nonrespiratory tract infection). Environmental contextual factors are the time of visit (daytime 8:00–18:00/nighttime 18:00–8:00) and presence of an accompanying person (yes/no).
Noise Sensitivity
Noise sensitivity was assessed using the Schutte Noise Sensitivity Questionnaire (NoiSeQ), which was employed to evaluate individuals’ subjective sensitivity and emotional responses to noise in daily environments. Originally developed by Schütte et al. [6] in 2007 for collecting noise sensitivity data, the instrument comprises 35 items measuring general sensitivity and psychological disturbance across five common daily situations: leisure time, work, home, social and sleep environments. Each item is rated on a 5-point scale from 1 (strongly disagree) to 5 (strongly agree), yielding a total score range of 35–175 points. The questionnaire demonstrated good internal consistency with a Cronbach’s α of 0.84 whilst exhibiting high and statistically significant correlations between subscales and with the total score. Although designed for general environments, the instrument’s ‘general sensitivity’ dimension and multicontext assessment framework demonstrate cross-environment applicability, having been validated across multiple cultural contexts with good cross-cultural reliability. [7,8]
Medical Experience
Patient medical experience was evaluated using the Patient Experience-Based Evaluation Scale for Medical Service Quality (PEES-50), developed by Tian et al. [9] This instrument is designed for outpatient populations and encompasses six dimensions: tangibility, reliability, assurance, responsiveness, empathy and cost-effectiveness, comprising a total of 29 items. All items employ a 5-point Likert scoring system (1 = strongly disagree, 5 = strongly agree). Dimensional scores are computed through the summation of its individual items, and the composite scale score represents the aggregate of all dimensional scores, yielding a potential range of 29–145 points. Elevated scores correspond to a positive healthcare encounter. The measurement tool displayed exceptional reliability in the current investigation, achieving a Cronbach’s α coefficient of 0.975.
Psychological Burden
Psychological burden was assessed using the 21-item Depression, Anxiety and Stress Scale (DASS-21), originally developed by Lovibond and Lovibond, [10] subsequently refined by Antony et al. [11] and cross-culturally adapted into Chinese by Wang et al. [12] The scale consists of 21 items distributed across three subscales: depression (e.g., ‘I felt down-hearted and blue’), anxiety (e.g., ‘I felt nervous’) and stress (e.g., ‘I found it hard to relax’). Responses are recorded on a 4-point Likert scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time), yielding a total score range of 0–63. High scores on each subscale indicate a great severity of the corresponding psychological symptom. The Cronbach’s α coefficients of the subscales were 0.89 for depression, 0.84 for anxiety and 0.88 for stress, and that of the total scale was 0.92, indicating good internal consistency.
Statistical Analysis
Statistical analysis was performed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA). Categorical data were presented as frequencies with percentages, n (%), and analysed using the Chi-square test. The normality of continuous data distribution was assessed using the Shapiro–Wilk test (P > 0.05 indicating normal distribution), and homogeneity of variances was verified by Levene’s test (P > 0.05 indicating equal variances). Normally distributed continuous data were expressed as mean ± standard deviation and compared using a t-test. Correlations were examined via Pearson correlation analysis with scatter plot visualisation; the strength of correlation was interpreted as weak for |r| < 0.3, moderate for 0.3 ≤ |r| < 0.5 and strong for |r| ≥ 0.5. Factors associated with noise sensitivity in fever clinic patients were analysed using multiple linear regression. Mediation effect analysis was conducted using the Bootstrap method with 1000 resampling iterations to calculate bias-corrected 95% confidence intervals. A four-step approach was employed to test the mediating role: Step 1 examined the total effect of the independent variable (noise sensitivity, X) on the dependent variable (medical experience, Y) (path c). Step 2 tested the effect of the independent variable (X) on the mediating variable (psychological burden, M) (path a). Step 3 evaluated the effect of the mediating variable (M) on the dependent variable (Y) (path b). Step 4 included the independent variable (X) and mediating variable (M) in the model to test the direct effect of X on Y (path c′). Mediation was considered present if paths a, b, c and c′ were all significant and the 95% confidence interval for the indirect effect (ab) did not include zero; c′ remaining significant indicated a partial mediation effect. As an exploratory analysis aimed at preliminarily investigating the mediating role of psychological burden, this study did not include covariates to simplify the model structure and focus on the association patterns amongst the core variables. A two-tailed P-value < 0.05 was considered statistically significant for all analyses.
RESULTS
Status of Noise Sensitivity in Adult Fever Clinic Patients
The range of NoiSeQ scores across the 200 fever clinic participants was 66–144 points with a mean value of 107.68 ± 16.78 points.
Univariate Analysis of Factors Influencing Noise Sensitivity in Fever Clinic Patients
Univariate analysis revealed that the total NoiSeQ scores demonstrated statistically significant differences based on waiting time and time of visit (all P < 0.05). No statistically significant differences were observed for other factors (all P > 0.05, Table 1).
Table 1.
Univariate analysis of factors influencing noise sensitivity in fever clinic patients
| Factor | Category | n | NoiSeQ total score | t | P |
|---|---|---|---|---|---|
| Gender | Male | 108 | 107.37 ± 16.68 | 0.282 | 0.778 |
| Female | 92 | 108.04 ± 16.98 | |||
| Age (years) | <45 | 112 | 107.19 ± 16.70 | 0.467 | 0.641 |
| ≥45 | 88 | 108.31 ± 16.96 | |||
| Educational level | High school or below | 130 | 107.35 ± 16.59 | 0.374 | 0.709 |
| College or above | 70 | 108.29 ± 17.24 | |||
| Residential location | Urban | 132 | 107.29 ± 16.62 | 0.460 | 0.646 |
| Rural | 68 | 108.44 ± 17.19 | |||
| Employment status | Employed | 111 | 107.43 ± 16.57 | 0.232 | 0.816 |
| Unemployed | 89 | 107.99 ± 17.13 | |||
| Marital status | Unmarried | 76 | 107.78 ± 17.13 | 0.063 | 0.948 |
| Married | 124 | 107.62 ± 16.63 | |||
| Comorbidities | No | 87 | 107.30 ± 17.16 | 0.281 | 0.779 |
| Yes | 113 | 107.97 ± 16.56 | |||
| Waiting time (minutes) | >60 | 55 | 111.85 ± 16.89 | 2.187 | 0.030 |
| ≤60 | 145 | 106.10 ± 16.52 | |||
| Body temperature at visit (°C) | ≤38.5 | 133 | 107.26 ± 16.56 | 0.494 | 0.622 |
| >38.5 | 67 | 108.51 ± 17.31 | |||
| Aetiology of fever | Respiratory Infection | 130 | 107.52 ± 16.30 | 0.189 | 0.851 |
| Non-respiratory Infection | 70 | 107.99 ± 17.75 | |||
| Time of visit | Night | 55 | 114.91 ± 13.77 | 3.883 | <0.001 |
| Daytime | 145 | 104.94 ± 17.04 | |||
| Accompanying person | Yes | 124 | 107.20 ± 16.60 | 0.514 | 0.608 |
| No | 76 | 108.46 ± 17.15 |
Note: NoiSeQ, Schutte Noise Sensitivity Questionnaire.
Multiple Linear Regression Analysis of Factors Influencing Noise Sensitivity in Fever Clinic Patients
The variables demonstrating statistically significant differences in the univariate analysis were entered as independent variables, with the total NoiSeQ score serving as the dependent variable in the multiple linear regression analysis. The results confirmed that waiting time and time of visit retained statistically significant associations with patient noise sensitivity (all P < 0.05; Table 2).
Table 2.
Multiple linear regression analysis of factors influencing noise sensitivity in fever clinic patients
| Factor | B | SE | β | t | P | 95% CI |
|---|---|---|---|---|---|---|
| Constant | 100.711 | 1.642 | 61.320 | - | - | |
| Waiting time | 11.143 | 2.667 | 0.297 | 4.179 | <0.001 | 5.884–16.402 |
| Time of visit | 14.198 | 2.667 | 0.379 | 5.324 | <0.001 | 8.939–19.457 |
Note: CI, confidence interval.
Correlations Amongst Noise Sensitivity, Medical Experience and Psychological Burden in Fever Clinic Patients
Pearson correlation analysis revealed that all subscales and total NoiSeQ score were negatively correlated with the corresponding dimensions and total PEES-50 score (r < 0, P < 0.05) but positively correlated with the depression, anxiety and stress subscales and total DASS-21 score (r > 0, P < 0.05). Further analysis indicated the strong negative correlation of total NoiSeQ score with total PEES-50 score (r = −0.594) and its strong positive correlation with total DASS-21 score (r = 0.547). Regarding specific dimensions, the total NoiSeQ score demonstrated strong correlations with cost-effectiveness (r = −0.516), empathy (r = −0.452), anxiety (r = 0.453) and stress (r = 0.440). Moderate correlations were observed with assurance (r = −0.409), reliability (r = −0.399), depression (r = 0.398) and responsiveness (r = −0.392). By contrast, its correlations with the remaining dimensions were weak. Detailed results are presented in Tables 3–4 and Figures 1–2.
Table 3.
Scores for medical experience and psychological burden in fever clinic patients
| Scale Name | Dimension / Total Score | Score |
|---|---|---|
| PEES-50 | Tangibility | 14.16 ± 3.41 |
| Reliability | 18.63 ± 3.73 | |
| Assurance | 15.47 ± 3.39 | |
| Responsiveness | 13.85 ± 3.23 | |
| Empathy | 12.36 ± 2.81 | |
| Cost-Effectiveness | 11.76 ± 2.74 | |
| Total Score | 86.22 ± 13.77 | |
| DASS-21 | Depression | 6.85 ± 1.50 |
| Anxiety | 8.67 ± 1.77 | |
| Stress | 7.63 ± 1.52 | |
| Total Score | 23.15 ± 3.78 |
Note: DASS-21, 21-item Depression, Anxiety and Stress Scale; PEES-50, Patient Experience-Based Evaluation Scale for Medical Service Quality.
Table 4.
Correlations between noise sensitivity and medical experience/psychological burden in fever clinic patients
| Dimension | Statistic | General sensitivity | Leisure situations | Work environments | Home settings | Social contexts | Sleep environments | NoiSeQ total score |
|---|---|---|---|---|---|---|---|---|
| Tangibility | r | −0.145 | −0.191 | −0.164 | −0.166 | −0.287 | −0.250 | −0.397 |
| P | 0.041 | 0.007 | 0.020 | 0.019 | <0.001 | <0.001 | <0.001 | |
| Reliability | r | −0.247 | −0.145 | −0.244 | −0.196 | −0.204 | −0.256 | −0.399 |
| P | <0.001 | 0.040 | <0.001 | 0.005 | 0.004 | <0.001 | <0.001 | |
| Assurance | r | −0.153 | −0.240 | −0.247 | −0.224 | −0.201 | −0.180 | −0.409 |
| P | 0.031 | <0.001 | <0.001 | 0.001 | 0.004 | 0.011 | <0.001 | |
| Responsiveness | r | −0.240 | −0.222 | −0.189 | −0.260 | −0.222 | −0.160 | −0.392 |
| P | <0.001 | 0.002 | 0.007 | <0.001 | 0.002 | 0.023 | <0.001 | |
| Empathy | r | −0.213 | −0.240 | −0.237 | −0.166 | −0.240 | −0.312 | −0.452 |
| P | 0.002 | <0.001 | <0.001 | 0.019 | <0.001 | <0.001 | <0.001 | |
| Cost-effectiveness | r | −0.201 | −0.360 | −0.181 | −0.251 | −0.258 | −0.338 | −0.516 |
| P | 0.004 | <0.001 | 0.010 | <0.001 | <0.001 | <0.001 | <0.001 | |
| PEES-50 total score | r | −0.280 | −0.318 | −0.296 | −0.294 | −0.328 | −0.344 | −0.594 |
| P | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
| Depression | r | 0.211 | 0.225 | 0.254 | 0.153 | 0.167 | 0.252 | 0.398 |
| P | 0.003 | 0.001 | <0.001 | 0.031 | 0.018 | <0.001 | <0.001 | |
| Anxiety | r | 0.151 | 0.265 | 0.262 | 0.149 | 0.282 | 0.248 | 0.453 |
| P | 0.033 | <0.001 | <0.001 | 0.035 | <0.001 | <0.001 | <0.001 | |
| Stress | r | 0.238 | 0.258 | 0.211 | 0.213 | 0.163 | 0.321 | 0.440 |
| P | <0.001 | <0.001 | 0.003 | 0.003 | 0.021 | <0.001 | <0.001 | |
| DASS-21 total score | r | 0.250 | 0.317 | 0.308 | 0.216 | 0.264 | 0.345 | 0.547 |
| P | <0.001 | <0.001 | <0.001 | 0.002 | <0.001 | <0.001 | <0.001 |
Note: DASS-21, 21-item Depression, Anxiety and Stress Scale; NoiSeQ, Schutte Noise Sensitivity Questionnaire; PEES-50, Patient Experience-Based Evaluation Scale for Medical Service Quality.
Figure 1.
Scatter plot depicting the correlation between NoiSeQ total score and PEES-50 total score.
Note: PEES-50, Patient Experience-Based Evaluation Scale for Medical Service Quality.
Figure 2.
Scatter plot illustrating the correlation between NoiSeQ total score and DASS-21 total score.
Note: DASS-21, 21-item Depression, Anxiety and Stress Scale.
Analysis of the Mediating Effect of Psychological Burden between Noise Sensitivity and Medical Experience
A conceptual framework suggesting psychological burden as a potential mediator between noise sensitivity and medical experience was proposed. Within the analytical model, noise sensitivity was designated as the independent variable (X), psychological burden as the mediating variable (M) and medical experience as the dependent variable (Y). The constructed multiple linear regression model confirmed statistically significant pathways: a (β = 0.547, P < 0.001), b (β = −0.565, P < 0.001) and c (β = −0.594, P < 0.001). The significance of path c’ (β = −0.285, P < 0.001) established psychological burden as a partial mediator in the relationship between noise sensitivity and medical experience. The mediation effect was quantified as ab = −0.069, with a 95% confidence interval from −0.102 to −0.041 (P < 0.001). The mediated proportion of the total effect was quantified as Effect M = ab/c = [0.547 × (−0.565)] / (−0.594) = 52.03%. This finding reveals that over half (52.03%) of the adverse influence of noise sensitivity on medical experience in fever clinic patients is attributable to the mechanism of intensified psychological burden [Table 5].
Table 5.
Analysis of the mediating effect of psychological burden between noise sensitivity and medical experience
| Equation | Independent variable | Dependent variable | R2 | B | SE | β | t | P |
|---|---|---|---|---|---|---|---|---|
| Equation c | Noise sensitivity | Medical experience | 0.352 | −0.487 | 0.047 | −0.594 | −10.382 | <0.001 |
| Equation a | Noise sensitivity | Psychological burden | 0.299 | 0.123 | 0.013 | 0.547 | 9.184 | <0.001 |
| Equation c’ | Noise sensitivity | Medical experience | 0.577 | −0.234 | 0.045 | −0.285 | −5.143 | <0.001 |
| Equation b | Psychological burden | Medical experience | 0.577 | −2.058 | 0.202 | −0.565 | −10.211 | <0.001 |
Note: SE, standard error.
DISCUSSION
Patients in fever clinics frequently exhibit heightened sensory sensitivity due to physiological discomfort, leading to a reduced tolerance threshold for ambient noises, such as public address announcements and medical equipment alarms, coupled with a diminished capacity to filter auditory signals. [13] As healthcare zones characterised by elevated environmental stress, fever clinics often suffer from the insufficient management of the acoustic environment. Common shortcomings include the absence of installed decibel monitoring equipment and inadequate optimisation of high-frequency noise sources, resulting in sustained patient exposure to unregulated sonic stimuli. [14]
This investigation revealed NoiSeQ measurements spanning 66–144 points amongst the 200 fever clinic patients, establishing a mean value of 107.68 ± 16.78 points. These quantitative outcomes reflect a notably elevated degree of noise sensitivity within this clinical population. With the upper quartile (P75) of the total score serving as a cut-off value, a common methodology in related research, the patients with scores >121 points were classified into a high noise-sensitivity subgroup. Accordingly, 46 (23.0%) patients in this cohort were identified as having high noise sensitivity. The acoustic environment within fever clinics is notably distinct from that of general outpatient settings, characterised by a complex combination of noise sources, including high-density patient flow, frequent public address announcements and continuous operation of medical equipment. Concurrently, the heightened noise sensitivity associated with the febrile state can predispose patients to increased activation of the sympathetic nervous system and stress responses upon noise exposure. This physiological state may exacerbate febrile symptoms and precipitate negative emotional responses, potentially evolving into medical fear and subsequent healthcare avoidance. Such cascade of effects can remarkably intensify negative patient experiences and may ultimately translate into diminished trust in the healthcare system. [15] Consequently, analysing noise sensitivity in fever clinic patients is crucial for accurately evaluating the impact of the environmental milieu on patient experience. This understanding is fundamental for implementing targeted measures aimed at enhancing patient compliance and engagement within fever clinic settings.
Univariate analysis revealed statistically significant differences in total NoiSeQ scores based on waiting time and time of visit (all P < 0.05). Multiple linear regression analysis further confirmed that prolonged waiting time and nighttime visits were significantly associated with increased noise sensitivity (all P < 0.05). The underlying mechanisms may be explained as follows: extended waiting duration leads to a prolonged depletion of physiological and psychological resources, subsequently impairing cognitive control capacity and resulting in a pronounced degradation of sensory filtering function, thereby heightening noise sensitivity. [16] Concurrently, the uncertainty inherent in prolonged waiting may exacerbate anxiety, potentially causing the hyperactivation of the amygdala which misinterprets noise as threat signals. This neurobiological response promotes increased cortisol secretion, ultimately elevating noise sensitivity. Patients visiting during nighttime hours often experience circadian troughs, during which the regulatory control of the prefrontal cortex over the limbic system is attenuated. This weakened top–down regulation amplifies emotional responses to noise and enhances perceptual sensitivity. Furthermore, nocturnal noise exposure impairs hippocampal function, consequently diminishing environmental adaptability. [17] The typically reduced staffing and heightened environmental unfamiliarity during night shifts may increase patient vigilance, thereby potentiating noise perception.
Pearson correlation analysis demonstrated that all subscales and total NoiSeQ score were negatively correlated with the corresponding dimensions and total PEES-50 score (r < 0, P < 0.05) but positively correlated with the depression, anxiety and stress subscales and total DASS-21 score (r > 0, P < 0.05). Psychological burden was identified as a partial mediator in the relationship between noise sensitivity and medical experience, accounting for 52.03% of the total effect. The underlying neurophysiological mechanisms may be explained as follows: noise acts as a potent stressor that, upon patient exposure, promotes the release of norepinephrine and heightens amygdala sensitivity to neutral auditory stimuli. This neural cascade facilitates the misinterpretation of noise as threatening signals, thereby augmenting psychological distress and burden. [18] Furthermore, the noise-induced disruption of prefrontal cortex function compromises emotional regulation capabilities, exacerbating anxiety symptoms. Concurrently, the suppressive effect of noise on hippocampal neurogenesis promotes depression-like behaviours, and chronic noise exposure may potentially reduce hippocampal volume, further aggravating psychological burden. [19] From a cognitive perspective, noise interferes with attentional allocation and consumes finite cognitive resources. This detrimental effect is compounded by the inherent negative impact of fever on prefrontal executive function, collectively impairing the integration of medical information and predisposing patients to negative evaluations of healthcare services. [20] Additionally, the observed partial mediating role of psychological burden in the noise sensitivity–medical experience relationship may stem from the noise-induced activation of the stress response system. This activation weakens functional connectivity between the amygdala and prefrontal cortex whilst strengthening amygdala–insula connectivity. Such neural reorganisation fosters and amplifies anxiety states, which directly lower tolerance for environmental stimuli − diminishing the perceived medical experience − and create an attentional bias towards negative contextual cues, thereby further compounding negative perceptions of the healthcare encounter. [21]
This study did not directly measure neurophysiological parameters, and the proposed mechanisms were primarily inferred from existing literature. Several limitations of this study warrant acknowledgement. Firstly, the actual noise levels within the fever clinic environment (e.g., objective decibel measurements) were not assessed. Solely relying on the subjective NoiSeQ questionnaire to evaluate noise sensitivity precludes the ability to definitively distinguish whether differences in patient experience are driven by the objective intensity of environmental noise or by inherent individual traits (e.g., an innate predisposition to noise sensitivity). This phenomenon represents a primary limitation. Future research should integrate objective noise monitoring data with subjective assessment scales to further elucidate the independent and interactive effects of environmental factors and personal predispositions. Secondly, although NoiSeQ demonstrates cross-cultural applicability, it was not specifically designed for hospital settings. Subsequent studies should develop or employ noise sensitivity instruments tailored to healthcare environments. Thirdly, this analysis did not control for covariates, such as age, underlying comorbidities or fever severity, which may affect the robustness of the mediation effect conclusions. Therefore, the findings should be interpreted with caution. Future investigations should incorporate these covariates to verify model reliability. Finally, the cross-sectional design precludes causal inference, necessitating further validation through prospective or experimental studies.
CONCLUSION
Noise sensitivity in fever clinic patients is influenced by waiting duration and time of visit. This auditory sensitivity directly compromises the medical experience and exerts indirect effects by exacerbating psychological burden. These findings suggest that implementing acoustic environment modifications and integrating psychological support measures represent promising approaches for enhancing healthcare service quality.
Availability of Data and Materials
Both materials and data presented in this article are available.
Author Contributions
Dan Li is responsible for writing the article and organizing the materials; YiLing Wang is in charge of the article’s structure, revising the article, and contacting for submission; are responsible for collecting materials and providing feedback on the article.
Ethics Approval and Consent to Participate
Following approval by the Ethics Committee of Xiaoshan Hospital affiliated to Hangzhou Normal University (Approval No: 202500142). All patients signed the informed consent form.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgement
The authors would like to express their gratitude to all researchers and clinicians whose work has contributed to the understanding and treatment of noise-induced hearing loss. We also appreciate the valuable insights and suggestions from our colleagues during the preparation of this manuscript. Additionally, we acknowledge the support from our affiliated institutions for providing resources and assistance throughout this study.
Funding Statement
This study received no specific grant from any funding agency, commercial, or not-for-profit sectors.
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