Abstract
Background:
Chronic pain in the neck, shoulder, waist, and legs is a primary public health concern, especially in occupational settings where musculoskeletal disorders are prevalent. Emerging evidence suggests that chronic exposure to low-frequency noise in industrial environments may exacerbate pain perception and psychological distress, but its full impact on musculoskeletal pain and associated psychological factors remains underexplored.
Methods:
This study focused on 256 employees from a local automobile manufacturing company who experienced shoulder, neck, lower back, and leg pains from August 2022 to August 2023. Participants were categorized into two groups according to their chronic exposure to low-frequency noise: the noise-exposed group (n = 119), who were exposed to noise levels of 0–200 Hz > 80 dB for more than 8 hour per day, and the non-noise group (n = 137), with less than 8 h/day during the same period. Data were collected on pain scores, psychological status, quality of life, and sleep quality using validated scales, including the Visual Analogue Scale, Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Medical Outcomes Study 36-item Short Form Health Survey, and Pittsburgh Sleep Quality Index.
Results:
A retrospective study was conducted involving 256 employees from an automobile manufacturing company, who were divided into a noise-exposed group (n = 119), chronically exposed to low-frequency noise, and a non-noise group (n = 137). The Noise-exposed group reported higher pain scores (5.04 ± 1.06 vs. 4.64 ± 1.35, P = 0.008) and increased anxiety (SAS: 28.16 ± 6.23 vs. 26.31 ± 5.77, P = 0.014) and depression scores (SDS: 29.57 ± 5.34 vs. 28.16 ± 4.89, P = 0.028). Quality of life was impacted, particularly physiological function (42.48 ± 6.08 vs. 44.26 ± 6.12, P = 0.020), although differences in joint function and sleep quality were not statistically significant (All P > 0.05).
Conclusion:
Chronic exposure to low-frequency noise was associated with heightened pain perception and psychological distress among workers, which influenced certain quality of life parameters.
Keywords: chronic noise exposure, low-frequency noise, musculoskeletal pain, occupational noise
KEY MESSAGES
-
(1)
Long-term exposure to low-frequency noise is associated with higher pain scores
-
(2)
Employees in the noise-exposed group exhibited higher levels of anxiety and depression, highlighting significant mental health issues.
-
(3)
Exposure to low-frequency noise led to impaired physiological function, affecting overall physical health status.
-
(4)
Overall quality of life significantly decreased in the noise-exposed group.
INTRODUCTION
Chronic pain affecting the neck, shoulder, waist, and legs is a pervasive issue with significant implications for individuals and public health systems globally.[1] Musculoskeletal disorders (MSDs) are a primarily occupational health issue, and they cause substantial socioeconomic burdens due to lost productivity and increased healthcare costs.[2] Various factors contribute to the development and exacerbation of MSDs, including biomechanical, psychological, and environmental stressors.[3] Noise exposure, especially chronic exposure to low-frequency noise, is increasingly recognized as a factor affecting not only hearing but also general health and well-being, which warrants close examination.[4]
Noise is an omnipresent occupational hazard in many industrial settings, including automobile manufacturing, where machinery often emits low-frequency noise.[5] These frequencies, which are typically below 500 Hz, are less easily masked by higher-frequency sounds and can penetrate enclosure structures; thus, they are more challenging to eliminate or control.[6] Chronic exposure to such noise frequencies has been linked to various adverse health effects, such as cardiovascular disturbances, stress-related illnesses, and cognitive impairments.[7] However, its potential impact on musculoskeletal pain perception and related psychological factors remains underexplored, but it is a critical area for understanding the full scope of occupational hazards.[8]
Recent research has begun to elucidate the effects of low-frequency noise on the musculoskeletal system and mental health. Noise-induced stress is a plausible explanation for heightened pain perception. Prolonged noise exposure can elevate the stress response of the body, which increases cortisol levels and augments pain perception.[9]
Moreover, low-frequency noise can interact with the musculoskeletal system, which leads to microinjuries or sustained muscle tension, especially in the neck and shoulders. This condition can result in musculoskeletal pain, which is consistent with the increased pain scores in noise-exposed groups.[10]
Psychologically, chronic exposure to noise can lead to psychiatric disturbances such as anxiety and depression, which are often comorbid with chronic pain. Anxiety can exacerbate pain perception by making individuals more hypervigilant and sensitive to painful stimuli.[11] Depression can increase pain scores by decreasing the ability of the body to inhibit pain through the descending pain modulatory systems.[12]
To evaluate the clinical and public health significance of our findings, we chose to assess anxiety, depression, and quality of life as key outcome measures.[4] Quality of life assessments highlight the physical, psychological, and social impacts of noise exposure, which emphasize the need for comprehensive health and safety practices in noisy work environments.
This study aims to bridge this research gap by investigating the effect of chronic exposure to low-frequency noise on neck, shoulder, waist, and leg pain among employees in an automobile manufacturing setting.
MATERIALS AND METHODS
Case Selection
This study focused on 256 patients experiencing shoulder, neck, lower back, and leg pains at Wuhan Fourth Hospital from August 2022 to August 2023. The participants were workers from a local automobile manufacturing company. Initial screening included a total of 300 employees. After the preliminary questionnaire survey, 44 employees who did not meet the inclusion criteria were excluded (including 20 who reported no pain symptoms, 14 with other illnesses, and 10 who did not complete all assessment items). Ultimately, 256 employees met the requirements for the study. Among them, 119 employees chronically exposed to low-frequency noise were categorized as the noise-exposed group (low-frequency noise intensity: 0–200 Hz, > 80 dB. Exposure time > 8 hours/day), while 137 employees not exposed to such noise during the same period served as the non-noise group (non-noise exposure time > 16 hours/day).[13] According to the guidelines on noise exposure from the World Health Organisation, long-term exposure to low-frequency noise should be limited to no more than 8 hours per day to minimize potential health risks. Therefore, this study set the exposure time for the noise-exposed group to greater than 8 h/day to ensure that the research outcomes reflect the actual impacts of prolonged exposure. Exposure to low-frequency noise was assessed using a sound level meter (Model NL-12, which was manufactured by Rion Co., Ltd., Tokyo, Japan) placed in various workstations within the plant. Measurements were conducted three times daily, with each session involving 2 h of sampling: morning shift (6:00–8:00), midday shift (12:00–14:00) and night shift (22:00–24:00). The sound level meter was set to measure noise levels in the frequency range of 0–200 Hz, which is consistent with the definition of low-frequency noise. Noise levels were recorded over a period of 1 month to capture typical exposure patterns. The sound level meter was calibrated before each measurement session to ensure accuracy. Noise levels were monitored and averaged. Workspaces with readings exceeding the threshold of 80 dB within the 0–200 Hz frequency range were classified as exposure areas, in accordance with Directive 2003/10/EC of the European Parliament and of the Council (dated 6 February 2003) on the minimum health and safety requirements regarding the exposure of workers to risks arising from physical agents (noise) issued by the European Commission.[14] The daily exposure time of workers in the area was counted. Data on general patient information, pain scores for shoulder, neck, lower back, and leg pain, psychological status, and quality of life were collected using a questionnaire survey on medical visit information at Wuhan Fourth Hospital.
The study received approval from the Institutional Review Board and Ethics Committee of the author’s hospital (KY2024-180-01). Despite the retrospective nature of the study, due to the administration of a questionnaire to participants, informed consent was obtained from all subjects according to institutional guidelines. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.[15]
Inclusion and Exclusion Criteria
Inclusion criteria: (1) Age over 18 years; (2) clinical confirmation of diagnosis for neck, shoulder, waist, or leg pain; (3) availability of complete and valid clinical data.
Exclusion criteria: (1) Patients with cardiovascular diseases, including acute coronary syndrome, severe heart failure, valvular heart disease and arrhythmias requiring intervention such as sustained ventricular tachycardia, symptomatic atrial fibrillation/flutter and type II second- or third-degree atrioventricular block; (2) patients with chronic liver diseases, such as cirrhosis or hepatitis, with liver function tests more than twice the upper limit of normal; (3) patients with chronic kidney diseases, who were defined by an estimated glomerular filtration rate (eGFR) < 60 mL/minute/1.73 m2; (4) patients with other significant organic lesions affecting major organs, such as active pulmonary diseases, severe gastrointestinal disorders or autoimmune conditions; (5) patients with a history of fractures, and patients currently experiencing fractures; (6) individuals with mental illnesses; (7) individuals with disorders of consciousness; (8) patients with chronic otitis media or hearing loss in one or both ears, which rendered them unable to undergo testing. These conditions were excluded because they can independently influence pain perception and psychological health, which could potentially confound the results.
Questionnaire Survey
A questionnaire survey and occupational health examination were conducted on the study participants. This approach aimed to collect data on occupational health testing and surveillance. Key areas of focus included gender, age, occupational history, exposure history to occupational hazards, past medical history, medication history, sensitivity to low-frequency noise, and overall health status.
Pain Rating
A Visual Analogue Scale (VAS) was utilized to evaluate the intensity of pain in patients before and after nursing intervention, with higher scores indicating greater pain severity. Pain assessment using the VAS was conducted during rest, daily activities, and nighttime on a scale of 10 cm. Patients were instructed to mark their pain level on a line ranging from “0,” indicating “no pain,” to “10,” representing “the worst pain imaginable.” The distance between “0” and the patients’ mark was measured to quantify the pain. The scale demonstrated a Cronbach’s alpha coefficient of 0.86, indicating good internal consistency.[16]
Joint Function
Joint function was evaluated using a standardized scoring system assessing mobility, stability, and pain-related functional limitations in four key areas: shoulder, neck, lumbar spine, and knee joints. Each joint was scored based on range of motion, stiffness, and performance during daily activities (e.g., lifting, walking, or bending), with higher scores indicating better functional capacity. The assessment was conducted by trained clinicians through physical examination and patient-reported outcomes.
Psychological State Rating
The Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS) were employed to assess the psychological state of the patients. Each scale has a maximum score of 100 points, with higher scores signifying more severe anxiety and depression. The SAS demonstrated a Cronbach’s alpha coefficient of 0.860, while the SDS showed a Cronbach’s alpha coefficient of 0.880, which means high reliability for both scales.[17,18]
Quality of Life Rating
The study utilized the Medical Outcomes Study 36-item Short Form Health Survey (MOS SF-36) to assess various dimensions of health, including physical functioning, role-physical, bodily pain, general health perception, vitality, social functioning, role-emotional, and mental health. This simplified health survey was a self-administered questionnaire measuring general health status, with each dimension being scored from 0 to 100. Higher scores reflect a better quality of life for the patient. The scale demonstrated a Cronbach’s alpha coefficient of 0.800, which suggests good reliability.[19]
Sleep Quality Score
The Pittsburgh Sleep Quality Index (PSQI) was employed to evaluate the sleep quality of participants. The PSQI consists of 19 self-reported questions divided into seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. Each component was rated on a scale of 0–3, with all components equally weighted. The component scores were summed to produce a global PSQI score, which ranged from 0 to 21, where a score of 5 or less signifies good sleep quality. The PSQI had a Cronbach’s alpha coefficient of 0.757, which signifies acceptable reliability.[20]
Statistical Analysis
The data were analyzed using SPSS 29.0 statistical software (SPSS Inc., Chicago, IL, USA). Categorical data were reported as (n [%], and the chi-square test was employed when the theoretical frequency (T) was ≥ 5, which yielded a test statistic denoted by χ2. If 1 ≤ T < 5, then the chi-square test was adjusted using a correction formula. The normality of continuous variables was assessed using the Shapiro–Wilk test, with P > 0.05 indicating a normal distribution. Data conforming to a normal distribution were presented as (X ± s), and normally distributed quantitative data were analyzed using independent samples t-tests. Data not conforming to a normal distribution were assessed using the Wilcoxon rank-sum test and reported as (median [25th percentile, 75th percentile]. A P-value less than 0.05 was considered statistically significant. [Figures 12] were generated using the R software package 3.0.2 (Free Software Foundation, Inc., Boston, MA, USA).
Figure 1.
Comparison of pain scores between two groups of patients. **P < 0.01. R software package 3.0.2 (Free Software Foundation, Inc., Boston, MA, USA).
Figure 2.
Comparison of psychological states between the two groups. (A) SAS and (B) SDS. SAS = Self-Rating Anxiety Scale, SDS = Self-Rating Depression Scale. *P < 0.05. R software package 3.0.2 (Free Software Foundation, Inc., Boston, MA, USA).
RESULTS
Baseline Characteristics of Participants
When analyzing the baseline characteristics of the noise-exposed group (n = 119) and the non-noise group (n = 137), no significant differences were found between the two groups in terms of age, gender distribution, body mass index, education level, cigarette smoking and alcohol drinking and the prevalence of hypertension and diabetes [Table 1]. No significant differences were noted in the duration of employment or sensitivity to low-frequency noise. Therefore, the baseline characteristics of the two groups are well-matched and suitable for further primary outcome comparative analysis. At the same time, the two groups demonstrated significantly different exposure times (P < 0.001), which confirms the validity of our grouping approach.
Table 1.
Baseline characteristics of participants
| Parameters | Total population (n = 256) | Noise-exposed group (n = 119) | Non-noise group (n = 137) | t/χ2 | P |
|---|---|---|---|---|---|
| Age (years) | 45.30 ± 1.48 | 45.26 ± 1.51 | 45.32 ± 1.46 | 0.314 | 0.754 |
| Gender (male/female) | 166 (64.84%)/90 (35.16%) | 72 (60.50%)/47 (39.50%) | 94 (68.61%)/43 (31.39%) | 1.837 | 0.175 |
| Body mass index (kg/m2) | 25.55 ± 3.36 | 25.31 ± 3.14 | 25.76 ± 3.55 | 1.052 | 0.294 |
| Degree of education | 3.504 | 0.173 | |||
| -Junior high school and below | 82 (32.03%) | 34 (28.57%) | 48 (35.04%) | ||
| -High school | 84 (32.81%) | 46 (38.66%) | 38 (27.74%) | ||
| -College diploma or above | 90 (35.16%) | 39 (32.77%) | 51 (37.23%) | ||
| Cigarette smoking [n (%)] | 148 (57.81%) | 63 (52.94%) | 85 (62.04%) | 2.163 | 0.141 |
| Alcohol drinking [n (%)] | 135 (57.73%) | 56 (47.06%) | 79 (57.66%) | 2.874 | 0.090 |
| Hypertension [n (%)] | 49 (19.14%) | 18 (15.13%) | 31 (22.63%) | 2.316 | 0.128 |
| Diabetes [n (%)] | 52 (20.31%) | 27 (22.69%) | 25 (18.25%) | 0.776 | 0.378 |
| Working years | 0.455 | 0.797 | |||
| <10 years | 81 (31.64%) | 39 (32.77%) | 42 (30.66%) | ||
| 10–20 years | 112 (43.75%) | 53 (44.54%) | 59 (43.07%) | ||
| > 20 years | 63 (24.61%) | 27 (22.69%) | 36 (26.28%) | ||
| Low-sensitive to LFN | 118 (46.09%) | 55 (46.22%) | 63 (45.99%) | 0.001 | 0.970 |
| High-sensitive to LFN | 138 (53.91%) | 64 (53.78%) | 74 (54.01%) | 0.001 | 0.970 |
| Daily exposure time to noise (hours) | 7.44 ± 2.25 | 9.84 ± 0.21 | 5.36 ± 0.34 | 127.253 | < 0.001 |
LFN = Low-frequency noise.
Pain Score and Joint Function Score
The average pain score in the noise-exposed group was significantly higher than in the non-noise group (P = 0.008) [[1]. However, no statistically significant difference was observed in joint function scores [Table 2]. These findings suggest that long-term exposure to low-frequency noise was associated with an increased perception of pain among employees, but it did not significantly affect joint function.
Table 2.
Comparison of joint function scores between two groups of patients
| Parameters | Total population (n = 256) | Noise-exposed group (n = 119) | Non-noise group (n = 137) | t | P |
|---|---|---|---|---|---|
| Shoulder joint | 32.63 ± 1.70 | 32.44 ± 1.77 | 32.79 ± 1.63 | 1.653 | 0.100 |
| Neck joint | 30.37 ± 2.39 | 30.16 ± 2.45 | 30.56 ± 2.33 | 1.339 | 0.182 |
| Lumbar joint | 35.64 ± 2.69 | 35.55 ± 3.51 | 35.72 ± 1.68 | 0.481 | 0.631 |
| Knee joint | 33.43 ± 2.23 | 33.17 ± 2.66 | 33.66 ± 1.76 | 1.708 | 0.089 |
Psychological State Rating
The SAS showed that the noise-exposed group had a higher mean score than the non-noise group (P = 0.014), which indicates increased levels of anxiety associated with noise exposure [2]. Similarly, the SDS scores were higher in the noise-exposed group, which suggests a greater prevalence of depressive symptoms among those chronically exposed to low-frequency noise. These findings highlight the impact of chronic exposure to noise on psychological well-being in employees.
Quality of Life Rating
The noise-exposed group had a lower mean score than the non-noise group (P = 0.020), which suggests that chronic exposure to low-frequency noise may adversely affect physiological functioning [Table 3]. However, no significant differences were observed in other quality of life parameters. These findings indicate that, while physiological function may be impacted, other aspects of quality of life were not significantly affected by noise exposure.
Table 3.
Comparison of quality of life scores between two groups of patients
| Parameters | Total population (n = 256) | Noise-exposed group (n = 119) | Non-noise group (n = 137) | t | P | |
|---|---|---|---|---|---|---|
| BP | 41.03 ± 6.37 | 40.78 ± 6.32 | 41.25 ± 6.44 | 0.591 | 0.555 | |
| PF | 43.43 ± 6.16 | 42.48 ± 6.08 | 44.26 ± 6.12 | 2.335 | 0.020 | |
| MH | 50.45 ± 7.49 | 50.32 ± 7.53 | 50.56 ± 7.48 | 0.256 | 0.799 | |
| SF | 48.16 ± 7.59 | 48.26 ± 7.36 | 48.07 ± 7.81 | 0.204 | 0.838 | |
| RE | 49.06 ± 7.17 | 48.77 ± 6.58 | 49.32 ± 7.66 | 0.609 | 0.543 | |
| RP | 41.43 ± 5.79 | 41.56 ± 5.84 | 41.32 ± 5.77 | 0.334 | 0.738 | |
| GH | 56.35 ± 4.42 | 56.77 ± 4.21 | 55.98 ± 4.57 | 1.420 | 0.157 | |
| VT | 45.68 ± 5.55 | 45.36 ± 5.85 | 45.96 ± 5.27 | 0.864 | 0.388 | |
BP = bodily pain, PF = physical functioning, MH = mental health, SF = social functioning, RE = role emotional, RP = role-physical, GH = general health perception, VT = vitality.
Sleep Quality
The sleep quality of participants was assessed using the PSQI. Table 4 summarizes the comparison of sleep quality scores between the noise-exposed and non-noise groups. The results indicate a trend towards poorer sleep quality among those exposed to noise; however, the differences were not statistically significant (P = 0.069).
Table 4.
Comparison of sleep quality scores between the two groups of patients
| Parameters | Total population (n = 256) | Noise-exposed group (n = 119) | Non-noise group (n = 137) | χ 2 | P |
|---|---|---|---|---|---|
| PSQI≤5 | 121 (47.27%) | 49 (41.18%) | 72 (52.55%) | 3.308 | 0.069 |
| PSQI > 5 | 135 (52.73%) | 70 (58.82%) | 65 (47.45%) |
PSQI = Pittsburgh Sleep Quality Index.
DISCUSSION
This study examines the effects of chronic exposure to low-frequency noise on pain perception, mental health, quality of life, and sleep among workers in an automobile plant. Key findings include higher pain scores in the exposed group, which suggests that prolonged exposure to noise may heighten pain perception by elevating stress and cortisol levels.[21] The stress response, which is marked by increased sympathetic nervous system activity, may exacerbate pain signals in the central nervous system.[22] Chronic stress may also impair the capacity of the body to manage pain effectively, which leads to an amplification of pain experiences.[23]
Exposure to low-frequency noise can affect pain perception through alterations in neurotransmission pathways. Studies have shown that chronic exposure to noise can lead to changes in the levels of neurotransmitters such as serotonin and norepinephrine, which play crucial roles in pain modulation. For instance, decreased serotonin levels can reduce the inhibitory control over pain pathways, which leads to increased pain sensitivity.[24] Similarly, elevated norepinephrine levels can enhance the excitatory signals in the central nervous system, which exacerbates pain perception.[8]
The central nervous system is a key mediator in the relationship between noise exposure and pain perception. Chronic exposure to low-frequency noise can alter the function of brain regions involved in pain processing, such as the anterior cingulate cortex and the insula. Brain regions sensitive to stress can become hyperactive due to chronic noise exposure, which results in heightened pain perception.[25] In addition, neuroimaging studies have shown that chronic exposure to noise can lead to structural changes in the brain, such as reduced grey matter volume in the prefrontal cortex, which is associated with pain regulation.[10]
Furthermore, low-frequency noise might interact with the musculoskeletal system, which influences proprioceptive and nociceptive pathways.[26] Chronic vibration from low-frequency noise can lead to micro-injuries or sustained muscle tension, particularly in areas such as the neck and shoulders that are prone to static loading and repetitive strain.[13] This condition can result in musculoskeletal pain, which is consistent with increased pain scores reported in the noise-exposed group.[27]
Psychologically, higher anxiety and depression scores in the noise-exposed group suggest a link between chronic noise exposure and psychiatric issues, which are commonly associated with chronic pain. Anxiety can exacerbate pain perception through altered attention processes, where individuals become hypervigilant and more sensitive to painful stimuli.[28] Depression can influence pain perception by affecting the descending pain modulatory systems, which reduces the ability of the body to inhibit pain and potentially leads to higher pain scores.[7]
The implications of noise exposure on anxiety and depression highlight its potential to disrupt cognitive and emotional processing. Disrupted sleep patterns, as indicated by PSQI scores, can exacerbate psychological stress and reduce quality of life. Sleep deprivation results in a cascade of hormonal and neurochemical changes that can elevate levels of pro-inflammatory cytokines, which, in turn, may stimulate pain pathways in the peripheral and central nervous systems.[29] Moreover, poor sleep quality is intrinsically linked to emotional dysregulation, which could further amplify perceived pain and psychological distress.[30]
From a broader perspective, the impact on quality of life, especially physiological function, highlights the extensive negative effects of noise exposure. Key aspects include physical functioning, such as the ability to perform daily activities without pain or limitation, which are critical for overall well-being.[26] Chronic exposure to low-frequency noise may subtly degrade physiological capacity, which leads to increased fatigue, reduced neuromuscular coordination, and poorer sleep, all of which can impair work-related tasks.[31]
Our study shows no significant differences in joint function scores, which indicates that noise exposure does not directly impair joint health but may affect pain perception through other pathways. Further research is needed to explore the interaction of psychological and emotional factors with environmental stressors to indirectly affect musculoskeletal health.[32]We must address the environmental and occupational factors of noise exposure and their cumulative impact on workers. Continuous noise pollution requires improved workplace health regulations. Therefore, the focus should be on noise reduction and the provision of personal protective equipment. Employers should conduct regular noise assessments and health screenings to identify at-risk individuals and provide timely interventions.[33]
While this study provides valuable insights into the effects of chronic exposure to low-frequency noise, several limitations must be acknowledged. First, the cross-sectional design limits our ability to infer causality between noise exposure and health outcomes. While associations are observed, the temporal sequence cannot be established, which is essential for identifying cause-and-effect relationships. Future research should employ longitudinal designs to better understand the temporal dynamics and potential causal mechanisms. Second, the relatively small sample size may reduce statistical power and limit the detection of subtle but potentially important effects. Third, self-reported measures for pain, psychological well-being, and sleep quality can introduce biases such as social desirability and recall bias. Future studies should incorporate objective measures, including physiological markers or wearable devices, to reduce these biases. Fourth, the focus of the study on a single industry and geographic area limits the generalizability of the findings. Future research should conduct multicenter studies with participants from diverse industries and regions to enhance external validity and provide a more comprehensive understanding of the effects of exposure to low-frequency noise. Fifth, we ignored potential confounding variables such as lifestyle factors, comorbid medical conditions, or other workplace stressors that might influence the outcomes. Lastly, the study lacked stratified analyses by age and gender, which should be addressed in future research to tailor occupational protection. We did not control for or measure biomechanical workload factors such as repetitive motions, poor posture, and mechanical vibration. These factors may contribute to musculoskeletal pain and could confound the observed associations between low-frequency noise and pain perception. Future studies should account for these variables to more accurately isolate the impact of noise exposure. In addition, refining the quantification and characterization of noise exposure could enhance the understanding of its effects. Addressing these gaps could strengthen future findings.
CONCLUSION
This study illustrates the profound impact of chronic exposure to low-frequency noise on pain perception, psychological well-being, and physiological function among workers. The interactions among environmental, psychological, and physiological factors are complex and multifaceted, which requires a multidisciplinary approach to unravel these connections. These findings provide a basis for enhancing occupational health measures, which involve reducing noise and offering mental health resources to improve the quality of life of employees.
Availability of Data and Materials
Research data, analysis results, and other information can be obtained from the corresponding author.
Author Contributions
Siyu Chen and Caixian Song: Writing − original Draft. Defang Ding and Siyu Chen: Conceptualization. Defang Ding and Caixian Song: Data curation.
Ethics Approval and Consent to Participate
The study received approval from the Institutional Review Board and Ethics Committee of Wuhan Fourth Hospital (KY2024-180-01). Despite the retrospective nature of the study, due to the administration of a questionnaire to participants, informed consent was obtained from all subjects according to institutional guidelines.
Conflict of Interest
No conflicts of interest exist in the submission of this manuscript.
Acknowledgment
None.
Funding Statement
None.
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Data Availability Statement
Research data, analysis results, and other information can be obtained from the corresponding author.
