Effects of Night-Shift Noise Exposure and Medical Equipment Alarms on Daytime Sleep Quality among Intensive Care Unit Nurses: The Mediating Role of Alarm Fatigue
Article information
Abstract
Purpose
This study examined the relationships between night-shift noise exposure, alarm fatigue, and daytime sleep quality in intensive care unit (ICU) nurses, and the mediating effects of alarm fatigue on the associations of noise exposure and medical equipment alarm frequency with daytime sleep quality.
Methods
This descriptive correlational study included 125 ICU nurses from a hospital in Busan, South Korea. Data were collected from April 21 to May 12, 2025. A GM-1356 meter measured noise levels in dBA. Validated Korean versions of the A Technology Hazard Survey and the Verran and Snyder-Halpern Sleep Scale assessed alarm fatigue and daytime sleep quality, respectively. Data were evaluated using correlation, hierarchical regression, and mediation analyses with SPSS version 27.0.
Results
The mean night-shift noise level was 54.24±2.95 dBA. Alarm fatigue was negatively correlated with daytime sleep quality (r=-.51, p<.001) and was the strongest predictor (β=-.47, p<.001). Night-shift noise exposure and alarm frequency were not directly associated with sleep quality. Alarm frequency was positively correlated with alarm fatigue (r=.22, p=.012), which mediated its association with sleep quality (indirect effect β=-.24, 95% CI [-0.45, −0.07]). Nurses caring for three patients had poorer sleep quality than those caring for two (β=-.19, p=.031).
Conclusion
ICU nurses were exposed to high night-shift noise levels and frequent alarms and experienced alarm fatigue and poor daytime sleep quality. Therefore, organizational interventions, including optimized alarm management and appropriate nurse staffing, are essential for reducing alarm fatigue and promoting patient safety and the well-being of nurses.
INTRODUCTION
Intensive care units (ICUs) are highly specialized clinical settings that require continuous 24-hour patient monitoring and immediate intervention. Consequently, they are characterized by high levels of environmental noise generated by the constant operation of medical devices, monitoring alarms, and repetitive caregiving activities. Within this demanding context, ICU nurses frequently experience compromised sleep quality despite the critical need for restorative daytime sleep following night shifts [1,2]. In particular, poor sleep quality has been reported to increase fatigue and drowsiness, and impair neurocognitive functions such as memory and reaction time, thereby increasing the risk of near misses [3,4]. Given the responsibilities of ICU nurses in caring for patients who are critically ill, sleep quality management is a pressing issue that extends beyond individual health and has direct implications for patient safety. As prior research [5] indicates that noise levels fluctuate across different ICU types, the specific department and patient-to-nurse ratio are pivotal factors that dictate the density of medical equipment and the intensity of nursing activities. Consequently, it is necessary to examine differences based on these general characteristics and to investigate the interrelationships among the study variables while accounting for these contextual influences.
Based on evidence that hospital noise increases patient stress and adversely affects recovery, the World Health Organization (WHO) recommends that average levels of hospital environmental noise be maintained at 35.0 dBA or lower, with maximum nighttime levels not exceeding 40.0 dBA [6]. However, actual ICU noise levels substantially exceed these recommendations, with mean levels reaching 65.5 dBA in international settings [7] and ranging from 54.3∼58.5 dBA in Korean hospitals [5,8]. Such high-noise environments adversely affect not only patients but also the nurses working within them, specifically influencing their fatigue and sleep quality [9]. Medical equipment alarms and environmental noise were identified as the primary sources, with noise levels exceeding 70 dBA under certain conditions [8]. Notably, unlike typical settings, the ICU involves the continuous 24-hour operation of monitoring and life-sustaining equipment, meaning noise problems are not limited to the daytime but persist throughout the night. In fact, one international study reported an average nighttime ICU noise level of 64.3 dBA [7], indicating that excessive noise persisted throughout the night. Consequently, ICU nurses are consistently exposed to high noise levels regardless of shift timing. Since ICU noise levels exhibit fluctuating patterns driven by varying workload intensities and human factors across different days and time periods [5], a granular analysis of these temporal variations is essential to accurately characterize the noise exposure patterns encountered by night-shift nurses. Chronic exposure to such noisy environments has been associated with impaired sleep quality and in more severe cases, the development of sleep disturbances [10], highlighting ICU noise as a significant factor influencing the sleep quality and overall health of nurses.
Among the various sources of ICU noise, clinical alarms generated by medical equipment are a major source. In principle, most medical devices used in ICUs are intended to enhance safety by promptly alerting healthcare providers to deviations from preset normal ranges [11]. In clinical practice, healthcare professionals are exposed to more than 1,000 alarms per shift [12]. Furthermore, 72∼ 99% of these alerts are false positives triggered by nonphysiological factors, such as patient movement, blood sampling, or temporary ventilator disconnection during tracheal suctioning, rather than true physiological changes [13-15]. Repeated exposure to false-positive alarms can lead to sensory overload and reduced sensitivity to alarm signals [13]. This effect may be particularly pronounced during the night when background noise is lower and high-frequency sounds, such as alarms, are perceived as louder and more disruptive [16]. Consequently, medical equipment alarms have become increasingly conspicuous at night, creating an environment of repetitive noise exposure that adversely affects both healthcare providers and patients and contributes to deteriorating sleep quality.
Alarm fatigue is defined as a phenomenon that occurs when healthcare providers become overwhelmed by the sheer number of alarm signals, which can result in alarm desensitization and, in turn, can lead to missed alarms or a delayed response [11]. Repeated exposure to hundreds of alarms has been shown to induce alarm fatigue, characterized by desensitization to alarm signals [12], which in turn leads to mental, sensory, and physical overload as well as sleep disturbances [17]. As alarm fatigue accumulates, clinical workflows may be disrupted and attention may be diverted, thereby increasing the risk of clinical errors such as omissions or delays in appropriate nursing responses [18]. In addition to increasing occupational burden, alarm fatigue poses a direct threat to patient safety. As reported in The Joint Commission's Sentinel Event Alert, an analysis of documented alarm-related cases showed that many of the 98 events resulted in severe outcomes, including death or permanent loss of function, with alarm fatigue identified as the most common contributing factor [19]. Consistent findings have been reported in previous studies, including research showing moderate to high levels of alarm fatigue among ICU nurses [20] and multinational studies of ICU nurses demonstrating the association of higher levels of alarm fatigue with poorer sleep quality [21]. In addition, nurses may remain mentally preoccupied with noise, stress, and fatigue even several hours after completing night shifts, suggesting that the effects of alarm exposure may persist beyond the work period and potentially interfere with post-shift recovery processes, including sleep [17]. Therefore, this study aimed to investigate the pathway through which noise exposure and medical equipment alarm frequency affect daytime sleep quality via alarm fatigue as a mediating variable.
Despite the growing international literature on noise and sleep in clinical settings direct application of these findings to Korean ICUs may be limited. The relationships among night-shift noise exposure, medical equipment alarms, alarm fatigue, and post-shift sleep may vary according to contextual factors such as nurse-to-patient ratios, ICU type, the density of monitoring devices, and unit-specific nighttime workflow patterns. Therefore, it is necessary to identify real-world noise exposure and alarm characteristics in Korean ICUs and to examine how these context-specific factors are associated with daytime sleep quality among ICU nurses.
Accordingly, this study comprehensively assessed night-shift noise exposure, characteristics of medical equipment alarm sounds, and levels of alarm fatigue in ICU nurses and examined their effects on daytime sleep quality. Using this approach, this study aimed to promote the health of ICU nurses while providing guidance to improve patient safety. Specifically, this study described the general characteristics of ICU nurses and the levels of key variables; measured night-shift noise levels by time period and day of the week across ICUs; assessed nurses' perceptions of medical equipment alarm sound characteristics during night shifts; examined correlations between night-shift noise exposure, alarm frequency, alarm fatigue, and daytime sleep quality; and identified factors influencing daytime sleep quality while verifying the mediating effect of alarm fatigue.
METHODS
1. Study Design
This study employed a descriptive correlational survey design to assess ICU noise levels and the characteristics of medical equipment alarm sounds during night shifts and to examine the relationships between alarm fatigue and daytime sleep quality among nurses working in the ICU.
2. Study Participants
Convenience sampling was used to recruit nurses working in the ICU of a university hospital in Busan, South Korea. Eligible participants directly participated in patient care, had at least three months of clinical experience, allowing independent nursing practice [22], and were engaged in night-shift work at the time of data collection. Only nurses who understood the study purpose and procedures and voluntarily agreed to participate were included in the final sample. Nurses in managerial or higher positions who did not provide direct patient care and those with less than three months of ICU experience were excluded. With a medium effect size of 0.15 based on Cohen's guidelines [23], significance level of 0.05, statistical power of 0.80, and 11 predictor variables (eight general characteristics, noise level, medical equipment alarm characteristics, and alarm fatigue), the minimum required sample size was calculated to be 123 participants using G*Power version 3.1.9.7 (Heinrich-Heine-Universitat, Dusseldorf). Assuming an attrition rate of 15%, a total of 145 questionnaires were distributed. After excluding questionnaires that were not returned or that contained insufficient responses, the final analysis included data from 125 participants.
3. Measurements
1) Extent of noise exposure during night shifts
A GM-1356 sound-level meter (Benetech, Shenzhen, China) measured noise levels during ICU night shifts, with a measurement range of 30∼130 dBA and an accuracy of ±1.5 dBA/dBC. The A-weighted decibel (dBA) scale closely approximates human auditory perception and is the most used weighting scale [6]. Measurements were conducted at two locations: the main nurses' station, where nurses spend most of their time, and near patient beds in the central area of the ICU [24]. Night-shift noise levels were defined as the average noise levels (by time period and day of the week) measured during night-shift hours (22:00∼07:00) across the six ICUs over one week. The noise exposure values of each nurse were determined by calculating the mean dBA recorded in their respective ward during their first night shift. Nurses working in the same ward on the same date were assigned an identical noise exposure value. The first night shift was selected as the reference point to ensure consistency in the timing of measurement and to minimize the confounding effects of cumulative fatigue from consecutive shifts, thereby reflecting noise exposure levels under standardized conditions.
2) Characteristics of medical equipment alarm sounds
The characteristics of the medical equipment alarm sounds were assessed using an Alarm Count Table from a medical equipment alarm survey originally developed by Baillargeon [25], translated by Cho et al. [26], revised and supplemented by Jeong and Kim [27], and further modified to suit the purpose of this study. The survey form documented the equipment generating the alarm and the type of alarm (valid or false-positive) at the time of the occurrence. The nurses were instructed to record information on alarm sounds generated by their assigned patients during their first night shift, and to document each alarm at the time of occurrence in order to minimize recall bias. Based on previous studies [12,15], alarm sounds were classified as follows. Valid alarms were defined as alarms triggered by a threshold exceedance that was clinically significant and required immediate intervention. False-positive alarms were subdivided into technical issues (alarms caused by technical factors such as sensor misplacement or equipment malfunction without actual threshold exceedance) and nontechnical issues (alarms exceeding the threshold limits but lacking clinical significance).
3) Alarm fatigue
In this study, alarm fatigue was measured using the A Technology Hazard Survey developed by Lopes [28] and translated and modified by Park [14]. The survey consisted of eight items assessing the experiences of alarm fatigue, frequency of false-positive alarms, interference with nursing tasks due to alarms, reduced trust in alarms, non-responsiveness to alarms, alarm-setting practices, perceived pressure related to alarm resolution, and alarm-induced stress. Each item was rated on a 5-point Likert scale ranging from "strongly disagree" (1 point) to "strongly agree" (5 points), yielding a total possible score of 40 points. Higher total scores indicate greater levels of alarm fatigue. The reliability coefficients of the survey were .73 and .81 (Cronbach's ⍺) in Park's study [14] and the present study, respectively.
4) Daytime sleep quality
Sleep quality was measured using the Verran and Snyder-Halpern (VSH) Sleep Scale developed by Snyder-Halpern and Verran [29] and translated by Kim and Kang [30]. This instrument consists of eight items that assess the frequency of nocturnal awakening, degree of movement during sleep, total sleep duration, sleep depth, sleep-onset latency, mood upon awakening, cause of awakening, and overall sleep satisfaction. Each item is scored on a scale of 0 to 10, yielding a total possible score of 80 points, with higher scores indicating better sleep quality. The reliability coefficients of the instrument were .82 and .86 (Cronbach's ⍺) at the time of development and in the present study, respectively.
4. Data Collection and Ethical Considerations
Following approval from the Institutional Review Board of Dong-A University (IRB approval number 2-1040709-AB-N-01-202503-IR-012-02), data were collected from April 21 to May 12, 2025. Prior to data collection, the researchers visited the hospital nursing administration in person to explain the purpose and rationale of the study and obtain permission for data collection. The researchers distributed the questionnaires during direct visits to each ICU. Permission to use the research instruments included in the questionnaire, namely the Medical Equipment Alarm Survey, A Technology Hazard Survey, and VSH Sleep Scale, was obtained from their respective developers. Participants voluntarily agreed to participate by providing written informed consent after reviewing the study information sheet, which described the study purpose and methods, right to withdraw at any time, and assurance of anonymity.
After confirming the ICU layout through hospital coordination, GM-1356 sound-level meters (Benetech, Shenzhen, China) were fixed at designated measurement positions and set to automatically record noise data at 8-second intervals continuously throughout the night shift hours over one consecutive week. The sound-level meters were installed at two locations: the main nurses' station, where nurses spend most of their time, and near patient beds in the central area of the ICU [24]. A Notification on Work Environment Measurement and Quality Control issued by the Ministry of Employment and Labor (Notice No. 2020-44, Article 27) recommended noise measurement within a hemispherical radius of 30 cm at the height of the worker's ears. However, the application of this criterion was limited considering the actual activity range of ICU nurses. Therefore, based on previous research [27] and considering feasibility and practical applicability, noise measurements were conducted at a reference distance of 50 cm from the equipment.
The nurses recorded the equipment name, alarm classification, and alarm content whenever an alarm occurred during their first night shift. Participants were instructed to evaluate their sleep quality based on the daytime sleep taken immediately following the completion of their first night shift. The questionnaire consisted of 24 items and required approximately 15 minutes to complete. Participants were informed that they could contact the principal investigator if they had any questions regarding the study. Participants placed the completed questionnaires in sealed opaque envelopes and stored them until collected by the researchers. Participants who consented to and completed all questionnaires were provided with a small token of appreciation. The data collected in this study were used solely for research purposes and will be stored for three years after study completion in accordance with relevant regulations, after which they will be destroyed.
5. Data Analysis
The collected data were analyzed using SPSS for Windows (version 27.0) with a two-tailed significance level of p<.05. The participants' general characteristics are presented as frequencies, percentages, means, and standard deviations. ICU noise exposure levels are presented as minimum and maximum values and means, whereas the frequencies of medical equipment alarm sounds are presented as frequencies and percentages. The levels of alarm fatigue and sleep quality are presented as means and standard deviations. Differences in sleep quality according to general characteristics were analyzed using independent t-tests or analysis of variance (ANOVA) after testing for normality of the sleep quality, and Scheffé's method was used for post-hoc analysis. Pearson's correlation coefficients were calculated to examine the relationships between ICU noise exposure, frequency of medical equipment alarms, alarm fatigue, and sleep quality. A hierarchical multiple regression analysis identified factors affecting sleep quality, including general characteristics, noise exposure, frequency of medical equipment alarms, and alarm fatigue. Hierarchical multiple regression analysis was conducted to control for general characteristics in the first step and to sequentially examine the additional explanatory power of the key independent variables, thereby verifying the relative influence of each variable group on daytime sleep quality. Prior to conducting hierarchical multiple regression analysis, the basic assumptions of regression were evaluated. Linearity and homoscedasticity were assessed by examining a scatterplot of standardized residuals against standardized predicted values. Normality of regression residuals was assessed using the Kolmogorov-Smirnov test and the Shapiro-Wilk test, along with visual inspection of the standardized residual histogram and the normal probability-probability (P-P) plot. To examine the mediating effect of alarm fatigue on the relationships between noise exposure, medical equipment alarm frequency, and sleep quality, a bootstrapping method was applied using PROCESS Macro Model 4. The total, direct, and indirect effects were estimated and the mediating effect was considered statistically significant when the 95% confidence interval (CI) for the indirect effect did not include zero.
RESULTS
1. Participants' General Characteristics and Values of Major Variables
Table 1 summarizes the participants' characteristics and major variables. The mean age of participants was 28.6 years; most were single (83.2%) and held a bachelor's degree (90.4%). The most common ICU type was the medical ICU (31.2%). The mean length of clinical experience was 5.96 years, with 53.6% reporting less than 5 years of experience. Regarding workload, the most frequent nurse-to-patient ratio was 1:3 (53.6%). More than half the participants (56.8%) reported experiencing a patient safety incident. The mean night-shift noise exposure level was 54.24 ±2.95 dBA (range: 48.68∼59.60 dBA), and the mean frequency of medical equipment alarms was 10.95±5.79 occurrences (range: 1∼28 occurrences). The mean alarm fatigue score was 28.86±4.80 points, and the mean daytime sleep quality score was 38.30±12.60 points.
General Characteristics and Descriptive Statistics of the Study Variables (N=125)
2. Comparison of Noise Levels by Time Period and Day of the Week During Night Shifts across ICUs
The noise level analysis across night-shift periods showed the lowest levels between approximately midnight and 2 AM, followed by an increase from approximately 5 AM to 6 AM. The neurological ICU (NCU) consistently exhibited the highest noise levels across all time periods, with the peak observed around 6 AM (59.50±1.72 dBA). In contrast, the remaining five ICUs had mean noise levels of 52.25 to 53.91 dBA. Noise levels differed significantly among ICUs at all time points (p<.001). Post-hoc analysis using Scheffé's method confirmed that the noise levels in the NCU were significantly higher than those in the other ICUs at all time points (Figure 1-A).
Noise Exposure Levels in Different Intensive Care Units According to Time and Day
Analysis by day of the week also demonstrated significant differences in noise levels among ICUs and across days (p<.001). Post-hoc analysis using Scheffé's method showed that the NCU consistently had higher noise levels than other ICUs on all days of the week, with the highest level recorded on Saturdays (59.59±4.04 dBA). The noise levels were significantly higher on Fridays and lower on Sundays than on other days (Figure 1-B).
3. Characteristics of Medical Equipment Alarm Sounds Occurring during ICU Night Shifts
Table 2 summarizes the characteristics of alarm sounds according to device type. Physiological monitors generated the highest number of alarms (n=836), and overall high rates of false-positive alarms were observed across devices. Specifically, false-positive alarms were prevalent for high-flow nasal cannula therapy (85.7%), intermittent pneumatic compression (69.6%), and ventilators (60.1%). In contrast, infusion pumps showed a relatively higher proportion of valid alarms (69.5%).
Number and Types of Medical Device Alarms
4. Correlations among Daytime Sleep Quality, Night Shift Noise Exposure, Medical Equipment Alarm Frequency, and Alarm Fatigue in ICU Nurses
Daytime sleep quality was negatively correlated with alarm fatigue (r=-.51, p<.001). In contrast, neither night-shift noise exposure level nor medical equipment alarm frequency was significantly correlated with sleep quality (p>.05). Alarm frequency was positively correlated with alarm fatigue (r=.22, p=.012). Noise exposure levels were not significantly correlated with alarm frequency or alarm fatigue (p>.05) (Table 3).
Correlations among Sleep Quality, Night-Shift Noise Exposure, Medical Equipment Alarm Frequency, and Alarm Fatigue in Intensive Care Unit Nurses (N=125)
5. Factors Influencing Daytime Sleep Quality and Mediating Effects of Alarm Fatigue in ICU Nurses
Hierarchical multiple regression analysis identified factors associated with daytime sleep quality among ICU nurses. Four models were constructed with general characteristics and independent variables entered sequentially. When the unit variable was included in the model, the VIF value for the noise exposure level rose to 12.256, and high VIF values were also observed for specific units (CCU=2.22, CSU=1.92, SCU=1.40, NCU=9.39, ECU=1.64). This indicated a significant multicollinearity issue between the unit and noise exposure level; therefore, the unit variable was excluded from the final regression model. Prior to the hierarchical multiple regression analysis, the assumptions of linearity, homoscedasticity, and normality of residuals were verified. The scatterplot of standardized residuals against standardized predicted values showed no systematic curvilinear pattern and no funnel-shaped distribution, supporting the assumptions of linearity and homoscedasticity. Normality of residuals was confirmed by the Shapiro-Wilk test (W=0.98, p=.079) and the Kolmogorov-Smirnov test (D=0.08, p=.050), with additional support from visual inspection of the histogram and normal P-P plot. Multicollinearity diagnostics showed tolerance values of ≥0.19 and variance inflation factors of≤5.18 for all variables, indicating no multicollinearity issues. The Durbin-Watson statistic for the final model was 2.21, indicating no autocorrelation. Model 1, which included general characteristics, explained 14% of the variance (R2=.14, adjusted R2=.07) and was statistically significant (F=1.96, p=.044). The number of patients per nurse had a significant negative effect on sleep quality (β=-.27, p=.003). In Models 2 and 3, which sequentially added noise exposure level and medical equipment alarm frequency, the explanatory power increased modestly to 16% (R2=0.16, adjusted R2=.08) and 17% (R2=.17, adjusted R2=.08), respectively. Neither noise exposure level (β=.15, p=.104) nor medical equipment alarm frequency (β=-.07, p =.485) showed significant effects, whereas the number of patients per nurse remained a significant negative predictor (β= -.30, p=.003). When alarm fatigue was added to Model 4, the explanatory power increased substantially to 34% (R2=.34, adjusted R2=.27), representing a 17% increase compared to Model 3, and the model was statistically significant (F=4.54, p<.001). Alarm fatigue (β=-.47, p<.001) and the number of patients per nurse emerged as a significant negative predictor (β=-.19, p=.031). In contrast, noise exposure level (β=.06, p=.451) and medical equipment alarm frequency (β=-.02, p=.823) did not have statistically significant effects on sleep quality (Table 4-A).
Factors Influencing Sleep Quality among Intensive Care Unit Nurses: Hierarchical Regression and Mediation Analysis (N=125)
The mediating effects of alarm fatigue on the relationships between noise exposure level, medical equipment alarm frequency, and daytime sleep quality were examined using PROCESS Macro (Table 4-B). In Model 1, which tested the effects of noise exposure level on daytime sleep quality mediated by alarm fatigue, the total (β=.19, p=.623), direct (β=.01, p =.978), and indirect (β=.04, 95% CI [-0.04, 0.14]) effects were not statistically significant, indicating no mediating effects of alarm fatigue. In Model 2, which tested the effect of medical equipment alarm frequency on daytime sleep quality mediated by alarm fatigue, the total effect was statistically significant (β=-.38, p=.043), whereas the direct effect was not (β=-.14, p=.413). The indirect effect was β=-.24 (95% CI [-0.45, −0.07]), and because the CI did not include zero, the mediating effect of alarm fatigue was statistically significant. Since the direct effect was not statistically significant while the indirect effect was significant, alarm fatigue was found to fully mediate the relationship between the frequency of medical equipment alarms and daytime sleep quality (Table 4-B).
DISCUSSION
This study examined night-shift noise exposure and medical equipment alarm characteristics in ICUs, assessed ICU nurses' alarm fatigue and sleep quality, and identified factors influencing daytime sleep quality.
Alarm fatigue has been identified as a major factor influencing daytime sleep quality among ICU nurses. Mediation analysis demonstrated that although the direct effect of medical equipment alarm frequency on sleep quality was not statistically significant, the indirect effect through alarm fatigue was significant, indicating a full mediating role of alarm fatigue. These full mediation findings suggest that the frequency of medical equipment alarms did not show a statistically significant direct effect on sleep quality; rather, its influence operated indirectly through the psychological and cognitive burden of alarm fatigue. This result is consistent with the findings of a multinational study of ICU nurses [21], which indicated that higher levels of alarm fatigue are associated with poorer sleep quality. Previous research has also shown that alarm fatigue can persist beyond work hours, leaving residual auditory impressions that contribute to sleep disturbances and psychological exhaustion [17]. Collectively, these findings indicate that improving daytime sleep quality in ICU nurses requires an interventional approach that extends beyond noise reduction; specifically, it highlights the importance of the indirect pathway of alleviating alarm fatigue by reducing the frequency of medical equipment alarms as a core intervention strategy. In particular, targeted interventions aimed at directly alleviating alarm fatigue should be prioritized, including the individualization of alarm threshold setings, implementation of protocols to reduce false-positive alarms, and provision of alarm management education for nursing staff.
The mean daytime sleep quality score among the ICU nurses was 38.30, which is comparable to the 38.4 value reported in a previous study using the same instrument [31]. This suggests that poor sleep quality among ICU nurses continues to be reported as a significant concern. In addition, daytime sleep quality was lower in nurses caring for three patients than in those caring for fewer patients, which may reflect increased workload and greater exposure to medical devices, potentially leading to higher alarm frequency and task demands. Similar findings have been reported among clinical nurses, where fatigue and absolute workload intensity were key factors influencing sleep quality [1], supporting the findings of the present study. Nurses with poor sleep quality have been reported to experience reduced reaction times and slower information processing, along with increased vulnerability to fatigue and drowsiness, which in turn increase the risk of near-miss medication errors [3,4]. Although near-miss events were not directly examined in this study, these findings suggest that deteriorated sleep quality associated with alarm fatigue could potentially compromise patient safety in ICU settings. Therefore, improving the sleep quality of ICU nurses should be recognized as a critical issue that extends beyond individual health promotion and is essential for maintaining the quality of nursing care and ensuring patient safety. However, since individual latent factors that could influence sleep quality—such as the participants' health status, psychological factors, and lifestyle habits—were not fully controlled, it is appropriate to interpret the results of this study within the context of statistical associations between work-related factors and sleep quality.
The level of noise exposure measured in ICUs in this study exceeded the WHO-recommended maximum nighttime level [6], which is consistent with findings from previous domestic studies [5,8]. In this study, night-shift noise levels varied by time period, with relatively lower levels between midnight and 2 AM and higher levels between 5 AM and 6 AM. These variations appear to reflect nursing activity patterns, as the period between midnight and 2 AM involves limited interventions to facilitate patient sleep, whereas the period between 5 AM and 6 AM corresponds to increased activity before and after shift handovers. This pattern is consistent with previous findings [8]. Across ICUs, the NCU demonstrated overall higher noise levels than other units, consistent with prior domestic research [5]. This may be attributed to the therapeutic characteristics of NCUs, including frequent reassessment of consciousness levels and the need for immediate interventions during the acute phase of care [5]. Noise levels also varied by day of the week; however, as these findings were based on a one-week observation period, caution is warranted in generalizing them as stable weekly patterns in ICUs, and future studies incorporating long-term repeated measurements are needed to confirm the reproducibility of these patterns.
Although the association between noise exposure and sleep quality was not statistically significant in the present study, Dwairi et al. [7] reported that nurses working in night-shift environments experienced fatigue and sleep disturbances related to noise exposure. In that study, discrepancies were observed between perceived and objectively measured noise levels, with perceived noise showing significant associations with psychological, emotional, and physical health as well as overall work performance, whereas objectively measured noise levels showed only limited associations [7]. These findings indicate that interpreting the effects of noise on sleep quality requires consideration of both objective noise levels and the nurses' subjective perceptions of noise. Accordingly, effective ICU noise management should adopt a tailored approach that considers patient characteristics and operational patterns, rather than relying solely on general noise reduction strategies.
In the present study, medical equipment alarm frequency did not show a statistically significant association with daytime sleep quality. The alarm frequency was the highest for physiological monitors, followed by ventilators and infusion pumps, supporting the findings of previous studies [26,27]. An examination of alarm characteristics revealed that more than half the alarms generated by physiological monitors and ventilators were false-positive alarms, whereas infusion pumps generated a higher proportion of valid alarms than false-positive alarms. These differences may be explained by the inherent sensitivity of physiological monitors and ventilators to patient movement and nursing care activities [15,18]. These findings underscore the need for both technical and organizational strategies to reduce false-positive alarms. In particular, during night shift hours, practical solutions are required to minimize unnecessary alarm activation, such as adjusting alarm volumes for physiological monitors and ventilators, and optimizing nursing activities to prevent avoidable alarm triggers.
In the present study, the mean level of alarm fatigue in ICU nurses was 28.86 points, which is comparable to the 28.63 points reported in Lee's study [20], indicating that ICU nurses experienced moderate to high levels of alarm fatigue. Although alarm fatigue was not significantly correlated with overall noise exposure level, it was significantly associated with the frequency of medical equipment alarms. This finding aligns with the conceptual definition of alarm fatigue as a cumulative phenomenon resulting from repetitive and excessive alarm stimuli, rather than from noise intensity alone. Accordingly, reducing alarm frequency requires not only alarm settings tailored to the clinical conditions of patients, but also the systematic identification of factors contributing to false-positive alarms.
Overall, the results of this study indicate that ICU nurses are continuously exposed to excessive levels of medical equipment alarms during clinical practice, and that night shift noise levels exceed WHO-recommended limits. Such noise exposure may contribute to the accumulation of alarm fatigue, which is associated with poorer physical and cognitive functioning among nurses; furthermore, through its mediating role, alarm fatigue may be linked to impaired sleep quality and have potential implications for patient safety. Internationally, alarm management protocols based on the CEASE Bundle, encompassing Communication, Electrodes, Appropriateness, Setup, and Education, have been developed and implemented to reduce false-positive alarms and enhance alarm management efficiency [32,33]. These approaches have been shown to improve alarm management competency and reduce alarm fatigue among ICU nurses [34]. Therefore, the development and implementation of structured alarm management protocols in domestic ICU settings that reflect local healthcare environments and staffing structures and enable the practical application of strategies, such as the CEASE Bundle, are warranted.
This study had several limitations. First, data were collected using convenience sampling of nurses working at a single tertiary hospital, which limits the generalizability of the findings to a broader population of ICU nurses. Second, the frequency of medical equipment alarms was based on self-recorded data, and the possibility of unrecorded alarms could not be excluded. Third, sleep quality was subjectively assessed using self-reported questionnaires, which may differ from objectively measured sleep quality. Fourth, this study did not fully control for individual latent factors that could influence sleep quality, such as the duration and cycle of night-only shift work, average sleep duration, caffeine intake, use of sleep aids, and the burden of childcare or household chores. Consequently, the findings should be interpreted as establishing statistical associations between work-related environment factors and sleep quality rather than definitive causal inferences. Fifth, noise measurements were collected at fixed locations within the ICU and may not have fully captured the individual noise exposures associated with the actual movement patterns and work activities of each nurse. To address these limitations, future research should incorporate objective alarm data obtained from medical equipment records and physiological measures of sleep quality, as well as longitudinal designs to examine changes over time. Moreover, multicenter studies combined with organizational-level support could strengthen the evidence base for effective alarm management strategies and contribute to academic advancement and improvements in nursing practice. Despite these limitations, this study goes beyond simply identifying associations between ICU noise exposure and sleep quality. By comprehensively analyzing the structural relationships among medical equipment alarm frequency, alarm fatigue, and sleep quality, this study provides empirical evidence for the full mediating role of alarm fatigue. In particular, by demonstrating that alarm frequency exerts an indirect effect on sleep quality through alarm fatigue rather than a direct one, this study offers a conceptual basis for understanding ICU noise not merely as a physical environmental factor, but as a cumulative process of cognitive and emotional burden.
CONCLUSION
This study was conducted to comprehensively examine the relationships between night-shift noise exposure, medical equipment alarms, alarm fatigue, and daytime sleep quality in ICU nurses and to identify the mediating role of alarm fatigue. The findings indicate that ICU nurses are continuously exposed to noise levels exceeding WHO recommendations and generally experience moderate to high levels of alarm fatigue. Although the noise exposure level was not directly associated with daytime sleep quality, the frequency of medical equipment alarms was significantly related to alarm fatigue, which has emerged as a key factor influencing daytime sleep quality. In particular, the frequency of medical equipment alarms contributes to deteriorated sleep quality through the mediating effect of alarm fatigue, suggesting that ICU noise-related issues should be understood not only as a problem of physical noise intensity but also as a cumulative process involving repetitive alarm stimuli and the cognitive and emotional responses of nurses. These results underscore that improving both sleep quality in ICU nurses and patient safety requires systematic strategies that extend beyond noise reduction and incorporate medical equipment alarm management and the mitigation of alarm fatigue. Accordingly, organizational-level interventions are required to reduce false-positive alarms, apply alarm settings tailored to the clinical conditions of patients, and strengthen the alarm management competencies of nurses.
Notes
CONFLICTS OF INTEREST
The authors declared no conflict of interest.
AUTHORSHIP
Study conception and design acquisition - Kim Min Ji and Kim Min Ju; Data collection - Kim Min Ji; Data analysis & Interpretation - Kim Min Ji and Kim Min Ju; Drafting & Revision of the manuscript - Kim Min Ji and Kim Min Ju.
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author upon reasonable request.