Assessing the Link Between Alarm Fatigue,Stress,and Perceived Performance Among Critical Care Nurses in Egypt

Abstract

Background

Alarm fatigue and psychological stress, such as anxiety and depression, are very common among critical care nurses and may be a contributing factor to impair their perceived performance in intensive care units.

Objectives

This study aimed to determine the levels of alarm fatigue, anxiety, depression, and perceived performance among critical care nurses and to analyze the correlation between them.

Methods

A cross-sectional descriptive correlational study was conducted among 200 critical care nurses working in 11 intensive care units across five public hospitals in Damanhur city, Egypt, between August and November 2023. Data were collected using the Alarm Fatigue Questionnaire, the Hospital Anxiety and Depression Scale (HADS), the Nursing Performance Instrument Questionnaire, and a demographic sheet.

Results

The mean score of critical care nurses’ perceived performance was 24.45 ± 7.44 out of 54, which falls within the low-performance category. About 29.5% of critical care nurses had experienced a high score of anxiety; less than half of them (47.5%) had experienced a high score of depression. The mean anxiety and depression scores were positively and significantly correlated with the mean score of alarm fatigue (r = .333, p = .001; r = .630, p < .001, respectively). In addition, the mean scores of alarm fatigue (r = −.536, p < .001) and psychological stress of anxiety (r = −.311, p < .001) and depression (r = −.312, p < .001) were negative and significantly correlated with critical care nurses’ performance.

Conclusions

Critical care nurses in intensive care units experience high levels of alarm fatigue, anxiety, and depression, which are negatively associated with their perceived performance. Addressing alarm management and providing psychological support for critical care nurses may help improve working conditions in intensive care settings.

Keywords

critical care nurses intensive care units clinical alarms fatigue anxiety depression

Introduction

The revolution in medical technology has led to the use of healthcare informatics devices and equipment with high-pitched alarm sounds in intensive care units (ICUs) (Shaoru et al., 2023). Accordingly, hemodynamic and ventilatory changes in critically ill patients are detected in ICUs using clinical alarm systems. Patients’ physiological factors, such as heart rate, respiration rate, and temperature, should be critically monitored every 4 hours (Khanna et al., 2025). The World Health Organization (WHO) has recommended that sound pressure levels in hospital rooms where patients are treated or observed should not exceed 35 decibels (dB) during the day and night (Bourji et al., 2020; WHO, 2018). According to the US Environmental Protection Agency, sound pressure levels in hospitals should be kept at a maximum of 45 dB during the day and 35 dB at night (Darbyshire et al., 2019). Despite such recommendations, noise levels in ICUs remain high (Seok et al., 2023).

Critical care nurses (CCNs) and patients are at risk for alarm fatigue and psychological stress in this noisy environment of ICUs, compromising their well-being (Akturan et al., 2022). The American Association of Critical-Care Nurses and the Commission have described them as a major problem in ICUs (Dee et al., 2022). Due to prolonged stays in these noisy environments caused by devices such as mechanical ventilators, cardiac monitors, and syringe pumps, CCNs are considered the main health care workers exposed to high levels of noise in ICUs (Michels et al., 2025). Such noise levels can arise from excessive false alarms, which can then cause alarm desensitization. Alarm desensitization can lead to over-sensitivity and troubleshooting for the ICU’s machine, increasing susceptibility to false alarms and leading to alarm fatigue (Asadi et al., 2022). The “cry-wolf” effect has been recognized by ICU staff as they are increasingly accustomed to alarm sounds as background noise in the work environment (Drew et al., 2014). Sensory overload due to an excessive number of alarms can lead to a delayed response to sirens or to ignoring them completely (Lewandowska et al., 2020). Most of these alarms are false and lead to alarm overload that may lead to accidents because the staff nurses try to reduce their numbers by deactivating alarm variables that need to be checked, reducing their volumes, silencing them, or accidentally altering their limit parameters (Xu et al., 2025).

Review of Literature

Alarm fatigue and psychological stress, including anxiety and depression, can reduce nurses’ productivity and understanding of their duties, thereby jeopardizing patient safety (Alkubati, Alsaqri, Alrubaiee, Almoliky, Alqalah, et al., 2024; Sacgaca et al., 2023). Alarm fatigue usually disturbs sleep and rest, impairs concentration and cognition, causes stress and fatigue, impedes communication, and increases the risk of accidents (Bourji et al., 2020; Scquizzato et al., 2020). Ignoring patient alarms can lead to clinical complications, with a significant risk of patient injury and even death (Alsuyayfi & Alanazi, 2022). Previous studies determined several factors that can compromise alarm safety, including a lack of alarm management policies and methods, poor usability of physiological monitors, a lack of standardized practices for alarm-lessening strategies, and nurse incompetence in physiological observers (Alkubati, Alsaqri, Alrubaiee, Almoliky, Alqalah, et al., 2024; Bach et al., 2018; Danquah & Asiamah, 2022).

Noise can also lead to decreased CCNs’ performance, fatigue or lack of energy, irreversible physical and mental exhaustion due to reduced rest periods, cognitive work overload, tiredness, and other psychological effects, and burnout due to an increase in anxiety levels and a decline in job satisfaction (Bourji et al., 2020; Lewandowska et al., 2020). Nursing performance is a collection of nursing behaviors and actions that nurses carry out with the goal of promoting the health and recovery of the patients in their care (Ravi et al., 2026; Sagherian et al., 2018). Previous studies showed that CCNs are subjected to extreme noise levels, anxiety, stress, fatigue or lack of energy, irreversible physical and mental exhaustion due to decreased resting periods, cognitive work overload, tiredness and other psychological effects (Alkubati, Alsaqri, Alrubaiee, Almoliky, Alqalah, et al., 2024; Lewandowska et al., 2020). Therefore, it is recommended to improve the design of ICUs and implement noise reduction guidelines to reduce the impact of noise on patients and staff (Al-Tarawneh et al., 2020).

Alarm fatigue is a particular risk for nursing personnel, who spend the majority of their time with patients and monitor their condition round-the-clock (Farajalla, Batran, et al., 2026), and is increasingly recognized as a serious problem related to patient safety in contemporary clinical practice (Nyarko et al., 2023). Previous studies have been conducted to explore causes of alarm fatigue (Michels et al., 2025; Shaoru et al., 2023) and the relationship between fatigue and performance (Farajalla et al., 2026; Kwon & Kim, 2026). In a study that was conducted in South Korea, alarm fatigue was negatively associated with nursing performance (Kwon & Kim, 2026).

This study was conceptualized based on Roy’s Adaptation Model, which provides a useful theoretical framework for understanding the potential relationships among alarm fatigue, psychological distress, and perceived performance among critical care nurses. This model views persons as adaptive systems that continuously respond to environmental stimuli via physiological and psychosocial coping processes (Roy, 2011). In high-intensity clinical environments like ICUs, frequent clinical alarms and heavy workloads can operate as environmental stimuli that test CCNs’ adaptive capacity. According to this concept, when environmental demands exceed coping resources, maladaptive responses such as psychological discomfort can arise, potentially affecting nurses’ work functioning and performance. Using this theoretical lens explains how alarm fatigue may be linked to psychological consequences such as anxiety and depression, as well as how these responses may be related to nurses’ perceived performance in critical care environments.

To date, however, a few studies have discussed the negative psychological effects of alarm fatigue and the factors that have contributed to its negative outcomes (Nyarko et al., 2024; Salameh et al., 2024; Seok et al., 2023). A recent study showed that nurses working in ICUs are more likely to have psychological problems than those in other wards (Lasalvia et al., 2020). Another recent study showed that alarm fatigue had a positive association with perceived stress (Kwon & Kim, 2026). While these international studies have highlighted alarm fatigue as an important patient safety concern in intensive care settings, research examining its psychological consequences and impact on nurses’ performance remains limited. Evidence from the Middle East and North Africa is also scarce. Furthermore, there is a lack of research on these relationships in the context of Egyptian CCNs. Therefore, this study aimed to examine the levels of alarm fatigue, anxiety, depression, and perceived performance among critical care nurses working in intensive care units, and to investigate the correlations between these variables.

Materials and Methods

Study Design

A cross-sectional design was used in this study. This design was appropriate because it allowed the researchers to assess the levels of alarm fatigue, anxiety, depression, and nurses’ perceived performance at a single point in time and to examine the associations between these variables among critical care nurses working in intensive care units. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies.

Study Settings and Participants

This study was conducted at 11 ICUs across five public hospitals in Damanhur city, Egypt: three general ICUs (Unit I with 15 beds, Unit II with 10 beds, and Unit III with 5 beds) at the Medical National Institute Hospital; an ICU with 8 beds for treating chest infections at the Chest Diseases Hospital; two ICUs with15 beds each at Itai Al-Baroud Hospital; two ICUs with 10 beds each at Kom Hamada Hospital; and three ICUs with 10 beds each at Kafr El Dawar Central Hospital. Information regarding ICU characteristics, including the number of beds and staffing patterns, was obtained from the administrative records of the participating hospitals.

A convenience sampling technique was used to recruit critical care nurses who were available during the data collection period and met the study inclusion criteria. This approach was chosen due to practical constraints related to nurses’ work schedules and the high workload in intensive care units, which limited the feasibility of probability-based sampling. The target population of this study included all critical care nurses working in intensive care units in public hospitals. The eligible participants consisted of critical care nurses working in 11 ICUs across five public hospitals in Damanhur city, Egypt during the study period. Nurses who had at least one year of ICU experience and were available during the data collection period were invited to participate. The one-year minimum experience criterion was applied to ensure that participants had sufficient exposure to the ICU environment and clinical alarm systems. Nurses who did not provide care to patients or who declined to participate were excluded from the study. A sample size of 199 CCNs was determined using the OpenEpi web-based calculator, Version 3.01 (www.openepi.com), based on the following criteria: 95% confidence level, 5% absolute precision and a population size of 410. The population size was obtained from the official staffing records of the participating hospitals’ nursing administrations. However, the questionnaire was distributed to 280 CCNs; of whom, 200 completed the questionnaire at a response rate of 71.4%.

Data Collection Tools

Data on socio-demographic characteristics (age, sex, marital status and level of education), and job-related information (nurse position, years of experience, nurse-to-patient ratio, usual work schedule, overtime work, number of shift hours, and the working ICU) were collected using pre-designed questionnaires. Three tools were used to collect data on alarm fatigue, psychological stress, and CCNs’ perceived performance in ICUs. All questionnaires were administered in English, which is the primary professional language used in the participating hospitals; therefore, no translation or cultural adaptation was required. The first tool was the Alarm Fatigue Questionnaire, a tool developed by Torabizadeh and colleagues in 2016 (Torabizadeh et al., 2017), which was used to assess alarm fatigue of CCNs. The questionnaire consisted of 13 questions with answers on a 5-point Likert scale: never (0), seldom (1), sometimes (2), usually (3), and always (4), except for items 2 and 9, which were scored in reverse order. In general, 11 items are scored out of 4, the total being 44; and 2 items are scored reversely, meaning the possible maximum is 0, so the possible maximum score is 44. On the other hand, if a respondent gets 0 for the positively scored items and 4 for the negatively scored items, then the total will be 8, i.e., the minimum. Higher scores indicate higher levels of alarm fatigue and a greater effect of alarm fatigue on CCNs’ perceived performance (Torabizadeh et al., 2017).

The second tool was the Hospital Anxiety and Depression Scale (HADS), which was adopted from Mehta et al., (2018); Mehta et al. (2018) to measure the psychological outcomes of alarm fatigue by assessing the presence and severity of anxiety and depression. This questionnaire consisted of 14 self-reporting items and included two subscales for anxiety and depression. Each item was measured on a 4-point (0–3) scale, so the scores for each of the two subscales ranged from 0 to 21. Scores of 11 or higher on either the anxiety and depression subscales were considered as a “significant anxiety or depression abnormality”, while scores of 8–10 were considered as a “borderline abnormality” and 0–7 as “normal” (Mehta et al., 2018).

The third tool was the CCNs’ Performance Instrument Questionnaire, which was adopted by Barker & Nussbaum, (2011); Barker & Nussbaum (2011), to assess CCNs’ perception of their performance in caring for critically ill patients. It consisted of 9 items, including questions about CCNs’ work-related tasks, e.g., “During a work shift, are there any changes in your muscle strength, endurance or physical energy that affect your ability to perform physical tasks associated with your job?”; “Do you sometimes find it necessary to take short-cuts in patient care?)”. The CCNs’ responses were measured using a 6-point Likert scale, with answers ranging from 1 for strongly disagree to 6 for strongly agree. Because the instrument includes negatively worded items, these items were reverse-coded prior to computing the total score. Therefore, the total scores ranged from 9 to 54, with higher scores indicating higher performance. For descriptive purposes, performance scores were categorized based on the percentage of the total possible score: low performance (<50% of the maximum score), moderate performance (50% to <75%), and high performance (≥75%) (Alhowaymel et al., 2025). The items of this tool measured physical (1, 4, 8), mental (5, 7) and general (2, 3, 6, 9) performance of CCNs (Sagherian et al., 2018).

Field Work

Self-administered questionnaires were used to collect the required data after approval by the institutional review board. The questionnaires were distributed to participants during their break time after explaining the purpose of the study. Participants were informed that their responses would remain anonymous and that no identifying information was required. After completion, the questionnaires were placed by the participants in sealed envelopes and deposited in a designated collection box located in the nursing unit. This procedure ensured that the researcher could not identify individual responses and helped minimize potential social desirability bias. Data were collected by one of the study authors (S.M.A), who is experienced in critical care nursing research, and the average time to complete the questionnaires was 20-30 minutes. Data were collected from August to November 2023.

Validity and Reliability

Face validity of the questionnaire was reviewed by five experts (two academic nursing faculty members and three ICU nursing professionals) to assess clarity, relevance, and comprehensibility of the items. This review process established the face validity of the instruments before data collection. A pilot study was conducted to test the questionnaire’s reliability and study feasibility by distributing the questionnaire to 20 CCNs whose data were excluded during analysis to avoid data contamination. The Alarm Fatigue Questionnaire, HADS, and the Performance Instrument Questionnaire all had good reliability, using Cronbach’s alpha coefficient (α= .89, .87, and .85, respectively).

Ethical Considerations

This study was ethically approved by the Ethics Committee of the Faculty of Nursing, Damanhour University, Egypt (Ethical Clearance: No/60-c-8-2022). CCNs were assured that their participation in this study was voluntary and that they had the right to withdraw at any time without giving any reason. The anonymity and confidentiality of the participants were guaranteed, and only aggregated data were shared.

Statistical Analysis

Data were analysed using the IBM SPSS Statistics, Version 27.0 (IBM Corp., Armonk, NY, USA). Prior to analysis, the dataset was screened for completeness, outliers, and inconsistencies. Questionnaires with incomplete responses on key variables were excluded from the final analysis to ensure data quality. Descriptive statistics, including frequencies, percentages, means, and standard deviations, were used to summarize participants’ characteristics and study variables. Normality was assessed by looking at the standardized residuals’ histograms and normal probability (P–P) plots, which revealed a fairly normal distribution. Multicollinearity was assessed using statistics from the Variance Inflation Factor (VIF) and Tolerance. Since all tolerance values (ranging from 0.335 to 0.853) were over the 0.10 threshold and all VIF values (ranging from 1.172 to 2.986) were below the commonly accepted cutoff of 5, multicollinearity did not jeopardize the stability of the regression model (Kim, 2019). Independent samples t-test was used to compare the scores of alarm fatigue, anxiety, depression and perceived performance of CCNs according to sex, overtime work, shift hours, and type of ICU, while analysis of variance (ANOVA) was used to compare the scores according to age category, staff position, marital status, years of experience, nurse-to-patient ratio, work schedule, and level of education. Pearson’s correlation coefficient (r) was used to measure the relationship between normally distributed quantitative variables. Multiple linear regression analysis was conducted to identify factors independently associated with critical care nurses’ perceived performance. The level of significance for all tests was set at p-value < .05.

Results

Characteristics of Study Participants

Table 1 shows that the largest age group of critical care nurses was ≤25 years (49%), followed by those aged 26–<35 years (43.5%). The majority of the CCNs were males (60.5%) and married (90.5%). More than half of CCNs (58%) had a bachelor’s degree, while more than one-third of them (35.5%) had 6 to 9 years of experience (41.5%). The most frequent nurse-to-patient ratio at ICUs was 1:2 (61%), and the most frequent work schedule was the regular morning shift (49%). Moreover, less than half of CCNs (45.5%) had overtime work, and most CCNs were working in general ICUs (64.5%).

Table 1.

Sociodemographic and Job-Related Characteristics of CCNs in ICUs (N=200)*

Characteristics	Categories	n	(%)
Age	≤25	98	(49.0)
	26– <35	87	(43.5)
	≥35	15	(7.5)
Sex	Male	121	(60.5)
Sex	Female	79	(39.5)
Level of education	Technical	53	(26.5)
	Bachelor	116	(58.0)
	Master	31	(15.5)
Marital status	Single	18	(9.0)
	Married	181	(90.5)
	Widowed	1	(0.5)
Years of experience	≤5	83	(41.5)
	6 - <10	71	(35.5)
	≥10	46	(23.0)
Nurse-to-patient ratio	1:1	43	(21.5)
	1:2	122	(61.0)
	1:3	35	(17.5)
Work schedule	Regular morning shift	98	(49.0)
	Regular night shift	41	(20.5)
	Two-shift rotation (morning/evening)	26	(13.0)
	Three-shift rotation	31	(15.5)
	Irregular schedule	4	(2.0)
Overtime work	Yes	91	(45.5)
Overtime work	No	109	(54.5)
Shift hours	≥12	113	(56.5)
Shift hours	<12	87	(43.5)
Type of ICU	Cardiac	71	(35.5)
Type of ICU	General	129	(64.5)

*The total number of respondents was 200; CCNs, critical care nurses; ICUs, intensive care units.

Comparison of Alarm Fatigue, Anxiety, Depression and Perceived Performance of CCNs According to Sociodemographic and Job-Related Characteristics

Table 2 shows a significant difference between the mean scores of alarm fatigue according to age (p = .001), years of experience (p = .006), nurse-to-patient ratio (p = .001), and the type of ICU (p = .001). The mean scores of anxieties were significantly different according to nurse-to-patient ratio (p = .001) and work schedule (p = .004), while the mean scores of depression were significantly different according to age (p = .009), years of experience (p = .042), and nurse-to-patient ratio (p = .037). On the other hand, the mean scores of CCNs’ perceived performance were significantly different according to age (p = .004), years of experience (p = .005), nurse-to-patient ratio (p = .003), work schedule (p = .001) and overtime work (p = .001).

Table 2.

Comparison of Alarm Fatigue, Anxiety, Depression and Perceived Performance of CCNs According to Sociodemographic and Job-Related Characteristics in ICUs (N=200)

Characteristics		n	Alarm fatigue	Anxiety	Depression	Performance
Age^b	≤25	98	25.6 ± 5.9	8.1 ± 4.7	11.4 ± 4.6	22.9 ± 7.2
	26– <35	87	22.0 ± 5.1	9.1 ± 4.2	9.6 ± 3.6	25.8 ± 7.2
	≥35	15	20.86 ± 5.19	6.80 ± 3.54	9.60 ± 3.48	26.46 ± 8.59
	t/f (p-value)		11.79 (.001)	2.12(.095)	4.887 (.009)	4.270 (.004)
Sex^a	Male	121	23.45 ± 5.98	8.42 ± 4.95	10.12 ± 4.42	23.75 ± 7.49
Sex^a	Female	79	24.01± 5.52	8.45 ± 3.64	11.05 ± 3.77	25.53 ± 7.28
	t/f (p-value)		-0.66 (.500)	-.04 (.966)	-1.53 (.092)	-1.65 (.099)
Level of education^b	Technical	53	24.88 ± 3.89	7.56 ± 4.94	11.01± 3.82	23.54 ± 6.57
	Bachelor	116	23.64 ± 6.78	8.89 ± 4.56	10.55 ± 4.67	24.75 ± 8.12
	Master	31	21.70 ± 3.66	8.22±2.81	9.35 ± 2.44	24.90 ± 6.16
	t/f (p-value)		3.00 (.052)	1.66 (.148)	1.57 (.209)	0.53 (.506)
Marital status^a	Single	18	23.22 ± 5.73	8.33± 3.39	9.61± 3.38	27.33 ± 10.01
	Married	181	23.73 ± 5.82	8.43± 4.57	10.58 ± 4.27	24.17 ± 7.13
	Widowed	1	-	-	-	-
	t/f (p-value)		.17 (.844)	.16 (.846)	.44 (.644)	1.49 (.226)
Years of experience^b	≤5	83	22.52 ± 4.23	8.20 ± 4.02	9.82 ± 2.50	22.60 ± 6.92
	6 - <10	71	22.60 ± 7.08	8.23 ± 3.91	9.85 ± 4.50	25.73 ± 8.39
	≥10	46	25.22 ± 4.97	8.84 ± 5.26	11.39 ± 4.52	25.82 ± 6.12
	t/f (p-value)		5.32 (.006)	.45 (.638)	3.40 (.035)	4.54 (.012)
Nurse-to-patient ratio^b	1:1	43	21.65 ± 5.25	6.51 ± 3.99	9.37 ± 3.87	26.68 ± 7.27
	1:2	122	21.68 ± 5.07	8.31± 4.55	9.8 0± 3.36	25.67 ± 8.64
	1:3	35	24.95 ± 5.84	9.15 ± 4.43	11.08 ± 4.42	23.38 ± 6.87
	t/f (p-value)		8.23 (.001)	5.84 (.003)	3.28 (.037)	3.48 (.032)
Work schedule^b	Regular morning shift	98	23.44 ± 5.54	6.88 ± 6.01	10.17 ± 3.96	27.96 ± 9.27
	Regular night shift	41	23.07 ± 5.35	7.04 ± 3.83	10.36 ± 4.41	27.29 ± 7.32
	Two-shift rotation	26	24.15 ± 7.59	8.67 ± 3.76	10.67 ± 3.96	23.90 ± 5.84
	Three-shift rotation	31	24.61 ± 5.61	10.67 ± 4.85	10.73 ± 3.90	23.00 ± 6.32
	Irregular schedule	4	25.00 ± 5.94	9.75 ± 5.50	13.75 ± 5.12	22.57 ± 6.87
	t/f (p-value)		.44 (.778)	4.11 (.003)	0.71 (.632)	4.93 (.001)
Overtime work^a	Yes	91	23.42 ± 5.96	8.49 ± 4.06	10.58 ± 3.49	22.88 ± 6.85
Overtime work^a	No	109	23.88 ± 5.66	8.39 ± 4.80	10.41 ± 4.71	26.34 ± 7.72
	t/f (p-value)		-.54 (.586)	.15 (.862)	.28 (.777)	3.35 (.001)
Shift hours^a	>12	113	24.01 ± 5.97	8.75 ± 4.96	10.57 ± 4.32	24.57 ± 7.34
Shift hours^a	<12	87	23.22 ± 5.55	8.19 ± 4.05	10.42 ± 4.11	24.36 ± 7.55
	t/f (p-value)		.95 (.337)	-.88 (.378)	-.25 (.803)	-.19 (.843)
Type of ICU^a	Cardiac	71	25.76 ± 6.52	8.30 ± 4.32	10.10 ± 3.76	24.74 ± 7.34
Type of ICU^a	General	129	22.52 ± 5.02	8.51 ± 4.56	11.19 ± 4.82	23.92 ± 7.66
	t/f (p-value)		3.90 (.001)	-.30 (.700)	1.77 (.077)	-.73 (.461)

^aIndependent t test.

^bANOVA test was conducted at p<.05.

Age showed significant differences in alarm fatigue (F = 11.79, p = .001), depression (F = 4.887, p = .009), and perceived performance (F = 4.270, p = .004), with nurses aged ≤25 years reporting higher alarm fatigue and depression and lower performance than older groups. Years of experience also demonstrated significant differences in alarm fatigue (F = 5.32, p = .006), depression (F = 3.40, p = .035), and performance (F = 4.54, p = .012), where nurses with ≥10 years of experience had higher alarm fatigue and depression but better perceived performance compared with those with fewer years of experience. Regarding workload factors, nurse-to-patient ratio significantly influenced alarm fatigue (F = 8.23, p = .001), anxiety (F = 5.84, p = .003), depression (F = 3.28, p = .037), and performance (F = 3.48, p = .032), with the 1:3 ratio showing the highest psychological distress and lowest performance. Work schedule showed significant subgroup differences in anxiety (F = 4.11, p = .003) and performance (F = 4.93, p = .001), particularly higher anxiety among nurses working three-shift rotations and higher performance among those working regular morning shifts. In addition, nurses without overtime work reported significantly higher perceived performance (t = 3.35, p = .001) compared with those working overtime, while cardiac ICU nurses reported significantly higher alarm fatigue than general ICU nurses (t = 3.90, p = .001).

Approximately 22.5% of critical care nurses had borderline anxiety scores and 29.5% had abnormal anxiety scores. Similarly, 29% of nurses had borderline depression scores and 47.5% had abnormal depression scores. Regarding perceived performance levels, 60.5% of nurses reported low performance, 34.5% moderate performance, and 5% high performance.

Pearson correlation analysis revealed that alarm fatigue score was positively correlated with anxiety (r = .333, p < .001) and depression scores (r = .630, p < .001). Anxiety score was also positively correlated with depression score (r = .171, p = .015). Additionally, perceived performance score was negatively correlated with alarm fatigue score (r = −.536, p < .001), anxiety (r = −.311, p < .001), and depression scores (r = −.312, p < .001), indicating that higher levels of alarm fatigue and psychological distress were associated with lower perceived performance among CCNs (Table 3).

Table 3.

Correlation Between Alarm Fatigue, Anxiety, Depression, and Perceived Performance Among CCNs (N=200)

Variables	1	2	3	4
1. Alarm fatigue	1
2. Anxiety	.333**	1
2. Anxiety	<.001
3. Depression	.630**	.171*	1
3. Depression	<.001	.015
4. Performance	−.536**	−.311**	−.312**	1
4. Performance	<.001	< .001	< .001

*Correlation is significant at the .05 level (2-tailed).

**Correlation is significant at the .01 level (2-tailed).

Multiple regression analysis indicated that the model explained 43.8% of the variance in CCNs’ perceived performance (R² = .438, Adjusted R² = .402, p < .001). Alarm fatigue (β = −.514, p < .001), regular night work schedule (β = −.424, p < .001), three-shift rotation (β = −.159, p = .047), and absence of overtime work (β = −.229, p < .001) were significant predictors of perceived performance (Table 4).

Table 4.

Multiple Regression of Factors Affecting CCNs’ Perceived Performance in ICUs

Variables	Categories	B	SE	β	t	95% CI for β	p-value	Tolerance	VIF
Age		.004	.114	.003	.036	-.22-.22	.971	.538	1.857
Years of experience		-.096	.176	-.045	-.546	-.44-.25	.586	.443	2.259
Nurse-to-patient ratio
	1:1
	1:2	-1.002	1.117	-.066	-.897	-3.20-1.20	.371	.558	1.791
	1:3	-.580	1.456	-.030	-.398	-3.4-2.29	.691	.542	1.846
Work schedule
	Regular morning
	Regular night	-6.306	1.408	-.424	-4.480	-9.08--3.52	.000	.335	2.986
	Two-shift rotation	-2.350	1.529	-.128	-1.538	-5.36-.66	.126	.436	2.296
	Three-shift rotation	-3.270	1.634	-.159	-2.001	-6.49--.04	.047	.474	2.108
	Irregular schedule	-2.952	3.170	-.056	-.931	-9.20-3.30	.353	.842	1.188
Overtime work
	Yes
	No	-3.413	.885	-.229	-3.854	-5.15--1.66	.000	.853	1.172
Alarm fatigue		-.661	.100	-.514	-6.585	-.85--.46	.000	.492	2.032
Anxiety		-.158	.103	-.095	-1.526	-.36-.04	.129	.782	1.279
Depression		.030	.127	.017	.234	-.22-.28	.816	.587	1.704

*Model p<.001; S. E, Std. Error; CI, Confidence Interval; VIF, variance inflation factor.

*R2=.438; Adjusted R2=.402.

Discussion

Noise in the ICU may be related to stress, which has various subjective and objective effects on the performance and mental competence of CCNs, as well as being a major risk factor for burnout (Lewandowska et al., 2020). Hospital-recommended noise levels are frequently exceeded in ICUs, posing a major threat to the psychological and physiological well-being of patients and staff (Farajalla et al., 2026). The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) has recognized noise as an important indicator for medical errors committed by both physicians and nurses, advising against having loud noise that may interfere with their effective communications (Clarke & Donaldson, 2008).

The present study revealed a link between alarm fatigue and its psychological stress outcomes of anxiety and depression among CCNs at ICUs in Damanhur hospitals, which also had a negative impact on CCNs’ performance. High abnormal anxiety and depression scores were common among approximately one-third and one-half of CCNs, respectively. Such scores should, however, be cautiously interpreted because the CCN might have overreported the data based on their self-perception (Farajalla et al., 2026). The present finding is consistent with earlier studies comparing the prevalence of anxiety and depression among healthcare providers in Egypt and Saudi Arabia (Arafa et al., 2021). The findings showed that the proportions of HCPs in Egypt who had mild-to-severe depression (78.5%) and mild-to-moderate anxiety (68.4%) were higher than those among HCPs in Saudi Arabia, being 51.7% and 41.7%, respectively (Arafa et al., 2021).

The positive correlation between anxiety and depression mean scores with the mean score of alarm fatigue can be due to the demanding workload in their ICUs, coupled with their lack of rest and other factors (e.g. scheduling, personal). In fact, most of the nurses who participated in this study reported a nurse-to-patient ratio of 1:2 in their ICUs, necessitating the monitoring of alarms for their patients. The hospital environment is becoming increasingly noisier as more sophisticated technologies are introduced, even though the safety that technology is supposed to provide is being disproved (Alshammari et al., 2024; Lewandowska et al., 2020). Such a technology can have a negative impact on patient outcomes if appropriate actions are not undertaken. Despite the vicious cycle between fatigue and anxiety or depression, it is important to address both because a decrease in anxiety can significantly reduce the likelihood of depression (Brown & Kroenke, 2009), while anxiety can significantly increase the risk of developing depression (Polikandrioti et al., 2018). In addition, nurses in ICUs are more prone to having alarm fatigue. This finding should be viewed as a warning to the ICU administrators, who should pay close attention to reduce the unnecessary alarms of devices and equipment. Special psychological support should be provided to CCNs who spend more time in a noisy environment and manage to reduce the shift time for CCNs in ICUs (Qtait et al., 2026). Manufacturers need to add safeguards to reduce unnecessary warnings, provide more intuitive and user-friendly systems, and ensure that individual alarms are given appropriate sounds based on their priority level (Au-Yeung et al., 2019).

The present study revealed a significantly negative correlation between alarm fatigue and CCNs’ performance, with approximately one-third of CCNs reporting moderate performance and more than half low performance. CCNs’ performance may decrease due to exposure to noisy ICU environments that increase the risk for psychological stress, anxiety and depression. Low CCNs’ performance could be attributed to the psychological impact of the high numbers of alarms in ICUs, which is consistent with other studies (Alkubati et al., 2025; Ardic et al., 2022; Babapour et al., 2022). The study’s findings can also be viewed via the lens of Roy’s Adaptation Model, which views persons as adaptive systems that respond to environmental stimuli via physiological and psychological processes (Roy, 2011). In intensive care units, frequent clinical alarms and intense workloads may be environmental stressors that test nurses’ adaptive capabilities. According to this theory, when environmental pressures exceed available coping resources, psychological responses such as anxiety and depression may arise, which may then be linked to nurses’ work functioning and perceived performance (Roy, 2011). Interpreting the findings from this theoretical perspective sheds light on how alarm fatigue may be linked to psychological discomfort and perceived work performance among critical care nurses.

Alarm management education can significantly reduce alert frequency and the prevalence of alarm fatigue among nursing professionals. Sliman et al. reported unsatisfactory levels of nurses’ knowledge and skills in alarm management (Sliman A et al., 2020). In the present study, CCNs’ performance showed a negative correlation with hospital anxiety and depression, indicating that psychological issues negatively associated with their perceived performance. Most of the false alarms in healthcare settings represent no viable threat to patients. False alarms can increase alarm fatigue and load and divert health-care personnel’ attention away from serious alerts that indicate actual or imminent damage (Alsuyayfi & Alanazi, 2022). Both patients and nursing staff may suffer catastrophic implications as a result of alarm fatigue (Lewandowska et al., 2020). Therefore, implementing alert management plans and assessing the amount of alarm fatigue are essential. Because nursing staff act as the frontline of patient care, they are the most affected group by alarm fatigue and have the opportunity to make changes and improve the safety of critically ill patients (Ejheisheh et al., 2025; Lee et al., 2021).

CCNs who care for patients may get desensitized to the beeps and buzzes of medical instruments, which are frequently made by mistake. Noise impairs nurses’ ability to concentrate, interferes with their cognitive functioning and increases physiological changes such as high blood pressure and heart rate that reduce staff productivity and patient satisfaction (Alsuyayfi & Alanazi, 2022). According to a study by Simons et al. (2018), there is an association between various noise parameters and sleep quality, and noise has a negative consequence on sleep quality (Simons et al., 2018). The Joint Commission’s safety goals, which include decreasing the harm caused by hospital alarm systems as one of their top concerns, would be more easily achieved with the help of combating alarm exhaustion (Clarke & Donaldson, 2008).

Clinical alarms of medical equipment in ICUs are a digital health hazard and one of the most important strategies for CCNs to be alerted to immediate or prospective hazards to critically ill patients (Shaoru et al., 2023). Fatigue leads to reduced performance and efficiency, as well as a higher risk of accidents. Fatigue impairs one’s capacity to reason clearly. As a result, exhausted people become unable to assess their own level of impairment and are unaware that they are not doing as well or safely as they would be if they were not fatigued. Numerous accidents in the real world have been caused by operator fatigue-related performance issues. Drew et al. (2014) reported that equipment should provide instructions to assist in better adjusting warning sets to patients. Because computers are more reliable than humans, there is a potential to enhance hemodynamic monitoring as well as reduce alarm fatigue using computers (Drew et al., 2014).

Strengths and Limitations

This study has several strengths. To the best of the researchers’ knowledge, it is among the first multicenter studies in Egypt to simultaneously examine alarm fatigue, anxiety, depression, and perceived performance among critical care nurses. The study included nurses from multiple intensive care units and used validated instruments with established psychometric properties to assess the study variables. Additionally, the sample size exceeded the minimum requirement determined through power analysis, enhancing the robustness of the statistical analyses. Despite these strengths, several limitations should be considered. The cross-sectional design of the study decreased the likelihood of a causal relationship and the generalizability of the findings. However, this can be expanded in different other settings. Longitudinal studies are needed to better understand the directionality of these associations. The use of convenience sampling may introduce selection bias and limit the generalizability of the findings to other critical care settings.

Additionally, certain variables that may influence alarm fatigue and psychological distress, such as specific sources of alarms in the ICU environment, were not assessed in the present study. Results should also be cautiously interpreted because they were self-reported, and the CCNs’ self-perception may have led to over-reporting. Also, the performance in this study was measured using a self-reported instrument, which reflects nurses’ perceived performance rather than objectively measured clinical performance. Additionally, although several demographic and work-related factors were examined, other potential confounding variables such as organizational support, workload intensity, and staffing levels were not fully assessed. Another limitation was that the questionnaire required approximately 20–30 minutes to complete and was administered during nurses’ break times, and survey fatigue may have occurred and could have influenced some responses. Future research should consider using probability sampling methods, longitudinal study designs, and multi-center data collection across different healthcare systems to improve generalizability and better examine the directionality of these relationships.

Implications for Nursing Practice

Based on the study findings, healthcare administrators should implement structured alarm management protocols and provide regular training programs to help critical care nurses effectively manage clinical alarms. This study suggests the actions that must be undertaken to develop universally accepted standards for alarm handling across all ICUs. Health authorities should also give more attention to psychological support for CCNs working in ICUs and provide them with strategies for time management in these noisy and stressful environments, which may lead to losing focus on the issues facing their patients.

Conclusions

Critical care nurses in ICUs experience high levels of alarm fatigue, anxiety and depression, which may interfere with their perceived performance. Accordingly, administrators in ICUs should pay special attention to manage and reduce the unnecessary and loud alarms of devices and equipment. In addition, special psychological support should be provided to CCNs who spend more time in noisy environments. To better understand the significance of these issues on CCNs’ performance and health, it may be helpful to investigate the relationship between alarm fatigue’s psychological effects and nursing performance. Such a relationship can help build techniques for coping with excessive alarms in ICUs by providing guidelines and recommendations.

Supplemental Material

Supplemental Material - Assessing the Link Between Alarm Fatigue, Stress, and Perceived Performance Among Critical Care Nurses in Egypt

Supplemental Material for Assessing the Link Between Alarm Fatigue, Stress, and Perceived Performance Among Critical Care Nurses in Egypt by Sameer A. Alkubati, Basma Salameh, Abdulsalam M Halboup, Ahmed Loutfy, Eddieson Astodello Pasay-an, Mohamed A. Zoromba, Heba E. El-Gazar, Ahmed El-Monshed, Mohamed A. Tlili, Shimmaa Mohamed Elsayed in Sage Open Nursing.

Supplemental Material

Supplemental Material - Assessing the Link Between Alarm Fatigue, Stress, and Perceived Performance Among Critical Care Nurses in Egypt

Footnotes

Acknowledgement

We would like to thank all CCNs who participated in this study.

ORCID iDs

Sameer A. Alkubati

Basma Basha

Mohamed A. Zoromba

Heba E. El-Gazar

Ethical Considerations

All methods were accomplished in harmony with the related guidelines and regulations, including the Declaration of Helsinki. The study was approved by the Ethical Committee at Faculty of Nursing, XXXX University (approval No/60-c-8-2022). Permission was obtained from the intended hospitals’ administrations and units’ managers before conducting the study. Informed consent was taken from the participants after explaining the study purposes, risks and benefits.

Consent to Participate

All participants were assured that their participation was voluntary, and they might withdraw at any time without any reasons. Anonymity and confidentiality of the participants were confirmed in which only aggregated data were used.

Author Contributions

Sameer A. Alkubati: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing. Basma Basha: Conceptualization, Writing – review & editing; Abdulsalam M Halboup: Conceptualization, Writing – review & editing; Ahmed Loutfy: Conceptualization, Writing – review & editing. Eddieson Pasay-an: Conceptualization, Writing – review & editing Mohamed A. Zoromba: Conceptualization, Writing – review & editing Heba E. El-Gazar: Conceptualization, Writing – review & editing Ahmed Hashem El-Monshed: Conceptualization, Writing – review & editing; Mohamed AYoub. Tlili: Conceptualization, Writing – review & editing. Shimmaa Elsayed: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. All authors read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data are available at request from the corresponding author.*

Supplemental Material

Supplemental material for this article is available online.

Appendix

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