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Abstract

Background

Sleep is essential for physiologic function and mental health stability, which promotes recovery from severe illness. However, poor sleep quality is a common complaint in the critical care unit. In intensive care units (ICUs), physical aspects of patient care are often prioritized over humanized aspects of care.

Objective

To assess the level of sleep quality and its associated factors among adult patients admitted to critical care units in Addis Ababa.

Method

A cross-sectional study design was conducted in three selected public hospitals. Over 4 months of the period we consecutively collected data among ICU patients, who were oriented to time, person, and place on the day of discharge. A modified freedman self-reporting and personal characteristics questionnaire was used. The data were entered into Epi-data version 4.4.6.0 and analyzed with SPSS 25. Both bivariate and multivariate analyses were conducted. A P-value of <.05 indicated statistical significance, and an adjusted odds ratio with a 95% confidence range was used to show the strength of the association.

Result

Of the total 102 participants, 57.8% of ICU patients had poor sleep quality. Four variables were identified as significant associations with poor sleep quality among ICU patients. These included elderly age (adjusted odd ratio [AOR] = 3.4; 95% confidence interval [CI]: 1.42, 7.93), comorbidity (AOR = 2.5; 95% CI: 1.24, 9.03), light exposure (AOR = 2.0; 95% CI: 1.16, 5.11), and the monitor's alarm (AOR = 1.7; 95% CI: 1.04, 8.23) were identified as significant association factors for poor sleep quality.

Conclusion and recommendation

Poor sleep quality was a major concern for ICU patients in this study. Elderly individuals, those with comorbidity, light exposure, and monitor alarms have been associated with poor sleep quality. Reducing nighttime light and adjusting alarm settings may significantly improve sleep quality, resulting in better recovery results in critical care units.

Keywords

sleep disorder environmental exposure risk factors intensive care units sleep deprivation

Introduction

Sleep is a cyclical state characterized by temporary unconsciousness, during which cognitive and sensory links to external stimuli are greatly decreased. It is made up of alternating phases of nonrapid eye movement and rapid eye movement sleep, which normally occur four to six times every night, with each cycle lasting around 90‒100 minutes. Overall, adults require approximately 7‒8 hours of sleep to maintain optimal health and functioning (Grimm, 2020; Patel et al., 2024).

An intensive care unit (ICU) is an extremely complicated environment in which critically ill patients are handled with intensive care monitoring and invasive devices (Elliott et al., 2011). However, poor sleep quality is a common complaint in critical care units (Brito et al., 2020; Dorsch et al., 2019). Patients also experience sleep disruption after they have been transferred from an ICU to a recovery room (Altman et al., 2017). Sleep disturbances substantially influence patients’ recovery from chronic diseases, especially in critical care settings. Disrupted sleep is associated with a variety of negative effects, including immunological dysfunction, decreased wound healing, and higher susceptibility to infections, which can impede recovery processes (Altman et al., 2017; Garbarino et al., 2021). Furthermore, sleep disturbance has an impact on the body functions that are critical to returning to a normal health state, including respiratory muscle weakness, cardiovascular effects resulting in hypertension, heart failure, and stroke, and hormonal changes resulting in insulin resistance in diabetics (Delaney et al., 2015; During & Kawai, 2017; Singh et al., 2022).

Review of Literature

A systematic review showed that about 66% of ICU patients experience poor sleep quality during their ICU stay (Shih et al., 2023). Studies revealed that the prevalence of poor sleep quality among ICU patients was significantly higher in different settings, in Australia 64% (Bihari et al., 2012), Iran 55% (Adib-Hajbaghery et al., 2012), Poland 50.1% (Lewandowska et al., 2019), India, 47% (Naik et al., 2018), and China 46.1% (Zhang et al., 2013). Furthermore, very little research has been conducted in Africa; a study of ICU patients in South Africa found that 70.6% of participants had poor sleep quality (Ehlers et al., 2013). Furthermore, in Egypt, a study conducted using the Freedman questionnaire found that 48% of ICU patients reported poor sleep quality (Magdy et al., 2019). Poor sleep quality is a significant public health concern in Ethiopia (Abate et al., 2024; Edmealem et al., 2024). However, there is limited data on sleep quality among critical care unit patients in Ethiopia.

Previous studies identified several factors that have been associated with poor sleep quality among critical care unit patients. These include elderly age, gender, environmental noise, heart monitoring alarms, light exposure, nursing care activities, pain and discomfort, stress and anxiety, frequency of admission, procedure and intervention, severity of illness, medication, bedside phone calling, medical conditions, and mechanical ventilation (Adell et al., 2021; Ahn et al., 2023; Bihari et al., 2012; Ding et al., 2017; Silva et al., 2024; Sundstrøm et al., 2021).

To our knowledge, no adequate studies have been undertaken to evaluate sleep quality among critical care unit patients in Addis Ababa. Hence, this study aimed to assess the level of sleep quality and identify its associated factors among adult patients in critical care units at selected public hospitals. By identifying key factors affecting sleep, this study aimed to lay a framework for encouraging preventative measures to improve patient recovery and general health. Improved sleep care may significantly contribute to better clinical outcomes, increasing recovery.

Methods

Study Design

A cross-sectional study was conducted among ICU patients who were admitted to ICUs. The study was conducted in the ICUs of selected public hospitals in Addis Ababa, the capital city of Ethiopia. The city has about 12 public hospitals. For this study, three public hospitals with advanced ICU services, large patient flow, and well-recorded systems were purposefully selected. These hospitals included the Alert Comprehensive specialized Hospital, Tikur Anbesa Specialized Hospital, and Saint Peter Specialized Hospital. The study period was from February 1 to June 30, 2020, by administering a self-rating Freedman sleep quality assessment questionnaire among ICU patients who were admitted to the selected public hospitals in Addis Ababa. The data were collected prospectively for 4 months.

Sample

All adult patients who were admitted to ICUs of public hospitals in Addis Ababa were included in the sample.

Study Population

The study included all patients admitted to ICUs from February 1 to June 30, 2020, at the selected public hospitals in Addis Ababa. A convenience sampling technique was used to recruit the participants.

Eligibility Criteria

Inclusion

This study included adult ICU patients (aged 18 and above) who had been hospitalized for at least two nights and were completely oriented to location, person, and time. This criterion ensured that participants received adequate exposure to the ICU setting, allowing for a more realistic assessment of how protracted stays affect sleep quality and mental orientation. By mandating this minimal stay, the study may be able to better capture the impacts of ICU ambient elements such as noise, light, and frequent medical interventions, all of which are well-documented disruptors of sleep patterns in critical care settings. Limiting the study to patients discharged between February 1 and May 30, 2020 ensures a consistent cohort that captures data relevant to that time period while avoiding variation caused by shifts in ICU procedures or seasonal changes.

Exclusion

Individuals with moderate-to-severe dementia, who had previous sleep disorders, did not speak the Amharic language, and were transferred to other facilities while unconscious were excluded from the study.

Sample Size Determination

The required sample size was determined using the single population proportion formula by taking the following assumptions: estimating proportion = 50%, 95% confidence level (Z = 1.96), and 9.7% margin of error (d) due to time and budget constraints, which yields 102. Convenience sampling was used in selecting patients who are easily accessible based on the inclusion criteria until fulfill the required sample size. This approach makes sure that every eligible patient is considered, resulting in a comprehensive and representative cohort for the study.

Sampling Technique

Of the 297 total patients admitted to the ICU during the data collection period, 102 were included by the convenient sampling technique because they met the inclusion criteria and agreed to participate in the study after receiving informed consent from the data collectors of each ICU. Study participants were screened every morning from Monday to Friday, starting from 8:30 to 12:30 a.m., at each ICU at the time of discharge (Figure 1).

Figure 1.

Schematic representation of ICU patients’ screening, enrollment, and participant in Freedman sleep quality questionnaire performed at the three ICUs in Addis Ababa, Ethiopia, 2020. ICU = intensive care unit.

Operational Definition

The operational definition of the Freedman sleep quality scale was used to categorize as good and poor sleep quality. This questionnaire assesses sleep quality and its disruptions among ICU patients. Patients rated their sleep on a 1 to 10 scale (1 = poor, 10 = excellent) both at home and in the ICU (Freedman et al., 1999). This assessment was repeated throughout their ICU stay. Daytime sleepiness was also rated on a 1 to 10 scale (1 = unable to stay awake, 10 = fully alert). Environmental factors were evaluated for their impact on sleep disruption. These included noise, light, nursing interventions, diagnostic tests, vital sign checks, blood draws, and medication administration. Specific ICU noises, such as telemetry alarms, ventilator sounds, pulse oximetry, staff communications, Intravenous pump alarms, suctioning, doctor's beepers, and TV/phone noise, were also assessed for their disruptive effects. Finally, sleep quality can be considered good if they report being satisfied with their sleep in the previous night. This is confirmed by a Freedman Sleep Quality Scale score of 7 or above (Freedman et al., 1999). A patient is considered to have poor sleep quality if they report being dissatisfied with their sleep the night before. This is confirmed by a Freedman Sleep Quality Scale score of less than 7 (Bihari et al., 2012; Freedman et al., 1999).

Data Collection Tools

The data was collected using a structured questionnaire based on a modified version of the Freedman sleep quality questionnaire (Freedman et al., 1999). Data was collected using a pretested, self-administered questionnaire and chart review (for clinically related variables). It was a self-rated scale with four major sections: demographics, clinical features, sleep quality components, and sleep-disrupting factors. All sleep quality questions in the questionnaire were graded on a 10-point scale from 1 (bad) to 10 (excellent), as was daytime sleepiness (1 = unable to remain awake; 10 = fully alert) during the duration of their ICU stay, as well as on the first day, midway, and end of their stay in the ICU. On a 10-point scale, participants rated many environmental factors that impact their sleep quality as disrupted (1 = no disruption, 10 = significant disturbance). The tool was verified with Cronbach's alpha global value of .933 (Adell et al., 2020).

Data Quality Control

Predata collecting training was provided for three BSc nurse data collectors, and adequately designed data collection equipment was constructed to ensure data quality. Three BSc nurses were assigned from each ICU to distribute the questionnaire daily for ICU patients. A pilot study of 15 questionnaires was done at Saint Paul's Millennium Medical College before 1 week of actual data collection. The primary investigator ensured that all data was complete on each data-collecting day.

Data Process and Analysis

The data was checked for completeness, cleaned, coded, and imported into Epi-Data Manager (version 4.4.6). The data was then analyzed with SPSS (version 25). The study variables were reported with descriptive statistics (frequency and percentage) for categorical variables and summary statistics (mean and standard deviation) for continuous variables.

We used a binary logistic regression to determine the association between each independent variable and the dependent variable. Adjusting for potential confounders, the multivariate analysis included variables with a P <.25 at a 95% confidence level from the bivariate analysis. We used the standard error to conduct a multicolinearity test to determine the linear connection between independent variables. Multicolinearity was checked using variable inflation factors, with a mean value of 1.32. We used the Hosmer–Lemeshow goodness-of-fit test to check model fit. The omnibus test was significant (P <.001), which showed the model was fitted. During the multivariate analysis, we evaluated the adjusted odds ratio with a 95% confidence interval (CI) to find associated factors for poor sleep quality among ICU patients. Variables with a P <.05 and a 95% CI were shown to be significantly associated with poor sleep quality. The final data was presented as text, tables, and figures.

Results

A total of 102 patients were eligible and participated in the study (Figure 1). The participants’ mean age was 43.3 (SD ± 13.7) years. About 53% of the study participants were female, and 60.8% were under 60 years old. Regarding the residence of participants, 62.7% of the participants were urban, and the remaining 37.3% were rural. Of the total participants, nearly 54% of them were married, and only 30% of the study participants graduated from college. Furthermore, about 32% of the study participants’ occupations were merchants (Table 1).

Table 1.

Distribution of sociodemographic characteristics among ICU patients of public hospitals in Addis Ababa, Ethiopia, 2020.

Variables	Categories	Frequency	Percentage
Sex	Female	54	52.9
Sex	Male	48	47.1
Age	<60 years	62	60.8
Age	≥60 years	40	39.2
Residence	Urban	64	62.7
Residence	Rural	38	37.3
Marital status	Married	55	53.9
	Single	37	36.3
	Others	10	9.8
Level of education	College graduated	31	30.4
	Complete secondary school	53	52.0
	Can read and write	18	17.6
Occupation	Merchant	33	32.4
	Employee	27	26.5
	Unemployment	23	22.5
	Housewife	19	18.6

Note. ICU = intensive care unit.

Sociodemographic Characteristics

Clinical-Related Characteristics

In this study, the mean length of stay in the ICU was 16.2 days + 3.27 SD. Almost one-third of respondents had a history of ICU hospitalization, with 66.7% experiencing extended stays (≥15 days). Only 21.6% of the study participants were brought to the ICU as a result of trauma, whereas 60% were hospitalized for medical reasons. Furthermore, 33.4% of the ICU patients had at least one comorbidity, with cardiac disease, diabetes, and hypertension being the most common. Furthermore, 33.4% of ICU patients had at least one comorbidity, with the most common being cardiac disease, diabetes mellitus, and hypertension (22.5%, 18.6%, and 14.7%, respectively). About 40.2% of the study participants received a mechanical ventilation during ICU stays (Table 2).

Table 2.

Distribution of clinical variables among ICU patients of public hospitals in Addis Ababa, Ethiopia, 2020.

Variables	Categories	Frequency	Percentage
Previous ICU admission	No	76	74.5
Previous ICU admission	Yes	26	25.5
Length of ICU stay	<15 days	34	33.3
Length of ICU stay	≥15 days	68	66.7
Trauma	No	80	78.4
Trauma	Yes	22	21.6
Medical	No	40	39.2
Medical	Yes	62	60.8
Surgical	No	84	82.4
Surgical	Yes	18	17.6
Comorbidity
At least one comorbidity	No	68	66.7
At least one comorbidity	Yes	34	33.4
Hypertension	No	87	85.3
Hypertension	Yes	15	14.7
Diabetic mellitus	No	83	81.4
Diabetic mellitus	Yes	19	18.6
Cardiac disease	No	79	77.5
Cardiac disease	Yes	23	22.5
Mechanical ventilation	No	61	59.8
Mechanical ventilation	Yes	41	40.2

Note. ICU = intensive care unit.

Environmental and Physiological-Related Factors

Based on the Freedman sleep quality scale on a 10-point scale, participants rated many environmental factors that impact their sleep quality as disrupted (1 = no disruption, 10 = significant disturbance). Accordingly, we categorized the 10-point scale into two categories: those who rated the score ≤5 on the Freedman sleep disruption factors were categorized as “no,” indicating it did not disrupt the sleep quality. In contrast, the ICU patients who rated >5 for the Freedman sleep disruption factors were categorized as “yes,” showing the factors significantly cause sleep disruption.

More than half (52%) of the participants rated the monitor alarm as a disrupting factor in sleep quality in the ICU. Nearly 30% of the participants reported that bedside phone calls disrupted sleep among ICU patients. The majority (57%) of the participants believed that talking was a significant cause of sleep quality. Furthermore, 37% of the participants also reported that doctors or nurses’ papers, pagers, and phone calls disrupted their sleep quality during their ICU stay. The most frequent causes of sleep disruption were light, pain, noise, and medication administration (71.6%, 63.7%, 61.8%, and 54.9%, respectively) (Table 3).

Table 3.

Distribution of noises and environmental factors that disrupt sleep quality of intensive care unit patients at public health in Addis Ababa, Ethiopia, 2020.

Variables	Categories	Frequency	Percentage
Monitor alarm	No	49	48.0
Monitor alarm	Yes	53	52.0
Finger probe	No	88	86.3
Finger probe	Yes	14	13.7
Nebulizer	No	67	65.7
Nebulizer	Yes	35	34.3
Bedside phone calling	No	72	70.6
Bedside phone calling	Yes	30	29.4
Oxygen administration	No	77	75.5
Oxygen administration	Yes	25	24.5
Talking	No	44	43.1
Talking	Yes	58	56.9
Television	No	92	90.2
Television	Yes	10	9.8
Doctors/nurses’ paper, pagers, and phone calling	No	64	627
Doctors/nurses’ paper, pagers, and phone calling	Yes	38	37.3
Light	No	29	28.4
Light	Yes	73	71.6
Noise	No	39	38.2
Noise	Yes	63	61.8
Anxiety	No	71	69.6
Anxiety	Yes	31	30.4
Pain	No	37	36.3
Pain	Yes	65	63.7
Physical examination	No	85	83.3
Physical examination	Yes	17	16.7
Medication administration	No	46	45.1
Medication administration	Yes	56	54.9
Vital sign measurement	No	59	57.8
Vital sign measurement	Yes	43	42.2
Blood sampling	No	87	85.3
Blood sampling	Yes	15	14.7
Doctors’/nurses’ activities	No	48	47.1
Doctors’/nurses’ activities	Yes	54	52.9

The most significant environmental-related self-reported factors affecting sleep quality among ICU patients were noise, pain, doctor/nurse activities, and light, with mean scores of 6.7, 5.6, 5.2, and 5.1, respectively (Figure 2). Furthermore, in terms of noise-related sleep disturbance factors, monitor alarms, talks, and phone calls were the most significant noise sources (Figure 3).

Figure 2.

Mean patient perceptions of sleep-disrupting factors in adult ICU patients at public hospitals in Addis Ababa, Ethiopia, 2020. 1 = None significant and 10 = Significant sleep-disrupting factors. ICU = intensive care unit.

Figure 3.

Mean patient perceptions of noise sleep-disrupting factors in adult ICU patients of public hospitals in Addis Ababa, Ethiopia, 2020. Noise disruptions are rated on a scale of 1 (no disruption) to 10 (significant sleep disruption). ICU = intensive care unit.

Prevalence of Sleep Quality

The study found that 57.8% of ICU patients reported poor sleep quality (95% CI: 48.4, 64.9). The majority of individuals, around 74.5%, reported satisfactory sleep quality at home. This showed that there was a substantial difference in sleep quality. The study found that sleep quality in the ICU was considerably worse than at home, with a mean score of 4.9 ± SD 1.2 (P = .002) (Figure 4).

Figure 4.

Comparison of patients’ perceived sleep quality between home and intensive care unit in Addis Ababa, Ethiopia, 2020.

Risk Factors Associated with Poor Sleep Quality

In bivariable logistic regression analysis, factors were found to be statistically significant for poor sleep quality among ICU patients. These factors, including age, residence, previous ICU hospitalization, comorbidity, length of stay, mechanical ventilation, light, pain, anxiety, and monitor alarm, were candidates for multivariable logistic regression analysis, all variables with a P-value <.25. After accounting for potential confounders, four variables were identified as having statistically significant associations with poor sleep quality among ICU patients. These included elderly age, comorbidity, light exposure, and the monitor's alarm, which were found to have a significant association with ICU patients’ poor sleep quality (Table 4).

Table 4.

Bivariate and multivariate logistic regression analysis of patients admitted to intensive care units at public hospitals in Addis Ababa, Ethiopia, 2020 (n = 102).

Covariates	Categories	Sleep quality		COR (95% CI)	AOR (95% CI)	P-value
Covariates	Categories	Poor	Good	COR (95% CI)	AOR (95% CI)	P-value
Age	<60 years	28 (45.2%)	34 (54.8%)	1	1
Age	≥60 years	31 (77.5%	9 (22.5%)	4.20 (2.34, 8.61)	3.4 (1.42, 7.93)	.002*
Previous ICU hospitalization	No	48 (63.2%)	28 (36.8%)	1	1
Previous ICU hospitalization	Yes	11 (42.3%)	15 (57.7%)	0.42 (0.23, 0.89)	0.56 (0.68, 2.61)	.421
Residence	Urban	35 (54.7%)	29 (45.3%)	1	1
Residence	Rural	25 (65.8%)	13 (34.2%	1.59 (0.89, 3.75)	1.21 (0.89, 3.21)	.095
Length of ICU stay	<15 days	17 (50.0%)	17 (50.0%)	0.79 (0.46, 2.18)	0.45 (0.74, 4.56)	.234
	≥15 days	42 (61.8%)	26 (38.2%)	1	1
Comorbidity	No	33 (48.5%)	35 (51.5%)	1	1
Comorbidity	Yes	26 (76.5%)	8 (23.5%)	3.44 (1.64, 10.12)	2.5 (1.24, 9.03)	.012*
Mechanical ventilation	No	33 (54.1%)	28 (45.9%)	1	1
Mechanical ventilation	Yes	26 (63.4%)	15 (36.6%)	1.47 (0.79, 2.74)	1.22 (0.87, 2.21)	.163
Light	No	10 (34.5%)	19 (65.5%	1	1
Light	Yes	40 (54.8%)	33 (45.2%)	2.31 (1.03, 5.41)	2.0 (1.16, 5.11)	.035*
Pain	No	20 (54.1%)	17 (45.9%)	1	1
Pain	Yes	39 (60.0%)	26 (40.0%)	1.28 (0.76, 3.29)	1.02 (0.84, 2.58)	.087
Anxiety	No	39 (54.9%)	32 (45.1%)	1	1
Anxiety	Yes	20 (64.5%)	11 (35.5%)	1.49 (1.02, 4.63)	1.12 (0.79, 3.21)	.074
Monitors alarm	No	24 (49.0%)	25 (51.0%)	1	1
Monitors alarm	Yes	35 (66.0%)	18 (34.0%)	2.03 (1.24, 5.61)	1.7 (1.04, 8.23)	.043*

Note. AOR = adjusted odd ratio; COR = crude odd ratio; ICU = intensive care unit; 95% CI = 95% confidence interval.

*Statistically significant.

Accordingly, the odds of poor sleep quality were three times higher among patients with an age greater than or equal to 60 years compared to those below 60 years (adjusted odd ratio [AOR] = 3.4; 95% CI: 1.42, 7.93). Patients who had at least one comorbidity were 2.5 times more likely to have poor sleep quality as compared to their counterparts (AOR = 2.5; 95% CI: 1.24, 9.03). The study participants who faced light exposure were two times more likely to experience poor sleep quality compared to their counterparts (AOR = 2.0; 95% CI: 1.16, 5.11). Furthermore, patients who believed the monitor alarm was the source of sleep disruption were 1.7 times more likely to have poor sleep quality than those who did not believe it was a disrupting factor (AOR = 1.7; 95% CI: 1.04, 8.23) (Table 4).

Discussion

This study assessed sleep quality and the associated factors among ICU patients at public hospitals of Addis Ababa, Ethiopia. According to this study's findings, 57.8% of the participants had poor sleep quality with (95% CI: 48.4, 64.9). Elderly age, comorbidity, light exposure, and the monitor's alarm were identified as associated factors of poor sleep quality among ICU patients.

The result of this study was comparable with that of a previous study in Iran, in which 55% of the ICU patients had poor sleep quality (Adib-Hajbaghery et al., 2012) and a study in Australia, 56% of ICU patients reported poor sleep quality (Martinez et al., 2021). The current study was also in line with a previous study in Poland, which reported that 50.1% of ICU patients experienced poor sleep quality (Lewandowska et al., 2019). However, the result of the current study was lower than that of a study conducted in South Africa, in which 70.6% of ICU patients reported poor sleep quality and this finding is also lower compared to the systematic and meta-analysis pooled prevalence of sleep disruption in the ICU patients, which reported 66% (Shih et al., 2023). This might be due to the difference in the short study period and the small sample size applied to the previous study (only 34 participants were included). Furthermore, the results of the current study were higher than those of the reported findings in China (46%; Zhang et al., 2013) and India (47%; Naik et al., 2018). This variation might be due to the small sample size and the difference in the study settings, and study population; the previous studies included the nurses’ point of view.

The ICU patients above the age or equal to 60 years were three times more likely to have poor sleep quality compared to patients under 60 years (AOR = 3.4; 95% CI: 1.42, 7.93). This finding was conformable to the study in Germany (Lemola & Richter, 2013) and north India (Naik et al., 2018). According to previous studies, people over 60 are more likely to have poor sleep quality than younger individuals (Corbo et al., 2023; Ryden & Alessi, 2022). A possible justification could be the increment of age is associated with hormonal changes that affect the sleep quality, including a reduction in melatonin production, which controls sleep cycles. This is supported by previous literature (Schlemmer et al., 2015).

The ICU patients who had at least one comorbidity were 2.5 times more likely to have poor sleep quality as compared to their counterparts (AOR = 2.5; 95% CI: 1.24, 9.03). This finding was in agreement with a previous study in Australia (Dagnew et al., 2024). Furthermore, a study in Tunisia found a high association between comorbidity and poor sleep quality (Moussa et al., 2023). This could be because patients with multiple comorbidities frequently take a variety of drugs, some of which might cause sleep disturbances. For example, many antidepressants or chronic pain drugs might cause daytime drowsiness and nighttime sleeplessness. Multiple health disorders might cause more stress and anxiety about one's health, making sleep more difficult. Worrying over controlling these situations might lead to racing thoughts and difficulties relaxing, making it hard to fall asleep (Dagnew et al., 2024; Novak & Lev-Ari, 2023).

The current study revealed that patients who faced light exposure were two times more likely to experience poor sleep quality compared to those with reducing light exposure (AOR = 2.0; 95% CI: 1.16, 5.11). This finding was also in line with the previous study conducted in Australia (Miranda-Ackerman et al., 2020; Siraji et al., 2023). The possible justification might be due to light has an important role in regulating the body's circadian rhythms, which control sleep and wake cycles. In the ICU, exposure to bright light, particularly at night, can cause misalignment of these rhythms, making it difficult to fall asleep and stay asleep.

Furthermore, patients who believed that monitor alarms were a source of sleep disturbance were 1.7 times more likely to experience poor sleep quality than those who did not identify alarms as a sleep-disturbing factor (AOR = 1.7; 95% CI: 1.04, 8.23). This finding contrasts a previous study conducted in Poland, which found no relationship between ventilator alarms and poor sleep, which patients rate as a sleep-disturbing factor. The variation might be due to the difference in the categorizing approach of the freedman self-rating on a 10-point scale for environmental factors. However, further research is needed to evaluate the relationship between monitor alarms and poor sleep quality among ICU patients. This study provides future researchers with baseline information, particularly in Ethiopia.

Limitation and Strength

Despite the relevance of this study's findings, which can contribute to improvements in the healthcare services in the ICU, it has notable limitations. The primary limitation is the data point is relatively small, which may restrict the generalizability of the results. Additionally, the absence of qualitative data to provide multiple perspectives further limits the depth of understanding. Another, limitation of this study was the COVID-19 pandemic, which caused disruptions in collecting data and limited access to participants since they were isolated. Nonetheless, these findings can serve as a valuable starting point for future, larger-scale studies that could address these limitations and offer more robust insights.

Implication for Practice

The findings highlight the importance of targeted interventions to enhance sleep quality among ICU patients, particularly those who are older or have comorbidities. To reduce disruptions, practical approaches might include limiting nocturnal light exposure and modifying alarm settings. Implementing a quiet, low-light environment can assist elderly people and others with health difficulties sleep better. By addressing these changeable environmental elements, ICU teams can improve patient comfort, potentially enhancing recovery outcomes and ICU care quality.

Conclusions

The magnitude of poor sleep quality among ICU patients was a major problem in the current study. Patients with elderly age and comorbidity have been identified as independent predictors of poor sleep quality in the ICU. In addition, patients who were exposed to light and monitor alarms reported poor sleep quality in the ICU. Implement strategies to reduce light exposure during the night and customize alarm settings could improve sleep quality. By following these recommendations, healthcare personnel may greatly enhance sleep quality for ICU patients while addressing the barriers provided by age, comorbidities, light exposure, and monitor alarms. This comprehensive approach not only improves patient comfort; but also promotes improved recovery outcomes in critical care.

Footnotes

Acknowledgments

The authors would like to thank data collectors, supervisors, and the respective hospitals involved in this study for their valuable contributions. The authors also want to appreciate Addis Ababa University, College of Medicine and Health Science for offering the chance and sponsor this study.

ORCID iDs

Taye Mezgebu Ashine

Tadesse Sahle Adeba

Edmialem Getahun Mesfin

Ethical considerations

Ethical clearance was obtained from the Research Committee of Addis Ababa University College of Medicine and Health Science, Department of Emergency Medicine, with Ref. no. EMCM/022/2020. Written consent was obtained from each participant. The letter of permission was written by the College of Medicine and Health Science Research directorate office for each hospital. A formal written letter was provided for each hospital. All information was kept confidential, and no individual identifiers were collected.

Authors’ Contributions

TMA, BMK, and AW participated in the study conception and design as well as in interpretation of data. KGT, TSA, EGM, and AHS participated in editing the manuscript and advised during data analysis. TAW and TMA performed the statistical analysis, as well as drafted and reviewed the manuscript. TMA and BMK edited the manuscript and prepared it for publication. All authors read and approved the final manuscript and agreed to submit 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

All data generated or analyzed are available in the article.

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Sleep Quality and Its Associated Factors among Adult Patients Admitted in the Intensive Care Units of Public Hospitals in Addis Ababa,Ethiopia

Abstract

Background

Objective

Method

Result

Conclusion and recommendation

Keywords

Introduction

Review of Literature

Methods

Study Design

Sample

Study Population

Eligibility Criteria

Inclusion

Exclusion

Sample Size Determination

Sampling Technique

Operational Definition

Data Collection Tools

Data Quality Control

Data Process and Analysis

Results

Sociodemographic Characteristics

Clinical-Related Characteristics

Environmental and Physiological-Related Factors

Prevalence of Sleep Quality

Risk Factors Associated with Poor Sleep Quality

Discussion

Limitation and Strength

Implication for Practice

Conclusions

Footnotes

Acknowledgments

ORCID iDs

Ethical considerations

Authors’ Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References