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Predictors of Sleep Quality in Patients Undergoing Open-Heart Surgery: A Hierarchical Regression Analysis A Short Running Title: Predictors of sleep after open-heart surgery

This study of 157 open-heart surgery patients in a cardiovascular ICU found that sleep quality is primarily predicted by symptom burden (pain and fatigue) and environmental stressors rather than demographic or clinical characteristics, explaining 35.1% of the variance in sleep outcomes.

Original authors: Ezgi ARSLAN, Zehra BALCIK

Published 2026-10-12

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CC BY 4.0

Original authors: Ezgi ARSLAN, Zehra BALCIK

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Sleep is a fundamental biological process, a quiet restoration that allows the body to heal, the immune system to strengthen, and the mind to reset. For anyone recovering from a major medical event, this rest is not merely a comfort but a critical component of recovery. Yet, for patients in an intensive care unit, particularly those who have just undergone open-heart surgery, sleep is often elusive. These environments are designed for life-saving precision, not for rest; they are filled with the constant hum of machines, the bright lights of monitoring stations, and the frequent interruptions of medical care. While it is widely understood that the hospital setting disrupts rest, the specific factors that prevent a patient from sleeping soundly after such a major operation have remained somewhat unclear. Researchers have long wondered whether a patient's age, the specific type of heart surgery they endured, or the physical pain they feel are the primary drivers of poor sleep, or if the chaotic environment itself holds the most weight.

A recent study conducted at a university hospital in western Turkey sought to untangle these complex influences. The researchers focused on 157 patients who had undergone open-heart surgery and were recovering in a cardiovascular intensive care unit. These patients had spent at least a full day in the unit, ensuring they were stable enough to participate. The team gathered data on the patients' backgrounds, such as their age and body mass index, as well as their medical history and how long they had been in the hospital. Crucially, they also measured three specific experiences: the intensity of pain the patients felt, their level of fatigue, and their perception of the stressors in their immediate environment, such as noise, light, and the feeling of being watched or interrupted. To gauge sleep, the patients were asked to rate their own sleep quality on a scale, describing how deep their sleep was, how often they woke up, and how long it took them to fall asleep.

The study revealed a clear and somewhat surprising hierarchy of what matters most for sleep in this setting. When the researchers first looked at the patients' personal characteristics, they found that age and body mass index explained only a small fraction of why some patients slept better than others. Adding clinical details, such as the specific type of heart disease the patient had or how many hours they had already spent in the intensive care unit, did not significantly improve the prediction. In fact, the study found that these demographic and clinical factors were not the main story. The real drivers of sleep quality emerged when the researchers examined the patients' immediate physical and environmental experiences.

The analysis showed that the strongest predictors of poor sleep were pain, fatigue, and the stress caused by the hospital environment. Patients who reported higher levels of pain or exhaustion, and those who felt more disturbed by the noise, lights, and frequent nursing interventions around them, had significantly worse sleep scores. In fact, when these three factors were combined, they explained more than a third of the variation in sleep quality among the patients, a much larger impact than age or medical diagnosis. Interestingly, the study found a positive link between age and sleep quality, suggesting that older patients in this group actually reported slightly better sleep than their younger counterparts, perhaps because they were better able to adapt to the hospital conditions or experienced less psychological distress.

The findings suggest that while a patient's medical history and age play a role, the quality of their rest is largely determined by factors that the medical team can actively manage. The study indicates that sleep in the intensive care unit is not just a passive result of a patient's illness but is heavily shaped by the care they receive and the environment they are in. The researchers concluded that to help these patients recover, the focus should shift toward reducing environmental stressors and managing symptoms more effectively. This means implementing strategies to lower noise and light levels, grouping medical tasks together to allow for longer periods of uninterrupted rest, and ensuring pain and fatigue are addressed proactively. By treating sleep as a modifiable outcome of care rather than an inevitable consequence of being in the hospital, medical teams can potentially support a smoother and more effective recovery for patients after open-heart surgery.

Technical Summary: Predictors of Sleep Quality in Patients Undergoing Open-Heart Surgery

Problem Statement
Sleep quality is a critical biological process for physiological homeostasis and recovery, yet it is frequently impaired in Intensive Care Unit (ICU) settings, particularly following open-heart surgery. While the multidimensional nature of sleep quality—shaped by patient characteristics, symptom burden, and environmental factors—is well-recognized, there is a lack of comprehensive studies utilizing multivariate analytical methods to identify independent predictors of sleep quality in post-cardiac surgery patients. Previous research often fails to separately examine the specific variables influencing sleep, leaving a gap in understanding whether demographic/clinical factors or modifiable nursing-sensitive factors (symptoms and environment) are the primary drivers of sleep disturbances in this population.

Methodology
This study employed a single-center, descriptive, and correlational design to examine predictors of sleep quality in 157 patients hospitalized in a cardiovascular surgery ICU in western Turkey following open-heart surgery.

Participants: Inclusion criteria required patients to be Turkish-speaking, cognitively able, have spent at least 24 hours in the ICU, and provide consent. Exclusion criteria included recent use of narcotics/sedatives, chronic sleep disorders (e.g., sleep apnea), psychiatric diagnoses, or sensory impairments.
Data Collection: Data were gathered between September 2025 and May 2026 using four instruments:
1. Patient Information Form: Captured sociodemographic (age, gender, BMI) and clinical data (diagnosis, length of stay, comorbidities).
2. Visual Analog Scale (VAS): Assessed pain and fatigue intensity (0–10 scale).
3. Intensive Care Unit Environmental Stressors Scale (ICUESS): A 42-item Likert scale measuring the degree of influence by environmental stressors (noise, light, care practices).
4. Richards–Campbell Sleep Questionnaire (RCSQ): A validated tool assessing sleep quality, depth, awakenings, and latency (0–100 scale).
Statistical Analysis: Data were analyzed using IBM SPSS Statistics 25.0. Descriptive statistics and Pearson correlation analyses were performed. A hierarchical multiple regression analysis was conducted in three steps to determine the variance in sleep quality explained by different variable groups:
- Model 1: Demographic variables (Age, BMI).
- Model 2: Clinical variables (Medical diagnosis, ICU length of stay).
- Model 3: Symptom and environmental variables (Pain, Fatigue, ICUESS scores).

Key Results

Descriptive Statistics: The mean age was 64.62 years, with 65.6% male participants. The mean sleep quality score (RCSQ) was 44.67 ± 28.24, indicating poor sleep. Mean pain and fatigue scores were 3.36 and 4.08, respectively, while the mean ICUESS score was 66.73.
Correlations: Sleep quality showed a significant positive correlation with age ( $r = 0.252, p = 0.001$ ) and significant negative correlations with pain ( $r = -0.406, p < 0.001$ ), fatigue ( $r = -0.348, p < 0.001$ ), and environmental stressors ( $r = -0.442, p < 0.001$ ). No significant correlation was found between BMI and sleep quality.
Hierarchical Regression Analysis:
- Model 1: Age and BMI explained 8.5% of the variance in sleep quality.
- Model 2: Adding medical diagnosis and ICU length of stay did not significantly increase the explained variance ( $\Delta R^2 = 0.002, p = 0.832$ ).
- Model 3: The inclusion of pain, fatigue, and environmental stressors significantly increased the explained variance by 26.3% ( $\Delta R^2 = 0.263$ ), bringing the total $R^2$ to 35.1% ( $p < 0.001$ ).
- Predictors: In the final model, ICU environmental stressors ( $\beta = -0.270$ ) and pain intensity ( $\beta = -0.265$ ) were the strongest negative predictors of sleep quality. Age remained a significant positive predictor ( $\beta = 0.162$ ). Fatigue was also a significant negative predictor ( $\beta = -0.159$ ). BMI, medical diagnosis, and length of stay were not significant predictors in the final model.

Key Contributions and Claims
The paper claims that sleep quality in post-open-heart surgery ICU patients is governed more by modifiable factors—specifically symptom burden (pain, fatigue) and perceived environmental stressors—than by static demographic or clinical characteristics.

Novelty: The study utilizes a hierarchical regression approach to isolate the independent contributions of different variable groups, demonstrating that clinical characteristics (diagnosis, length of stay) do not add explanatory power beyond basic demographics, whereas symptom and environmental factors explain a substantial portion of the variance.
Counter-intuitive Finding: Contrary to some literature suggesting older age correlates with worse sleep, this study found a positive correlation between age and sleep quality. The authors suggest this may be due to older patients adapting more easily to the ICU environment compared to younger patients who may experience greater psychological distress or environmental sensitivity.
Clinical Significance: The authors argue that because pain, fatigue, and environmental stressors are modifiable through nursing care, they represent the most actionable targets for intervention. The study posits that improving sleep quality is not merely a consequence of the patient's clinical condition but a care outcome that can be shaped by the ICU team.

Limitations and Scope
The authors modestly acknowledge several limitations:

The cross-sectional design precludes causal inferences.
The single-center setting limits generalizability to other ICU environments.
Sleep quality was measured via self-report (RCSQ) rather than objective methods like polysomnography.
Variables such as anxiety, depression, sedative medication use, and preoperative sleep habits were not included in the analysis.

Conclusion
The study concludes that to enhance sleep quality in cardiovascular ICUs, nursing interventions should prioritize the management of pain and fatigue and the reduction of environmental stressors (noise, light, care interruptions) rather than focusing solely on demographic or clinical risk factors. The authors recommend routine sleep assessment and the integration of sleep-friendly practices, such as clustering care activities and implementing quiet time protocols, to support postoperative recovery.

Technical Summary: Predictors of Sleep Quality in Patients Undergoing Open-Heart Surgery

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