Clinical Care Interaction and Patient Sleep in the Intensive Care Unit: A Secondary Data Analysis.

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Learn more: PMC Disclaimer | PMC Copyright Notice Nurs Crit Care . 2026 Apr 13;31(3):e70482. doi: 10.1111/nicc.70482 Search in PMC Search in PubMed View in NLM Catalog Add to search Clinical Care Interaction and Patient Sleep in the Intensive Care Unit: A Secondary Data Analysis Aliya Islam Aliya Islam 1 School of Nursing, Midwifery and Social Work, University of Queensland, Brisbane, Queensland, Australia Find articles by Aliya Islam 1 , Lori Delaney Lori Delaney 2 School of Nursing and Midwifery, University of Southern Queensland, Ipswich, Queensland, Australia Find articles by Lori Delaney 2, ✉ Author information Article notes Copyright and License information 1 School of Nursing, Midwifery and Social Work, University of Queensland, Brisbane, Queensland, Australia 2 School of Nursing and Midwifery, University of Southern Queensland, Ipswich, Queensland, Australia * Correspondence: Lori Delaney ( [email protected] ) ✉ Corresponding author. Revised 2026 Jan 14; Received 2025 Jul 3; Accepted 2026 Mar 4; Issue date 2026 May. © 2026 The Author(s). Nursing in Critical Care published by John Wiley & Sons Ltd on behalf of British Association of Critical Care Nurses. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMC Copyright notice PMCID: PMC13071779  PMID: 41969025 ABSTRACT Background Sleep is a fundamental biophysiological requirement, essential for both physiological and psychological health. Patients admitted to intensive care are vulnerable to sleep disturbances due to continuous monitoring and the intensity of care provided. Despite this, the relationship between clinical care interactions and sleep quality remains underexplored. Aim To examine the association between clinical interactions, patient acuity and inflammatory biomarkers on sleep, and to characterise ICU patient sleep using objective and subjective measures. Study Design A secondary data analysis of a clinical trial conducted within a 36‐bed tertiary intensive care unit. The primary study employed a within‐subjects design, integrating patient self‐reported sleep experiences (Richard‐Campbell's Sleep Questionnaire) and Actigraphy (biophysiological sleep monitoring) to characterise the sleep quality of ICU patients. Data on clinical interactions were extracted from electronic patient records (Metavision: iMDsoft). Results Patients ( n = 37) reported poor sleep quality, with mean RCSQ scores below 50 mm across all subscales. Actigraphy reported a median nocturnal sleep duration of 5.6 h (IQR 303.3–422 min), with a mean of 33.4 awakenings per hour (SD ± 16.3). Patients experienced a median of nine interactions per hour, with clinical assessments (32.8%) being the most frequent. Higher SOFA scores correlated with increased clinical interaction ( r s (35) = 0.33, p = 0.05), whilst lower APACHEII scores were associated with greater sleep disturbance ( r s (33) = −0.39, p = 0.02). Conclusions Intensive care patients experience impaired sleep characterised by fragmentation and poor perceived quality. Whilst frequent nocturnal interactions were documented, these did not appear to be the primary contributor to patients' perceived poor sleep quality. Patients with reduced acuity were highly susceptible to impaired quality of sleep, indicating the need to implement tailored strategies to support sleep in the ICU setting. Relevance to Clinical Practice Routine clinical care practices need to be re‐evaluated to better align with the physiological requirements for sleep. Lower acuity patients cared for in ICU are disproportionately experiencing sleep disturbance and poor‐quality sleep. Therefore, future ICU models of care must integrate sleep‐supportive strategies as a component of standard care. Trial Registration: The primary study was formally registered as a clinical trial with the Australian New Zealand Clinical Trial Registry (ACTRN12615000945527) Keywords: actigraphy, clinical care, intensive care, Richard‐Campbell sleep questionnaire, sleep, sleep disturbance Impact Statements What is known about the topic ○ Sleep in intensive care is highly fragmented and reduced in duration and quality, with sleep acquired through both the day and night. ○ Poor sleep is associated with adverse outcomes, including an increased risk of delirium and may persist post‐ICU discharge. ○ Assessment of sleep is poorly integrated as a component of clinical care, with sleep frequently deprioritised despite its biophysiological importance. What this paper adds ○ Lower acuity patients cared for in ICU experience a high level of sleep disturbance and report poor quality of sleep. ○ Routine‐driven clinical interactions are a primary modifiable contributor to sleep disruption, irrespective of the patient's level of acuity or monitoring needs. ○ There is a need to consider processes to reconfigure models of care and non‐pharmacological sleep protocols as essential for sleep continuity and enhancing recovery in the ICU. 1. Introduction Sleep is a fundamental biological requirement, essential for cognitive function, physical health and psychological recovery. Alongside nutrition and exercise, sleep is recognised as one of the three pillars of health, playing a critical role in mitigating chronic disease and supporting well‐being [ 1 , 2 ]. In the context of critical illness, sleep assumes a greater importance in patient recovery, yet patients cared for in intensive care frequently experience significant sleep disruption. The implications of sleep disturbance are not benign and have been linked with a myriad of adverse effects, including impaired immune responses, metabolic disturbances, delirium, psychological distress and increased mortality [ 3 , 4 , 5 , 6 , 7 ]. Despite decades of research, sleep remains poorly prioritised in ICU care planning, with limited integration into routine clinical practice. 2. Background Sleep disturbance in the intensive care unit (ICU) is frequently attributed to intrinsic and extrinsic factors. Intrinsic factors encompass the complex interplay of physiological responses to critical illness resulting in increased inflammatory biomarkers (cytokines, interleukins, C‐reactive protein), which disrupt circadian rhythms and the homeostatic drive that co‐ordinates normal sleep [ 3 , 8 ]. Conversely, extrinsic factors (environmental noise, light and clinical interactions) are commonly reported by staff and patients as the primary cause of poor quality of sleep and typical sleep architecture [ 9 , 10 , 11 ]. Polysomnographic studies indicate that ICU patients achieve < 5 h of nocturnal sleep [ 7 , 12 ], with approximately 50% of their total sleep time (TST) being acquired during daylight hours [ 13 , 14 ], and < 8% spent in the restorative phases of sleep: Slow wave sleep (SWS) and Rapid Eye Movement (REM) sleep [ 7 , 12 ]. Extrinsic factors that contribute to poor sleep have become the primary focus of ICU sleep research, as they are considered to be highly modifiable. The intensive nature of clinical care provided in the ICU often requires hourly assessments and a range of procedures that occur regardless of time, which can negatively impact of patients' quality of sleep. As a result, patients are frequently awakened for physiological assessments (e.g., neurological observations, vital signs), medication administration, repositioning and investigations (e.g., chest radiology, blood pathology), without regard for circadian timing. Studies indicate that these routine care interactions are among the most commonly perceived causes of sleep disturbance in the ICU setting. Ahn et al. [ 3 ] reported that 43% of patients identified nursing procedures as a primary barrier to sleep. Whilst Adell et al. [ 9 ], reported that ICU patients frequently describe sleep disturbance related to nocturnal patient assessments and loud conversations as equally disruptive to those caused by monitoring alarms and equipment. Observational studies indicate that patients can experience up to 60 clinical interactions per hour, resulting in highly fragmented sleep [ 15 , 16 , 17 ]. More recent research reveals that mechanically ventilated patients experience greater sleep disturbance than non‐ventilated patient, primarily related to pain, nursing interventions and alarms [ 18 ]. Electroencephalogram based research indicates that each nursing intervention contributes to measurable sleep fragmentation irrespective of sedation with a mean of 13.2 (± 0.93) nursing care event per patient per [ 19 ]. Despite the increasing awareness of the negative impacts of poor‐quality sleep, it remains an aspect of care that is commonly deprioritised within task‐driven models of care. Although ICU nurses acknowledge the detrimental impact of nocturnal care activities on patient sleep, clinical priorities and cultural norms often limit the deferral of non‐urgent interventions [ 20 ]. As a result, there is growing evidence indicating that ICU environments need to minimise the avoidable disruptions, including those emanating from routine care to improve the quality of sleep among critically ill patients. 3. Aims and Objectives of Study The purpose of this secondary data analysis was to explore the relationship between clinical care interactions and sleep quality in the ICU. This study aims to: (1) Evaluate the impact of clinical care (healthcare activities and perceived noise levels) on ICU patients' sleep quality, (2) Characterise the sleep experiences of ICU patients using both subjective and objective sleep assessment measures and (3) Examine the associations between clinical interactions, patient acuity and inflammatory biomarkers. Whilst previous research has primarily explored sleep disturbance in isolation, focusing on either environmental factors, patient perceptions or polysomnographic sleep architecture, this secondary analysis integrates patient‐reported sleep experiences, objective sleep metrics, clinical interactions, patient acuity and inflammatory biomarkers. By examining nursing care activities in relation to measurable sleep fragmentation in combination with markers of physiological stress, this study advances existing observational research towards a more mechanistic understanding of sleep disturbance in critical illness. As a result, it challenges the perspective of sleep disturbance as an inevitable consequence of the intensive care environment, to positioning routine clinical practices as modifiable contributions to reducing the quality of sleep. This study provides empirical insights to inform nursing‐led strategies, care clustering and prioritisation of sleep within task‐driven models of ICU care. 4. Design and Methods This study involved a secondary data analysis of a pre‐existing dataset derived from a primary observational study conducted between June 2017 and September 2019. The dataset included the integration of actigraphy, clinical interaction data and validated subjective measures of sleep, providing clinically relevant data reflective of current ICU practices to explore sleep in the ICU and how clinical care practice may impact on sleep quality (ACTRN12615000945527). The primary study aimed to evaluate the reliability and feasibility of Actigraphy (ACTG) as a sleep monitoring method in the ICU compared with polysomnography, the gold standard for sleep monitoring and subjective sleep assessment using the Richards‐Campbell Sleep Questionnaire (RCSQ). Concurrent environmental monitoring inclusive of noise, sound and clinical interactions was also undertaken. The primary study recruited 80 participants (Cohen's power primer with an alpha value of 0.05%; power 80%) who underwent concurrent sleep monitoring and environmental monitoring for a duration of 24 h. 4.1. Setting and Participants Participants included in this secondary analysis were adults (≥ 18 years) admitted to the ICU for an anticipated duration of greater than 24 h and met the following inclusion criteria: Glasgow Coma Scale (GCS) Score of 15/15, Richmond Agitation‐Sedation Scale (RASS) score between a +2 (moderately sedated) and −3 (agitated) (inter‐rater reliability ĸ = 0.91) [ 21 ], a negative confusion assessment method (CAM) (inter‐rater reliability ĸ = 0.84–0.96) [ 22 ] and were not receiving sedation (Propofol, Midazolam) via continuous infusion. Only participants who completed the RCQS were included in the secondary analysis ( n = 37) (Figure 1 ). FIGURE 1. Open in a new tab Consort diagram of participant recruitment. 4.2. Data Collection Tools 4.2.1. Richard‐Campbell's Sleep Questionnaire Patients' perceived quality of sleep was assessed using the RCSQ, which has been previously validated against polysomnography (Cronbach's α = 0.92) [ 23 ]. The RCSQ is a five‐item visual analogue scale, containing five subscales: sleep depth, sleep latency, number of awakenings, efficiency and sleep quality. The sixth optional question was included within the original RCSQ to evaluate the patient's experience of perceived noise levels in the clinical setting. Scoring of the RCSQ involved the aggregation of the mean score derived from the five subscales to provide an overall score indicative of the patient's perceived sleep quality, with higher scores indicative of better sleep quality. Scores < 50 are considered indicators of poor quality of sleep [ 24 ]. The RCSQ was administered to patients between 07:00–09:00 h the morning following sleep monitoring. 4.2.2. Actigraphy Biophysiological assessment of sleep was conducted via Actigraphy (ACTG) (Actiwatch Plus, Spectrum, Phillips Respironics) which measures motion through piezoelectric sensors to infer sleep–wake patterns [ 25 ], using a threshold sensitivity setting of 40 (medium) counts per 30‐s epoch. Activity levels above 40 were classified as wake periods, whilst activity counts below 40 were classified as sleep [ 12 ]. Data derived from ACTG included total sleep time, number of awakenings, sleep onset latency and sleep fragmentation [ 25 ]. ACTG monitoring was initiated between 13:00–16:00 h and continued for a duration of 24 h. Programming of the ACTG was undertaken using Actiware version 6.0.9; Phillips Respironics. 4.2.3. Clinical Interactions Clinical interactions were defined as any direct patient activity involving physical contact or likely to disrupt sleep continuity. Interactions were documented within the patient's electronic medical record (Metavision) and were categorised into the following; vital signs (e.g., blood pressure, heart rate, respiratory rate, oxygen saturation, temperature), assessments (e.g., GCS, respiratory and neurovascular), investigations (arterial blood gas, blood pathology, chest x‐ray, blood glucose levels and continuous renal replacement therapy), procedures (e.g., line insertion, intubation and extubation), hygiene care (e.g., mouth, eyes, personal cares, oral suctioning), repositioning (e.g., sitting out of bed, pressure area care), ward rounds (e.g., medical and allied health), ventilation (e.g., changes in mechanical ventilation settings, initiation or removal of non‐invasive ventilation and suctioning), nutrition (e.g., nasogastric tube cares, meals and oral fluids), pharmacology (e.g., medication administration). Clinical interactions occurring within 3 min of each other were considered part of a cluster or bundle of interventions, to distinguish between isolated care activities and grouped episodes of care. Clinical interactions were logged throughout the 24 h using Metavision to denote clinical care activities and interactions over three shifts: morning (07:00–15:00 h), afternoon (15:00–21:30 h) and night shifts (21:30–07:00 h). 4.3. Data Analysis Analysis of the ACTG data was performed using Actiware software (version 6.0.9; Phillips Respironics), with data scored as sleep or awake at 30‐s epochs using the predetermined activity count within the software. The Shapiro–Wilkes test was applied to assess normalcy due to the small sample size ( n < 50) [ 26 ]. Descriptive statistics: means and standard deviations applied, or median and interquartile ranges reported where applicable. Categorical data was analysed using frequencies and percentages. Associations between sleep variables and clinical interactions were explored using Spearman Rho. A p value of < 0.05 was applied to determine statistical significance. Data analysis was conducted via Statistical Package for Social Sciences (SPSS) (version 29). This secondary data analysis is reported in accordance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 4.4. Ethical Considerations and Trial Registration The study was granted low‐risk ethical approval (October 2019) by the Human Research and Ethics Committee (ETHL5.16.071). The primary study was formally registered as a clinical trial with the Australian New Zealand Clinical Trial Registry (ACTRN12615000945527). 5. Results 5.1. Participant Demographics A total of 37 patients, representing 46% of the original study cohort, met the inclusion criteria for this secondary data analysis. The mean age of participants was 61 years (SD ± 16.1 years), with 51% ( n = 19) being female. The majority of participants were classified as high‐dependency patients (51.4%, n = 19) based on the ICU medical team's assessment of clinical care needs and acuity. One participant was classed as a ward patient; however, their care remained aligned to HDU care regimes provided in the ICU. The mean APACHEII score was 17 (SD ± 5.8), with an associated mean relative risk of mortality of 28 (SD ± 15.6) (Refer to Table 1 ). TABLE 1. Patient demographics and clinical characteristics ( n = 37). Characteristic Value Age (years), mean ± SD 62 ± 16.1 Sex, n (%) Male 18 (49%) Female 19 (51%) Anthropometrics Weight (kg), mean ± SD 89 ± 21.0 Height (cm), mean ± SD 172 ± 7.2 BMI, median (IQR) 28.6 (24.3–33.5) APACHE II admission diagnosis, n (%) Respiratory 15 (40.5%) Cardiac 8 (21.6%) Neurological 1 (2.7%) Trauma (excluding head injury) 3 (8.1%) Sepsis 6 (16.2%) General surgery 3 (8.1%) Gastrointestinal 1 (2.7%) Clinical area, n (%) ICU 17 (45.9%) HDU 19 (51.4%) Ward 1 (2.7%) Length of stay ICU, hours, median (IQR) 119 (70.5–200.5) Hospital, hours, mean ± SD 499 ± 386.8 Severity of illness APACHE II score, mean ± SD 17 ± 5.8 APACHE II relative risk, mean ± SD 28 ± 15.6 SOFA score, mean ± SD 11.1 ± 3.9 RASS score at monitoring start, n (%) Drowsy 6 (15.8%) Alert and calm 26 (68.4%) Restless 5 (13.2%) Oxygen therapy, n (%) No oxygen 5 (13.5%) Low flow oxygen 11 (29.7%) High flow oxygen 15 (40.5%) Non‐invasive ventilation (NIV) 4 (10.8%) Mechanical ventilation 2 (5.4%) Known sleep disorder, n (%) Yes 6 (16.2%) No 31 (83.8%) Inflammatory markers White cell count, ×10 9 /L, median (IQR) 9.7 (8.2–12.5) C‐reactive protein, mg/L, mean ± SD 123.5 ± 99.9 Open in a new tab Abbreviations: APACHEII, acute physiological and chronic health evaluation; HDU, high‐dependency unit; ICU, Intensive Care Unit; IQR, interquartile range; SD, standard deviation; SOFA, sequential organ failure assessment. 5.2. Sleep Profile of Patients in ICU Patients perceived their quality of sleep to be poor, with a mean RCSQ score of 47.5 (SD ± 22.7). Sleep scores were found to be < 50 across all subscales of the RCSQ, with sleep latency being the lowest reported item (M 45.5 ± 26.9) (Table 2 ). Noise within the ICU environment was reported as elevated and perceived as disruptive to sleep with a mean score of 52 (SD ± 27.3). The perceived experience of noise and its impact on sleep quality was found to be positively correlated ( r s (36) = 0.47, p = 0.02). TABLE 2. Patient self‐reported sleep quality assessed via the Richards‐Campbell sleep questionnaire ( n = 37). RCSQ domain Mean ± SD Sleep depth 45.7 ± 27.3 Sleep latency 45.4 ± 26.9 Awakenings 47.3 ± 22.3 Returning to sleep 49.8 ± 28.0 Overall sleep quality 48.9 ± 27.4 Perceived noise level 52.0 ± 27.3 RCSQ total score 47.5 ± 22.7 Open in a new tab Sleep data derived from ACTG indicated a TST of 8.1 h (median = 433 min, IQR 272–689 min) across the 24‐h monitoring period. More than 70% of patients' TST was acquired during the nocturnal period (22:00–06:00 h) with a median of 337 min (5.6 h) (IQR 303.3–422 min) (Table 3 ). Nocturnal sleep was highly fragmented with a mean of 33.4 awakenings (SD ± 16.3) and a median sleep efficiency of 71.7% (IQR 63.3–85.7). Total sleep time reported by ACTG demonstrated a poor correlation with the total RCSQ score ( r s (35) = 0.6, p = 0.75) and patient perceived sleep quality as measured by the RCSQ (Question 5) ( r s (35) = −0.02, p = 0.89). TABLE 3. Biophysiological sleep profile of ICU patients via actigraphy ( n = 34). Domain 24‐hour period (median [IQR] or mean ± SD) Overnight (22:00–06:00) (median [IQR] or mean ± SD) Total sleep time (minutes) 433 (272–689) 337 (303.3–422) Wake after sleep onset (minutes) 22.3 (11–36) 42.5 (28–64.5) Sleep latency (minutes) 36 (17–79) 32.5 (0.5–66.5) Number of awakenings 26.5 ± 18.7 33.4 ± 16.3 Fragmentation index 61.5 (54.8–74.5) 38.7 (25.8–46.6) Sleep efficiency (%) 55.8 (43.4–73.4) 71.7 (63.3–85.7) Open in a new tab 5.3. Clinical Interactions Patients experienced a mean of 93.7 (SD ± 21.7, 144 max—42 min) clinical interactions over the 24‐h monitoring period. The frequency of clinical interactions was found to be highest during the afternoon shift ( M = 35, SD ± 6.6) compared to the morning shift ( M = 31.6, SD ± 9.9), with the night shift reporting a reduced frequency of interactions ( M = 28.7, SD ± 9.9). However, the differences in the frequency of interactions across the three shifts did not differ significantly ( p > 0.05) (Figure 2 ). FIGURE 2. Open in a new tab The frequency of clinical interactions provided over a 24‐h Continuum. During the nocturnal period (22:00–06:00 h), patients experienced a median of 28 interactions. The distribution of clinical activity across the 24‐h period indicated that nurse‐led interactions accounted for the highest proportion of interventions, with assessment accounting for 33% of all interactions, followed by vital signs monitoring (15%) and investigations (13%). Ward rounds (1%), ventilation (2%) and nutrition (4%) were the least commonly performed interventions (Figure 3 ). FIGURE 3. Open in a new tab The frequency of clinical interaction types provided to patients in the ICU overnight and across a 24‐h continuum. 5.4. Impact of Clinical Interactions on Sleep The frequency of clinical interactions was found not to significantly impact on patients' TST ( r s (33) = −0.09, p = 0.62) or sleep efficiency ( r s (34) = −0.16, p = 0.35). Patients' perceived sleep experience was found to not be influenced by the number of clinical interactions that they received ( r s (35) = 0.05, p = 0.75), suggesting that patients with a higher number of interventions did not experience better or poorer sleep. A positive correlation was found between SOFA score and frequency of clinical interactions ( r s (35) = 0.33, p = 0.05). Patients experiencing higher levels of sleep fragmentation were associated with lower APACHEII scores ( r s (33) = −0.39, p = 0.02). The presence of increased CRP levels was associated with reduced frequency of clinical interactions ( r s (35) = −0.32, p = 0.05), but with poorer self‐reported sleep quality ( r s (35) = −0.43, p = 0.02). 6. Discussion The findings of this secondary analysis indicate patients cared for in the ICU experience reduced quality of sleep characterised by high levels of sleep fragmentation, despite achieving an overall normal TST. Although 70% of the TST was acquired during the nocturnal period (22:00–06:00 h), patients experienced a median of 33.4 awakenings overnight and reduced sleep efficiency, indicating high levels of disrupted sleep. Patients assessed as having higher levels of organ dysfunction (SOFA score) experienced more frequent clinical interactions. Whilst patients with lower acuity (APACHEII score) and increased inflammatory biomarkers (CRP) experienced poorer quality of sleep, indicating how vulnerable patients' sleep is to the disruptive effects of critical illness and the ICU environment. These findings reflect the complex nature of the ICU environment and that patients are unlikely to achieve quality sleep, which is considered critical for both physiological and psychological recovery. Both objective and subject assessments of sleep indicated that patients experienced compromised sleep quality whilst in ICU. Although patients achieved an average of a total 8.1 h of sleep across a 24‐h period, only 5.6 h occurred during the night (22:00–06:00 h), with the remaining sleep distributed across daytime hours. The implication of restricted sleep is substantial, with sleep fragmentation being associated with impaired autonomic regulation, immune suppression and increased cardiovascular strain [ 27 , 28 , 29 , 30 ]. Importantly, there is emerging evidence that suggests sleep architecture may be a biomarker for ICU patient outcomes, with a loss of sleep spindles and K complexes within non‐rapid eye movement sleep has been linked to increased risks of delirium, hospital length of stay and mortality [ 4 , 31 , 32 ]. Our findings were primarily observed in a population largely composed of non‐intubated patients, who exhibited similar sleep disturbances to those reported among intubated ICU patients [ 33 , 34 ]. This convergence suggests that irrespective of the patient's level of acuity or ventilation status, admission to the ICU has detrimental impacts on sleep. Our findings indicate patients with lower acuity scores (APACHEII scores) experienced higher levels of sleep fragmentation, potentially due to their responsiveness to the ICU environment. As a result, sleep disturbance may be exacerbated by standardised clinical care routines including frequent patient assessments and increased staff interactions with responsive patients. Therefore, a conscientious effort is required to implement sleep‐supportive strategies to promote circadian rhythm regulation and consolidated sleep. Implementing care approaches that identify patients who are safe to reduce overnight interactions and bundling care activities to allow for consolidated time for sleep are potentially effective interim strategies. As the disrupted sleep patterns established in the ICU are known to persist post‐ICU discharge, with long‐term consequences on cognitive function and quality of life [ 7 , 35 , 36 ]. The frequency of clinical interactions was found to be high among patients with higher levels of organ dysfunction (SOFA Score) and reflects previous studies that identified increased care interventions in patients with greater illness severity, along with longer durations in ICU length of stay [ 37 , 38 ]. The relationship between clinical acuity and sleep disturbances has been consistently highlighted in the literature, not only as a direct consequence of increased interventions but also as a factor resulting in sleep disturbance [ 4 , 7 , 39 , 40 ]. The presence of this relationship highlights the tensions that exist between meeting the intensive care needs of critically ill patients and providing an environment conducive to sleep. Consequently, as patients transition to recovery phases of critical illness, establishing patient centred care approaches that support sleep and normalise sleep–wake patterns are potentially important. The frequency and distribution of clinical interactions are often considered to be contributors to the sleep fragmentation experienced, offering limited opportunities for uninterrupted rest. Patients experienced a median of nine interactions per hour, with the greatest frequency occurring during the morning shifts, followed by night and afternoon shifts. Overnight, patients experience up to 73 clinical interactions (median 28) which is consistent with previous studies which reported 20–50 disruptions per night due to routine care [ 4 , 41 ]. Although no direct correlation was observed between clinical interaction frequency and TST or perceived sleep quality, the pattern and clustering of clinical interaction likely contributed to reduced continuity of sleep. Our findings suggest that the frequency and nature of interventions largely reflect established nursing procedures and workflow patterns within the ICU. These patterns are consistent with earlier reports suggesting that entrenched practices may be a significant contributor to sleep disruption in critically ill patients [ 17 , 20 , 37 , 42 , 43 ]. The ability to distinguish between clinically necessary interventions and those driven by habitual, task‐oriented workflows is a factor that can impede clinical decision‐making, along with a lack of perceived autonomy in making care decisions for complex patients. Documented activities such as hourly assessments, bathing and early morning radiological procedures are potentially performed as routine activities based on workplace customs. The culture of ‘busyness’ in nursing can undermine clinical judgement and can be exacerbated when nurses may lack autonomy in the management of ICU patients. Yoder et al. [ 44 ] observed the frequency of vital sign monitoring reflected habitual behaviour rather than a risk‐stratified approach to potential clinical deterioration. These frequent and, at times, potentially unnecessary interactions prevent patients from achieving uninterrupted sleep. The findings call into question the assumption that frequent care equates to better care. Rather, frequent, non‐essential interactions may hinder recovery by limiting sleep, and as a result, a need to shift towards a more patient‐centred, recovery‐orientated model of care. Lis et al. [ 45 ] report that enhancing nurses' autonomy and addressing organisational impediments to sleep management could improve ICU patients' sleep quality. The integration of sleep‐promoting protocols in ICU settings has been increasingly supported in the literature as part of a broader cultural shift towards recognising sleep as a critical component of patient recovery. Several studies have demonstrated the efficacy of non‐pharmacological intervention bundles, such as minimising light and noise, clustering care and promoting circadian alignment in improving sleep outcomes without compromising patient safety [ 46 , 47 ]. Knauert et al. [ 46 ] demonstrated that implementing such protocols led to a 32% reduction in nighttime interventions. Similarly, research by Wang et al. [ 48 ] and Locihová et al. [ 47 ] highlighted that structured sleep strategies not only improve sleep outcomes but also support interdisciplinary awareness of the therapeutic value of sleep. These findings suggest that embedding sleep‐focused processes within routine ICU care can decrease sleep fragmentation overnight, protect sleep continuity and support a more restorative care environment. 7. Limitations Whilst this study's findings provide some important clinical insights into the structure and frequency of clinical care and its impact on sleep, it is acknowledged that the sample size of this study is limited and not adequately powered. Therefore, the findings may not be generalisable to the broader ICU population and may not reflect the intricacies of the relationship between study variables. The study population within the secondary analysis was comprised of non‐intubated patients cared for within the ICU and may not be representative of the acuity or complexity of patients more broadly cared for in ICU environments. This heterogeneity in patient conditions, with varying illnesses, also introduces variability in how different conditions impact sleep, which further complicates the ability to draw definitive conclusions. Additionally, many ICU patients generally expect to experience poor sleep as well as disturbances, which may have resulted in predetermined bias in their perceptions and responses. 8. Implications for Practice Despite decades of evidence, sleep remains an underaddressed challenge in the context of ICU care. This study indicates that all patients regardless of acuity are vulnerable to sleep disturbance in the ICU; as a result, standard ICU practices, including routine assessment and monitoring, may inadvertently prioritise task‐based care over individualised needs. Interventions that cluster care activities, reduce non‐essential overnight disruption and support normal sleep–wake cycles could yield improvements in both sleep quality and recovery outcomes. Whilst environmental modifications such as noise reduction and circadian lighting are beneficial, they must be implemented alongside clinical workflow changes to achieve meaningful improvement. The reported findings reiterate the need to prioritise sleep as a critical component of patient care and enhance sleep quality through the implementation of sleep‐supportive protocols, including clustering care, and minimising nighttime disturbance, which are core components of critical care outcomes. 9. Conclusion Sleep is an essential biophysiological process that requires prioritisation in the care coordination of ICU patients. Non‐ventilated patients are highly susceptible to sleep disturbance due to their increased level of interaction with the ICU environment. The frequency of clinical interaction observed in the study suggests that there exist limited opportunities to achieve consolidated sleep and that sleep may not be prioritised in the coordination of patients' clinical care. Therefore, developing sleep protocols and re‐evaluating clinical workflows is required to centre sleep as an important component in the recovery of ICU patients. Author Contributions Aliya Islam: data analysis, drafting and editing of manuscript: Lori Delaney: methodology, editing and revisions of manuscript, supervision. Funding The authors have nothing to report. Ethics Statement The primary study was approved by the Human Research and Ethics Committee (October 2019) (ETHL5.16.071). Consent Written informed consent was obtained from all the participants prior to the enrolment into the primary study. Conflicts of Interest The authors declare no conflicts of interest. Acknowledgements This research was undertaken as a part of an Honours degree program and nil funding was attributed to the research. We acknowledge the work and contributions of the researcher who led the primary research study. Open access publishing facilitated by University of Southern Queensland, as part of the Wiley ‐ University of Southern Queensland agreement via the Council of Australasian University Librarians. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References 1. McNamara S., Nichols T., Dash S., de Courten M., and Calder R., “Sleep: A Core Pillar of Health and Wellbeing: Improving Population Sleep Health to Reduce Preventable Illness and Injury–Policy Evidence Review,” (2023). 2. Vodovotz Y., Barnard N., Hu F. B., et al., “Prioritized Research for the Prevention, Treatment, and Reversal of Chronic Disease: Recommendations From the Lifestyle Medicine Research Summit,” Frontiers in Medicine 7 (2020): 585744. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Ahn Y. H., Lee H. Y., Lee S. M., and Lee J., “Factors Influencing Sleep Quality in the Intensive Care Unit: A Descriptive Pilot Study in Korea,” Acute and Critical Care 38, no. 3 (2023): 278–285, 10.4266/acc.2023.00514. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Boyko Y., Toft P., Ørding H., Lauridsen J. T., Nikolic M., and Jennum P., “Atypical Sleep in Critically Ill Patients on Mechanical Ventilation Is Associated With Increased Mortality,” Sleep and Breathing 23, no. 1 (2019): 379–388. [ DOI ] [ PubMed ] [ Google Scholar ] 5. Daou M., Telias I., Younes M., Brochard L., and Wilcox M. E., “Abnormal Sleep, Circadian Rhythm Disruption, and Delirium in the ICU: Are They Related?,” Frontiers in Neurology 11 (2020): 549908. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Garbarino S., Lanteri P., Bragazzi N. L., Magnavita N., and Scoditti E., “Role of Sleep Deprivation in Immune‐Related Disease Risk and Outcomes,” Communications Biology 4, no. 1 (2021): 1304. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Marchasson L., Rault C., Le Pape S., et al., “Impact of Sleep Disturbances on Outcomes in Intensive Care Units,” Critical Care 28, no. 1 (2024): 331. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Richards K. C., Wang Y.‐Y., Jun J., and Ye L., “A Systematic Review of Sleep Measurement in Critically Ill Patients,” Frontiers in Neurology 11 (2020): 542529, 10.3389/fneur.2020.542529. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Adell M. B., Barrachina L. G., Andrés E. B., et al., “Factors Affecting Sleep Quality in Intensive Care Units,” Medicina Intensiva (English Edition) 45, no. 8 (2021): 470–476. [ DOI ] [ PubMed ] [ Google Scholar ] 10. Bani Younis M., Hayajneh F., and Alshraideh J. A., “Effect of Noise and Light Levels on Sleep of Intensive Care Unit Patients,” Nursing in Critical Care 26, no. 2 (2021): 73–78. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Reinke L., Haveman M., Horsten S., et al., “The Importance of the Intensive Care Unit Environment in Sleep—A Study With Healthy Participants,” Journal of Sleep Research 29, no. 2 (2020): e12959. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Delaney L. J., Litton E., Melehan K. L., Huang H.‐C., Lopez V., and Van Haren F., “The Feasibility and Reliability of Actigraphy to Monitor Sleep in Intensive Care Patients: An Observational Study,” Critical Care 25, no. 1 (2021): 1–12, 10.1186/s13054-020-03447-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Elías M. N., Munro C. L., and Liang Z., “Daytime‐To‐Nighttime Sleep Ratios and Cognitive Impairment in Older Intensive Care Unit Survivors,” American Journal of Critical Care 30, no. 2 (2021): e40–e47. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Munro C. L., Liang Z., Elías M. N., Ji M., Chen X., and Calero K., “Sleep and Activity Patterns Are Altered During Early Critical Illness in Mechanically Ventilated Adults,” Dimensions of Critical Care Nursing 40, no. 1 (2021): 29–35. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Çelik S., Öztekin D., Akyolcu N., and İşsever H., “Sleep Disturbance: The Patient Care Activities Applied at the Night Shift in the Intensive Care Unit,” Journal of Clinical Nursing 14, no. 1 (2005): 102–106, 10.1111/j.1365-2702.2004.01010.x. [ DOI ] [ PubMed ] [ Google Scholar ] 16. Gabor J. Y., Cooper A. B., Crombach S. A., et al., “Contribution of the Intensive Care Unit Environment to Sleep Disruption in Mechanically Ventilated Patients and Healthy Subjects,” American Journal of Respiratory and Critical Care Medicine 167, no. 5 (2003): 708–715, 10.1164/rccm.2201090. [ DOI ] [ PubMed ] [ Google Scholar ] 17. Tamburri L. M., DiBrienza R., Zozula R., and Redeker N. S., “Nocturnal Care Interactions With Patients in Critical Care Units,” American Journal of Critical Care 13, no. 2 (2004): 102–112. [ PubMed ] [ Google Scholar ] 18. Erbay Dalli Ö. and Kelebek Girgin N., “Sleep Quality and Disruptive Factors in Intensive Care Units: A Comparison Between Mechanically Ventilated and Spontaneously Breathing Patients,” Nursing in Critical Care 30, no. 4 (2025): e70097. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Matsuura Y., Ohno Y., Natori H., et al., “Electroencephalography‐Based Evaluation of the Impact of Night‐Time Nursing Care on Sleep During Intensive Care Unit Administration After Cardiac Surgery,” Scientific Reports 15, no. 1 (2025): 22913. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Gellerstedt L., Medin J., Kumlin M., and Rydell Karlsson M., “Nursing Care and Management of Patients' Sleep During Hospitalisation: A Cross‐Sectional Study,” Journal of Clinical Nursing 28, no. 19–20 (2019): 3400–3407, 10.1111/jocn.14915. [ DOI ] [ PubMed ] [ Google Scholar ] 21. Ely E. W., Truman B., Shintani A., et al., “Monitoring Sedation Status Over Time in ICU Patients: Reliability and Validity of the Richmond Agitation‐Sedation Scale (RASS),” Journal of the American Medical Association 289, no. 22 (2003): 2983–2991, 10.1001/jama.289.22.2983. [ DOI ] [ PubMed ] [ Google Scholar ] 22. Han J. H., Wilson A., Graves A. J., et al., “Validation of the Confusion Assessment Method for the Intensive Care Unit in Older Emergency Department Patients,” Academic Emergency Medicine 21, no. 2 (2014): 180–187. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Ritmala‐Castren M., Axelin A., Richards K. C., Mitchell M. L., Vahlberg T., and Leino‐Kilpi H., “Investigating the Construct and Concurrent Validity of the Richards‐Campbell Sleep Questionnaire With Intensive Care Unit Patients and Home Sleepers,” Australian Critical Care 35, no. 2 (2022): 130–135. [ DOI ] [ PubMed ] [ Google Scholar ] 24. Locihová H., Axmann K., Žiaková K., Šerková D., and Černochová S., “Sleep Quality Assessment in Intensive Care: Actigraphy vs Richards‐Campbell Sleep Questionnaire,” Sleep Science 13, no. 4 (2020): 235–241. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Medrzycka‐Dabrowska W., Lewandowska K., Kwiecień‐Jaguś K., and Czyż‐Szypenbajl K., “Sleep Deprivation in Intensive Care Unit – Systematic Review,” Open Medicine 13, no. 1 (2018): 384–393, 10.1515/med-2018-0057. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Mishra P., Pandey C. M., Singh U., Gupta A., Sahu C., and Keshri A., “Descriptive Statistics and Normality Tests for Statistical Data,” Annals of Cardiac Anaesthesia 22, no. 1 (2019): 67–72. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Kuna K., Szewczyk K., Gabryelska A., et al., “Potential Role of Sleep Deficiency in Inducing Immune Dysfunction,” Biomedicine 10, no. 9 (2022): 2159. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Poluektov M. G., “Sleep and Immunity,” Neuroscience and Behavioral Physiology 51, no. 5 (2021): 609–615. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Schlagintweit J., Laharnar N., Glos M., et al., “Effects of Sleep Fragmentation and Partial Sleep Restriction on Heart Rate Variability During Night,” Scientific Reports 13, no. 1 (2023): 6202. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Yang H., Goldstein M. R., Vazquez M., Williams J. P., and Mullington J. M., “Effects of Sleep and Sleep Deficiency on Autonomic Function in Humans,” Current Opinion in Endocrine and Metabolic Research 18 (2021): 268–274. [ Google Scholar ] 31. Grigg‐Damberger M. M., Hussein O., and Kulik T., “Sleep Spindles and K‐Complexes Are Favorable Prognostic Biomarkers in Critically Ill Patients,” Journal of Clinical Neurophysiology 39, no. 5 (2022): 372–382. [ DOI ] [ PubMed ] [ Google Scholar ] 32. Lee H., Mizrahi M. A., Hartings J. A., et al., “Continuous Electroencephalography After Moderate to Severe Traumatic Brain Injury,” Critical Care Medicine 47, no. 4 (2019): 574–582. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Oxlund J., Knudsen T., Sörberg M., Strøm T., Toft P., and Jennum P. J., “Sleep Quality and Quantity Determined by Polysomnography in Mechanically Ventilated Critically Ill Patients Randomized to Dexmedetomidine or Placebo,” Acta Anaesthesiologica Scandinavica 67, no. 1 (2023): 66–75. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Prajapat B., Gupta N., Chaudhry D., Santini A., and Sandhya A. S., “Evaluation of Sleep Architecture Using 24‐Hour Polysomnography in Patients Recovering From Critical Illness in an Intensive Care Unit and High Dependency Unit: A Longitudinal, Prospective, and Observational Study,” Journal of Critical Care Medicine 7, no. 4 (2021): 257–266. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Fukamachi Y., Sakuramoto H., Sato T., et al., “Prevalence, Risk Factors, and Intervention of Long‐Term Sleep Disturbance After Intensive Care Unit Discharge: A Scoping Review,” Cureus 17, no. 4 (2025): e83011. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Weinhouse G. L., Kimchi E., Watson P., and Devlin J. W., “Sleep Assessment in Critically Ill Adults: Established Methods and Emerging Strategies,” Critical Care Explorations 4, no. 2 (2022): e0628. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Giusti G. D., Tuteri D., and Giontella M., “Nursing Interactions With Intensive Care Unit Patients Affected by Sleep Deprivation: An Observational Study,” Dimensions of Critical Care Nursing 35, no. 3 (2016): 154–159. [ DOI ] [ PubMed ] [ Google Scholar ] 38. Kawamoto E., Ito‐Masui A., Esumi R., Imai H., and Shimaoka M., “How ICU Patient Severity Affects Communicative Interactions Between Healthcare Professionals: A Study Utilizing Wearable Sociometric Badges,” Frontiers in Medicine 7 (2020): 606987. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Elliott R., Rai T., and McKinley S., “Factors Affecting Sleep in the Critically Ill: An Observational Study,” Journal of Critical Care 29, no. 5 (2014): 859–863. [ DOI ] [ PubMed ] [ Google Scholar ] 40. Kamdar B. B., Needham D. M., Collop N. A., Watson P. L., and Rowden A. M., “Sleep Deprivation in Critical Illness: Its Role in Physical and Psychological Recovery,” Journal of Intensive Care Medicine 28, no. 2 (2013): 97–111. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Locihová H., Axmann K., and Žiaková K., “Sleep‐Disrupting Effects of Nocturnal Nursing Interventions in Intensive Care Unit Patients: A Systematic Review,” Journal of Sleep Research 30, no. 4 (2021): e13223. [ DOI ] [ PubMed ] [ Google Scholar ] 42. Freedman N. S., Kotzer N., and Schwab R. J., “Patient Perception of Sleep Quality and Etiology of Sleep Disruption in the Intensive Care Unit,” American Journal of Respiratory and Critical Care Medicine 159, no. 4 (1999): 1155–1162, 10.1164/ajrccm.159.4.9806141. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Le A., Friese R. S., Hsu C. H., Wynne J. L., Rhee P., and O'Keeffe T., “Sleep Disruptions and Nocturnal Nursing Interactions in the Intensive Care Unit,” Journal of Surgical Research 177, no. 2 (2012): 310–314. [ DOI ] [ PubMed ] [ Google Scholar ] 44. Yoder J. C., Yuen T. C., Churpek M. M., Arora V. M., and Edelson D. P., “A Prospective Study of Nighttime Vital Sign Monitoring Frequency and Risk of Clinical Deterioration,” JAMA Internal Medicine 173, no. 16 (2013): 1554. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Lis K., Sak‐Dankosky N., and Czarkowska‐Pączek B., “Nurses' Autonomy in Sleep Management Improves Patients' Sleep Quality: A Cross‐Sectional Study,” Nursing in Critical Care 27, no. 3 (2022): 326–333. [ DOI ] [ PubMed ] [ Google Scholar ] 46. Knauert M. P., Pisani M., Redeker N., et al., “Pilot Study: An Intensive Care Unit Sleep Promotion Protocol,” BMJ Open Respiratory Research 6 (2019): e000411, 10.1136/bmjresp-2019-000411. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Locihová H., Axmann K., Padyšáková H., and Fejfar J., “Effect of Non‐Pharmacological Nursing Interventions on the Quality of Sleep in Intensive Care Patients: A Systematic Review,” Journal of Clinical Nursing 27, no. 9–10 (2018): 1822–1834. [ Google Scholar ] 48. Wang W., Cao X., Luan J., Zhang Q., Cai C., and Han J., “Nurse‐Led Evidence‐Based Quality Improvement Programme to Improve Intensive Care Unit Patient Sleep Quality,” Nursing in Critical Care 30, no. 3 (2025): e70028. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. 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