Abstract
Objective:
To investigate the effect of a multidimensional comprehensive management plan with noise control as the core, based on the Software, Hardware, Environment, Liveware (SHEL) risk assessment model, on rehabilitation exercise compliance, balance ability, and sleep quality in patients after knee arthroplasty (KA).
Methods:
This retrospective observational study was conducted on 90 patients who underwent KA at Daqing People’s Hospital between January 2022 and December 2024. Patients were categorized into an observation group and a control group (45 cases each) based on the ward management and nursing protocols received during hospitalization. The control group received routine nursing care and ward management, whereas the observation group received a comprehensive ward management plan with noise control as the core, based on the SHEL model, in addition to the routine care. Rehabilitation exercise compliance, balance ability (Berg Balance Scale [BBS], and sleep quality (Pittsburgh Sleep Quality Index [PSQI] were compared between the two groups at 5 and 10 days postoperatively. Postoperative complications were also recorded.
Results:
Compared with the control group, the observation group showed significantly lower mean daytime and nighttime noise levels in the ward during hospitalization, lower PSQI scores, and higher functional exercise compliance and BBS scores at 5 and 10 days postoperatively (all P < 0.05). There was no significant difference in the incidence of postoperative complications between the two groups (P > 0.05).
Conclusion:
The comprehensive noise management scheme based on the SHEL model was associated with improved sleep quality, rehabilitation exercise compliance, and balance function in patients after KA. This model may have good clinical application value in environmental management following orthopedic surgery.
Keywords: knee arthroplasty, noise, treatment adherence, sleep quality
KEY MESSAGES
-
(1)
Ward noise management based on the SHEL model (Software, Hardware, Environment, Liveware) significantly reduced mean noise levels in patients undergoing knee arthroplasty.
-
(2)
The SHEL model management improved sleep quality, as evidenced by lower Pittsburgh Sleep Quality Index scores at 1 week and 1 month after surgery.
-
(3)
Patients receiving SHEL model-based noise management demonstrated better rehabilitation exercise compliance and balance ability compared with those receiving routine care.
-
(4)
Systematic noise control addressing multiple dimensions may promote postoperative recovery and has important value for nursing practice.
INTRODUCTION
Knee arthroplasty (KA) is an effective treatment for end-stage knee osteoarthritis, rheumatoid arthritis and other diseases. It can significantly relieve joint pain, reconstruct joint function and improve the quality of life of patients.[1] Clinical studies have shown that patients undergoing KA generally have low compliance with rehabilitation exercises, which seriously affects rehabilitation outcomes.[2] The factors affecting rehabilitation compliance are complex, including subjective factors, such as the patient’s pain tolerance and understanding of rehabilitation, as well as objective factors, such as ward environment and nursing measures. Among these, ward environmental factors, especially noise interference, have become an important variable that cannot be ignored.[3] The World Health Organization (WHO) has made clear guidelines on the noise levels in the hospital environment, recommending that noise levels should not exceed 35 dB during daytime, with an equivalent sound level of 30 dB at night, and a maximum sound level of 40 dB.[4] However, due to various factors such as treatment operations, personnel movement and equipment operation, noise levels in hospital wards frequently exceed these recommended limits in clinical practice.[5]
After surgery, patients often experience a sensitive state of the nervous system due to surgical trauma, pain stimulation, and systemic stress response, leading to a substantial decrease in their tolerance to environmental noise.[6] Continuous noise interference not only directly disrupts patients’ sleep structure, leading to sleep disorders such as difficulty falling asleep and fragmented sleep, but also triggers negative emotions such as anxiety and irritability through neuroendocrine regulation, further inhibiting patients’ enthusiasm for rehabilitation, thereby reducing their initiative and compliance in rehabilitation exercises.[7]
The Software, Hardware, Environment, Liveware (SHEL) model is an internationally recognized medical risk assessment tool, which consists of four dimensions: software (S), hardware (H), environment (E), and liveware (L). Its core principle is to identify potential risks in each dimension from a system perspective and establish a closed-loop management. It has been widely used in medical risk analysis and nursing quality improvement.[8] This model can identify potential risk factors in each dimension through a system and formulate targeted and feasible countermeasures, which can avoid the occurrence of adverse events at their source and reduce their incidence.[9] Therefore, this study applied the SHEL model to the management practice of KA postoperative wards, implemented a comprehensive management plan with centered on noise control, and investigated its association with patient rehabilitation exercise compliance, balance ability, and sleep quality.
MATERIALS AND METHODS
Study Subjects
Among the 109 patients who underwent unilateral primary KA at Daqing People’s Hospital between January 2022 and December 2024, 19 patients were excluded (11 undergoing bilateral KA and 8 with severe complications), and 90 patients were ultimately included. According to the ward management and nursing measures received during hospitalization, patients admitted before January 2024 were assigned to the control group (n = 45), whereas those admitted since January 2024 were assigned to the observation group (n = 45). The demographic characteristics and clinical data were extracted from the hospital’s medical record system and compared between groups.
Inclusion criteria: ① met the indications for KA;[10] ② aged 45–75 years; and ③ were conscious and had normal cognitive function. Exclusion criteria: ① simultaneous bilateral knee replacement; ② severe cardiovascular or cerebrovascular diseases, liver or kidney dysfunction or malignant tumors; ③ hearing impairment or mental illness; and ④ a history of balance dysfunction or falls.
The study was approved by the Ethics Committee of Daqing People’s Hospital (Approval KY [2024] No. 018). Written informed consent was obtained from all individual participants included in the study. This study was conducted in strict adherence to the principles of the Declaration of Helsinki.[11]
Treatment Methods
Both groups were recruited from the same orthopedic ward under consistent hospital layout, facilities, and routine care protocols. The ward formally implemented the SHEL model-based noise management in January 2024; therefore, patients admitted between January 2022 and December 2023 (receiving routine ward management) were assigned to the control group (n = 45), whereas patients admitted between January 2024 and December 2024 (receiving SHEL model-based noise management) were assigned to the observation group (n = 45). This study adopts a historical controlled design. Baseline data were comparable between the two groups, including surgical indications, implant system, surgical team, anesthesia and postoperative analgesia protocols, nursing staff configuration, rehabilitation pathways, and routine postoperative nursing procedures. The comparison results show that the main difference between the two groups is whether a SHEL model-based noise management plan has been implemented.
All patients received unilateral KA. The control group received routine clinical nursing care and standardized ward management procedures. The specific nursing care procedures were as follows:
(1) Postoperative basic medical care: 24-h routine dynamic monitoring of vital signs was implemented, with closely monitoring of blood pressure, heart rate, body temperature, and other indicators; stepwise pain management was implemented according to postoperative pain scores, and analgesics were administered as appropriate; Aseptic techniques were strictly followed during incision care, with regular dressing changes to observe wound healing, and standardized anti-infection treatment was provided to reduce the risk of postoperative infection. (2) Standardized rehabilitation guidance: Personalized rehabilitation exercise plans were developed for patients in accordance with the clinical management pathway for KA. Early postoperative guidance included basic rehabilitation exercises, including isometric quadriceps contraction, ankle pumping, and joint mobility exercises, with the intensity and duration of exercise gradually adjusted according to patients’ recovery. Before discharge, comprehensive home rehabilitation education was provided, including key points of home exercise, precautions, and handling of abnormal situations. A rehabilitation guidance manual was distributed, with follow-up guidance provided after discharge. (3) Routine ward basic management: The wards were cleaned daily to maintain a clean and hygienic environment. Windows were opened regularly for ventilation in accordance with hospital infection control requirements to ensure air circulation in the wards. Ward lighting was managed according to the patients’ rest needs, and unnecessary lighting equipments were turned off at night to create a comfortable resting environment. In daily work, medical staff were verbally reminded to keep quiet when performing operations. Family members and visitors were also reminded to keep the wards quiet. However, no specific noise control system has been established, nor have any targeted and systematic ward noise control measures been implemented.
In addition to all measures implemented by the control group, the observation group received a comprehensive ward management plan centered on noise control based on the SHEL model.
Identification of risk factors using the SHEL Model:[12] Through literature review, expert consultation and clinical observation, risk factors related to noise in the ward after KA were identified as follows. Software (S): insufficient awareness of noise control among medical staff, non-standard operating procedures, and a lack of targeted health education; Hardware (H): ageing medical equipment generating abnormal noise, nursing tools (e.g., trolleys, bed rails) lacking noise-reduction design, and inadequate sound insulation facilities; Environment (E): an unreasonable ward layout, frequent personnel movement, and a high frequency of nighttime medical operations; Liveware (L): insufficient awareness of the harm of noise among patients and their families, nonstandardized visitor behavior, and excessively loud communication among medical staff.
Based on the risk factors identified across the four dimensions, the research team has formulated targeted improvement measures for each dimension as follows:
Software aspects (S): ① The “Ward Noise Control Operation Specifications” were developed to clarify the noise control requirements for medical staff during diagnosis, treatment operations and communication, including handling items gently and peaking quietly during nighttime communication; ② Specialized exercise was provided on the harm of noise to postoperative recovery, the application of the SHEL model and noise reduction operation techniques. Competency assessments were conducted after the training to ensure that all staff had mastered the techniques; ③ A noise monitoring and feedback mechanism was established, with the responsible nurse recording the ward noise level daily and holding nursing quality-control meetings weekly to analyze the causes of excessive noise and continuously improve the management process.
Hardware aspects (H): ① Noise reduction renovation of ward facilities: Install silent rubber pads on the legs of beds and tables and chairs, install soundproof strips on ward doors, and equip nursing carts with shock-absorbing wheels and add lubricant regularly; ② Medical equipment management: Perform regular maintenance on equipment such as monitors and infusion pumps, set alarm volume thresholds for equipment, adjust non-emergency alarms to low volume, and turn off idle equipment at night; ③ Provide personalized noise reduction devices: Earplugs, eye masks and portable white noise players were provided to patients according to their needs.
Environmental aspects (E): ① Optimize ward layout: Separate the rehabilitation exercise area from the rest area to reduce noise interference during exercise; ② Rationally plan treatment time: Nonemergency procedures were performed during the daytime (8:00–18:00) and unnecessary treatment procedures and personnel movement were minimized from 22:00 to 6:00 the next day; ③ Set “quiet periods”: 21:00–7:00 the next day was set as the quiet period. Post conspicuous signs at the ward entrance, limit the number of visitors, and prohibit loud conversations and the use of electronic devices with speakerphone in the ward.
Liveware aspects (L): ① Patient and family education: Upon admission, patients and their families were provided with a “Ward Noise Control Manual,” explaining the impact of noise on postoperative sleep and recovery through pictures, texts, and short video demonstrations, and guiding patients to cooperate with noise control measures; ② Visitor management: Before visiting, visitors were informed of visiting guidelines, with the number of visitors limited to no more than two per visit and each visiting time limited to ≤30 min; ③ Optimization of communication among medical staff: The Connect-Introduce-Communicate-Ask-Respond-Exit (CICARE) communication model was adopted to avoid loud discussions of patients’ conditions in the ward. Discussions were conducted at the nurses’ station or in the examination room when necessary.
Observation Indicators
Rehabilitation Exercise Compliance
Rehabilitation exercise compliance was assessed using the Orthopaedic Patient Compliance with Functional Exercise Scale.[13] The scale consists of 15 items and uses a 5-point Likert scale (1–5 points), with a total score of 15–75 points. Higher scores indicate better compliance. Assessments were conducted at 5 and 10 days postoperatively. This scale has good reliability and validity, with a Cronbach’s α coefficient of 0.85 and a split‑half reliability of 0.82.
Balance Ability
The Berg Balance Scale (BBS)[14] was used to assess balance ability. The scale includes 14 items, including sitting to standing, standing independently, and standing with eyes closed. Each item is scored from 0 to 4 points, and the total score ranges from 0 to 56 points. A score of <40 points indicates a risk of falling, and a score of 41–56 points indicates good balance function. The assessment was conducted at 5 and 10 days postoperatively. The BBS shows excellent reliability and validity, with a Cronbach’s α of 0.91 and an intraclass correlation coefficient of 0.89.
Sleep Quality
The Pittsburgh Sleep Quality Index (PSQI)[15] was used to assess sleep quality. The scale has 18 items in total, with a total score of 0–21. Higher scores indicate worse sleep quality. The assessment was conducted at 5 and 10 days postoperatively. The PSQI has good reliability and validity, with a Cronbach’s α of 0.83 and good construct validity.
Incidence of Complications
The incidence of complications such as infection, joint stiffness, and deep vein thrombosis within 10 days after surgery was recorded in both groups of patients.
Noise Level Monitoring
Ward noise level monitoring data were extracted from the hospital’s nursing quality control medical record system. A portable sound level meter (Hangzhou Aihua Instrument Co., Ltd., AWA5636; accuracy: ±1.5 dB) was used for noise monitoring. Based on the hospital noise measurement practices reported by de Lima Andrade et al.,[4] and in accordance with the clinical workflow of our hospital, the data collection time was set to 8:00–18:00 daily after surgery (covering the main diagnosis and treatment as well as the rehabilitation period) and 22:00–6:00 the next day (the main sleep and rest period). Each measurement lasted 5 min, and the mean value was recorded. Fixed-point monitoring measurements were conducted at the center of the ward, 1.2 m above the floor. The mean daytime and nighttime noise levels during hospitalization, calculated from postoperative day 1 to the day before discharge, were used as quantitative indicators for evaluating the effectiveness of noise reduction management.
Basic Nursing Quality Indicators
The Visual Analog Scale (VAS) scores at 5 days postoperatively and the cumulative analgesic use within 72 h after surgery were extracted from the hospital’s electronic medical record system in order to verify the comparability of the basic nursing levels between the two groups.
In this study, “prenursing” refers to the day before KA, when the responsible nurse completed all scale assessments. Regarding the data at 5 days postoperatively: All patients were hospitalized, and PSQI, BBS, functional exercise compliance scale assessments and noise monitoring were completed in the hospital. Regarding the data at 10 days postoperatively: Some patients had been discharged, and the PSQI, BBS, and functional exercise compliance scales were evaluated through outpatient follow-up by uniformly trained research nurses. Complications information was obtained from outpatient medical records and patient reports. Because noise monitoring data were only available during hospitalization, the noise data on postoperative day 10 were not analyzed. All follow-up procedures were standardized to ensure the consistency and accuracy of data collection.
Statistical Analyses
Statistical analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA). The normality test of continuous variables was conducted using the Shapiro–Wilk test combined with Q–Q visualization for comprehensive judgment. Continuous data that conforms to normality is represented as mean ± standard deviation (SD), and independent sample t test is used for inter-group comparison. Repeated-measures ANOVA was used to test the group main effect, time main effect and group × time interaction effect, and Bonferroni correction was applied for pairwise comparisons post hoc. The count data are expressed as frequency (%) and analyzed using the χ2 test or Fisher’s exact test. To control for the influence of potential confounding factors, this study further used multivariate covariance analysis to compare the main outcome measures (PSQI, BBS, and functional exercise compliance score) on postoperative day 10 between groups. The covariates included age, body mass index (BMI), preoperative PSQI score, preoperative BBS score, VAS pain score on postoperative day 5, and cumulative analgesic consumption 72 h after surgery. The analysis of covariance (ANCOVA) results were reported as adjusted mean ± standard error (SE). A P < 0.05 was considered statistically significant.
RESULTS
Comparison of Baseline Data
No significant intergroup differences were observed in baseline characteristics, including sex, age, BMI, comorbidities, preoperative function, disease type, anesthesia method, and postoperative analgesia regimen (all P > 0.05), indicating acceptable comparability and minimizing confounding bias related to temporal changes in clinical practice [Table 1].
Table 1.
Comparison of Baseline Data between the Two Groups of Patients
| Groups (n = 45) | Observation Group | Control Group | t/χ2 | P |
|---|---|---|---|---|
| Sex (male/female) | 21 (46.67)/24 (53.33) | 25 (55.56)/20 (44.44) | 0.711 | 0.399 |
| Age | 63.40 ± 5.96 | 61.60 ± 6.25 | 1.398 | 0.166 |
| Disease type (bone knee/rheumatoid arthritis) | 38 (84.44)/7 (15.56) | 40 (88.89)/5 (11.11) | 0.385 | 0.535 |
| Surgical side (left/right) | 22 (48.89)/23 (51.11) | 20 (44.44)/25 (55.56) | 0.179 | 0.673 |
| Comorbidities (present/absent) | 32 (71.11)/13 (28.89) | 29 (64.44)/16 (35.56) | 0.458 | 0.499 |
| BMI (kg/m2) | 25.32 ± 2.41 | 24.98 ± 2.53 | 0.642 | 0.522 |
| Disease severity (moderate/severe) | 26 (57.78)/19 (42.22) | 28 (62.22)/17 (37.78) | 0.196 | 0.658 |
| Operation duration (min) | 82.65 ± 11.32 | 84.11 ± 12.07 | 0.579 | 0.564 |
| Anesthesia method (combined spinal–epidural anesthesia/general anesthesia) | 39 (86.67)/6 (13.33) | 41 (91.11)/4 (8.89) | 0.450 | 0.502 |
| Postoperative hospital stay/day | 7.25 ± 1.36 | 7.51 ± 1.42 | 0.863 | 0.390 |
Note: BMI: body mass index.
Comparison of Postoperative Basic Nursing Quality Indicators
No intergroup significant differences were observed in VAS score (3.38 ± 1.01 vs. 3.51 ± 0.79, t = 0.700, P = 0.486) and cumulative analgesic consumption (41.61 ± 11.84 mg vs. 43.05 ± 12.17 mg, t = 0.571, P = 0.570) [Table 2].
Table 2.
Comparison of Postoperative Basic Nursing Quality Indicators between Two Groups of Patients
| Groups (n = 45) | Observation Group | Control Group | t | P |
|---|---|---|---|---|
| VAS pain score on postoperative day 5/point | 3.38 ± 1.01 | 3.51 ± 0.79 | 0.700 | 0.486 |
| Cumulative analgesic consumption within 72 h postoperatively/mg | 41.61 ± 11.84 | 43.05 ± 12.17 | 0.571 | 0.570 |
Note: VAS: Visual Analog Scale.
Comparison of Ward Noise Levels
The mean day and night noise levels in the observation group were both significantly lower than those in the control group during hospitalization (both P < 0.05) [Table 3].
Table 3.
Comparison of Ward Noise Levels between the Two Groups of Patients
| Groups (n = 45) | Observation Group | Control Group | t | P |
|---|---|---|---|---|
| Mean daytime noise level during hospitalization/dB | 70.68 ± 3.10 | 88.52 ± 3.50 | 25.604 | <0.001 |
| Mean nighttime noise level during hospitalization/dB | 49.92 ± 2.84 | 60.05 ± 3.18 | 15.954 | <0.001 |
Comparison of Sleep Quality
PSQI scores of the observation group at 5 and 10 days postoperatively were significantly lower than those of the control group (both P < 0.05). Repeated-measures ANOVA showed significant group effect, time effect, and group × time interaction effect (all P < 0.05) [Table 4]. After adjusting for confounding factors using multivariate covariance analysis, the PSQI scores of the observation group were still significantly lower than those of the control group (adjusted mean: 6.87 ± 0.32 vs. 9.19 ± 0.32, F = 26.202, P < 0.001).
Table 4.
Comparison of PSQI Scores at Different Postoperative Time Points between the Two Groups of Patients
| Groups (n = 45) | Observation Group | Control Group | t | P |
|---|---|---|---|---|
| Before nursing | 14.27 ± 2.58 | 13.91 ± 2.58 | 0.654 | 0.515 |
| 5 days after surgery | 9.67 ± 2.15a | 11.98 ± 2.19a | 5.048 | <0.001 |
| 10 days after surgery | 6.87 ± 1.96bc | 9.18 ± 1.98bc | 5.563 | <0.001 |
| F time | F = 155.909, P < 0.001 | |||
| F groups | F = 26.267, P < 0.001 | |||
| F time×groups | F = 10.375, P < 0.001 | |||
Note: PSQI: Pittsburgh Sleep Quality Index. a indicates comparison between before and 5 days after surgery within the group, P < 0.05; b indicates comparison between before and 10 days after surgery within the group, P < 0.05; c indicates comparison between 5 and 10 days after surgery within the group, P < 0.05.
Comparison of Rehabilitation Exercise Compliance
The rehabilitation exercise compliance scores of the observation group at 5 and 10 days postoperatively were significantly higher than those of the control group (both P < 0.05) [Table 5]. After adjusting for confounding factors using multivariate covariance analysis, the compliance scores of the observation group were still significantly higher than those of the control group (adjusted mean: 65.55 ± 0.58 vs. 53.99 ± 0.58, F = 193.686, P < 0.001).
Table 5.
Comparison of Postoperative Functional Exercise Compliance Scores between the Two Groups at Different Time Points
| Groups (n = 45) | Observation group | Control group | t | p |
|---|---|---|---|---|
| 5 days after surgery | 57.49 ± 4.42 | 48.33 ± 4.54 | 9.691 | <0.001 |
| 10 days after surgery | 65.51 ± 3.49 c | 54.16 ± 4.31c | 13.764 | <0.001 |
Note: c indicates comparison between 5 and 10 days after surgery within the group, P < 0.05.
Comparison of Balance Ability
BBS scores of the observation group at 5 and 10 days postoperatively were significantly higher than those of the control group (both P < 0.05). Repeated-measures ANOVA showed significant group effect, time effect, and group × time interaction effect (P < 0.05) [Table 6]. After adjusting for confounding factors using multivariate covariance analysis, BBS scores of the observation group were still significantly higher than those of the control group (adjusted mean: 46.99 ± 0.53 vs. 40.99 ± 0.53, F = 62.790, P < 0.001).
Table 6.
Comparison of BBS Scores at Different Postoperative Time Points between the Two Groups of Patients
| Groups (n = 45) | Observation Group | Control Group | t | P |
|---|---|---|---|---|
| Before nursing | 37.20 ± 3.51 | 36.89 ± 3.59 | 0.416 | 0.678 |
| 5 days after surgery | 42.53 ± 3.41a | 39.67 ± 3.48a | 5.587 | <0.001 |
| 10 days after surgery | 46.89 ± 3.51bc | 41.13 ± 3.58bc | 7.709 | <0.001 |
| F time | F = 90.573, P < 0.001 | |||
| F groups | F = 45.782, P < 0.001 | |||
| F time×groups | F = 13.696, P < 0.001 | |||
Note: BBS: Berg Balance Scale. a indicates comparison between before and 5 days after surgery within the group, P < 0.05; b indicates comparison between before and 10 days after surgery within the group, P < 0.05; c indicates comparison between 5 and 10 days after surgery within the group, P < 0.05.
Comparison of Postoperative Complications
The incidence of postoperative complications in the observation group was lower than that in the control group (6.67% vs. 13.33%), but the difference was not statistically significant (P > 0.05) [Table 7].
Table 7.
Comparison of Postoperative Complication Rates between the Two Groups of Patients
| Groups (n = 45) | Infect | Stiff Joints | Deep Vein Thrombosis | Overall Incidence |
|---|---|---|---|---|
| Observation group | 1 (2.22) | 1 (2.22) | 1 (2.22) | 3 (6.67) |
| Control group | 2 (4.44) | 1 (2.22) | 3 (6.67) | 6 (13.33) |
| Statistic | — | |||
| P | 0.485 |
DISCUSSION
Sleep is essential for tissue repair and immune function recovery of patients after KA, and ward noise is a key environmental factor affecting postoperative sleep quality.[16] Studies have shown that postoperative patients are 30%–50% more sensitive to noise than the general population. Frequent noise exposure at night can disrupt sleep architecture, resulting in difficulty falling asleep, shallow sleep, and more frequent awakenings, which in turn affects the wound healing and rehabilitation process.[17] The SHEL model,[18] as a systematic risk assessment tool, can comprehensively identify the root causes of noise across four dimensions: software, hardware, environment, and liveware, overcoming the limitations of traditional single-dimensional noise management.
This study showed that the PSQI scores of the observation group at 5 and 10 days postoperatively were significantly lower than those of the control group, suggesting a significant correlation between the SHEL model-based noise control program and the improvement of sleep quality. This finding aligns with the study by Nien et al.[19] which applied white noise intervention to improve patients’ sleep quality in an ICU environment, both confirming the positive effect of improving the sound environment in the ward on enhancing patients’ sleep. The mechanism by which noise affects sleep may be related to the activation of the stress response. Continuous noise exposure can activate the hypothalamic–pituitary–adrenal axis through the auditory pathway, leading to increased cortisol secretion, which in turn affects the function of the sleep–wake regulation center, prolongs sleep latency and reduces sleep efficiency.[20] On the other hand, this comprehensive management, through multidimensional synergistic effects such as noise control, environmental optimization, and humanistic care, can help reduce stress levels and may improve sleep quality. In addition, personalized noise reduction tools (e.g., earplugs, white noise players) can further reduce the impact of environmental noise and help patients establish a stable sleep pattern.[21]
Meanwhile, although the management measures of the four dimensions of SHEL in this study mainly aimed to reduce the noise level in the ward, their effects may not be limited to noise reduction. The systematic nature of the SHEL model determines that its effect stems from the synergistic effects of software, hardware, environment, and liveware, including the reduction of noise exposure, as well as the synchronous improvement of doctor–patient communication quality, ward order perception, and patient social support perception. Therefore, the observed improvement in clinical outcomes should be understood as the overall effect of the SHEL-based comprehensive management program, rather than the isolated effect of noise reduction.
Rehabilitation exercise compliance is a key factor affecting functional recovery after KA, and poor sleep quality and negative emotions are important reasons for reduced compliance.[22] Studies have pointed out[23] that poor psychological state and low pain tolerance are independent risk factors for insufficient adherence to rehabilitation exercises in patients who underwent KA, and noise interference can further aggravate anxiety and pain perception by affecting sleep, forming a vicious cycle. The present study showed that the functional exercise compliance scale scores of the observation group at each time point after surgery were significantly higher than those of the control group, suggesting that the SHEL model-based comprehensive nursing care plan centered on noise control was associated with improved rehabilitation exercise compliance. Several mechanisms may explain this finding. First, good sleep quality can reduce the body’s fatigue state and improve patients’ physical strength and energy to participate in rehabilitation exercises. Second, this plan provided patients with a more comfortable rehabilitation atmosphere by optimizing the ward environment, which may help alleviate negative emotions such as anxiety and irritability, and improve patients’ confidence and self-efficacy in rehabilitation.[24] In addition, a positive nurse–patient trust relationship is an important factor in promoting patients’ overall treatment compliance.[25] The SHEL model emphasizes the importance of standardized operation and effective communication among medical staff, which helps to enhance patients’ trust in nursing care, thereby improving their rehabilitation exercise compliance.[26]
Patients after KA generally have weakened balance ability due to impaired joint proprioception and decreased muscle strength. Poor balance recovery increases the risk of falls and affects the rehabilitation process.[27] A study reported that proprioceptive exercise can significantly improve patients’ balance ability after KA and reduce the risk of falls; however, the rehabilitation effect depends on the patient’s compliance and physical condition.[28] In the present study, the observation group showed a significant improvement in rehabilitation exercise compliance after implementing a comprehensive ward management plan centered on noise control, enabling patients to complete rehabilitation exercises in a more standardized manner, thereby promoting balance ability recovery. The BBS scores at 5 and 10 days postoperatively were significantly higher than those in the control group. At the same time, the incidence of postoperative complications in the observation group was lower than that in the control group, which was associated with the improvement of sleep quality and the improvement of rehabilitation exercise compliance. Good sleep can enhance immune function and reduce the infection risk; standardized rehabilitation exercises can promote blood circulation and reduce the probability of deep vein thrombosis and joint stiffness. In addition, a comprehensive management plan centered on noise control may help alleviate stress response, alleviate stress-induced vasoconstriction and inflammatory cytokine release by reducing noise exposure, and also help reduce the incidence of complications.[29]
The theoretical basis of this study mainly comes from the systematic risk assessment concept of the SHEL model, which has been proven to be effective in medical and nursing risk management. The present study extends the model to ward noise management, enriching the application scenarios of the SHEL model in clinical nursing and providing new ideas for postoperative environmental management. In terms of the selection of outcome indicators, the Functional Exercise Compliance Scale, BBS, and PSQI are all internationally recognized standardized assessment tools with good reliability and validity, and have been widely used in the rehabilitation evaluation of patients who underwent KA, ensuring the reliability and comparability of the research results.[30,31] Based on the findings of this study, careful control of the inclusion and exclusion criteria of research subjects is important to ensure sample homogeneity in future clinical studies. A comprehensive SHEL model-based ward management scheme centered on noise control requires multidimensional quality control to ensure the standardization of medical staff operation, hardware modification, environmental management, thereby reducing implementation bias. Standardized scales should be used to assess outcomes, and outcome assessors should receive uniform training to ensure the reliability and validity of rehabilitation compliance, balance ability, and sleep quality assessment. Although there was no statistical difference in complications among the groups in this study, it is still necessary to extend the follow-up time, increase the sample size for further observation, and conduct in-depth analysis of the association between noise level and each outcome indicator in order to improve the clinical applicability of the study.[32]
This study has certain limitations. First, this study adopted a historical control design. Although no significant differences were observed in key measurable confounding variables between the two groups, the potential influence of unmeasured confounding factors, such as the accumulation of nursing experience over time and changes in patients’ rehabilitation cognition, cannot be completely excluded. Second, the daytime (8:00–18:00) and nighttime (22:00–6:00) noise monitoring periods in this study were defined according to the clinical workflow of our hospital, which did not fully correspond to the WHO Guidelines for community noise, which define the daytime period as 6:00–22:00 (16 h) and nighttime period as 22:00–6:00 (8 h). This difference may limit the comparability of findings with studies that adopt standard WHO time periods. Third, the SHEL model was mainly used as a tool for systematic identification of risk factors and the development of improvement measures, without conducting individualized dynamic risk assessment and risk-stratified intervention. Finally, due to the retrospective design, variables such as anxiety and depression status, family accompaniment, and frequency of nighttime nursing operations were not recorded and could not be included in statistical analyses. These factors may independently affect patients’ sleep quality and rehabilitation compliance. Therefore, the observed intergroup differences should be interpreted as evidence of an association rather than a causal relationship. Future research should adopt a prospective randomized controlled design and systematically collect data on psychosocial factors and nursing environmental variables. In noise measurement, internationally recognized time periods should be uniformly used, or at least the definition of measurement periods should be reported in detail to facilitate cross-study comparisons. Based on this, a dynamic risk assessment system should be constructed to achieve hierarchical and precise noise management strategies, further confirming the findings of this study.
CONCLUSION
The SHEL model-based comprehensive ward management plan centered on noise control was associated with reduced ward noise levels, improved patients’ sleep quality, better rehabilitation exercise compliance, and enhanced balance function in patients after KA through systematic management across software, hardware, environment, and liveware dimensions. This management model demonstrates potential clinical value in promoting postoperative recovery.
Availability of Data and Materials
The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to privacy and ethical restrictions (approval from the Ethics Committee of Daqing People’s Hospital), the raw data are not publicly available.
Author Contributions
Lei Li contributed to Research Design, Data Collection, Data Analysis, and Paper Writing; XingYin Song contributed to Research Design and Data Curation; YanZe Meng contributed to Data Curation and Data Analysis; Xue Feng was responsible for Research Design, Data Analysis, Reviewing and Editing, Communication Coordination, Ethical Review, Copyright and Licensing, and Follow-up. All authors read and approved the final manuscript.
Ethics Approval and Consent to Participate
This study was approved by the Ethics Committee of Daqing People’s Hospital (Approval No.: Lun Approval KY [2024] No. 018). All procedures followed the ethical standards of the institutional research committee and the Declaration of Helsinki (2013 revision, Fortaleza, Brazil). Written informed consent was obtained from all individual participants included in the study.
Conflicts of Interests
The authors declare that they have no competing interests.
Acknowledgment
The authors thank the nursing staff of the Department of Orthopedics at Daqing People’s Hospital for their contributions to the routine clinical implementation of the ward noise management protocol during the study period. We also express our gratitude to all patients whose medical records were included in this retrospective analysis.
Funding Statement
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to privacy and ethical restrictions (approval from the Ethics Committee of Daqing People’s Hospital), the raw data are not publicly available.