
==== Front
Nurs Open
Nurs Open
10.1002/(ISSN)2054-1058
NOP2
Nursing Open
2054-1058
John Wiley and Sons Inc. Hoboken

39312275
10.1002/nop2.70049
NOP270049
NOP-2023-May-0832.R3
Research Methodology: Discussion Paper ‐ Methodology
Research Methodology: Discussion Paper ‐ Methodology
Exploring the factors affecting the readiness for hospital discharge after total knee arthroplasty: A structural equation model approach
Li et al.
Li Na https://orcid.org/0000-0002-2630-1149
1
Guo Manjie 2
You Simeng 3
Ji Hong https://orcid.org/0000-0003-4266-9186
1 3 honghongji-2005@163.com

1 Department of Nursing The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital Jinan China
2 Beijing Hospital of Traditional Chinese Medicine Capital Medical University Beijing China
3 School of Nursing and Rehabilitation Shandong University Jinan China
* Correspondence
Hong Ji, Department of Nursing, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jing Shi Road, Jinan, 250014, Shandong Province, China.
Email: honghongji-2005@163.com

23 9 2024
9 2024
11 9 10.1002/nop2.v11.9 e7004931 8 2024
09 5 2023
12 9 2024
© 2024 The Author(s). Nursing Open published by John Wiley & Sons Ltd.
https://creativecommons.org/licenses/by-nc/4.0/ 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.

Abstract

Aim

To investigate the factors that influence readiness for hospital discharge in Chinese patients after total knee arthroplasty and to identify priorities for nursing interventions.

Design

A cross‐sectional study.

Methods

From January to August 2022, data were collected from 339 patients at two tertiary A‐level hospitals in Jinan, Shandong Province. SPSS 26.0 and Mplus 8.3 software were used for statistical analysis.

Results

Results from multiple linear regression showed that patients' age, residence status, education level, knee pain during sleep, quality of discharge teaching, self‐efficacy for rehabilitation, pain control knowledge, and social support were factors influencing their readiness for hospital discharge. The results of the structural equation model had shown that there were also indirect effects of the education level, knee pain during sleep, quality of discharge teaching, and pain control knowledge.

Conclusion

Patients' readiness for hospital discharge needs further improvement, hence physicians and nurses should judiciously allocate medical resources and concentrate their efforts on high‐risk groups characterized by low readiness for hospital discharge.

Implications for the Profession and Patient Care

This study underscores the importance of physicians and nurses prioritizing key factors such as age, residency status, education level, and social support in total knee arthroplasty patients to enhance their readiness for hospital discharge. By implementing targeted discharge planning, effective pain management, and comprehensive rehabilitation education, healthcare providers can improve patient outcomes.

Impact

This study identified key factors influencing readiness for hospital discharge in total knee arthroplasty patients, guiding targeted nursing interventions to improve post‐operative care.

Reporting Method

STROBE.

Patient or Public Contribution

The participants recruited for this study were actively engaged in the data collection process.

influencing factors
readiness for hospital discharge
structural equation model
total knee arthroplasty
Research Hospital Association of Shandong Province2022018 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:23.09.2024
Li, N. , Guo, M. , You, S. , & Ji, H. (2024). Exploring the factors affecting the readiness for hospital discharge after total knee arthroplasty: A structural equation model approach. Nursing Open, 11 , e70049. 10.1002/nop2.70049 39312275
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pmc1 INTRODUCTION

With the rise in the aging and obese population, there has been a noticeable increase in the total knee arthroplasty (TKA) procedures performed annually (Overgaard et al., 2020). In China, specifically, the number of TKA cases has surged from 50,000 a decade ago to almost 400,000, showcasing a remarkable growth rate of approximately 27.43% per year. This upward trajectory continues, and it is anticipated that TKA will emerge as one of the most prevalent surgical procedures in the coming decade (Singh et al., 2019).

In response to the healthcare management challenges from the increasing number of TKA, hospitals are taking measures to reduce the length of stay of patients (Iorio et al., 2020; Morrell et al., 2021). While shorter hospital stays offer the advantage of reducing the length of a patient's hospitalization, they may also present the disadvantage of limiting the available time for discharge preparation. Consequently, patients' readiness for hospital discharge (RHD) can be adversely affected. Evidence recognizes RHD as a pivotal factor in ensuring patient safety during the discharge process (Galvin et al., 2017). Key indicators of RHD encompass physical stability, ample support, psychological ability, and comprehensive information and knowledge. However, evidence has suggested that most physicians and nurses use clinical laboratory indicators to determine discharge times while paying little attention to patients' feelings (Wang, Wang, et al., 2021; Zhao et al., 2020). A large‐sample study confirms that only 47.8% of inpatients report feeling ready to be discharged when they leave the hospital (Mabire et al., 2019), indicating that the patient's RHD is at a low level and needed our attention. Moreover, further research findings have revealed a compelling association between patients' self‐reported RHD and various post‐discharge outcomes, including complication rates, readmission rates, mortality, and overall quality of life (Siow et al., 2019; You et al., 2022). At the same time, post‐TKA patients suffer from prolonged recovery time at home, lower post‐operative satisfaction (Choi & Ra, 2016), higher likelihood of complications, and higher 90‐day readmission rates (Gold et al., 2016). Research has demonstrated a clear correlation between RHD in post‐TKA patients and essential factors such as pain management, walking proficiency, knee functionality, and stair climbing capability (Causey‐Upton et al., 2019). These factors play a pivotal role in determining the success of their post‐operative rehabilitation outcomes. Therefore, in order to enhance post‐operative satisfaction and overall quality of life for TKA patients, it is imperative to comprehensively comprehend the present state and influential factors affecting their RHD.

2 BACKGROUND

Meleis' theory of transformation posits that transformation is a complex phenomenon that encompasses various aspects, including alterations in an individual's health status, role relationships, expectations, and abilities (Meleis et al., 2000). This comprehensive process is influenced by four fundamental factors: the nature of the transition, the conditions surrounding the transition, the provision of therapeutic care, and the response model (Ramsay et al., 2014). Aligned with the research objectives of this study, the independent variables employed to explore the factors influencing patients' RHD include the nature of transition (disease‐related information), the conditions of transition (encompassing general demographic information, self‐efficacy, social support, and pain control knowledge), and the quality of discharge teaching as a measure of care therapy.

Upon an extensive review of existing studies examining factors influencing RHD, it becomes apparent that a significant number of them employ linear regression (Hydzik et al., 2021; Qian et al., 2021) or logistic regression analyses as the primary statistical methods (Wang et al., 2021; Zhang et al., 2021). These studies have only explored the effects of the independent variables on the outcome variable, without deeply investigating the intrinsic links that exist between each variable. However, the structural equation model (SEM) enables a comprehensive analysis of the effects of individual variables on outcome variables and the interrelationships that exist between them (Brandmaier et al., 2013). In summary, the main goal of this study is to develop a SEM to investigate the factors that impact patients' RHD after TKA. This SEM will facilitate a comprehensive analysis of the pathways and intensity of influence that each factor has on RHD, allowing for a deeper understanding of their interrelationships. It will help nurses to understand the crucial elements in discharge education and optimize the allocation of healthcare resources. A systematic evaluation has unveiled that a significant portion of existing discharge instructions are generic and met with low acceptance from patients (Pellet et al., 2020). Consequently, the specific aim of this study is to provide doctors and nurses with valuable guidance in formulating personalized and evidence‐based discharge plans while elucidating the crucial aspects of discharge education. Ultimately, this study seeks to enhance patients' RHD outcomes, improving the quality of care and support that they receive.

3 THE STUDY

3.1 Aim and objective

The aim of this study is to investigate the factors influencing RHD in post‐TKA patients. The objectives are to identify key demographic, clinical, and psychosocial factors, assess the direct and indirect effects using an SEM, and prioritize nursing interventions to enhance their RHD.

4 METHODS

4.1 Design

A cross‐sectional study, descriptive design to explain post‐TKA patients' RHD and to analyse the relevant factors.

4.2 Instrument with validity and reliability

4.2.1 General information questionnaires

Based on a literature review and experts consultation, the general information questionnaire was designed by the researcher. The demographic information included age, sex, marital status, residency status, place of residence, education level, working status, per capita income (RMB), and medical insurance. The disease‐related information included height, weight (used to calculate BMI), number of TKA, type of surgery, co‐morbidities, length of hospital stay (days), and post‐operative stay (days).

4.2.2 Numeric rating scale (NRS)

This scale comprises a 10 cm long straight line, where a higher score indicates greater pain severity experienced by the patient and can be evaluated by nurses (Hawker et al., 2011). Specifically, in this research, the scale was employed to gauge the level of knee pain at rest, after activity, and during sleep among patients undergoing TKA before hospital discharge.

4.2.3 Quality of discharge teaching scale (QDTS)

This self‐assessment scale, which has been culturally adapted and applied to the Chinese population (Guan & Feng, 2023), is utilized to gauge patients' perceptions of the quality of discharge teaching. Comprising three dimensions and 24 items, each rated on a scale from 0 to 10, higher scores on this scale indicate better perceived quality of discharge teaching by the patients.

4.2.4 Self‐efficacy for rehabilitation outcome scale (SER)

The scale employed in this study is specifically tailored to evaluate patients' self‐efficacy for rehabilitation following TKA and has been used in the Chinese population (Wang et al., 2014). Consisting of two dimensions and 12 items, each scored between 0 and 10, higher scores on the scale indicate greater self‐efficacy in rehabilitation.

4.2.5 Postoperative pain control knowledge questionnaire

The scale, developed by Chinese scholar Wen Mei (2007), is primarily utilized to evaluate the knowledge of pain control among post‐operative patients. The questionnaire comprises eight items, each scored on a scale of 0–5. Higher scores on the questionnaire indicate a higher level of patient knowledge regarding postoperative pain control.

4.2.6 Social support rating scale (SSRS)

The scale used in this study is a general scale developed for assessing the social support of inpatients, and consists of three dimensions with a total score of 12–66 (Xiao, 1994). Higher scores on the social support scale indicate a higher level of social support for the patient.

4.2.7 Readiness for hospital discharge scale (RHDS)

The original version of the RHDS was developed by American scholars Weiss and Piacentine (2006) and later culturally adapted for Chinese use by Lin et al. (2014). In this study, the Chinese version of the RHDS was employed, encompassing three dimensions: personal status (items 1–3), adaptive capacity (items 4–8), and anticipatory support (items 9–12). Each item is scored on a scale of 0 to 10, where higher scores indicate better resilience to hospital discharge. The item mean score of the RHDS is calculated by dividing the total score by the number of items. RHDS scores were then categorized into four levels of discharge readiness (Wang, Lv, et al., 2021): very high (9–10), high (8–8.9), moderate (7–7.9), and low (<7).

4.3 Sampling and recruitment

4.3.1 Setting

The study was conducted in the Department of Bone and Joint Surgery at two tertiary A‐level hospitals in Jinan, Shandong Province. Both departments have comparable levels of TKA medical care with 42 and 45 beds respectively, and the average length of stay for post‐TKA patients is 5–7 days.

4.3.2 Sampling and study population

Based on the cross‐sectional study calculation formula and concerning existing studies on RHD, the sample size was calculated to be 171 cases (Guan & Feng, 2023). To construct an SEM, a minimum sample size of at least 10 times the number of independent variables is required (Thompson, 2000). This study involves a total of 25 independent variables. Taking into consideration a 15% rate of invalid questionnaires, the determined minimum sample size required for this study is 288. We used convenience sampling method to select post‐TKA patients from January to August 2022 at the target hospital for questionnaire survey with 339 valid questionnaires were collected. Patients who meet the following criteria are included in this study: (1) the Osteoarthritis Guidelines diagnose knee osteoarthritis (Abramoff & Caldera, 2020); (2) post‐TKA; discharged today and have received health education; (3) without significant cognitive and language difficulties, can understand the questionnaire entries; (4) voluntary participation in this study and signing of the informed consent form. Nevertheless, patients with other serious diseases and postoperative complications will be excluded. Both hospitals in the study have the same discharge criteria for post‐TKA patients, that is, no significant postoperative complications, good physical status, knee extension of 0 degrees and flexion greater than or equal to 90 degrees.

4.4 Data collection

The survey for this study was conducted primarily by two postgraduate nursing students who were interning in bone and joint surgery and had direct access to patients. The survey was conducted in a quiet ward on the day of discharge and averaged approximately 20 minutes per patient. Given the age and literacy level of the participants, the survey was consented to be a one‐to‐one question‐and‐answer format, with participants responding verbally and then transcribed by the investigator.

4.5 Statistical analysis

First, after double‐checking and removing questionnaires with obvious logical errors, we ensured the quality and reliability of the data. Then, using SPSS 26.0 and Mplus 8.3 software for statistical analysis, the test level was two‐sided α = 0.05 (p < 0.05 indicated a statistical difference). Multiple linear regression was used to select the independent factors affecting RHD in post‐TKA patients. Using Mplus 8.3 software, a SEM was constructed and analysed using the maximum likelihood (ML) estimation method. The significance of the multiple mediating effects of the mediating variables was tested and verified by applying the Bootstrap method (Bootstrap =2000).

4.6 Ethical considerations

The present study was approved by the Ethics Committee (2022‐R‐021). Before the survey, the researcher explained the purpose and significance of this study to the patients, who made a timely decision and signed an informed consent form. During the survey, the researcher monitored the patient's condition without disturbing them, and participants could stop answering at any time. After the survey, the researcher patiently answered the patients' questions and promptly responded to the medical staff about the problems. Patient information would be kept anonymously to ensure that the relevant data would be used only for the study.

5 RESULTS

In this study, 350 questionnaires were distributed. After excluding those with obvious logical errors, 339 questionnaires remained for analysis. The effective recovery rate was 96.86%.

5.1 Reliability testing of scales

All the scales used in this study demonstrated good reliability. Specifically, the QDTS had a Cronbach's alpha coefficient of 0.859. The SER outcome scale showed a Cronbach's alpha coefficient of 0.912. The Postoperative Pain Control Knowledge questionnaire had a Cronbach's alpha coefficient of 0.755. For the SSRS, the Cronbach's alpha coefficient was 0.731. Additionally, the RHDS exhibited a Cronbach's alpha coefficient of 0.860 overall, with its three dimensions showing coefficients of 0.794, 0.845, and 0.769, respectively.

5.2 Current status of RHD in post‐TKA patients

The total RHDS score on discharge day of post‐TKA patients was (92.12 ± 6.43) and the item mean score was (7.68 ± 0.54), which was at a moderate level (7–7.9) (Wang et al., 2021).

5.3 Results of single factor analysis

Table 1 presented the results of the analysis conducted on general demographics and disease‐related information. This study identified several variables that exhibited statistical significance in relation to RHD among post‐TKA patients. These variables included age, marital status, residence status, education level, work status, per capita income, medical insurance, number of TKA, type of surgery, and the number of co‐morbidities.

TABLE 1 Characteristics of categorical variables and univariate analysis.

Variables	n (%)	RHDS score	t/H/F value	p value	
Age(years)	45–59	73 (21.5)	96.05 ± 4.33	53.315	<0.001	
60–74	227 (67.0)	91.89 ± 5.89	
75–89	39 (11.5)	86.10 ± 7.62	
Sex	Male	80 (23.6)	92.65 ± 7.22	0.838	0.403	
Female	259 (76.4)	91.96 ± 6.16	
Marital status	Married	273 (80.5)	92.63 ± 6.35	−2.984	0.003	
Divorced/Widowed	66 (19.5)	90.03 ± 6.35	
Residence status	Alone	18 (5.3)	84.06 ± 5.27	−5.727	<0.001	
Not alone	321 (94.7)	92.58 ± 6.19	
Residency	Rural	188 (55.5)	91.80 ± 6.55	1.115	0.329	
Town	48 (14.2)	93.35 ± 6.10	
City	103 (30.4)	92.14 ± 6.33	
Education level	Primary and below	147 (43.3)	88.46 ± 5.83	70.062	<0.001	
Junior	115 (33.9)	93.47 ± 4.98	
High and above	77 (22.7)	97.12 ± 5.20	
Working status	Jobless	177 (52.2)	90.41 ± 6.38	14.174	<0.001	
Leaving/retired	106 (31.3)	93.95 ± 6.15	
In‐service	56 (16.5)	94.07 ± 5.62	
Per capita income (RMB)	<2000	173 (51.0)	90.28 ± 6.43	13.952	<0.001	
2000–3000	78 (23.0)	92.51 ± 5.72	
3001–4000	36 (10.6)	95.03 ± 6.19	
>4000	52 (15.3)	95.65 ± 5.29	
Medical insurance	Resident insurance	224 (66.1)	91.07 ± 6.19	−4.318	<0.001	
Employee insurance	115 (33.9)	94.17 ± 6.41	
BMI	Low	3 (0.9)	92.67 ± 5.13	0.972	0.406	
Normal	62 (18.3)	90.87 ± 6.56	
Overweight	126 (37.2)	92.33 ± 6.41	
Obesity	148 (43.7)	92.46 ± 6.40	
Number of TKA	The first time	288 (85.0)	91.74 ± 6.45	−2.640	0.009	
The second time	51 (15.0)	94.29 ± 5.87	
Type of surgery	Unilateral TKA	295 (87.0)	92.35 ± 6.24	1.701	0.090	
Bilateral TKA	44 (13.0)	90.59 ± 7.46	
Number of co‐morbidities	0	91 (26.8)	94.81 ± 5.58	13.451	<0.001	
1–2	228 (67.3)	91.36 ± 6.37	
≥3	20 (5.9)	88.65 ± 6.92	
Note: BMI is Body Mass Index. ‘Low’ means <18.5; ‘Normal’ means 18.5–23.9; ‘Overweight’ means 24–27.9; ‘Obesity’ means ≥28.

The findings presented in Table 2 demonstrated that the correlation coefficients between length of hospitalization, degree of knee pain during sleep, and RHD were significantly higher. Furthermore, the study suggested that variables such as quality of discharge teaching, self‐efficacy for rehabilitation, pain control knowledge, and social support exhibited a positive association with RHD.

TABLE 2 Correlation analysis between variables (r).

Variables	Personal status	Adaptive capacity	Anticipated support	RHDS score	
Length of hospitalization	−0.093	−0.176**	−0.061	−0.152**	
Length of post‐operative	−0.061	−0.140**	−0.034	−0.111*	
Degree of knee pain at rest	−0.317**	−0.411**	−0.152**	−0.391**	
Degree of knee pain after activity	−0.405**	−0.437**	−0.189**	−0.449**	
Degree of knee pain during sleep	−0.419**	−0.438**	−0.204**	−0.460**	
Quality of discharge teaching	0.393**	0.289**	0.318**	0.412**	
Self‐efficacy for rehabilitation	0.501**	0.455**	0.201**	0.496**	
Pain control knowledge	0.129*	0.199**	0.015	0.159**	
Social support	0.281**	0.258**	0.452**	0.406**	
Note: **is: at 0.01 level (two‐tailed), significant correlation. *is: at 0.05 level (two‐tailed), significant correlation.

5.4 Results of multiple linear regression analysis

In this study, RHD was the dependent variable, and variables with statistically significant differences in univariate and correlation analyses were the independent variables. These variables were included in a multiple linear regression analysis using an input method, with their respective assignments shown in Table 3. The findings revealed that the regression equation included eight independent variables, which account for 66.8% of the variance. The adjusted R 2 for this regression model was 66.8%, with an F value of 43.538 and a significance level of p < 0.001. For further details, please refer to Table 4.

TABLE 3 Independent and dummy variables associated with RHD in patients (n = 339).

Independent variable	Assignment method	
Age(years)	45–59 = 1; 60–74 = 2; 75–89 = 3	
Marital status	Married = 0; Divorced/Widowed = 1	
Residence status	Not alone = 0; Alone = 1	
Education level	Primary and below = 1; Junior = 2; High and above = 3	
Working status	Dummy Variable 1: Jobless = 0; Leaving/retired = 1; In‐service = 0

Dummy Variable 2: Jobless = 0; Leaving/retired = 0; In‐service = 1

	
Per capita income (RMB)	<2000 = 1; 2000–3000 = 2; 3001–4000 = 3;

>4000 = 4

	
Medical insurance	Resident insurance = 1; Employee insurance = 2	
Number of TKA	The first time = 1; The second time = 2	
Number of co‐morbidities	0 = 1; 1–2 = 2; ≥3 = 3	
Continuous variables	Original values	

TABLE 4 Multiple linear regression analysis of RHD in patients.

Independent variables	Partial regression coefficient	Standard error	Standardized regression coefficients	t value	p value	
(Constant)	43.213	5.269	—	8.201	<0.001	
Age	−2.433	0.456	−0.215	−5.334	<0.001	
Residence status	−4.086	1.066	−0.143	−3.832	<0.001	
Education level	2.409	0.410	0.295	5.879	<0.001	
Knee pain during sleep	−1.356	0.181	−0.274	−7.475	<0.001	
Quality of discharge teaching	0.198	0.032	0.223	6.201	<0.001	
Self‐efficacy for rehabilitation	0.201	0.039	0.193	5.125	<0.001	
Pain control knowledge	0.093	0.042	0.071	2.204	0.028	
Social support	0.122	0.040	0.109	3.019	0.003	

5.5 Results of SEM

This study utilized Mplus 8.3 software and employed the ML method for model fitting and parameter estimation. Non‐significant paths were removed to obtain the final SEM. The developed model demonstrated a χ2/DF value of 2.022, an RMSEA value of 0.055, a CFI value of 0.979, a TLI value of 0.954, and an SRMR value of 0.045, all of which met the requirements for SEM fitting. Figure 1 provided a visual representation of the SEM. The path coefficients in the SEM indicated the extent of association between RHD and various influencing factors among post‐TKA patients. The specific values for each path in this study were presented in Table 5.

FIGURE 1 Structure equation model diagram (standardized path coefficients).

TABLE 5 Effect of variables in structural equation modelling on the effect of RHD in patients.

Routes	Direct effect	Indirect effect	Total effect	
RHD<‐Age	−0.240	—	−0.240	
RHD<‐Education level	0.295	0.116	0.411	
RHD<‐Residence status	−0.178	—	−0.178	
RHD<‐Knee pain during sleep	−0.278	−0.104	−0.382	
RHD<‐Quality of discharge teaching	0.235	0.065	0.299	
RHD<‐Self‐efficacy for rehabilitation	0.207	—	0.207	
RHD<‐Pain control knowledge	0.074	0.023	0.097	
RHD<‐Social support	0.106	—	0.106	
Note: ‘—’ is blank data.

6 DISCUSSION

6.1 Analysis of current status of RHD in post‐TKA patients

The findings of this study revealed that the overall level of RHD among post‐TKA patients was not high, indicating a need for targeted interventions by clinicians and nurses. When comparing these results with those of other similar studies, it was observed that post‐TKA patients displayed lower levels of RHD compared to certain medical‐surgical patients (Mehraeen et al., 2022; Zhao et al., 2020). There might be two reasons for this disparity. Firstly, as TKA is an invasive procedure, patients often experience pain and functional limitations during the recovery process. It is worth noting that up to one‐fifth of the patients may also develop kinesiophobia, which can hinder their rehabilitation (Cai et al., 2018). Secondly, in comparison to other surgical operations, post‐TKA patients require a more extended period of home‐based rehabilitation (Buus et al., 2021), which can lead to increased uncertainty after discharge and subsequently impact their RHD.

6.2 Analysis of the direct effects of influencing factors on RHD in post‐TKA patients

According to SEM results, age, education level, residence status, knee pain during sleep, quality of discharge teaching, self‐efficacy for rehabilitation, pain control knowledge and social support had a significant direct impact on RHD in post‐TKA patients, all of which were greater than 0.1. Analysing these factors, it could be found that physicians and nurses should focus on age, education level, residency status, and social support. Additionally, they could intervene in knee pain during sleep, the quality of discharge teaching, and self‐efficacy for rehabilitation.

6.2.1 The effect of age, education level, residence status and social support on RHD

In this study, it was found that age and residence status had a negative effect on RHD, while education level and social support had a positive effect on RHD. Older patients, who often face multiple chronic conditions like hypertension and diabetes, tended to have lower RHD, consistent with previous studies (Zhang et al., 2021; Qian et al., 2021). Aging was also associated with declines in physical and cognitive function, which could impair self‐care and adherence to discharge instructions. Additionally, patients with lower education levels showed reduced comprehension of discharge instructions, aligning with findings from Qian et al. (2021). Furthermore, patients who live alone after discharge required special attention from healthcare providers (Xiong et al., 2021) due to potential deficits in physical and emotional support (Kolarczyk et al., 2023). Similarly, low social support scores correlated with lower RHD (Zhang, Tang, et al., 2021). In summary, physicians and nurses should focus on patients who are older, have low education level, live alone, and have low social support scores.

6.2.2 The effect of knee pain during sleep on RHD

Knee pain is a significant problem for post‐TKA patients, occurring in about 60% of patients (Price et al., 2018). Results of the present study also confirmed that pain can affect patients' RHD, in line with the results of existing studies (Dewinter et al., 2016). Although multimodal analgesia was considered the optimal measure for pain management after TKA (Li et al., 2019), there were still some patients with severe pain problems. This indicated that physicians and nurses need to refine existing management programs and develop individualized pain management based on the patient's physical variability. At the same time, nurses should offer improved health education and psychological interventions for patients, including pre‐operative pain education to correct misconceptions, providing life care instructions such as proper positioning of the operated limb to enhance blood flow and reduce pain, and timely assessing the patient's knee pain level to apply medication or ice for relief (Wang et al., 2022). Additionally, using music or positive thinking interventions can help divert the patient's attention, alleviate anxiety and depression, and reduce pain sensitivity (Engel et al., 2021; Laframboise‐Otto et al., 2021).

6.2.3 The effect of quality of discharge teaching on RHD

Previous studies have indicated that the quality of discharge teaching plays a crucial role in affecting the patients' RHD (Guan & Feng, 2023; Zhang et al., 2021a), consistent with the results of this study. Hospital discharge is a transitional phase for patients, during which nurses need to be involved in implementing and preparing it. Whereas discharge education is an important part of nursing work, quality teaching is closely linked to better self‐care after discharge and can also enhance patients' RHD (Guan & Feng, 2023). When nurses have excellent teaching skills, it may take less content to produce the desired results, but in the opposite case, there may be over‐compensation of content. Nurse managers can enhance the teaching skills of nurses by conducting regular health education contests and excellent educator experience sharing. In addition, a systematic review showed that most of the discharge instructions in clinical practice were currently universal and not well accepted by patients (Pellet et al., 2020), so nurses should spend more time listening to patients' problems and providing personalized solutions.

6.2.4 The effect of self‐efficacy for rehabilitation on RHD

In this study, the effect of self‐efficacy for rehabilitation on RHD in post‐TKA patients was consistent with the many studies that had found self‐efficacy to be a positive contributor to RHD (Franck et al., 2023). The rehabilitation of post‐TKA patients played a pivotal role in determining the surgical outcome and was vital for their attainment of independence. Results of a study by Cai et al. (2018) demonstrated that patients' self‐efficacy for rehabilitation served as a significant predictor of post‐TKA kinesiophobia, which in turn influenced their adherence to post‐operative exercise. Hence, healthcare professionals can employ interventions based on self‐efficacy theory to address patients' cognition, motivation, and emotions, thereby facilitating their early mobilization and discharge. Furthermore, implementing positive thinking interventions (Dowsey et al., 2019) can help alleviate kinesiophobia in TKA patients and enhance their adherence to rehabilitation exercises, ultimately preparing them for an early discharge.

6.3 Analysis of the indirect effects of influencing factors on RHD in post‐TKA patients

The results of this study demonstrated that pain control knowledge, knee pain during sleep, the quality of discharge teaching, and education level exerted both direct and indirect effects on RHD in post‐TKA patients. The education level could influence patients' RHD through three pathways, wherein the quality of discharge teaching, self‐efficacy for rehabilitation, and social support serve as mediating variables. Each pathway contributed to 21.875%, 5.938%, and 8.438% of the total effect value, respectively. Above data implied that the education level was important for patients' RHD, which was consistent with the previous findings (Qian et al., 2021; Xiong et al., 2021). The theory of knowledge‐attitude‐practice also points out that knowledge is a prerequisite for people to change their cognition and take action (Tahani & Manesh, 2021), and the education level is closely related to the patient's ability to accept knowledge. This indicated that healthcare professionals should prioritize patients with lower levels of education and enhance the duration and frequency of health education to improve the quality of discharge teaching and self‐efficacy for rehabilitation. Simultaneously, patients should be encouraged to utilize available social and medical resources, actively seek support from family and friends, strengthen their social support network, and enhance their RHD.

Knee pain during sleep could impact patients' RHD by influencing their social support and self‐efficacy for rehabilitation. On the one hand, knee pain during sleep negatively impacted the patient's sleep quality and physical strength, resulting in a reduction in their engagement in social activities and utilization of support (Brandstetter et al., 2017), ultimately affecting their RHD. On the other hand, individuals experiencing pain during sleep were more susceptible to anxiety and depression, which further hindered their recovery confidence and impeded the overall recovery process (Sorel et al., 2022). To summarize, the indirect impact of knee pain during sleep on a patient's RHD comprised 32.010% of the total effect. It was recommended to establish an interdisciplinary post‐TKA pain specialist team, consisting of professionals in nutrition, anaesthesia, and rehabilitation, to collaborate and effectively alleviate patients' knee pain during sleep. This approach aimed to enhance social support, boost self‐efficacy for rehabilitation, and ultimately improve the patient's overall RHD.

Post‐TKA patients' pain control knowledge had the potential to positively influence their RHD through self‐efficacy for rehabilitation, accounting for 7.188% of the total effect. Previous studies had shown a significant positive correlation between patients' knowledge and comprehension with their RHD (Liu & Jiang, 2017). Knee pain posed a significant barrier to functional rehabilitation for post‐TKA patients, and the patients' pain control knowledge could impact their outcomes in pain management after the surgery. Moreover, the rehabilitation outcome following TKA directly impacted surgical outcomes and physical abilities, potentially leading to prolonged hospital stays and reduced RHD. Hence, it is of utmost importance for nurses to proactively impart pain control knowledge to patients upon their admission. This approach aimed to enhance patients' awareness of post‐operative pain and their ability to express it accurately, ultimately reducing post‐operative pain, improving rehabilitation efficacy, and facilitating early RHD.

6.4 Strength and limitation of the work

This study provides critical insights into factors affecting RHD in post‐TKA patients, with a comprehensive analysis using SEM. But, this research still had some limitations. First, the variables we included are not comprehensive enough, which will be refined in a future study. Second, the subjects of this study were all from Jinan, Shandong Province, with limited extrapolation of results. Third, this study used a one‐on‐one question‐and‐answer format to collect patient data, which may have some reporting bias.

6.5 Recommendations for further research

Future studies should broaden the scope to explore variations in RHD among post‐TKA patients across different geographic regions. Additionally, longitudinal studies are recommended to investigate the causal relationships between variables, which could inform more effective interventions to enhance RHD.

7 CONCLUSION

In this study, post‐TKA patients had an intermediate level of RHD, which suggested that physicians and nurses should pay attention to the assessment and intervention of their RHD. The SEM results revealed that healthcare professionals should prioritize interventions targeting knee pain during sleep, quality of discharge teaching, and self‐efficacy for rehabilitation in post‐TKA patients. Furthermore, for patients with lower education levels, limited pain control knowledge, and severe knee pain during sleep, interventions could also be focused on improving their RHD through enhancements in the quality of discharge teaching, social support, and self‐efficacy for rehabilitation.

AUTHOR CONTRIBUTIONS

Na Li and Hong Ji conceptualized and designed the study. Data collection was carried out by Na Li and Simeng You, while Na Li and Manjie Guo conducted the data analysis. Hong Ji was responsible for the final statistical examination. The manuscript was drafted by Na Li, with Hong Ji providing critical review and revisions.

FUNDING INFORMATION

This study was funded by Research Hospital Association of Shandong Province (2022018).

CONFLICT OF INTEREST STATEMENT

The authors unequivocally declare that this study is free from any existing or potential conflicts of interest.

ETHICS STATEMENT

This study was approved by the Ethics Committee of the School of Nursing and Rehabilitation, Shandong University (2022‐R‐021). Strictly follow the principles of voluntariness, fairness, usefulness, and harmlessness during the investigation process.

ACKNOWLEDGEMENTS

We are very grateful to all patients who participated in this study. The author also wishes to thank the hospital department teachers and mentors for their support and guidance.

DATA AVAILABILITY STATEMENT

The first author of this study, Li Na, can be contacted for relevant data if the request is reasonable.
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