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Nursing
Original research
Classification of kinesiophobia in patients after cardiac surgery under extracorporeal circulation in China: latent profile and influencing factors analysis from a cross-sectional study
  1. Wenhui Xing1,2,
  2. Jing Jing Piao3,
  3. Tong Ren1,
  4. Yujing Liang1,
  5. Qi Li2,
  6. Yanmei Gu2,4,
  7. Rong Wang1
  1. 1 Department of Cardiovascular Surgery, The Sixth Medical Centre of PLA General Hospital, Beijing, China
  2. 2 Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
  3. 3 Kobe University Graduate School of Health Sciences, Kobe, Hyogo, Japan
  4. 4 Hebei Key Laboratory of Health Care with Traditional Chinese Medicine, Shijiazhuang, Hebei, China
  1. Correspondence to Dr Rong Wang; wangrongd{at}126.com

Abstract

Objective To investigate the potential classification of kinesiophobia in patients after cardiac surgery under extracorporeal circulation from a psychosocial perspective, and analyse the characteristic differences among different latent levels of patients.

Study design This is a cross-sectional study of Chinese adults after cardiac surgery under extracorporeal circulation, aged 18 years and older, recruited from a tertiary hospital in North China.

Methods This study uses latent profile analysis to identify potential classifications of kinesiophobia in questionnaires from 348 patients undergoing cardiac surgery under extracorporeal circulation. Multiple logistic regression analysis was used to evaluate the influencing factors at different latent classifications.

Results The average performance of each indicator in Model 3 is best suited for analysis, Entropy=0.873 and bootstrap likelihood ratio test (p)＜0.0001. The result of regression equation shows postoperative time (p<0.001), age, self-efficacy, pain and social support level (p<0.05) were the factors influencing the potential profile classification of patients after cardiac surgery under extracorporeal circulation.

Conclusion The study identified three distinct classifications of patients: the low kinesiophobia group, the moderate kinesiophobia—high-risk perceived symptoms group and the high kinesiophobia—high exercise avoidance group (HK-HEAG). Addressing kinesiophobia, especially in older male patients during the early postoperative period, is crucial. Enhancing self-efficacy seems effective in reducing kinesiophobia, while increasing social support may not be as beneficial for the HK-HEAG. These findings provide a basis for implementing preventive interventions in cardiac rehabilitation.

Trial registration number The research is registered with the Chinese Clinical Trial Registry (ChiCTR2200057895).

  • Coronary heart disease
  • Cross-Sectional Studies
  • MENTAL HEALTH
  • Nursing Care

Data availability statement

No data are available.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi-org.ezproxy.u-pec.fr/10.1136/bmjopen-2024-083909

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    STRENGTHS AND LIMITATIONS OF THIS STUDY

    • It is a comprehensive investigation conducted in China, specifically examining the factors that potentially influence the level of kinesiophobia in patients who have undergone cardiac surgery with extracorporeal circulation.

    • This study indicatively uses latent profile analysis to classify kinesiophobia of patients after cardiac surgery under extracorporeal circulation.

    • Self-report questionnaires in data collection may influence the assessment of kinesiophobia in an objective approach.

    • The adaptability of this model is limited to the Chinese background.

    Introduction

    Kinesiophobia is defined as an irrational and excessive fear of carrying out a physical movement.1 Previous studies have reported that psychological factors, such as kinesiophobia, are a significant barrier to patient participation in cardiac rehabilitation (CR).2–6 In the context of cardiac disease, it is mostly described as a fear of physical activity due to the apprehension of worsening cardiac disease or the possibility of inducing adverse outcomes. Kinesiophobia was detected in 65% of individuals with chronic heart failure and in 86.26% of patients with a first-time acute myocardial infarction.7 8 According to a previous study, high levels of kinesiophobia can negatively impact not only the performance of daily activities but also CR engagement.9

    Research has shown that kinesiophobia has an influence and plays an intermediate role in attendance at CR.1 As a mediator, kinesiophobia is influenced by predictive factors and has indirect effects. General health and muscle endurance increased the probability of attendance at CR, while self-rated anxiety had the opposite effect. There have been studies exploring whether there are positive changes in kinesiophobia based on CR, with higher levels of aerobic capacity and lower levels of physical activity compared with patients with low levels of kinesiophobia. Results showed a significant reduction in kinesiophobia after an exercise-based CR programme.10 CR is an important step in the recovery process after cardiac surgery. It is a comprehensive strategy that is aimed at improving a person’s physical, psychological and social functioning.11–13 Studies have shown that exercise-based CR can not only reduce mortality and hospital admissions for cardiovascular disease but also improve quality of life and mental well-being.12 14 15 CR’s effectiveness and importance are recommended as level IA by most international cardiovascular societies.16–18

    Kinesiophobia is a psychological disorder, and we should pay more attention to those subjective factors that are self-influenced and in constant change. In a potential profile analysis of kinesiophobia in patients with coronary heart disease (CHD),19 objective demographic information was included in the analysis, and the results showed that patients could be divided into three potential types: ‘low fear type’, ‘intermediate fear type’ and ‘high fear type’. However, among the influencing factors of kinesiophobia, objective factors cannot be interfered with by medical staff. In addition, research supports that kinesiophobia is positively correlated with age,19 but the explanation for these potential differences in age has not been studied. From the perspective of social psychology, the research results of Zhang20 et al showed that it was important to alleviate kinesiophobia for patients with low subjective social status, but the mechanism of how social support produced positive effects in different kinesiophobia classifications has not been clarified. Clinical professionals should collect objective influence factors as predictive factors, focusing on targeted interventions based on the patient’s own subjective factors.

    Few studies have investigated the effects of kinesiophobia in patients after cardiac surgery under extracorporeal circulation. The number of cardiac surgeries has increased tremendously in recent years. In China, cardiac surgery volume increased by 8% in 2020 compared with 2012.21 Extracorporeal circulation replaces cardiopulmonary function in a non-physiological way during cardiac surgery, and the lung function of patients is significantly decreased after the operation,22 23 while the blood is in a hypercoagulable state after the operation, and there is a risk of thrombosis.24 Exercise is the main form of CR, and early postoperative activity is beneficial to patients to reduce postoperative pulmonary complications and thrombotic events.25 26 However, due to various reasons, patients with kinesiophobia caused a decline in exercise compliance.1 The factors of kinesiophobia are complex and highly heterogeneous. From the social psychology perspective, the classification of kinesiophobia in patients after cardiac surgery under extracorporeal circulation has not been well characterised. Previous studies27 28 mainly evaluated patients’ kinesiophobia classification from the total score of the scale, which may have the same total score but the score of each item varies greatly. This study fills that gap. Latent profile analysis (LPA) is an ‘individual-centric’ statistical analysis method that can homogenise sequential data, explore the characteristics of groups without categories and ethnic differences in groups, and then analyse their respective influencing factors in different subgroups.29 We hypothesised that patients with kinesiophobia after cardiac surgery could be accurately divided into three subgroups using the LPA method, and the features between the groups were well distinguished. Based on the classification results of this study, it provides a reliable reference for clinical medical staff to intervene in cases of kinesiophobia.

    Materials and methods

    Study design

    In this cross-sectional study, subgroups of kinesiophobia characteristics and associated factors in patients after heart surgery were investigated. All participants were recruited from a tertiary hospital in North China and completed the questionnaire from April 2022 to April 2023.

    Participants

    Participants who met the inclusion criteria were provided with information about the study prior to inclusion, as well as consent and willingness to engage in this study after being fully informed of its objectives.

    The participants met the following inclusion and exclusion criteria.

    Inclusion criteria

    1. Advised by the doctor to participate in CR.

    2. Patients who underwent cardiac surgery under extracorporeal circulation (eg, coronary artery bypass grafting and cardiac valve replacement) 3 months prior to the survey.

    3. Adults aged 18–75 years.

    4. Conscious, mentally and psychologically competent and able to complete the questionnaire.

    Exclusion criteria

    1. Contraindication to CR (eg, uncontrollable or unstable angina, severe arrhythmias, etc).

    2. Refusal to provide personal information for participation in the questionnaire.

    3. Recent severe family events (eg, malignancy), psychological instability, individuals actively expressing depressive and anxious tendencies or are suspected of having mild cognitive impairment.

    Study tools

    Sociodemographic questionnaire

    Sociodemographic data were collected, including gender, age, education level, marital status, vocational type, average monthly household income, current residence, smoking status, alcohol consumption, surgical operation approach and postoperative time.

    Tampa Scale for Kinesiophobia Heart

    The Chinese version of the Tampa Scale for Kinesiophobia Heart (TSK-SV Heart) was used to assess the kinesiophobia levels of patients.30 This scale consists of 17 items that assess danger, fear, avoidance and dysfunction. The questions were evaluated using the 4-point Likert scale (1=strongly disagree, 2=disagree, 3=agree and 4=strongly agree). A score of 37 or higher indicates a high level of kinesiophobia.9 31

    Cardiac Exercise Self-Efficacy Instrument

    The Cardiac Exercise Self-Efficacy Instrument (CESEI) was developed by Hickey et al to measure exercise self-efficacy in CR patients.32 In 2021, a Chinese version of CESEI was developed through translation, back translation and cultural adjustment.33 The Chinese version of the CESEI includes 16 items corresponding to one dimension, which are scored on a scale of 1–5. The total score is determined by the sum of the items scored. The higher the score, the higher the patient’s self-efficacy in CR. The Cronbach’s alpha for the Chinese version of the CESEI is 0.941.

    Social Support Rating Scale

    The Social Support Rating Scale (SSRS) was used to examine the levels of social support among the participants.34 The SSRS comprises 10 items divided into 3 categories: objective support, subjective support and social support use. Low, medium and high levels of social support are represented by total scores of 0–22, 23–44 and 45–66, respectively. The Cronbach’s alpha of the scale is 0.81.

    Multi-dimensional Fatigue Inventory

    The Multi-dimensional Fatigue Inventory (MFI-20) was used to determine participants' fatigue levels.35 General weariness, physical fatigue, reduced activity, diminished motivation and mental fatigue are the five categories of the MFI-20. Responses were given using the 5-point Likert scale ranging from 1 (yes, this is true) to 5 (no, this is not true). This scale has a Cronbach’s alpha of 0.882, and it is regularly used to assess patient weariness with good reliability.

    Hospital Anxiety and Depression Scale

    The Hospital Anxiety and Depression Scale (HADS) was used to determine the level of anxiety and depression in participants.36 The HADS comprises 14 items with 4 possible answers ranging from 0 to 4, as well as 2 subscales: anxiety and depression. The HADS score indicates the severity of anxiety or depression. The higher the HADS score, the more severe the anxiety or sadness. This scale has been tested in a variety of countries.

    Numerical Rating Scale

    The Numerical Rating Scale (NRS) is accurate, concise and more feasible. It was once considered the gold standard for pain assessment by the American Pain Society.37 Patients are asked to select a single number representing the intensity of their pain on a scale of 1–11 (0=no pain and 10=worst pain). A score of 7–8 is classified as severe pain, indicating that the pain is intense.

    Statistical analysis

    The statistical analysis was conducted using SPSS 22.0 software. For normally distributed quantitative data, descriptive statistics were presented as mean±SD, and group comparisons were performed using differential analysis. Qualitative data were described using frequencies and percentages, and group comparisons were performed using the χ2 test. To establish the LPA model, the Mplus 8.3 software was used. The TSK-SV Heart score of patients after heart surgery was used as the model’s observed variable. The initial model category was set to 1, and the number of model levels was gradually increased. Model fit was assessed using various criteria, including the Akaike information criteria (AIC), Bayesian information criteria (BIC), sample-size-adjusted BIC (aBIC), entropy index, Lo–Mendell–Rubin likelihood ratio test (LMRT) and bootstrap likelihood ratio test (BLRT). Smaller values of AIC, BIC and aBIC indicate better model fit. A higher entropy value closer to 1 suggests a higher probability of accurate individual classification. LMRT and BLRT were used to compare the fit of k-individual models to k−1 models. Based on the results of the LPA of kinesiophobia, a multiple logistic analysis was performed to explore the factors influencing the latent profile classification of patients’ kinesiophobia after heart surgery. The statistical tests were two-tailed, and a difference of p<0.05 was considered statistically significant.

    Results

    Convenience sampling was employed with 412 participants who underwent cardiac surgery under extracorporeal circulation. 42 participants were excluded according to the inclusion and exclusion criteria, leaving 370 eligible participants in the study. 18 questionnaires had more than five blanks or missed important information, while another four questionnaires had the same answer choice for more than five consecutive questions. All the above 22 questionnaires had been excluded. Finally, 348 questionnaires were left for analysis (figure 1).

    Figure 1
    Figure 1

    Flow diagram of participants.

    Demographic characteristics of the participants

    In the current study, 248 male participants (71.26%) and 100 female participants (28.74%) aged 18–45 years (18.97%), 46–65 years (40.80%) and 66–75 years (40.23%) were included. 252 participants (72.41%) underwent a conventional approach, while 101 participants (27.58%) used sternum-sparing approach. 95 participants (27.29%) were 3–6 months postoperative, 132 participants (37.93%) were 7–12 months postoperative, 90 patients (25.86%) were 13–18 months postoperative and 31 patients (8.90%) were 19–24 months postoperative. Detailed characteristics of the participants are displayed in online supplemental table 1.

    Supplemental material

    LPA of the participants’ kinesiophobia scores

    LPA was conducted to identify the heterogeneity of kinesiophobia in patients after cardiac surgery under extracorporeal circulation. Four models were initially constructed based on model fit indicators AIC, BIC, aBIC, entropy, LMRT and BLRT. As the classification number increases (online supplemental table 2), the AIC, BIC and aBIC values gradually decrease and reach a minimum in Model 4 with LMRT value 0.131 (>0.05), while the matching indicators in Model 3 all fit well (<0.05). Furthermore, there was no cross in the three levels, as shown in figure 2. Therefore, Model 3 of kinesiophobia after heart surgery was accepted in the current study.

    Supplemental material

    Figure 2
    Figure 2

    Latent profile analysis classification.

    In Model 3, the different groups were named as the low kinesiophobia group (LKG), moderate kinesiophobia—high-risk perceived symptoms group (MK-HRPSG) and high kinesiophobia—high exercise avoidance group (HK-HEAG). The LKG (72/348, 20.6%) had low scores in all items with a TSK-SV Heart score of (34.08±4.12). The MK-HRPSG (148/348, 42.6%) had moderate scores on all items with a TSK-SV Heart score of (48.91±7.07). The HK-HEAG (128/348, 36.8%) had high scores in all items with a TSK-SV Heart score of (51.81±6.07).

    One-way analysis of variance of different potential classification impact factors

    There were differences in different potential classifications of kinesiophobia in participants, and there were statistically significant differences in the degree distribution of age, postoperative time, pain, social support and self-efficacy (p<0.05), as shown in online supplemental table 3.

    Supplemental material

    Multiple logistic regression analysis of potential classification factors

    Taking LKG as a reference to conduct disordered multi-classification logistic regression analysis, the classifications of kinesiophobia were taken as dependent variables, with significant variables in the above analysis as independent variables and covariables. The results showed that age, postoperative time, self-efficacy, pain and social support were factors influencing the potential classification of kinesiophobia (p<0.05), as shown in online supplemental table 4.

    Supplemental material

    Discussion

    Characteristics of participants

    The data for this study were collected from a tertiary hospital in the country with an ample volume of cardiac surgeries. In this study, there was a higher proportion of male participants compared with female participants (male:female=2.48:1), which aligns with findings from previous studies.38–41 It is noteworthy that female participants constitute no more than 30% of the study population in research trials.40–42

    There are several factors contributing to this gender disparity. First, mortality rates and risk for the most prevalent cardiovascular diseases consistently tend to be higher among men than women.43 Moreover, women face disparities in wealth, income and access to resources, which can hinder their timely access to medical care. A study revealed that women with low incomes, low levels of education and residing in deprived areas are more likely to delay seeking medical attention.44 Lastly, a lack of awareness among women about the importance of CHD and emergency care contributes to delays in seeking medical attention.45 A report from the European Heart Survey found that women aged 60 years and above were less likely to undergo coronary artery bypass grafting(CABG) compared with men, whereas they were more likely to receive percutaneous coronary intervention (PCI), adding to the gender differences in surgical treatment.46

    Analysis of a 3-classification model of kinesiophobia score

    Among the models tested, Model 3 exhibited significant characteristics. As our results indicate, Model 4 had the best-fitting AIC, BIC and entropy values. Model 4 allows variances and covariances to be freely estimated and varied across profiles. However, this improvement in fit came at the expense of differences when compared with other models. Model 4 had a lower LMRT value (0.131) in particular. Ultimately, we selected Model 3 due to its relatively low AIC and BIC values, along with a high entropy value of 0.873, suggesting accurate classification of participants into the appropriate profile. Furthermore, the LMRT and BLRT values in Model 3 were low (0.05 and 0.01, respectively), indicating good model fit.

    The researchers established a score of 37 as the threshold for determining the presence of kinesiophobia.47 A score of 37 or higher indicates a high level of kinesiophobia.27 Notably, participants in the LKG in Model 3, obtained a mean score of (34.08±4.12), indicating a lower degree of kinesiophobia. This finding aligns with previous studies.27 48 The MK-HRPSG in Model 3 scored lower on items 8 (2.00±0.73) and 16 (1.90±0.75) compared with an average item score of 2.33. These items belong to the risk perception dimension. The results suggest that participants in the MK-HRPSG exhibit a heightened perception of risk, which may result in decreased adherence to recommended treatments.49 In contrast, participants in the HK-HEAG in Model 3 obtained higher scores on item 2 (3.43±0.55) and lower scores on item 4 (2.86±0.84) compared with the average item score of 3.03. These particular items are indicative of exercise avoidance tendencies. Consequently, participants in the HK-HEAG in Model 3 may demonstrate greater resistance to and avoidance of physical activity when advised by their doctors regarding exercise prescriptions.50

    Based on the presented results, Model 3 effectively minimises individual heterogeneity by considering latent traits, resulting in the identification of distinct subgroups. The results of this study showed potential classifications of kinesiophobia, which were mainly affected by age, postoperative time, self-efficacy, pain and social support.

    Factors affecting classification

    We observed that age under 45 years did not play a role in influencing the classification of kinesiophobia. In this study, age was analysed categorically as a rank variable, which differed from previous studies that treated age as a continuous variable.19 Remarkably, our findings emphasised the significance of age above 45 years, specifically indicating that patients aged over 45 years were more likely to exhibit tendencies towards HK-HEAG. These results align with existing research showing higher levels of kinesiophobia among older adults,51 which is consistent with our research. The increased kinesiophobia with ageing can be attributed to factors such as physical frailty, which not only leads to a decrease in energy but also heightens the fear of injury and falling. In addition, it is difficult for the elderly to acquire scientific knowledge about kinesiophobia,52 which further aggravates exercise avoidance. However, Gunn et al 53 reported an adverse association between age and kinesiophobia among adults, suggesting that older individuals may have more available time and exercise experience, which reduces their anxiety towards potentially harmful activities. It is important to note that generalising findings based on age is not appropriate, as age under 45 years did not prove to be a significant factor in our study. Future studies should focus on exploring the kinesiophobia classification in older adults.

    There is a time effect of kinesiophobia in the postoperative period. Our study discovered that the period between 3 and 6 months after surgery is a critical time frame for kinesiophobia concerns. The level of kinesiophobia decreased with the increase in postoperative time,54 55 and postoperative time was not a factor affecting the classification of kinesiophobia after 6 months. The longer the postoperative period, the less likely they were to be classified as HK-HEAG. This finding aligns with previous studies conducted on patients following an acute coronary artery disease event, which reported that kinesiophobia scores were highest (32.5) at baseline, decreasing to 30.9 after 2 weeks and 30.1 after 4 months, suggesting a decline in kinesiophobia over time.56 This trend may be due to the gradual recovery of exercise capacity and cardiac function over time, as patients can gradually tolerate increased exercise and feel the benefits of participating in it. Early postoperative activity has been shown to improve functional recovery time,57 especially through early postoperative activities on the floor. Clinical staff should help patients overcome their kinesiophobia as early as possible to promote their engagement in CR as soon as possible, improve their cardiopulmonary function, reduce the incidence of postoperative venous thrombosis58 and minimise the length of hospital stay.59

    Self-efficacy plays an important role in kinesiophobia classifications.60 In our study, we observed a negative association between self-efficacy and kinesiophobia, which aligns with previous research findings.53 61 Patients with high self-efficacy scores were more likely to be classified as MK-HRPSG. According to Schwarzer et al’s theory, individuals with low self-efficacy face difficulties accepting their health status and have lower confidence and expectations regarding exercise.62 They exhibit extreme reluctance to seek help when faced with unexpected traumatic events during exercise and are more prone to kinesiophobia.63 However, patients with high self-efficacy demonstrate favourable psychological adaptation and coping skills when faced with heart surgery, enabling them to approach challenges more proactively.64 Consequently, enhancing self-efficacy is an effective measure for preventing and alleviating kinesiophobia, and various interventions focused on increasing self-efficacy are currently available in postoperative settings.65–67 Further studies are needed to determine whether their use in patients undergoing cardiac surgery results in positive outcomes. Additionally, the inclusion of pain measurements in kinesiophobia assessments is essential.68 The fear-avoidance model theory suggests that if patients perceive pain as a frightening stimulus and experience an exacerbation of pain, they adopt negative coping mechanisms to avoid activities that trigger pain, thus exhibiting kinesiophobia.69 Therefore, it is necessary to provide patients with education on pain perception, help them understand the benefits of exercise, relieve their fear of pain and enhance their confidence in engaging in physical activity.70

    Social support emerges as the primary factor influencing kinesiophobia in MK-HRPSG patients. Social support was negatively correlated with the classification level of kinesiophobia.71 It is consistent with the results of a qualitative study on 16 female patients by Keessen et al.72 In accordance with social support theory,73 74 individuals with ample social support are more inclined to confide their negative emotions to family, friends and social networks. This, in turn, boosts their confidence in facing discomfort and diminishes kinesiophobia. Notably, our observations revealed no significant correlation between social support and kinesiophobia in the HK-HEAG. As a result, we postulate that alternative interventions should be explored to alleviate kinesiophobia in HK-HEAG patients, other than the domain of social support.

    Limitations of this study

    This study has taken a step in the direction of defining and understanding kinesiophobia in patients in North China. It is possible that other patients with different cultural backgrounds may produce different results. In addition, it is important to emphasise that methodological problems in the research design limit our interpretations. Self-report questionnaires in data collection may also influence the assessment of kinesiophobia in an objective approach. Finally, the LPA method has advantages in group classification, in which the selection process is decided by researchers according to the comprehensive judgement of indicators. Thus, it is likely that the results involve some subjectivity.

    Conclusions

    This study uses LPA to identify potential classifications of kinesiophobia in patients after cardiac surgery under extracorporeal circulation. The findings indicate that patients fall into three distinct classifications: LMG, MK-HRPSG and HK-HEAG. It is crucial for clinical staff to prioritise addressing kinesiophobia, particularly in older male patients during the early postoperative period. Furthermore, enhancing self-efficacy shows promise as an effective method for reducing kinesiophobia, while increasing social support may not yield desirable outcomes in the HK-HEAG. These findings offer a valuable evidence-based foundation for implementing preventative interventions to address kinesiophobia during CR for patients undergoing cardiac surgery. It is important to note that this study is cross-sectional, and future research should consider expanding the sample size and conducting longitudinal studies to validate the obtained results.

    Data availability statement

    No data are available.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants and was approved by the ethical review committee of the Sixth Medical Centre of PLA General Hospital (Beijing, China, HZKY-PJ-2022-2). The research was registered with the Chinese Clinical Trial Registry (ChiCTR2200057895). Participants gave informed consent to participate in the study before taking part.

    Acknowledgments

    The study was undertaken during the COVID-19 pandemic. We sincerely thank the clinicians and nurses who supported the research by assisting with data collection. We also thank the study participants.

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    Footnotes

    • Contributors WX and JJP are joint first authors. RW obtained funding. WX designed the study. JJP, TR and YL collected the data. WX and QL were involved in data cleaning and analysed the data. YG contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. All authors have read and approved the final manuscript. RW is the guarantor of this study.

    • Funding This work was supported by the National Key Research and Development Program of China (grant number 2022YFB4700805).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.