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Background <p>A nomogram model was developed to assess the risk of kinesiophobia in post-cardiac surgery patients, aiming to provide a basis for the early identification of high-risk individuals and guide clinical interventions.</p> Methods <p>This study employed a cross-sectional design and recruited post-cardiac surgery patients admitted to a tertiary Grade A hospital in Shanghai between June 2024 and June 2025. Univariate analysis and logistic regression analysis were performed using SPSS 27.0 to identify independent risk factors for kinesiophobia. Internal validation of the model was carried out in R 4.4.3 using the Bootstrap method with 1,000 resamples. The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), and a nomogram was generated to visually represent the model.</p> Results <p>A total of 477 valid questionnaires were collected, among which 292 patients exhibited kinesiophobia, corresponding to a prevalence of 61.22%. Logistic regression analysis indicated that pain intensity, social support level, fatigue, Type D personality, and educational level were independent risk factors for kinesiophobia in post-cardiac surgery patients (<i>P</i> &lt; 0.05). The calibration curve, receiver operating characteristic (ROC) curve, and DCA all demonstrated the model’s high quality.</p> Conclusion <p>Post-cardiac surgery patients are at a high risk of developing kinesiophobia. The nomogram model developed in this study can provide healthcare professionals with a practical tool for the early identification of high-risk individuals and the implementation of targeted interventions.</p>

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Analysis of factors influencing kinesiophobia in post-cardiac surgery patients and construction of a nomogram prediction model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Zihan Hong,
  • Mengjun Zhan,
  • Yao Shi,
  • Junjun Gu

摘要

Background

A nomogram model was developed to assess the risk of kinesiophobia in post-cardiac surgery patients, aiming to provide a basis for the early identification of high-risk individuals and guide clinical interventions.

Methods

This study employed a cross-sectional design and recruited post-cardiac surgery patients admitted to a tertiary Grade A hospital in Shanghai between June 2024 and June 2025. Univariate analysis and logistic regression analysis were performed using SPSS 27.0 to identify independent risk factors for kinesiophobia. Internal validation of the model was carried out in R 4.4.3 using the Bootstrap method with 1,000 resamples. The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), and a nomogram was generated to visually represent the model.

Results

A total of 477 valid questionnaires were collected, among which 292 patients exhibited kinesiophobia, corresponding to a prevalence of 61.22%. Logistic regression analysis indicated that pain intensity, social support level, fatigue, Type D personality, and educational level were independent risk factors for kinesiophobia in post-cardiac surgery patients (P < 0.05). The calibration curve, receiver operating characteristic (ROC) curve, and DCA all demonstrated the model’s high quality.

Conclusion

Post-cardiac surgery patients are at a high risk of developing kinesiophobia. The nomogram model developed in this study can provide healthcare professionals with a practical tool for the early identification of high-risk individuals and the implementation of targeted interventions.