Early Postoperative Kinesiophobia Model for Lumbar Degenerative Disease Patients

Clinical data from 402 patients with lumbar degenerative diseases was used to construct a prediction model for early postoperative kinesiophobia. The dataset includes a training set of 301 patients and a test set of 101 patients, collected from two Chinese hospitals between January and November 2023. Researcher Zhumei Fang published this model and associated data on figshare in 2026.

Use Cases

• Predict kinesiophobia risk using patient features like age, sex, and pain intensity via logistic regression or tree-based models.
• Analyze the relationship between psychological factors (anxiety, depression, self-efficacy) and postoperative fear of movement.
• Validate the reported nomogram model's performance metrics (AUC, accuracy, sensitivity) on the provided test set of 101 patients.
• Investigate the prevalence of kinesiophobia, reported as 62.69% in the full cohort, across different patient subgroups.
• Assess model calibration using the provided Hosmer-Lemeshow test statistics (χ²=8.32, P=0.822 for training set).

Strengths

• Dataset includes a clearly defined training set (301 patients) and an independent test set (101 patients) for model validation.
• Model performance is reported with specific metrics, including an AUC of 0.931 on the training set and 0.874 on the test set.
• Study design follows the TRIPOD checklist for transparent reporting of multivariable prediction models.

Limitations

• The dataset is small in scale (402 total patients), which may limit the generalizability of the predictive model.
• Data collection was restricted to two hospitals in a specific region of China, potentially introducing geographic and institutional bias.
• The underlying patient-level data columns and values are not visible in the provided description, limiting direct analysis.

Provenance

Source

Zhumei Fang, via figshare.

Collection Method

Cross-sectional study collecting clinical and survey data from patients post lumbar fusion surgery.

Time Range

January 2023 to November 2023.

Freshness

Data covers patient records from 2023; the dataset was last updated on the platform in April 2026.

Geography

Patients from the Second Affiliated Hospital of Fujian Medical University and Quanzhou Orthopedic-Traumatological Hospital in China.