Development and internal validation of a nomogram for sleep quality among Chinese medical students: a cross-sectional study
Abstract Background Poor sleep quality is common among Chinese medical students. Identifying its predictors is essential for implementing individualized interventions. However, clinical prediction models targeting sleep quality in this population remain scarce. This study aimed to develop and valida...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-025-23504-7 |
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| Summary: | Abstract Background Poor sleep quality is common among Chinese medical students. Identifying its predictors is essential for implementing individualized interventions. However, clinical prediction models targeting sleep quality in this population remain scarce. This study aimed to develop and validate a nomogram to predict poor sleep quality among Chinese medical students. Methods A cross-sectional study was used to collect data among Chinese medical students at the Hubei University of Medicine. A total of 2893 medical students were randomly divided into training (70%) and validation (30%) groups. Multivariable Firth logistic regression analysis was performed to examine factors associated with sleep quality. Thereafter, these factors were used to develop a nomogram for predicting sleep quality. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). Results A total of 70.4% of medical students in the study reported poor sleep quality. The predictors of sleep quality included grade, gender, self-assessment of interpersonal relationships, and self-assessment of health status. The scores of the nomogram ranged from 0 to 189, and the corresponding risk ranged from 0.50 to 0.95. The calibration curve showed that the nomogram had good classification performance. The area under the curve (AUC) of the ROC for the training group is 0.676, and that for the validation group is 0.702. The DCA demonstrated that the model also had good net benefits. Conclusions The nomogram prediction model has sufficient accuracies, good predictive capabilities, and good net benefits. The model can also provide a reference for predicting the sleep quality of medical students. |
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| ISSN: | 1471-2458 |