BACKGROUND: Frailty among older adults has received widespread attention from society, especially among nappers. The objective of this study was to develop a frailty prediction model for nappers. METHODS: The data source was the China Health and Retirement Longitudinal Study, with a cohort of 1830 older nappers. We used the least absolute shrinkage and selection operator to screen the best predictors from multiple factors, logistic regression analysis to explore the best predictors of frailty in older nappers, and nomogram to establish a prediction model. A calibration curve was used to evaluate the precision of the model, and the predictive performance was assessed by analyzing the area under the characteristic and decision curves. RESULTS: The prevalence of frailty among older nappers was 28.9 % (528/1830). Chronic diseases, physical activity, sleep quality, pain, fatigue, depression, nap duration, and nighttime sleep duration were the best predictive factors for frailty in older nappers. The area under the curve (AUC) in the training set was 0.751 (95 % confidence interval [CI] = 0.724-0.779) with a specificity of 0.662 and sensitivity of 0.711. The AUC in the validation set was 0.781 (95 % CI = 0.749-0.812) with a specificity of 0.730 and sensitivity of 0.714. The Hosmer-Lemeshow test values were both p >
0.05. The nomogram model showed good concordance and accuracy. CONCLUSION: We constructed a nomogram that serves as a valuable and convenient instrument for assessing the prevalence of frailty among older nappers.