How to predict the electronic health literacy of Chinese primary and secondary school students?: establishment of a model and web nomograms

BACKGROUND: The internet has become an important resource for the public to obtain health information. Therefore, the ability to obtain and use such resources has become important for health literacy. This study aimed to establish a prediction model of Chinese students’ electronic health literacy (EHL) to guide government policymaking and parental interventions, identify the predictors of EHL in Chinese students using random forests, and establish a corresponding prediction model to help policymakers and parents determine whether primary and secondary school students have high EHL. METHODS: This is a cross-sectional study. From June to August 2021, a cluster sample survey was conducted with 1,300 students from seven primary and secondary schools in Shaanxi Province, China. We evaluated 1,235 primary and secondary school students using the e-health literacy scale. The data were divided into training and testing datasets in a 70:30 ratio for further analysis using random forest. The predictive accuracy of the score was measured using the area under the receiver operating characteristic curve. We also used decision curve analysis to determine the usefulness of the prediction model by quantifying the net benefits at different threshold probabilities in the validation dataset. RESULTS: We found that 33.6% of students had high EHL. The univariate analysis showed that age (P < 0.001), grade (P < 0.001), employment status (P < 0.001), household location (P < 0.001), parental phubbing behavior (P < 0.001), and general self-efficacy (P < 0.001) were significantly associated with EHL. A random forest classification model was developed with the training dataset (872 students), and seven variables were confirmed as important: age, grade, employment status, father education level, game time, parental phubbing behavior, and general self-efficacy. The validation of the model showed good discrimination, with an area under the curve of 0.975 in the training dataset and 0.738 in the testing dataset. The model was translated into an online risk calculator, which is freely available (https://xietao.shinyapps.io/DynNomapp/). CONCLUSIONS: In this study, an intuitive tool to predict the EHL of Chinese primary and secondary school students was developed and validated. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13421-4.