Background: Health literacy has been defined as the degree to which individuals have the capacity to obtain, process, and understand the basic health information and services needed to make appropriate health decisions. Currently, few studies have validated the causal pathways of determinants of health literacy through the use of statistical modeling. The purpose of the present study was to develop and validate a health literacy model at an individual level that could best explain the determinants of health literacy and the associations between health literacy and health behaviors even health status. Methods: Skill-based health literacy test and a self-administrated questionnaire survey were conducted among 3222 Chinese adult residents. Path analysis was applied to validate the model. Results: The model explained 38.6% of variance for health literacy, 11.7% for health behavior and 2.3% for health status: (GFI = 0.9990; RMR = 0.0521; χ² = 10.2151, P = 0.1159). Education has positive and direct effect on prior knowledge (β = 0.324) and health literacy (β = 0.346). Health literacy is also affected by prior knowledge (β = 0.245) and age (β = -0.361). Health literacy is a direct influencing factor of health behavior (β = 0.101). The most important factor of health status is age (β = 0.107). Health behavior and health status have a positive interaction effect. Conclusion: This model explains the determinants of health literacy and the associations between health literacy and health behaviors well. It could be applied to develop intervention strategies to increase individual health literacy, and then to promote health behavior and health status.

Internet addiction is a mental health problem that affects a significant number of people worldwide. Our study attempted to investigate the prevalence of Internet addiction among Chinese adolescents and to explore the predictors of Internet addiction and its association with well-being. A total of 10,988 adolescents from nine different cities in China were surveyed using the Diagnostic Questionnaire (DQ) for Internet addiction, the Center for Epidemiologic Studies Depression Scale, the Rosenberg Self-esteem Scale, and the Adolescent's Satisfaction with Life Scale. The mean age of the whole sample was 17.2 years (ranging from 13 to 23 years). The prevalence rate of Internet addiction among the surveyed adolescents was 7.5%, which was influenced by gender and grade (χ² = 74.027, p < 0.001; χ² = 7.162, p < 0.05). The breadth of extracurricular activities, the age when people used Internet for the first time, and whether people used Internet for the first time in Internet bar were significant predictors of Internet addiction (β = −0.065, p < 0.001; β = −0.101, p < 0.001; β = 0.545, p < 0.001). Finally, our study found evidence demonstrating the link between Internet addiction and well-being. Increased symptoms of problematic use were associated with decreased self-esteem (F = 258.344, p < 0.001), satisfaction with life (F = 232.428, p < 0.001), and increased depression (F = 607.062, p < 0.001).

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: With the release of the Health China Action (2019–2030), family health is receiving increasing attention from experts and scholars. But at present, there is no family health scale in China that involves multidimensional and interdisciplinary commonality.Aim: To translate a Short Form of the Family Health Scale (FHS-SF) and to test the reliability and validity of the Chinese version of the FHS-SF.Method: A Short Form of the Family Health Scale was Chinese translated with the consent of the original author. A total of 8912 residents were surveyed in 120 cities across China using a multistage sampling method, with gender, ethnicity, and education level as quota variables. Seven hundred fifty participants were selected to participate in this study, and 44 participants were randomly selected to be retested 1 month later.Results: The Cronbach’s alpha of the Chinese version of a Short Form the Family Health Scale was 0.83,the Cronbach’s alphas of the four subscales ranged from 0.70 to 0.90, the retest reliability of the scale was 0.75, the standardized factor loadings of the validation factor analysis were above 0.50, GFI = 0.98; NFI = 0.97; RFI = 0.95; RMSEA = 0.07, all within acceptable limits.Conclusion: The Chinese version of a Short Form the Family Health Scale has good reliability and validity and can be used to assess the level of family health of Chinese residents.

Background: Patients with type 2 diabetes (T2DM) have an increasing need for personalized and Precise management as medical technology advances. Artificial intelligence (AI) technologies on mobile devices are being developed gradually in a variety of healthcare fields. As an AI field, knowledge graph (KG) is being developed to extract and store structured knowledge from massive data sets. It has great prospects for T2DM medical information retrieval, clinical decision-making, and individual intelligent question and answering (QA), but has yet to be thoroughly researched in T2DM intervention. Therefore, we designed an artificial intelligence-based health education accurately linking system (AI-HEALS) to evaluate if the AI-HEALS-based intervention could help patients with T2DM improve their self-management abilities and blood glucose control in primary healthcare. Methods: This is a nested mixed-method study that includes a community-based cluster-randomized control trial and personal in-depth interviews. Individuals with T2DM between the ages of 18 and 75 will be recruited from 40-45 community health centers in Beijing, China. Participants will either receive standard diabetes primary care (SDPC) (control, 3 months) or SDPC plus AI-HEALS online health education program (intervention, 3 months). The AI-HEALS runs in the WeChat service platform, which includes a KBQA, a system of physiological indicators and lifestyle recording and monitoring, medication and blood glucose monitoring reminders, and automated, personalized message sending. Data on sociodemography, medical examination, blood glucose, and self-management behavior will be collected at baseline, as well as 1,3,6,12, and 18 months later. The primary outcome is to reduce HbA1c levels. Secondary outcomes include changes in self-management behavior, social cognition, psychology, T2DM skills, and health literacy. Furthermore, the cost-effectiveness of the AI-HEALS-based intervention will be evaluated. Discussion: KBQA system is an innovative and cost-effective technology for health education and promotion for T2DM patients, but it is not yet widely used in the T2DM interventions. This trial will provide evidence on the efficacy of AI and mHealth-based personalized interventions in primary care for improving T2DM outcomes and self-management behaviors. Trial registration: Biomedical Ethics Committee of Peking University: IRB00001052-22,058, 2022/06/06; Clinical Trials: ChiCTR2300068952, 02/03/2023.