Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.

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.

Association of glucose control and stages of change for multiple self-management behaviors in patients with diabetes: A latent profile analysis.

OBJECTIVE: To determine whether the joint use of the transtheoretical model and latent profile analysis could help us better understand the shared characteristics of patients with diabetes and explore the association of patients' latent classes and glucose control. METHODS: Five hundred twenty-three (523) patients with diabetes were included in the study. The questionnaire evaluated patients' stages of change for medication-taking, diet control, exercise, and glucose-monitoring. Latent profile analysis was performed based on the four indicators. RESULTS: Patients were classified into four latent groups and defined as follows: good medication-taking/good lifestyle (GM/GL, 41.7%), poor medication-taking/poor lifestyle (PM/PL, 27.7%), good medication-taking/poor lifestyle (GM/PL, 21.6%), and poor medication-taking/good lifestyle (PM/GL, 9.0%). Patients in the PM/PL group were generally younger and better educated while those in the GM/GL group exhibited the opposite pattern. Compared with patients in the PM/PL group, those in the PM/GL and GM/GL groups had significantly lower HbA1c values (PM/GL: standardized ß = -0.694, P =  0.007; GM/GL: standardized ß = -0.499, P =  0.003). CONCLUSION: With the help of the transtheoretical model and latent profile analysis, future study could cluster homogeneous patients before the initiation of intervention and provide tailored instructions to different types of patients accordingly. PRACTICE IMPLICATIONS: A combination of the transtheoretical model and latent profile analysis could shed some light into future diabetic interventions.

[Health literacy survey among permanent resident in Daxing District of Beijing].

OBJECTIVE: To understand status of health literacy and analyze its influencing factors among permanent residents in Daxing district of Beijing. METHODS: Multistage stratified cluster random sampling method was used to recruit 807 permanent residents 18-79 years old. A questionnaire was used to collect information about normal social-demographic characters,the basic knowledge and concept literacy, healthy lifestyle and behavior literacy, and health skill literacy and so on. RESULTS: The average awareness rate of health literacy was 71.1%. In the three aspects of health literacy, the residents' average awareness rates of the basic knowledge and concept literacy, healthy lifestyle and behavior literacy, and health skill literacy were 66.9%, 77.5% and 72.9%, respectively. Only 14.6% of the residents had adequate health literacy. The residents' average possession rate of the basic knowledge and concept literacy, healthy lifestyle and behavior literacy, and health skill literacy were 13.6%, 51.1% and 52.5%, respectively. Health literacy was significantly different among people with different education level, occupation and average monthly household income. Logistic regression analysis showed that the main factors that influenced health literacy were education level and age. Passing percentages were increased with education level and age. CONCLUSION: The rate of the residents' health literacy was low. There is an urgent need to strengthen health education and promotion in the population and spread health literacy related knowledge, in order to improve their health literacy level.