Globally, symptomatic type 1 diabetes (T1D) prevalence varies markedly. The International Diabetes Federation 11thEdition Atlas/T1D Index Version 3.0 estimated 2025 numbers for 202 countries/territories ("countries"), and projected to2040. The T1D Index model, a Markov model with sub-models for incidence-over-time, adult incidence, and mortality-over-time, was updated with recent population-based T1D incidence, mortality and prevalence studies. For countries without studies, data were extrapolated from countries with similarsettings. There are estimated 9.5 million people living with T1D globally (compared to 8.4 million in 2021, a 13 % increase), with 1.0 million of these aged 0-14, and 0.8 million aged 15-19 years. In lower-income countries, prevalent cases increased by 20 % from 1.8 million in 2021 to 2.1 million in 2025. Incident cases in 2025 are an estimated 513,000 (164,000 aged 0-14 and 58,000 aged 15-19 years), with incidence increasing by 2.4 % in the last year. Premature deaths are estimated at 174,000, with 17.2 % of these due to non-diagnosis soon after clinical onset. The estimated remaining life expectancy of a 10-year-old child diagnosed with T1D in 2025 varies between countries from 6 to 66 years. There are still no data available for 119 countries. The projectedT1D population for 2040 is estimated to be14.7 million. The number of global T1D cases is rising quickly, especially in lower-income settings, due to increasing diagnosed incidence, falling mortality and ageing, and population growth. Contemporary data are unavailable for over 50% of all countries, highlighting need for epidemiological studies.

Background: Diabetes poses heavy social and economic burdens worldwide. Diabetes management apps show great potential for diabetes self-management. However, the adoption of diabetes management apps by diabetes patients is poor. The factors influencing patients’ intention to use these apps are unclear. Understanding the patients’ behavioral intention is necessary to support the development and promotion of diabetes app use. Objective: This study aimed to identify the determinants of patients’ intention to use diabetes management apps based on an integrated theoretical model. Methods: The hypotheses of our research model were developed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). From April 20 to May 20, 2019, adult patients with diabetes across China, who were familiar with diabetes management apps, were surveyed using the Web-based survey tool Sojump. Structural equation modeling was used to analyze the data. Results: A total of 746 participants who met the inclusion criteria completed the survey. The fitness indices suggested that the collected data fit well with the research model. The model explained 62.6% of the variance in performance expectancy and 57.1% of the variance in behavioral intention. Performance expectancy and social influence had the strongest total effects on behavioral intention (β=0.482; P=.001). Performance expectancy (β=0.482; P=.001), social influence (β=0.223; P=.003), facilitating conditions (β=0.17; P=.006), perceived disease threat (β=0.073; P=.005), and perceived privacy risk (β=–0.073; P=.012) had direct effects on behavioral intention. Additionally, social influence, effort expectancy, and facilitating conditions had indirect effects on behavioral intention that were mediated by performance expectancy. Social influence had the highest indirect effects among the three constructs (β=0.259; P=.001). Conclusions: Performance expectancy and social influence are the most important determinants of the intention to use diabetes management apps. Health care technology companies should improve the usefulness of apps and carry out research to provide clinical evidence for the apps’ effectiveness, which will benefit the promotion of these apps. Facilitating conditions and perceived privacy risk also have an impact on behavioral intention. Therefore, it is necessary to improve facilitating conditions and provide solid privacy protection. Our study supports the use of UTAUT in explaining patients’ intention to use diabetes management apps. Context-related determinants should also be taken into consideration.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The diabetes disease burden in China is heavy, and mobile apps have great potential for diabetes management. However, there is a lack of investigation of diabetes app use among Chinese diabetes patients and diabetologists. The perspectives and attitudes of diabetes patients and diabetologists regarding diabetes apps are also not clear. Objective: Our objectives were to investigate diabetes patients’ and diabetologists’ use, attitudes and perspectives, as well as patients’ needs, with respect to diabetes apps to provide information about the design of diabetes apps and how to best promote their use. Methods: Diabetes patients and diabetologists throughout China were surveyed on the WeChat (Tencent Corp) network using Sojump (Changsha ran Xing InfoTech Ltd) from January 23, 2018, to July 30, 2018. Differences among groups were compared using chi-square tests. Multivariate regression analysis was used to identify associations of demographic and basic disease information with app usage among adult patients. Results: Overall, 1276 individuals from 30 provincial administrative regions responded to the patient survey; among them, the overall app awareness rate was 29.94% (382/1276), and usage was 15.44% (197/1276). The usage was higher among type 1 diabetes (T1DM) patients than among type 2 diabetes (T2DM) patients (22.8%, 108/473 VS 10.8%, 79/733; P<0.001). Multivariate regression analysis showed that diabetes type, age, education, family income and location were associated with app use in adult patients. The need for and selection of diabetes apps were a little different between T1DM and T2DM patients. The reasons why patients discontinued the use of an app included limited time (29.9%, 59/197), complicated operations (25.4%, 50/197), ineffectiveness for glycemic control (24.4%, 48/197) and cost (19.3%, 38/197). Of the 610 responders to the diabetologist survey, 40.5% (247/610) recommended diabetes apps to patients, and 25.2% (153/608) used diabetes apps to manage patients. The greatest obstacles to diabetologists’ use of apps to manage diabetes patients include limited time (46.1% , 280/608), issues of legality (21.2%, 129/608), patients’ distrust (17.8%, 108/608) and issues with charging (10.9%, 66/608). Conclusions: The awareness and use of diabetes apps in Chinese diabetic patients and the proportion of diabetologists using diabetes apps to manage patients are low. Designing apps targeting different patient needs and conducting high-quality randomized controlled trials (RCT) will improve the effectiveness of apps, provide evidence for patients to choose suitable apps, and be conducive to the promotion of app use.

Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve the self-management skills is an essential way to help patients enhance their metabolic control and quality of life. Artificial intelligence (AI) technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge. The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. This paper reviews and discusses the most recent applications of AI techniques to various aspects of diabetes education. With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management and lifelong educational interventions.