期刊论文详细信息
BMC Public Health
Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study
Study Protocol
Aijuan Ma1  Yumei Han2  Yadi Gan3  Yuhui Shi4  Hewei Min4  Xinying Sun4  Yibo Wu4  Xiaohui Guo5  Mingzi Li6 
[1] Beijing Center for Disease Control and Prevention, Beijing, China;Beijing Medical Examination Center, Beijing, China;Daxing District Center for Disease Control and Prevention of Beijing, Beijing, China;Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China;Peking University First Hospital, Beijing, China;School of Nursing, Peking University, Beijing, China;
关键词: Type 2 diabetes;    Artificial intelligence;    Intelligent question and answering;    Mobile health;    Mixed-methods study;   
DOI  :  10.1186/s12889-023-16066-z
 received in 2023-04-27, accepted in 2023-06-06,  发布年份 2023
来源: Springer
PDF
【 摘 要 】

BackgroundPatients 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.MethodsThis 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.DiscussionKBQA 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 registrationBiomedical Ethics Committee of Peking University: IRB00001052-22,058, 2022/06/06; Clinical Trials: ChiCTR2300068952, 02/03/2023.

【 授权许可】

CC BY   
© The Author(s) 2023

【 预 览 】
附件列表
Files Size Format View
RO202309143319931ZK.pdf 1377KB PDF download
40648_2023_255_Article_IEq12.gif 1KB Image download
42492_2023_141_Article_IEq5.gif 1KB Image download
MediaObjects/12951_2023_1957_MOESM2_ESM.tif 6188KB Other download
【 图 表 】

42492_2023_141_Article_IEq5.gif

40648_2023_255_Article_IEq12.gif

【 参考文献 】
  • [1]
  • [2]
  • [3]
  • [4]
  • [5]
  • [6]
  • [7]
  • [8]
  • [9]
  • [10]
  • [11]
  • [12]
  • [13]
  • [14]
  • [15]
  • [16]
  • [17]
  • [18]
  • [19]
  • [20]
  • [21]
  • [22]
  • [23]
  • [24]
  • [25]
  • [26]
  • [27]
  • [28]
  • [29]
  • [30]
  • [31]
  • [32]
  • [33]
  • [34]
  • [35]
  • [36]
  • [37]
  • [38]
  • [39]
  • [40]
  • [41]
  • [42]
  • [43]
  • [44]
  • [45]
  • [46]
  • [47]
  • [48]
  • [49]
  • [50]
  • [51]
  • [52]
  • [53]
  • [54]
  • [55]
  • [56]
  • [57]
  • [58]
  • [59]
  • [60]
  • [61]
  • [62]
  • [63]
  • [64]
  • [65]
  • [66]
  • [67]
  • [68]
  • [69]
  • [70]
  • [71]
  • [72]
  • [73]
  • [74]
  • [75]
  文献评价指标  
  下载次数:3次 浏览次数:1次