Chen Li, Wei Li, Yanru Shao, Zhigang Xu, Junyan Song, Yan Wang
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引用次数: 0
Abstract
Background: Type 2 diabetes mellitus (T2DM) poses a critical global health burden, requiring effective health education to enhance patient self-management. Artificial intelligence (AI) offers personalized and scalable solutions; however, comprehensive syntheses of its applications in T2DM health education are scarce.
Objective: Guided by the Arksey and O'Malley scoping review framework, this study maps AI-based health education interventions for T2DM by evaluating technologies, effectiveness, and challenges.
Methods: Seven academic databases (PubMed, Web of Science, Embase, Scopus, EBSCO, the Cochrane Library, the Joanna Briggs Institute (JBI) Database, and Wiley Online Library) were searched for studies published from 2008 to March 2025, identifying 14 eligible interventional studies involving 32,478 adult T2DM patients receiving AI-based health education.
Results: (1) Technological Diversity: Interventions included mobile apps (eg, FoodLens, TRIO system), chatbots, intelligent platforms, and machine learning algorithms, focusing on diet, glucose monitoring, and lifestyle management. (2) Effectiveness: AI interventions enhanced glycemic control, yielding reductions in glycosylated hemoglobin (HbA1c) of up to 2.59%, improved self‑management adherence (60-85%), and produced positive psychological outcomes (eg, increased self‑efficacy); efficacy varied by intervention duration and user engagement. (3) Challenges: Key barriers included technical complexity, low long-term engagement, digital literacy gaps, and data privacy concerns.
Conclusion: AI holds substantial potential for T2DM health education via personalized, accessible interventions. Future research should address technological hurdles, prioritize user-centered design, and integrate AI into healthcare systems to ensure sustainability and equity.
背景：2型糖尿病（T2DM）是一个严重的全球健康负担，需要有效的健康教育来增强患者的自我管理。人工智能（AI）提供个性化和可扩展的解决方案；然而，目前对其在2型糖尿病健康教育中的应用还缺乏全面的综合研究。目的：在Arksey和O'Malley范围审查框架的指导下，本研究通过评估技术、有效性和挑战来绘制基于人工智能的2型糖尿病健康教育干预措施。方法：检索7个学术数据库（PubMed、Web of Science、Embase、Scopus、EBSCO、Cochrane图书馆、Joanna Briggs研究所（JBI）数据库和Wiley在线图书馆），检索2008年至2025年3月发表的研究，确定14项符合条件的介入研究，涉及32,478名接受人工智能健康教育的成年T2DM患者。结果：(1)技术多样性：干预措施包括移动应用程序（如FoodLens、TRIO系统）、聊天机器人、智能平台和机器学习算法，重点关注饮食、血糖监测和生活方式管理。(2)有效性：人工智能干预增强了血糖控制，糖化血红蛋白（HbA1c）降低高达2.59%，提高了自我管理的依从性（60-85%），并产生了积极的心理结果（例如，提高了自我效能）；效果随干预持续时间和用户参与度而变化。(3)挑战：主要障碍包括技术复杂性、长期参与度低、数字素养差距和数据隐私问题。结论：人工智能通过个性化的、可获得的干预措施在T2DM健康教育方面具有巨大的潜力。未来的研究应解决技术障碍，优先考虑以用户为中心的设计，并将人工智能整合到医疗系统中，以确保可持续性和公平性。
期刊介绍：
An international, peer-reviewed, open access, online journal. The journal is committed to the rapid publication of the latest laboratory and clinical findings in the fields of diabetes, metabolic syndrome and obesity research. Original research, review, case reports, hypothesis formation, expert opinion and commentaries are all considered for publication.
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