Uetova Ekaterina, Hederman Lucy, Ross Robert, O'Sullivan Dympna
School of Computer Science, Technological University Dublin, Dublin, Ireland.
School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.
Digit Health. 2024 Oct 29;10:20552076241277693. doi: 10.1177/20552076241277693. eCollection 2024 Jan-Dec.
With the increasing global burden of chronic diseases, there is the potential for conversational agents (CAs) to assist people in actively managing their conditions. This paper reviews different types of CAs used for chronic condition management, delving into their characteristics and the chosen study designs. This paper also discusses the potential of these CAs to enhance the health and well-being of people with chronic conditions.
A search was performed in February 2023 on PubMed, ACM Digital Library, Scopus, and IEEE Xplore. Studies were included if they focused on chronic disease management or prevention and if systems were evaluated on target user groups.
The 42 selected studies explored diverse types of CAs across 11 health conditions. Personalization varied, with 25 CAs not adapting message content, while others incorporated user characteristics and real-time context. Only 12 studies used medical records in conjunction with CAs for conditions like diabetes, mental health, cardiovascular issues, and cancer. Despite measurement method variations, the studies predominantly emphasized improved health outcomes and positive user attitudes toward CAs.
The results underscore the need for CAs to adapt to evolving patient needs, customize interventions, and incorporate human support and medical records for more effective care. It also highlights the potential of CAs to play a more active role in helping individuals manage their conditions and notes the value of linguistic data generated during user interactions. The analysis acknowledges its limitations and encourages further research into the use and potential of CAs in disease-specific contexts.
随着全球慢性病负担的日益加重,对话代理(CA)有潜力帮助人们积极管理自身病情。本文回顾了用于慢性病管理的不同类型的CA,深入探讨了它们的特点以及所采用的研究设计。本文还讨论了这些CA在改善慢性病患者健康和福祉方面的潜力。
2023年2月在PubMed、ACM数字图书馆、Scopus和IEEE Xplore上进行了检索。如果研究聚焦于慢性病管理或预防,且对系统在目标用户群体上进行了评估,则纳入这些研究。
所选的42项研究探讨了11种健康状况下的多种类型的CA。个性化程度各不相同,25个CA不调整消息内容,而其他的则纳入了用户特征和实时情境。只有12项研究将医疗记录与CA结合用于糖尿病、心理健康、心血管问题和癌症等病症。尽管测量方法存在差异,但这些研究主要强调了改善健康结果以及用户对CA的积极态度。
结果强调CA需要适应不断变化的患者需求,定制干预措施,并纳入人力支持和医疗记录以提供更有效的护理。它还突出了CA在帮助个人管理病情方面发挥更积极作用的潜力,并指出了用户交互过程中产生的语言数据的价值。该分析承认其局限性,并鼓励进一步研究CA在特定疾病背景下的使用和潜力。