TY - JOUR AU - Keller, Roman AU - Hartmann, Sven AU - Teepe, Gisbert Wilhelm AU - Lohse, Kim-Morgaine AU - Alattas, Aishah AU - Tudor Car, Lorainne AU - Müller-Riemenschneider, Falk AU - von Wangenheim, Florian AU - Mair, Jacqueline Louise AU - Kowatsch, Tobias PY - 2022 DA - 2022/1/7 TI -预防和管理2型糖尿病的数字行为改变干预:系统市场分析乔- J地中海互联网Res SP - e33348六世24 - 1 KW -数字健康公司KW -医疗KW - 2型糖尿病KW -预防KW -管理KW -会话代理KW -数字行为改变干预千瓦投资KW -即时适应干预KW -数字医疗KW -糖尿病KW -代理KW -行为AB -背景:技术进步提供新的机会2型糖尿病的预防和管理。风险投资公司一直在投资提供数字行为改变干预(dbci)的数字糖尿病公司。然而，对于支持这些干预措施的科学证据，以及这些干预措施在多大程度上利用了新型技术驱动的自动化开发，如会话代理(ca)或即时自适应干预(JITAI)方法，我们知之甚少。目的:我们的目标是确定为2型糖尿病管理和预防提供dbci的最受资助的公司，审查支持dbci的科学证据水平，确定哪些dbci被质量保证机构认可为基于证据的项目，并检查这些dbci包括CAs和JITAI机制等新型自动化方法的程度。方法:使用2个风险投资数据库(Crunchbase Pro和Pitchbook)进行系统搜索，以确定提供2型糖尿病预防和管理干预措施的资金最多的公司。通过PubMed、谷歌Scholar和dbci网站确定与已确定的dbci相关的科学出版物，并提取有关干预有效性的数据。采用美国疾病控制与预防中心的糖尿病预防识别计划(DPRP)对识别状态进行识别。对dbci的出版物、网站和移动应用程序进行了关于干预特征的审查。截至2021年6月15日，为2型糖尿病提供dbci的16家资金最多的公司共获得了24亿美元的资金。 Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A1c (HbA1c) outcomes between the intervention and control groups. However, all the studies reported HbA1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs. Conclusions: Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions. SN - 1438-8871 UR - //www.mybigtv.com/2022/1/e33348 UR - https://doi.org/10.2196/33348 UR - http://www.ncbi.nlm.nih.gov/pubmed/34994693 DO - 10.2196/33348 ID - info:doi/10.2196/33348 ER -