School of Nursing, UT Health San Antonio, TX, USA.
University of Utah, College of Nursing, Salt Lake City, UT, USA.
J Diabetes Sci Technol. 2022 Jul;16(4):812-824. doi: 10.1177/19322968211036430. Epub 2021 Aug 11.
A 2017 umbrella review defined the technology-enabled self-management (TES) feedback loop associated with a significant reduction in A1C. The purpose of this 2021 review was to develop a taxonomy of intervention attributes in technology-enabled interventions; review recent, high-quality systematic reviews and meta-analyses to determine if the TES framework was described and if elements contribute to improved diabetes outcomes; and to identify gaps in the literature.
We identified key technology attributes needed to describe the active ingredients of TES interventions. We searched multiple databases for English language reviews published between April 2017 and April 2020, focused on PwD (population) receiving diabetes care and education (intervention) using technology-enabled self-management (comparator) in a randomized controlled trial, that impact glycemic, behavioral/psychosocial, and other diabetes self-management outcomes. AMSTAR-2 guidelines were used to assess 50 studies for methodological quality including risk of bias.
The TES Taxonomy was developed to standardize the description of technology-enabled interventions; and ensure research uses the taxonomy for replication and evaluation. Of the 26 included reviews, most evaluated smartphones, mobile applications, texting, internet, and telehealth. Twenty-one meta-analyses with the TES feedback loop significantly lowered A1C.
Technology-enabled diabetes self-management interventions continue to be associated with improved clinical outcomes. The ongoing rapid adoption and engagement of technology makes it important to focus on uniform measures for behavioral/psychosocial outcomes to highlight healthy coping. Using the TES Taxonomy as a standard approach to describe technology-enabled interventions will support understanding of the impact technology has on diabetes outcomes.
2017 年的一项伞式综述定义了与 A1C 显著降低相关的技术支持的自我管理(TES)反馈循环。本次 2021 年综述的目的是制定一个技术支持干预措施属性分类法;综述最近高质量的系统综述和荟萃分析,以确定 TES 框架是否得到描述,以及各要素是否有助于改善糖尿病结局;并确定文献中的空白。
我们确定了描述 TES 干预措施的关键技术属性。我们在多个数据库中搜索了 2017 年 4 月至 2020 年 4 月期间发表的英语语言综述,重点是接受糖尿病护理和教育的 PwD(人群)(干预措施),使用技术支持的自我管理(比较组)进行随机对照试验,影响血糖、行为/心理社会和其他糖尿病自我管理结局。使用 AMSTAR-2 指南评估了 50 项研究的方法学质量,包括偏倚风险。
TES 分类法的制定是为了标准化技术支持干预措施的描述;并确保研究使用分类法进行复制和评估。在纳入的 26 项综述中,大多数评估了智能手机、移动应用程序、短信、互联网和远程医疗。有 21 项荟萃分析中 TES 反馈循环显著降低了 A1C。
技术支持的糖尿病自我管理干预措施继续与改善的临床结局相关。技术的快速采用和参与正在进行,因此关注行为/心理社会结局的统一措施以突出健康应对方式非常重要。使用 TES 分类法作为描述技术支持干预措施的标准方法将有助于理解技术对糖尿病结局的影响。