Sergio Diez Alvarez, Antoni Fellas, Katie Wynne, Derek Santos, Dean Sculley, Shamasunder Acharya, Pooshan Navathe, Xavier Gironès, Andrea Coda
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引用次数: 0
Abstract
Background: The use of smart technology in the management of all forms of diabetes mellitus has grown significantly in the past 10 years. Technologies such as the smartwatch have been proposed as a method of assisting in the monitoring of blood glucose levels as well as other alert prompts such as medication adherence and daily physical activity targets. These important outcomes reach across all forms of diabetes and have the potential to increase compliance of self-monitoring with the aim of improving long-term outcomes such as hemoglobin A1c (HbA1c).
Objective: This systematic review aims to explore the literature for evidence of smartwatch technology in type 1, 2, and gestational diabetes.
Methods: A systematic review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to August 2023), Embase (January 1980 to August 2023), Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, ACM Digital Libraries, and Web of Science databases. Type 1, type 2, and gestational diabetes were eligible for inclusion. Quantitative studies such as prospective cohort or randomized clinical trials that explored the feasibility, usability, or effect of smartwatch technology in people with diabetes were eligible. Outcomes of interest were changes in blood glucose or HbA1c, physical activity levels, medication adherence, and feasibility or usability scores.
Results: Of the 8558 titles and abstracts screened, 5 studies were included for qualitative synthesis in this review. A total of 322 participants with either type 1 or type 2 diabetes mellitus were included in the review. A total of 4 studies focused on the feasibility and usability of smartwatch technology in diabetes management. One study conducted a proof-of-concept randomized clinical trial including smartwatch technology for exercise time prescriptions for participants with type 2 diabetes mellitus. Adherence of participants to smartwatch technology varied between included studies, with one reporting input submissions of 58% and another reporting that participants logged 50% more entries than they were required to. One study reported significantly improved glycemic control with integrated smartwatch technology, with increased exercise prescriptions; however, this study was not powered and required a longer observational period.
Conclusions: This systematic review has highlighted the lack of robust randomized clinical trials that explore the efficacy of smartwatch technology in the management of patients with type 1, type 2, and gestational diabetes. Further research is required to establish the role of integrated smartwatch technology in important outcomes such as glycemic control, exercise participation, drug adherence, and diet monitoring in people with all forms of diabetes mellitus.
背景:在过去10年中,智能技术在各种形式糖尿病管理中的应用显著增长。像智能手表这样的技术已经被提议作为一种帮助监测血糖水平的方法,以及其他警报提示,如药物依从性和日常体育活动目标。这些重要的结果适用于所有形式的糖尿病,并有可能增加自我监测的依从性,以改善血红蛋白A1c (HbA1c)等长期结果。目的:本系统综述旨在探讨智能手表技术在1型、2型和妊娠期糖尿病中的应用。方法:检索Ovid MEDLINE和CINAHL数据库进行系统评价。在Ovid MEDLINE(1966年1月至2023年8月)、Embase(1980年1月至2023年8月)、Cochrane Central Register of Controlled Trials (Central, Cochrane Library,最新一期)、CINAHL(1982年起)、IEEE Xplore、ACM数字图书馆和Web of Science数据库中使用所有确定的关键词和索引项进行第二次搜索。1型、2型和妊娠期糖尿病符合纳入条件。定量研究,如前瞻性队列或随机临床试验,探索智能手表技术在糖尿病患者中的可行性、可用性或效果是合格的。感兴趣的结果是血糖或糖化血红蛋白的变化、身体活动水平、药物依从性以及可行性或可用性评分。结果:在筛选出的8558篇标题和摘要中,有5篇研究被纳入本综述的定性综合。共有322名1型或2型糖尿病患者被纳入本综述。共有4项研究关注智能手表技术在糖尿病管理中的可行性和可用性。一项研究进行了一项概念验证的随机临床试验,其中包括为2型糖尿病患者提供运动时间处方的智能手表技术。在被纳入的研究中,参与者对智能手表技术的依从性各不相同,一项研究报告的输入提交率为58%,另一项研究报告的输入量比要求的多50%。一项研究报告称,集成智能手表技术显著改善了血糖控制,增加了运动处方;然而,这项研究没有动力,需要更长的观察期。结论:本系统综述强调了缺乏可靠的随机临床试验来探索智能手表技术在1型、2型和妊娠糖尿病患者管理中的疗效。需要进一步的研究来确定集成智能手表技术在各种糖尿病患者的血糖控制、运动参与、药物依从性和饮食监测等重要结果中的作用。
期刊介绍:
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636.
The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.
JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.