Fisher Institute of Health and Well-Being, College of Health, Ball State University, Muncie, IN, United States.
Department of Nutrition and Health Sciences, College of Health, Ball State University, Muncie, IN, United States.
JMIR Mhealth Uhealth. 2020 Apr 28;8(4):e15400. doi: 10.2196/15400.
Diabetes and obesity have become epidemics and costly chronic diseases. The impact of mobile health (mHealth) interventions on diabetes and obesity management is promising; however, studies showed varied results in the efficacy of mHealth interventions.
This review aimed to evaluate the effectiveness of mHealth interventions for diabetes and obesity treatment and management on the basis of evidence reported in reviews and meta-analyses and to provide recommendations for future interventions and research.
We systematically searched the PubMed, IEEE Xplore Digital Library, and Cochrane databases for systematic reviews published between January 1, 2005, and October 1, 2019. We analyzed 17 reviews, which assessed 55,604 original intervention studies, that met the inclusion criteria. Of those, 6 reviews were included in our meta-analysis.
The reviews primarily focused on the use of mobile apps and text messaging and the self-monitoring and management function of mHealth programs in patients with diabetes and obesity. All reviews examined changes in biomarkers, and some reviews assessed treatment adherence (n=7) and health behaviors (n=9). Although the effectiveness of mHealth interventions varied widely by study, all reviews concluded that mHealth was a feasible option and had the potential for improving patient health when compared with standard care, especially for glycemic control (-0.3% to -0.5% greater reduction in hemoglobin A) and weight reduction (-1.0 kg to -2.4 kg body weight). Overall, the existing 6 meta-analysis studies showed pooled favorable effects of these mHealth interventions (-0.79, 95% CI -1.17 to -0.42; I2=90.5).
mHealth interventions are promising, but there is limited evidence about their effectiveness in glycemic control and weight reduction. Future research to develop evidence-based mHealth strategies should use valid measures and rigorous study designs. To enhance the effectiveness of mHealth interventions, future studies are warranted for the optimal formats and the frequency of contacting patients, better tailoring of messages, and enhancing usability, which places a greater emphasis on maintaining effectiveness over time.
糖尿病和肥胖已成为流行疾病和代价高昂的慢性病。移动医疗（mHealth）干预对糖尿病和肥胖管理的影响很有前景；然而，研究表明 mHealth 干预的效果存在差异。
本综述旨在根据综述和荟萃分析报告的证据评估 mHealth 干预措施在糖尿病和肥胖治疗和管理方面的有效性，并为未来的干预措施和研究提供建议。
我们系统地检索了 PubMed、IEEE Xplore 数字图书馆和 Cochrane 数据库，以获取 2005 年 1 月 1 日至 2019 年 10 月 1 日期间发表的系统评价。我们分析了符合纳入标准的 55604 项原始干预研究的 17 项综述。其中，有 6 项综述被纳入我们的荟萃分析。
这些综述主要集中在移动应用程序和短信的使用以及 mHealth 计划的自我监测和管理功能，这些功能在糖尿病和肥胖患者中得到了应用。所有综述都检查了生物标志物的变化，一些综述评估了治疗依从性（n=7）和健康行为（n=9）。尽管 mHealth 干预的效果因研究而异，但所有综述都得出结论，mHealth 是一种可行的选择，与标准护理相比，具有改善患者健康的潜力，特别是在血糖控制（糖化血红蛋白降低 0.3%至 0.5%）和体重减轻（体重减轻 1.0 至 2.4 公斤）方面。总体而言，现有的 6 项荟萃分析研究表明，这些 mHealth 干预措施具有有利的综合效果（-0.79，95%CI-1.17 至-0.42；I2=90.5）。
mHealth 干预措施很有前景，但关于其在血糖控制和体重减轻方面的有效性的证据有限。未来的研究应开发基于证据的 mHealth 策略，使用有效的措施和严格的研究设计。为了提高 mHealth 干预措施的效果，未来的研究需要关注最佳格式和与患者联系的频率、更好地调整信息以及提高可用性，这些都更加注重随着时间的推移保持效果。
