School of Nursing and Midwifery, University of Plymouth, Devon, UK.
The University of Plymouth Centre for Innovations in Health and Social Care: A JBI Centre of Excellence, Devon, UK.
JBI Evid Synth. 2022 Sep 1;20(9):2195-2243. doi: 10.11124/JBIES-21-00294.
The objective of this review was to map the knowledge related to the use of mobile health (mHealth) as a primary mode of intervention for the prevention and management of gestational diabetes mellitus and its long-term implications among women at risk of or diagnosed with gestational diabetes mellitus. We also sought to understand if mHealth for women at risk of or diagnosed with gestational diabetes mellitus incorporated relevant behavior change theory and techniques.
Prevention and management of gestational diabetes mellitus and its associated adverse outcomes are important to maternal and infant health. Women with gestational diabetes mellitus report high burden of disease management and barriers to lifestyle change post-delivery, which mHealth interventions may help to overcome. Evidence suggests apps could help gestational diabetes mellitus prevention and management; however, less is known about broader applications of mHealth from preconception to interconception, and whether relevant behavior change techniques are incorporated.
Studies that focused on mHealth use as the primary mode of intervention for the prevention and management of gestational diabetes mellitus and its long-term implications were considered for inclusion. Telehealth or telemedicine were excluded as these have been reviewed elsewhere.
Six databases were searched: MEDLINE, CINAHL, Embase, Cochrane Library, Scopus, and TRIP. No limits were applied to database exploration periods to ensure retrieval of all relevant studies. Gray literature sources searched were OpenGrey, ISRCTN Registry, ClinicalTrials.gov, EU Clinical Trials Register, and ANZCTR. Two reviewers independently screened abstracts and assessed full texts against the inclusion criteria. Data were extracted using an adapted version of the JBI data extraction instrument. Data are presented in narrative form accompanied by tables and figures.
This review identified 2166 sources, of which 96 full texts were screened. Thirty eligible reports were included, covering 25 different mHealth interventions. Over half (n = 14) of the interventions were for self-managing blood glucose during pregnancy. Common features included tracking blood glucose levels, real-time feedback, communication with professionals, and educational information. Few (n = 6) mHealth interventions were designed for postpartum use and none for interconception use. Five for postpartum use supported behavior change to reduce the risk oftype 2 diabetes and included additional features such as social support functions and integrated rewards. Early development and feasibility studies used mixed methods to assess usability and acceptability. Later stage evaluations of effectiveness typically used randomized controlled trial designs to measure clinical outcomes such as glycemic control and reduced body weight. Three mHealth interventions were developed using behavior change theory. Most mHealth interventions incorporated two behavior change techniques shown to be optimal when combined, and those delivering behavior change interventions included a wider range. Nevertheless, only half of the 26 techniques listed in a published behavior change taxonomy were tried.
mHealth for gestational diabetes mellitus focuses on apps to improve clinical outcomes. This focus could be broadened by incorporating existing resources that women value, such as social media, to address needs, such as peer support. Although nearly all mHealth interventions incorporated behavior change techniques, findings suggest future development should consider selecting techniques that target women's needs and barriers to engagement. Lack of mHealth interventions for prevention of gestational diabetes mellitus recurrence and type 2 diabetes mellitus suggests further development and evaluation are required.
本综述旨在绘制与使用移动健康（mHealth）作为预防和管理妊娠糖尿病及其长期影响的主要干预模式相关的知识图谱，尤其是针对有妊娠糖尿病风险或已被诊断为妊娠糖尿病的女性。我们还试图了解针对有妊娠糖尿病风险或已被诊断为妊娠糖尿病的女性的 mHealth 是否纳入了相关的行为改变理论和技术。
预防和管理妊娠糖尿病及其相关不良后果对母婴健康非常重要。患有妊娠糖尿病的女性报告称，在产后管理疾病方面负担沉重，并且难以改变生活方式，而 mHealth 干预可能有助于克服这些问题。有证据表明，应用程序可能有助于预防和管理妊娠糖尿病；然而，对于从受孕前到受孕期间的 mHealth 的更广泛应用，以及是否纳入了相关的行为改变技术，我们了解得较少。
纳入了专注于将 mHealth 用作预防和管理妊娠糖尿病及其长期影响的主要干预模式的研究。排除了远程医疗或远程医疗，因为它们已在其他地方进行了综述。
共搜索了 6 个数据库：MEDLINE、CINAHL、Embase、Cochrane 图书馆、Scopus 和 TRIP。为了确保检索到所有相关研究，数据库探索期间未设置任何限制。还搜索了灰色文献来源，包括 OpenGrey、ISRCTN 注册处、ClinicalTrials.gov、欧盟临床试验注册处和 ANZCTR。两名审查员独立筛选摘要，并根据纳入标准评估全文。使用 JBI 数据提取工具的改编版本提取数据。数据以叙述形式呈现，并附有表格和图形。
本综述共确定了 2166 个来源，其中 96 篇全文进行了筛选。有 30 篇符合条件的报告被纳入，涵盖了 25 种不同的 mHealth 干预措施。其中超过一半（n=14）的干预措施是用于自我管理怀孕期间的血糖。常见的功能包括跟踪血糖水平、实时反馈、与专业人员的沟通以及提供教育信息。只有 6 项 mHealth 干预措施是为产后使用而设计的，没有一项是为受孕前使用而设计的。有 5 项用于产后使用的干预措施支持行为改变，以降低 2 型糖尿病的风险，并且包括社交支持功能和集成奖励等额外功能。早期开发和可行性研究使用混合方法评估可用性和可接受性。后期评估有效性的研究通常使用随机对照试验设计来衡量临床结果，如血糖控制和体重减轻。有 3 项 mHealth 干预措施是使用行为改变理论开发的。大多数 mHealth 干预措施都结合了两种被证明是最佳组合的行为改变技术，而那些提供行为改变干预措施的则包括了更广泛的技术。尽管在已发布的行为改变分类法中列出了 26 种技术，但只尝试了其中的一半。
针对妊娠糖尿病的 mHealth 主要集中在改善临床结果的应用程序上。通过纳入女性重视的现有资源（如社交媒体）来满足需求，如同伴支持，可以扩大这一重点。尽管几乎所有的 mHealth 干预措施都纳入了行为改变技术，但研究结果表明，未来的发展应考虑选择针对女性需求和参与障碍的技术。缺乏针对妊娠糖尿病复发和 2 型糖尿病的 mHealth 干预措施表明需要进一步开发和评估。
