Mobile health as a primary mode of intervention for women at risk of, or diagnosed with, gestational diabetes mellitus

OBJECTIVE: 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. INTRODUCTION: 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. INCLUSION CRITERIA: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.