Technology-based interventions for prevention of type 2 diabetes following gestational diabetes: systematic review and meta-analysis

Background
Previous gestational diabetes incurs an 8-fold risk of developing type 2 diabetes, but lifestyle change can prevent or delay progression. Technology-based interventions may help overcome challenges women face in making postpartum lifestyle changes.

Objective
This study aimed to assess whether technology-based diabetes prevention interventions improve outcomes related to the onset of type 2 diabetes among women with a previous diagnosis of gestational diabetes.

Methods
Cochrane Central Register of Controlled Trials, CINAHL, Embase, PsycINFO, and Midwives Information and Resource Service were searched to October 2025 using subject headings and free-text terms. Titles and abstracts were independently screened by 2 authors, as were retrieved full-text articles. Studies were eligible if they examined technology-based diabetes prevention interventions delivered between gestational diabetes diagnosis and any time post partum, assessing anthropometric outcomes, glycemic control, health behavior, or psychological outcomes. Risk of bias was assessed by 1 reviewer using the National Institute for Clinical Excellence checklist, and certainty of evidence was assessed by 2 reviewers using the Grading of Recommendations Assessment, Development, and Evaluation. Data were summarized narratively, and results were pooled, where possible, using a random effects model.

Results
This review identified 15 studies, including 1257 participants. Pooled analysis of 7 studies showed significantly greater weight loss among those receiving technology-based interventions (mean difference –1.01, SE 0.35, 95% CI –1.86 to –0.16 kg; P=.03). Interventions delivered using technology only showed increased weight loss (mean difference –1.13, 95% CI –3.12 to 0.86 kg) as did those with a longer follow-up (mean difference –1.58, 95% CI –3.93 to 0.76 kg) compared with combined technology and telemedicine approaches (mean difference –0.89, 95% CI –2.51 to 0.73 kg) and studies with shorter follow-up (mean difference –0.7, 95% CI –1.21 to –0.18 kg), but these differences were not significant (mode of delivery: χ21=0.08; P=.78; follow-up: χ21=1.06; P=.30). Meta-analysis showed no significant differences in BMI (mean difference –0.22, SE 0.1, 95% CI –0.4 to –0.01 kg/m2; P=.27; n=2 studies), fasting glucose (mean difference –0.03, SE 0.16, 95% CI –0.49 to 0.49 mmol/L; P=.99; n=4 studies), 2-hour glucose (mean difference 0.12, SE 0.19, 95% CI –0.47 to 0.72 mmol/L; P=.56; n=4 studies), hemoglobin A1c(mean difference –0.01%, SE 0.02%, 95% CI –0.24% to 0.23%; P=.74; n=2 studies), or homeostasis model assessment of insulin resistance (mean difference 0.07, SE 0.02, 95% CI –0.16 to 0.31; P=.16; n=2). Certainty of evidence for all pooled outcomes was very low.

Conclusions
Technology-based interventions may help support women in reducing their risk of type 2 diabetes following gestational diabetes mellitus, but substantial heterogeneity, significant risk of bias, and very low certainty in the evidence mean that the findings should be interpreted cautiously. Trials with larger samples and longer follow-up are required to draw firm conclusions.

Trial Registration
PROSPERO CRD42024324019; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024324019