Bridging Gaps and Understanding Disparities in Gestational Diabetes Mellitus to Improve Perinatal Outcomes

Daneshmand, Sean; Stortz, Sharon K; Morrisey, Robin; Faksh, Arij

doi:10.2337/ds19-0013

Cited by 12 publications

(6 citation statements)

References 60 publications

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“…Along with close fetal surveillance, maternal behavior modification is often seen as an effective strategy for HRP management (HRPM). 20 , 21 For instance, for GDM, modified dietary interventions, exercise, glucose monitoring, and/or medications result in improved outcomes in maternal glycemic control and birth outcomes. 22–24 As shown in Figure 1 , oftentimes, critical information needed for patient engagement is distributed in the form of prenatal ultrasounds, discharge summary, lab reports in paper and electronic formats across personal health records, text messages, and individual websites (eg, educational resources).…”

Section: Introductionmentioning

confidence: 99%

Digital health technologies for high-risk pregnancy management: three case studies using Digilego framework

Myneni,

Zingg,

Singh

et al. 2024

JAMIA Open

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Objective High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health. Methods We describe three studies that leverage core methods from Digilego digital health development framework to (1) conduct large-scale social media analysis (n = 55 301 posts) to understand population-level patterns in women’s needs, (2) architect a digital repository to enable women curate HRP related information, and (3) develop a digital platform to support PPD prevention. We applied a combination of qualitative coding, machine learning, theory-mapping, and programmatic implementation of theory-linked digital features. Further, we conducted preliminary testing of the resulting products for acceptance with sample of pregnant women for GDM/HTN information management (n = 10) and PPD prevention (n = 30). Results Scalable social computing models using deep learning classifiers with reasonable accuracy have allowed us to capture and examine psychosociobehavioral drivers associated with HRPM. Our work resulted in two digital health solutions, MyPregnancyChart and MomMind are developed. Initial evaluation of both tools indicates positive acceptance from potential end users. Further evaluation with MomMind revealed statistically significant improvements (P < .05) in PPD recognition and knowledge on how to seek PPD information. Discussion Digilego framework provides an integrative methodological lens to gain micro-macro perspective on women’s needs, theory integration, engagement optimization, as well as subsequent feature and content engineering, which can be organized into core and specialized digital pathways for women engagement in disease management. Conclusion Future works should focus on implementation and testing of digital solutions that facilitate women to capture, aggregate, preserve, and utilize, otherwise siloed, prenatal information artifacts for enhanced self-management of their high-risk conditions, ultimately leading to improved health outcomes.

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Section: Introductionmentioning

confidence: 99%

Digital health technologies for high-risk pregnancy management: three case studies using Digilego framework

Myneni,

Zingg,

Singh

et al. 2024

JAMIA Open

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“…The first step towards optimal management of GDM and prevention or delay of subsequent T2DM is diagnosis. GDM screening for all pregnant women has therefore been recommended by several professional bodies [4,5]. Currently, only a minority of women get screened worldwide for GDM [5], using many GDM testing and diagnostic criteria that have not been standardised despite efforts to do so [4] (See Table 1 for different GDM diagnostic criteria).…”

Section: Introductionmentioning

confidence: 99%

Integrating Gestational Diabetes Screening and Care and Type 2 Diabetes Mellitus Prevention After GDM Into Community Based Primary Health Care in South Africa-Mixed Method Study

Mutabazi¹,

Bonong²,

Trottier³

et al. 2022

Int J Integr Care

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Background: Despite high gestational diabetes mellitus (GDM) prevalence in South Africa (9.1% in 2018), its screening and management are not well integrated into routine primary health care and poorly linked to post-GDM prevention of type 2 diabetes mellitus (T2DM) in South Africa's fragmented health system. This study explored women's, health care providers' and experts' experiences and perspectives on current and potential integration of GDM screening and prevention of T2DM post-GDM within routine, community-based primary health care (PHC) services in South Africa.Methods: This study drew on the Behaviour Change Wheel (BCW) framework and used a mixed method, sequential exploratory design for data collection, analysis and interpretation. Individual semi-structured interviews were conducted with key informants (n = 5) from both national and provincial levels and health care providers (n = 18) in the public health system of the Western Cape Province. Additionally, focus group discussions (FGDs) with Community Health Workers (CHWs n = 15) working with clinics in the Western Cape province. A further four FGDs and brief individual exit interviews were conducted with women with GDM (n = 35) followed-up at a tertiary hospital: Groote Schuur Hospital (GSH). Data collection with women diagnosed and treated for GDM happened between March and August 2018.Thematic analysis was the primary analytical method with some content analysis as appropriate. Statistical analysis of quantitative data from the 35 exit interview questionnaires was conducted, and correlation with qualitative variables assessed using Cramér's V coefficient.Results: Shortage of trained staff, ill-equipped clinics, socio-economic barriers and lack of knowledge were the major reported barriers to successful integration of GDM screening and postnatal T2DM prevention. Only 43% of women reported receiving advice about all four recommendations to improve GDM and decrease T2DM risk (improve diet, reduce sugar intake, physical exercise and regularly take medication). All participants supported integrating services within routine, community-based PHC to universally screen for GDM and to prevent or delay development of T2DM after GDM.

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“…Awareness of the adverse outcomes associated with GDM has been a driver for substantial improvements in perinatal care for pregnant women with GDM in recent years [ 13 ]. The first-line treatment for GDM involves lifestyle changes, e.g., modified diet and increased physical activity, and nearly two-thirds of women can achieve glycaemic targets with this approach [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Gestational Diabetes Mellitus and Infant Adiposity at Birth: A Systematic Review and Meta-Analysis of Therapeutic Interventions

Herath

Beckett

Hills

et al. 2021

JCM

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Exposure to untreated gestational diabetes mellitus (GDM) in utero increases the risk of obesity and type 2 diabetes in adulthood, and increased adiposity in GDM-exposed infants is suggested as a plausible mediator of this increased risk of later-life metabolic disorders. Evidence is equivocal regarding the impact of good glycaemic control in GDM mothers on infant adiposity at birth. We systematically reviewed studies reporting fat mass (FM), percent fat mass (%FM) and skinfold thicknesses (SFT) at birth in infants of mothers with GDM controlled with therapeutic interventions (IGDMtr). While treating GDM lowered FM in newborns compared to no treatment, there was no difference in FM and SFT according to the type of treatment (insulin, metformin, glyburide). IGDMtr had higher overall adiposity (mean difference, 95% confidence interval) measured with FM (68.46 g, 29.91 to 107.01) and %FM (1.98%, 0.54 to 3.42) but similar subcutaneous adiposity measured with SFT, compared to infants exposed to normal glucose tolerance (INGT). This suggests that IGDMtr may be characterised by excess fat accrual in internal adipose tissue. Given that intra-abdominal adiposity is a major risk factor for metabolic disorders, future studies should distinguish adipose tissue distribution of IGDMtr and INGT.

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Bridging Gaps and Understanding Disparities in Gestational Diabetes Mellitus to Improve Perinatal Outcomes

Cited by 12 publications

References 60 publications

Digital health technologies for high-risk pregnancy management: three case studies using Digilego framework

Digital health technologies for high-risk pregnancy management: three case studies using Digilego framework

Integrating Gestational Diabetes Screening and Care and Type 2 Diabetes Mellitus Prevention After GDM Into Community Based Primary Health Care in South Africa-Mixed Method Study

Gestational Diabetes Mellitus and Infant Adiposity at Birth: A Systematic Review and Meta-Analysis of Therapeutic Interventions

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