Leveraging Technology to Improve Diabetes Care in Pregnancy

Systematic development and refinement of a user-centered evidence-based digital toolkit for supporting self-care in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a pregnancy complication affecting many women, requiring changes in behaviours, which command them to learn self-care practices shortly. Digital interventions have been developed to support women with GDM. However, they have often overlooked women's needs and characteristics and failed to frame self-care theories into their design. To address this issue, we adopted a mixed methods approach to develop and refine a user-centred, evidence-based digital Toolkit for supporting self-care in GDM, providing behavioural and educational content, particularly about nutrition. To inform the development and refinement of the Toolkit, we conducted a literature review, observed sixty-six nutrition appointments, interviewed eleven dietitians and seventeen patients, and held co-creation sessions with two dietitians, all of which were analysed using a deductive Thematic Analysis. To validate the Toolkit, we conducted a survey with seventeen healthcare professionals, which was analysed using descriptive statistics. The final version of the NUTRIA Toolkit consists of four main modules with thirty-eight artefacts, including behavioural tools to assist women in GDM management. Despite some limitations, this study robustly endorsed the development and refinement of a user-centred, evidence-based Toolkit for supporting self-care in GDM, aiming for future feasibility and trial testing.

Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things

Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing health care delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven clinical decision support systems (CDSSs) can predict glucose trends and provide tailored evidence-based treatments with real-time adjustments as insulin resistance changes with placental growth. Additionally, mobile health (mHealth) applications facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship. KEY POINTS: · The IoMT expands how patients interact with their health care.. · AI has widespread application in the care of pregnancies complicated by diabetes.. · A need for validation and black-box methodologies challenges the application of AI-based tools.. · As research in AI grows, considerations for data privacy and ethical dilemmas will be required..

Personalized and Culturally Tailored Features of Mobile Apps for Gestational Diabetes Mellitus and Their Impact on Patient Self-Management: Scoping Review

Background

Gestational diabetes mellitus (GDM) is an increasingly common high-risk pregnancy condition requiring intensive daily self-management, placing the burden of care directly on the patient. Understanding personal and cultural differences among patients is critical for delivering optimal support for GDM self-management, particularly in high-risk populations. Although mobile apps for GDM self-management are being used, limited research has been done on the personalized and culturally tailored features of these apps and their impact on patient self-management.

Objective

This scoping review aims to explore the extent to which published studies report the integration and effectiveness of personalized and culturally tailored features in GDM mobile apps for patient self-management support.

Methods

We examined English-language peer-reviewed articles published between October 2016 and May 2023 from PubMed, CINAHL, PsycINFO, ClinicalTrials.gov, Proquest Research Library, and Google Scholar using search terms related to digital tools, diabetes, pregnancy, and cultural tailoring. We reviewed eligible articles and extracted data using the Arskey and O'Malley methodological framework.

Results

Our search yielded a total of 1772 articles after the removal of duplicates and 158 articles for full-text review. A total of 21 articles that researched 15 GDM mobile apps were selected for data extraction. Our results demonstrated the stark contrast between the number of GDM mobile apps with personalized features for the individual user (all 15 mobile apps) and those culturally tailored for a specific population (only 3 of the 15 mobile apps). Our findings showed that GDM mobile apps with personalized and culturally tailored features were perceived to be useful to patients and had the potential to improve patients' adherence to glycemic control and nutrition plans.

Conclusions

There is a strong need for increased research and development to foster the implementation of personalized and culturally tailored features in GDM mobile apps for self-management that cater to patients from diverse backgrounds and ethnicities. Personalized and culturally tailored features have the potential to serve the unique needs of patients more efficiently and effectively than generic features alone; however, the impacts of such features still need to be adequately studied. Recommendations for future research include examining the cultural needs of different ethnicities within the increasingly diverse US population in the context of GDM self-management, conducting participatory-based research with these groups, and designing human-centered mobile health solutions for both patients and providers.

Stakeholder Engagement in Type 1 Diabetes Research, Quality Improvement, and Clinical Care

The integration of stakeholder engagement (SE) in research, quality improvement (QI), and clinical care has gained significant traction. Type 1 diabetes is a chronic disease that requires complex daily management and care from a multidisciplinary team across the lifespan. Inclusion of key stakeholder voices, including patients, caregivers, health care providers and community advocates, in the research process and implementation of clinical care is critical to ensure representation of perspectives that match the values and goals of the patient population. This review describes the current framework for SE and its application to research, QI, and clinical care across the lifespan.