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Background: The prevalence of women diagnosed with gestational diabetes mellitus (GDM) is increasing dramatically. Mobile technologies to enhance patient self-management offer many advantages for women diagnosed with GDM. However, to our knowledge, although mobile health (mHealth) and telemedicine systems for GDM management exist, evidence on their cultural and digital health literacy appropriateness levels is limited.
Objective: This review aimed to search and assess the literature on mHealth and telemedicine systems designed for women diagnosed with GDM. Our assessment of these technologies focused on their cultural and digital health literacy appropriateness as well as the systems' effectiveness in improving glycemic control and maternal and infant outcomes.
Methods: We conducted a scoping review using a framework adapted from Arksey and O'Malley. Four electronic databases were searched for relevant studies: PubMed, MEDLINE (EBSCO), Web of Science, and Scopus. The databases were searched between January 2010 and January 2022. The inclusion criteria were pregnant women diagnosed with GDM, use of telemedicine for monitoring and management, and vulnerable or disadvantaged patients. We used terms related to mobile apps and telemedicine: GDM, vulnerable populations, periphery, cultural appropriateness, and digital health literacy. Studies were screened and selected independently by 2 authors. We extracted the study data on a Microsoft Excel charting table and categorized them into final themes. The results were categorized according to the cultural and digital health literacy features presented.
Results: We identified 17 studies that reported on 12 telemedicine and mHealth app interventions. We assessed the studies in three domains: cultural appropriateness, digital health literacy, and maternal and infant outcomes. In the literature, we found that existing digital technologies may improve glycemic control and diabetes self-management. However, there is a lack of assessment of cultural and digital health literacy appropriateness for pregnant women diagnosed with GDM. Considerations in app design regarding cultural appropriateness were found in only 12% (2/17) of the studies, and only 25% (3/12) of the interventions scored ≥3 out of 5 in our assessment of digital health literacy.
Conclusions: mHealth and telemedicine can be an effective platform to improve the clinical management of women with GDM. Although studies published on the use of mHealth and telemedicine systems exist, there is a limited body of knowledge on the digital health literacy and cultural appropriateness of the systems designed for women diagnosed with GDM. In addition, as our study was restricted to the English language, relevant studies may have been excluded. Further research is needed to evaluate, design, and implement better tailored apps regarding cultural and digital literacy appropriateness for enhancing pregnant women's self-management as well as the effectiveness of these apps in improving maternal and infant health outcomes.
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http://dx.doi.org/10.2196/37844 | DOI Listing |
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J Orthop Res
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Interdisciplinary Orthopedics, Department of Orthopedics Surgery, Aalborg University Hospital, Aalborg, Denmark.
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Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
The rapid evolution of digital tools in recent years after COVID-19 pandemic has transformed diagnostic and therapeutic practice in neurology. This shift has highlighted the urgent need to integrate digital competencies into the training of future specialists. Key innovations such as telemedicine, artificial intelligence, and wearable health technologies have become central to improving healthcare delivery and accessibility.
View Article and Find Full Text PDFJ Imaging Inform Med
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Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
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