[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290732-105":59,"doc-detail-290732-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","digital-health-and-machine-learning-technologies-for-blood-glucose-monitoring-and-management-of-gestational-diabetes","Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes","","Innovations in digital health and machine learning are reshaping clinical pathways by enabling ubiquitous monitoring through wearable devices and smartphones. This paper reviews digital health and machine learning technologies for gestational diabetes, including sensor technologies for blood glucose monitoring and models used in clinical and commercial settings. It highlights gaps in clinical readiness, despite one in six mothers affected, and argues for interpretable methods, home-based “virtual ward” capabilities, and affordable, sustainable solutions across diverse socioeconomic and resource settings.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/digital-health-and-machine-learning-technologies-for-blood-glucose-monitoring-and-management-of-gestational-diabetes/290732/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/digital-health-and-machine-learning-technologies-for-blood-glucose-monitoring-and-management-of-gestational-diabetes/290732.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the focus of the paper on gestational diabetes?","Question",{"text":113,"@type":114},"The paper reviews digital health and machine learning technologies used for blood glucose monitoring and management in gestational diabetes, including sensors and clinical/commercial models.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Why is digital health adoption described as insufficient?",{"text":118,"@type":114},"It states that, despite the high prevalence of gestational diabetes, digital health applications were underdeveloped, especially for techniques deployable in clinical practice.",{"name":120,"@type":111,"acceptedAnswer":121},"What future directions does the paper emphasize?",{"text":122,"@type":114},"It calls for clinically interpretable machine learning methods, adaptation of clinically proven devices for home self-management via “virtual ward” and virtual consultation, and ensuring affordability and sustainability for women with different backgrounds and resources.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290732,1789642464,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes  \nHuiqi Lu, Member, IEEE, Xiaorong Ding , Member, IEEE, Jane E Hirst, Yang Yang, Jenny  \nYang , Lucy Mackillop, David Clifton  \nAbstract— Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes ̶ a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.  \nIndex Terms— blood glucose sensors, gestational diabetes, digital health, machine learning, patient monitoring.  \nI. INTRODUCTION  \nGestational diabetes mellitus (GDM) is defined by the World Health Organization (WHO) as carbohydrate intolerance resulting in hyperglycemia-high blood glucose-of variable severity with onset or first recognition during pregnancy [1] . It is one of the most common non-communicable medical complications during pregnancy. The prevalence of GDM is increasing rapidly worldwide. In 2021, the average prevalence of GDM was 16.7% globally, and was highest in Southeast Asia at 25.9%, as reported based on the population, the diagnostic criteria used, and geographical locations [2-6] . GDM is associated with both short-and long-term adverse health consequences for mothers and children. During pregnancy, GDM is associated with an increased risk of pre-eclampsia, increased fetal growth leading to macrosomia, shoulder dystocia, birth trauma and neonatal hypoglycaemia [7, 8] . Women with GDM also have a 50% risk of developing type 2 diabetes in the decade following their pregnancy [9, 10]. This makes GDM a condition of great public health interest in the fight against the global epidemic of type 2 diabetes.  \nDuring a healthy pregnancy, particularly in the second half of pregnancy, placental hormones favour a state of insulin resistance, resulting in increased insulin secretion from the pancreatic β-cells [11] . There is a higher risk of developing hyperglycaemia for women in whom insulin secretion is inadequate, or those with higher peripheral insulin resistance [10] .  \nThe most widely accepted test for diagnosing GDM is the 75-gram oral glucose tolerance test (OGTT), recommended by the WHO [12] and UK National Institute for Health and Care Excellence (NICE)  \n[13] . Using the WHO/IADPSG criteria, GDM is diagnosed if the woman has either a fasting plasma glucose level of 5.1 mmol/L or above, 1-hour ≥10 .0 mmol/L, or 2-hour ≥ 8.5 mmol/L. However, these thresholds are not globally agreed. Many counties, including the UK, recommend their own national thresholds to diagnose GDM. This diversity in diagnosis is also reflected in a lack of universal clinical management and monitoring targets","cbCaitmy9Ov0rAyJ","https://ap.wps.com/l/cbCaitmy9Ov0rAyJ","pdf",2394431,"English","# Introduction\n## Definition and epidemiology of gestational diabetes\n## Diagnostic criteria and variability\n## Glycaemic management and monitoring\n## Digital health technologies and machine learning context","[{\"question\":\"What is the focus of the paper on gestational diabetes?\",\"answer\":\"The paper reviews digital health and machine learning technologies used for blood glucose monitoring and management in gestational diabetes, including sensors and clinical/commercial models.\"},{\"question\":\"Why is digital health adoption described as insufficient?\",\"answer\":\"It states that, despite the high prevalence of gestational diabetes, digital health applications were underdeveloped, especially for techniques deployable in clinical practice.\"},{\"question\":\"What future directions does the paper emphasize?\",\"answer\":\"It calls for clinically interpretable machine learning methods, adaptation of clinically proven devices for home self-management via “virtual ward” and virtual consultation, and ensuring affordability and sustainability for women with different backgrounds and resources.\"}]","Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes | PDF",48]