Impact of Remote Monitoring Technologies for Assisting Patients With Gestational Diabetes Mellitus: A Systematic Review

Call to action for a life course approach

Gestational diabetes remains the most common medical disorder in pregnancy, with short-term and long-term consequences for mothers and offspring. New insights into pathophysiology and management suggest that the current gestational diabetes treatment approach should expand from a focus on late gestational diabetes to a personalised, integrated life course approach from preconception to postpartum and beyond. Early pregnancy lifestyle intervention could prevent late gestational diabetes. Early gestational diabetes diagnosis and treatment has been shown to be beneficial, especially when identified before 14 weeks of gestation. Early gestational diabetes screening now requires strategies for integration into routine antenatal care, alongside efforts to reduce variation in gestational diabetes care, across settings that differ between, and within, countries. Following gestational diabetes, an oral glucose tolerance test should be performed 6-12 weeks postpartum to assess the glycaemic state. Subsequent regular screening for both dysglycaemia and cardiometabolic disease is recommended, which can be incorporated alongside other family health activities. Diabetes prevention programmes for women with previous gestational diabetes might be enhanced using shared decision making and precision medicine. At all stages in this life course approach, across both high-resource and low-resource settings, a more systematic process for identifying and overcoming barriers to preventative care and treatment is needed to reduce the current global burden of gestational diabetes.

Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety

BACKGROUND

Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed.

AIM

This study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students.

METHODS

This study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used.

RESULTS

No significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety.

CONCLUSION

Midwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.

Ultrasensitive Hierarchical AuNRs@SiO2@Ag SERS Probes for Enrichment and Detection of Insulin and C-Peptide in Serum

Introduction

Insulin and C-peptide played crucial roles as clinical indicators for diabetes and certain liver diseases. However, there has been limited research on the simultaneous detection of insulin and C-peptide in trace serum. It is necessary to develop a novel method with high sensitivity and specificity for detecting insulin and C-peptide simultaneously.

Methods

A core-shell-satellites hierarchical structured nanocomposite was fabricated as SERS biosensor using a simple wet-chemical method, employing 4-MBA and DTNB for recognition and antibodies for specific capture. Gold nanorods (Au NRs) were modified with Raman reporter molecules and silver nanoparticles (Ag NPs), creating SERS tags with high sensitivity for detecting insulin and C-peptide. Antibody-modified commercial carboxylated magnetic bead@antibody served as the capture probes. Target materials were captured by probes and combined with SERS tags, forming a "sandwich" composite structure for subsequent detection.

Results

Under optimized conditions, the nanocomposite fabricated could be used to detect simultaneously for insulin and C-peptide with the detection limit of 4.29 × 10-5 pM and 1.76 × 10-10 nM in serum. The insulin concentration (4.29 × 10-5-4.29 pM) showed a strong linear correlation with the SERS intensity at 1075 cm-1, with high recoveries (96.4-105.3%) and low RSD (0.8%-10.0%) in detecting human serum samples. Meanwhile, the C-peptide concentration (1.76 × 10-10-1.76 × 10-3 nM) also showed a specific linear correlation with the SERS intensity at 1333 cm-1, with recoveries 85.4%-105.0% and RSD 1.7%-10.8%.

Conclusion

This breakthrough provided a novel, sensitive, convenient and stable approach for clinical diagnosis of diabetes and certain liver diseases. Overall, our findings presented a significant contribution to the field of biomedical research, opening up new possibilities for improved diagnosis and monitoring of diabetes and liver diseases.

Mitigating Health-Related Uncertainties During Pregnancy: The Role of Smart Health Monitoring Technologies

Pregnancy is a time filled with uncertainties, which can be challenging and lead to fear or anxiety for expectant parents. Health monitoring technologies that allow monitoring of the vital signs of both the mother and fetus offer a way to address health-related uncertainties. But are smart health monitoring technologies (SHMTs) actually an effective means to reduce uncertainties during pregnancy, or do they have the opposite effect? Using conceptual reasoning and phenomenological approaches grounded in existing literature, this Viewpoint explores the effects of SHMTs on health-related uncertainties during pregnancy. The argument posits that while SHMTs can alleviate some health-related uncertainties, they may also create new ones. This is particularly the case when the abundance of vital data overwhelms pregnant persons, leads to false-positive diagnoses, or raises concerns about the accuracy and analysis of data. Consequently, it is concluded that the use of SHMTs is not a cure-all for overcoming health-related uncertainties during pregnancy. Since the use of such monitoring technologies can introduce new uncertainties, it is important to carefully consider where and for what purpose they are used, use them sparingly, and promote a pragmatic approach to uncertainties.Using conceptual reasoning and phenomenological approaches grounded in existing literature, the effects of SHMTs on health-related uncertainties during pregnancy are explored. The argument posits that while SHMTs can alleviate some health-related uncertainties, they may also create new ones. This is particularly the case when the abundance of vital data overwhelms pregnant persons, leads to false-positive diagnoses, or raises concerns about the accuracy and analysis of data. Consequently, it is concluded that the use of SHMTs is not a cure-all for overcoming health-related uncertainties during pregnancy. Since the use of such monitoring technologies can introduce new uncertainties, it is important to carefully consider where and for what purpose they are used, use them sparingly, and promote a pragmatic approach to uncertainties.

Effectiveness of remote monitoring for glycemic control on maternal-fetal outcomes in women with gestational diabetes mellitus: A meta-analysis

BACKGROUND

The current pandemic and future public health emergencies highlight the importance of evaluating a telehealth care model. Previous studies have reached mixed conclusions about the effectiveness of remote monitoring on glycemic control and maternal and infant outcomes in women with gestational diabetes mellitus (GDM).

OBJECTIVES

This meta-analysis aimed to evaluate the effectiveness of remote blood glucose monitoring for women with gestational diabetes mellitus and to provide evidence-based guidance on the management of women with gestational diabetes mellitus for policymakers and healthcare providers during situations such as pandemics or natural disasters.

METHODS

The Cochrane Library, PubMed, Web of Science, EBSCO, Embase, Medline, CINAHL databases, and ClinicalTrials.gov were systematically searched from their inception to July 10, 2021. Randomized controlled trials (RCTs) published in English with respect to remote blood glucose monitoring in women with GDM were included in the meta-analysis. Two independent reviewers performed data extraction and assessed the quality of the studies. Risk ratios, mean differences, 95% confidence intervals, and heterogeneity were calculated.

RESULTS

A total of 1265 participants were included in the 11 RCTs. There were no significant differences in glycemic control and maternal-fetal outcomes between the remote monitoring group and a standard care group, which included glycosylated hemoglobin (HbA1c), fasting blood glucose, mean 2-h postprandial blood glucose, caesarean birth, gestational weight gain, shoulder dystocia, neonatal hypoglycemia, and other outcomes.

CONCLUSION

This meta-analysis reveals that it is unclear if remote glucose monitoring is preferable to standard of care glucose monitoring. To improve glycemic control and maternal-fetal outcomes during the current epidemic or other natural disasters, the implementation of double-blind RCTs in the context of simulating similar disasters remains to be studied in the future.

Pre-pregnancy Weight and Racial-Ethnic Disparities in Pregnancy-Associated Conditions in the State of Georgia: A Population-Based Study

INTRODUCTION

We investigate racial-ethnic disparities in pre-pregnancy obesity and pregnancy weight gain, which are known to increase the risk of pregnancy-associated conditions.

METHODS

We used 4-year (2017-2020) combined Georgia Pregnancy Risk Assessment Monitoring System data (N = 3208) to investigate racial-ethnic disparities in the incidence of gestational hypertension (GHT), gestational diabetes mellitus (GDM), and postpartum depression (PPD) and their associated risk with pre-pregnancy overweight/obesity after controlling for demographic and other confounders using regression modeling. The geographic distributions of hypertension and PPD rates at the county level were compared to the patterns of racial-ethnic populations and hospitals.

RESULTS

The PPD rates were higher among Asian (17.6), Hispanic (14.4), and Black (14.3); GDM was highest among Asian (16.0) mothers; and GHT was the highest among Black (11.7) followed by White mothers (9.0). Pre-pregnancy overweight and obese conditions increased the odds of hypertension in Black (2 ½ times) and White (> 3 ½ times) mothers. Premature birth increased the odds of hypertension (2-3 times) in all mothers. Pre-pregnancy weight also increased the odds of GDM (3-7 times) in these racial groups. Premature birth increases the odds twice as likely for PPD in Hispanic and White mothers. The convergence of high PPD and hypertension rates with high proportions of racial and ethnic minorities, and lack of hospital presence, indicates areas where healthcare interventions are required.

CONCLUSIONS

These findings underscore the importance of promoting a healthy pre-pregnancy weight to reduce the burden of maternal morbidity and pregnancy outcomes in general. More comprehensive prenatal monitoring using technological interventions for self-care has a great promise of being effective in maintaining a healthy pregnancy.

Telemedicine in the treatment of gestational diabetes: An observational cohort study on pregnancy outcomes and maternal satisfaction

AIMS

Gestational diabetes treatment requires several outpatient consultations from diagnosis until delivery in order to prevent hyperglycaemia, which is associated with maternal and fetal complications. There is limited evidence in the literature about telemedicine superiority in improving pregnancy outcomes for women with gestational diabetes. The primary aim of the study was to evaluate maternal and fetal outcomes, while the secondary aim was to estimate the degree of satisfaction with gestational diabetes treatment, comparing telemedicine versus outpatient care.

METHODS

This observational cohort study involved 60 consecutive women with gestational diabetes treated at the Diabetology Unit of Ferrara: 27 were followed up through a weekly remote control method (telemedicine group) and 33 in ambulatory clinics every 2 or 3 weeks (conventional group). After giving birth, 56 women responded to the modified Oxford Maternity Diabetes Treatment Satisfaction Questionnaire to assess their satisfaction with diabetes care.

RESULTS

No statistically significant differences were found in most of the maternal and neonatal parameters evaluated in both groups. The questionnaire scores were positive in all areas investigated. Telemedicine follow-up made women feel more controlled (p = 0.045) and fit better with their lifestyle (p = 0.005). It also emerged that almost all women treated with telemedicine would recommend this method to a relative or a friend.

CONCLUSIONS

Telemedicine follow-up proved to be safe both in terms of metabolic control and pregnancy outcomes; furthermore, it significantly decreased the need for outpatient consultations and increased women's satisfaction. Studying the impact of telemedicine is also necessary, considering the current difficulties associated with the Sars-COV-2 pandemic.

Analysis of the management and therapeutic performance of diabetes mellitus employing special target

Diabetes mellitus (DM) is a chronic metabolic condition characterized predominantly by hyperglycemia. The most common causes contributing to the pathophysiology of diabetes are insufficient insulin secretion, resistance to insulin's tissue-acting effects, or a combination of both. Over the last 30 years, the global prevalence of diabetes increased from 4% to 6.4%. If no better treatment or cure is found, this amount might climb to 430 million in the coming years. The major factors of the disease's deterioration include age, obesity, and a sedentary lifestyle. Finding new therapies to manage diabetes safely and effectively without jeopardizing patient compliance has always been essential. Among the medications available to manage DM on this journey are glucagon-like peptide-1 agonists, thiazolidinediones, sulphonyl urease, glinides, biguanides, and insulin-targeting receptors discovered more than 10 years ago. Despite the extensive preliminary studies, a few clinical observations suggest this process is still in its early stages. The present review focuses on targets that contribute to insulin regulation and may be employed as targets in treating diabetes since they may be more efficient and secure than current and traditional treatments.

Effects of online support and social media communities on gestational diabetes: A systematic review

BACKGROUND

Gestational diabetes mellitus (GDM) is a common complication in pregnancy that can lead to negative maternal and fetal outcomes. Online support interventions have been suggested as a potential tool to improve the management of GDM.

OBJECTIVE

This systematic review aimed to summarize the effectiveness of social media and online support interventions for the management of GDM.

METHODS

We conducted a thorough systematic search across Web of Science, Scopus, and PubMed, following PRISMA guidelines, and supplemented it with a manual search. Our results included both qualitative and quantitative research. We rigorously assessed quantitative studies for bias using ROBINS-I and RoB 2 tools, ensuring the reliability of our findings.

RESULTS

We incorporated a total of 22 studies, which were comprised of ten qualitative and twelve quantitative studies. Online support interventions were found to have a positive impact on promoting self-care and improving healthcare outcomes for women with GDM. Individualized diet and exercise interventions resulted in lower odds of weight gain and GDM diagnosis, while online prenatal education increased breastfeeding rates. In addition, telemedicine options reduced the need for in-person clinical visits and improved patient satisfaction.

CONCLUSIONS

Online support interventions show potential to improve outcomes in patients with GDM in this small literature review. Future research is also necessary to determine the effectiveness of different types of online interventions and identify strategies to improve engagement and the quality of the information provided through online resources.

Effectiveness of a comprehensive telemedicine intervention replacing standard care in gestational diabetes: a randomized controlled trial

AIMS

Telemedicine improves glycemic and perinatal outcomes when used as an adjunct to standard care in gestational diabetes (GDM). Little is known about its effectiveness when used instead of standard care. We aimed to compare the outcomes of telemedicine care and the standard care in women with GDM.

METHODS

In a single-center, parallel, randomized controlled trial, women were randomized to: (1) a telemedicine group, sending glucose readings via an application installed on a smartphone and monthly individual video calls replacing on-site visits or (2) standard care group with routine monthly on-site visits. The primary outcome was the effectiveness of glycemic control. The secondary outcomes were gestational weight gain (GWG) and perinatal data, including birth weight, gestational age, the incidence of the offspring large for gestational age, preterm birth, preeclampsia and cesarean section.

RESULTS

A total of 106 women were randomized to the telemedicine (n = 54) and the standard care group (n = 52). The telemedicine group demonstrated less postprandial measurements above the glycemic target (10.4% [3.9-17.9] vs. 14.6% [6.5-27.1]; p = 0.015), together with lower average postprandial glucose (5.6 ± 0.3 vs. 5.9 ± 0.4; p = 0.004). Percentage of cesarean section was lower in the telemedicine group (9 (17.3%) vs. 18 (35.3%); p = 0.038).

CONCLUSIONS

Telemedicine offers an effective alternative to delivering care to women with GDM. Trial registration NCT05521893, ClinicalTrials.gov Identifier URL: https://www.

CLINICALTRIALS

gov/ct2/show/NCT05521893?term=NCT05521893&draw=2&rank=1.

The Effect of Maternal Dietary Patterns on Birth Weight for Gestational Age: Findings from the MAMI-MED Cohort

Limited evidence exists on the effects of maternal dietary patterns on birth weight, and most studies conducted so far did not adjust their findings for gestational age and sex, leading to potentially biased conclusions. In the present study, we applied a novel method, namely the clustering on principal components, to derive dietary patterns among 667 pregnant women from Catania (Italy) and to evaluate the associations with birth weight for gestational age. We identified two clusters reflecting distinct dietary patterns: the first one was mainly characterized by plant-based foods (e.g., potatoes, cooked and raw vegetables, legumes, soup, fruits, nuts, rice, wholemeal bread), fish and white meat, eggs, butter and margarine, coffee and tea; the second one consisted mainly of junk foods (sweets, dips, salty snacks, and fries), pasta, white bread, milk, vegetable and olive oils. Regarding small gestational age births, the main predictors were employment status and primiparity, but not the adherence to dietary patterns. By contrast, women belonging to cluster 2 had higher odds of large for gestational age (LGA) births than those belonging to cluster 1 (OR = 2.213; 95%CI = 1.047-4.679; p = 0.038). Moreover, the odds of LGA increased by nearly 11% for each one-unit increase in pregestational BMI (OR = 1.107; 95%CI = 1.053-1.163; p < 0.001). To our knowledge, the present study is the first to highlight a relationship between adherence to an unhealthy dietary pattern and the likelihood of giving birth to a LGA newborn. This evidence adds to the current knowledge about the effects of diet on birth weight, which, however, remains limited and controversial.

Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning

Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of 0.03±0.013 and an R2 of 63±19%, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an R2 up to 92%. The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase.

How Wearable Sensors Can Support the Research on Foetal and Pregnancy Outcomes: A Scoping Review

The application of innovative technologies, and in particular of wearable devices, can potentially transform the field of antenatal care with the aim of improving maternal and new-born health through a personalized approach. The present study undertakes a scoping review to systematically map the literature about the use wearable sensors in the research of foetal and pregnancy outcomes. Online databases were used to identify papers published between 2000-2022, from which we selected 30 studies: 9 on foetal outcomes and 21 on maternal outcomes. Included studies focused primarily on the use of wearable devices for monitoring foetal vital signs (e.g., foetal heart rate and movements) and maternal activity during pregnancy (e.g., sleep patterns and physical activity levels). There were many studies that focused on development and/or validation of wearable devices, even if often they included a limited number of pregnant women without pregnancy complications. Although their findings support the potential adoption of wearable devices for both antenatal care and research, there is still insufficient evidence to design effective interventions. Therefore, high quality research is needed to determine which and how wearable devices could support antenatal care.

STORK: Collaborative Online Monitoring of Pregnancies Complicated with Gestational Diabetes Mellitus

Background: A novel digital platform, named STORK, was developed in the COVID-19 pandemic when clinic visits were restricted. A study of its clinical use during the pandemic was conducted. The study aims to advance the state of the art in monitoring and care of pregnancies complicated with gestational diabetes mellitus (GDM) via online collaboration between patients and care providers. Methods: This study involved 31 pregnant women diagnosed with GDM and 5 physicians. Statistical comparisons were made in clinic-visit frequency and adverse outcomes between the STORK group and a historical control group of 32 women, compatible in size, demographics, anthropometrics and medical history. Results: The average number of submitted patient measurements per day was 3.6±0.4. The average number of clinic visits was 2.9±0.7 for the STORK group vs. 4.1±1.1 for the control group (p<0.05). The number of neonatal macrosomia cases was 2 for the STORK group vs. 3 for the control group (p>0.05); no other adverse incidents. Conclusions: The patient compliance with the pilot use of STORK was high and the average number of prenatal visits was reduced. The results suggest the general feasibility to reduce the average number of clinic visits and cost, with enhanced monitoring, case-specific adaptation, assessment and care management via timely online collaboration.