Artificial Intelligence Model of Drive-Through Vaccination Simulation

Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence

The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could transform clinical interventions and outcomes, but it risks dehumanization and increased unethical practices. The field of clinical nutrition and dietetics is no exception. This article finds a multitude of developments underway, which include the use of AI for malnutrition screening; predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions; aiding interventions, such as estimating nutrient intake; applying precision nutrition, such as measuring postprandial glycemic response; and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI-development protocols, benchmarked to care recommendations, with rigorous large-scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.

Transforming Aquaculture through Vaccination: A Review on Recent Developments and Milestones

Aquaculture has rapidly emerged as one of the fastest growing industries, expanding both on global and on national fronts. With the ever-increasing demand for proteins with a high biological value, the aquaculture industry has established itself as one of the most efficient forms of animal production, proving to be a vital component of global food production by supplying nearly half of aquatic food products intended for human consumption. As in classic animal production, the prevention of diseases constitutes an enduring challenge associated with severe economic and environmental repercussions. Nevertheless, remarkable strides in the development of aquaculture vaccines have been recently witnessed, offering sustainable solutions to persistent health-related issues challenging resilient aquaculture production. These advancements are characterized by breakthroughs in increased species-specific precision, improved vaccine-delivery systems, and innovations in vaccine development, following the recent advent of nanotechnology, biotechnology, and artificial intelligence in the -omics era. The objective of this paper was to assess recent developments and milestones revolving around aquaculture vaccinology and provide an updated overview of strengths, weaknesses, opportunities, and threats of the sector, by incorporating and comparatively discussing various diffuse advances that span across a wide range of topics, including emerging vaccine technologies, innovative delivery methods, insights on novel adjuvants, and parasite vaccine development for the aquaculture sector.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

BACKGROUND

Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

OBJECTIVE

We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

METHODS

We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS

A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization.

CONCLUSIONS

The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

Discrete-event simulation study of a COVID-19 mass vaccination centre

The global spread of COVID-19 and the declaration of the pandemic status made by the World Health Organization (WHO) led to the establishment of mass vaccination campaigns. The challenges posed by the request to immunise the entire population necessitated the set-up of new vaccination sites, named Mass Vaccination Centres (MVCs), capable of handling large numbers of patients rapidly and safely. The present study focused on the evolution of MVC performances, in terms of the maximum number of vaccinated patients and primary resource utilisation ratio, while involving statistics belonging to the patient dimension. The research involved the creation of a digital model of the MVC, using the Discrete-Event Simulation (DES) software (FlexSim Healthcare), and consequent what-if analyses. The results were derived from the study of an existing facility, located within a sports centre in the province of Bergamo (Italy) and operating with an advanced MVC organisational model, in compliance with the national anti-SARS-CoV-2 legislation. The research provided additional evidence on innovative MVC organisational models, identifying an optimal MVC configuration. Besides, the obtained results remain relevant for countries where a significant portion of the population has not yet addressed the emergency, either for upcoming vaccination treatments. Furthermore, the methodology adopted in the present article proved to be a valuable resource in the analysis of the healthcare processes.

Solve study: a study to capture global variations in practices concerning laparoscopic cholecystectomy

BACKGROUND

There is a lack of published data on variations in practices concerning laparoscopic cholecystectomy. The purpose of this study was to capture variations in practices on a range of preoperative, perioperative, and postoperative aspects of this procedure.

METHODS

A 45-item electronic survey was designed to capture global variations in practices concerning laparoscopic cholecystectomy, and disseminated through professional surgical and training organisations and social media.

RESULTS

638 surgeons from 70 countries completed the survey. Pre-operatively only 5.6% routinely perform an endoscopy to rule out peptic ulcer disease. In the presence of preoperatively diagnosed common bile duct (CBD) stones, 85.4% (n = 545) of the surgeons would recommend an Endoscopic Retrograde Cholangio-Pancreatography (ERCP) before surgery, while only 10.8% (n = 69) of the surgeons would perform a CBD exploration with cholecystectomy. In patients presenting with gallstone pancreatitis, 61.2% (n = 389) of the surgeons perform cholecystectomy during the same admission once pancreatitis has settled down. Approximately, 57% (n = 363) would always administer prophylactic antibiotics and 70% (n = 444) do not routinely use pharmacological DVT prophylaxis preoperatively. Open juxta umbilical is the preferred method of pneumoperitoneum for most patients used by 64.6% of surgeons (n = 410) but in patients with advanced obesity (BMI > 35 kg/m2, only 42% (n = 268) would use this technique and only 32% (n = 203) would use this technique if the patient has had a previous laparotomy. Most surgeons (57.7%; n = 369) prefer blunt ports. Liga clips and Hem-o-loks® were used by 66% (n = 419) and 30% (n = 186) surgeons respectively for controlling cystic duct and (n = 477) 75% and (n = 125) 20% respectively for controlling cystic artery. Almost all (97.4%) surgeons felt it was important or very important to remove stones from Hartmann's pouch if the surgeon is unable to perform a total cholecystectomy.

CONCLUSIONS

This study highlights significant variations in practices concerning various aspects of laparoscopic cholecystectomy.

Multi-Disciplinary Design and Implementation of a Mass Vaccination Clinic Mobile Application to Support Decision-Making

Mass vaccination clinics are complex systems that combine professionals who do not typically work together. Coordinating vaccine preparation and patient intake is critically important to maintain patient flow equilibrium, requiring continuous communication and shared decision-making to reduce vaccine waste.

OBJECTIVES

(1) To develop a mobile application (app) that can address the information needs of vaccination clinic stakeholders for end-of-day doses decision-making in mass immunization settings; and (2) to understand usability and clinical implementation among multi-disciplinary users.

METHODS

Contextual inquiry guided 71.5 hours of observations to inform design characteristics. Rapid iterative testing and evaluation were performed to validate and improve the design. Usability and integration were evaluated through observations, interviews, and the system usability scale.

RESULTS

Designing the app required consolidating contextual factors to support information and workload needs. Twenty-four participants used the app at four clinics who reported its effectiveness in reducing stress and improving communication efficiency and satisfaction. They also discussed positive workflow changes and design recommendations to improve its usefulness. The average system usability score was 87 (n = 22).

DISCUSSION

There is significant potential for mobile apps to improve workflow efficiencies for information sharing and decision-making in vaccination clinics when designed for established cultures and usability, thereby providing frontline workers with greater time to focus on patient care and immunization needs. However, designing and implementing digital systems for dynamic settings is challenging when healthcare teams constantly adapt to evolving complexities. System-level barriers to adoption require further investigation. Future research should explore the implementation of the app within global contexts.

A queueing Network approach for capacity planning and patient Scheduling: A case study for the COVID-19 vaccination process in Colombia

This paper considers the problem of patient scheduling and capacity planning for the vaccination process during the COVID-19 pandemic. The proposed solution is based on a non-linear mathematical modeling approach representing the dynamics of an open Jackson Network and a Generalized Network. To test these models, we proposed three objective functions and analyzed different configurations of the process corresponding to various levels of the models' parameters as well as the conditions present in the case study. To assess the computational performance of the models, we also experimented with larger instances in terms of number of steps or stations used and number of patients scheduled. The computational results show how parameters such as the minimum percentage of patients served, the maximum occupation allowed per station and the objective functions used have an impact on the configuration of the process. The proposed approach can support the decision-making process in vaccination centers to efficiently assign human and material resources to maximize the number of patients vaccinated while ensuring reasonable waiting times, number of patients in queue and servers' utilization rates, which in turn are key to avoid overcrowding and other negative conditions in the system that could increase the risk of infections.

Implementation of the drive-through strategy for COVID-19 vaccination: an experience report

OBJECTIVE

To describe the experience of implementing a satellite vaccination unit in a drive-through system during a campaign against COVID-19.

METHOD

This is an experience report carried out in a drive-through vaccination satellite unit. The study development was guided by the triad structure-process-results, proposed by Donabedian.

RESULTS

The unit was structured in a soccer stadium, allowing it to serve large audiences safely. Care flow occurred in stages and professionals were organized by sectors, with emphasis on the nursing team' work. Initially, screening was performed; later, users went to the registration sector, and, finally, they were forwarded to the application station. The unit also had emergency sectors, a cold chain, space for professionals and a Basic Health Unit as a point of support. In 25 days of operation, 9698 doses were administered, with 1.8% of doses lost.

CONCLUSION

The implementation of this system required planning, structure, process development and intense team articulation, with emphasis on the fundamental and strategic role of nurses in different points of action and leadership.

Implementação da estratégia drive-through para vacinação COVID-19: um relato de experiência

RESUMO Objetivo: Descrever a experiência de implementação de uma unidade satélite de vacinação em sistema drive-through, durante a campanha contra COVID-19. Método: Trata-se de um relato de experiência, realizado em uma unidade satélite de vacinação em sistema drive-through. O desenvolvimento do estudo foi norteado pela tríade estrutura-processo-resultados, proposta por Donabedian. Resultados: A unidade foi estruturada em um estádio de futebol, permitindo o atendimento de grandes públicos de forma segura. O fluxo de atendimento ocorreu por etapas, e os profissionais foram organizados por setores, com destaque para atuação da equipe de enfermagem. Inicialmente, realizou-se a triagem, posteriormente, o usuário dirigia-se ao setor de cadastramento, e, por fim, era encaminhado à estação de aplicação. A unidade contava também com os setores de urgência e emergência, rede de frio, espaço para os profissionais e uma Unidade Básica de Saúde como ponto de apoio. Em 25 dias de atuação, foram administradas 9698 doses, com 1,8% de doses perdidas. Conclusão: A implementação deste sistema exigiu planejamento, estrutura, desenvolvimento de processos e intensa articulação em equipe, com destaque para o papel fundamental e estratégico do enfermeiro em diferentes pontos de atuação e liderança.

Critical Concepts for COVID-19 Mass Vaccination Site Operations

Mass vaccination campaigns have been used effectively to limit the impact of communicable disease on public health. However, the scale of the coronavirus disease (COVID-19) vaccination campaign is unprecedented. Mass vaccination sites consolidate resources and experience into a single entity and are essential to achieving community ("herd") immunity rapidly, efficiently, and equitably. Health care systems, local and regional public health entities, emergency medical services, and private organizations can rapidly come together to solve problems and achieve success. As medical directors at several mass vaccination sites across the United States, we describe key mass vaccination site concepts, including site selection, operational models, patient flow, inventory management, staffing, technology, reporting, medical oversight, communication, and equity. Lessons learned from experience operating a diverse group of mass vaccination sites will help inform not only sites operating during the current pandemic, but also may serve as a blueprint for future outbreaks of highly infectious communicable disease.

Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation

Drive-through clinics have previously been utilized in vaccination efforts and are now being more widely adopted for COVID-19 vaccination in different parts of the world by offering many advantages including utilizing existing infrastructure, large daily throughput and enforcing social distancing by default. Successful, effective, and efficient drive-through facilities require a suitable site and keen focus on layout and process design. To demonstrate the role that high fidelity computer simulation can play in planning and design of drive-through mass vaccination clinics, we used multiple integrated discrete event simulation (DES) and agent-based modelling methods. This method using AnyLogic simulation software to aid in planning, design, and implementation of one of the largest and most successful early COVID-19 mass vaccination clinics operated by UCHealth in Denver, Colorado. Simulations proved to be helpful in aiding the optimization of UCHealth drive through mass vaccination clinic design and operations by exposing potential bottlenecks, overflows, and queueing, and clarifying the necessary number of supporting staff. Simulation results informed the target number of vaccinations and necessary processing times for different drive through station set ups and clinic formats. We found that modern simulation tools with advanced visual and analytical capabilities to be very useful for effective planning, design, and operations management of mass vaccination facilities.

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

OBJECTIVE

To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

MATERIALS AND METHODS

We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.

RESULTS

In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.

DISCUSSION

Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.

CONCLUSION

There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic

The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.

WeChat-assisted dietary and exercise intervention for prevention of gestational diabetes mellitus in overweight/obese pregnant women: a two-arm randomized clinical trial

PURPOSE

This study aimed to examine the influence of a WeChat-based dietary and exercise intervention on gestational diabetes mellitus (GDM) prevention in overweight/obese pregnant women in Beijing.

METHODS

Overweight/obese pregnant women were recruited in the early stages of pregnancy. After screening by include and exclude standards, eligible women were randomly divided into two groups, intervention and control groups. The control group received a general advice session about pregnancy nutrition and weight management. The intervention group received three face-to-face sessions about personalized dietary and exercise intervention, with the help of WeChat as a monitoring tool to promote treatment plan adherence. At 24-28 weeks of pregnancy, GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Gestational weight gain (GWG), maternal and neonatal outcomes were also collected.

RESULTS

This study analyzed 215 participants. At the mid-trimester, 42 (37.8%) women in the control group were diagnosed with GDM (n = 111) versus 25 (24.5%) in the intervention group (n = 104; p < 0.05). The intervention group gained 11.2 ± 4.9 kg during the whole gestation period, with 4.9 ± 3.1 kg-weight increment in the first 25 weeks of pregnancy, versus 13.4 ± 5.0 kg and 6.9 ± 3.2 kg in the first 25 weeks in the control group (between groups: p < 0.001/p = 0.002). Incidence of macrosomia was not significantly lower in the intervention group than in the control group (8/7.9% vs 11/9.9%) (p > 0.05). No significant difference was found in the rate of natural labor and occurrence of perinatal complications (e.g., preterm birth, gestational hypertension, and preeclampsia) between the groups (p > 0.05).

CONCLUSIONS

The WeChat-assisted dietary and exercise intervention was effective in reducing the occurrence of GDM and excessive weight gain in overweight/obese pregnant women. Disseminating knowledge of pregnancy and childbirth through social media platforms like WeChat could be an important part of antenatal care.