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BackgroundArtificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people’s preferences for AI clinicians and traditional clinicians are worth exploring. ObjectiveWe aimed to quantify and compare people’s preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people’s preferences were affected by the pressure of pandemic. MethodsWe used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people’s preferences for different diagnosis methods. ResultsIn total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P

BackgroundThe influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. ObjectiveThis study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. MethodsPearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. ResultsThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P

BackgroundGestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. ObjectiveThis study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. MethodsAn AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. ResultsThe areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. ConclusionsOur prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.

People across the world have been greatly affected by the ongoing coronavirus disease (COVID-19) pandemic. The high infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospitals is particularly problematic for recently delivered mothers and currently pregnant women who require professional antenatal care. Online antenatal care would be a preferable alternative for these women since it can provide pregnancy-related information and remote clinic consultations. In addition, online antenatal care may help to provide relatively economical medical services and diminish health care inequality due to its convenience and cost-effectiveness, especially in developing countries or regions. However, some pregnant women will doubt the reliability of such online information. Therefore, it is important to ensure the quality and safety of online services and establish a stable, mutual trust between the pregnant women, the obstetric care providers and the technology vis-a-vis the online programs. Here, we report how the COVID-19 pandemic brings not only opportunities for the development and popularization of online antenatal care programs but also challenges.

BackgroundIn December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. ObjectiveThe aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. MethodsWe adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. ResultsAfter removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics’ themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. ConclusionsTopic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.

Background: Virtual reality (VR) can be used to build many different scenes aimed at reducing study-related stress. However, only few academic experiments on university students for preference testing have been performed. Objective: This study aims to assess the preference of VR games for stress and depression treatment using a discrete choice experiment (DCE). Methods: A total of 5 different attributes were selected based on the depression therapy parameters and attributes related to VR: (1) treatment modality; (2) therapy duration; (3) perceived remission rate; (4) probability of adverse events; and the (5) monthly cost of adding treatment to a discrete choice experiment. By comparing different attributes and levels, we could draw some conclusions about the depression therapy testing preference for university students; 1 university student was responsible for VR scene development and 1 for participant recruitment. Results: The utility value of different attributes for "0% Probability of adverse events" was higher than others (99.22), and the utility value of VR treatment as the most popular treatment method compared with counseling and medicine treatment was 80.95. Three parameter aspects (different treatments for depression) were statistically significant (P<.001), including "0%" and "50%" of "Probability of adverse events" and "¥500" (a currency exchange rate of ¥1 [Chinese yuan]=US $0.15 is applicable) of "The monthly cost of treatment." Most individuals preferred 12 months as the therapy duration, and the odds ratio of "12 months" was 1.095 (95% CI 0.945-1.270) when compared with the reference level (6 months). Meanwhile, the cheapest price (¥500) of depression therapy was the optimum choice for most students. Conclusions: People placed great preference on VR technology psychological intervention methods, which indicates that VR may have a potential market in the treatment of psychological problems. However, adverse events and treatment costs need to be considered. This study can be used to guide policies that are relevant to the development of the application of VR technology in the field of psychological pressure and depression treatment. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the association between physical activity (PA) and risk of cesarean delivery. Material and methods: 197 singleton pregnant women recruited in this study. Participants were divided into vaginal and cesarean delivery group. PA based objectively monitoring between the two groups was compared. Logistic regression was used to analyze the association between PA and cesarean delivery. Results: Moderate PA (MPA) of cesarean delivery group was less in the first (21.5 vs 27.5 min/day; p = 0.006) and second trimester (19.4 vs 26.8 min/day; p = 0.001). Light PA of cesarean delivery group was less (195.9 vs 217.3 min/day; p = 0.006) with more sedentary time (551.7 vs 529.1 min/day; p = 0.041) in the third trimester. Increased risk of cesarean delivery was noted in cases with MPA < 37.8 min/day compared to MPA = 37.8 min/day (aOR 2.62; 95% CI 1.09 to 6.32; p = 0.031) in the first trimester. MPA < 17.9 min/day in the second trimester increased the risk of cesarean delivery (aOR 3.01; 95% CI 1.57 to 5.75; p = 0.001) compared to MPA = 17.9 min/day. Conclusions: MPA in the first two trimesters were associated with the risk of cesarean section. Women should increase MPA from early pregnancy. [ABSTRACT FROM AUTHOR]

Aim: The present study aimed to investigate the construct structure behind the psychosocial response, behavioral response, prenatal depression, and post-traumatic stress disorder (PTSD) in pregnant women during the COVID-19 pandemic in China. Method: The validated Chinese version of the Edinburgh Postnatal Depression Scale (EPDS), PTSD CheckList (PCL)-6, and two newly established scales for COVID-19-related psychological and behavioral responses were used. Structural equation modeling (SEM) analysis was applied to evaluate the structural relationships of psychological and behavioral responses during the COVID-19 pandemic. Results: Of the 1,908 mothers who completed the questionnaires, 1,099 met the criteria for perinatal depression, and 287 were positively screened for PTSD, where 264 women exceed the cut-off points for both. Pregnant women with full-time or part-time jobs tended to have the lowest scores of EPDS (10.07 ± 5.11, P < 0.001) and stress levels (23.85 ± 7.96, P = 0.004), yet they were more likely to change their behavior in accordance with the COVID-19 outbreak (13.35 ± 3.42, P = 0.025). The structural model fit the data (χ² = 43.260, p < 0.001) and resulted in satisfactory fit indices (CFI = 0.984, TLI = 0.959, RMSEA = 0.072, and χ²/df = 10.815), all path loadings were significant (p < 0.05). The SEM indicates that the level of QoL was attributable to the occurrence of PND, leading to PTSD, and COVID-19 related behavioral and psychological responses. Conclusion: The inter-relationships between the COVID-19-related psychosocial and behavioral responses have been assessed, indicating that the pandemic increased the burden of perinatal depression. Psychoeducation, as well as other psychological interventions, may be needed to alleviate the COVID-19-based anxiety and increase their engagement in protective behaviors. [ABSTRACT FROM AUTHOR]