The Effect of Online Supervised Exercise throughout Pregnancy on the Prevention of Gestational Diabetes in Healthy Pregnant Women during COVID-19 Pandemic: A Randomized Clinical Trial.

(1) Background: to examine the effect of an online supervised exercise program during pregnancy on the prevention of GDM, and on maternal and childbirth outcomes. (2) Methods: we conducted a randomized clinical trial (NCT04563065) in 260 pregnant women without obstetric contraindications who were randomized into two study groups: intervention group (IG, N = 130) or control group (CG, N = 130). An online supervised exercise program was conducted from 8-10 to 38-39 weeks of pregnancy. (3) Results: no significant differences were found at baseline in maternal characteristics; nevertheless, certain outcomes showed a favorable trend towards the IG. A lower number and percentage of GDM cases were found in the IG compared to the CG (N = 5/4.9% vs. N = 17/16.8%, p = 0.006). Similarly, fewer cases of excessive maternal weight gain (N = 12/11.8% vs. N = 31/30.7%, p = 0.001) were found in the IG, and a lower percentage of instrumental deliveries (N = 8/11.3% vs. N = 13/15.1%) and c-sections (N = 7/9.9% vs. N = 20/23.3%, p = 0.046). (4) Conclusions: an online supervised exercise program can be a preventative tool for GDM in healthy pregnant women.

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets.

BACKGROUND: The issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States. OBJECTIVE: The aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets. METHODS: Twitter's streaming application programming interface was used to collect a random 1% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract-level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract-level food desert status. RESULTS: We found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics. CONCLUSIONS: Social media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract-level measures of food sentiment and healthiness, are associated with census tract-level food desert status.

Exercise throughout Pregnancy Prevents Excessive Maternal Weight Gain during the COVID-19 Pandemic: A Randomized Clinical Trial.

The purpose of this study was to examine the effects of a virtual exercise program throughout pregnancy during the COVID-19 pandemic on maternal weight gain. A randomized clinical trial (NCT NCT04563065) was performed. In total, 300 pregnant individuals were assessed for eligibility, and a total of 157 were randomized, of which 79 were in the control group (CG), and 78 were in the intervention group (IG). Those in the intervention group participated in a virtual supervised exercise program throughout pregnancy, 3 days per week. Fewer pregnant participants exceeded the weight gain recommendations in the IG group than in the CG (n = 4/5.9% vs. n = 31/43.1%, p = 0.001). Weight gain during pregnancy was lower in the IG than in the CG (9.96 ± 3.27 kg vs. 12.48 ± 4.87 kg, p = 0.001). Analysis of subgroups based on pre-pregnancy body mass index, showed significant differences in excessive maternal weight gain between study groups in normal-weight (IG, n = 0/0% vs. CG, n = 10/25%, p = 0.001) and those with overweight (IG, n = 2/18% vs. CG, n = 12/60%, p = 0.025). A virtual supervised exercise program throughout pregnancy could be a clinical tool to manage maternal weight gain during the COVID-19 pandemic by controlling excessive gain.

Influence of a Virtual Exercise Program throughout Pregnancy during the COVID-19 Pandemic on Perineal Tears and Episiotomy Rates: A Randomized Clinical Trial.

The complications associated with COVID-19 confinement (impossibility of grouping, reduced mobility, distance between people, etc.) influence the lifestyle of pregnant women with important associated complications regarding pregnancy outcomes. Therefore, perineal traumas are the most common obstetric complications during childbirth. The aim of the present study was to examine the influence of a supervised virtual exercise program throughout pregnancy on perineal injury and episiotomy rates during childbirth. A randomized clinical trial design (NCT04563065) was used. Data were collected from 98 pregnant women without obstetric contraindications who attended their prenatal medical consultations. Women were randomly assigned to the intervention (IG, N = 48) or the control group (CG, N = 50). A virtual and supervised exercise program was conducted from 8-10 to 38-39 weeks of pregnancy. Significant differences were found between the study groups in the percentage of episiotomies, showing a lower episiotomy rate in the IG (N = 9/12%) compared to the CG (N = 18/38%) (χ2 (3) = 4.665; p = 0.031) and tears (IG, N = 25/52% vs. CG, N = 36/73%) (χ2 (3) = 4.559; p = 0.033). A virtual program of supervised exercise throughout pregnancy during the current COVID-19 pandemic may help reduce rates of episiotomy and perineal tears during delivery in healthy pregnant women.

A facile assay for rapid detection of COVID-19 antibodies.

The outbreak of new coronavirus disease (COVID-19) has quickly spread all over the world. Real time reverse transcriptase polymerase chain reaction (rRT-PCR) for nucleic acid detection has become the standard method for clinical diagnosis of COVID-19 infection. But these rRT-PCR tests have many inherent limitations, and carry a high false negative rate. It is an urgent to develop a method to accurately identify the vast infected patients and asymptomatic viral carriers from the population. In this article, we present the principle and procedure of developing a colloidal gold immunochromatographic assay (GICA) for rapid detection of COVID-19-specific antibodies. The detection kit can be used to detect immunoglobulin M (IgM) and IgG of COVID-19 in human blood samples within 15 minutes, and to identify different stages of viral infection. Test results can be digitalized using an office scanner and a FiJi software with appropriate confidence interval (CI) setting. Based on analysis from 375 samples, we calculated that overall sensitivity and specificity of the assay were 95.85% and 97.47%, respectively. Compared with rRT-PCR, this assay has many advantages including convenience and rapid detection. The detection kit can be widely used in hospitals, clinics and laboratories for rapid screening of both symptomatic and asymptomatic COVID-19 carriers in large scale.