Background Excessive gestational weight gain (EGWG) places women at increased risk for complications during pregnancy and also increases the likelihood that they will remain overweight after pregnancy. The Institute of Medicine (IOM) has recommended weight gain guidelines based on pre-pregnancy body mass index (BMI), but evidence-based strategies to achieve these goals are limited. Objective This review discusses factors associated with EGWG with the goal of identifying targets for future intervention. Methods A search was performed using the PubMed database to identify all English-language papers published between 1995 and 2014 related to excessive weight gain in pregnancy. Papers were grouped by theme: preconception BMI, sociodemographics, diet and exercise, psychosocial characteristics, and type of prenatal care. Results Studies found that women who were overweight or obese at the time of conception were at higher risk of EGWG and that increased physical activity protected against EGWG. Studies on diet and sociodemographic characteristics were inconclusive. Psychological factors, specifically accurate perceptions of BMI, also appear to play a role in EGWG. Limited studies on methods of prenatal care delivery did not show improvement of weight parameters with group compared to one-on-one visits. Conclusion Pre-pregnancy BMI is most strongly associated with EGWG, indicating that healthy weight habits throughout adult life may be especially important in periods of expected weight change, such as pregnancy. To decrease EGWG, providers should focus on improving pre-conception BMI through appropriate counseling on healthy eating and increased physical activity as well as encouraging pregnant women to continue moderate exercise during pregnancy when appropriate.
INTRODUCTION: Approximately 50% of vaginitis cases are misdiagnosed because of human error with the current diagnostic method of light microscopy wet mount. Untreated vaginitis may lead to complications of increased risk of sexually transmitted infections or preterm birth. Use of artificial intelligence (AI) and immunofluorescence scanning microscopy can aid in determining the health of the vaginal microbiome. METHODS: Institutional review board approval was obtained for the study. Participants who fit the inclusion and exclusion criteria were consented for participation. Patient demographic information was de-identified and recorded. Three swabs from each patient were collected: one for traditional wet-mount diagnosis, and two for DayZ Vaginal Health Assessment Assay analysis. The targets of interest recorded were clue cells, yeast pseudohyphae, and trichomonads. The evaluation compared wet-mount findings to the automated AI algorithm results. RESULTS: In preliminary data analysis of 58 patient samples, AI had an accuracy of 95% in finding trichomonads, 91% in candida, and 90% in clue cells. The AI detected trichomonads on one sample that was missed by the expert reader, detected pseudohyphae on three samples that were undetected by the expert reader, and detected clue cells in 17 samples that were undetected by the expert reader. These data are preliminary as the sample collection and data analysis are ongoing. CONCLUSION: The assay has shown superior ability to diagnose wet-prep findings compared to standard wet-mount evaluation. This new technology could provide a reliable source to determine a quick and accurate diagnosis for patients with symptomatic or asymptomatic vaginitis.
