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BACKGROUND: Maternal-fetal attachment (MFA) has been reported to be associated with the postpartum mother-infant relationship. Seeing the fetus through ultrasound might influence MFA, and the effect could be increased by more realistic images, such as those generated in virtual reality (VR). OBJECTIVE: The aim was to determine the effect of fetal images generated in VR on MFA and depressive symptoms through a prenatal-coaching mobile app. METHODS: This 2-arm parallel randomized controlled trial involved a total of 80 pregnant women. Eligible women were randomly assigned to either a mobile app-only group (n=40) or an app plus VR group (n=40). The VR group experienced their own baby's images generated in VR based on images obtained from fetal ultrasonography. The prenatal-coaching mobile app recommended health behavior for the pregnant women according to gestational age, provided feedback on entered data for maternal weight, blood pressure, and glucose levels, and included a private diary service for fetal ultrasound images. Both groups received the same app, but the VR group also viewed fetal images produced in VR; these images were stored in the app. All participants filled out questionnaires to assess MFA, depressive symptoms, and other basic medical information. The questionnaires were filled out again after the interventions. RESULTS: Basic demographic data were comparable between the 2 groups. Most of the assessments showed comparable results for the 2 groups, but the mean score to assess interaction with the fetus was significantly higher for the VR group than the control group (0.4 vs 0.1, P=.004). The proportion of participants with an increased score for this category after the intervention was significantly higher in the VR group than the control group (43% vs 13%, P=.005). The feedback questionnaire revealed that scores for the degree of perception of fetal appearance all increased after the intervention in the VR group. CONCLUSIONS: The use of a mobile app with fetal images in VR significantly increased maternal interaction with the fetus. TRIAL REGISTRATION: ClinicalTrials.gov NCT04942197; https://clinicaltrials.gov/ct2/show/NCT04942197.
Assuntos
Aplicativos Móveis , Realidade Virtual , Lactente , Humanos , Gravidez , Feminino , Cuidado Pré-Natal , Período Pós-Parto , FetoRESUMO
OBJECTIVES: To evaluate the effect of maternal age to the cesarean section rate of twin pregnancies in late preterm and term gestation. METHODS: A retrospective study was performed on twin pregnancies delivered at Seoul National University Bundang Hospital from June 2003 to December 2020. Preterm births before 34 weeks of gestation were excluded, and only live births were analyzed. The patients were classified into four groups according to maternal age (<30, 30-34, 35-39, and ≥40 years). The primary outcome was the rate of cesarean section. RESULTS: The median value of maternal body mass index, the rate of assisted reproductive technology, dichorionic twin pregnancy, preeclampsia, and gestational diabetes increased significantly according to the maternal age group (all p<0.05). Among a total of 2,075 twin pregnancies, the rates of cesarean section were 65, 74, 80, and 95% for groups with maternal age under 30, 30-34, 35-39, and ≥40 years, respectively (p<0.001). The cesarean section rates after a trial of labor were 22, 22, 28, and 63%, respectively (p=0.032). Maternal old age was an independent risk factor for cesarean section after a trial of labor in both nulliparous and multiparous women after adjusting for confounding factors. CONCLUSIONS: The rate of cesarean section in twin pregnancies significantly increased as maternal age increased, even in multiparous women.
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Trabalho de Parto , Gravidez de Gêmeos , Adulto , Cesárea , Feminino , Humanos , Recém-Nascido , Idade Materna , Gravidez , Resultado da Gravidez/epidemiologia , Estudos RetrospectivosRESUMO
With the growing complexity and volume of data, visualizations have become more intricate, often requiring advanced techniques to convey insights. These complex charts are prevalent in everyday life, and individuals who lack knowledge in data visualization may find them challenging to understand. This paper investigates using Large Language Models (LLMs) to help users with low data literacy understand complex visualizations. While previous studies focus on text interactions with users, we noticed that visual cues are also critical for interpreting charts. We introduce an LLM application that supports both text and visual interaction for guiding chart interpretation. Our study with 26 participants revealed that the in-situ support effectively assisted users in interpreting charts and enhanced learning by addressing specific chart-related questions and encouraging further exploration. Visual communication allowed participants to convey their interests straightforwardly, eliminating the need for textual descriptions. However, the LLM assistance led users to engage less with the system, resulting in fewer insights from the visualizations. This suggests that users, particularly those with lower data literacy and motivation, may have over-relied on the LLM agent. We discuss opportunities for deploying LLMs to enhance visualization literacy while emphasizing the need for a balanced approach.
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OBJECTIVES: To determine the effects of cerclage on twin pregnancies. METHODS: A multicenter, retrospective, cohort study was conducted at 10 tertiary centers using a web-based data collection platform. The study population included twin pregnancies delivered after 20 weeks of gestation. Patients with one or two fetal deaths before 20 weeks of gestation were excluded. Maternal characteristics, including prenatal cervical length (CL) and obstetric outcomes, were retrieved from the electronic medical records. RESULTS: A total of 1,473 patients had available data regarding the CL measured before 24 weeks of gestation. Seven patients without CL data obtained prior to cerclage were excluded from the analysis. The study population was divided into two groups according to the CL measured during the mid-trimester: the CL ≤2.5 cm group (n = 127) and the CL >2.5 cm group (n = 1,339). A total of 127 patients (8.7%) were included in the CL ≤2.5 cm group, including 41.7% (53/127) who received cerclage. Patients in the CL >2.5 cm group who received cerclage had significantly lower gestational age at delivery than the control group (hazard ratio (HR): 1.8; 95% confidence interval (CI): 1.11-2.87; p = .016). Patients in the CL ≤2.5 cm group who received cerclage had a significantly higher gestational age at delivery than the control group (HR: 0.5; 95% CI: 0.30-0.82; p value = .006). CONCLUSIONS: In twin pregnancies with a CL ≤2.5 cm, cerclage significantly prolongs gestation. However, unnecessary cerclage in women with a CL >2.5 cm may result in a higher risk of preterm labor and histologic chorioamnionitis although this study has a limitation originated from retrospective design.
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Cerclagem Cervical , Resultado da Gravidez , Gravidez de Gêmeos , Humanos , Feminino , Gravidez , Cerclagem Cervical/estatística & dados numéricos , Cerclagem Cervical/métodos , Estudos Retrospectivos , Gravidez de Gêmeos/estatística & dados numéricos , Adulto , Resultado da Gravidez/epidemiologia , Medida do Comprimento Cervical , Nascimento Prematuro/prevenção & controle , Nascimento Prematuro/epidemiologia , Idade Gestacional , Incompetência do Colo do Útero/cirurgiaRESUMO
Partitioning a dynamic network into subsets (i.e., snapshots) based on disjoint time intervals is a widely used technique for understanding how structural patterns of the network evolve. However, selecting an appropriate time window (i.e., slicing a dynamic network into snapshots) is challenging and time-consuming, often involving a trial-and-error approach to investigating underlying structural patterns. To address this challenge, we present MoNetExplorer, a novel interactive visual analytics system that leverages temporal network motifs to provide recommendations for window sizes and support users in visually comparing different slicing results. MoNetExplorer provides a comprehensive analysis based on window size, including (1) a temporal overview to identify the structural information, (2) temporal network motif composition, and (3) node-link-diagram-based details to enable users to identify and understand structural patterns at various temporal resolutions. To demonstrate the effectiveness of our system, we conducted a case study with network researchers using two real-world dynamic network datasets. Our case studies show that the system effectively supports users to gain valuable insights into the temporal and structural aspects of dynamic networks.