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Nulliparity

Nulliparity, the state of never having given birth, is an important factor in reproductive health research.
This MeSH term encompasses studies on the unique physiological and epidemiological aspects of individuals who have not experienced pregnancy or childbirth.
Nulliparity can influence risk profiles for various conditions, from gynecological disorders to cardiovascular disease.
Researchers investigating nulliparity require efficient tools to locate and analyze the latest discoveries across published literature, preprints, and patent data.
PubCompare.ai's AI-driven platform offers a smarter, more effeicient way to advance nulliparity research, with poweful search and comparative analysis capabilities to enhance reproducibility and accuracy.

Most cited protocols related to «Nulliparity»

1
Labor Progression and Duration Analysis
We used data from the Consortium on Safe Labor, a multicenter retrospective observational study that abstracted detailed labor and delivery information from electronic medical records in 12 clinical centers (with 19 hospitals) across 9 American College of Obstetricians and Gynecologists (ACOG) U.S. districts from 2002 to 2008. 87% of births occurred in 2005 – 2007. Detailed description of the study was provided elsewhere.5 Briefly, participating institutions extracted detailed information on maternal demographic characteristics, medical history, reproductive and prenatal history, labor and delivery summary, postpartum and newborn information. Information from the neonatal intensive care unit (NICU) was linked to the newborn records. Data on labor progression (repeated, time-stamped cervical dilation, station and effacement) were extracted from the electronic labor database. To make our study population reflect the overall U.S. obstetric population and to minimize the impact of the various number of births from different institutions, we assigned a weight to each subject based on ACOG district, maternal race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic and others), parity (nulliparas vs. multiparas) and plurality (singleton vs. multiple gestation). We first calculated the probability of each delivery with these four factors according to the 2004 National Natality data. Then, based on the number of subjects each hospital contributed to the database, we assigned a weight to each subject .5 We applied the weight to the current analysis. This project was approved by the Institutional Review Boards of all participating institutions.
There were a total of 228,668 deliveries in the database. A total of 62,415 parturients were selected. Figure 1 depicts the sample selection process for the current analysis. Women were grouped by parity (0, 1, 2+). We used a repeated-measures analysis with 8th degree polynomial model to construct average labor curves by parity.6 In this analysis, the starting point was set at the first time when the dilation reached 10 cm (time = 0) and the time was calculated backwards (e.g., 60 minutes before the complete dilation, -60 minutes). After the labor curve models had been computed, the x-axis (time) was reverted to a positive value, i.e., instead of being -12 → 0 hours, it became 0 → 12 hours.
To estimate duration of labor, we used an interval-censored regression7 to estimate the distribution of times for progression from one integer centimeter of dilation to the next (called “traverse time”) with an assumption that the labor data are log-normally distributed.8 (link) The median and 95th percentiles were calculated. Because multiparous women tended to be admitted at a more advanced stage labor than nulliparous women, many multiparous women did not have information on cervical dilation prior to 4 cm. Therefore, the labor curve for multiparous women started at 5 cm rather than at 4 cm as for nulliparous women.
Finally, to address the clinical experience wherein a woman is first observed at a given dilation and then measured periodically, we calculated cumulative duration of labor from admission to any given dilation up to the first 10 cm in nulliparas. The same interval censored regression approach was used. We provide the estimates according to the dilation at admission (2.0 or 2.5 cm, 3.0 or 3.5 cm, 4.0 or 4.5 cm, 5.0 or 5.5 cm) because women admitted at different dilation levels may have different patterns of labor progression. We then plotted the 95th percentiles of the duration of labor from admission as a partogram. All statistical analyses were performed using SAS version 9.1 (PROC MIXED for the repeated-measures analysis and PROC LIFEREG for interval censored regression). Since the objective of this paper is to describe labor patterns and estimate duration of labor without comparing among various groups, no statistical tests were performed.
Zhang J., Landy H.J., Branch D.W., Burkman R., Haberman S., Gregory K.D., Hatjis C.G., Ramirez M.M., Bailit J.L., Gonzalez-Quintero V.H., Hibbard J.U., Hoffman M.K., Kominiarek M., Learman L.A., Van Veldhuisen P., Troendle J, & Reddy U.M. (2010). Contemporary Patterns of Spontaneous Labor With Normal Neonatal Outcomes. Obstetrics and gynecology, 116(6), 1281-1287.
Publication 2010
Dilatations, Cervical Disease Progression Epistropheus Ethics Committees, Research Ethnicity Gynecologist Hispanics Infant, Newborn Mothers Nulliparity Obstetric Delivery Obstetrician Obstetric Labor Pathological Dilatation Reproduction Woman
2
Nulliparous Labor Progress and Oxytocin
The sample included 1177 women who participated in a study on labour progress and oxytocin augmentation between October 1998 and December 2003 at Sahlgrenska University Hospital in Gothenburg and Ryhov County Hospital in Jönköping, Sweden. The study is described in detail elsewhere [21 (link)]. In brief, the antenatal clinics provided written information about the study as well as information about the follow-up questionnaire to healthy nulliparous women in their third trimester. Inclusion criteria were nulliparity, a singleton fetus in cephalic presentation, uncomplicated pregnancy, spontaneous onset of active labor with regular contractions and an effaced cervix dilated not less than four centimeters and gestational age of between 37+0 and 41+6 weeks at admission to the delivery ward. Informed consent was obtained from all respondents who were mailed the questionnaire one month postpartum.
Dencker A., Taft C., Bergqvist L., Lilja H, & Berg M. (2010). Childbirth experience questionnaire (CEQ): development and evaluation of a multidimensional instrument. BMC Pregnancy and Childbirth, 10, 81.
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Publication 2010
Cervix Uteri Fetus Gestational Age Nulliparity Obstetric Delivery Oxytocin Pregnancy Woman
3
Maternal Age and Preterm Birth: A Retrospective Cohort Analysis
This is a retrospective cohort study using data obtained from the QUARISMA randomized controlled trial [17 (link)]. QUARISMA was a cluster intervention trial designed to assess the effectiveness of a complex intervention with background information and audits targeting a general population in terms of safe and sustainable reduction in the rate of caesarean sections. The intervention targeted physicians and nurses, involved audits of indications for cesarean delivery, provision of feedback to health professionals, and implementation of best practices. It took place in 32 hospitals in the province of Quebec, Canada, from 2008 to 2011 and enabled to collect information on more than 184 000 pregnancies. Trained staff collected information on standardized individual records. In this trial, hospitals were the units of randomization and women were the units of analysis. By designating hospitals as the units of randomization (clusters), the study ensured that all women within a given maternity unit were assigned to the same trial group, thereby reducing the risk of contamination of the intervention effect. Ethics approval was obtained by the Ethics research board of CHU Sainte-Justine (Montreal) under the Study Number 2604, for the completion of the trial, for the creation of the database and for the present study.
Inclusion criteria were those of the QUARISMA trial: birth at or after 24 gestational weeks of a fetus weighing >500 grams; and maternal age >20 years. Non-inclusion criteria were multiple pregnancies, fetal malformations and intra-uterine fetal demise.
Five maternal age categories were defined: 20–24, 25–29, 30–34, 35–39 and 40 years and older. Groups of age were compared based on maternal history: past drug use, nulliparity, and medical history including chronic hypertension, diabetes mellitus, renal and cardiac disease, thrombophilia, systemic erythematous lupus and inflammatory bowel disease. Characteristics of the current pregnancy were also studied: drug use, smoking, use of assisted reproductive technologies, and occurrence of an invasive procedure (chorionic villus sampling or amniocentesis). Additionally, groups of age were also compared according to maternal and obstetrical complications: hypertensive complications (gestational hypertension, pre-eclampsia and eclampsia), gestational diabetes and placenta praevia. All comparisons used chi-square test.
The odds ratios for preterm birth (<37 weeks) and very preterm birth (< 32 weeks) were calculated for different age groups before and after adjustment by multivariate logistic regression for known risk factors, maternal characteristics and gestational complications. For these analyses, the reference group corresponded to the group with the lowest rate of prematurity. As our analyses did not focus on the intervention of the primary trial (caesarean section) and since this intervention did not condition the relationship between the explanatory variables and the outcome studied in our paper; we did not performed mixed model analyses accounting for cluster (hospitals).
Preterm birth <37 weeks was divided into spontaneous and iatrogenic preterm birth. For both conditions, risk factors were studied using multivariate logistic analyses after adjustment on covariates. Iatrogenic delivery was defined as performance of a cesarean delivery before onset of labor or induction of labor using cervical ripening or oxytocin.
Results were considered significant when p<0.05. All statistical analyses were performed with the use of SAS software, version 9.3 (SAS Institute)
Fuchs F., Monet B., Ducruet T., Chaillet N, & Audibert F. (2018). Effect of maternal age on the risk of preterm birth: A large cohort study. PLoS ONE, 13(1), e0191002.
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Publication 2018
Age Groups Amniocentesis Assisted Reproductive Technologies Birth Cesarean Section Diabetes Mellitus Eclampsia Erythema Fetal Malformations Fetus Gestational Diabetes Health Personnel Heart Diseases High Blood Pressures Inflammatory Bowel Diseases Kidney Labor, Induced Lupus Vulgaris Mothers Nulliparity Nurses Obstetric Delivery Oxytocin Pharmaceutical Preparations Physicians Placenta Previa Pre-Eclampsia Pregnancy Pregnancy Complications Premature Birth Thrombophilia Transient Hypertension, Pregnancy Woman
4
Sleep Disordered Breathing and Pregnancy Outcomes
Details of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be parent and sleep disordered breathing substudy methods have been previously published.20 (link)21 (link) Briefly, the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be parent study was conducted at 8 clinical sites and managed by an independent Data Coordinating Center. Inclusion criteria for the parent study were nulliparity (no prior delivery ≥20 weeks’ gestation) and a viable singleton pregnancy at the time of screening (60–136 (link) weeks’ gestation). Women were excluded from the sleep disordered breathing substudy if they were currently on continuous positive airway pressure (CPAP) treatment for sleep disordered breathing, had severe asthma requiring continuous oral steroid therapy for more than 14 days, or suffered from a condition requiring oxygen supplementation. The sleep disordered breathing substudy was designed and powered to test the primary hypothesis that sleep disordered breathing occurring early or appearing later in pregnancy is associated with an increased incidence of preeclampsia. Secondary aims were to examine the association between sleep disordered breathing and gestational hypertension and GDM.
For the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be-sleep disordered breathing substudy, Level 3 home sleep tests were performed using a 6-channel monitor that was self-applied by the participant twice during pregnancy, first between 60–150 weeks of pregnancy and then again between 220–310 weeks. Sleep study data were downloaded at the study site and electronically transmitted to a central Sleep Reading Center. The scoring and quality control protocol has been previously published.20 (link) Sleep studies were scored using the following definitions:
All apnea and hypopnea events were annotated and later linked with oxygen saturation values. Participants, investigators and care providers were blinded to the sleep test results unless urgent alert criteria were identified. Urgent alert studies included those with an AHI >50 events/hour or severe hypoxemia (oxygen saturation of <90% for ≥10% of sleep time). Criteria for urgent alerts were developed by expert consensus from members of the study team and approved by the Advisory and Safety Monitoring Board IRB approval was obtained at each site and informed consent was obtained from each participant.
For our primary analyses AHI was treated as a dichotomous variable with an AHI ≥ 5 defining the presence of sleep disordered breathing. To examine exposure-response relationships between increased AHI and pregnancy outcomes we also ran analyses grouping participants into four AHI groups: AHI=0, 0For any participant with hypertension, proteinuria, or a related condition documented in the chart, a detailed chart review was required by a site investigator or a staff member certified for abstraction of complicated charts. Appendix 2, available online at http://links.lww.com/xxx, outlines our study definitions of hypertensive disorders. Cases that presented atypically and were difficult to classify according to study criteria were adjudicated by the principal investigators and final classification was reached by consensus. As part of chart abstraction, the onset of the hypertensive disorder (gestational age; antepartum, intrapartum, postpartum) was ascertained. For analysis, preeclampsia was defined as all cases of mild, severe, or superimposed preeclampsia or eclampsia, regardless of the timing of onset. A hypertensive disorder related to pregnancy was defined as all cases of preeclampsia and antepartum gestational hypertension.
GDM was defined by one of the following glucose tolerance testing (GTT) criteria: 1) fasting 3-hour 100 gram GTT with two abnormal values: fasting ≥95 mg/dL, 1-hour ≥180 mg/dL, 2-hour ≥155 mg/dL, 3-hour ≥140 mg/dL; 2) fasting 2-hour 75 gram GTT with one abnormal value: fasting ≥92 mg/dL, 1-hour ≥180 mg/dL, 2-hour ≥153 mg/dL; 3) non-fasting 50 gram GTT ≥200 mg/dL if no fasting 3-hour or 2-hour GTT was performed. In addition to GTT data, chart abstractors recorded if a diagnosis of GDM was made during the course of clinical care. If no GTT data were available, the information from chart abstraction was used for GDM classification. Women with pre-gestational diabetes were excluded from analysis of GDM.
A detailed description of our sample size calculation has been previously published.20 (link) In summary, we aimed to enroll 3630 women in the SDB substudy anticipating that this would yield approximately 180 women (5%) with sleep disordered breathing in early pregnancy and 360 (10%) in late pregnancy. With these assumptions, and setting the Type I error at 2-sided α =0.05, the target sample size yields at least 80% power to detect a relative risk of 2.0 (1.8) for preeclampsia for women with sleep disordered breathing in early (late) pregnancy, assuming a 7% incidence of preeclampsia among the unexposed.22 (link),23 (link)Descriptive statistics were used to characterize the study population by AHI category. Chi-square tests assessed associations with characteristics that were categorical and analysis of variance F-tests were used for continuous measurements. Crude and adjusted odds ratios and 95% confidence intervals were calculated from univariate and multivariate logistic regression models to relate level of sleep disordered breathing in early and mid-pregnancy to hypertensive disorders of pregnancy and to GDM. Adjustment covariates included maternal age (≤21, 22–35, and >35 years), body mass index (BMI; <25, 25 to <30, ≥30 kg/m2), chronic hypertension (yes, no), and for mid-pregnancy, rate of weight gain per week between early and mid-pregnancy assessments, treated as a continuous variable. These covariates were selected prior to analysis of results based on data supporting an association between these variables and both sleep disordered breathing and the adverse pregnancy outcomes of interest.24 (link),25 (link) To avoid overfitting, other potential covariates were first evaluated for confounding on the relationship of sleep disordered breathing (AHI≥5) with hypertensive disorders of pregnancy and with GDM using a criteria of >10% modification in the sleep disordered breathing odds ratio by inclusion of the potential covariate. The interactions of BMI with hypertensive disorders of pregnancy on sleep disordered breathing and with GDM on sleep disordered breathing were investigated for early and mid-pregnancy assessments. Exposure-response relationships were assessed in post-hoc tests for linear and quadratic trends in the log-odds across the AHI categories using orthogonal contrasts. For all analyses, women with pregnancy losses prior to 200 weeks’ gestation were excluded.
All tests were performed at a nominal significance level of α=0.05. All single degree of freedom tests were 2-sided. No correction was made for multiple comparisons. Analyses were conducted using SAS 9.3/9.4 software.
Facco F.L., Parker C.B., Reddy U.M., Silver R.M., Koch M.A., Louis J.M., Basner R.C., Chung J.H., Nhan-Chang C.L., Pien G.W., Redline S., Grobman W.A., Wing D.A., Simhan H.N., Haas D.M., Mercer B.M., Parry S., Mobley D., Hunter S., Saade G.R., Schubert F.P, & Zee P.C. (2017). Association Between Sleep-Disordered Breathing and Hypertensive Disorders of Pregnancy and Gestational Diabetes Mellitus. Obstetrics and gynecology, 129(1), 31-41.
Publication 2016
Apnea Asthma Clinical Trials Data Monitoring Committees Continuous Positive Airway Pressure Contrast Media Diagnosis Eclampsia Gestational Age Glucose High Blood Pressures Immune Tolerance Index, Body Mass Mothers Nulliparity Obstetric Delivery Oxygen Oxygen Saturation Parent Polysomnography Pre-Eclampsia Pregnancy Sleep Steroids Therapeutics Transient Hypertension, Pregnancy Woman
5
Preeclampsia Risk Factor Assessment Protocol
We identified publications investigating the association between pre-eclampsia and at least one risk factor in a previous pregnancy or in the current pregnancy. We examined those risk factors described in the published guidelines and reviews13 (link)
14 (link)
15 (link)
16 (link)
17 (link)
18 (link)
19 (link) that were patient specific, that were readily recalled by a woman or abstracted from her prior pregnancy record, and that a general clinician could ascertain in the first trimester of pregnancy. For these reasons, and the observation that a family history in risk assessment tends to have a low sensitivity (that is, low recall),20 (link) we did not assess family history of pre-eclampsia as a risk factor. We also limited our selection to large sample cohort studies because they tend to be more representative of the general population than small single centre studies and they have sufficient statistical power to assess less prevalent, but potentially important, risk factors.21 (link)
Selected risk factors from a previous pregnancy included a history of pre-eclampsia, placental abruption, fetal intrauterine growth restriction, and stillbirth.
Current pregnancy risk factors included nulliparity, advanced maternal age, high body mass index (BMI), chronic hypertension, prepregnancy diabetes mellitus (type 1 or type 2), chronic kidney disease, systemic lupus erythematosus, antiphospholipid antibody syndrome, assisted reproduction, and multiple pregnancy.
The resulting papers were first screened by title and abstract. Full text articles were obtained if they met all of the following screening criteria: a cohort study design with a minimum sample size of 1000 pregnancies; the study evaluated the relation between one or more of the aforementioned risk factors and the outcome of pre-eclampsia; the authors provided the number of pre-eclampsia events among their participants with and without a given risk factor, to enable the calculation of pooled effect sizes, as described below.
Full text papers were included in the final dataset if they met the aforementioned screening criteria and also evaluated each risk factor up to 16 weeks’ gestation or earlier (as aspirin might be more efficacious when initiated before this gestational age6 (link)
7 (link)
8 (link)).
Two authors (EB and KM), both of whom are medical students, screened studies and abstracted data. EB screened all citations retrieved from the database searches, and both authors evaluated the eligibility of the full text articles. Disagreements were resolved by discussion or in consultation with a third author (JGR). If two published studies evaluated the same cohort of women, we included the study with the largest number of women or the greatest number of relevant outcomes. Study authors were not contacted.
Bartsch E., Medcalf K.E., Park A.L, & Ray J.G. (2016). Clinical risk factors for pre-eclampsia determined in early pregnancy: systematic review and meta-analysis of large cohort studies. The BMJ, 353, i1753.
Publication 2016
Abruptio Placentae Antiphospholipid Syndrome Aspirin Chronic Kidney Diseases Diabetes Mellitus, Insulin-Dependent Eligibility Determination Fetal Growth Retardation Health Risk Assessment High Blood Pressures Hypersensitivity Index, Body Mass Lupus Erythematosus, Systemic Mental Recall Nulliparity Patients Pre-Eclampsia Pregnancy Reproduction Students, Medical Woman

Most recents protocols related to «Nulliparity»

1
Factors Influencing Safer Sex Negotiation
Three sets of explanatory variables were selected based on the findings of existing empirical studies. One, eight socio-demographic characteristics associated with safer sex negotiation in previous studies were selected. These are age [3 (link)], child marriage [44 ], education [32 (link)], parity [45 (link)], media exposure [1 (link)], religion [4 (link)], work status [2 (link)], and experience of female genital mutilation [46 (link)]. Age was grouped into three categories 15–24, 25–34, and 35 years or older. Child marriage was measured as ‘yes’ if the age at first marriage was less than eighteen years, and ‘no’ if otherwise. Parity was grouped as ‘nulliparity’ if women have had no previous live birth, ‘primiparity’ if women have had only a child, ‘multiparity’ if women have had between two to four live births, and ‘grand multiparity’ if women have had five or more live births. This is in line with the categorization of women’s parity in the literature [47 (link)–49 (link)]. Media exposure was derived from the frequencies of reading newspapers, listening to the radio, and watching television through the generation of a composite index. The index was divided into three parts to reflect low, moderate, and high media exposure.
Two, six relational characteristics were selected based on findings in relevant existing studies. These are healthcare autonomy [50 (link)], financial autonomy [51 (link)], household wealth quintile [32 (link)], partners’ education [32 (link)], ownership of assets [32 (link)], and type of marriage [52 (link)]. Healthcare autonomy was based on who had the final say on women’s healthcare, while financial autonomy was based on who had the final say on spending women’s earnings. In both cases, women had autonomy if they had the final say either solely or jointly with partners. Ownership of assets was based on ownership of land/house either solely or jointly with a partner. Three, two gender norms, namely, attitude to wife-beating and male controlling behavior, and two societal characteristics, namely place of residence and geo-political zone of residence were included as control variables in the study. Attitude to wife-beating was grouped as ‘supportive’ if women accepted any condition for wife-beating, and non-supportive if wife-beating was rejected given all conditions. Male controlling behavior was derived from women’s responses to whether the husband or partner desire to limit the wife’s contact, desire to know her movement, gives no permission to meet friends, alleges unfaithfulness, or is jealous of the wife’s interaction with other men. The four control variables have been found to be correlates of safer sex negotiation or other women’s sexual outcomes in previous studies [2 (link)–4 (link), 12 (link), 32 (link)].
Solanke B.L., Kupoluyi J.A., Awoleye A.F., Adewole O.E, & Babalola O.B. (2023). Women’s ability to negotiate safer sex with partners by contraceptive status among a nationally representative sample of married women in Nigeria. Contraception and Reproductive Medicine, 8, 17.
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Publication 2023
Child Friend Households Husband Jealousy Males Movement Nulliparity Wife Woman
2
Postpartum Hemorrhage and Obstetric Complications
Primary postpartum hemorrhage: Postpartum blood loss was visually estimated by the midwives and nurse, during which they made a quantitative estimate of the amount of blood lost. In direct blood collection, all blood lost during the postpartum period (except for the placenta and membranes) is contained in a disposable plastic collector bag, which is attached to a plastic sheet and placed under the woman's buttocks. When the bleeding stops, the bag could be gravimetrically weighed, allowing for a direct measurement (28 (link), 29 (link)).
Hemodynamic instability: It defined as any instability in the blood which changes (the pulse rate, the respiratory rate, the temperature, the blood pressure, the status of the skin and mucous membranes), which can lead to inadequate arterial blood flow to organs (3 ).
Prolonged labor: It is a failure of labor to progress and can be determined by the labor stage and whether the cervix has thinned and opened appropriately during labor (30 ).
Onset of labor: a series of continuous, progressive contractions of the uterus, additionally characterized by a bloody show and rupture of the amniotic sac (a bag of water), which is self-reported by the parturient or by a clinician report (30 ).
Prolonged latent phase of first stage: It had been defined as a nullipara who has not entered the active phase 20 h after the onset of the latent phase and a multipara who has not entered the active phase 14 h after the onset of the latent phase (30 ).
Prolonged in active first stage labor: A dilatation of cervix <1–2 cm/h after a women reaches the active phase (≥6 cm) is considered a delay in progress of labor (30 ).
Prolonged second stage of labor: this stage covers more than 2.5 h duration for nulliparous and 1 h in multiparous (30 ).
Obstructed labor is defined as labor with little or no progress despite strong uterine contractions confirmed through vaginal and abdominal examination (30 ).
Retained placenta: A placenta that was actively controlled during the third stage of labor and has not undergone placental expulsion within 30 min of the baby's birth (31 (link)).
Uterine atony is defined as a soft and weak uterus after delivery, and it happens when the uterine muscles don't contract enough to clamp the placental blood vessels shut after childbirth (30 , 31 (link)).
Zenebe G.A., Zenebe W.A., Ewunie T.M, & Dires S. (2023). Primary postpartum hemorrhage and associated factors among delivering women in Gedeo Zone, Southern Ethiopia. Frontiers in Medicine, 10, 1096501.
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Publication 2023
Abdomen Amnion Arteries Birth BLOOD Blood Circulation Blood Pressure Blood Vessel Buttocks Cervix Uteri Debility Dilatations, Cervical Hemodynamics Immediate Postpartum Hemorrhage Midwife Mucous Membrane Myometrium Nulliparity Nurses Obstetric Delivery Obstetric Labor Placenta Placenta, Retained Postpartum Hemorrhage Pulse Rate Respiratory Rate Skin Tissue, Membrane Uterine Contraction Uterine Inertia Uterus Vagina Woman
3
Maternal Characteristics and Prepregnancy BMI
Demographic and medical information was obtained through structured questionnaires during study clinic visit 1. Covariates included maternal age (18–24, 25–29, 30–34, ≥35 years); education level (≤high school, high school/GED, some college, ≥college); household income (USD < 50,000, USD 50,000–99,999, USD 100,000–149,999, USD ≥ 150,000); nulliparity, physical activity (metabolic equivalent of task (METs) per week); total energy intake during pregnancy (kcal per day) and additional dietary intake data from the FFQ. Race and ethnicity were categorized into five groups: non-Hispanic White (White), non-Hispanic Black (Black), Asian and Pacific Islander, Hispanic, and other. People who identified as multi-racial or had an unknown race and ethnicity were placed into the other race and ethnicity category due to the small sample size.
Weight measurements were extracted from the electronic health record (EHR). Pre-pregnancy BMI was calculated as pre-pregnancy body weight (kilograms) divided by height (meters) squared. Pre-pregnancy body weight was defined as a measured weight within 12 months before pregnancy (78%). If a measurement was not available in this timeframe, a self-reported pre-pregnancy weight or pregnancy weight measured before 10 weeks’ gestation was used (22%). Pre-pregnancy BMI was categorized into three groups to examine effect modification (BMI < 25.0, 25.0–29.9, ≥30.0).
Liu E.F., Zhu Y., Ferrara A, & Hedderson M.M. (2023). Dietary Quality Indices in Early Pregnancy and Rate of Gestational Weight Gain among a Prospective Multi-Racial and Ethnic Cohort. Nutrients, 15(4), 835.
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Publication 2023
Asian Persons Clinic Visits Ethnicity Hispanics Households Metabolic Equivalent Nulliparity Pacific Islander Americans Pregnancy
4
Retrospective Study on Intragastric Balloon Efficacy
This retrospective study was conducted in a specialized hospital for bariatric surgery. It included all the obese individuals who underwent an IGB insertion between January 2016 and June 2019. Patients with the following characteristics were included: aged ≥18 years, with a BMI of more than 25 kg/m2 and eligible for intragastric balloon insertion [16 (link)], who had failed supervised weight reduction programs (including diet and exercise).
Exclusion criteria included patients younger than 18 years, patients with hiatal hernia, patients with previous gastric surgery, and patients who were not followed up for at least 6 months after removal of the IGB. Patients who were receiving GLP1-antagonists were excluded from our study too.
The hospital records of the patients fulfilling the inclusion criteria were retrieved and the following data were recorded: demographic data, initial BMI, smoking status, co-morbidities, parity [nullipara, para for 1–4 parities and grandpara for more than 4 parities] [17 (link)], results of pre-IGB insertion laboratory investigations, complications encountered during the period from insertion to IGB removal, adherence to diet and exercise programs, percentage of excess weight reduction, and BMI at the time of IGB removal.
Bawahab M.A., Abbas K.S., Maksoud W.M., Abdelgadir R.S., Altumairi K., Alqahtani A.R., Alzahrani H.A, & Bhat M.J. (2023). Factors Affecting Weight Reduction after Intragastric Balloon Insertion: A Retrospective Study. Healthcare, 11(4), 600.
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Publication 2023
antagonists Bariatric Surgery Diet EHMT1 protein, human Hiatal Hernia Nulliparity Obesity Operative Surgical Procedures Patients Stomach Weight Reduction Programs Youth
5
Vascular Function in Elite Female Runners
The present cross-sectional study is part of a larger study of the same population. The main aim was to investigate vascular function in Norwegian female elite long-distance runners.17 (link) Overlap includes age, height, weight, training data, fat mass, hormone levels and the LEA in Female Questionnaire (LEAF-Q). Power analysis was done for the main outcome, endothelial function, in the overall study on vascular function. However, in addition, post hoc power analysis has been performed for the primary objective of the current study and presented in the results section. Written information about the study was distributed to female runners in the top 20 long-distance running statistics lists in Norway in 2019 and announcements on social media. Recruitment focused on long-distance running (footraces ranging from 5 km to half-marathon), including trail running. According to the recently published paper from McKay et al,18 (link) 10 runners qualified as ‘elite’ by competing at the international level (including track, road and trail running), and 5 runners competed at the national level, representing ‘highly trained’ runners.18 (link) The whole runners’ group will be referred to as ‘elite runners’ hereafter. A control group of physically inactive women was recruited among students at the University of Oslo. The inclusion criteria were age 18–35 years, healthy, non-smoking and nullipara. Training criteria were a minimum of 8-hour endurance training weekly for the runners and a maximum of 2-hour training weekly for the controls. Sixteen runners and 17 physically inactive women were recruited between October 2019 and January 2020. All participants signed written informed consent. One runner and two controls were withdrawn from the study due to injury or challenges during the COVID situation. The final analysis included 15 runners and 15 controls. Both study groups were Caucasian.
The study was conducted during three different test days. The overall study17 (link) included vascular function tests, performed on the first test day. In addition, the participants answered the LEAF-Q.19 (link) At the second visit, all blood samples were collected. The BMD and maximal oxygen consumption (VO2max) were measured at the third visit.
Kyte K.H., Haakstad L.A., Hisdal J., Sunde A, & Stensrud T. (2023). Bone health in Norwegian female elite runners: a cross-sectional, controlled study. BMJ Open Sport — Exercise Medicine, 9(1), e001472.
Publication 2023
BLOOD Blood Physiological Phenomena Caucasoid Races Endothelium Females Hematologic Tests Hormones Injuries Marathon composite resin Nulliparity Oxygen Consumption Student TNFSF10 protein, human Woman

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Spss 21 statistical software by IBM
Sourced in United States
SPSS 21 is a statistical software package developed by IBM. It provides advanced analytics capabilities for data management, analysis, and reporting. SPSS 21 offers a range of statistical techniques, including regression analysis, hypothesis testing, and data mining, to help users gain insights from their data.
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Sas statistical software by SAS Institute
Sourced in United States, Austria, Japan, Cameroon
SAS statistical software is a comprehensive data analysis and visualization tool. It provides a wide range of statistical procedures and analytical capabilities for managing, analyzing, and presenting data. The software is designed to handle large and complex datasets, allowing users to perform advanced statistical modeling, regression analysis, and data mining tasks. The core function of the SAS statistical software is to enable users to extract insights and make data-driven decisions.
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Spss 28 by IBM
Sourced in United States, Germany, United Kingdom, Japan
SPSS 28.0 is a statistical software package developed by IBM. It provides tools for data management, analysis, and visualization. The core function of SPSS 28.0 is to enable users to perform a variety of statistical analyses, including regression, correlation, and hypothesis testing.
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Stata version 11 by StataCorp
Sourced in United States, Denmark, United Kingdom
STATA version 11 is a software package for statistical analysis, data management, and graphics. It provides a wide range of statistical tools and functions for researchers, analysts, and professionals in various fields. The core function of STATA version 11 is to perform advanced statistical analysis and modeling on data.
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Spss 24 by IBM
Sourced in United States, United Kingdom, Japan, Germany, Austria, Belgium, France, Denmark
SPSS 24.0 is a statistical software package developed by IBM. It provides data management, analysis, and reporting capabilities. The software is designed to handle a wide range of data types and is commonly used for social science research, market research, and business analytics.
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Spss software version 20 by IBM
Sourced in United States, United Kingdom, Japan, China
SPSS software version 20.0 is a statistical analysis software package developed by IBM. It is designed to handle a wide range of data analysis tasks, including data management, statistical modeling, and reporting. The software provides a comprehensive set of tools for data manipulation, analysis, and visualization, making it a popular choice for researchers, analysts, and decision-makers across various industries.

More about "Nulliparity"

Nulliparity, the state of never having given birth, is a crucial factor in reproductive health research.
This term encompasses studies on the unique physiological and epidemiological aspects of individuals who have not experienced pregnancy or childbirth.
Nulliparity can influence risk profiles for various conditions, from gynecological disorders to cardiovascular disease.
Researchers investigating nulliparity require efficient tools to locate and analyze the latest discoveries across published literature, preprints, and patent data.
PubCompare.ai's AI-driven platform offers a smarter, more efficient way to advance nulliparity research, with powerful search and comparative analysis capabilities to enhance reproducibility and accuracy.
Nulliparity is closely related to other reproductive health concepts such as primiparity (the state of having given birth for the first time) and multiparity (the state of having given birth multiple times).
These terms are often used in statistical analyses using software like SAS version 9.4, Stata 15, SPSS Statistics version 22, and the Statistical Package for the Social Sciences (SPSS) software version 22.0.
Researchers may also utilize SPSS 21 statistical software, SAS statistical software, SPSS 28.0, STATA version 11, SPSS 24.0, and SPSS software version 20.0 to study the epidemiological and clinical implications of nulliparity.
By leveraging PubCompare.ai's advanced features, researchers can quickly locate and compare the latest protocols, findings, and best practices related to nulliparity from a diverse range of sources.
This enhanced search and analysis capabilty can lead to more reproducible and accurate research outcomes, ultimately advancing our understanding of this important reproductive health factor.