Dataset Information

Pregnant Users' Perceptions of the Birth Plan Interface in the "My Prenatal Care" App: Observational Validation Study.

ABSTRACT: BACKGROUND:Birth plans are meant to be a declaration of the expectations and preferences of pregnant woman regarding childbirth. The My Prenatal Care app engages pregnant women in an educational intervention for a healthy pregnancy. We hypothesized that users' positive perception of an in-app birth plan is a relevant step for establishing direct communication between pregnant women and the health care team, based on an online report available on the app. OBJECTIVE:This study aimed to evaluate pregnant women's perception about the communicability of birth-plan preparation using a mobile app. METHODS:This was an observational, exploratory, descriptive study. The methodology was user centered, and both qualitative and quantitative approaches were employed. The tools of the communicability evaluation method were applied. Overall, 11 pregnant women evaluated their experience of using a birth-plan prototype interface. The evaluation was performed in a controlled environment, with authorized video recording. There were 8 task-oriented interactions proposed to evaluate interface communicability with users when using the Birth Plan menu. For evaluating perceptions and experiences, a survey with structured and open-ended questions in addition to the free expression of participants was conducted. The primary outcomes assessed were interface communicability and user's perception of the Birth Plan prototype interface in the My Prenatal Care mobile app. Secondarily, we involved users in the prototyping phase of the interface to identify bottlenecks for making improvements in the app. RESULTS:Regarding users' performance in accomplishing previously prepared tasks, we found that 10 of 11 (91%) women were capable of completing at least 6 of 8 (75%) tasks. A positive relationship was found between the number of communicability problems and the success of completing the tasks. An analysis of the records revealed three communicability breakdowns related to the data entry, save, and scrollbar functions. The participants freely expressed suggestions for improvements such as for the save function and the process of sharing the birth-plan form upon completion. CONCLUSIONS:Users had a positive perception of the Birth Plan menu of the My Prenatal Care app. This user-centered validation enabled the identification of solutions for problems, resulting in improvements in the app.

SUBMITTER: Moraes Carrilho J

PROVIDER: S-EPMC6458531 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Pregnant Users' Perceptions of the Birth Plan Interface in the "My Prenatal Care" App: Observational Validation Study.

Moraes Carrilho Juliana J Oliveira Isaias José Ramos IJR Santos Dimitri D Osanan Gabriel Costa GC Cruz-Correia Ricardo João RJ Reis Zilma Silveira Nogueira ZSN

JMIR formative research 20190328 1

<h4>Background</h4>Birth plans are meant to be a declaration of the expectations and preferences of pregnant woman regarding childbirth. The My Prenatal Care app engages pregnant women in an educational intervention for a healthy pregnancy. We hypothesized that users' positive perception of an in-app birth plan is a relevant step for establishing direct communication between pregnant women and the health care team, based on an online report available on the app.<h4>Objective</h4>This study aimed ...[more]

PMID: 30920372

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Project description:BACKGROUND:Pain volatility is an important factor in chronic pain experience and adaptation. Previously, we employed machine-learning methods to define and predict pain volatility levels from users of the Manage My Pain app. Reducing the number of features is important to help increase interpretability of such prediction models. Prediction results also need to be consolidated from multiple random subsamples to address the class imbalance issue. OBJECTIVE:This study aimed to: (1) increase the interpretability of previously developed pain volatility models by identifying the most important features that distinguish high from low volatility users; and (2) consolidate prediction results from models derived from multiple random subsamples while addressing the class imbalance issue. METHODS:A total of 132 features were extracted from the first month of app use to develop machine learning-based models for predicting pain volatility at the sixth month of app use. Three feature selection methods were applied to identify features that were significantly better predictors than other members of the large features set used for developing the prediction models: (1) Gini impurity criterion; (2) information gain criterion; and (3) Boruta. We then combined the three groups of important features determined by these algorithms to produce the final list of important features. Three machine learning methods were then employed to conduct prediction experiments using the selected important features: (1) logistic regression with ridge estimators; (2) logistic regression with least absolute shrinkage and selection operator; and (3) random forests. Multiple random under-sampling of the majority class was conducted to address class imbalance in the dataset. Subsequently, a majority voting approach was employed to consolidate prediction results from these multiple subsamples. The total number of users included in this study was 879, with a total number of 391,255 pain records. RESULTS:A threshold of 1.6 was established using clustering methods to differentiate between 2 classes: low volatility (n=694) and high volatility (n=185). The overall prediction accuracy is approximately 70% for both random forests and logistic regression models when using 132 features. Overall, 9 important features were identified using 3 feature selection methods. Of these 9 features, 2 are from the app use category and the other 7 are related to pain statistics. After consolidating models that were developed using random subsamples by majority voting, logistic regression models performed equally well using 132 or 9 features. Random forests performed better than logistic regression methods in predicting the high volatility class. The consolidated accuracy of random forests does not drop significantly (601/879; 68.4% vs 618/879; 70.3%) when only 9 important features are included in the prediction model. CONCLUSIONS:We employed feature selection methods to identify important features in predicting future pain volatility. To address class imbalance, we consolidated models that were developed using multiple random subsamples by majority voting. Reducing the number of features did not result in a significant decrease in the consolidated prediction accuracy.

Project description:There is a lack of standardized measurement tools globally to assess knowledge, attitudes, and perceptions of expecting women toward prenatal screening. The purpose of this systematic review was to identify reasons women pursue or decline prenatal screening and compare the strengths and limitations of available measurement tools used to assess pregnant women's perceptions, knowledge, and attitudes toward prenatal screening. This review followed the five-step York methodology by Arksey and O'Malley and incorporated recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis checklist for the extraction, analysis, and presentation of results. The five steps consisted of: (1) identification of the research questions; (2) searching for relevant studies; (3) selection of studies relevant to the research questions; (4) data charting; and (5) collation, summarization, and reporting of results. Four online databases (PubMed, Embase, Web of Science, and Cochrane Library) were selected after the librarian's development of a detailed search strategy. The Rayyan platform was used between June 2023 and August 2023 to epitomize the articles produced from our search. A total of 68 eligible studies were included in the analysis. The top five major reasons for declining prenatal screening uptake included (1) being unsure of the risk of prenatal screening and harm to the baby or miscarriage (n = 15), (2) not considering action such as termination of pregnancy for prenatal screening to be considered as necessary (n = 14), (3) high cost (n = 12), (4) lack of knowledge about testing procedures and being anxious about the test (n = 10), and (5) being worried about probability of false negative or false positive results (n = 6). Only 32 studies utilized scientifically validated instruments. Difficulties in capturing representative, adequately sized samples inclusive of diverse ethnicities and demographics were pervasive. Findings highlight the need for rigorous validation of research measurement methodologies to ensure the accuracy and applicability of resulting data regarding the assessment of prenatal screening perceptions, knowledge, and attitudes across diverse female populations.Registration: N/A.

Project description:BackgroundRural and urban differences in health outcomes and behaviors have been well-documented, with significant rural health disparities frequently highlighted. Mobile health (mHealth) apps, such as meditation apps, are a novel method for improving health and behaviors. These apps may be a critical health promotion strategy during the COVID-19 pandemic and could potentially be used to address rural health disparities. However, limited research has assessed whether meditation app health outcomes are associated with rural and urban residence, and it is unclear whether disparities in health and behaviors between rural and urban populations would persist among meditation app users.ObjectiveWe aimed to explore associations between rural or urban status, psychological outcomes, and physical activity among users of a mobile meditation app. We further aimed to explore associations between rural or urban status and perceived effects of COVID-19 on stress, mental health, and physical activity, and to explore changes in these outcomes in rural versus urban app users over time.MethodsThis study was a secondary analysis of a national survey conducted among subscribers to the meditation app Calm. Eligible participants completed online baseline surveys from April to June 2020, and follow-up surveys from June to September 2020, assessing demographics, psychological outcomes, physical activity, and perceived effects of COVID-19 on stress, mental health, and physical activity.ResultsParticipants (N=8392) were mostly female (7041/8392, 83.9%), non-Hispanic (7855/8392, 93.6%), and White (7704/8392, 91.8%); had high socioeconomic status (income ≥US $100,000: 4389/8392, 52.3%; bachelor's degree or higher: 7251/8392, 86.4%); and resided in a metropolitan area core (rural-urban commuting area code 1: 7192/8392, 85.7%). Rural or urban status was not associated with baseline stress, depression, anxiety, pre-COVID-19 and current physical activity, or perceived effects of COVID-19 on stress, mental health, and physical activity. Repeated-measures models showed overall decreases in depression, anxiety, and perceived effects of COVID-19 on physical activity from baseline to follow-up, and no significant changes in stress or perceived effects of COVID-19 on stress and mental health over time. Models also showed no significant main effects of rural or urban status, COVID-19 statewide prevalence at baseline, or change in COVID-19 statewide prevalence.ConclusionsWe did not find associations between rural or urban status and psychological outcomes (ie, stress, depression, and anxiety), physical activity, or perceived effects of COVID-19 on stress, mental health, and physical activity. Rural or urban status does not appear to drive differences in outcomes among meditation app users, and the use of mHealth apps should continue to be explored as a health promotion strategy in both rural and urban populations. Furthermore, our results did not show negative cumulative effects of COVID-19 on psychological outcomes and physical activity among app users in our sample, the majority of whom were urban, White, female, and of high socioeconomic status. Further research is needed to investigate meditation app use as a health promotion strategy in rural and urban populations.

Project description:BackgroundPrenatal exposure to metals may play an important role in fetal growth. However, the epidemiologic evidence for certain metals is sparse, and most of the existing research has focused on evaluating single metals in highly exposed target populations.ObjectivesWe evaluated associations of cadmium, lead, manganese, selenium, and total mercury exposures during pregnancy with fetal growth using data from mother-infant pairs participating in the National Children's Study.MethodsPrenatal metal exposures were measured using maternal blood collected from 6 to 32 weeks of gestation. Birth outcomes, including gestational age, birthweight, birth length, head circumference, and ponderal index, were ascertained through physical measurement at birth or abstraction from medical records. Regression coefficients and their 95% confidence intervals were estimated from multivariable linear regression models in the overall study population as well as among male and female infants. We further evaluated pairwise metal-metal interactions.ResultsSex-specific associations were observed for lead, with inverse associations for birthweight, birth length, head circumference, and gestational age observed only among female infants. Sex-specific associations were also observed for selenium, with a positive association for birthweight observed among male infants; selenium was also positively associated with ponderal index and inversely associated with birth length among female infants. Overall, total mercury was inversely associated with birthweight and ponderal index, and the association with birthweight was stronger among female infants. No significant associations were observed with cadmium and manganese. In the metal-metal interaction analyses, we found evidence of a synergistic interaction between lead and total mercury and antagonistic interaction between selenium and total mercury with selected birth outcomes.ConclusionsOur findings suggest that prenatal exposure to metals may be related to birth outcomes, and infant sex may modify these associations.

Dataset Information

Pregnant Users' Perceptions of the Birth Plan Interface in the "My Prenatal Care" App: Observational Validation Study.

Publications

Pregnant Users' Perceptions of the Birth Plan Interface in the "My Prenatal Care" App: Observational Validation Study.

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