Established Evidence Results

Intervention Components (click on component to see a list of all articles that use that intervention): Media Campaign (Print Materials, Radio, TV) Mobile Apps

Intervention Description: The Growing and Glowing campaign was a digital intervention designed to increase access to pregnancy-related health information for Black women in Hillsborough County, Florida. The campaign was based on multiple theories of behavior change and aimed to address the issue of low birthweight, which is disproportionately experienced by Black women. The campaign content was delivered through social media channels and a website, and was tailored to the unique needs of the target population. The content included short videos of local trusted healthcare experts and illustrated imagery, and covered topics such as weight gain and nutrition, prenatal care, general empowerment, and COVID-19. The campaign also featured prenatal care providers who were actively practicing in the area and provided connections to community resources. The campaign was launched publicly in March 2020 and ran for the first year. The campaign was evaluated using two cross-sectional surveys and digital metrics from Google Analytics. The results showed significant improvements in pregnancy-related intentions, awareness of local resources, and the importance of prenatal care among women aware of the campaign. , ,

Intervention Results: The results of the Growing and Glowing campaign showed significant improvements in pregnancy-related intentions among Black women in Hillsborough County, Florida. The campaign attained 1,234 followers, 805,437 impressions, and a reach of 19,875. The web series videos were viewed almost 27,000 times, with 89% average viewer retention, and the website attracted 2,634 unique page views. The evaluation surveys revealed significant improvements in positive pregnancy-related intentions, including intentions to talk about nutrition with a doctor, intentions to discuss weight and exercise, and positive trends in intentions to discuss breastfeeding and the baby’s weight. Additionally, women aware of the campaign had significantly higher awareness of local resources and the importance of prenatal care. Despite the limitations of the small sample size, the data collected provided important insights into pregnancy-related knowledge and attitudes of Black women, particularly during the COVID-19 pandemic.

Conclusion: The study concluded that the Growing and Glowing campaign, a digital intervention designed for and by Black women, was effective in delivering pregnancy-related health information to the target population in Hillsborough County, Florida. The campaign achieved significant improvements in pregnancy-related intentions and increased awareness of local resources and the importance of prenatal care among women aware of the campaign. The results also highlighted the potential of digital interventions to reach women who may fall outside traditional health advertising in a cost-effective manner, especially during a time when women are engaging in fewer in-person care visits and spending more time online. The study emphasized the benefits of digital advertising in reaching specific populations and the ability to rapidly pivot messages based on local circumstances, ensuring the conveyance of timely, important information. Additionally, the study underscored the need for creative solutions paired with rigorous evaluation methods to establish an evidence base for best practices in reaching pregnant Black women. Despite the limitations of the study, the data collected provided important information on pregnancy-related knowledge and attitudes of Black women, particularly during the COVID-19 pandemic.

Study Design: The study design was a pre-post evaluation of the Growing and Glowing campaign, which aimed to increase access to pregnancy-related health information for Black women in Hillsborough County, Florida. The evaluation included two cross-sectional surveys, one conducted before the campaign implementation and the other conducted after the first year of the campaign. The surveys examined pregnancy-related knowledge, attitudes, intentions, and behaviors, in alignment with the two theories underpinning the campaign strategy and content. The surveys were conducted using Qualtrics panels and digital advertisements on social media platforms, such as Facebook and Instagram, and recruitment focused on oversampling Black women. The study also used digital metrics from Google Analytics to understand the campaign’s reach and engagement across all platforms. ,

Setting: The study was conducted in Hillsborough County, Florida, which is located in the southeastern United States. The Growing and Glowing campaign was designed to reach Black women in this area and provide them with pregnancy-related health information tailored to their unique needs

Population of Focus: The target audience of the Growing and Glowing campaign was Black women in Hillsborough County, Florida. The campaign was designed to provide pregnancy-related health information tailored to the unique needs of this population, with a focus on addressing the issue of low birthweight, which is disproportionately experienced by Black women. The campaign messaging adopted a reproductive empowerment lens and focused on educating women on areas related to low birthweight, including weight gain and nutrition, prenatal care, general empowerment, and COVID-19. The campaign content was delivered through social media channels and a website, and was based on multiple theories of behavior change. ,

Sample Size: he baseline survey included 162 respondents, and the follow-up survey included 265 respondents. In both surveys, efforts were made to oversample Black women, and the majority of the respondents identified as Black. The sample size of the surveys may have limited statistical significance in results, which is a challenge for any study reaching a small audience at the county level. Despite these limitations, the data collected as part of this study provide important information on pregnancy-related knowledge and attitudes of Black women, particularly during the COVID-19 pandemic

Age Range: The age range of the respondents in both the baseline and follow-up surveys was 18 to 65 years old. The surveys included similar age ranges, income ranges, and proportion of responses who selected “Other” for their race.

Intervention Components (click on component to see a list of all articles that use that intervention): Text Messaging Mobile Apps

Intervention Description: The intervention in the study involved providing experimental participants with a smartphone loaded with a specially designed app. The app included features such as vegetable-based recipes, food tips, and strategies for healthier meal preparation and grocery shopping. Experimental participants also received a three-month data plan for the smartphone. Additionally, participants were given two different extra vegetables for each of four weekly pantry distributions. Control participants, on the other hand, received only the extra vegetables for the weekly distributions

Intervention Results: After 3-4 weeks of additional 'test vegetables', cooks at experimental pantries had made 38 % more preparations with these items than control cooks (P = 0·03). Ten weeks following baseline, experimental pantries also scored greater gains in using a wider assortment of vegetables than control pantries (P = 0·003). Use of the app increased between mid-experiment and final measurement (P = 0·0001)

Conclusion: The app appears to encourage household cooks to try new preparation methods and widen their incorporation of vegetables into family diets. Further research is needed to identify specific app features that contributed most to outcomes and to test ways in which to disseminate the app widely.

Study Design: A randomized controlled trial with repeated measures across 10 weeks.

Setting: Clients of fifteen community food pantry distributions

Population of Focus: Clients of food pantries

Sample Size: 289

Age Range: 9/15/2024

Intervention Components (click on component to see a list of all articles that use that intervention): Text Messaging Mobile Apps

Intervention Description: The intervention described in the study focused on the Cooking Matters Mobile Application (CM App), which was developed by Share Our Strength in partnership with Savvy Apps. The CM App is a mobile phone-based resource designed for low-income parents and caregivers of young children (pregnancy/infant to age 5 years) to assist with meal planning and preparation

Intervention Results: Attitudes and self-efficacy related to CM App's subject matter and functions (meal planning; recipe use; creating and using a shopping list) were measured via surveys and interviews. Mean (sd) responses were positive towards 'meal planning' and 'shopping and cooking' (4·17 (0·63) and 3·49 (0·86) on a 5-point Likert scale, respectively). Interviewees described meal planning and preparation behaviours as intrinsic, based on habit, and influenced by family preference and food costs. Early adopters of the CM App may already be engaged in and/or are motivated to engage in the targeted health behaviours.

Conclusion: Users may benefit most from incorporating into their routines new ways to prepare easy, cost-efficient, healthy meals at home that their families will enjoy.

Study Design: Mixed methods

Setting: Community-based

Population of Focus: Familes

Sample Size: 461

Age Range: 0-18

Intervention Components (click on component to see a list of all articles that use that intervention): Mobile Apps

Intervention Description: The study examided the use of a mobile prenatal care app called Babyscripts. The app was designed to deliver educational content and remotely monitor blood pressure and weight for expectant mothers. The educational content was based on American College of Obstetricians and Gynecologists (ACOG) standards and refined by a committee of four board-certified obstetricians at the George Washington University (GWU) School of Medicine. The app sent educational content to the expectant mother at gestation-appropriate times throughout the pregnancy, covering topics such as pregnancy progression, preexisting risk hazards such as alcohol intake, smoking, or drug abuse, advice to address these risk hazards, dietary and nutritional content, breastfeeding information, guidelines for appropriate weight gain, and warning signs for pregnancy complications. The app also integrated with a Wi-Fi-connected scale and blood pressure cuff to provide both feedback and alerts depending on the readings. The alerts were created to provide early warnings to patients and providers about aberrant data points with the hope of providing early detection of hypertensive disorders of pregnancy and abnormal weight gain, indicating an increased risk of gestational diabetes, nutritional deficiency, or edema associated with preeclampsia.

Intervention Results: The results of the study indicated that the use of the mobile prenatal care app, Babyscripts, was associated with a reduced number of in-person obstetric (OB) visits during pregnancy for low-risk patients. The average number of in-person OB visits during pregnancy was 7.8 for the experimental group using the app, compared to 10.2 for the control group receiving usual care. This reduction in in-person visits was statistically significant (P=.01). Importantly, there was no statistical difference in patient satisfaction (P>.05) or provider satisfaction (P>.05) between the experimental and control groups. This suggests that the use of the mobile prenatal care app did not compromise patient or provider satisfaction with prenatal care. Additionally, the study found that the reduced in-person visit schedule facilitated by the app did not lead to a reduction in patient or provider satisfaction.

Conclusion: The conclusion drawn from the study is that the use of the mobile prenatal care app, Babyscripts, resulted in a reduction in the number of in-person obstetric visits during pregnancy for low-risk patients, without compromising patient or provider satisfaction with prenatal care. The findings suggest that the app was effective in facilitating a reduced in-person visit schedule while maintaining patient and provider satisfaction. The study also highlighted the potential of mobile health apps in addressing the perceived barriers to reducing in-person visits for prenatal care. The authors concluded that the app did not replace in-person visits but rather complemented the current activities that occur at each visit, potentially allowing for more individualized discussions during in-person visits. The study also emphasized the importance of future research to identify predictors of adverse clinical outcomes in various populations to mitigate the risk of adverse events

Study Design: The app aimed to facilitate the integration of prenatal care into the lives of pregnant women and provide them with educational programs and remote monitoring capabilities. Additionally, the study highlighted that pregnant women are highly engaged with their health care decisions during pregnancy and may be more receptive to educational programs that can be delivered through a mobile health app.

Setting: The study setting for the research described in the provided PDF file was two obstetric (OB) and gynecology (GYN) offices in the United States: one in downtown Washington, DC, and one in suburban Maryland. Prenatal care was provided at both locations by OB and GYN physicians and nurse midwives. Low-risk women were cared for by obstetrician-gynecologists at both locations, and all deliveries took place at the GWU hospital in Washington, DC. The educational components and clinical triggers were developed and refined at GWU in Washington, DC, United States, working in conjunction with a local mobile health technology firm 1EQ and their product Babyscripts.

Population of Focus: The study focused on the use of a mobile prenatal care app, specifically designed to deliver educational content and remotely monitor blood pressure and weight for expectant mothers

Sample Size: 88

Age Range: The age range for the participants in the study was between 18 and 40 years. Women aged between 18 and 40 years, presenting for a first-trimester verification of pregnancy or new OB visit, and who were considered low-risk were eligible for enrollment in the study. Additionally, participants were required to regularly use a mobile phone and be fluent in English.

Intervention Components (click on component to see a list of all articles that use that intervention): EMR Reminder Targeting Interventions to Focused Groups Educational Material Mobile Apps Online Material/Education/Blogging

Intervention Description: The study primarily focuses on developing a data-driven primary intervention approach using machine learning and electronic health records (EHR) data to identify pregnant women at risk for postpartum depression (PPD) . The intervention aligns with a discernible strategy of leveraging machine learning algorithms to predict PPD risk based on EHR data, with the potential for early prevention, diagnosis, and intervention . The study does not analyze a multicomponent intervention; rather, it focuses on the development and validation of a machine learning algorithm for PPD risk prediction using EHR data. The intervention strategy is centered around leveraging data-driven approaches to identify at-risk individuals and potentially tailor therapeutic interventions, screening timelines, and preventive strategies for PPD

Intervention Results: The study analyzed a total of 15,197 deliveries from January 2015 to June 2018, and the prevalence of depression was 6.7% (N=1,010) and 6.5% (N=3,513) in the WCM and NYC-CDRN datasets, respectively . The machine learning algorithm was able to predict PPD risk with an area under the receiver operating characteristic curve (AUC-ROC) of 0.83 (95% CI: 0.81-0.85) in the training dataset and 0.80 (95% CI: 0.77-0.83) in the validation dataset . The study found significant differences in age, the number of emergency department visits, and racial distribution between PPD and non-PPD groups in the training and validation data . The study demonstrates that a data-driven primary intervention approach using machine learning and EHR data may be leveraged to reduce the healthcare provider burden of identifying PPD risk.

Conclusion: Machine learning-based models incorporating EHR-derived predictors, could augment symptom-based screening practice by identifying the high-risk population at greatest need for preventive intervention, before development of PPD.

Study Design: The study design was a prospective cohort study that used electronic health records (EHR) data to develop and validate a machine learning algorithm for predicting the risk of postpartum depression (PPD) among pregnant women . The study used two EHR datasets containing data on 15,197 women from 2015 to 2018 at a single site and 53,972 women from 2004 to 2017 at multiple sites as development and validation sets, respectively . The study included all pregnant women with fully completed antenatal care procedures who had live births of infants, and the exclusion criteria were maternal age below 18 or above 45, or lack of outpatient, inpatient, or emergency room encounter information in the EHR data within 1 year following childbirth . The study was approved by the Institutional Review Board at Weill Cornell Medicine (IRB protocol# 1711018789), and data extraction and analysis were performed in 2019 . The study used a well-defined outcome measure of PPD diagnosis within 1 year following childbirth, and the machine learning algorithm was able to predict PPD risk with a high degree of accuracy

Setting: The study setting for the development dataset was a single site, and the validation dataset included data from multiple health systems across New York City affiliated with the Patient-Centered Outcomes Research Institute funded New York City Clinical Data Research Network data (NYC-CDRN) . Therefore, the study setting primarily involved healthcare institutions and systems in New York City, USA.

Population of Focus: The target audience for the study includes healthcare professionals, researchers, and policymakers involved in maternal and mental health, as well as professionals working with electronic health records (EHR) and machine learning applications in healthcare. Additionally, the findings of the study may be of interest to organizations and institutions involved in developing and implementing predictive models for identifying and addressing the risk of postpartum depression among pregnant women.

Sample Size: The study included a total of 15,197 deliveries from January 2015 to June 2018 in the development dataset, and 53,972 deliveries from August 2004 to October 2017 in the validation dataset . These datasets were used to develop and validate a machine learning algorithm for predicting the risk of postpartum depression among pregnant women.

Age Range: The study included pregnant women within a specific age range. The exclusion criteria for the study were maternal age below 18 or above 45 . Therefore, the age range of the included pregnant women in the study was 18 to 45 years old.