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The current study examined the frequency and predictors of older adults' engagement with symptom reporting in COVIDWATCHER, a mobile health (mHealth) citizen science application. Citizen science is a type of participatory research that leverages information provided by community members. There were 1,028 COVIDWATCHER participants who engaged with symptom reporting between April 2020 and January 2021. Approximately 13.5% ( n = 139) were adults aged ≥65 years. We used a Wilcoxon test to compare the mean frequency of engagement with symptom reporting by older adults (i.e., aged ≥65 years) to younger adults (i.e., aged ≤64 years) and multivariable linear regression to explore the predictors of engagement with symptom reporting. There was a significant difference in engagement with symptom reporting between adults aged ≥65 years compared to those aged ≤64 years ( p < 0.001). In our final model, age (β = 26.0; 95% confidence interval [14.8, 34.2]) was a significant predictor for engagement with symptom reporting. These results help further our understanding of older adult engagement with mHealth-enabled citizen science for symptom reporting. [ Journal of Gerontological Nursing, 49 (4), 6–11.]

The incidence of women entering into pregnancy with BMI indicating overweight or obesity is rising with concurrent increases in adverse complications such as gestational diabetes. Although several studies have examined the compositional changes to the microbiome across BMI classifications, there has been no investigation regarding changes in microbial function during pregnancy.A total of 105 gastrointestinal microbiome biospecimens were used in this analysis. Biospecimens were sequenced by using the Illumina NovaSeq 6000 shotgun metagenomics platform.Findings indicate an enrichment in microbiota from the phylum Firmicutes across all pregravid BMI groups with a decrease in α diversity in groups with BMI indicating obesity or overweight compared with a group with BMI indicating normal weight (p = 0.02). More specifically, women with BMI indicating obesity or overweight had enrichment in Bifidobacterium bifidum and B. adolescentis. Women with BMI25 kg/mOur analysis suggests that there are differences in microbial composition and function between BMI groups. Future research should consider how these changes contribute to specific clinical outcomes during pregnancy.

To explore experiences of symptoms of suspected or confirmed COVID-19 illness among women using the CovidWatcher mobile citizen science app.Convergent parallel mixed-methods design.Twenty-eight self-identified women consented for follow-up after using CovidWatcher. Participants' ages ranged from 18 to 83 years old.We collected data via semistructured, virtual interviews and surveys: the COVID-19 Exposure and Family Impact Survey and Patient-Reported Outcomes Measurement Information System measures. We used directed content analysis to develop codes, categories, themes, and subthemes from the qualitative data and summarized survey data with descriptive statistics.We derived five themes related to symptom experiences: (a) Physical Symptoms, (b) Mental Health Symptoms, (c) Symptom Intensity, (d) Symptom Burden, and (e) Symptom Trajectories. Subthemes reflected more nuanced experiences of suspected or confirmed COVID-19 disease. For those without COVID-19, anxiety and mental health symptoms were still present. Of those who attested to one of the PROMIS-measured symptoms, all but one had at least mild severity in one of their reported symptoms.This study demonstrates the cross-cutting impact of the COVID-19 pandemic on individuals who identify as women. Future research and clinical practice guidelines should focus on alleviating physical and mental health symptoms related to the ongoing pandemic, regardless of COVID-19 diagnosis. Furthermore, clinicians should consider how patients can use symptom reconciliation apps and tracking systems.

Over half of American adults are diagnosed with a chronic condition, with an increasing prevalence being diagnosed with multiple chronic conditions. These adults are at higher risk for having unrelieved, co-occurring symptoms, known as symptom clusters.To identify symptom phenotypes of patients diagnosed with four common chronic conditions, specifically, cancer, chronic obstructive pulmonary disease, heart failure, and/or type 2 diabetes mellitus, and to understand factors that predict membership in symptomatic phenotypes.We conducted a retrospective, cross-sectional analysis using participant responses (N=14,127) to All of Us Research Program, a National Institutes of Health biomedical database, survey questions. We performed hierarchical clustering to generate symptom phenotypes of fatigue, emotional distress, and pain and used multinomial regression to determine if demographic, healthcare access and utilization, and health-related variables predict symptom phenotype.Four phenotypes, one asymptomatic or mildly symptomatic and three highly symptomatic (characterized by severe symptoms, severe pain, and severe emotional distress), were identified. The percentage of participants belonging to the severe symptoms phenotype increased with the number of chronic conditions. Most notably, foregoing or delaying medical care and rating mental health as poor or fair increased the odds of belonging to a highly symptomatic phenotype.We found meaningful relationships between demographic, healthcare access and utilization, and health-related factors and symptom phenotypes. With the increasing trends of American adults with one or more chronic conditions and a demand to individualize care in the precision health era, it is critical to understand the factors that lead to unrelieved symptoms.

The Future of Nursing 2020 to 2030 report explicitly addresses the need for integration of nursing expertise in designing, generating, analyzing, and applying data to support initiatives focused on social determinants of health (SDOH) and health equity. The metrics necessary to enable and evaluate progress on all recommendations require harnessing existing data sources and developing new ones, as well as transforming and integrating data into information systems to facilitate communication, information sharing, and decision making among the key stakeholders. We examine the recommendations of the 2021 report through an interdisciplinary lens that integrates nursing, biomedical informatics, and data science by addressing three critical questions: (a) what data are needed?, (b) what infrastructure and processes are needed to transform data into information?, and (c) what information systems are needed to "level up" nurse-led interventions from the micro-level to the meso- and macro-levels to address social determinants of health and advance health equity?

Newly emerging infectious diseases (EIDs), like the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, are becoming increasingly common. Due to geographic, political, social, behavioral, and genomic differences, some populations are more vulnerable to infectious disease spread than others. The purpose of this article is to present a framework for research and practice response to emergent infectious diseases that addresses multiple transdisciplinary actions to limit exposure or mitigate adverse outcomes for individuals and communities. Recent experience with new strains of emergent infectious diseases reinforces the importance of intervening at multiple levels, from genomics to political messaging to create multipronged, transdisciplinary interventions to contain the threat. In particular, incorporation of genomics into public health nursing practice of infectious diseases management can enhance existing regional-, community-, and individual-level health promotion and protection efforts, thus impacting long-term health outcomes.

Variability in the symptom experience in patients diagnosed with chronic conditions may be related to social determinants of health (SDoH). The purpose of this critical review was to (1) summarize the existing literature on SDoH and symptom clusters (i.e., multiple, co-occurring symptoms) in patients diagnosed with common chronic conditions, (2) evaluate current variables and measures used to represent SDoH, (3) identify gaps in the evidence base, and (4) provide recommendations for the incorporation of SDoH into future symptom cluster research. We identified 118 articles including information on SDoH in chronic condition symptom cluster research. Articles primarily focused on cancer populations. Few articles had the explicit purpose of investigating relationships between SDoH and symptom clusters, and the inclusion of SDoH was often limited to variables used to describe samples. Future studies should be designed to "move beyond Table 1" in their utilization of SDoH as variables and examine relationships between SDoH and symptom clusters. Attention should be paid to the appropriateness of measures being used to collect information on SDoH, and analysis methods that estimate causal connections between variables should be considered. Research regarding the relationship of SDoH with symptom clusters in patients with chronic conditions has the potential to reveal mechanisms of symptom disparities and guide changes to alleviate these disparities.

Nurse scientists are generating, acquiring, distributing, processing, storing, and analyzing greater volumes of complex omics data than ever before. To take full advantage of big omics data, to address core biological questions, and to enhance patient care, however, genomic nurse scientists must embrace data science. Intended for readership with limited but expanding data science knowledge and skills, this article aims to provide a brief overview of the state of data science in genomic nursing. Our goal is to introduce key data science concepts to genomic nurses who participate at any stage of the data science lifecycle, from research patient recruitment to data wrangling, preprocessing, and analysis to implementation in clinical practice to policy creation. We address three major components in this review: (1) fundamental terminology for the field of genomic nursing data science, (2) current genomic nursing data science research exemplars, and (3) the spectrum of genomic nursing data science roles as well as education pathways and training opportunities. Links to helpful resources are included throughout the article.

The intestinal microbiota changes throughout pregnancy and influences maternal metabolic adaptations to support fetal growth. Obesity induces alterations to the microbiota that include decreased microbial diversity and shifts in microbial composition, though specific species changes are inconsistent between published studies. In animal models, probiotics and exercise moderate maternal weight gain and partially correct the maternal microbiota. Supplemental Escherichia coli, however, exacerbate maternal obesity during the perinatal period, lending weight to the theory that inflammation-induced gut epithelial barrier leak influences metabolic dysregulation. Although birth weight is not always altered when offspring are exposed to an obesogenic diet during gestation, insulin resistance and lipid metabolism are impacted through adulthood in association with this exposure and can lead to increased body weight in adulthood. Postnatal offspring growth is accelerated in response to maternal overnutrition during lactation. Offspring microbiota, metabolism, and behavior are altered in response to early exposure to high fat and high sucrose diets. Consequences to this exposure include impaired glucose and insulin homeostasis, fatty liver, and neurobehavioral deficits that can be ameliorated by improving the microbial environment. In this mini review, we provide an overview of the use of translational animal models to understand the mechanisms associated with changes to the gastrointestinal microbiota due to maternal obesity and the microbial impact on the metabolic changes of pregnancy.

Background Preterm birth (n = 182) from the InterGEN Study (2014–2019). In this study, we determine if differences in DNAm exist between women who reported a preterm birth in the last 3–5 years compared to those who had full-term births by using two different approaches: epigenome-wide association study (EWAS) and genome-wide co-methylation analyses. Results Though no significant CpG sites were identified using the EWAS approach, we did identify significant modules of co-methylation associated with preterm birth. Co-methylation analyses showed correlations with preterm birth in gene ontology and KEGG pathways. Functional annotation analysis revealed enrichment for pathways related to central nervous system and sensory perception. No association was observed between DNAm age and preterm birth, though larger samples are needed to confirm this further. Conclusions We identified differentially methylated gene networks associated with preterm birth in African American women 3–5 years after birth, including pathways related to neurogenesis and sensory processing. More research is needed to understand better these associations and replicate them in an independent cohort. Further study should be done in this area to elucidate mechanisms linking preterm birth and later epigenomic changes that may contribute to the development of health disorders and maternal mood and well-being.

Background: Maternal obesity is a well-known risk factor for significant obstetric and neonatal complications. The influence of the gastrointestinal microbiome in the setting of maternal obesity during pregnancy is less understood. The purpose of this systematic review is to synthesize the literature on the relationships between maternal obesity and excessive gestational weight gain (EGWG) and the composition of maternal and child gastrointestinal microbiomes. Method: We searched CINHAL, OVID Medline, Web of Science, and PubMed for relevant literature using medical subject heading terms related to obesity, pregnancy, and the gastrointestinal microbiome. We assessed 249 articles for potential inclusion using the preferred reporting items for systematic review and meta-analyses framework and deemed 11 articles as relevant for this review. Results: Maternal obesity was associated with significant microbial changes in both maternal and infant fecal microbiome biospecimens including increases in Bacteroidetes, Firmicutes, and the Actinobacteria phyla and decreases in Bifidobacteria. However, inconsistencies in uniform taxonomic results across all studies mean that evidence of specific microbial associations with obesity and EGWG is inconclusive. Conclusion: Our findings suggest that both maternal and child gastrointestinal microbiome composition is altered in the setting of maternal obesity and EGWG during pregnancy. Future microbiome studies should concentrate on the investigation of metagenomic sequencing to elucidate microbial function rather than solely taxonomic composition. More diverse populations of mothers should be sampled to address health disparities and adverse outcomes of underrepresented populations. Finally, analytic pipelines should be standardized across studies to aid in reproducibility.

Background American Indian elders have one of the lowest life expectancies in the United States. Disproportionate disease burden, socioeconomic disparities, and higher rates of violence across the lifespan are thought to contribute to higher rates of elder abuse. Elder abuse and higher rates of trauma exposure are linked with adverse outcomes. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines informed the methodology and assessment of the literature on elder abuse among American Indians. Results Of the nine studies published in the last 30 years, rates of elder abuse varied by study, location, and tribal affiliation from 4.3% to 45.9%. Large studies with comparison populations found higher rates for American Indians. There was a consensus for three risk factors: substance abuse, mental health problems, and caregiving issues. Importance of tribal norms, the notion of respect conferred to elders, and the concept of acculturation were major culturally relevant themes. Perceived tribal norms and strengths, for example, respect for elders, were at odds with abuse experiences, particularly financial exploitation and neglect. Historical trauma, shame, and fear impacted reporting. There was little consistency in study designs, most were qualitative or mixed methods, samples were small, there was no common measurement tool or time frame for abuse, and there was only one intervention study. Implications High rates of abuse suggest healthcare providers should be encouraged to screen and intervene despite the lack of empirical evidence. Providers should not assume that traditional culturally ascribed strengths, such as honor and respect for elders, provide any degree of protection against elder abuse.

Novel discoveries in genomics and other omics sciences are rapidly redefining our understanding of health and disease as well as advancing the development of targeted therapeutics for improving health outcomes. The scale of these findings, fueled by vast increases in computing power and new techniques in data analytics, easily supersedes that of phenomena observed using more traditional research approaches. Until recently, the classification and diagnosis of disease has involved rather subjective processes, whereby signs and late symptom patterns are linked with clinical outcomes. However, symptom patterns, disease trajectories, and health outcomes are complex entities characterized by a wide range of clinical manifestations and progression patterns. The burgeoning fields of data science and bioinformatics are opening opportunities for nurse scientists to quantify disease heterogeneity by defining and categorizing disease phenotypes and endotypes. Nurse scientists and clinicians can play a critical role in engaging patients and the larger scientific community in these efforts. The purpose of this article is to provide an introduction to concepts critical to understanding and quantifying heterogeneity in the onset and progression of chronic disease. To present and exemplify key concepts, we (1) discuss evidence for heterogeneity in the onset and progression of Type 1 diabetes, (2) link emerging research approaches in data science with principles in network science and systems biology to lay the groundwork for stratifying subclinical and advanced chronic disease, thus expanding the purview of symptom science, and (3) describe the computational skills needed to engage in these analyses.

Objective In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT). Materials and methods A comprehensive literature search of 1964 articles from PubMed and EMBASE was narrowed to 21 eligible articles. Data related to purpose, text source, number of users and/or posts, evaluation metrics, and quality indicators were recorded. Results Pain (n = 18) and fatigue and sleep disturbance (n = 18) were the most frequently evaluated symptom clinical content categories. Studies accessed ePAT from sources such as Twitter and online community forums or patient portals focused on diseases, including diabetes, cancer, and depression. Fifteen studies used NLP as a primary methodology. Studies reported evaluation metrics including the precision, recall, and F-measure for symptom-specific research questions. Discussion NLP and text mining have been used to extract and analyze patient-authored symptom data in a wide variety of online communities. Though there are computational challenges with accessing ePAT, the depth of information provided directly from patients offers new horizons for precision medicine, characterization of sub-clinical symptoms, and the creation of personal health libraries as outlined by the National Library of Medicine. Conclusion Future research should consider the needs of patients expressed through ePAT and its relevance to symptom science. Understanding the role that ePAT plays in health communication and real-time assessment of symptoms, through the use of NLP and text mining, is critical to a patient-centered health system.

Objective Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information documented in EHR free-text narratives. Materials and Methods Our search of 1964 records from PubMed and EMBASE was narrowed to 27 eligible articles. Data related to the purpose, free-text corpus, patients, symptoms, NLP methodology, evaluation metrics, and quality indicators were extracted for each study. Results Symptom-related information was presented as a primary outcome in 14 studies. EHR narratives represented various inpatient and outpatient clinical specialties, with general, cardiology, and mental health occurring most frequently. Studies encompassed a wide variety of symptoms, including shortness of breath, pain, nausea, dizziness, disturbed sleep, constipation, and depressed mood. NLP approaches included previously developed NLP tools, classification methods, and manually curated rule-based processing. Only one-third (n = 9) of studies reported patient demographic characteristics. Discussion NLP is used to extract information from EHR free-text narratives written by a variety of healthcare providers on an expansive range of symptoms across diverse clinical specialties. The current focus of this field is on the development of methods to extract symptom information and the use of symptom information for disease classification tasks rather than the examination of symptoms themselves. Conclusion Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives. Efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.

Objective We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process. Materials and Methods Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards of quality for reporting systematic reviews, a protocol for study eligibility was developed a priori and registered in the PROSPERO database. Using predetermined inclusion criteria, studies published from database inception through February 2021 were identified from 5 databases. The Joanna Briggs Institute Critical Appraisal Checklist for Quasi-experimental Studies was adapted to determine the study quality and the risk of bias of the included articles. Results Eleven studies representing 8 unique NLP systems met the inclusion criteria. These studies demonstrated moderate study quality and exhibited heterogeneity in the study design, setting, and intervention type. All 11 studies evaluated the NLP system’s performance for identifying eligible participants; 7 studies evaluated the system’s impact on time efficiency; 4 studies evaluated the system’s impact on workload; and 2 studies evaluated the system’s impact on recruitment. Discussion NLP systems in clinical research eligibility prescreening are an understudied but promising field that requires further research to assess its impact on real-world adoption. Future studies should be centered on continuing to develop and evaluate relevant NLP systems to improve enrollment into clinical studies. Conclusion Understanding the role of NLP systems in improving eligibility prescreening is critical to the advancement of clinical research recruitment.

The gut microbiome appears to play an important role in human health and disease. However, only little is known about how variability in the gut microbiome contributes to individual differences during early and sensitive stages of brain and behavioral development. The current study examined the link between gut microbiome, brain, and behavior in newborn infants (N = 63; M [age] = 25 days). Infant gut microbiome diversity was measured from stool samples using metagenomic sequencing, infant functional brain network connectivity was assessed using a resting state functional near infrared spectroscopy (rs-fNIRS) procedure, and infant behavioral temperament was assessed using parental report. Our results show that gut microbiota composition is linked to individual variability in brain network connectivity, which in turn mediated individual differences in behavioral temperament, specifically negative emotionality, among infants. Furthermore, virulence factors, possibly indexing pathogenic activity, were associated with differences in brain network connectivity linked to negative emotionality. These findings provide novel insights into the early developmental origins of the gut microbiome-brain axis and its association with variability in important behavioral traits. This suggests that the gut microbiome is an important biological factor to consider when studying human development and health.

Big data and data science are essential to enabling precision health approaches for symptom science. In contrast to big data, which is defined by the attributes of the data, data science explicitly focuses on extraction of value from data. Consequently, data science requires a technical pipeline that is informed by a research question and supported by data governance policies. The pipeline typically includes sets of tools that support: (1) extraction/ingestion, (2) wrangling (pre-processing using semi-automated tools), (3) computation and analysis, (4) modeling and application, and (5) reporting and visualization. In this chapter, we describe how big data and data science can advance precision health approaches for symptom science, summarize current challenges to use of big data and data science methods, and present future research directions.

Incorporating information regarding the gut microbiota into psychobiological research promises to shed new light on how individual differences in brain and cognitive development emerge. However, the investigation of the gut-brain axis in development is still in its infancy and poses several challenges, including data analysis. Considering that the gut microbiome is an eco-system containing millions of bacteria, one needs to utilize a breadth of methodologies and data analytic techniques. The present review serves two purposes. First, this review will inform developmental psychobiology researchers about the emerging study of the gut-brain axis in development and second, this review will propose methodologies and data analytic strategies for integrating microbiome data in developmental research.

Background Implicit bias has been documented in candidate selection within academic medicine. Gender bias is exposed when writers systematically use different language to describe attributes of male and female applicants. This study examined the presence of gender bias in recommendation letters for surgical residency candidates. Study Design Recommendation letters for 2016 to 2017 surgery resident applicants selected for interview at an academic institution were analyzed using qualitative text analysis, quantitative text mining, and topic modeling. Dedoose, QDA Miner, and RStudio analytic software were used for analysis. Results There were 332 letters of recommendation for 89 applicants (51% male) analyzed. Of 265 letter writers, 86% were male, 21% chairs, and 50% professors. Average word count was 404. Letter writers for male compared with female applicants had a significantly higher average word count (male = 421, SD 144; female = 388, SD 140, p = 0.035). Standout adjectives (eg exceptional), reference to awards, achievement, ability, hardship, leadership, scholarship, and use of applicant's name were most often applied to male applicants. Comments on positive general terms (eg delightful), grindstone words (eg hard-working), physical description, doubt raisers, and work ethic were most often applied to female applicants. Topic modeling and term frequencies revealed achievement words (performance, career, leadership, and knowledge) used more often with male applicants, while caring words (care, time, patients, and support) were used more often with female applicants. Conclusions Gendered differences examined through language and text exist in surgical residents' recommendation letters. Implementing tools to help faculty write recommendation letters with meaningful content and editing letters for reflections of stereotypes may improve the resident selection process by reducing bias.

Increasing incidence of sexually transmitted diseases (STDs) has prompted the public health and technology communities to innovate new measures to understand how individuals use Internet resources to attain relevant information, particularly for sensitive or stigmatized conditions. The purpose of this study is to examine recent health information seeking and needs of the r/STD community, a subreddit focused exclusively on STDs. We found that the majority of posts crowdsource information about intermediate, non-reportable STDs such as human papillomavirus (HPV). Crowdsourced information in this community focused on symptoms, treatment, as well as the social and emotional aspects of sexual health such as fear of misdiagnosis. From our analysis, it is clear that online communities focused on discussion of health symptoms have the ripe potential to influence information-seeking behavior and consumer action.

Revolutionary advances in genomic technology have allowed researchers to address biological questions about cell types, states, and gene regulation at the scale of single cells. However, the ability to characterize gene expression and function of individual cells brings with it new data-related challenges, such as dimensionality, feature reduction, and noise reduction. The central objective of this research was to use existing methods in a novel application of single-cell gene expression data to better characterize sub-populations of cell types in various regions of the brain. This research approach used a computational bioinformatics pipeline for single-cell RNA-sequencing (RNA-seq) normalization and clustering. Data science methodologies, such as autoencoding and text mining, were adapted to identify candidate gene sets that distinguish different types of cells in the central nervous system. Then, the functional themes of these gene sets were inferred using a combination of functional enrichment of gene ontology terms and topic modeling. Topic modeling revealed various functional themes among the clusters, in some cases reinforcing the results of biomarker analysis, and in other cases providing further insight into potential functional differences between clusters. For one cluster in the cortex, an immune theme emerged with stemmed-words as specific as “immun” and “antigen” appearing in the results. In the hippocampus, clusters determined to be neurons could be further differentiated as themes related to various organs were identified. One of these clusters featured a vascular theme with words related to “endotheli.” Future applications of these methods intend to expound upon specific cellular processes, in relation to immune function, and translational research on neurological disease states.

To examine the effects of intimate partner violence (IPV) at varied time points in the perinatal period on inadequate and excessive gestational weight gain.Retrospective cohort using population-based secondary data.Pregnancy Risk Assessment Monitoring System and birth certificate data from New York City and 35 states.Data were obtained for 251,342 U.S. mothers who gave birth from 2004 through 2011 and completed the Pregnancy Risk Assessment Monitoring System survey 2 to 9 months after birth.The exposure was perinatal IPV, defined as experiencing physical abuse by a current or ex-partner in the year before or during pregnancy. Adequacy of gestational weight gain (GWG) was categorized using 2009 Institute of Medicine guidelines. Weighted descriptive statistics and multivariate logistic regression models were used.Approximately 6% of participants reported perinatal IPV, 2.7% reported IPV in the year before pregnancy, 1.1% reported IPV during pregnancy only, and the remaining 2.5% reported IPV before and during pregnancy. Inadequate GWG was more prevalent among participants who experienced IPV during pregnancy and those who experienced IPV before and during pregnancy (23.3% and 23.5%, respectively) than in participants who reported no IPV (20.2%; p .001). Participants who experienced IPV before pregnancy only were significantly more likely to have excessive GWG (p .001). Results were attenuated in the multivariate modeling; only participants who experienced IPV before pregnancy had weakly significant odds of excessive GWG (adjusted odds ratio = 1.14, 95% CI [1.02, 1.26]).The association between perinatal IPV and inadequate GWG was explained by confounding variables; however, women who reported perinatal IPV had greater rates of GWG outside the optimal range. Future studies are needed to determine how relevant confounding variables may affect a woman's GWG.

This study attempts to advance theorizing about health policy advocacy with combinations of narrative focus and a statistical map in an attempt to increase state legislators' support for policies to address the issue of obesity by reducing food deserts. Specifically, we examine state legislators' responses to variations in narrative focus (individual vs. community) about causes and solutions for food deserts in U.S. communities, and a statistical map (presence vs. absence) depicting the prevalence of food deserts across the United States. Using a Web-based randomized experiment (N=496), we show that narrative focus and the statistical map interact to produce different patterns of cognitive response and support for policies to reduce the prevalence of food deserts. The presence of a statistical map showing the prevalence of food deserts in the United States appeared to matter only when combined with an individual narrative, offsetting the fact that the individual narrative in isolation produced fewer thoughts consistent with the story's persuasive goal and more counterarguments in opposition to environmental causes and solutions for obesity than other message conditions. The image did not have an impact when combined with a story describing a community at large. Cognitive responses fully mediated message effects on intended persuasive outcomes. We conclude by discussing the study's contributions to communication theory and practice.