African American women have lower levels of leisure time physical activity compared to White American women. Interventions to improve physical activity have mixed benefits for African American women, even when guided by theory. Understanding how theoretical constructs used in physical activity interventions relate to changing behavior may provide direction for more successful interventions. The study aimed to examine the relationships among social cognitive constructs (self-efficacy, social support from group behavioral meetings, outcome expectations/realizations), and change in physical activity from baseline to 48 weeks in African American women participating in a lifestyle physical activity program. A secondary data analysis of longitudinal data using a correlational design was conducted using data from a 48-week physical activity randomized controlled trial (RCT). The RCT included a group behavioral meeting component with one of three telephone intervention conditions (no calls, personal motivation calls, or automated motivational calls) randomly assigned across six community healthcare sites. The participants were 260 sedentary, midlife African American women with no major signs or symptoms of cardiovascular disease who completed baseline and 48-week assessments of the RCT. Measures included self-efficacy for change in overcoming barriers to physical activity at 24 weeks, physical and psychological outcome realizations at 24 weeks, social support from group behavioral meetings at 24 weeks, and physical activity (self-report and device-measured) change from baseline to 48 weeks. In a hierarchical regression model predicting change in self-reported time spent in weekly moderate-vigorous physical activity at 48 weeks, psychological outcome realizations at 24 weeks were significant positive predictors. In a hierarchical regression model for change in device-measured daily steps at 48 weeks, a self-efficacy change at 24 weeks was a significant positive predictor. Attention should be given to increasing self-efficacy to overcome physical activity barriers and achieve self-identified physical and psychological outcomes in physical activity programs.

Rita H. Pickler, PhD, RN, FAAN, is Editor, Nursing Research. Accepted for publication November 29, 2020. The authors have no funding or conflicts of interest to disclose. Dr. Bruner, author of the commentary, “How Nurse Scientists Can Stay Productive and On Track During the Pandemic,” declined to provide a response to the “Letter.”

Rita H. Pickler, PhD, RN, FAAN, is Editor of Nursing Research, and the Director of the PhD and MS in Nursing Science Programs, The Ohio State University College of Nursing, Columbus. Accepted for publication October 1, 2020. The Editor has no conflicts of interest to disclose. Corresponding author: Rita H. Pickler, PhD, RN, FAAN, PhD & MS in Nursing Science Programs, The Ohio State University College of Nursing, 1585 Neil Avenue, Columbus, OH 43210 (e-mail: [email protected]).

Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes. We aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations. First, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse- and physician-authored common EHR note types. Compared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent. Using our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.

Nurses are a high-risk group for presenteeism: When one is present at work but not fully engaged. Presenteeism can occur because of multiple work, personal, and event factors and is linked to negative nurse, patient, and organizational outcomes. A model that accounts for the multiple antecedents that lead to presenteeism, as well as its consequences, is needed. The aim of this study was to evaluate our proposed presenteeism in nursing model among registered nurses in the United States by examining the fit of the data with the model. Four hundred forty-seven registered nurses who provide direct patient care completed a cross-sectional survey on presenteeism, its antecedents and consequences, and demographics. Structural equation modeling was used to test relationships in the proposed model and overall model fit. The presenteeism in nursing model holistically accounted for significant presenteeism antecedents and consequences. There were significant relationships between work environment, perceived stress and work-life balance, and presenteeism. There were also significant relationships between presenteeism and missed care, and burnout. These relationships align with prior research and identify antecedents and consequences of presenteeism. This model can be used by practitioners and researchers to develop interventions addressing presenteeism to improve health system, nurse, and patient outcomes.

Accurate, real-time models to predict hospital adverse events could facilitate timely and targeted interventions to improve patient outcomes. Advances in computing enable the use of supervised machine learning (SML) techniques to predict hospital-onset infections. The purpose of this study was to trial SML methods to predict urinary tract infections (UTIs) during inpatient hospitalization at the time of admission. In a large cohort of adult hospitalizations in three New York City acute care facilities (N = 897,344), we used two SML methods-neural networks and decision trees-to predict having a hospital-onset UTI using data available and accessible on the first day of admission at healthcare facilities in the United States. Performance for both neural network and decision tree models were superior compared to logistic regression methods. The decision tree model had a higher sensitivity compared to neural network, but a lower specificity. SML methods show potential for automated accurate UTI risk stratification using electronic data routinely available at admission; this could relieve nurses from the burden of having to complete and document additional risk assessment forms in the electronic medical record. Future studies should pilot and test interventions linked to the risk stratification results, such as short nursing educational modules or alerts triggered for high-risk patients.

Nursing Research: 5/6 2020 - Volume 69 - Issue 3 - p E26-E188 doi: 10.1097/NNR.0000000000000430

Rita H. Pickler, PhD, RN, FAAN, is Editor of Nursing Research and The FloAnn Sours Easton Professor of Child and Adolescent Health Director, PhD Program and MS in Nursing Science Program, The Ohio State University College of Nursing, Columbus. The Editor has no conflicts of interest to disclose. Accepted for publication February 21, 2020. Corresponding author: Rita H. Pickler, PhD, RN, FAAN, The Ohio State University College of Nursing, 324 Newton Hall, 1585 Neil Avenue, Columbus, OH 43210 (e-mail: [email protected]).

African American women (AAW) are diagnosed with more aggressive forms of breast cancer when compared to White women and are more likely to die before the age of 45 years. Researchers have suggested that psychological stress could be a precursor to breast cancer. AAW experience a unique form of stress that is a result of living at the intersection of socially constructed roles of race, gender, and class (intersectionality). No studies have used the paradigm of Black Feminist Thought to examine breast cancer disparities among AAW. The purpose of this study was to examine how the intersection of race, gender, and class influences mental and physiological well-being among AAW with breast cancer. A phenomenological design was used. Ten AAW ages 45-60 years were recruited from across the United States. Semistructured interviews were conducted and analyzed using a modified Husserlian approach of descriptive phenomenology. The interviews were audiotaped and transcribed verbatim, and themes were identified using Giorgi's method. Four themes emerged: (a) altruism (selfless giving and caring for others), (b) silent strength (strength in silence while enduring life in the intersection), (c) existential invisibility (an essential, obscured presence in society), and (d) marginalization (living at the margins of society). Marginalization manifested in two forms: (a) passive marginalization (circumstances in which women removed themselves mentally or physically from societal adversity) and (b) active marginalization (circumstances in which women were overlooked, devalued, or ostracized by others). This study supports the use of Black Feminist Thought to garner knowledge that adds to the discussion of breast cancer disparities. The themes identified suggest other upstream psychosocial risk factors for the development of breast cancer in AAW that stem from life within the societal intersection.

Preterm birth is a risk factor for elevated blood pressure in childhood and the development of hypertension and cardiometabolic disease in adulthood; however, mechanisms for the development of both are poorly understood. Rapid weight gain early in childhood may serve as a driver directly and indirectly through cortisol levels found to be elevated in early childhood in individuals born preterm. The objective of this pilot study was to examine the effect sizes of the relationships between weight gain and blood pressure in toddlers born very preterm. A secondary aim was to note any mediating effect of cortisol on the relationships between weight gain and blood pressure. A cross-sectional design with a convenience sample of 36 toddlers who were born very preterm was used to examine the relationships between postnatal weight gain, cortisol, and blood pressure at follow-up. Many of the participants experienced rapid weight gain in the first 12 months of life. Mean systolic and diastolic readings were 94 and 56.6, respectively. Diastolic blood pressure readings were obtained from 23 participants and the majority were elevated. Weight gain was associated with diastolic blood pressure with a medium effect size. A mediating role with cortisol was not supported.Although findings need to be validated in a larger sample, the blood pressure elevations in this sample were alarming. If readings continue to amplify as these children age, the fact that elevations are already present during the toddler period could indicate more significant cardiovascular disease in adulthood for this population. Rapid weight gain in early life may be a driver for elevated blood pressure even during early childhood in individuals born preterm.