Web-Based Tailored Nursing Intervention to Support Medication Self-management: A Qualitative Study of the Experience of Kidney Transplant Recipients Optimal adherence to immunosuppressive medication is essential to kidney graft success. A Web-based tailored virtual nursing intervention was developed to promote medication adherence and support self-management among kidney transplant recipients. A qualitative study was undertaken in a hospital setting in Montreal (Canada) to document how users experience the intervention and to explore medication intake self-management behaviors. To participate, transplant recipients had to be at least 18 years old and had to have completed at least one computer session of the intervention. Semistructured interviews were conducted with 10 participants (two women, eight men) with a mean age of 47.8 years. They reported receiving their latest renal transplant on average 10.6 years prior. Content analysis of the interview transcripts yielded five major themes: (1) kidney transplant is a gift from life; (2) routinization of medication intake; (3) intervention is a new and positive experience; (4) using the intervention offers many benefits; and (5) individual relevance of the intervention. Patient experience shows the intervention is acceptable and can help better manage medication intake. Results also underscore the importance of offering the intervention early in the care trajectory of transplant recipients. Web-based tailored virtual nursing interventions could constitute an easily available adjunct to existing specialized services. Licensing options have been granted for the marketing of VIH-TAVIE. The study was funded by the Kidney Foundation of Canada (2013–2015), the Canadian Institutes of Health Research (2012–2013), and the Research Chair in Innovative Nursing Practices. J.C. has received a clinical research bursary (senior) from the Fonds de recherche du Québec - Santé (2013–2017) to support her research program on innovative virtual interventions intended for persons living with a chronic health problem. The TAVIE platform was developed with the financial support of the Réseau Sidami du FRSQ. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: José Côté, PhD, RN, 850 St-Denis, Montreal, Québec, Canada H2X 0A9 (jose.cote@umontreal.ca). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Knowledge Discovery With Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics. The purpose of this article is to explain a knowledge discovery and data mining approach that was employed to discover knowledge about hospital-acquired catheter-associated urinary tract infections from multiple data sources, including electronic health records and nurse staffing data. Three different machine learning techniques are described: decision trees, logistic regression, and support vector machines. The decision tree model created rules to interpret relationships among associated factors of hospital-acquired catheter-associated urinary tract infections. The logistic regression model showed what factors were related to a higher risk of hospital-acquired catheter-associated urinary tract infections. The support vector machines model was included to compare performance with the other two interpretable models. This article introduces the examples of cutting-edge machine learning approaches that will advance secondary use of electronic health records and integration of multiple data sources as well as provide evidence necessary to guide nursing professionals in practice. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Jung In Park, PhD, RN, 100D Berk Hall, University of California, Irvine, CA 92697 (junginp@uci.edu). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Defining Menstrual Literacy With the Aim of Evaluating Mobile Menstrual Tracking Applications For the estimated 75 million people in the United States who menstruate, understanding menstrual health as a critical “vital sign” is an important aspect of managing personal health. Unsurprisingly, in the past decade, menstrual tracking applications have become increasingly popular, with more than 300 applications available for download and an estimated 200 million downloads worldwide. This study had two purposes. The first was to formulate a definition for menstrual literacy—a baseline of knowledge and skills for understanding anatomical and biological facts of menstruation, caring for the menstruating body, and completing menstrual care tasks—by building on prior work about health literacy and by conducting content analysis of eight Web sites containing information about menstruation. The second was to evaluate a maximum variation sample of 17 menstrual tracking applications; here, features and functions related to the concepts about menstrual literacy identified in a content analysis were compared. These applications had insufficient support for facilitating menstrual literacy, especially for teen and perimenopausal users. The article discusses these disconnects and subsequent design opportunities for menstrual tracking applications to facilitate more robust support of menstrual literacy and overall health of people who menstruate. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding Author: Jordan Eschler, PhD, School of Communication Northwestern University, 710 N Lake Shore Dr, 15th Floor, Abbott Hall, Chicago, IL 60611 (jordan.eschler@northwestern.edu). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

The Purpose of Bedside Robots: Exploring the Needs of Inpatients and Healthcare Professionals Robotic systems are used to support inpatients and healthcare professionals and to improve the efficiency and quality of nursing. There is a lack of scientific literature on how applied robotic systems can be used to support inpatients. This study uses surveys and focus group interviews to identify the necessary aspects and functions of bedside robots for inpatients. A total of 90 healthcare professionals and 108 inpatients completed the questionnaire, and four physicians and five nurses participated in the focus group interviews. The most highly desired functionalities were related to patient care and monitoring, including alerting staff, measuring vital signs, and sensing falls. Nurses and physicians reported different needs for human-robot interaction. Nurses valued robotic functions such as nonverbal expression recognition, automatic movement, content suggestion, and emotional expressions. The results of the patients' open-ended questions and healthcare professionals' focus groups indicate that the purpose of the robots should primarily be treatment and nursing. Participants believe bedside robots would be helpful but have concerns regarding safety and utility. This study attempts to determine which aspects of robots may increase their acceptance. Our findings suggest that if robots are used in healthcare institutions, they may improve the effectiveness of care. This material is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (no. 10063098, “Telepresence Robot System Development for the Support of POC (Point of Care) Service Associated With ICT Technology”). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Jeongeun Kim, PhD, RN, College of Nursing, Seoul National University Research Institute of Nursing Science, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea (kim0424@snu.ac.kr). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Representing Nursing Data With Fast Healthcare Interoperability Resources: Early Lessons Learned With a Use Case Scenario on Home-Based Pressure Ulcer Care Healthcare communities are rapidly embracing Health Level 7's Fast Healthcare Interoperability Resources standard as the next-generation messaging protocol to facilitate data interoperability. Implementation-friendly formats for data representation and compliance to widely adopted industry standards are among the strengths of Fast Healthcare Interoperability Resources that are accelerating its wide adoption. Research confirms the advantages of Fast Healthcare Interoperability Resources in increasing data interoperability in mortality reporting, genetic test sharing, and patient-generated data. However, few studies have investigated the application of Fast Healthcare Interoperability Resources in nursing-specific domains. In this study, a Fast Healthcare Interoperability Resources document was generated for a use case scenario in a home-based, pressure ulcer care setting. Study goals were to describe the step-by-step process of generating a Fast Healthcare Interoperability Resources artifact and to inform nursing communities about the advantages and challenges in representing nursing data with Fast Healthcare Interoperability Resources. Overall, Fast Healthcare Interoperability Resources effectively represented the majority of the data included in the use case scenario. A few challenges that could potentially cause information loss were noted such as the lack of standardized concept codes for value encoding and the difficulty directly connecting an observation to a related condition. Continuous evaluations in diverse nursing domains are needed in order to gain a more thorough insight on potential challenges that Fast Healthcare Interoperability Resources holds in representing nursing data. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Hyeoneui Kim, PhD, MPH, RN, FAAN, Duke University School of Nursing, 307 Trent Dr, Durham, NC 27710 (hyeoneui.kim@duke.edu). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.cinjournal.com). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Home Monitoring to Track Activity and Sleep Patterns Among Older Adults: A Feasibility Study Measuring changes in activity and sleep over time is important for research and practice. While commercially available home monitoring systems passively track these parameters, the feasibility, acceptability, and usefulness of new products need to be evaluated. We tested a commercially available system for providing long-term data on activity and sleep with 10 single women (mean age, 86.5 years) who were monitored in their homes. Motion detectors, a bed sensor, door sensor, and chair sensor were installed for 3 months to collect data. Other measures, objective actigraphy data from 1 week and self-report, provided data for comparison. Sleep and activity data were similar across measures; the most active participant had the highest scores on all activity measures including sensor data. Participants were generally positive about the monitoring system, but participants varied in their awareness levels of the presence of the equipment. Use of the sensor system was feasible in this pilot study and acceptable to participants. The study also illustrates challenges researchers can encounter when working with a commercial company. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Helen W. Lach, PhD, RN, CNL, FGSA, FAAN, 3525 Caroline Mall, St Louis, MO 63104 (helen.lach@slu.edu). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Living With Intelligent Sensors: Older Adult and Family Member Perceptions This qualitative study is part of a larger randomized prospective intervention study that examined the clinical and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. It explored the perceptions of older adults and family members on the sensor system's usefulness, impact on daily routine, privacy, and sharing of health information. This study was conducted in 13 assisted-living facilities in Missouri, and 55 older adults were interviewed. Data were collected over five points in time with a total of 188 interviews. From these five participant interview iterations, the following themes emerged: (1) understanding and purpose, (2) daily life and benefits, (3) impact on privacy, and (4) sharing of information. Three themes emerged from one round of family interviews: (1) benefits of bed sensors, (2) family involvement/staff interaction, and (3) privacy protection versus sensor benefits. The sensor suite was regarded as helpful in maintaining independence, health, and physical functioning. Responses suggest that the willingness to adopt the sensor suite was motivated by both a decline in functional status and a desire to remain independent. Participants were willing to share their health data with providers and select family members. Recommendations for future practice are provided. This research was made possible through funding from the National Institute of Nursing Research, National Institutes of Health (grant 1R01NR014255), Intelligent Sensor System for Early Illness Alerts in Senior Housing. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Colleen Galambos, PhD, ACSW, LCSW-C, FGSA, Helen Bader School of Social Welfare, Enderis Hall, Room 1157, PO Box 786, Milwaukee, WI 53201 (galamboc@uwm.edu). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

The Effects of Simulation-Based Advanced Life Support Education for Nursing Students Advanced life support education for nursing students is very important because nurses are first responders in emergency situations. The purpose of this study was to identify the effects of simulation-based advanced life support education on nursing students' knowledge, performance, self-efficacy, and teamwork. A nonequivalent control group posttest-only design was used. Fourth-year nursing students were randomly assigned to either simulation-based Korean Advanced Life Support (n = 30) or lecture-based education (n = 30) groups. Data were analyzed using descriptive statistics and the Mann-Whitney U test. The experimental group showed statistically significant higher scores in knowledge (P < .001), performance (P < .001), and self-efficacy (P = .049) when compared with the control group. However, there was no significant difference in teamwork scores between the two groups (P = .529). The 4.5-hour simulation-based Korean Advanced Life Support education was more effective than the 4.5-hour lecture-based education for nursing students in terms of knowledge, performance, and self-efficacy. Nurse educators should adopt simulation-based advanced life support education into the curriculum for the optimal competence of nursing students. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Young Sook Roh, PhD, RN, Red Cross College of Nursing, Chung-Ang University, 84 Heukseok-ro Dongjak-gu, Seoul, Republic of Korea 06974 (aqua@cau.ac.kr). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Improving Prediction of Fall Risk Using Electronic Health Record Data With Various Types and Sources at Multiple Times Inpatient falls are among the most common adverse events threatening patient safety. Although many studies have developed predictive models for fall risk, there are some drawbacks. First, most previous studies have relied on an incident-reporting system alone to identify fall events. Thus, it has been found that falls are more likely to be underreported. Second, there has been a controversy on how to select accurate representative values for patient status data across multiple times and various data sources in electronic health records. Given this background, this study used nurses' progress notes as a complementary data source to detect fall events. In addition, we developed criteria including coverage, currency, and granularity in order to integrate electronic health records data documented at multiple times in various data types and sources. Based on this methodology, we developed three models, logistic regression, Cox proportional hazard regression, and decision tree, to predict risk of patient falls and evaluate the predictive performance of these models by comparing the results to results from the Hendrich II Fall Risk Model. The findings of this study will be used in a clinical decision support system to predict risk of falling and provide evidence-based tailored recommendations in the future. This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2018R1A2A2A05022021). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Hyeoun-Ae Park, PhD, RN, FAAN, FACMI, College of Nursing, Seoul National University, Daehak-ro 103, Jongno-gu, Seoul 03080, South Korea (hapark@snu.ac.kr). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

NimbleMiner: An Open-Source Nursing-Sensitive Natural Language Processing System Based on Word Embedding This study develops and evaluates an open-source software (called NimbleMiner) that allows clinicians to interact with word embedding models with a goal of creating lexicons of similar terms. As a case study, the system was used to identify similar terms for patient fall history from homecare visit notes (N = 1 149 586) extracted from a large US homecare agency. Several experiments with parameters of word embedding models were conducted to identify the most time-effective and high-quality model. Models with larger word window width sizes (n = 10) that present users with about 50 top potentially similar terms for each (true) term validated by the user were most effective. NimbleMiner can assist in building a thorough vocabulary of fall history terms in about 2 hours. For domains like nursing, this approach could offer a valuable tool for rapid lexicon enrichment and discovery. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Maxim Topaz, PhD, RN, 560 W 168th St, New York, NY 10032 (mtopaz80@gmail.com). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.