Mitigating urinary incontinence condition using machine learning.

BACKGROUND: Urinary incontinence (UI) is the inability to completely control the process of releasing urine. UI presents a social, medical, and mental issue with financial consequences. OBJECTIVE: This paper proposes a framework based on machine learning for predicting urination time, which can benefit people with various degrees of UI. METHOD: A total of 850 data points were self-recorded by 51 participants to investigate how different factors impact urination time. The participants were instructed to record input data (such as the time of consumption and the number of drinks) and output data (i.e., the time the individual urinated). Other factors, such as age and BMI, were also considered. The study was conducted in two phases: (1) data was prepared for modeling, including missing values, data encoding, and scaling; and (2) a classification model was designed with four output classes of the next urination time: < = 30 min, 31-60 min, 61-90 min, > 90 min. The model was built in two steps: (1) feature selection and (2) model training and testing. Feature selection methods such as lasso regression, decision tree, random forest, and chi-square were used to select the best features, which were then used to train an extreme gradient boosting (XGB) algorithm model to predict the class of the next urination time. RESULT: The feature selection steps resulted in nine features considered the most important features affecting UI. The accuracy, precision, recall, and F1 score of the XGB predictive model are 0.70, 0.73, 0.70, and 0.71, respectively. CONCLUSION: This research is the first step in developing a machine learning model to predict when a person will need to urinate. A precise predictive instrument can enable healthcare providers and caregivers to assist people with various forms of UI in reliable, prompted voiding. The insights from this predictive model can allow future apps to go beyond current UI-related apps by predicting the time of urination using the most relevant factors that impact voiding frequency.

Use of Notification and Communication Technology (Call Light Systems) in Nursing Homes: Observational Study.

BACKGROUND: The call light system is one of the major communication technologies that link nursing home staff to the needs of residents. By providing residents the ability to request assistance, the system becomes an indispensable resource for patient-focused health care. However, little is known about how call light systems are being used in nursing homes and how the system contributes to safety and quality of care for seniors. OBJECTIVE: This study aimed to understand the experiences of nursing home staff who use call light systems and to uncover usability issues and challenges associated with the implemented systems. METHODS: A mix of 150 hours of hypothetico-deductive (unstructured) task analysis and 90 hours of standard procedure (structured) task analysis was conducted in 4 different nursing homes. The data collected included insights into the nursing home's work system and the process of locating and responding to call lights. RESULTS: The data showed that the highest alarm rate is before and after mealtimes. The staff exceeded the administration's expectations of time to respond 50% of the time. In addition, the staff canceled 10.0% (20/201) of call lights and did not immediately assist residents because of high workload. Furthermore, the staff forgot to come back to assist residents over 3% of the time. Usability issues such as broken parts, lack of feedback, lack of prioritization, and low or no discriminability also contributed to the long response time. More than 8% of the time, residents notified the staff about call lights after they waited for a long time, and eventually, these residents were left unattended. CONCLUSIONS: Nursing homes that are still using old call light systems risk the continuation of usability issues that can affect the performance of the staff and contribute to declining staff and resident outcomes. By incorporating feedback from nurses, nursing home management will better understand the influence that the perceptions and usability of technology have on the quality of health care for their residents. In this study, it has been observed that the call light system is perceived to be an important factor affecting the outcomes of the care process and satisfaction of both residents and staff as well as the staff's performance. It is important to recognize that communication and notification technology contributes to the challenges the staff faced during their work, making their working conditions more difficult and challenging.

Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients.

Cardiovascular disease is the leading cause of mortality among nonalcoholic steatohepatitis (NASH) patients who undergo liver transplants. In the present study, machine learning algorithms were used to identify important risk factors for cardiovascular death and to develop a prediction model. The Standard Transplant Analysis and Research data were gathered from the Organ Procurement and Transplantation Network. After cleaning and preprocessing, the dataset comprised 10,871 patients and 92 features. Recursive feature elimination (RFE) and select from model (SFM) were applied to select relevant features from the dataset and avoid overfitting. Multiple machine learning algorithms, including logistic regression, random forest, decision tree, and XGBoost, were used with RFE and SFM. Additionally, prediction models were developed using a support vector machine, Gaussian naïve Bayes, K-nearest neighbors, random forest, and XGBoost algorithms. Finally, SHapley Additive exPlanations (SHAP) were used to increase interpretability. The findings showed that the best feature selection method was RFE with a random forest estimator, and the most critical features were recipient and donor blood type, body mass index, recipient and donor state of residence, serum creatinine, and year of transplantation. Furthermore, among all the outcomes, the XGBoost model had the highest performance, with an accuracy value of 0.6909 and an area under the curve value of 0.86. The findings also revealed a predictive relationship between features and cardiovascular death after liver transplant among NASH patients. These insights may assist clinical decision-makers in devising strategies to prevent cardiovascular complications in post-liver transplant NASH patients.

The Cost of Frontline Nursing: Investigating Perception of Compensation Inadequacy During the COVID-19 Pandemic.

Background: Nursing professionals experienced greater levels of stress and burnout during the COVID-19 pandemic. Studies examining stress and burnout have found a relationship between compensation and burnout. However, further studies are needed to examine the relationship between the mediating effects of supervisor and community support and coping strategies and the effects of burnout on compensation. Objective: The purpose of this study is to build on previous burnout research by examining the mediation effects of supervisor and community support and coping strategies on the relationship between sources of stress and burnout on feelings of compensation inadequacy, or the desire for more compensation. Methods: Using Qualtrics survey responses from 232 nurses, this study used correlation testing and mediation analyses of indirect, direct, and total effects to explore the relationships between critical factors influencing stress, burnout, nurses' use of coping skills, and the perception of supervisor and community support on perceived compensation inadequacy. Results: This study found that the support domain has a significant and positive direct effect on compensation, with supervisor support increasing the desire for additional compensation. Support was also found to have a significant and positive indirect effect and a significant and positive total effect on the desire for additional compensation. This study's results also found that coping strategies had a significant, direct positive effect on the desire for additional compensation. While problem solving and avoidance increased the desire for additional compensation, transference had no significant relationship. Conclusion: This study found evidence of the mediation effect of coping strategies on the relationship between burnout and compensation.

The effect of COVID-19 vaccine tele-educational program on vaccine hesitancy and receiving the vaccine among women planning for pregnancy, pregnant or breast-feeding mothers.

BACKGROUND: COVID-19 hesitancy among women planning to become pregnant, who are pregnant, and who are breast-feeding is still a global phenomenon. Unfortunately, there is a lack of national educational programs that provide those groups of people with the information they need about the vaccine. OBJECTIVE: This study investigated the effect of the COVID-19 vaccine tele-educational program on vaccine hesitancy and receiving the vaccine among women planning for pregnancy, pregnant and breast-feeding mothers. METHODS: This study implemented a quasi-experimental pre-post design and was conducted in Jordan. It was a two-time study and had two groups of women; 220 women participated in the control group, and 205 women participated in the intervention group (those received the tele-educational program). All participating women answered the demographic characteristics sheet and the Arabic version of Hesitancy About COVID-19 Vaccination Questionnaire twice. RESULTS: Results showed that after conducting the program the interventional group reported significantly higher vaccination rate and lower mean score of hesitancy than the control group (M = 24.67, SD = 5.11; M = 27.45, SD = 4.92; respectively) t (423) = -4.116, p-value < 0.001. Moreover, before the program, women in the intervention group reported significantly higher levels of hesitancy compared to those in the same group after the program (M = 28.35, SD = 4.91; M = 24.66, SD = 5.11; respectively) t (204) = 17.83, p-value < 0.001. CONCLUSIONS: The study concluded that awareness of pregnant women after being given the tele-education program about COVID-19 vaccination decreased their hesitancy and improved their willingness to participate in the COVID-19 vaccination. Therefore, health workers should focus on providing scientific-based information about the vaccine to reduce the doubts of pregnant women about participating in the COVID-19.

Ultra-fine transformation of data for normality.

Normally distributed data is crucial for the application of large-scale statistical analysis. To statisticians, the most important assumptions of statistical users are the adequacy of the data and the normal distribution of the data. However, users are constantly forced to deal with unusual data. This includes changing the method used to be less sensitive to non-normal data or transforming that data to normal data. In addition, common mathematical transformation methods (for example, Box-Cox) do not work on complex distributions, and each method works on limited data shapes. In this paper, a novel approach is presented to transform any data into normally distributed data. We refer to our approach as the Ultra-fine transformation method. The article's novelty is that the proposed approach is powerful enough to accurately transform any data with any distribution to the standard normal distribution. Besides this approach's usefulness, it is simple in both theory and in application, and users can easily retrieve the original data from its transformed state. Therefore, we recommend using this method for the data used in the statistical method, even if the data are normal.

Investigating Frontline Nurse Stress: Perceptions of Job Demands, Organizational Support, and Social Support During the Current COVID-19 Pandemic.

Background: While frontline nurses employ coping alternatives to help deal with occupational stress resulting from unprecedented challenges during the COVID-19 pandemic, their access to necessary resources is unclear. Objective: This study aims to explore nurses' mental health in Alabama hospitals during the COVID-19 outbreak and investigate the impact of organizational and community support on nurse stressor levels, physio-psychosocial responses, and coping strategies employed. Methods: A cross-sectional survey was developed to bridge our understanding of stress, support, and coping mechanisms and distributed to nurses working with COVID-19-infected patients in hospital settings in Alabama. A total of 232 frontline nurses responded to 79 items in four domains (stressors, physio-psychosocial symptoms, coping, and support) between May 6, 2020, and June 30, 2020. A two-way ANOVA, regression analysis, and mediation of effects were used to analyze the data. Results: This study found that both social support and use of coping strategies contributed to the reduction of physio-psychosocial symptoms. Differences were found in how older frontline nurses perceived the efficacy of social support and certain coping strategies. This study provides further evidence of the importance of organizational support in addressing the harmful physio-psychosocial symptoms experienced by nurses.

Listening to the Voice of the Hospitalized Child: Comparing Children's Experiences to Their Parents.

BACKGROUND: Pediatric patients can provide feedback about their healthcare experiences. However, most do not and are instead represented by their parents. A widely accepted notion is that pediatric patients lack the capacity, vocabulary, and preferences needed to answer meaningful questions related to their healthcare. However, because the pediatric patients' experience can differ from the perspective of their parents, the use of proxy reporting ultimately reduces the hospital's ability to address the concerns of pediatric patients directly. OBJECTIVES: The overall goals of this survey study were (1) to identify the key domains whereby pediatric patient and parent preferences differ and (2) to investigate the extent to which pediatric patients perceive their healthcare experiences differently from their parents. METHODS: This study employed an adult version, an older child version, and a younger child version of a 47-question survey instrument divided into seven key domains: experience with the admission process, interactions with nurses, interactions with doctors, care in the hospital, hospital environment, experience before discharge, and overall score. The instrument was developed to survey children (ages 4-17) recently discharged from the hospital and their parents or guardian. RESULTS: The findings suggest pediatric patients reported interest in engaging with their doctors about their healthcare. However, pediatric patients younger than 13 years old reported having fewer pediatric doctors ask them about their healthcare, with the youngest group reporting the worst experiences. Moreover, differences in perceptions were identified between pediatric patients and their parents regarding hospital quality and performance. Pediatric patients reported less pain and higher satisfaction regarding the cleanliness of the room and their experience in the admission process than their parents. Patient and parent responses were significantly different within each of the seven domains. CONCLUSION: The results speak to the larger discussion of whether current hospital settings can engage with small children at the level necessary to capture meaningful feedback about their healthcare experience. By providing additional evidence to the contrary, this survey study brings further context to misconceptions regarding pediatric patients' involvement as crucial actors in the patient-and-family-centered care model.

Capturing nurses' perception of communicative technologies in nursing facilities: Survey instrument development.

BACKGROUND: Despite the common perception of the call light system as crucial for patient safety, there are usability gaps between what features staff in nursing facilities need to address an alarm quickly and how the call system is designed. A survey instrument was developed to investigate nursing home staff experiences in using call light technology. METHODS: A survey instrument was developed and distributed to staff in two nursing facilities in Up-State, New York (N = 278). The strength and direction of relationships between pairs of variables were measured using Pearson correlation and analysis of variance to investigate the perceived effect of staff's perception of the call light system on the work system's elements, process and staff outcomes. RESULTS: The study found correlations between the job-level workload and the noise in the unit (r = 0.272, p = .000), between job-level workload and the ease of locating call lights (r = 0.154, p = .023), and between job stress and burnout (r = 0.176, p = .009). Only 64% of nursing home staff believed that the call light system reflected urgent needs, with younger staff relating to this group. The staff gave the work environment a high score, which corresponds to excessive noises caused by the call light alarm, according to 74% of the staff. These associations stem from gaps caused by a lack of user feedback in the design and acquisition process, leading to usability issues that reduce performance and satisfaction over time. CONCLUSION: The study found that the type of call light system used is associated with usability challenges that often impeded the performance of the nursing home staff's response to residents. These insights can improve the selection of a new call light system that avoids usability issues and challenges identified by end-users.

Identifying Patterns of Turnover Intention Among Alabama Frontline Nurses in Hospital Settings During the COVID-19 Pandemic.

INTRODUCTION: Current research about frontline nurse stress and turnover intention lacks context related to rural communities' plight in providing organizational resources during the current COVID-19 pandemic. These implications have been particularly underexamined in the United States, whose regional differences may influence how frontline nurses perceive the access and utility of organizational resources. This study investigates if anxiety and stress while working during the current COVID-19 pandemic contribute to frontline nurses' desire to leave their current position in Alabama hospital settings. MATERIAL AND METHODS: A cross-sectional survey was developed and distributed as a Qualtrics survey to frontline nurses using social media and professional contacts. A total of 111 frontline nurse respondents within May 19-June 12, 2020 were included in this study. RESULTS: A significant correlation was found between gender (p= 0.002), marital status (p= 0.000) and seniority (p= 0.049) on turnover intention. A nurse's perceived anxiety and stress related to their patients' acuity (r= 0.257, p= 0.004), their personal health as a risk factor (r= 0.507, p= 0.000), their patient assignments (r= 0.239, p= 0.01), their personal protective equipment (r= 0.412, p= 0.000), and their psychological support (r= 0.316, p= 0.001) correspond to higher turnover intention among nurses working with patients infected with COVID-19. CONCLUSION: Perceived resource loss in task autonomy, PPE, and psychosocial support increased turnover intention among frontline nurses in Alabama. Research is needed to understand how intrinsic motivations and social support influence individual nurse staff's perceptions of resource loss and job demands. Further, more research is necessary to examine the implications of rurality and place in discussing turnover intention and organizational resources across multiple health systems.

Perspectives of Nursing Homes Staff on the Nature of Residents-Initiated Call Lights.

Little research has been conducted to capture the perceptions of nursing home staff when using the call light system. There is also a lack of information regarding the effects that these perceptions of the call light system have on their workload, safety, quality of care, or overall satisfaction. In response to the high volume of complaints from residents and their families regarding long response times to call light alarms, we developed this exploratory cross-sectional survey study. This study aims to capture nursing home staff experiences while using a call light system; to investigate the challenges the staff face when using the system; and to determine how these challenges contribute to their workload, performance, and satisfaction. A survey instrument was developed and distributed to all 153 of the nursing staff, certified nursing assistants, and licensed practical nurses in a nursing home in upstate New York. A total of 105 completed surveys were retrieved for an overall response rate of 68.63%. Descriptive analysis, Pearson correlation, and the Kruskal-Wallis test were used to analyze the collected data. The results showed a significant correlation between the processes of being notified and locating call light alarms and workload. The staff reported many usability challenges that may contribute to longer response time such as lack of prioritization, low/no discriminability, noise, and overwrite previous alarm. In addition, 78% of the staff agreed that responding to a call light can prevent serious harm; however, 56% of the staff agreed that call light system is not meaningful; and around 78% think that call light system is disruptive in the environment and source for constant noise. The study finds that incorporating the insights provided by nursing home staff may improve the acceptance of new and existing technology, which ultimately improves the delivery of care through greater usability.

Major Stressors and Coping Strategies of Frontline Nursing Staff During the Outbreak of Coronavirus Disease 2020 (COVID-19) in Alabama.

BACKGROUND: Studies suggest that nursing staff during pandemics such as H1N1 Influenza and COVID-19 exhibit higher than usual stress levels due to an increasingly overburdened healthcare system and increasing infection rates. This study aims to investigate the major stressors and coping strategies reported by nurses working directly with potentially infectious patients in Alabama, United States, during the COVID-19 pandemic. MATERIALS AND METHODS: A cross-sectional questionnaire study was conducted with nursing staff working in hospital settings in the state. The questionnaire was completed by 109 nurses working in hospitals that treated COVID-19 patients. RESULTS: Around 71% of the nursing staff were concerned about receiving more COVID-19 patients and exhibited heightened workload-related stress resulting from taking care of infected patients. The study found that most nurses (82%) are stressed about getting their friends and family infected. Overall, younger, less experienced nurses reported more stress levels compared to older, senior-level nurses. Findings suggest that many nurses fail to perceive protective measures as an effective coping strategy, with only 75% reporting problem-solving strategies such as hand washing and wearing a face mask, and only 60% avoiding public transportation and crowded spaces. Findings also suggest a lack of organizational support including psychiatric assistance, with no nurses reportedly seeking psychological therapy. CONCLUSION: The COVID-19 pandemic increased the stress level of the nursing staff in Alabama. The study finds that the cases in the state of AL are still increasing dramatically, which can overwhelme the healthcare system and escalate nurse stress levels.

Multi-Agent-Based Urban Vegetation Design.

Urban vegetation is an essential element of the urban city pedestrian walkway. Despite city forest regulations and urban planning best practices, vegetation planning lacks clear comprehension and compatibility with other urban elements surrounding it. Urban planners and academic researchers currently devote vital attention to include most of the urban elements and their impact on the occupants and the environment in the planning stage of urban development. With the advancement in computational design, they have developed various algorithms to generate design alternatives and measure their impact on the environment that meets occupants' needs and perceptions of their city. In particular, multi-agent-based simulations show great promise in developing rule compliance with urban vegetation design tools. This paper proposed an automatic urban vegetation city rule compliance approach for pedestrian pathway vegetation, leveraging multi-agent system and algorithmic modeling tools. This approach comprises three modules: rule compliance (T-Rule), street vegetation design tool (T-Design), and multi-agent alternative generation (T-Agent). Notably, the scope of the paper is limited to trees, shrubbery, and seating area configurations in the urban pathway context. To validate the developed design tool, a case study was tested, and the vegetation design tool generated the expected results successfully. A questionnaire was conducted to give feedback on the use of the developed tool for enhancing positive experience of the developed tool. It is anticipated that the proposed tool has the potential to aid urban planners in decision-making and develop more practical vegetation planting plans compared with the conventional Two-Dimensional (2D) plans, and give the city occupants the chance to take part in shaping their city by merely selecting from predefined parameters in a user interface to generate their neighborhood pathway vegetation plans. Moreover, this approach can be extended to be embedded in an interactive map where city occupants can shape their neighborhood greenery and give feedback to urban planners for decision-making.

Evaluating a smartwatch notification system in a simulated nursing home.

AIMS: This paper evaluates a high-fidelity prototype of a smartwatch-based communication system for nursing homes. The system could improve communication between residents and caregivers, thus reducing staff response time and improving residents' safety. BACKGROUND: Across the country, nursing homes are often cited as having serious quality and safety issues. Deficiencies in the communication systems that connect residents with caregivers significantly impact the quality of care. Most nursing homes still use traditional call light systems, that give visual and auditory cues to caregivers, who may or may not be in proximity to the system. This study evaluates a smartwatch communication system developed to quickly relay a resident's requests for assistance to staff anywhere in the nursing home. METHOD: Certified Nursing Assistants (CNAs) employed by nursing homes were recruited to perform routine tasks using both a traditional system and a smart system. Response time to call lights, staff performance and outcomes were measured through direct and recorded observations and through the surveys of participants. RESULTS: The smartwatch system reduced staff response time to call lights from bedrooms by 40%, from bathrooms by 58% and from bed exit alarms by 29%. The smartwatch system also reduced perceived workload by about 50% and was highly accepted by the CNAs. CONCLUSION: A smartwatch-based notification system may increase staff performance and decrease workload, thus leading to greater staff and patient satisfaction, which could ultimately lead to better quality of care and patient safety. RELEVANCE TO CLINICAL PRACTICE: This study provides empirical evidence for the usefulness of smartwatches in facilitating the notification and communication processes in healthcare settings and is also among the first to actually examine a smartwatch system in a healthcare setting.