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No abstract available As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths. There is an increasing need for highly accessible health management platforms for comprehensive symptoms of Parkinson disease. Mobile apps encompassing nonmotor symptoms have been rarely developed since these symptoms are often subjective and difficult to reflect what individuals actually experience. The study developed an app for comprehensive symptom management and evaluated its usability and feasibility. A single-group repeated measurement experimental design was used. Twenty-two participants used the app for 6 weeks. Monitoring of nonmotor symptoms, games to address motor symptoms, and medication management were incorporated in the app. Quantitative outcomes were self-assessed through an online questionnaire, and one-on-one telephone interviews were conducted to understand the user's point of view. The successful experience of self-monitoring had improved participants' self-efficacy (Z = −3.634, P This study aimed to explore digital literacy among community-dwelling older adults in urban South Korea. A semistructured interview guide was developed using the Digital Competence ( 2.0 framework, which emphasizes the competencies for full digital participation in five categories: information and data literacy, communication and collaboration, content creation, safety, and problem-solving. The data were analyzed using combined inductive and deductive content analysis. Inductive analysis identified three main categories: perceived ability to use digital technology, responses to digital technology, and contextual factors. In the results of deductive analysis, participants reported varying abilities in using digital technologies for information and data literacy, communication or collaboration, and problem-solving. However, their abilities were limited in handling the safety or security of digital technology and lacked in creating digital content. Responses to digital technology contain subcategories of perception (positive or negative) and behavior (trying or avoidance). Regarding contextual factors, aging-related physical and cognitive changes were identified as barriers to digital literacy. The influence of families or peers was viewed as both a facilitator and a barrier. Our participants recognized the importance of using digital devices to keep up with the trend of digitalization, but their digital literacy was mostly limited to relatively simple levels. Game-based virtual reality simulation programs can capitalize on the advantages of non–face-to-face education while effectively stimulating the interest of trainees and improving training efficiency. This study aimed to develop a game-based virtual reality simulation program for nervous system assessment and to evaluate the effects of the program on the learning attitudes of nursing students. Using a one-group pretest-posttest design, 41 senior nursing students were enrolled, and their learning attitudes (self-directed learning attitude, academic self-efficacy, flow-learning experience, and learning presence) were evaluated. The effect of the program was statistically significant in self-directed learning attitude (t = −2.27, P = .027) and learning presence (t = −3.07, P = .003), but the difference was not statistically significant in academic self-efficacy (t = −1.97, P = .054) and learning flow (t = −0.74, P = .459). The virtual gaming simulation program can be used to effectively replace field training in situations wherein field training is limited, such as during the COVID-19 pandemic. Vulnerable populations face challenges gaining access to quality healthcare, which places them at a high risk for poor health outcomes. Using patient portals and secure messaging can improve patient activation, access to care, patient follow-up adherence, and health outcomes. Developing and testing quality improvement strategies to help reduce disparities is vital to ensure patient portals benefit all, especially vulnerable populations. This quality improvement initiative aimed to increase enrollment in a patient portal, use secure messages, and adhere to follow-up appointments. Before the project, no patients were enrolled in the portal at this practice site. Over 8 weeks, 61% of invited patients were enrolled in the patient portal. Eighty-five percent were Medicaid recipients, and the others were underinsured. Eight patients utilized the portal for secure messaging. The follow-up appointment attendance rate was better in the enrolled patients than in those who did not enroll. The majority of survey respondents reported satisfaction in using the patient portal. Patient portal utilization and adoption in vulnerable groups can improve when a one-on-one, hands-on demonstration and technical assistance are provided. This study aims to develop a virtual reality–based education program for managing behavioral and psychological symptoms of dementia for family carers of persons living with dementia and investigate the feasibility for users. The program was developed through literature review, interviews with family carers, surveys, and expert content validity assessment. User feasibility was evaluated quantitatively through a questionnaire on usefulness, ease of use, and satisfaction, and qualitatively through participant interviews. The program was produced in two parts, Type 1 and Type 2, consisting of three and six episodes, respectively. Participants showed a high level of satisfaction with overall program scores of 4.28 ± 0.66 and 4.34 ± 0.41 for the two evaluations. Participants also expressed that both programs were helpful, Type 1 for achieving changes in attitude associated with more understanding of persons living with dementia and Type 2 for acquiring coping methods through communication training. Use of the virtual reality device was not inconvenient and was identified as helpful due to the high immersion experience. Results of this study confirmed that family carers had no resistance to education using new technologies such as virtual reality devices and that virtual reality–based education could be effective for training family carers. Delirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods (2005-2009 and 2015-2020). Randomly selected care episodes were annotated with a template, including delirium symptoms, treatment methods, and adverse events. The patients were then manually classified into two groups: nondelirious (n = 257) and possibly delirious (n = 172). The data were analyzed quantitatively and descriptively. According to the data, the documentation of symptoms such as disorientation, memory problems, motoric behavior, and disorganized thinking improved between periods. Yet, the key symptoms of delirium, inattention, and awareness were seldom documented. The professionals did not systematically document the possibility of delirium. Particularly, the way nurses recorded structural information did not facilitate an overall understanding of a patient's condition with respect to delirium. Information about delirium or proposed care was seldom documented in the discharge summaries. Advanced machine learning techniques can augment instruments that facilitate early detection, care planning, and transferring information to follow-up care. This study aimed to develop a Mobile Application to Prevent Recurrent Stroke to prevent recurrent stroke by enhancing self-management and to evaluate its effects on stroke survivors' health outcomes. The Mobile Application to Prevent Recurrent Stroke was developed based on social cognitive theory and the model in order of analysis, design, development, implementation, and evaluation process. The Mobile Application to Prevent Recurrent Stroke consisted of health management contents such as information about stroke, its associated risk factors, and required skills to conduct self-management with tailored support and counseling. A quasi-experimental preintervention and postintervention design was used involving a total of 54 stroke survivors. The experimental group (n = 27) was provided the Mobile Application to Prevent Recurrent Stroke for 8 weeks, whereas the control group (n = 27) received an education booklet. The result revealed that medication adherence (P = .002), healthy eating habit (P To examine whether psychosocial needs in diabetes care are associated with carbohydrate counting and if carbohydrate counting is associated with satisfaction with diabetes applications' usability, a randomized crossover trial of 92 adults with type 1 or 2 diabetes requiring insulin therapy tested two top-rated diabetes applications, mySugr and OnTrack Diabetes. Survey responses on demographics, psychosocial needs (perceived competence, autonomy, and connectivity), carbohydrate-counting frequency, and application satisfaction were modeled using mixed-effect linear regressions to test associations. Participants ranged between 19 and 74 years old (mean, 54 years) and predominantly had type 2 diabetes (70%). Among the three tested domains of psychosocial needs, only competence—not autonomy or connectivity—was found to be associated with carbohydrate-counting frequency. No association between carbohydrate-counting behavior and application satisfaction was found. In conclusion, perceived competence in diabetes care is an important factor in carbohydrate counting; clinicians may improve adherence to carbohydrate counting with strategies designed to improve perceived competence. Carbohydrate-counting behavior is complex; its impact on patient satisfaction of diabetes application usability is multifactorial and warrants consideration of patient demographics such as sex as well as application features for automated carbohydrate counting. 
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