Introduction: To reduce morbidity and mortality rates, hemodialysis patients are advised to restrict their fluid intake. However, fluid restriction is the biggest stressor for the patients. This study explored the barriers and facilitators of fluid restriction among Korean hemodialysis patients. Method: This qualitative descriptive study was guided by the ecological model of health behavior. This study involved five focus group (23 patients) and individual interviews (4 patients) at two hospitals in Korea. Audio-recorded transcripts were analyzed using qualitative content analysis. Results: Six intrapersonal, three interpersonal, and four organization factors affecting fluid restriction adherence were identified. Barriers included physiological symptoms, lack of self-control, social relationships, social stigma, lack of practical education, Korean food culture, and lack of public health and policy support. Facilitators included risk perception, self-assessment, knowledge, stimulated sweating, and social support. Discussion: The findings suggest that socioecological factors should be considered when designing fluid restriction programs for hemodialysis patients.

Aims and objectives The main purpose of this study was to identify the best fall‐risk assessment tool, among the Morse Fall Scale, the Johns Hopkins fall‐risk Assessment Tool and the Hendrich II fall‐risk Model, for a tertiary teaching hospital. The study also analysed fall‐risk factors in the hospital, focusing on the items of each fall assessment tool. Methods Data on falls were obtained from the patient safety reports and electronic nursing records of a tertiary teaching hospital. A retrospective study was conducted to compare the sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, Youden index and accuracy of the Morse Fall Scale, the Johns Hopkins fall‐risk Assessment Tool and the Hendrich II fall‐risk Model. This study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology guideline for reporting case–control studies. Results By analysing the association between falls and the items included in the three tools, we identified significant fall‐risk factors such as gait, dizziness or vertigo, changes in mental status, impulsivity, history of falling, elimination disorder, drugs affecting falls, and depression. Conclusions The Hendrich II fall‐risk Model had the best predictive performance for falls of the three tools, considering the highest in the area under the curve and the Youden index that comprehensively analysed sensitivity and specificity, while the Johns Hopkins fall‐risk Assessment Tool had the highest accuracy. The most significant fall‐risk predictors are gait, dizziness or vertigo, change in mental state, and history of falling. Relevance to clinical practice To improve the fall assessment performance of the Morse Fall Scale at the study hospital, we propose that it be supplemented with four most significant fall‐risk predictors identified in this study.

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