Digital health interventions to promote healthy lifestyle in hemodialysis patients: an interventional pilot study.

Low physical activity has been associated with poor prognosis in hemodialysis (HD) patients. Interventions to maintain healthy lifestyle in this population are important to reduce mortality. This study aimed to evaluate the effectiveness of digital health interventions (DHIs) for improving the physical activity and health-related quality of life (HRQoL) in HD patients. The 24-week prospective study enrolled 31 clinically stable HD patients. All participants were assigned home exercises and provided with wearable devices. Dietary and exercise information was uploaded to a health management platform. Suggestions about diet and exercise were provided, and a social media group was created. Physical performance testing was performed at baseline and during weeks 4, 8, 12, 16 and 24. HRQoL and nutritional status were evaluated. A total of 25 participants completed the study. After the interventions, the daily step count increased 1658 steps. The 10-time-repeated sit-to-stand test reduced by 4.4 s, the sit-to-stand transfers in 60 s increased 12 repetitions, the distance of six-minute walk test (6MWT) increased by 55.4 m. The mental health components and burden of kidney disease of the Kidney Disease Quality of Life survey, and subjective global assessment (SGA) scores improved. By Spearman correlation, the monthly step count correlated positively with 6MWT and SGA. DHIs that combined wearable devices, a health management platform, and social media could strengthen physical activity and improve the HRQoL and nutrition of maintenance HD patients. The results outline a new model to promote healthy lifestyle behaviors in HD patients.

Constructing a prediction model for physiological parameters for malnutrition in hemodialysis patients.

A retrospective analysis of the improvement in the health condition of patients undergoing hemodialysis was done to understand the important factors that can affect malnutrition in these patients. In this study, data from patients who underwent hemodialysis between 2010 and 2015 in a regional hospital in Yunlin County were collected from the Taiwan Society of Nephrology-Kidney Transplantation database. A total of 1049 medical records from 300 patients with age over 20 and underwent hemodialysis were collected for this study. A decision tree C5.0 and logistic regression were used to identify 40 independent variables, as well as the association of the dependent variable albumin. Then, the C5.0 decision tree, logistic regression, and support vector machine (SVM) methods were applied to find a combination of factors that contributed to malnutrition in patients undergoing hemodialysis. Predictive models were established. Finally, a receiver operating characteristic curve and confusion matrix was used to evaluate the standard of performance of these models. All analytical methods indicated that "age" was an important factor. In particular, the best predictive model was the SVM-model 4, with a training accuracy rate of 98.95% and test accuracy rate of 66.89%, identified that "age" and 15 other important factors were the most related to hemodialysis. The findings of this study can be used as a reference for clinical applications.