Effects of a mobile application on improving self‐management of adult patients receiving peritoneal dialysis: A randomized controlled trial

医学 形状记忆合金* 随机对照试验 物理疗法 生活质量(医疗保健) 干预(咨询) 自我管理 社会认知理论 腹膜透析 疾病管理 疾病 内科学 护理部 心理学 数学 组合数学 机器学习 计算机科学 帕金森病 心理治疗师
作者
Yoon Jung Chae,Hee Sun Kim
出处
期刊:Japan Journal of Nursing Science [Wiley]
卷期号:21 (1) 被引量:7
标识
DOI:10.1111/jjns.12555
摘要

Abstract Aim This study aimed to develop a mobile application for improving self‐management and to evaluate its effects in adult patients on peritoneal dialysis (PD). Methods This randomized controlled trial was registered with the Korean Clinical Research Information Service Registry (KCT0007267) and conducted at a tertiary hospital. A self‐management mobile application (SMA) was developed based on social cognitive theory and the ADDIE (analysis, design, development, implementation, and evaluation) model. The SMA includes information about disease management; self‐recording of data on diet, exercise, medication, and health behavior; and healthcare providers' support and feedback. Participants aged 19–65 years were randomly allocated to the intervention group ( n = 27) using the SMA for 10 weeks, and the control group ( n = 26) receiving usual care. PD‐related knowledge and self‐efficacy, PD‐related health behavior, biomarkers, and health‐related quality of life (HRQoL) were surveyed pretest/posttest and analyzed using SPSS 23.0. Results Compared to the controls, the intervention group showed significant improvement in PD‐related knowledge and health behavior, albumin, and hemoglobin. HRQoL domains of symptoms/problems of kidney disease and disease impact on daily activity were improved in the intervention group. Conclusion The SMA is an effective intervention for enhancing health behaviors as well as improving the HRQoL of patients with PD. Without any limitations on time or location, patients with PD can easily use the SMA to monitor their health conditions, efficiently manage their disease, and perform PD‐related behaviors. Nurses can implement high‐quality tailored healthcare by using patients’ lifelog data from the SMA.
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