The Combination of Sensor Digital Kariasa Early Detection Prototype and Health Education for Self-Management in Preventing Recurrent Ischemic Stroke.

Autor: Kariasa, I Made, Nurachmah, Elly, Setyowati, S., Koestoer, Raldi Artono
Předmět:
Zdroj: SAGE Open Nursing; 12/6/2022, Vol. 8, p1-10, 10p
Abstrakt: Introduction: Recurrent stroke is one of the concerns that not only causes functional disability but also economic and psychosocial problems. Self-management is one of the indicators to predict recurrent stroke. Field observations indicate there is currently no tool to increase the survivors' self-awareness. Objective: The study aimed to investigate if an early detection tool and health education can improve patient self-awareness toward self-management in ischemic stroke patients in order to prevent recurrent ischemic stroke. Methods: This study consisted of two stages. In the first stage, the study used research and development methods to develop a digital sensor tool named Sensor Digital Kariasa (SenDiKa). In the second stage, the study used a quasi-experimental design with a pretest–posttest control group involving 44 postischemic stroke patients who were selected by using consecutive sampling. The subjects were divided into intervention and control groups, and the length of the intervention was 12 weeks. Results: This study found a significant difference between the two groups (P <.001). The intervention group who used the early detection tool and received health education showed better self-management compared to the control group. The use of SenDiKa early detection prototype and health education for self-management was perceived useful and gave positive effect to the improvement of self-management in poststroke patients to prevent recurrent stroke. Conclusion: The combination of SenDiKa early detection prototype and health education for self-management can be used for patients to identify the major risk factors of recurrent stroke, such as blood pressure, blood sugar, and cholesterol. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index