FEATURESMobile Application as a Learning Aid for Nurses and Nursing Students to Identify and Care for Stroke Patients Pretest and Posttest ResultsBaccin, Camila Rosalia Antunes DNS; Dal Sasso, Grace T. Marcon PhD; Paixão, Crysttian Arantes PhD; de Sousa, Paulino Artur Ferreira PhDAuthor Information Author Affiliations: Clinical Technologies, Information and Informatics in Health and Nursing Research Group, Florianópolis (Drs Baccin and Dal Sasso); Department of Nursing and the Graduate Program in Nursing at the Federal University of Santa Catarina (Dr Dal Sasso); Federal University of Santa Catarina, Campus of Curitibanos (Dr Paixão), Brazil; and Nursing School of Porto, Portugal (Dr de Sousa). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Camila Rosalia Antunes Baccin, DNS, Rua Pereira Oliveira, 287 Bairro Brusque, Lages, Santa Catarina, Brazil CEP 88.501320 (firstname.lastname@example.org). CIN: Computers, Informatics, Nursing: July 2020 - Volume 38 - Issue 7 - p 358-366 doi: 10.1097/CIN.0000000000000623 Buy Metrics Abstract Cerebrovascular accident is a serious public health problem and requires the attention of professionals who can detect, diagnose, and provide care in a timely fashion. A quantitative quasi-experimental study was conducted using a mobile app called mSmartAVC for clinical evaluation of nursing care at the bedside. The study aimed at measuring the knowledge of nurses and nursing students in the detection and care of cerebrovascular accident. In this study, a total of 115 nurses from health services in the South of Brazil and 35 nursing students of a community university participated. The stages focused on development, modeling of clinical cases, problem-based learning, pretest (before) app use, and posttest (after) use of the app. The results of the pretest and posttest corrections showed a substantial statistical difference (P < .001), indicating a significant knowledge gain after the use of the app, particularly in terms of the detection scales and interpretation of the imaging tests. The mSmartAVC app used at the bedside supported decision-making for detection and nursing care. It was possible to confirm that the use of mobile apps plays an essential role as a learning tool for nurses and nursing students. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.