Cáceres Rivera Diana Isabel, Rojas Luz Mileyde Jaimes, Rojas Lyda Z, Gomez Diana Canon, Castro Ruiz David Andrés, López Romero Luis Alberto
Facultad de Enfermería, Universidad Cooperativa de Colombia, Bucaramanga, Colombia.
Centro de Investigaciones, Fundación Cardiovascular de Colombia, Floridablanca, Colombia.
JMIR Form Res. 2024 Mar 18;8:e33868. doi: 10.2196/33868.
Advances in health have highlighted the need to implement technologies as a fundamental part of the diagnosis, treatment, and recovery of patients at risk of or with health alterations. For this purpose, digital platforms have demonstrated their applicability in the identification of care needs. Nursing is a fundamental component in the care of patients with cardiovascular disorders and plays a crucial role in diagnosing human responses to these health conditions. Consequently, the validation of nursing diagnoses through ongoing research processes has become a necessity that can significantly impact both patients and health care professionals.
We aimed to describe the process of developing a mobile app to validate the nursing diagnosis "intolerance to physical activity" in patients with acute myocardial infarction.
We describe the development and pilot-testing of a mobile system to support data collection for validating the nursing diagnosis of activity intolerance. This was a descriptive study conducted with 11 adults (aged ≥18 years) who attended a health institution for highly complex needs with a suspected diagnosis of coronary syndrome between August and September 2019 in Floridablanca, Colombia. An app for the clinical validation of activity intolerance (North American Nursing Diagnosis Association [NANDA] code 00092) in patients with acute coronary syndrome was developed in two steps: (1) operationalization of the nursing diagnosis and (2) the app development process, which included an evaluation of the initial requirements, development and digitization of the forms, and a pilot test. The agreement level between the 2 evaluating nurses was evaluated with the κ index.
We developed a form that included sociodemographic data, hospital admission data, medical history, current pharmacological treatment, and thrombolysis in myocardial infarction risk score (TIMI-RS) and GRACE (Global Registry of Acute Coronary Events) scores. To identify the defining characteristics, we included official guidelines, physiological measurements, and scales such as the Piper fatigue scale and Borg scale. Participants in the pilot test (n=11) had an average age of 63.2 (SD 4.0) years and were 82% (9/11) men; 18% (2/11) had incomplete primary schooling. The agreement between the evaluators was approximately 80% for most of the defining characteristics. The most prevalent characteristics were exercise discomfort (10/11, 91%), weakness (7/11, 64%), dyspnea (3/11, 27%), abnormal heart rate in response to exercise (2/10, 20%), electrocardiogram abnormalities (1/10, 9%), and abnormal blood pressure in response to activity (1/10, 10%).
We developed a mobile app for validating the diagnosis of "activity intolerance." Its use will guarantee not only optimal data collection, minimizing errors to perform validation, but will also allow the identification of individual care needs.
健康领域的进展凸显了将技术作为有健康问题风险或已出现健康问题的患者诊断、治疗及康复基本组成部分加以应用的必要性。为此,数字平台已证明其在识别护理需求方面的适用性。护理是心血管疾病患者护理的基本组成部分,在诊断人类对这些健康状况的反应方面发挥着关键作用。因此,通过持续研究过程对护理诊断进行验证已成为一项必要工作,这会对患者和医护人员产生重大影响。
我们旨在描述开发一款移动应用程序以验证急性心肌梗死患者“身体活动不耐受”这一护理诊断的过程。
我们描述了一个移动系统的开发及预测试,该系统用于支持数据收集以验证活动不耐受的护理诊断。这是一项描述性研究,研究对象为2019年8月至9月间在哥伦比亚弗洛里达布兰卡一家针对有高度复杂需求的医疗机构就诊、疑似患有冠状动脉综合征的11名成年人(年龄≥18岁)。分两步开发了一款用于急性冠状动脉综合征患者活动不耐受(北美护理诊断协会 [NANDA] 编码00092)临床验证的应用程序:(1)护理诊断的操作化;(2)应用程序开发过程,包括对初始需求的评估、表格的开发和数字化以及预测试。使用κ指数评估两名评估护士之间的一致程度。
我们开发了一种表格,其中包括社会人口统计学数据、医院入院数据、病史、当前药物治疗以及心肌梗死溶栓风险评分(TIMI - RS)和全球急性冠状动脉事件注册(GRACE)评分。为确定定义特征,我们纳入了官方指南、生理测量数据以及诸如派珀疲劳量表和博格量表等量表。预测试的参与者(n = 11)平均年龄为63.2(标准差4.0)岁,82%(9/11)为男性;18%(2/11)小学教育未完成。对于大多数定义特征,评估者之间的一致性约为80%。最常见的特征为运动不适(10/11,91%)、虚弱(7/11,64%)、呼吸困难(3/11,27%)、运动时心率异常(2/10,20%)、心电图异常(1/10,9%)以及活动时血压异常(1/10,10%)。
我们开发了一款用于验证“活动不耐受”诊断的移动应用程序。其使用不仅将确保最佳数据收集,将验证过程中的误差降至最低,还将有助于识别个体护理需求。