设计支持双相情感障碍患者的移动应用程序的最小功能集:基本功能和要求的建议。

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2023-06-06 eCollection Date: 2023-06-01 DOI:10.1007/s41666-023-00134-5
Saeedeh Heydarian, Alia Shakiba, Sharareh Rostam Niakan Kalhori
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引用次数: 0

摘要

对提供心理健康服务的移动应用程序进行的研究得出结论,精神障碍患者倾向于使用此类应用程序来保持心理健康平衡。技术可能有助于管理和监测双相情感障碍(BP)等问题。这项研究分四个步骤进行,以确定为BP患者设计移动应用程序的特点,包括(1)文献搜索,(2)分析现有的移动应用程序以检查其效率,(3)采访BP患者以发现他们的需求,以及4)使用动态叙事调查探索专家的观点。文献搜索和手机应用程序分析产生了45个功能,后来在专家们对该项目进行调查后,这些功能减少到了30个。这些特征包括:情绪监测、睡眠时间表、能量水平评估、易怒、言语水平、沟通、性活动、自信心水平、自杀念头、内疚感、注意力集中度、攻击性、焦虑、食欲、吸烟或吸毒、血压、患者体重和药物副作用、提醒、情绪数据量表,收集数据的图表,将收集的数据提交给心理学家,教育信息,使用应用程序向患者发送反馈,以及情绪评估的标准测试。分析的第一阶段应该考虑专家和患者的观点调查,情绪和药物跟踪,以及与其他处于相同情况的人的沟通是最需要考虑的特征。本研究已经确定了用于管理和监测双相情感障碍患者的应用程序的必要性,以最大限度地提高效率,最大限度地减少复发和副作用。
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The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements.

Research conducted on mobile apps providing mental health services has concluded that patients with mental disorders tend to use such apps to maintain mental health balance technology may help manage and monitor issues like bipolar disorder (BP). This study was conducted in four steps to identify the features of designing a mobile application for BP-affected patients including (1) a literature search, (2) analyzing existing mobile apps to examine their efficiency, (3) interviewing patients affected with BP to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Literature search and mobile app analysis resulted in 45 features, which were later reduced to 30 after the experts were surveyed about the project. The features included the following: mood monitoring, sleep schedule, energy level evaluation, irritability, speech level, communication, sexual activity, self-confidence level, suicidal thoughts, guilt, concentration level, aggressiveness, anxiety, appetite, smoking or drug abuse, blood pressure, the patient's weight and the side effects of medication, reminders, mood data scales, diagrams or charts of the collected data, referring the collected data to a psychologist, educational information, sending feedbacks to patients using the application, and standard tests for mood assessment. The first phase of analysis should consider an expert and patient view survey, mood and medication tracking, as well as communication with other people in the same situation are the most features to be considered. The present study has identified the necessity of apps intended to manage and monitor bipolar patients to maximize efficiency and minimize relapse and side effects.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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