捕获发作预测所需的实时健康数据的数字干预:形成性共同设计和可用性研究的协议 (ATMOSPHERE研究)
Emily E V Quilter1, Samuel Downes1, Mairi Therese Deighan1
1School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom.
JMIR research protocols
|September 19, 2024
概括
在ATMOSPHERE研究中,开发了一个可穿戴的原型,用于在中实时预测发作. 这种以人为中心的技术旨在改善发作管理和患者的生活质量.
科学领域:
- 神经学 神经学
- 数字健康数字健康
- 可穿戴技术可穿戴技术
背景情况:
- 是一种慢性神经系统疾病,其特点是不可预测的发作,严重影响患者的生活质量.
- 现有的治疗方法无法完全控制大量患者的发作.
- 发作的不可预测性是一个主要问题,推动了对先进的发作预测解决方案的需求.
研究的目的:
- 开发和评估使用可穿戴技术进行数字干预,以实时,个性化预测发作.
- 报告工作流的协议,专注于设计和测试用于捕获实时输入数据的原型,用于预测建模.
- 在合作中设计一个原型并进行"野外"可用性研究以进行改进.
主要方法:
- 基于人的方法用于原型设计和可用性测试.
- 第1阶段涉及与患者和医疗保健专业人员共同设计会议,以定义用户要求.
- 第二阶段将是一项"野外"的可用性研究,部署一个功能原型为期4周,收集混合方法可行性,可接受性和参与度的数据.
主要成果:
- 第1阶段的共同设计涉及22名个人,导致了一个功能性原型,可以跟踪基于证据和个性化的发作沉物.
- 即将进行的第二阶段可用性研究预计将为原型改进和未来开发提供见解.
- 第二阶段预计将于2024年最后一个季度完成.
结论:
- 该ATMOSPHERE研究正在开发一个以用户为中心的,非侵入性的可穿戴设备,用于预测发作.
- 协作设计和可用性测试旨在解决对预测性预测技术的需求.
- 这项技术利用预测分析和个性化机器学习来潜在地改善管理和生活质量.
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