用智能手表持续监测患者的数字平台:纵向前性队列研究
Kaio Jia Bin1, Lucas Ramos De Pretto1, Fábio Beltrame Sanchez1
1Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
JMIR formative research
|September 12, 2023
概括
这项研究开发了智能手表数据的安全数字模型,确保隐私符合巴西的一般数据保护法,用于远程患者监控和临床决策.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 可穿戴技术可穿戴技术
背景情况:
- COVID-19大流行加速了数字化转型,增加了对智能手表等可穿戴设备的依赖,以持续监测生命体征.
- 确保可穿戴设备个人健康数据的安全性和隐私性对于其在临床决策中的采用至关重要.
- 巴西的Lei Geral de Proteção de Dados (LGPD) 规定了处理个人数据的严格规则,包括智能手表的健康信息.
研究的目的:
- 开发一种数字化解决方案,用于从可穿戴设备捕获和安全传输数据.
- 确保收集的健康数据随时可用于临床和研究目的,同时遵守法律规定.
- 创建一个支持医疗保健中安全和敏捷数据利用的模型.
主要方法:
- 一个功能模型是根据巴西的Lei Geral de Proteção de Dados (LGPD) 设计的.
- 智能手表数据通过物联网 (IoT) 匿名传输,以便在医院环境中以后识别.
- 一项为期24周的临床试验涉及80名志愿者 (COVID-19幸存者和对照组),以评估平台同步,智能手表准确度和模拟远程监控中的精度.
主要成果:
- 在35周的试验期间,收集了超过1120万条记录,没有系统停机.
- 连续心率数据同步率在24小时内为66%,在2天内为79%,在一周内为91%.
- 分析显示,智能手表对氧和度,血压和心率的测量具有很高的准确性和精度,而COVID-19和对照组之间没有显著差异.
结论:
- 开发的功能模型有效地捕获智能手表数据,并将其匿名提供给医疗保健服务.
- 该模型确保符合数据保护立法,使其能够用于临床决策支持.
- 在模拟的医院外环境中验证了准确性和精度,该解决方案支持远程患者监控.
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