LINC:在数字表型研究中保持高质量的被动数据的框架.
Elombe Calvert1, Erlend Lane1, Matthew Flathers1
1Department of Psychiatry, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02115, USA.
Scientific reports
|February 22, 2026
概括
通过系统化设备设置,参与者参与和数据监控的程序,LINC框架改善了数字表型中的被动数据收集. 这种方法在现实智能手机研究中显著提高了数据质量.
科学领域:
- 数字健康数字健康
- 移动健康 (mHealth) 服务提供商
- 行为科学 行为科学
背景情况:
- 基于智能手机的数字表型化在收集高质量的被动数据方面面临挑战,原因是技术障碍和用户参与度低.
- 现有的方法通常严重依赖于数据归算或基于模型的方法,这些方法可能不会准确地反映现实世界的行为.
研究的目的:
- 引入LINC (启动,交互,通知,正确),这是一个最佳实践框架,用于增强在数字表型研究中的被动数据收集.
- 为实现LINC提供实际资源,而不需要专门的技术专业知识.
主要方法:
- 该LINC框架系统化了四个关键领域的操作程序:设备/应用程序配置,参与者参与,实时数据监控和数据收集中断的故障排除.
- 为了证明该框架的可行性,研究人员使用了一项大型观察性研究 (n=373),在2-3周内检查了社交媒体使用和青少年心理健康.
主要成果:
- 该研究使用LINC框架实现了基于GPS的中位数被动数据质量0.92 (IQR:0.59-0.98).
- 75%的参与者超过了0.59的数据质量得分,25%超过了0.98,这表明数据质量很高.
结论:
- 在基于智能手机的数字表型研究中,LINC框架是实现高质量的被动数据的可行和有效方法.
- 这种系统化的方法为归算和基于模型的方法提供了切实可行的替代方案,在现实研究环境中提高了数据可靠性.
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