了解生态瞬间评估方法,感知行为和响应能力之间的关系:交叉研究分析
Diane Cook1, Aiden Walker1, Bryan Minor1
1Department of Psychology, College of Arts and Sciences, Washington State University, 3160 Folsom Blvd, Sacramento, WA, 95816, United States, 1 5093354985.
JMIR mHealth and uHealth
|April 10, 2025
概括
生态瞬间评估 (EMA) 的响应率和质量受到参与者因素和研究设计的影响. 优化提示时间和持续时间可以增强对福祉研究的数据收集.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 数据科学数据科学数据科学
背景情况:
- 生态瞬间评估 (EMA) 收集实时福利数据,但在响应一致性和准确性方面面临挑战.
- 传感器数据集成提供了一种方法来对EMA的响应进行上下文化,并分析模式.
研究的目的:
- 在各种研究中检查EMA反应模式及其与感知行为的关系.
- 调查影响EMA响应率 (RR) 和质量的因素,包括提示时间,问题数量和行为背景.
- 探索参与者人口统计和研究设计对EMA数据收集的影响.
主要方法:
- 分析了9项临床研究中来自454名参与者的146,753个EMA提示.
- 收集并发的传感器数据 (智能手表/智能家居),以提取行为数字标记.
- 检查响应率,完整性,质量,与传感器数据对齐,以及响应系列的建模.
主要成果:
- EMA的平均响应率 (RR) 为79.95%,其中88.37%的响应被完全完成.
- 晚上和平日观察到更高的反应率,年龄组之间存在差异.
- RR与EMA问题的数量有负相关性,与传感器检测到的活动和在家的时间有正相关性;随着时间的推移,响应质量下降.
结论:
- 欧洲药物管理局的反应模式受到参与者的人口统计和研究参数的重大影响.
- 定制EMA策略,例如在活动过渡附近快速定时并最大限度地减少交互持续时间,可以改善RR和数据质量.
- 像游戏化这样的策略可以提高参与者的参与度,并保持响应差异,以获得更强大的数据.
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