强度被动数据信号与暴饮暴食行为模式之间的关系:从动态系统方法来看
Qinxin Shi1,2, Laura M Thornton3, Robyn Kilshaw1
1Department of Psychology, University of Utah.
概括
来自可穿戴设备的被动数据可以帮助检测神经性 булимия (BN) 和暴饮暴食障碍 (BED) 中的暴饮暴食事件. 步骤模式的稳定性,而不是平均值,可能表明正在接近的狂欢.
科学领域:
- 数字健康数字健康
- 饮食障碍 研究 研究 研究
- 行为科学 行为科学
背景情况:
- 暴饮暴食类型的饮食障碍,包括神经性 булимия (BN) 和暴饮暴食障碍 (BED),对健康构成重大挑战.
- 目前监测暴饮暴食的方法通常依赖于自我报告,这可能是不准确的.
- 将技术整合到医疗保健中为客观数据收集提供了新的途径.
研究的目的:
- 调查被动数据 (心率,动图) 对于识别暴饮暴食事件的有用性.
- 探索动态系统理论对BN或BED患者的被动健康数据的应用.
- 为了确定被动数据模式是否可以作为过度饮食事件的早期指标.
主要方法:
- 利用动态系统理论和基于技术的健康监测.
- 通过恢复记录应用程序收集实时的饮食行为数据.
- 在30天内从1019名参与者的Apple Watches中收集了包括心率和动图在内的被动数据.
主要成果:
- 与对照期相比,在狂饮前2小时的平均心率或步数没有发现显著差异.
- 在导致狂饮事件的动态中,观察到动量和稳定的显著差异.
- 步骤模式的稳定性,而不是平均步骤数量,成为即将到来的暴饮暴食事件的潜在可检测指标.
结论:
- 被动动动图数据,特别是步骤模式稳定性,显示为检测即将到来的暴饮暴食情节的非侵入性指标具有前途.
- 对可穿戴数据的动态系统分析可能会增强对BN和BED的早期检测和干预.
- 未来的研究应该专注于改进这些被动数据指标,以便在饮食障碍管理中临床应用.
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