通过智能手机衍生的传感器和使用数据预测日常生活中的食物渴望
Thomas Schneidergruber1, Jens Blechert2,3, Samuel Arzt1
1Department Creative Technologies, University of Applied Sciences Salzburg, Salzburg, Austria.
Frontiers in digital health
|July 12, 2023
概括
研究人员现在可以使用智能手机传感器数据预测食物渴望,从而实现及时的数字干预. 这种被动数据收集方法可以根据环境和用户行为模式预测渴望状态.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 人与计算机的交互
背景情况:
- 食物的渴望与不健康的饮食行为有关,如过度饮食和过度饮食.
- 渴望每天都在波动,受到各种内部和外部环境的影响.
- 预测食物渴望可以促进主动的数字干预.
研究的目的:
- 为了确定即将到来的食物渴望是否可以通过被动智能手机传感器数据来检测和预测.
- 评估预测渴望的可行性,而不需要频繁的参与者问卷.
主要方法:
- 从56名参与者收集了14天内每天6次的瞬间食物渴望评级,每天6次.
- 使用了被动智能手机传感器数据 (噪音,光,运动,屏幕活动,通知,一天中的时间) 记录在渴望评级之前.
- 从预测变量中排除了地理位置数据.
主要成果:
- 预测了个人高与低的渴望评级,曲线下的平均面积 (AUC) 为0.78.
- 这种预测模型在85%的参与者中以14%的速度超过了使用过去渴望数据的基线模型.
- 实现的AUC被认为是上限,需要使用更大的数据集进行验证以进行培训,验证和测试.
结论:
- 通过分析通过智能手机传感器和使用模式捕获的外部和内部情况,可以预测食物渴望状态.
- 这种预测能力使得准时适应性干预成为可能.
- 被动数据收集将参与者的负担降至最低,为可扩展的数字健康解决方案铺平了道路.
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