在关于饮食行为和背景的数字纵向数据中揭示过度饮食模式
Farzad Shahabi1,2, Boyang Wei3,4, Christopher Romano3
1Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. farzad.shahabi@northwestern.edu.
NPJ digital medicine
|September 17, 2025
概括
过度饮食是一个主要的健康问题. 该研究使用可穿戴传感器和心理数据确定了五种不同的过度饮食模式,为个性化肥胖干预铺平了道路.
科学领域:
- 行为科学 行为科学
- 肥胖问题研究研究
- 数字健康数字健康
背景情况:
- 过度饮食是导致肥胖的重要因素,这是一个紧迫的公共卫生问题.
- 了解过度饮食行为的细微差别对于开发有效的干预措施至关重要.
研究的目的:
- 调查导致肥胖个体过度饮食的行为,心理和上下文因素.
- 通过被动感应和生态瞬间评估 (EMA) 来识别不同的过度进食表型.
主要方法:
- "感觉为什么"研究监测了65名在自由生活环境中患有肥胖的人.
- 数据收集涉及穿戴式摄像头,移动应用程序,饮食召回和657天的EMA.
- 半监督学习应用于EMA衍生特征,以识别过度饮食的表型.
主要成果:
- 使用EMA和被动传感数据,高准确度预测过量进食事件 (平均AUROC=0.86,平均AUPRC=0.84).
- 他们确定了五种不同的过度饮食表型:"外卖宴会"",晚餐餐厅狂欢"",晚餐渴望"",不受控制的快乐饮食"和"压力驱动的晚餐吃东西".
- 这些表型突出了影响过度饮食的因素的复杂相互作用.
结论:
- 过度饮食是多方面的,受行为,心理和环境因素的结合影响.
- 鉴定到的过度饮食表型为开发个性化干预措施打击肥胖提供了基础.
- 被动感应和EMA数据为现实环境中的饮食行为提供了宝贵的见解.
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