计算模型的验证,以表征从视频编码的食中累积摄入曲线的计算模型
Alaina L Pearce1,2, Timothy R Brick3,4
1Social Science Research Institute, Pennsylvania State University, University Park, PA, United States.
Frontiers in nutrition
|August 17, 2023
概括
计算模型可以从视频中准确地描述饮食模式,即使没有连续的体重数据. 这种方法允许对儿童和饮食障碍患者的饮食动态进行详细分析.
科学领域:
- 计算建模计算建模
- 行为科学是一种行为科学.
- 营养科学 营养科学
背景情况:
- 观察编码捕捉了饮食行为,但错过了动态时间模式.
- 通用饮食监测器捕捉了饮食动态,但有严格的协议不适合儿童.
- 需要一种方法来分析严格的协议限制的人群的饮食模式.
研究的目的:
- 评估计算模型从视频编码的食中表征累积摄入曲线的能力.
- 为了确定视频编码的食是否可以取代连续的食重量测量,以分析摄入曲线.
- 为了评估使用平均咬伤大小对模型性能的影响.
主要方法:
- 累积摄入曲线使用基斯利夫的二次方程和托马斯的逻辑普通微分方程 (LODE) 模型进行估计.
- 模拟数据包括恒定咬伤,可变咬伤和咬伤测量误差 (平均咬伤大小).
- 用参数恢复,合适度和预测错误来评估模型性能.
主要成果:
- 在所有模拟数据条件中,模型性能一致.
- 在视频编码的食物中使用平均咬伤大小并没有显著影响累积摄入曲线估计的准确性.
- 洛德模型显示的参数比二次模型更为明显,这表明对个人饮食模式的敏感性更高.
结论:
- 计算模型可以有效地描述从视频编码的饭菜中的累积摄入曲线,即使有平均咬伤大小估计.
- 这种方法将饮食行为的研究扩展到儿童和饮食障碍患者等人群.
- 这些发现为识别与过度消费相关的饮食模式和开发有针对性的治疗提供了新的途径.
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