Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.
Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.
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DOI：
10.1371/journal.pone.0153085
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
Seto E;Hua J;Wu L;Shia V;Eom S;Wang M;Li Y
智能手机应用程序(App)有助于收集有关行为多个方面的数据，这些数据有助于确定基线模式，并监测旨在促进更健康生活方式的干预措施的进展情况。基于个体的模型可以用来检验行为，如饮食，是否与某些类型模式相对应。本文的目的是展示基于个体的建模方法与一个人的饮食行为，以及这种方法的价值比较典型的回归模型。利用手机APP，从中国西南快速发展的城市昆明的一所大学招收的12名大学生中收集了2周的体力活动和生态瞬时评估数据，以及6天的饮食数据。收集了整个2周的手机GPS数据，根据这些数据确定了每个受试者在活动空间中暴露在不同食物环境中的情况。使用手机加速度计测量体力活动。手机EMA用于评估自我报告的情绪/感觉。餐点和食物组的份量是通过带语音注释的餐点视频确定的。基于个体的回归模型被用来将受试者描述为以下4种饮食类型之一：那些由一天中的时间决定常规份量的人，那些份量能平衡体力活动(能量平衡)的人，那些份量受情绪影响的人，以及那些份量与食物环境有关的人。所有参与者都观察到了对基于电话的行为评估的充分遵守。在所有个体中，有868种食物被记录在案，其中水果、谷物和乳制品占主导地位。平均而言，每个参与者记录了218小时的加速测量和35次EMA反应。对于一些受试者，常规模型能够解释高达47%的份量变化，而能量平衡模型能够解释88%以上的份量变化。在我们所有的受试者中，食物环境是饮食模式的重要预测因素。一般来说，将所有受试者组合到一个集合模型中比单独为每个个体建模效果更差。类型学建模方法对于理解我们队列中的个体饮食行为是有用的。这种方法可能适用于对其他人类行为的研究，特别是那些收集对个人的重复测量的行为，以及那些涉及基于智能手机的行为测量的行为。
Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person’s eating behavior, and the value of such approach compared to typical regression models. Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject’s activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments. Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately. A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement.