Datasheet3_Predicting food craving in everyday life through smartphone-derived sensor and usage data.csv

BackgroundFood craving relates to unhealthy eating behaviors such as overeating or binge eating and is thus a promising target for digital interventions. Yet, craving varies strongly across the day and is more likely in some contexts (external, internal) than in others. Prediction of food cravings ahead of time would enable preventive interventions.
ObjectiveThe objective of this study was to investigate whether upcoming food cravings could be detected and predicted from passive smartphone sensor data (excluding geolocation information) without the need for repeated questionnaires.
MethodsMomentary food craving ratings, given six times a day for 14 days by 56 participants, served as the dependent variable. Predictor variables were environmental noise, light, device movement, screen activity, notifications, and time of the day recorded from 150 to 30 min prior to these ratings.
ResultsIndividual high vs. low craving ratings could be predicted on the test set with a mean area under the curve (AUC) of 0.78. This outperformed a baseline model trained on past craving values in 85% of participants by 14%. Yet, this AUC value is likely the upper bound and needs to be independently validated with longer data sets that allow a split into training, validation, and test sets.
ConclusionsCraving states can be forecast from external and internal circumstances as these can be measured through smartphone sensors or usage patterns in most participants. This would allow for just-in-time adaptive interventions based on passive data collection and hence with minimal participant burden.

背景：食物渴求与过量进食、暴食等不健康饮食行为密切相关，因此是数字干预的极具潜力的靶标。然而，食物渴求在一天内波动幅度极大，且在部分（外部、内部）情境下的发生概率显著高于其他情境。提前预测食物渴求，可助力开展预防性干预。
研究目的：本研究旨在探究，无需重复填写问卷的前提下，能否通过被动采集的智能手机传感器数据（不包含地理位置信息），检测并预测即将出现的食物渴求。
研究方法：56名参与者连续14天、每日进行6次瞬时食物渴求评分，以此作为因变量。预测变量包括评分前150至30分钟内采集的环境噪音、光照、设备移动、屏幕使用情况、通知推送以及当日时段信息。
研究结果：在测试集上，对个体高、低食物渴求评分的预测平均受试者工作特征曲线下面积（area under the curve, AUC）达到0.78。该模型在85%的参与者中，相比以过往食物渴求值训练的基准模型性能提升了14%。然而，该AUC值大概率为性能上限，需通过可划分为训练集、验证集与测试集的更长数据集开展独立验证。
研究结论：对于多数参与者而言，可通过智能手机传感器或使用模式采集的外部与内部情境信息，预测其食物渴求状态。该方法可基于被动数据采集开展即时自适应干预，从而最大限度降低参与者的负担。