Understanding the social context of eating is crucial for promoting healthy eating behaviors. Multimodal smartphone sensor data could provide valuable insights into eating behavior, particularly in mobile food diaries and mobile health apps. However, research on the social context of eating with smartphone sensor data is limited, despite extensive studies in nutrition and behavioral science. Moreover, the impact of country differences on the social context of eating, as measured by multimodal phone sensor data and self-reports, remains under-explored. To address this research gap, our study focuses on a dataset of approximately 24K self-reports on eating events provided by 678 college students in eight countries to investigate the country diversity that emerges from smartphone sensors during eating events for different social contexts (alone or with others). Our analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique trends in each country. We further studied how user and country-specific factors impact social context inference by developing machine learning models with population-level (non-personalized) and hybrid (partially personalized) experimental setups. We showed that models based on the hybrid approach achieve AUC scores up to 0.75 with XGBoost models. These findings emphasize the importance of considering country differences in building and deploying machine learning models to minimize biases and improve generalization across different populations.

翻译：理解饮食的社会背景对促进健康饮食行为至关重要。多模态智能手机传感器数据可为饮食行为提供宝贵洞察，尤其在移动饮食日记和移动健康应用中。然而，尽管营养学与行为科学领域已开展广泛研究，基于智能手机传感器数据的饮食社会背景研究仍较为有限。此外，通过多模态手机传感器数据与自我报告测量的国家差异对饮食社会背景的影响尚待深入探究。为弥补这一研究空白，本研究聚焦涵盖来自8个国家678名大学生约2.4万份饮食事件自我报告的数据集，考察不同社会背景（独自或与他人共餐）下饮食事件中智能手机传感器所呈现的国家多样性。分析表明，部分饮食事件期间的智能手机使用特征在不同国家间具有相似性，而另一些特征则呈现各国特有趋势。我们进一步通过构建人口层面（非个性化）与混合（部分个性化）实验设置的机器学习模型，研究用户及国家特定因素对社会背景推断的影响。结果显示，采用XGBoost模型的混合方法可实现高达0.75的AUC分数。这些发现强调了在构建与部署机器学习模型时考虑国家差异的重要性，以减少偏差并提升在不同人群中的泛化能力。