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：理解饮食的社交情境对于促进健康饮食行为至关重要。多模态智能手机传感器数据能够为饮食行为提供有价值的洞察，尤其是在移动饮食记录和移动健康应用中。然而，尽管营养学和行为科学领域已有广泛研究，但利用智能手机传感器数据研究饮食社交情境的工作仍然有限。此外，通过多模态手机传感器数据和自我报告测量的饮食社交情境受国家差异的影响尚未得到充分探索。为填补这一研究空白，本研究聚焦于一个包含约2.4万份饮食事件自我报告的数据集，这些报告来自八个国家的678名大学生，旨在探究不同社交情境（独自进食或与他人共餐）下，智能手机传感器在饮食事件中展现的国家多样性。分析表明，尽管某些智能手机使用特征在各国间相似，但其他特征在每个国家呈现出独特趋势。我们进一步通过开发基于群体层面（非个性化）和混合（部分个性化）实验设置的机器学习模型，研究了用户和国家特定因素对社交情境推断的影响。结果显示，采用混合方法的XGBoost模型最高可获得0.75的AUC分数。这些发现强调了在构建和部署机器学习模型时考虑国家差异的重要性，以减少偏差并提升模型在不同人群中的泛化能力。