# Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity

arXiv: https://arxiv.org/abs/2306.00709 · HTML: https://mlsanity.com/paper/2306.00709/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training](https://mlsanity.com/paper/2404.17391/) (2024)
- [Are Anxiety Detection Models Generalizable? A Cross-Activity and Cross-Population Study Using Wearables](https://mlsanity.com/paper/2504.03695/) (2025)
- [DySTAN: Joint Modeling of Sedentary Activity and Social Context from Smartphone Sensors](https://mlsanity.com/paper/2512.02025/) (2025)

## Abstract (arXiv)

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.