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

Abstract

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.

25th ACM International Conference on Multimodal Interaction (ICMI)

References in corpus (6)

• Smartphone Sensing for the Well-being of Young Adults: A Review
• The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations
• Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK
• Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing
• Alone or With Others? Understanding Eating Episodes of College Students with Mobile Sensing
• Sensing Eating Events in Context: A Smartphone-Only Approach

Cited by in corpus (3)

• M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training
• DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling
• Learning About Social Context from Smartphone Data: Generalization Across Countries and Daily Life Moments