The social context of eating plays a crucial role in promoting healthy eating behaviors, and smartphone sensing data can provide valuable insights into eating behavior through mobile food diaries and health applications. However, research on the social context of eating with smartphone sensor data is limited, especially regarding country differences. To address this gap, a study was conducted using a smartphone sensing dataset from eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the UK) to investigate the country diversity that emerges from smartphone sensors during eating events for different social contexts (alone or with others). The study focused on approximately 24K self-reports on eating events provided by 678 college students. The analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique behaviors in each country. To further understand how user and country-specific factors impact social context inference in mobile food diaries and health sensing systems, machine learning models were developed with population-level (non-personalized) and hybrid (partially personalized) experimental setups. The hybrid approach achieved AUC scores up to 0.75 with XGBoost models. These findings have implications for future research on mobile food diaries and health sensing systems as they emphasize the importance of considering country differences when building and deploying machine learning models to minimize biases and improve generalization across different populations. Overall, this study highlights the importance of understanding the social context of eating through smartphone sensing data to promote healthy eating behaviors effectively. It also emphasizes the need to consider country differences when developing machine learning models for mobile food diaries and health sensing systems to ensure their effectiveness across diverse populations.
- When we eat, the people and environment around us can affect how healthy our eating habits are.
- Smartphones can help us keep track of what we eat and give us information about our eating habits.
- Not a lot of research has been done on how different countries' cultures affect their eating habits when using smartphones to track them.
- A study was done in eight different countries to see how people use their smartphones while eating alone or with others.
- Some smartphone behaviors were similar across all countries, but some were different depending on where people lived.
Definitions
- Social context: The people and environment around us that can influence our behavior
- Smartphone sensing data: Information collected by a smartphone's sensors (like the camera or microphone) to understand user behavior
- Mobile food diaries: Apps that allow users to record what they eat throughout the day
- Health applications: Apps that focus on tracking health-related information like exercise, sleep, and diet
- Country diversity: Differences in culture or behavior between different countries
- Machine learning models: Computer programs that learn from data and make predictions based on patterns they find
- AUC scores: A measure of how well a machine learning model predicts outcomes (higher scores mean better predictions)
- Generalization: The ability for a machine learning model to work well with new data it hasn't seen before
Understanding the Social Context of Eating Through Smartphone Sensing Data
Eating is a social activity, and the context in which we eat can have a major impact on our health. For example, eating with others has been linked to healthier food choices and improved nutrition. However, understanding how different countries approach eating as a social activity is challenging due to limited research on the topic. To address this gap, researchers recently conducted a study using smartphone sensing data from eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay and the UK) to investigate country-level differences in eating behavior during different social contexts (alone or with others). The results of their study provide valuable insights into how mobile food diaries and health applications can be used to promote healthy eating behaviors across diverse populations.
The Study
The study focused on approximately 24K self-reports on eating events provided by 678 college students from the eight countries mentioned above. The analysis revealed that while some smartphone usage features during eating events were similar across countries (e.g., duration), other features exhibited unique behaviors in each country (e.g., number of messages sent). To further understand how user and country-specific factors impact social context inference in mobile food diaries and health sensing systems, machine learning models were developed with population-level (non-personalized) and hybrid (partially personalized) experimental setups. The hybrid approach achieved AUC scores up to 0.75 with XGBoost models.
Implications for Future Research
These findings have implications for future research on mobile food diaries and health sensing systems as they emphasize the importance of considering country differences when building and deploying machine learning models to minimize biases and improve generalization across different populations. It also highlights the need for more research into understanding how different cultures view eating as a social activity so that effective interventions can be designed to promote healthy behaviors among diverse populations around the world.
Conclusion
Overall, this study highlights the importance of understanding the social context of eating through smartphone sensing data to promote healthy eating behaviors effectively across diverse populations around the world. By leveraging machine learning techniques such as XGBoost models alongside population-level datasets from multiple countries, researchers are able to gain valuable insights into how people interact with one another while dining – information that could ultimately help us create better interventions for promoting healthier dietary habits globally
Understanding the social context of eating is crucial for promoting healthy eating behaviors by providing timely interventions. Multimodal smartphone sensing data has the potential to provide valuable insights into eating behavior, particularly in mobile food diaries and mobile health applications. However, research on the social context of eating with smartphone sensor data is limited, despite extensive study 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, we present a study using a smartphone sensing dataset from eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the UK). Our study focuses on a set of approximately 24K self-reports on eating events provided by 678 college students 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 behaviors 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 have implications for future research on mobile food diaries and mobile health sensing systems, emphasizing the importance of considering country differences in building and deploying machine learning models to minimize biases and improve generalization across different populations.
