TABLE 1.

Examples of big data contributions to obesity research, by research dimension¹

Research dimension	Research question	Data needed	Potential data sources	Methods needed	Potential outcomes
Food/nutrient intake and eating behavior—nutritional theme	How do food characteristics (taste, texture) interact with social setting to control satiety?	Rich food composition and features database [volume/variety] Objective measures of social setting: eating regularity and duration, family, school, and peer eating activity [volume/variety] Timing of food intake and objective/subjective satiety measures in real-time, not, e.g., daily average [velocity]	National food consumption surveys EFSA food composition database (97) Publicly available nutrition and food composition applications—as documented (98, 99)	Automated or semiautomated food profiling/food composition spectrometry in FAIR databases Automated meal sensing (wearable sensors/AI-driven meal picture analysis, observational restaurants) Mobile-based surveys	Research: integrated biological, economic, and social sciences understanding of the determinants of food intake Innovations: novel food types with high satiety index Policies: health policy promoting specific eating practices in schools, families, and workplaces, according to healthy and sustainable FBDGs; tailored to context
Social food environments—psycho-social theme	What role does food and diet portrayal in digital media (advertising and social media) play in food and beverage intake?	Real-time food-intake assessments and subjective/objective determinants of satiety [volume, velocity] Social media and real-world advertising exposure logs [variety] Source of digital media advertising/marketing (e.g., via industry or from users themselves)	National food consumption surveys Social media content, behavior and network data via APIs/web scraping Regional/local census and statistics bureaus	Mobile/sensor-based food intake measurements Citizen/crowd-sourced mapping tools Monitoring of digital advertisements (e.g., via Web browser/application plugins)	Research: role of social media, influence, and social networks in obesogenic behaviors Innovations: effective healthy diet–targeted advertisement campaigns/nudging Policies: food advertisement policies for more targeted restriction/promotion of certain foods
Built environment—environmental theme	What combinations of features of the built environment influence physical activity levels and weight status?	GIS-derived points of interest per region [volume/variety] Socioeconomic, education, ability, and health characteristics [volume/variety] Real-time, GPS-correlated physical activity measurement [volume/velocity]	National food consumption surveys GIS/Google/Foursquare USDA Food Environment Atlas Regional/local census and statistics bureaus (e.g., Eurostat) Map the Meal Gap (US) (100) Food Environment Atlas (US) (101) Global Physical Activity Observatory (102)	Computational tools for scraping environment characteristics, linked in real-time to user activity Mobile-based tracking of anthropometry and activity levels	Research: relation between built environment, physical activity, health-related behaviors, and obesity prevalence Innovations: interactive intervention/policy design tools; tools for real-time monitoring of policies and interventions Policies: toward healthier, physical-activity-promoting, and accessible urban and suburban environments

¹AI, artificial intelligence; API, Application Programming Interface; EFSA, European Food Safety Authority; FAIR, findable, accessible, interoperable, reusable; FBDG, food-based dietary guideline; GIS, Geographic Information Systems; GPS, Global Positioning System.