A Methodology for Obtaining Objective Measurements of Population Obesogenic Behaviors in Relation to the Environment

TL;DR

This paper introduces a comprehensive methodology using wearable sensors and online data to objectively measure and analyze population behaviors related to obesity in relation to their environment, aiding public health efforts.

Contribution

It presents a novel, technology-based approach to monitor obesogenic behaviors and environmental factors, integrating data sources while ensuring privacy and data quality.

Findings

Effective correlation of behaviors with environmental factors

Feasibility of large-scale data collection using wearable sensors

Enhanced understanding of obesogenic behavior patterns

Abstract

The way we eat and what we eat, the way we move and the way we sleep significantly impact the risk of becoming obese. These aspects of behavior decompose into several personal behavioral elements including our food choices, eating place preferences, transportation choices, sleeping periods and duration etc. Most of these elements are highly correlated in a causal way with the conditions of our local urban, social, regulatory and economic environment. To this end, the H2020 project "BigO: Big Data Against Childhood Obesity" (http://bigoprogram.eu) aims to create new sources of evidence together with exploration tools, assisting the Public Health Authorities in their effort to tackle childhood obesity. In this paper, we present the technology-based methodology that has been developed in the context of BigO in order to: (a) objectively monitor a matrix of a population's obesogenic behavioral elements using commonly available wearable sensors (accelerometers, gyroscopes, GPS), embedded in smart phones and smart watches; (b) acquire information for the environment from open and online data sources; (c) provide aggregation mechanisms to correlate the population behaviors with the environmental characteristics; (d) ensure the privacy protection of the participating individuals; and (e) quantify the quality of the collected big data.