2 research outputs found
Inferring the Spatial Distribution of Physical Activity in Children Population from Characteristics of the Environment
Obesity affects a rising percentage of the children and adolescent
population, contributing to decreased quality of life and increased risk for
comorbidities. Although the major causes of obesity are known, the obesogenic
behaviors manifest as a result of complex interactions of the individual with
the living environment. For this reason, addressing childhood obesity remains a
challenging problem for public health authorities. The BigO project
(https://bigoprogram.eu) relies on large-scale behavioral and environmental
data collection to create tools that support policy making and intervention
design. In this work, we propose a novel analysis approach for modeling the
expected population behavior as a function of the local environment. We
experimentally evaluate this approach in predicting the expected physical
activity level in small geographic regions using urban environment
characteristics. Experiments on data collected from 156 children and
adolescents verify the potential of the proposed approach. Specifically, we
train models that predict the physical activity level in a region, achieving
81% leave-one-out accuracy. In addition, we exploit the model predictions to
automatically visualize heatmaps of the expected population behavior in areas
of interest, from which we draw useful insights. Overall, the predictive models
and the automatic heatmaps are promising tools in gaining direct perception for
the spatial distribution of the population's behavior, with potential uses by
public health authorities.Comment: Accepted version to be published in 2020, 42nd Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Montreal, Canad
A Methodology for Obtaining Objective Measurements of Population Obesogenic Behaviors in Relation to the Environment
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.Comment: This paper has been accepted for publication at the Statistical
Journal of the International Association for Official Statistics,
https://content.iospress.com/journals/statistical-journal-of-the-iao