arXiv subjects

Ioannis Ioakimidis

Publications and source records attributed to Ioannis Ioakimidis.

3 recordsLinked to original sources

arXiv2020

Collecting big behavioral data for measuring behavior against obesity

Obesity is currently affecting very large portions of the global population. Effective prevention and treatment starts at the early age and requires objective knowledge of population-level behavior on the region/neighborhood scale. To this end, we present a system for extracting and collecting behavioral information on the individual-level objectively and automatically. The behavioral information is related to physical activity, types of visited places, and transportation mode used between them. The system employs indicator-extraction algorithms from the literature which we evaluate on publicly available datasets. The system has been developed and integrated in the context of the EU-funded BigO project that aims at preventing obesity in young populations.

cs.CY↗

arXiv2020

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.

cs.CY↗

arXiv2019

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.

cs.CY↗