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

Diou, Christos; Sarafis, Ioannis; Papapanagiotou, Vasileios; Alagialoglou, Leonidas; Lekka, Irini; Filos, Dimitrios; Stefanopoulos, Leandros; Kilintzis, Vasileios; Maramis, Christos; Karavidopoulou, Youla; Maglaveras, Nikos; Ioakimidis, Ioannis; Charmandari, Evangelia; Kassari, Penio; Tragomalou, Athanasia; Mars, Monica; Nguyen, Thien-An Ngoc; Kechadi, Tahar; O’Donnell, Shane; Doyle, Gerardine; Browne, Sarah; Malley, Grace O’; Heimeier, Rachel A.; Riviou, Katerina; Koukoula, Evangelia; Filis, Konstantinos; Hassapidou, Maria; Pagkalos, Ioannis; Ferri, Daniel; Perez, Isabel; Delopoulos, Anastasios

doi:10.1016/j.appet.2020.104924

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…The OPdashboard constitutes a live system, where more and more schools are added over time. By April 2020, BigO was deployed in 33 schools and 2 pediatric clinics in 5 different European cities (Athens, Thessaloniki, and Larissa from Greece; Stockholm from Sweden; and Dublin from Ireland) [ 40 ]. Ethical approval was received from each country separately.…”

Section: Resultsmentioning

confidence: 99%

“…More than 3700 children, aged 9 to 18 years, participated in data collection, between the start of March 2018 until the end of March 2020, providing approximately 107 years of accelerometry data and 73 years of GPS data [ 40 ]. This was the first time such an amount of big data has been collected contemporaneously, on an individual basis within a region, allowing for the extraction of childhood obesogenic behavior accurately.…”

Section: Resultsmentioning

confidence: 99%

“…A steady flow of recruitment was followed; however, an expected decrease occurred during the summer because of the summer holidays and school closure. Most of the children used the app during the 2-week period, while a small minority continued to use it for more than 2 weeks More than 3700 children, aged 9 to 18 years, participated in data collection, between the start of March 2018 until the end of March 2020, providing approximately 107 years of accelerometry data and 73 years of GPS data [40]. This was the first time such an amount of big data has been collected contemporaneously, on an individual basis within a region, allowing for the extraction of childhood obesogenic behavior accurately.…”

Section: Data Overviewmentioning

confidence: 99%

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Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Filos

Lekka

Kilintzis

et al. 2021

JMIR Mhealth Uhealth

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Background Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project “BigO: Big Data Against Childhood Obesity” aims to tackle childhood obesity by creating new sources of evidence based on big data. Objective This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app. Methods The OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children’s obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard’s functionality was evaluated during a focus group discussion with public health experts. Results The preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children’s eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children’s physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard’s potential to aid in a better understanding of the interplay between children’s obesogenic behaviors and the environment, and improvements were suggested. Conclusions Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Data Overviewmentioning

confidence: 99%

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Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Filos

Lekka

Kilintzis

et al. 2021

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

show abstract

“…The intake monitoring solutions that we have seen so far can be used in both experimental studies, as well as in realworld applications at individual or population level. In this section, we outline three categories of use-cases, and discuss practical considerations resulting from previous experience in the SPLENDID (Maramis et al, 2014), i-Prognosis (Hadjidimitriou et al, 2016 and BigO (Diou et al, 2020) projects.…”

Section: Use-cases and Impact In Eating Behavior Research And Practicementioning

confidence: 99%

“…A similar approach was followed in the BigO project (Filos et al (2021); Diou et al (2020)), in which approximately 4000 children and adolescent participants used their mobile phones to measure physical activity behavior, as well as statistics related to visits to various location types. The intake information provided by the users of BigO was only based on pictures and self-reports, but would have been much richer if the proposed intake measurement methods had also been applied.…”

Section: Use For Policy Decision Supportmentioning

confidence: 99%

Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned

Diou,

Kyritsis,

Papapanagiotou

et al. 2022

Preprint

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The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurement of in-meal eating behavior. These allow the study of eating behavior outside the laboratory in free-living conditions, without the need for video recordings and laborious manual annotations. In this paper, we present a high-level overview of our recent work on intake monitoring using a smartwatch, as well as methods using an in-ear microphone. We also present evaluation results of these methods in challenging, real-world datasets. Furthermore, we discuss use-cases of such intake monitoring tools for advancing research in eating behavior, for improving dietary monitoring, as well as for developing evidence-based health policies. Our goal is to inform researchers and users of intake monitoring methods regarding (i) the development of new methods based on commercially available devices, (ii) what to expect in terms of effectiveness, and (iii) how these methods can be used in research as well as in practical applications.

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Knowledge management tools and mechanisms for evidence-informed decision-making in the WHO European Region: a scoping review

El-Jardali,

Bou-Karroum,

Hilal

et al. 2023

Health Res Policy Sys

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Background Knowledge management (KM) emerged as a strategy to promote evidence-informed decision-making. This scoping review aims to map existing KM tools and mechanisms used to promote evidence-informed health decision-making in the WHO European Region and identify knowledge gaps. Methods Following the Joanna Briggs Institute (JBI) guidance for conducting scoping reviews, we searched Medline, PubMed, EMBASE, the Cochrane library, and Open Grey. We conducted a descriptive analysis of the general characteristics of the included papers and conducted narrative analysis of the included studies and categorized studies according to KM type and phase. Results Out of 9541 citations identified, we included 141 studies. The KM tools mostly assessed are evidence networks, surveillance tools, observatories, data platforms and registries, with most examining KM tools in high-income countries of the WHO European region. Findings suggest that KM tools can identify health problems, inform health planning and resource allocation, increase the use of evidence by policymakers and stimulate policy discussion. Conclusion Policymakers and funding agencies are called to support capacity-building activities, and future studies to strengthen KM in the WHO European region particularly in Eastern Europe and Central Asia. An updated over-arching strategy to coordinate KM activities in the WHO European region will be useful in these efforts.

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

Cited by 3 publications

References 16 publications

Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned

Knowledge management tools and mechanisms for evidence-informed decision-making in the WHO European Region: a scoping review

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