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

Abstract

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.

The impact of COVID-19 on Physical Activity of Czech children

“…This was true both for the boys and the girls. We have not confirmed the increase in habitual outdoor PAs like other studies [ 26 , 44 ]. A recently published study by Ng et al on Czech adolescents’ remote school and health experiences during spring lockdown reports more PA.…”

“…The weekly activities mean score was lower during-COVID compared with pre-COVID, but not enough to be statistically significant. The most active weekday was Saturday for both males and females, which is in line with other studies underlying the importance of accessibility to outdoor spaces for sufficient PA during the pandemic [19,26,44].…”

“…The COVID-19 pandemic has created unprecedented situations by which strict community lockdowns have negatively affected the movement and health behavior in children [ 11 ]. Negative consequences like increasing obesity [ 12 , 13 ], pain [ 14 ], depression, anxiety, feelings of loneliness [ 15 – 17 ], and sleep disturbances [ 18 , 19 ] are closely related to PA levels [ 31 , 43 , 44 ]. Our findings are similar to others [ 28 , 30 , 45 ] demonstrating a significant decline in PA in children during COVID compared to pre-COVID.…”

“…Our findings are similar to others [ 28 , 30 , 45 ] demonstrating a significant decline in PA in children during COVID compared to pre-COVID. This problem is quite complex however, due to the multifactorial nature of various changes in PA during COVID such as: social status and family income [ 19 ], city dwelling versus villages or suburbs [ 44 ], parental education levels [ 18 ], pre-pandemic sports habits [ 46 ], the level of national restrictions, and age [ 26 ] among others.…”

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

“…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.…”