Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard
BackgroundObesity 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 hea...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2021-07-01
|
| Series: | JMIR mHealth and uHealth |
| Online Access: | https://mhealth.jmir.org/2021/7/e26290 |
| _version_ | 1797735904293421056 |
|---|---|
| author | Dimitris Filos Irini Lekka Vasileios Kilintzis Leandros Stefanopoulos Youla Karavidopoulou Christos Maramis Christos Diou Ioannis Sarafis Vasileios Papapanagiotou Leonidas Alagialoglou Ioannis Ioakeimidis Maria Hassapidou Evangelia Charmandari Rachel Heimeier Grace O'Malley Shane O’Donnell Gerardine Doyle Anastasios Delopoulos Nicos Maglaveras |
| author_facet | Dimitris Filos Irini Lekka Vasileios Kilintzis Leandros Stefanopoulos Youla Karavidopoulou Christos Maramis Christos Diou Ioannis Sarafis Vasileios Papapanagiotou Leonidas Alagialoglou Ioannis Ioakeimidis Maria Hassapidou Evangelia Charmandari Rachel Heimeier Grace O'Malley Shane O’Donnell Gerardine Doyle Anastasios Delopoulos Nicos Maglaveras |
| author_sort | Dimitris Filos |
| collection | DOAJ |
| description |
BackgroundObesity 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.
ObjectiveThis 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.
MethodsThe 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.
ResultsThe 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.
ConclusionsOur 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. |
| first_indexed | 2024-03-12T13:05:49Z |
| format | Article |
| id | doaj.art-dbed3fa0109e49c9bb58c14f359d3a03 |
| institution | Directory Open Access Journal |
| issn | 2291-5222 |
| language | English |
| last_indexed | 2024-03-12T13:05:49Z |
| publishDate | 2021-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR mHealth and uHealth |
| spelling | doaj.art-dbed3fa0109e49c9bb58c14f359d3a032023-08-28T17:01:32ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222021-07-0197e2629010.2196/26290Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention DashboardDimitris Filoshttps://orcid.org/0000-0001-5613-652XIrini Lekkahttps://orcid.org/0000-0001-9305-6561Vasileios Kilintzishttps://orcid.org/0000-0002-9783-6757Leandros Stefanopouloshttps://orcid.org/0000-0002-2682-5639Youla Karavidopoulouhttps://orcid.org/0000-0003-3648-9533Christos Maramishttps://orcid.org/0000-0002-6417-0784Christos Diouhttps://orcid.org/0000-0002-2461-1928Ioannis Sarafishttps://orcid.org/0000-0001-8449-1705Vasileios Papapanagiotouhttps://orcid.org/0000-0001-6834-5548Leonidas Alagialoglouhttps://orcid.org/0000-0002-8361-0589Ioannis Ioakeimidishttps://orcid.org/0000-0002-7208-1722Maria Hassapidouhttps://orcid.org/0000-0003-1027-5772Evangelia Charmandarihttps://orcid.org/0000-0002-0851-6998Rachel Heimeierhttps://orcid.org/0000-0003-4798-1312Grace O'Malleyhttps://orcid.org/0000-0002-2421-3866Shane O’Donnellhttps://orcid.org/0000-0002-3778-705XGerardine Doylehttps://orcid.org/0000-0001-8516-4431Anastasios Delopouloshttps://orcid.org/0000-0001-8220-8486Nicos Maglaverashttps://orcid.org/0000-0002-4919-0664 BackgroundObesity 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. ObjectiveThis 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. MethodsThe 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. ResultsThe 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. ConclusionsOur 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.https://mhealth.jmir.org/2021/7/e26290 |
| spellingShingle | Dimitris Filos Irini Lekka Vasileios Kilintzis Leandros Stefanopoulos Youla Karavidopoulou Christos Maramis Christos Diou Ioannis Sarafis Vasileios Papapanagiotou Leonidas Alagialoglou Ioannis Ioakeimidis Maria Hassapidou Evangelia Charmandari Rachel Heimeier Grace O'Malley Shane O’Donnell Gerardine Doyle Anastasios Delopoulos Nicos Maglaveras Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard JMIR mHealth and uHealth |
| title | Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard |
| title_full | Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard |
| title_fullStr | Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard |
| title_full_unstemmed | Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard |
| title_short | Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard |
| title_sort | exploring associations between children s obesogenic behaviors and the local environment using big data development and evaluation of the obesity prevention dashboard |
| url | https://mhealth.jmir.org/2021/7/e26290 |
| work_keys_str_mv | AT dimitrisfilos exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT irinilekka exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT vasileioskilintzis exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT leandrosstefanopoulos exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT youlakaravidopoulou exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT christosmaramis exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT christosdiou exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT ioannissarafis exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT vasileiospapapanagiotou exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT leonidasalagialoglou exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT ioannisioakeimidis exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT mariahassapidou exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT evangeliacharmandari exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT rachelheimeier exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT graceomalley exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT shaneodonnell exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT gerardinedoyle exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT anastasiosdelopoulos exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard AT nicosmaglaveras exploringassociationsbetweenchildrensobesogenicbehaviorsandthelocalenvironmentusingbigdatadevelopmentandevaluationoftheobesitypreventiondashboard |