Your browser doesn't support javascript.
Big Data Collection in Vulnerable Populations: Application to Real World Clinical and Public Health Settings
Obesity Facts ; 14(SUPPL 1):163-164, 2021.
Artigo em Inglês | EMBASE | ID: covidwho-1255726
ABSTRACT

Introduction:

Obesity is a major public health problem worldwide and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposures at the individual, community, and societal levels. However, monitoring and evaluating such changes is challenging. Development in the fields of behaviour change science, public health, clinical paediatrics, technology, citizen science and Big Data analytics can be harnessed to implement multidisciplinary research addressing the prevention and treatment of child and adolescent obesity at a population level. The H2020 project “BigO Big Data Against Childhood Obesity” (http//bigoprogram.eu) is one example of such research efforts. Following the emergence of the COVID-19 pandemic, the BigO research team in Ireland adapted study procedures to ensure collection of Big Data could continue with modified procedures.

Aim:

To present the approach used for BigO data collection in Ireland during the COVID-19 pandemic and to explore changes in data collection over time.

Methods:

Step 1 reviewed and sought approval for ethical and regulatory procedures relevant to the collection, monitoring, and storage of personal data collected during the COVID-19 pandemic in children from the general population and those attending a multidisciplinary clinical service for severe obesity. Step 2 explored recruitment strategies and the informed consent and assent process. Step 3 explored the collection of anonymized data including photographs of meals, beverages and advertisements, physical activity metrics, and masked GPS data using geo-hash representing geographical area rather than detailed coordinates. Following aggregation, analysis, and visualization of collected data descriptive statistics were used to explore patterns of behaviour in the population over time in order to better understand whether the system could be used to monitor behaviours through a period of significant societal change.

Results:

New ethical approval was granted for the updated methods. From 88 secondary schools approached to participate in the study, five agreed to commence an online consent process with parents and students. In the school setting, 700 children were eligible for study inclusion and 178 consented to participate, respectively. For the clinical study, images of outdoor advertisements collected from participants in Ireland changed during lock-down periods as children had less access to outdoor space and use of study smartwatches was discontinued to adhere to local infection control policies. Please see Table 1 for further details on results of the clinical study.

Conclusions:

Real-time collection of Big Data was possible through a period of societal upheaval though the expected volume of data was reduced. Such data may prove an important tool for monitoring interventions at the level of the individual child or at the population level for this vulnerable group.

Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: EMBASE Tipo de estudo: Estudo prognóstico Idioma: Inglês Revista: Obesity Facts Ano de publicação: 2021 Tipo de documento: Artigo

Similares

MEDLINE

...
LILACS

LIS


Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: EMBASE Tipo de estudo: Estudo prognóstico Idioma: Inglês Revista: Obesity Facts Ano de publicação: 2021 Tipo de documento: Artigo