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; O’Malley, Grace; Heimeier, Rachel A.; Riviou, Katerina; Koukoula, Evangelia; Filis, Konstantinos; Hassapidou, Maria; Pagkalos, Ioannis; Ferri, Daniel; Perez, Isabel; Delopoulos, Anastasios

doi:10.1109/embc44109.2020.9175361

Cited by 9 publications

(4 citation statements)

References 16 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These data are anonymized and used to create statistical models to understand how behavior and the environment influence the prevalence of childhood obesity including its underlying factors. If BigO could be adapted to target children under 5 years of age through their families, it could provide direct contribution to the monitoring of this indicator ( 71 ).…”

Section: Resultsmentioning

confidence: 99%

Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization’s Triple Billion Targets

Fraisl

See

Estevez

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

Achieving the health and well-being related Sustainable Development Goals (SDGs) and the World Health Organization’s (WHO) Triple Billion Targets depends on informed decisions that are based on concerted data collection and monitoring efforts. Even though data availability has been increasing in recent years, significant gaps still remain for routine surveillance to guide policies and actions. The COVID-19 crisis has shown that more and better data and strengthened health information systems are needed to inform timely decisions that save lives. Traditional sources of data such as nationally representative surveys are not adequate for addressing this challenge alone. Additionally, the funding required to measure all health and well-being related SDG indicators and Triple Billion Targets using only traditional sources of data is a challenge to achieving efficient, timely and reliable monitoring systems. Citizen science, public participation in scientific research and knowledge production, can contribute to addressing some of these data gaps efficiently and sustainably when designed well, and ultimately, could contribute to the achievement of the health and well-being related SDGs and Triple Billion Targets. Through a systematic review of health and well-being related indicators, as well as citizen science initiatives, this paper aims to explore the potential of citizen science for monitoring health and well-being and for mobilizing action toward the achievement of health and well-being related targets as outlined in the SDG framework and Triple Billion Targets. The results demonstrate that out of 58 health and well-being related indicators of the SDGs and Triple Billion Targets covered in this study, citizen science could potentially contribute to monitoring 48 of these indicators and their targets, mostly at a local and community level, which can then be upscaled at a national level with the projection to reach global level monitoring and implementation. To integrate citizen science with official health and well-being statistics, the main recommendation is to build trusted partnerships with key stakeholders including National Statistical Offices, governments, academia and the custodian agencies, which is mostly the WHO for these health and well-being related targets and indicators.

show abstract

Section: Resultsmentioning

confidence: 99%

Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization’s Triple Billion Targets

Fraisl

See

Estevez

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Decisions between union and intersection-based methods for integrated datasets, and the development of robust statistical approaches for gene prioritization are essential for enhancing bioinformatics analyses. Additionally, the creation and application of extensive "big" datasets, (e.g., the BigO project) [57], require advancements in statistical, machine learning, artificial intelligence, and database methodologies, enabling bioinformatics to address complex obesity-related questions more effectively.…”

Section: The Promise Of Bioinformaticsmentioning

confidence: 99%

Considerations on efforts needed to improve our understanding of the genetics of obesity

Ghosh,

Bouchard

2024

Int J Obes

View full text Add to dashboard Cite

“…For example, the revised RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework (22) describes the need to assess organisational and patient characteristics and perspectives to understand specific contexts. Limited data suggest that there is no consistent approach to assessment, diagnosis, management, or signposting for obesity in paediatric services in practice locally, but rather standalone interventions or partial interventions may be delivered in routine practice by individuals and services regionally (23)(24)(25)(26)(27). In Ireland, to realise the implementation of the HSE model of care for treating obesity, a fundamental understanding is required around (a) what HPs can currently deliver in terms of treatment and (b) what is needed to build capacity throughout the health system to improve identification, clinical assessment, treatment access, and treatment delivery and outcomes related to obesity in CYP.…”

Section: Introductionmentioning

confidence: 99%

Addressing child and adolescent obesity management in Ireland: identifying facilitators and barriers in clinical practice

Ferdous¹,

Arthurs²,

Tully³

et al. 2023

Front. Pediatr.

View full text Add to dashboard Cite

BackgroundIreland’s Model of Care for the Management of Overweight and Obesity outlines a plan for treating adolescent and child obesity (CO). However, engagement with key stakeholders is required to support its implementation and improve health services.AimThis study aims to map the perceived barriers and facilitators related to CO management across healthcare settings, professional disciplines, and regions in the Republic of Ireland (ROI).Materials and methodsAn online cross-sectional survey of registered healthcare professionals (HPs), designed to adhere to the Consolidated Framework for Implementation Research (CFIR), was co-developed by a project team consisting of researchers, healthcare professionals, and patient advocates. The survey was pilot tested with project stakeholders and distributed online to professional groups and via a social media campaign, between September 2021 and May 2022, using “SurveyMonkey.” Data were summarised using descriptive statistics and thematic analyses. Themes were mapped to the CFIR framework to identify the type of implementation gaps that exist for treating obesity within the current health and social care system.ResultsA total of 184 HPs completed the survey including nurses (18%), physicians (14%), health and social care professionals (60%), and other HPs (8%). The majority were female (91%), among which 54% reported conducting growth monitoring with a third (32.6%) giving a diagnosis of paediatric/adolescent obesity as part of their clinical practice. Nearly half (49%) of the HPs reported having the resources needed for clinical assessment. However, 31.5% of the HPs reported having enough “time,” and almost 10% of the HPs reported having no/limited access to suitable anthropometric measurement tools. Most HPs did not conduct obesity-related clinical assessments beyond growth assessment, and 61% reported having no paediatric obesity training. CFIR mapping identified several facilitators and barriers including time for clinical encounters, suitable materials and equipment, adequate training, perceived professional competency and self-efficacy, human equality and child-centredness, relative priorities, local attitudes, referral protocols, and long waiting times.ConclusionsThe findings provide actionable information to guide the implementation of the Model of Care for the Management of Overweight and Obesity in Ireland. Survey findings will now inform a qualitative study to explore implementation barriers and facilitators and prioritise actions to improve child and adolescent obesity management.

show abstract

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

Cited by 9 publications

References 16 publications

Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization’s Triple Billion Targets

Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization’s Triple Billion Targets

Considerations on efforts needed to improve our understanding of the genetics of obesity

Addressing child and adolescent obesity management in Ireland: identifying facilitators and barriers in clinical practice

Contact Info

Product

Resources

About