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

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canad

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions

The Effect of a Comprehensive Life-Style Intervention Program of Diet and Exercise on Four Bone-Derived Proteins, FGF-23, Osteopontin, NGAL and Sclerostin, in Overweight or Obese Children and Adolescents

The adipose and bone tissues demonstrate considerable interconnected endocrine function. In the present study, we determined the concentrations of fibroblast growth factor-23 (FGF-23), osteopontin, neutrophil gelatinase-associated lipocalin (NGAL) and sclerostin in 345 children and adolescents who were overweight or obese (mean age ± SD mean: 10.36 ± 0.16 years; 172 males, 173 females; 181 prepubertal; and 164 pubertal) before and after their participation in a comprehensive life-style intervention program of diet and exercise for one year. Following the one-year life-style interventions, there was a significant decrease in BMI (p < 0.01), FGF-23 (p < 0.05), osteopontin (p < 0.01) and NGAL (p < 0.01), and an increase in sclerostin (p < 0.01) concentrations. BMI z-score (b = 0.242, p < 0.05) and fat mass (b = 0.431, p < 0.05) were the best positive predictors and waist-to-height ratio (WHtR) (b = −0.344, p < 0.05) was the best negative predictor of the change of osteopontin. NGAL concentrations correlated positively with HbA1C (b = 0.326, p < 0.05), WHtR (b = 0.439, p < 0.05) and HOMA-IR (b = 0.401, p < 0.05), while BMI (b = 0.264, p < 0.05), fat mass (b = 1.207, p < 0.05), HDL (b = 0.359, p < 0.05) and waist circumference (b = 0.263, p < 0.05) were the best positive predictors of NGAL. These results indicate that FGF-23, osteopontin, NGAL and sclerostin are associated with being overweight or obese and are altered in relation to alterations in BMI. They also indicate a crosstalk between adipose tissue and bone tissue and may play a role as potential biomarkers of glucose metabolism. Further studies are required to delineate the physiological mechanisms underlying this association in children and adolescents

A National e-Health Program for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence in Greece

Obesity in childhood and adolescence represents one of the most challenging public health problems of the 21st century owing to its epidemic proportions worldwide and the associated significant morbidity, mortality and public health costs. In Greece, the prevalence of overweight and obesity in childhood and adolescence exceeds 30–35%. To address the increasing prevalence of overweight and obesity in children and adolescents in our country, we developed the ‘National e-Health Program for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence’, which provides specific and detailed guidance to all primary health care physicians about the personalized management of children and adolescents with overweight or obesity. In the present study we evaluated 2400 children and adolescents [mean age ± SEM: 10.10 ± 0.09 years.; Males: 1088, Females: 1312; Obesity (n = 1370, 57.1%), Overweight (n = 674, 28.1%), normal BMI (n = 356, 14.8%)], who followed the personalized multi-disciplinary management plan specified by the ‘National e-Health Program for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence’, and were studied prospectively for 1 year. We demonstrated that at the end of the first year, the prevalence of obesity decreased by 32.1%, the prevalence of overweight decreased by 26.7%, and the cardiometabolic risk factors improved significantly. These findings indicate that our National e-Health Program is effective at reducing the prevalence of overweight and obesity in childhood and adolescence after one year of intervention in the largest sample size reported to date

The Effect of a Life-Style Intervention Program of Diet and Exercise on Irisin and FGF-21 Concentrations in Children and Adolescents with Overweight and Obesity

Overweight and obesity in childhood and adolescence represent major public health problems of our century, and account for increased morbidity and mortality in adult life. Irisin and Fibroblast Growth Factor 21 (FGF-21) have been proposed as prognostic and/or diagnostic biomarkers in subjects with obesity and metabolic syndrome, because they increase earlier than other traditional biomarkers. We determined the concentrations of Irisin and FGF-21 in children and adolescents with overweight and obesity before and after one year of a life-style intervention program of diet and physical exercise and explored the impact of body mass index (BMI) reduction on the concentrations of Irisin, FGF-21 and other cardiometabolic risk factors. Three hundred and ten (n = 310) children and adolescents (mean age ± SD: 10.5 ± 2.9 years) were studied prospectively. Following one year of the life-style intervention program, there was a significant decrease in BMI (p = 0.001), waist-to-hip ratio (p = 0.024), waist-to-height ratio (p = 0.024), and Irisin concentrations (p = 0.001), and an improvement in cardiometabolic risk factors. There was no alteration in FGF-21 concentrations. These findings indicate that Irisin concentrations decreased significantly as a result of BMI reduction in children and adolescents with overweight and obesity. Further studies are required to investigate the potential role of Irisin as a biomarker for monitoring the response to lifestyle interventions and for predicting the development of cardiometabolic risk factors

A Comprehensive, Multidisciplinary, Personalized, Lifestyle Intervention Program Is Associated with Increased Leukocyte Telomere Length in Children and Adolescents with Overweight and Obesity

Leucocyte telomere length (LTL) is a robust marker of biological aging and is associated with obesity and cardiometabolic risk factors in childhood and adolescence. We investigated the effect of a structured, comprehensive, multidisciplinary, personalized, lifestyle intervention program of healthy diet and physical exercise on LTL in 508 children and adolescents (239 males, 269 females; 282 prepubertal, 226 pubertal), aged 10.14 ± 0.13 years. Participants were classified as obese (n = 267, 52.6%), overweight (n = 174, 34.2%), or of normal BMI (n = 67, 13.2%) according to the International Obesity Task Force (IOTF) cutoff points and were studied prospectively for one year. We demonstrated that LTL increased significantly after 1 year of the lifestyle interventions, irrespective of gender, pubertal status, or body mass index (BMI). Waist circumference was the best negative predictor of LTL at initial assessment. The implementation of the lifestyle interventions also resulted in a significant improvement in clinical (BMI, BMI z-score and waist to height ratio) and body composition indices of obesity, inflammatory markers, hepatic enzymes, glycated hemoglobin (HbA1C), quantitative insulin sensitivity check index (QUICKI), and lipid profile in all participants. These findings indicate that the increased LTL may be associated with a more favorable metabolic profile and decreased morbidity later in life

Novel e-Health Applications for the Management of Cardiometabolic Risk Factors in Children and Adolescents in Greece

Obesity in childhood and adolescence represents a major health problem. Novel e-Health technologies have been developed in order to provide a comprehensive and personalized plan of action for the prevention and management of overweight and obesity in childhood and adolescence. We used information and communication technologies to develop a “National Registry for the Prevention and Management of Overweight and Obesity” in order to register online children and adolescents nationwide, and to guide pediatricians and general practitioners regarding the management of overweight or obese subjects. Furthermore, intelligent multi-level information systems and specialized artificial intelligence algorithms are being developed with a view to offering precision and personalized medical management to obese or overweight subjects. Moreover, the Big Data against Childhood Obesity platform records behavioral data objectively by using inertial sensors and Global Positioning System (GPS) and combines them with data of the environment, in order to assess the full contextual framework that is associated with increased body mass index (BMI). Finally, a computerized decision-support tool was developed to assist pediatric health care professionals in delivering personalized nutrition and lifestyle optimization advice to overweight or obese children and their families. These e-Health applications are expected to play an important role in the management of overweight and obesity in childhood and adolescence

Longitudinal Association of Telomere Dynamics with Obesity and Metabolic Disorders in Young Children

In adults, short leukocyte telomere length (LTL) is associated with metabolic disorders, such as obesity and diabetes mellitus type 2. These associations could stem from early life interactions between LTL and metabolic disorders. To test this hypothesis, we explored the associations between LTL and metabolic parameters as well as their evolution over time in children with or without obesity at baseline. Seventy-three (n = 73) children attending our Outpatient Clinic for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence, aged 2–10 years (mean ± SD: 7.6 ± 2.0 years), were followed for 2 to 4 years. Anthropometric, clinical, and biological (including LTL by Southern blot) measurements were performed annually. Baseline LTL correlated negatively with BMI (p = 0.02), fat percentage (p = 0.01), and blood glucose (p = 0.0007). These associations persisted after adjustments for age and sex. No associations were found between LTL attrition during the follow-up period and any of the metabolic parameters. In young children, obesity and metabolic disturbances were associated with shorter telomeres but were not associated with more pronounced LTL attrition. These results suggest that short telomeres contribute to the development of obesity and metabolic disorders very early in life, which can have a major impact on health

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project “BigO: Big data against childhood obesity” used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions