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

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          Abstract

          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.

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          Most cited references30

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          Insulin resistance and atherosclerosis.

          The epidemic of obesity in the developed world over the last two decades is driving a large increase in type 2 diabetes and consequentially setting the scene for an impending wave of cardiovascular morbidity and mortality. It is only now being recognized that the major antecedent of type 2 diabetes, insulin resistance with its attendant syndrome, is the major underlying cause of the susceptibility to type 2 diabetes and cardiovascular disease. In metabolic tissues, insulin signaling via the phosphatidylinositol-3-kinase pathway leads to glucose uptake so that in insulin resistance a state of hyperglycemia occurs; other factors such as dyslipidemia and hypertension also arise. In cardiovascular tissues there are two pathways of insulin receptor signaling, one that is predominant in metabolic tissues (mediated by phosphatidylinositol-3-kinase) and another being a growth factor-like pathway (mediated by MAPK); the down-regulation of the former and continued activity of the latter pathway leads to atherosclerosis. This review addresses the metabolic consequences of the insulin resistance syndrome, its relationship with atherosclerosis, and the impact of insulin resistance on processes of atherosclerosis including insulin signaling in cells of the vasculature.
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            Effective treatment of eating disorders: Results at multiple sites.

            We report the results of a study based on 1,428 patients with eating disorders treated at 6 clinics. These patients were consecutively referred over 18 years and used inpatient and outpatient treatment. The subjects were diagnosed with anorexia nervosa, bulimia nervosa, or an eating disorder not otherwise specified. Patients practiced a normal eating pattern with computerized feedback technology, they were supplied with external heat, their physical activity was reduced, and their social habits restored to allow them to return to their normal life. The estimated rate of remission for this therapy was 75% after a median of 12.5 months of treatment. A competing event such as the termination of insurance coverage, or failure of the treatment, interfered with outcomes in 16% of the patients, and the other patients remained in treatment. Of those who went in remission, the estimated rate of relapse was 10% over 5 years of follow-up and there was no mortality. These data replicate the outcomes reported in our previous studies and they compare favorably with the poor long-term remission rates, the high rate of relapse, and the high mortality rate reported with standard treatments for eating disorders.
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              Decelerated and linear eaters: effect of eating rate on food intake and satiety.

              Women were divided into those eating at a decelerated or linear rate. Eating rate was then experimentally increased or decreased by asking the women to adapt their rate of eating to curves presented on a computer screen and the effect on food intake and satiety was studied. Decelerated eaters were unable to eat at an increased rate, but ate the same amount of food when eating at a decreased rate as during the control condition. Linear eaters ate more food when eating at an increased rate, but less food when eating at a decreased rate. Decelerated eaters estimated their level of satiety lower when eating at an increased rate but similar to the control condition when eating at a decreased rate. Linear eaters estimated their level of satiety similar to the control level despite eating more food at an increased rate and higher despite eating less food at a decreased rate. The cumulative satiety curve was fitted to a sigmoid curve both in decelerated and linear eater under all conditions. Linear eaters rated their desire to eat and estimated their prospective intake lower than decelerated eaters and scored higher on a scale for restrained eating. It is suggested that linear eaters have difficulty maintaining their intake when eating rate is dissociated from its baseline level and that this puts them at risk of developing disordered eating. It is also suggested that feedback on eating rate can be used as an intervention to treat eating disorders.
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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                12 May 2020
                May 2020
                : 12
                : 5
                : 1380
                Affiliations
                [1 ]Division of Endocrinology, Metabolism and Diabetes, First Department of Pediatrics, National and Kapodistrian University of Athens Medical School, “Aghia Sophia” Children’s Hospital, 11527 Athens, Greece; peniokassari@ 123456gmail.com (P.K.); evangelia.charmandari@ 123456googlemail.com (E.C.)
                [2 ]Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
                [3 ]Department of Dietetics, Nutrition and Sport, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne VIC 3086, Australia; G.Moschonis@ 123456latrobe.edu.au
                [4 ]Department of Nutrition and Dietetics, Harokopio University of Athens, 70 El Venizelou Avenue, Kallithea, 17671 Athens, Greece; manios@ 123456hua.gr
                [5 ]Department of Biosciences and Nutrition, Karolinska Institutet, 17177 Stockholm, Sweden; Ioannis.Ioakimidis@ 123456ki.se
                [6 ]Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; diou@ 123456mug.ee.auth.gr (C.D.); adelo@ 123456eng.auth.gr (A.D.)
                [7 ]Department of Medicine, Lab of Computing Medical Informatics and Biomedical Imaging Technologies, Aristotle University of Thessaloniki Medical School, 54124 Thessaloniki, Greece; 6.leandros@ 123456gmail.com (L.S.); lekka@ 123456auth.gr (E.L.); nicmag@ 123456med.auth.gr (N.M.)
                Author notes
                [* ]Correspondence: nansymou@ 123456hotmail.com ; Tel.: +30-6937687555
                Author information
                https://orcid.org/0000-0002-2846-7820
                https://orcid.org/0000-0003-3009-6675
                https://orcid.org/0000-0002-2461-1928
                https://orcid.org/0000-0002-2682-5639
                https://orcid.org/0000-0001-8220-8486
                https://orcid.org/0000-0002-0851-6998
                Article
                nutrients-12-01380
                10.3390/nu12051380
                7284613
                32408523
                90fdba7c-5b86-4bdb-845e-a591db9d9bca
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 23 March 2020
                : 07 May 2020
                Categories
                Review

                Nutrition & Dietetics
                childhood obesity,e-health,registries,big data,algorithms,pedobesity,cardiometabolic risk factors

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