Now showing 1 - 4 of 4
  • Publication
    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.
    Scopus© Citations 9  585
  • Publication
    Mobile health (mHealth) applications for children in treatment for obesity: A randomised feasibility study
    The W82GO Service delivers evidence-based obesity treatment to families of children and adolescents with obesity (BMI>98th percentile) and has a positive impact on obesity.
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  • Publication
    Establishing consensus on key public health indicators for the monitoring and evaluating childhood obesity interventions: a Delphi panel study
    Background: Childhood obesity is influenced by myriad individual, societal and environmental factors that are not typically reflected in current interventions. Socio-ecological conditions evolve and require ongoing monitoring in terms of assessing their influence on child health. The aim of this study was to identify and prioritise indicators deemed relevant by public health authorities for monitoring and evaluating childhood obesity interventions. Method: A three-round Delphi Panel composed of experts from regions across Europe, with a remit in childhood obesity intervention, were asked to identify indicators that were a priority in their efforts to address childhood obesity in their respective jurisdictions. In Round 1, 16 panellists answered a series of open-ended questions to identify the most relevant indicators concerning the evaluation and subsequent monitoring of interventions addressing childhood obesity, focusing on three main domains: built environments, dietary environments, and health inequalities. In Rounds 2 and 3, panellists rated the importance of each of the identified indicators within these domains, and the responses were then analysed quantitatively. Results: Twenty-seven expert panellists were invited to participate in the study. Of these, 16/27 completed round 1 (5 9% response rate), 14/16 completed round 2 (87.5% response rate), and 8/14 completed the third and final round (57% response rate). Consensus (defined as > 70% agreement) was reached on a total of 45 of the 87 indicators (49%) across three primary domains (built and dietary environments and health inequalities), with 100% consensus reached for 5 of these indicators (6%). Conclusion: Forty-five potential indicators were identified, pertaining primarily to the dietary environment, built environment and health inequalities. These results have important implications more widely for evaluating interventions aimed at childhood obesity reduction and prevention.
      45Scopus© Citations 6
  • Publication
    Mobile Health Apps in Pediatric Obesity Treatment: Process Outcomes From a Feasibility Study of a Multicomponent Intervention
    Background: Multicomponent family interventions underline current best practice in childhood obesity treatment. Mobilehealth (mHealth) adjuncts that address eating and physical activity behaviors have shown promise in clinical studies.Objective: This study aimed to describe process methods for applying an mHealth intervention to reduce the rate of eating andmonitor physical activity among children with obesity.Methods: The study protocol was designed to incorporate 2 mHealth apps as an adjunct to usual care treatment for obesity.Children and adolescents (aged 9-16 years) with obesity (BMI ≥98th centile) were recruited in person from a weight managementservice at a tertiary health care center in the Republic of Ireland. Eligible participants and their parents received informationleaflets, and informed consent and assent were signed. Participants completed 2 weeks of baseline testing, including behavioraland quality of life questionnaires, anthropometry, rate of eating by Mandolean, and physical activity level using a smart watchand the myBigO smartphone app. Thereafter, participants were randomized to the (1) intervention (usual clinical care+Mandoleantraining to reduce the rate of eating) or (2) control (usual clinical care) groups. Gender and age group (9.0-12.9 years and 13.0-16.9years) stratifications were applied. At the end of a 4-week treatment period, participants repeated the 2-week testing period.Process evaluation measures included recruitment, study retention, fidelity parameters, acceptability, and user satisfaction.Results: A total of 20 participants were enrolled in the study. A web-based randomization system assigned 8 participants to theintervention group and 12 participants to the control group. Attrition rates were higher among the participants in the interventiongroup (5/8, 63%) than those in the control group (3/12, 25%). Intervention participants undertook a median of 1.0 training mealusing Mandolean (25th centile 0, 75th centile 9.3), which represented 19.2% of planned intervention exposure. Only 50% (9/18)of participants with smart watches logged physical activity data. Significant differences in psychosocial profile were observedat baseline between the groups. The Child Behavior Checklist (CBCL) mean total score was 71.7 (SD 3.1) in the interventiongroup vs 57.6 (SD 6.6) in the control group, t-test P<.001, and also different among those who completed the planned protocolcompared with those who withdrew early (CBCL mean total score 59.0, SD 9.3, vs 67.9, SD 5.6, respectively; t-test P=.04). Conclusions: A high early attrition rate was a key barrier to full study implementation. Perceived task burden in combination with behavioral issues may have contributed to attrition. Low exposure to the experimental intervention was explained by poor acceptability of Mandolean as a home-based tool for treatment. Self-monitoring using myBigO and the smartwatch was acceptable among this cohort. Further technical and usability studies are needed to improve adherence in our patient group in the tertiary setting.
      160Scopus© Citations 18