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Health related quality of life impairment in children with overweight is highly context dependent
Oral Abstracts alone, ATC largely had no impact on outcomes. Secondary pre-post assessment of the Loozit® program showed mean reductions [95% CI] in BMI z-score (−0.13 [−0.20, −0.06]) and WHtR (−0.02 [−0.03, −0.01]) in both arms, with significant improvements in total cholesterol, triglycerides and most psychosocial outcomes. Conclusion: The Loozit® group program is feasible to deliver medium-long term as a community-based adolescent weight management intervention. However, adjunctive ATC did not provide further benefits to the group program. Further work is needed to optimize technological support for adolescent weight-loss maintenance. http://dx.doi.org/10.1016/j.orcp.2012.08.079 O078 Preventing obesity among adolescent girls in lowincome secondary schools: One-year outcomes of the NEAT Girls cluster randomized controlled trial D. Lubans 1,∗ , P. Morgan 1 , A. Okely 2 , D. Dewar 1 , C. Collins 3 , M. Batterham 4 , R. Callister 5 , T. Finn 1 , R. Plotnikoff 1 1 Priority
Research Centre in Physical Activity and Nutrition, School of Education, University of Newcastle, Australia 2 Interdisciplinary Educational Research Institute, University of Wollongong, Australia 3 Priority Research Centre in Physical Activity and Nutrition, School of Health Sciences, University of Newcastle, Australia 4 Centre for Statistical and Survey Methodology, University of Wollongong, Australia 5 Priority Research Centre in Physical Activity and Nutrition, School of Biomedical Sciences and Pharmacy, University of Newcastle, Australia Aim: To evaluate the impact of a 12-month multi-component school-based obesity prevention program for adolescent girls, known as NEAT Girls (Nutrition and Enjoyable Activity for Teen Girls). Method: Twelve secondary schools in low-income communities in the Hunter and Central Coast regions of New South Wales, Australia were randomized to the NEAT Girls intervention or a wait list control group. Participants were adolescent girls aged 12—14 years (N = 357). The multi-component school-based intervention was based on Social Cognitive Theory and included teacher professional development, enhanced school sport sessions, interactive seminars, nutrition workshops, lunchtime physical activity sessions, handbooks and pedometers for self-monitoring, parent newsletters, and text messaging for social support. The
39 following outcomes were assessed at baseline and 12 months: height and weight [to calculate body mass index (BMI) and BMI z-score], percentage body fat (bioelectrical impedance analysis), physical activity (accelerometers), screen time (questionnaire), dietary intake (food frequency questionnaire) and self-esteem (questionnaire). Results: After 12 months, changes in BMI (adjusted mean difference [95% CI] = −0.19 [−0.70 to 0.33]), BMI z-score (−0.08 [−0.20 to 0.04]), and percentage body fat (−1.09 [−2.88 to 0.70]) were in favor of the intervention, but were not statistically different from those in the control group. Changes in screen time were statistically significant (−30.67 min/day [−62.43 to −1.06]), but there were no group by time effects for physical activity, dietary behavior or self-esteem. Conclusion: A school-based intervention tailored for adolescent girls from schools located in lowincome communities did not significantly reduce BMI gain. However, changes in body composition were of a magnitude similar to previous studies and may be associated with clinically important health outcomes. http://dx.doi.org/10.1016/j.orcp.2012.08.080 O079 Health related quality of life impairment in children with overweight is highly context dependent S. Petersen 1,2,3,∗ , M. Moodie 1,2 , H. Mavoa 2 , G. Faeamani 4 , G. Waqa 5 , K. Fotu 5 , B. Swinburn 1 1 WHO
Collaborating Centre for Obesity Prevention, Deakin University, Melbourne, Australia 2 Deakin Health Economics, Deakin University, Melbourne, Australia 3 Clinical Sciences, Umeå University, Sweden 4 School of Population Health, University of Auckland, New Zealand 5 Fiji School of Medicine, CMNHS, Fiji National University, Suva, Fiji Aim: The literature indicates that overweight/obesity may be associated with HRQoL impairment. This study investigates the relationship between HRQoL and overweight/obesity in children living in two high income countries (Australia, New Zealand [NZ]), and two lower middle-income countries (Fiji, Tonga). Method: The study population included over nineteen thousand 10—18-year-old children participating in the Pacific Obesity Prevention in Communities (OPIC) Project; 2955 from Australia, 5639 from NZ (of which 826 identified themselves
40 as Tongan), 8947 from Fiji, and 2164 from Tonga. Height and weight was measured and weight status classified according to the IOTF-BMI cut-offs. Self-reported HRQoL was assessed by the PedsQL (adolescent module). Results: Overweight/obesity was found in around 40% of the children, but prevalence varied between countries. In Australia and NZ, there was a stepwise decrease in HRQoL by increasing weight status, a pattern which was also seen in the Tongan subgroup in NZ. In Fiji, HRQoL was impaired in children who had overweight, but not in those who had obesity, whereas in Tonga, HRQoL increased stepwise with rising weight status. Thus the negative relationship between HRQoL and higher weight status found in Australia, NZ and also Tongan children in NZ was not seen in Fijian children and was the opposite of that seen in children living in Tonga. Conclusion: There seem to be a contrast between the HRQoL—weight status relationship in children from higher and lower income countries and country of living may have greater influence on this relationship than ethnicity. http://dx.doi.org/10.1016/j.orcp.2012.08.081 O080 Obesity and built environment: Does the association hold longitudinally? M. Daniel 1,2,∗ , C. Paquet 1 , N. Howard 1 , N. Coffee 1 , A. Taylor 3 , R. Adams 4 , G. Hugo 5 1 Social
Epidemiology and Evaluation Research Group, Sansom Institute for Health Research and School of Population Health, University of South Australia, Adelaide, Australia 2 Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Victoria, Australia 3 Population Research and Outcome Studies, Discipline of Medicine, The University of Adelaide, Australia 4 The Health Observatory, The Queen Elizabeth Hospital Campus, The University of Adelaide, Australia 5 Discipline of Geography, Environment and Population, The University of Adelaide, Australia Aim: Mostly cross-sectional but few longitudinal studies have linked aspects of the built environment to obesity. This study investigated whether characteristics of one’s local food environment and public open space (POS) predicted change in central obesity over 10 years of follow up. Method: Biomedical cohort data collected across three waves between 2000 and 2010 were used to assess change in central obesity (≥94 cm
Oral Abstracts (males)/≥80 cm (females)). Built environmental features within 1000-m road distance of participants’ residence were obtained using a Geographic Information System. Food environment was operationalised as the number of fast-food restaurants, and unhealthful and healthful food stores. POS was characterised by the number, median size, and greenness of such areas. Analyses were conducted on participants with at least two clinical measurements and who had not moved before the second wave of data collection (n = 2796). Binary growth models accounting for spatial clustering and participants’ age, gender, education and income were used to estimate associations between baseline residential area characteristics and 10-year change in obesity status across the three waves of follow up. Results: No evidence was found of associations between participants’ local food environment or POS, and change in central obesity over 10 years. Conclusion: These results do not support a longitudinal association between the built environment and central obesity. Research considering the dynamic nature of the built environment and assessing how both the environment and obesity co-vary over time is necessary, however, before ruling out built environmental effects on changes in obesity status. http://dx.doi.org/10.1016/j.orcp.2012.08.082 O081 Added sugar intake of Australian children and adolescents J. Louie 1,2 , A. Rangan 3 , V. Flood 1 , T. Gill 2,∗ 1 School
of Health Sciences, Faculty of Health and Behavioral Sciences, The University of Wollongong, Australia 2 Cluster for Public Health Nutrition, Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, The University of Sydney, Australia 3 Discipline of Nutrition and Metabolism, School of Molecular Biosciences, The University of Sydney, Australia Aim: To examine the added sugars intake of Australian children and adolescents. Method: Data from the 2007 Australian National Children’s Nutrition and Physical Activity Survey was used. ‘Added sugars’ was defined as any forms of sugars (including fruit juice concentrate) added to the food during manufacturing or cooking. Added sugars content of foods were estimated based on the composition, recipe and/or ingredients list (where available) of the foods. Under- and over-reporters identified using the Goldberg cut-
Research Centre in Physical Activity and Nutrition, School of Education, University of Newcastle, Australia 2 Interdisciplinary Educational Research Institute, University of Wollongong, Australia 3 Priority Research Centre in Physical Activity and Nutrition, School of Health Sciences, University of Newcastle, Australia 4 Centre for Statistical and Survey Methodology, University of Wollongong, Australia 5 Priority Research Centre in Physical Activity and Nutrition, School of Biomedical Sciences and Pharmacy, University of Newcastle, Australia Aim: To evaluate the impact of a 12-month multi-component school-based obesity prevention program for adolescent girls, known as NEAT Girls (Nutrition and Enjoyable Activity for Teen Girls). Method: Twelve secondary schools in low-income communities in the Hunter and Central Coast regions of New South Wales, Australia were randomized to the NEAT Girls intervention or a wait list control group. Participants were adolescent girls aged 12—14 years (N = 357). The multi-component school-based intervention was based on Social Cognitive Theory and included teacher professional development, enhanced school sport sessions, interactive seminars, nutrition workshops, lunchtime physical activity sessions, handbooks and pedometers for self-monitoring, parent newsletters, and text messaging for social support. The
39 following outcomes were assessed at baseline and 12 months: height and weight [to calculate body mass index (BMI) and BMI z-score], percentage body fat (bioelectrical impedance analysis), physical activity (accelerometers), screen time (questionnaire), dietary intake (food frequency questionnaire) and self-esteem (questionnaire). Results: After 12 months, changes in BMI (adjusted mean difference [95% CI] = −0.19 [−0.70 to 0.33]), BMI z-score (−0.08 [−0.20 to 0.04]), and percentage body fat (−1.09 [−2.88 to 0.70]) were in favor of the intervention, but were not statistically different from those in the control group. Changes in screen time were statistically significant (−30.67 min/day [−62.43 to −1.06]), but there were no group by time effects for physical activity, dietary behavior or self-esteem. Conclusion: A school-based intervention tailored for adolescent girls from schools located in lowincome communities did not significantly reduce BMI gain. However, changes in body composition were of a magnitude similar to previous studies and may be associated with clinically important health outcomes. http://dx.doi.org/10.1016/j.orcp.2012.08.080 O079 Health related quality of life impairment in children with overweight is highly context dependent S. Petersen 1,2,3,∗ , M. Moodie 1,2 , H. Mavoa 2 , G. Faeamani 4 , G. Waqa 5 , K. Fotu 5 , B. Swinburn 1 1 WHO
Collaborating Centre for Obesity Prevention, Deakin University, Melbourne, Australia 2 Deakin Health Economics, Deakin University, Melbourne, Australia 3 Clinical Sciences, Umeå University, Sweden 4 School of Population Health, University of Auckland, New Zealand 5 Fiji School of Medicine, CMNHS, Fiji National University, Suva, Fiji Aim: The literature indicates that overweight/obesity may be associated with HRQoL impairment. This study investigates the relationship between HRQoL and overweight/obesity in children living in two high income countries (Australia, New Zealand [NZ]), and two lower middle-income countries (Fiji, Tonga). Method: The study population included over nineteen thousand 10—18-year-old children participating in the Pacific Obesity Prevention in Communities (OPIC) Project; 2955 from Australia, 5639 from NZ (of which 826 identified themselves
40 as Tongan), 8947 from Fiji, and 2164 from Tonga. Height and weight was measured and weight status classified according to the IOTF-BMI cut-offs. Self-reported HRQoL was assessed by the PedsQL (adolescent module). Results: Overweight/obesity was found in around 40% of the children, but prevalence varied between countries. In Australia and NZ, there was a stepwise decrease in HRQoL by increasing weight status, a pattern which was also seen in the Tongan subgroup in NZ. In Fiji, HRQoL was impaired in children who had overweight, but not in those who had obesity, whereas in Tonga, HRQoL increased stepwise with rising weight status. Thus the negative relationship between HRQoL and higher weight status found in Australia, NZ and also Tongan children in NZ was not seen in Fijian children and was the opposite of that seen in children living in Tonga. Conclusion: There seem to be a contrast between the HRQoL—weight status relationship in children from higher and lower income countries and country of living may have greater influence on this relationship than ethnicity. http://dx.doi.org/10.1016/j.orcp.2012.08.081 O080 Obesity and built environment: Does the association hold longitudinally? M. Daniel 1,2,∗ , C. Paquet 1 , N. Howard 1 , N. Coffee 1 , A. Taylor 3 , R. Adams 4 , G. Hugo 5 1 Social
Epidemiology and Evaluation Research Group, Sansom Institute for Health Research and School of Population Health, University of South Australia, Adelaide, Australia 2 Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Victoria, Australia 3 Population Research and Outcome Studies, Discipline of Medicine, The University of Adelaide, Australia 4 The Health Observatory, The Queen Elizabeth Hospital Campus, The University of Adelaide, Australia 5 Discipline of Geography, Environment and Population, The University of Adelaide, Australia Aim: Mostly cross-sectional but few longitudinal studies have linked aspects of the built environment to obesity. This study investigated whether characteristics of one’s local food environment and public open space (POS) predicted change in central obesity over 10 years of follow up. Method: Biomedical cohort data collected across three waves between 2000 and 2010 were used to assess change in central obesity (≥94 cm
Oral Abstracts (males)/≥80 cm (females)). Built environmental features within 1000-m road distance of participants’ residence were obtained using a Geographic Information System. Food environment was operationalised as the number of fast-food restaurants, and unhealthful and healthful food stores. POS was characterised by the number, median size, and greenness of such areas. Analyses were conducted on participants with at least two clinical measurements and who had not moved before the second wave of data collection (n = 2796). Binary growth models accounting for spatial clustering and participants’ age, gender, education and income were used to estimate associations between baseline residential area characteristics and 10-year change in obesity status across the three waves of follow up. Results: No evidence was found of associations between participants’ local food environment or POS, and change in central obesity over 10 years. Conclusion: These results do not support a longitudinal association between the built environment and central obesity. Research considering the dynamic nature of the built environment and assessing how both the environment and obesity co-vary over time is necessary, however, before ruling out built environmental effects on changes in obesity status. http://dx.doi.org/10.1016/j.orcp.2012.08.082 O081 Added sugar intake of Australian children and adolescents J. Louie 1,2 , A. Rangan 3 , V. Flood 1 , T. Gill 2,∗ 1 School
of Health Sciences, Faculty of Health and Behavioral Sciences, The University of Wollongong, Australia 2 Cluster for Public Health Nutrition, Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, The University of Sydney, Australia 3 Discipline of Nutrition and Metabolism, School of Molecular Biosciences, The University of Sydney, Australia Aim: To examine the added sugars intake of Australian children and adolescents. Method: Data from the 2007 Australian National Children’s Nutrition and Physical Activity Survey was used. ‘Added sugars’ was defined as any forms of sugars (including fruit juice concentrate) added to the food during manufacturing or cooking. Added sugars content of foods were estimated based on the composition, recipe and/or ingredients list (where available) of the foods. Under- and over-reporters identified using the Goldberg cut-