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Results: To date, data has been collected on 2000 par-icipants aged 5–24 years. Results from the initial pilotesting (n = 32; mean age = 11.9 years, SD = 5.1) found dif-erences in objective and subjective (MARCA) measurementf physical activity. Accelerometer derived time spent inight activity (400 min/day) was greater than that measuredith the MARCA (280 min/day); however accelerometer-erived time spent in moderate (100 min/day) and vigorous25 min/day) activity was generally lower than that for self-eport (moderate 140 min/day and vigorous 50 min/day).elevision viewing constituted the main sedentary pastimeor both school days (80 min/day) and non-school days180 min/day). Prevalence of other screen activities (videoames and computers) was greater on non-school days60 min/day) compared to non-school days (20 min). Datarom the final sample will be presented.

Discussion: This is the first study of its kind in Newealand and provides both accelerometer (objective) andARCA (subjective) measures of physical activity and

edentary behaviours in children and adolescents. Thisroject represents a joint collaboration between researchers athe University of Auckland, University of South Australia andlinders University. Because of the similar measures used,ndings from this study can be compared with data from theustralian National Kids Eat Kids Play Survey.

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29

hysical inactivity and other chronic disease-relatedifestyle risk factors in a sample of Canadian youth

. Plotnikoff ∗, N. Karunamuni, J. Spence, K. Storey, L.orbes, K. Raine, C. Wild, L. McCargar

University of Alberta

Introduction: A detailed understanding of health-relatedehaviours among youth can provide valuable insights thatay be used to guide intervention programs. The purpose

f this study was to examine the prevalence, clustering, agerends, and gender differences of chronic-disease related riskactors in a large sample of Canadian youth, by collectingata through a web-based platform.

Methods: A large, randomly selected sample of youth aged1–17 years (N = 4932) was recruited from schools in therovince of Alberta, Canada for the Web-Survey of Physi-al Activity and Nutrition (Web-SPAN) Study. In total, 363chools were contacted, of which 193 (53%) agreed to par-icipate. Participants reported their physical activity, foodntake, smoking behaviour, height and weight and a host ofelated social-cognitive measures. A measurement validation

ub-study was completed assessing diet and measured BMIith approximately 500 (10%) of the sample. Analyses of the

tudy proper examined the prevalence, trends and clusteringf risk factors and behaviours.

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cine in Sport 12 (2010) e1–e232 e63

Results: 57% were physically inactive, 36% had a high-fatntake, 41% had a poor diet according to national recommen-ations for daily fruit and vegetable intake, 6% were smokersnd 21% were overweight or obese. Approximately 82% boysnd 88% girls had at least one risk factor for chronic dis-ase. Odds of having multiple risks (i.e. having 0or 1, versus+ risk factors) was 1.52 (p < 0.001; 95% CI: 1.33–1.73) inemales compared to males; with a 17% increase in oddsf multiple risks per each additional year of age (OR:1.17;< 0.001; 95%CI:1.10–1.23) across both sexes. Almost onefth of males and females had three or more health-riskactors. Trend analysis for PA revealed a significant nega-ive linear trend with age for boys (F = 28.79; p < 0.001) andirls (F = 81.90; p < 0.001). Fat-intake displayed a significantositive linear trend for boys (F = 4.85; p < 0.01). Signifi-ant positive linear trends were observed for increased BMI,ategorized by IOTF cutoffs (F = 4.16; p < 0.05) for boys.

Conclusions: Results of this study may be helpful in guid-ng intervention efforts. A web-based platform is also aeasible and efficient method of data collection for assess-ng physical activity and other lifestyle risk factors for thisopulation.

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30

year changes in physical activity and perceptions of theeighbourhood environment

. Duncan ∗, K. Mummery

Institute for Health and Social Science Research, Centralueensland University Australia

Introduction: Characteristics of the built environment areecognised as important correlates of physical activity andhere is limited research in this area that examines theseelationships outside of major metropolitan areas. Addition-lly, there are few longitudinal studies examining changesn the environment and physical activity level. This studyims to examine changes in perceptions of the neighbourhoodnvironment and changes in two physical activity outcomesminutes of walking for recreation, total minutes of physicalctivity) over a 7 year period in a large regional city.

Methods: In August–September 2001,1281 adults com-leted telephone interviews (46.6% response rate) providingnformation on demographics, physical activity self-efficacy,ocial support, perceptions of the neighbourhood envi-onment and physical activity level. Perceptions of theeighbourhood environment assessed perceptions related toersonal and traffic safety, access to commercial and phys-cal activity destinations and aesthetics using fifteen items.

hese items were summed to create an overall index of theerceived neighbourhood environment. Participation in mod-rate and vigorous intensity activity, recreational walking andransport walking were assessed separately using the Active

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ustralia questionnaire. Of the 1281 participants at baseline,46 agreed to be recontacted and follow-up interviews wereonducted with 368 adults in September 2008 using the sameurvey instrument. The response rate for the follow-up sur-ey was 73.2%. Relationships between changes in activityevels and perceptions of the environment were examinedsing logistic regression, results are presented as both unad-usted and adjusted OR for demographics, self-efficacy, socialupport and residential self-selection.

Results: There was no statistical significant associa-ion between increased total minutes of physical activitynd perceptions of the environment in unadjusted analy-is (OR = 1.46, 95% CI 0.94–2.28) or adjusted analysesOR = 1.41, 95% CI 0.89-2.24). Significant relationshipsere observed between increases in walking for recre-

tion and perceptions of the environment in both unadjustedOR = 2.27, 95% CI 1.32-3.89) and adjusted analysesOR = 2.36, 95% CI 1.35–4.11).

Conclusion: The observation that favourable changes ineighbourhood environments are associated with favourablehanges activity over a 7 year period confirm previous resultsf cross sectional studies observing that higher activity levelsre associated with more favourable neighbourhood envi-onments. The lack of association between total minutes ofhysical activity and perceptions of the neighbourhood likelyeflects the characteristics of the neighbourhood environmentssessed.

This work was supported by a Grant-in-Aid (G07B3114)rom the National Heart Foundation of Australia.

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31

hanging association of Australian parents’ physi-al activity and their children’s sport participation:985–2004

. Dollman

University of South Australia

Introduction: The socio-ecological milieu of children’shysical activity is changing over time, perhaps causing per-urbations within the causal “web” that explains physicalctivity behaviours. This may partly explain the equivocal lit-rature in this area. While parental role modelling has gainedcceptance as a correlate of children’s physical activity, it isnclear if its relative importance is changing. Accordingly,his study examined associations of parent physical activitynd children’s sport participation in 1985 and 2004.

Methods: In 1985, children in 10 South Australian schoolsarticipated in the Australian Schools Health and Fitness

urvey (ASHFS), and children in eight of these schools par-

icipated in a repeat survey in 2004. Sampling methods anddministration protocols for the AHFS questionnaire weredentical in both surveys. Items relevant to this study were:

1wCa

cine in Sport 12 (2010) e1–e232

umber of organised sports played in the previous 12 months“Sport”); and children’s perceptions of whether mother andather exercise at least twice weekly (Parent PA, responseptions: yes; no; don’t know). Parent PA responses wereombined: both active; one active; neither active. ANOVAompared Parent PA on Sport, separately by sex and surveyear, among children (9–15y) from the 8 schools common tooth surveys (1985:179 girls, 211 boys; 2004: 210 girls, 218oys).

Results: Parent PA differed between surveys (p < 0.0001).n 1985,19% reported neither parent active while 36%eported both active, compared with 32% and 28% in 2004.n 1985, there were no differences (p > 0.05) in Sport betweenarent PA categories among boys (both active 2.5 ± 1.7;ne active 2.3 ± 1.5; neither active 2.2 ± 1.4) and girls (both.1 ± 1.6; one 1.8 ± 1.5; neither 1.7 ± 1.5). Among boys in004, Sport was higher in those with both active parents2.6 ± 1.5) than those with one (2.0 ± 1.5, p = 0.009) andeither (1.6 ± 1.4, p < 0.0001) active, while those with onective parent differed significantly from those with neitherctive (p = 0.04). Among girls in 2004, Sport was also highern those with both active parents (2.5 ± 1.7) than those withne (2.0 ± 1.5, p = 0.03) and neither (1.3 ± 1.3, p < 0.0001)ctive, while those with one differed from those with neitherp = 0.04) active.

Conclusions: In the 2004 sample only, children whoerceived both parents to be active reported highest sportarticipation, while those with neither parent active reportedowest sport participation. This underscores the current rolef parents as socialising agents for physical activity. Moreenerally, the results suggest that intervention design shoulde founded on the most recent available evidence of children’shysical activity correlates.

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hildhood obesity - are we there yet?

. Olds ∗, C. Maher, K. Ferrar, G. Tomkinson

University of South Australia

Background: The popular media, health experts andesearchers in Australia and overseas talk about an “obesitypidemic” with exponentially increasing rates of obesity andverweight. The aim of this study was to examine recentrends in the prevalence of childhood overweight and obesityn Australia and overseas.

Method: 41 surveys of childhood weight status conductedn Australia between 1985 and 2008 were reviewed. The stud-es included data on 264,905 Australians aged between 2 and

8 years, including raw data on 70,758 children. Childrenere classified as overweight or obese using the criteria ofole et al. (2007). Prevalence estimates were adjusted forge, and plotted separately for boys and girls against year of