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during all classroom activities. Systematic momentary observa-tions were collected at 5 min intervals (n = 226 observations) onstudent’s use of technology, posture and activity, and learningbehaviours including student collaboration (whole class, partner,or individual activities) and teacher behavioural control (behaviourcorrections and positive feedback).

Results: Students spent 188.9 (32.2 SD) minutes per school daysedentary, 147.1 (28.6) min per day in light activity, 8.3 (5.0) minper day in moderate activity and 10.8 (8.1) min per day in vigorousactivity. The majority of sedentary time was spent in uninter-rupted bouts of 10–20 min. Students used tablets for a maximumof 26 min per day. The most common postures while using tabletswere sitting in chairs at desks (41.7% of students), sitting on thefloor (36.3%), and standing (16.4%). Results from ANOVA show thatpostures also varied with learning behaviours, for example whenstudents participated in large group activities involving the entireclass, more students were sitting on the floor (64.8%) comparedto during individual (14.2%, p < .001), partner (25.9%, p < .001), andtablet (36.3%, p = .040) activities. There were no differences in thenumber of behavioural corrections or feedback between types ofactivities or postures. Although teachers made fewer behaviouralcorrections during tablet use, the difference was not significant.

Discussion: Tablet computer use appears to provide potentialto change classroom posture and movement. These findings canhelp inform classroom tablet computer use policies and practices toimprove activity and posture in classrooms. Data on how the imple-mentation of a contemporary learning space affects these outcomeswill be reported.

http://dx.doi.org/10.1016/j.jsams.2014.11.291

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Active video games: Are they an effectiveapproach to reducing sedentary time andincreasing physical activity in children?

L. Straker ∗, E. Howie, R. Abbott, A. Smith

Curtin University, Australia

Introduction: There is widespread community concern thatchildren spend too much time being sedentary and too little timebeing physically active. Leisure time screen use is thought to be amajor contributor to children’s activity profiles, with TV viewing,computer use and electronic games commonly responsible for con-siderable sedentary exposure. Many children spend 30–90 min perday playing electronic games. Whilst traditional electronic gamesrequire very little body movement, active video games such asMicrosoft Xbox Kinect, Sony PlayStation Move and Nintendo Wiirequire arm, leg or torso movement. Replacing sedentary electronicgame time with active video game time may be an effective strat-egy to improve children’s activity profiles whilst allowing them theenjoyment of playing electronic games. Playing active video gamesmay be useful by direct replacement of sedentary time with activ-ity, or via indirect pathways of improved attitudes towards self andactivity or improved motor skills.

Methods: Fifty six 10–12 year old children completed partici-pation in a within-subjects randomized controlled trial comparingsedentary time and physical activity in three conditions: no homeaccess to electronic games, home access to sedentary electronicgames, and home access to active video games. Sedentary time andlight, moderate and vigorous activity were measured using Acti-cal accelerometers worn on the hip for a week. Self-esteem wasmeasured by Harter’s Self-Perception Profile for Children and lik-ing of physical activity was measured using the Physical ActivityEnjoyment Scale.

Results: Home access to active video games compared to seden-tary electronic games resulted in a small decrease in sedentarytime after school (6.2 min/day 95%CI 1.4–11.4) and a small increasein moderate/vigorous physical activity after school (3.2 min/day95%CI 0.9–5.5) but no significant changes in overall daily sedentarytime or physical activity. There were neither significant changesin child self-esteem nor in reported enjoyment of physical activityfollowing home access to active video games.

Discussion: Current active video games offerings do not appearto be successful in enhancing children’s habitual activity profilesdirectly, and have limited evidence of potential indirect effectsthrough improved attitudes to self and activity. Related laboratorystudies have found that children move differently when playing vir-tual games compared with real world games, suggesting motor skillimprovements may be limited also. Technology use is an importantcomponent of the lives of many children and determining effectiveways for children to engage with technology in a manner whichsupports health and development needs to be a priority.

http://dx.doi.org/10.1016/j.jsams.2014.11.292

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Physical activity, physical education andacademic performance

D. Telford 1,3,∗, R. Cunningham 2,W. Abhayaratna 2,3, R. Telford 1, L. Olive 2

1 University of Canberra, Australia2 Australian National University, Australia3 Canberra Hospital, Australia

Background: Our general objective was to examine relation-ships between the development of physical literacy and academicdevelopment. Specifically, we determined firstly the effects ofphysical education (PE) taught by specialists on the literacy andnumeracy development of primary school children as assessed bythe national NAPLAN tests, and secondly whether the NAPLANscores were related to the cardio-respiratory fitness (CRF) of chil-dren at both the school and individual levels.

Methods: Between grades 3 and 5, 620 boys and girls in29 Australian elementary public schools received approximately150 min/w of PE and sport. For 312 children this included anintervention of 90 min/w of PE taught by specialists (BluearthFoundation www.bluearth.org) while the remaining childrenreceived all PE from generalist classroom teachers, this being ourcontrol group. Characteristics of the PE programs were assessedby systematic observation of the teachers and pupils using SOFITrecords. Government assessments of writing, numeracy, and read-ing proficiency (NAPLAN testing) were accessed, and we measuredpercent body fat (%BF, DEXA) and CRF (multistage run).

Results: With adjustment for socio-economic status, and anaverage improvement of 65 points across all children, specialist-taught PE produced an 11 point greater improvement in numeracy(p < 0.03) than generalist teacher conducted PE, with trends in thesame direction for writing (10 points) and reading (6 points). Fur-thermore, schools with better average fitness in their studentsachieved better numeracy and writing scores (p < 0.001); and at theindividual child level, this relationship was sustained but weaker(p = 0.03). There were no significant relationships between aca-demic performance and %BF.

Conclusions: Well-designed specialist-taught PE facilitatedbetter academic development in elementary school children, pro-viding strong support for the premise that development of physicalliteracy enhances academic progress as well. The stronger relation-ships between academic performance and fitness at the school level

e66 Thursday 16 October Papers / Journal of Science and Medicine in Sport 18S (2014) e23–e71

than at the child level suggests a ‘school culture’ effect, where theconcurrent attention to fitness and academic achievement by tea-chers within a particular school may partly explain the relationshipbetween academic achievement and fitness.

http://dx.doi.org/10.1016/j.jsams.2014.11.293

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ActiveSmart physical activity behaviour changeprogram

M. Rudez 1,∗, K. Luten 2

1 Department of Sport and Recreation, Australia2 UrbanTrans, Australia

Introduction: ActiveSmart is the Department of Sport andRecreation’s (DSR) personalised behaviour change program that istailored to engage individuals and households in adopting or main-taining physical activity. The primary objective of ActiveSmart isto increase participation in all forms of physical activity to buildsocial capital, community connectedness and improve health. Theapproach is facilitative and responsive to the needs of participantsand results in an uptake of active recreation, sport and an increasein the use of local infrastructure. ActiveSmart has been delivered inthe Cities of Rockingham 2009, Geraldton 2011 (Phase 1) and a topup service to Geraldton participants in 2013 (Phase 2).

Methods: ActiveSmart Geraldton Phase 1: Formative Researchwas conducted to understand physical activity levels andbehaviour; Welcome Letters were sent inviting 10,000 house-holds to participate; Recruitment of 4089 households into theprogram via telephone and community events; Local Resourcesand Small Steps to help people get started were identified throughrecruitment conversations; Resource Packs were hand deliveredto participants homes; Coaching and Goal Setting was providedto 2371 participants who opted into the intense service, whichincluded a phone call every 3–4 weeks to offer support and facili-tate goal setting over a six month period; Evaluation involved preand post surveys to measure the impact of the program. ActiveS-mart Geraldton Phase 2 re-engaged 922 households from Phase 1participants to build upon the previous program and determine thelongitudinal impact 18 months later, this involved a welcome letterand 2 follow up phone calls.

Results: ActiveSmart Geraldton Phase 1 participants reporteda 15 min increase in physical activity per person, per day; 19% ofparticipants moved from insufficient to sufficient levels of physicalactivity; and 21% of participants moved from doing no vigorousphysical activity to doing at least one session per week. Participantsreported feeling better, more connected and achieving goals.

ActiveSmart Geraldton Phase 2 resulted in an additional 5 minincrease in physical activity per person, per day (above the 15 minincrease gained in Phase 1); and an additional 4.5% of participantsmoved from insufficient to sufficient levels of physical activity. ABenefit Cost Analysis found $1 invested in ActiveSmart saves $25in the health budget over 10 years.

Discussion: This project effectively moves people beyond justawareness of the need to be active – it gives them the supportneeded to actually get started and sustain new behaviours.

http://dx.doi.org/10.1016/j.jsams.2014.11.294

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Testing the efficacy of a gender-tailoredintervention to prevent weight regain in men:The SHED-IT Weight Loss Maintenance RCT

M. Young 1,2,∗, C. Collins 1,3, R. Callister 1,4,R. Plotnikoff 1,2, C. Doran 1,5, P. Morgan 1,2

1 Priority Research Centre in Physical Activity andNutrition, University of Newcastle, Australia2 School of Education, University of Newcastle,Australia3 School of Health Sciences, University of Newcastle,Australia4 School of Biomedical Sciences and Pharmacy,University of Newcastle, Australia5 School of Medicine and Public Health, University ofNewcastle, Australia

Introduction: Despite short-term efficacy, many weight lossstudies demonstrate poor long-term results and have difficultyrecruiting males. The aim of this study was to determine whethera gender-tailored Weight Loss Maintenance Program significantlyimproved maintenance of lost weight up to 12 months after aninitial weight loss program in a sample of overweight/obese men.

Methods: The study was a two-phase, randomised controlledtrial. In Phase I (3 months) 209 men received the previously devel-oped SHED-IT Weight Loss Program. In Phase II (12 months) 92 menwho lost ≥4 kg were randomised to (i) a maintenance group whoreceived the 6-month SHED-IT Weight Loss Maintenance Program(n = 47), or (ii) a self-help control group (n = 45). The maintenanceprogram encouraged men to engage in key behaviours linked tosuccessful WLM and operationalised Social Cognitive Theory. Toimprove scalability, the program included no face-to-face contactor tailored components. Assessments occurred at ‘entry’ (start ofPhase I), ‘baseline’ (start of Phase II), ‘6-month’ (post-test) and ‘12-month’ (follow-up; primary endpoint). The primary outcome wasweight change during Phase II (i.e. from ‘baseline’ to 12 monthsafter randomization). Secondary outcomes included physical activ-ity (PA; pedometry) and fruit and vegetable (F&V) intake.

Results: At entry, mean weight was 105.6 kg (sd 14.1). Dur-ing Phase I, weight decreased by 7.3 kg (sd 2.5) (p < 0.001) and PAincreased by 1559 steps/day (sd 2578) (p < 0.001), but no increasewas observed for F&V intake (0.1 serves/day (sd 2.2); p = 0.81).In Phase 2, (at 12 months), intention-to-treat linear mixed mod-els revealed no significant between-group difference for weightregain (−1.5 kg; 95% confidence interval [CI], −3.7,0.7) with themaintenance group regaining 0.6 kg (95%CI, −0.9,2.2 kg) and thecontrol group regaining 2.1 kg (95%CI, 0.5,3.7). At 12 months, PAimprovements were maintained in both groups with no between-group difference (−22 steps/day; 95% CI −1273,1229). A significantbetween-group difference was observed at 12 months for F&Vintake (+1.1 serves/day; 95% CI, 0.1,2.0), favoring the maintenancegroup.

Discussion: Although no significant difference was observedbetween groups, the maintenance group only regained 8% of lostweight, which is considerably less than the 30–35% rate of regaincommonly reported in the first year after treatment. However, thiseffect may have been masked by the relative success of the weightloss-only group, who outperformed other control groups in theliterature. Overall, both SHED-IT arms demonstrated positive find-ings for PA and weight, but longer term follow-up may be requiredto determine the true contribution of the additional maintenanceprogram.

http://dx.doi.org/10.1016/j.jsams.2014.11.295