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David Rowe 1, David McMinn 2, Shemane Murtagh 1, Norah Nelson 1 1 Physical Activity for Health Research Group University of Strathclyde 2 Rowett Institute University of Aberdeen
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Children’s physical activity levels Potential for active transport Purpose: To investigate immediate, medium- term, and long-term effects of a school- based intervention on children’s walking to/from school and overall physical activity
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Curricular intervention (6 weeks) Developed and piloted as part of McKee et al. (2007) Introductory activities Write and draw activities, target setting, etc. Lesson plans e.g., the local environment, mapping skills, the heart and lungs, safety, etc. Teachers’ resource handbook Worksheets, activity cards, homework Pupil pack “My travel challenge”, progress chart, fluorescent stickers, etc.
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McKee et al. (2007) Quasi-experiment, two schools (n = 55) Distance walked and distance by car, measured via self-report (route mapping) Stage of change, barriers and motivations Walking: Intervention 198 772 m/day; Control 242 285 m/day Car: Intervention 2018 933 m/day; Control 933 947m/day
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SE-CAT study added: Objective measures of physical activity Larger sample Stratified sample (high and low SES) Assess variety of theoretical model parameters (Barriers, Motivations, Self-Efficacy, TPB, Habit) Follow-up (maintenance) measures (5-mth, 12- mth) Evaluates seasonal effect Assesses parent perceptions Process evaluation Pilot (McMinn et al., 2012, BMC Public Health)
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InterventionComparisonTime Data collection Aug/Sep 2009 Data collection TG Oct/Nov 2009 Mar-May 2010 Jun 2010 Oct/Nov 2010 Jun 2011 McMinn et al. (2012) Preventive Medicine
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Physical activity measures (main behavioral outcome) Total daily, morning commute, afternoon commute Route characteristics Actigraph GT1M, NL-1000, Trackstick (GPS) Travel diary (home journey) Mode Arrival time Implementation (teacher checklist)
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Parent and child questionnaires Socioeconomic (car ownership, income, home ownership, home SIMD, etc.) Stage of Change Barriers, facilitators, benefits Self-efficacy TPB Attitude, subjective norms, perceived behavioral control, intention Habit Walking Car/bus
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Active commuting/physical activity Actigraph-determined steps and MVPA To school (a.m. commute), from school (p.m. commute), and total day
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163 children (P5; 8-9 yr) in 5 schools Actigraph GT1M accelerometer steps (a.m./p.m. commute and total day), during one school week Measurement points were baseline (fall), post- intervention (winter), 5 months (spring), and 12 months (fall). Travelling Green intervention delivered by teachers immediately after baseline in the initial intervention group (I; n = 79), and following 5-month measures in the comparison group (C; n = 84) Data analysis: 2-way (Group*Time) factorial ANOVAs Post hoc inspection of mean plots Cohen’s d effect sizes (0.2=small; 0.5=medium; > 0.80=large) 10
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11 Significant Group*Time interaction for a.m. commute steps (p <.05, η 2 =.02) The I and C groups diverged following the I group intervention for 5 months, and converged following the C group intervention d = 0.03 d = 0.25 d = 0.49 d = 0.17
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12 Nonsignificant Group*Time interaction for p.m. commute steps (p >.05) Main effects tests indicated significantly higher (p Winter < Spring = Fall). d = 0.28 d = 0.19 d = 0.23 d = 0.20
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13 Significant Group*Time interaction for daily steps (p <.001, η 2 =.05). For daily steps, the I and C groups diverged following the I group intervention for 5 months, and converged following the C group intervention. d = -0.37 d = 0.13 d = 0.30 d = 0.00
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Children’s school travel and daily physical activity are subject to seasonal change Regardless of whether it is delivered in fall or spring, a 6-week school-based intervention can beneficially influence the morning school commute and daily physical activity of upper elementary school children Future interventions should increase involvement of parents, and/or be implemented in upper elementary age 14
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Strengths: Objective measure of commuting behavior Rigorous determination of commute time Investigation of medium- and long-term maintenance Limitations: Nonequivalence at baseline for total PA and p.m. commute Non-compliance with data collection protocols (not wearing accelerometer) Quasi-experimental design 15
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16 david.rowe@strath.ac.uk
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