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Supporting Young Adolescents’ Physical Activity Across Multiple Locations Jordan Carlson 1, Tarrah Mitchell 1,2, Kelsey Borner 1,2, Brian Saelens 3, Jasper.

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Presentation on theme: "Supporting Young Adolescents’ Physical Activity Across Multiple Locations Jordan Carlson 1, Tarrah Mitchell 1,2, Kelsey Borner 1,2, Brian Saelens 3, Jasper."— Presentation transcript:

1 Supporting Young Adolescents’ Physical Activity Across Multiple Locations Jordan Carlson 1, Tarrah Mitchell 1,2, Kelsey Borner 1,2, Brian Saelens 3, Jasper Schipperijn 4, Jacqueline Kerr 5, Terry Conway 5, Lawrence Frank 6, Jim Chapman 7, Karen Glanz 8, Kelli Cain 5, & James Sallis 5 Children’s Mercy Hospital 1 ; University of Kansas 2 ; Seattle Children’s Hospital 3 ; Syddansk Universitet 4 ; University of California, San Diego 5 ; University of British Columbia 6 ; Urban Design 4 Health 7 ; University of Pennsylvania 8 Conclusions More physical activity opportunities are needed at school (and at home), given the large amount of time spent there. Location-specific physical activity interventions are not likely to decrease physical activity in other locations by more than 5%. The home neighborhood is particularly important, so supporting neighborhood activity and active travel appear promising for increasing overall physical activity in young adolescents. Background Multiple settings/locations contribute to youth’s overall moderate-to- vigorous physical activity (MVPA). Understanding the role of key locations can inform intervention. Research Questions 1.How many minutes of MVPA (and what proportion of overall MVPA) are accrued in each of 5 key locations? 2.What proportion of time spent in each location is spent in MVPA? 3.Do adolescents compensate for high levels of activity in a given location by being less active in another location? Results Most (40%) of adolescents’ physical activity was at school, but only 4.8% of school time was spent physically active – the lowest of all locations. 10% of time spent in the home and school neighborhoods was physically active – the highest of all locations. Days when an individual youth had more school MVPA (relative to his/her average in that location), he/she had less at home MVPA and less other location MVPA. Days when an individual youth had more near home MVPA (relative to his/her average in that location), he/she had more near school MVPA and more at home MVPA. Methods 549 adolescents wore GPS (GlobalSat DG-100) and accelerometers (Actigraph) during waking hours for an average of 5 days. Minimum of 8 hours of wear-time for both devices for day to be included. Spatial analyses to identify time spent in locations (GIS & SQL): At home (50-meter radius circular buffer around home address). Near home (1-km street-network buffer around home address). At school (15-meter buffer around school parcel). Near school (1-km street-network buffer around school address). Other locations (everywhere else). MVPA was estimated using Evenson cut points (≥2296 cpm). Mixed-effects regression accounted for nested data. Contact: Jordan Carlson, PhD, jacarlson@cmh.edu Question 1. MVPA accrued in each location Question 2. Proportion of location time spent in MVPA Question 3. Within-participant associations in MVPA minutes/day across the 5 locations (i.e., compensation analyses) Unstandardized B (SE), Min/day p MVPA associations across locations Near home → Near school 0.10 (0.05).045 Near home → Other locations -0.05 (0.04).180 Near school → Other locations 0.01 (0.08).882 Near home → At school -0.05 (0.03).142 Near school → At school -0.08 (0.09).384 At school → At home -0.05 (0.02).044 At school → Other locations -0.05 (0.03).049 Near home → At home 0.14 (0.05).002 Near school → At home -0.03 (0.04).479 Other locations → At home 0.02 (0.02).302 GlobalSat DG-1000 GPS Device Actigraph Accelerometer Devices Sample Characteristics N 549 Mean (SD) age 14.1 (1.4) Percent female 49.9% Percent non-White or Hispanic 31.3% Percent parents with college degree 64.7% Percent residing in Seattle, WA (vs. Baltimore, MD) region 51.8% Percent from high income neighborhood 46.3% Mean (SD) BMI percentile 64.0 (26.6) Percent obese 10.0% Mean (SD) MVPA min/day (weighted week) 39.4 (20.1)


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