Using accelerometers and global positioning system devices to assess gender and age differences in children’s school, transport, leisure and home based physical activity

Abstract

BackgroundKnowledge on domain-specific physical activity (PA) has the potential to advance public health interventions and inform new policies promoting children’s PA. The purpose of this study is to identify and assess domains (leisure, school, transport, home) and subdomains (e.g., recess, playgrounds, and urban green space) for week day moderate to vigorous PA (MVPA) using objective measures and investigate gender and age differences.MethodsParticipants included 367 Danish children and adolescents (11–16 years, 52% girls) with combined accelerometer and Global Positioning System (GPS) data (mean 2.5 days, 12.7 hrs/day). The Personal Activity and Location Measurement System and a purpose-built database assessed data in 15-second epochs to determine PA and assign epochs to 4 domains and 11 subdomains. Frequencies and proportions of time spent in MVPA were determined and differences assessed using multi-level modeling.ResultsMore than 90% of MVPA was objectively assigned to domains/subdomains. Boys accumulated more MVPA overall, in leisure, school and transport (all p < 0.05). Children compared with adolescents accumulated more MVPA, primarily through more school MVPA (p < 0.05). Boys spent a large proportion of time accumulating MVPA in playgrounds, active transport, Physical Education, sports facilities, urban green space and school grounds. Girls spent a significant proportion of time accumulating MVPA in active transport and playgrounds. No gender or age differences were found in the home domain.ConclusionsLarge variations were found in PA frequency and intensity across domains/subdomains. Significant gender differences were found, with girls being less active in almost all domains and subdomains. Objectively measured patterns of PA across domains/subdomains can be used to better tailor PA interventions and inform future policies for promoting child PA.

Locations of Physical Activity as Assessed by GPS in Young Adolescents

“…Previous research assessing land use type of physical activity locations in youth suggests that nonhome and nonschool physical activity is spread across multiple land use types, including green spaces, streets, retail locations, and other residential locations. [14][15][16] Although a small amount of overall physical activity occurred near home and near school (ie, the home and school neighborhoods), these locations may be the most promising for intervention. This is because the proportion of location time spent in physical activity for the near-home and near-school locations, ∼10%, was higher than for the other 3 locations assessed.…”

“…Most previous studies investigating physical activity locations in youth have used self-report methods and focused primarily on the home or school, 12,13 with the exception of 2 Global Positioning System (GPS)-based studies conducted in Europe and 1 in Canada that may not generalize to the United States. [14][15][16] It is possible that different amounts of physical activity in specific locations account for some of the demographic differences often observed in youth physical activity, so understanding how locations relate to demographic differences in youth physical activity could inform location-specific intervention strategies to reduce health disparities.…”

Contribution of Walking to School to Individual and Population Moderate-Vigorous Intensity Physical Activity: Systematic Review and Meta-Analysis

“…that it makes only a small contribution to population MVPA , probably a combination 246 of low prevalence of active commuting to school, limited MVPA during the commute, 247 short commuting distances (18,22,27,35,42), and the fact that so many days are not 248 schooldays (12,18). If walking to school is going to make a much greater contribution 249 to population MVPA in future, the prevalence, duration, and MVPA content of 250 walking to school must all be increased substantially.…”

Perceived spatial stigma, body mass index and blood pressure: a global positioning system study among low-income housing residents in New York City

“…Consistent with other studies (Zenk et al, 2011;Hurvitz and Moudon, 2012;McCluskey et al, 2012;Wiehe et al, 2013;Yan et al, 2014;Clark et al, 2014;Dessing et al, 2014;Harrison et al, 2014;Klinker et al, 2014;Yen et al, 2015), GPS tracking of the sample was conducted for one week. Prior to distribution, we programmed the GPS device to log in 30-second intervals (so if a participant wore the GPS device for an hour, and had no data loss it would have 120 GPS points recorded) (Duncan et al, 2014c).…”