Pedestrian traffic safety and outdoor active play among 10–13 year olds living in a mid-sized city

Abstract

This cross-sectional study examined the independent and interactive associations between objective and perceived measures of neighborhood pedestrian traffic safety and outdoor active play. A total of 458 children aged 10–13 years from Kingston, Canada were studied in 2015–2016. Outdoor active play was measured over 7 days using data from activity logs, accelerometers, and Global Positioning System loggers. Geographic Information System data were collected within 1 km of participants' homes and used to create traffic volume, traffic calming, traffic speed, and pedestrian infrastructure indexes. Parents' perceptions of these pedestrian safety domains were obtained by questionnaire. Most of the pedestrian safety measures were not significantly associated with outdoor active play, and there were no interactions between the objective and perceived measures (p > 0.3). The significant relationships are listed here. Children whose parents perceived moderate or high traffic speeds in their neighborhood had outdoor active play values that were 0.35 (SE = 0.10, p = 0.021) and 0.20 (SE = 0.15, p = 0.048) SD units higher, respectively, then children whose parents perceived low traffic speed. By comparison to children from neighborhoods in the lowest tertile, children from the highest traffic volume tertile had higher outdoor active play levels (0.26, SE = 0.12, p = 0.029), while children from neighborhoods in the moderate traffic calming tertile (−0.28, SE = 0.11, p = 0.008) and the moderate pedestrian infrastructure tertile (−0.25, SE = 0.11, p = 0.023) had lower outdoor active play levels.

Children’s outdoor active mobility behaviour and neighbourhood safety: a systematic review in measurement methods and future research directions

“…The remainder (12/25 studies; 48%) used objective measures, including 2/25 studies (8%) used body mass index (BMI) measures, with remaining used an accelerometer activity tracker in five studies (two Actigraphs, two Acticals, and two GT1M Actigraphs), an Accusplit pedometer (AH 120M8 KS10), a GPS spatial location tracker in five studies (two Garmin Forerunner 220 s, VGPS-900, GSM22, and QStarz BT-Q1000/BT-Q1000XT), and an ArcGIS to derive walkability index in one study. Some studies used a single tool such as an accelerometer [ 33 , 36 , 45 ] or GPS [ 50 , 52 ] while a few studies combined more than one, such as a GPS with an accelerometer and travel diary/activity log [ 40 , 41 ]. Variation of utilised tools and the discrete output units across studies are illsutrated below, Fig.…”

“…Step count from accelerometers was used in three studies [ 33 , 36 , 41 , 45 ], while another study used parent questionnaires [ 44 ] to calculate children’s active play (both by frequency and duration of participation in vigorous-intensity or moderate-intensity activities and by reaching 60 min of daily PA based on the recommended guidelines). Another study depicted intensity by measuring children’s daily average minutes of outdoor active play [ 40 ]. Activity space: Two studies used children’s movements across space to determine the size and geometry of movements when visiting places.…”

“…For example, epochs, which is the interval of time for capturing rapid transitions of an activity, varied among and within the tools used. Using global positioning systems, studies used one-second [ 52 ], five-second [ 28 ] and 10-s epochs resampled to 30-s [ 45 ], and 15-s [ 40 ] epochs, while other manuscripts that used an accelerometer adopted 30-s epochs [ 41 , 45 ]. Additionally, reviewed studies used a range of varied inclusion criteria (i.e.…”

“…The activity space-based measure (1/25 studies; 4%) delineates the geometry of a child’s activity space and differs from the earlier group in that it is an irregular convex polygon composed of joining points (a minimum of three) representing the destinations visited by children [ 49 ]. A pre-defined buffer radius (using a distance based on earlier research findings such as 1 km around the participant’s home) can also depict the buffer-based neighbourhood (4/25 studies; 16%) [ 16 , 34 , 40 , 42 ].…”

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

“…According to generation mechanisms, the parameters are divided into 2 categories: one related to the central nervous system and the anther one is related to the peripheral nervous system. e en, human subjective evaluation test method and physiological parameter acquisition method are adopted [42]. At the same time, in order to avoid the impact on physiological fatigue caused by long-time driving experiments about the experimental results, the design time for each experiment is 30 minutes.…”