TY - JOUR
AU - Stewart,, Julia
AB - Abstract Objective Boys experience more injuries as pedestrians than girls. The aim of this study was to compare how boys and girls cross streets in order to identify factors that differentially influence their injury risk as pedestrians. Methods Using a fully immersive virtual reality (VR) system interfaced with a 3D movement measurement system, various measures of children’s street-crossing behaviors were taken. Results At the start of the crossing, boys selected smaller (riskier) inter-vehicle gaps to cross into than girls. Subsequently, as they crossed, they showed greater attention to traffic, shorter start delay, and more evasive action than girls, which are strategies that could reduce risk as a pedestrian. Despite these efforts, however, boys experienced more hits and close calls than girls. Conclusion To enhance their safety as pedestrians, girls adopt a proactive approach and select larger inter-vehicle gaps to cross into, whereas boys apply a reactive approach aimed at managing the risk created by having selected smaller (riskier) gaps. Girls’ proactive approach yielded safer outcomes than boys’ reactive strategy. children, injury risk, pedestrians, sex differences Introduction Unintentional injuries are the leading cause of mortality for youth under the age of 19 years [World Health Organization (WHO), 2008]. For pedestrian injuries, elementary school children 5–9 years of age experience these at disproportionately high rates, making them a high-risk group (National Highway Traffic Safety Administration, 2018). Environmental factors, such as road design and traffic speed and volume, influence children’s risk of pedestrian injury (Mueller et al., 1990; Roberts et al., 1995). However, how children cross streets also can increase their risk of injury (Hoffrage et al., 2003; Wazana et al.,1997). Boys are almost twice as likely as girls to experience fatal pedestrian injuries, with an overall death rate of 13.8 per 100,000 for boys and 7.5 per 100,000 for girls (WHO, 2008). This sex difference suggests that crossing patterns differ for boys and girls in substantive ways. There are surprisingly few studies, however, that have compared the crossing behaviors of boys and girls beyond determining what inter-vehicle gap size they chose when standing at the curb. The current study addressed this issue by comparing both the attention patterns and crossing behaviors of boys and girls and determining how behavioral risk of injury arises differentially as they cross streets. Crossing streets is a complex perceptual-action task, particularly for children because they have limited pedestrian experience and underdeveloped perceptual and cognitive systems. One must attend to traffic-based information for cars (e.g., vehicle speed, distance, volume) and integrate this information over time so dynamic changes in vehicle direction, distance, and speed are perceived and one can estimate time of arrival and select a safe inter-vehicle gap. Additionally, one must precisely time one’s movement through the gap to avoid being hit as one crosses. Many skills are implicated in safely crossing a street (for an extensive review see Schwebel et al., 2012). One must allocate attention to traffic, identify safe inter-vehicle gaps, and cross into these gaps with the speed and synchronization needed to avoid being hit by an approaching vehicle that may be varying over time in location, speed, and distance (see Plumert & Kearney, 2014 for further discussion). Past research reveals both age and sex differences in children’s street-crossing skills. Younger school-age children often show inadequate attention to traffic (Demetre, 1997), and there is speculation that the development of attention throughout childhood contributes to age-related improvements in street crossing (Barton, 2006). Choosing too small an inter-vehicle gap and unsafe crossing locations (e.g., midblock between parked cars) are additional risk factors associated with injury for younger pedestrians (Agran et al., 1994; Barton & Schwebel, 2007; DiMaggio & Durkin, 2002). Children also delay the initiation of a crossing longer than adults for similarly sized gaps, which can increase their risk of being hit when crossing (Pitcairn & Edlmann, 2000; Schwebel et al., 2012). Finally, deciding when to cross also poses risk for young pedestrians because children 5–12 years of age have difficulty understanding the interrelationships between time, distance, and velocity simultaneously (Demetre et al., 1992). They also rely more on vehicle distance than speed in making roadside crossing decisions (Connelly et al., 1998), whereas adults integrate both sources of information in crossing streets safely (Connelly et al., 1998). In terms of sex differences in crossing skills, the findings are sparse and mixed. One study found that boys made slightly safer gap decisions at ages 5–6 years and 11–12 years but not at 8–9 years (Connelly et al., 1998). Another study found no sex differences in gap choice in children 5–8 years (Barton & Schwebel, 2007). The one commonality in both studies is that they measured gap choice without children actually crossing the road. This is problematic, however, because te Velde et al. (2005) have shown that gap choice varies depending on whether it is a cognitive decision alone (as in these past studies) or tied to an actual crossing that involves both perceptual and motor processes and is more complex. Indeed, recent evidence suggests two anatomically discrete neurological pathways for perception that is coupled with motor processes (i.e., picking up a pen) and perception that is not (i.e., observing the size, shape, and location of the pen). Thus when one initiates an action that is dependent on the dynamic integration of perceptual cues and motor adjustments, such as stepping into the path of an approaching vehicle, this activates a different structure in the cerebral cortex (dorsal stream) that processes and transmits information quite differently than the structure that is activated when one is simply observing the approaching vehicle (ventral stream) (Milner & Goodale, 2008). Thus, as noted by Corbett and Morrongiello (2017) it is critical that perception of traffic be coupled with movement into traffic to gain the most accurate representation of child pedestrian behavior. The present study examined sex differences in gap choice when children were actually crossing the street and considered how these choices impact in-road behaviors differentially for girls and boys. A previous study using a different virtual reality system (i.e., CAVE system that projects images on the walls and floors) than used herein (i.e., head-mounted display, HMD) examined how boys and girls walk across a virtual one-lane road (O'Neal et al., 2018). No sex differences were found in the crossing measures taken (e.g., gap selection, timing of entry), however, the authors did not examine evasive actions as the children walked. Moreover, research directly comparing children’s performance in these two virtual reality (VR) systems reveals safer crossings in the HMD than CAVE, related to performance differences in perceptual-motor coupling (e.g., HMD yielded faster walking speed and greater precision in timing their entry into gaps; Mallaro et al., 2017). Additionally, recent evidence, using a fully immersive HMD virtual reality simulator shows that children engage in evasive actions as they cross in response to greater risk in some traffic conditions. Thus, when tested in ways that naturally couple perceptual and motor behaviors, and with measures that enable one to capture complex behavioral sequences, children show strategic pedestrian capabilities. The current study aimed to build on these findings and examine boys’ and girls’ attention and street-crossing behaviors from initiation through to completion in order to determine if strategies that differentially influence their safety could be identified. Methods Power Analysis To estimate a reasonable sample size for the proposed analysis, a power analysis was conducted using G*Power and based on the work by Erdfelder et al. (1996). It should be noted that there are very few past studies that have addressed sex differences in pedestrian behaviors and only one that has done so in a fully immersive VR environment assessing direct motor/perceptual processes in the roadway . Based on this study and others using this system, we anticipated a medium to large effect size. To ensure a conservative power estimate we calculated that, with a power of 0.95 and a Type 1 error probability of p = .05, using a repeated-measures analysis of variance (ANOVA) to assess a within-between interaction, a sample size of 116 would be required to detect a small to moderate effect size of f = .17. Participants The sample comprised 133 children (52% male; range: 8–10 years; M = 9.08 years, SD = 1.03 years) who were recruited throughout the local community by hanging posters and distributing information letters wherever parents and children gathered (e.g., swim and hockey facilities, children’s libraries). Participants were developing normally, as reported by their parents who were asked about their child’s development in the areas of social-emotional, perceptual, behavioral, and learning domains. No participant had any immediate family member who had ever been injured by a car as a pedestrian. This suburban community is in Southwestern Ontario and includes 180,000 residents. The sample was almost entirely Caucasian children (93%) from predominantly middle-upper income families (77% earned above $80,000 per annum), with parents who were well educated (69% having completed a university or graduate degree). Approximately 5%, 7%, and 4% of parents did not report ethnicity, income, or education, respectively. Materials Screening questionnaire The Simulator Sickness Questionnaire assessed each child’s history of migraine headaches, claustrophobia, motion sickness, and dizziness/nausea (Kennedy et al., 1993). The questionnaire was completed over the phone with all parents prior to booking an appointment. Any children who had experienced these symptoms in the past were excluded from the sample (N = 1). Virtual reality and movement tracking system The system was constructed in an 8 m × 5 m room using an 8-camera optical-motion tracking system (PPTH by Worldviz) which fed position data to specialized software (Vizard), using a high-level scripting language (Python). Participants view the virtual environment through a Virtual Research Systems 1280 × 1024 resolution stereoscopic HMD with a 60° diagonal field of view, weighing approximately 1.5 pounds. An Inertia Cube 3, which was used to track children’s head movements, was mounted on the HMD. This is a 3 degrees of freedom (i.e., X, Y, and Z coordinates) orientation-tracking system that uses accelerometers, gyroscopic, and magnetic sensors to track the orientation of a participant’s head to allow for real-time changes in the virtual environment that correspond with the orientation of the participant’s head. The virtual environment is made up of a two-lane street (2.75 m/lane) with a double yellow line down the center, along with sidewalks and homes set back from the road. The virtual environment’s realism is enhanced by the inclusion of trees, shadows, and textures, and corresponding sounds of traffic movement (e.g., Doppler effect). Each participant controls the direction and speed of their movement, creating the opportunity for them to adjust their behavior as they cross (e.g., speeding up to evade an approaching vehicle). All traffic flowed in one direction, from the left of the participant, and in the closest lane only. Crossing indices were recorded as the child crossed this lane of traffic. Crossing Measures The crossing measures included gap selection, start delay, evasive action, and high-risk time left to spare (HRTLS). These are defined and described in Table I. Table I. Pedestrian Measure and Concept Definitions Measure/concept . Definition . Start delay The time (in seconds) between the rear bumper of the first car in the chosen gap crossing the participant's path and the time when s/he steps off the curb. TLS The time remaining (in seconds) for the approaching car to intersect with the child’s path. Calculated as the time left (in seconds) between the child and the approaching car when the child exits the path of the approaching car. When TLS = 0, the child was hit by the vehicle. High-risk TLS The proportion of trials with a TLS ranging from 0 (hit) to .75 s. Gap choice The inter-vehicle gap size (for the gap that the participant entered) measured in seconds rather than distance units. Calculated as the time in seconds from the rear bumper of the first car in the gap passing the participant and the arrival of the front bumper of the second car at the same point. Time car in view The time (in seconds) while in the path of an approaching vehicle that the child was looking at the vehicle, calculated based on whether the vehicle was visible in the VR headset. EA The difference in velocity (in m/s) from the point of entering the gap to the maximum velocity while in the path of the car. Measure/concept . Definition . Start delay The time (in seconds) between the rear bumper of the first car in the chosen gap crossing the participant's path and the time when s/he steps off the curb. TLS The time remaining (in seconds) for the approaching car to intersect with the child’s path. Calculated as the time left (in seconds) between the child and the approaching car when the child exits the path of the approaching car. When TLS = 0, the child was hit by the vehicle. High-risk TLS The proportion of trials with a TLS ranging from 0 (hit) to .75 s. Gap choice The inter-vehicle gap size (for the gap that the participant entered) measured in seconds rather than distance units. Calculated as the time in seconds from the rear bumper of the first car in the gap passing the participant and the arrival of the front bumper of the second car at the same point. Time car in view The time (in seconds) while in the path of an approaching vehicle that the child was looking at the vehicle, calculated based on whether the vehicle was visible in the VR headset. EA The difference in velocity (in m/s) from the point of entering the gap to the maximum velocity while in the path of the car. Note. EA = evasive action; TLS = time left to spare; VR = virtual reality. Open in new tab Table I. Pedestrian Measure and Concept Definitions Measure/concept . Definition . Start delay The time (in seconds) between the rear bumper of the first car in the chosen gap crossing the participant's path and the time when s/he steps off the curb. TLS The time remaining (in seconds) for the approaching car to intersect with the child’s path. Calculated as the time left (in seconds) between the child and the approaching car when the child exits the path of the approaching car. When TLS = 0, the child was hit by the vehicle. High-risk TLS The proportion of trials with a TLS ranging from 0 (hit) to .75 s. Gap choice The inter-vehicle gap size (for the gap that the participant entered) measured in seconds rather than distance units. Calculated as the time in seconds from the rear bumper of the first car in the gap passing the participant and the arrival of the front bumper of the second car at the same point. Time car in view The time (in seconds) while in the path of an approaching vehicle that the child was looking at the vehicle, calculated based on whether the vehicle was visible in the VR headset. EA The difference in velocity (in m/s) from the point of entering the gap to the maximum velocity while in the path of the car. Measure/concept . Definition . Start delay The time (in seconds) between the rear bumper of the first car in the chosen gap crossing the participant's path and the time when s/he steps off the curb. TLS The time remaining (in seconds) for the approaching car to intersect with the child’s path. Calculated as the time left (in seconds) between the child and the approaching car when the child exits the path of the approaching car. When TLS = 0, the child was hit by the vehicle. High-risk TLS The proportion of trials with a TLS ranging from 0 (hit) to .75 s. Gap choice The inter-vehicle gap size (for the gap that the participant entered) measured in seconds rather than distance units. Calculated as the time in seconds from the rear bumper of the first car in the gap passing the participant and the arrival of the front bumper of the second car at the same point. Time car in view The time (in seconds) while in the path of an approaching vehicle that the child was looking at the vehicle, calculated based on whether the vehicle was visible in the VR headset. EA The difference in velocity (in m/s) from the point of entering the gap to the maximum velocity while in the path of the car. Note. EA = evasive action; TLS = time left to spare; VR = virtual reality. Open in new tab Procedure Each session lasted about 1.5 hr. Following the granting of written consent by the parent and their child, each child accompanied a research assistant to the VR testing room, while their parent remained in the waiting room. Each child completed two phases. In the Familiarization phase, a research assistant demonstrated how to cross the street while wearing the headset as the child watched a computer monitor; they also demonstrated what would happen if they were to be hit by a car (the vehicle disappears just before the point of impact and a siren sound is played). Following this, the child’s head was fitted with the VR headset; knob adjustments allowed the child to adjust the headset to improve fit, and the clarity of letters shown on the screen. The child was then positioned on the virtual curb, facing the two-way street, and instructed to cross the street, turn, and walk back to the curb. They repeated this 10 times with no cars appearing during this phase. Previous research has demonstrated that by the end of a 10 trial familiarization stage, children are fully accustomed to the VR equipment (Kennedy et al., 2000); our own pilot testing (using 15 practice trials) confirmed that by trial 10 children reached a stable walking velocity that did not increase significantly from trials 10 to 15. In the Testing phase, traffic was presented and the child was told to cross when it was safe to do so. On each of 10 trials, vehicle gaps were presented in an incremental and increasing order, with two gaps of each size (i.e., 2, 2, 2.5, 2.5, 3, 3, 3.5, 3.5, 4, 4, 4.5, 4.5, 5, 5, 6, 6 s). Cars traveled at either a slow (5 trials at 30 km/hr) or fast (5 trials at 70 km/hr) speed; speed condition was randomized across the 10 trials. Analytic Approach Descriptive and parametric statistics (ANOVA, regression) were applied to characterize the data and compare the pre-crossing and crossing behaviors between boys and girls; descriptive statistics are given in Table II. Preliminary data checking procedures were applied before analyses were conducted, including examining dependent variables for violations in normality. Violations of normality were corrected using Log transformations; however, the raw means are reported herein to aid in interpretation. Linearity, normality of residuals and assessment for multivariate outliers, based on Cook’s distance were conducted on an analysis-by-analysis basis with individual outliers assessed for undue influence before removal. Repeated-measures ANOVAs were conducted to assess for violations of sphericity for within-participant effects and determine if adjustment to the degrees of freedom (Greenhouse–Geisser correction) was warranted. The ‘time attending to traffic’ variables were rounded to the nearest 10th of a second and analyzed using Negative Binomial regression. Table II. Descriptive Statistics Giving the Mean (SD) for Gap, Start Delay (s), EA and HRTLS (close calls) a Function of Vehicle Speed and Gender Speed (km/hr) . Sex . Gap choice (in seconds) . Start delay . EA . Time car in view (in ms) . HRTLS . 30 Males 2.80 (0.64)** 0.66 (0.19)** 0.32 (0.11) 83 (180)** 0.58 (0.26)** Females 3.21 (0.68) 0.76 (0.21) 0.30 (0.13) 29 (106) 0.36 (0.26) 70 Males 2.45 (0.38) 0.81 (0.20) 0.26 (0.08) 80 (174)* 0.88 (0.17) Females 2.59 (0.40 0.93 (0.20) 0.26 (0.16) 37 (110) 0.84 (0.21) Speed (km/hr) . Sex . Gap choice (in seconds) . Start delay . EA . Time car in view (in ms) . HRTLS . 30 Males 2.80 (0.64)** 0.66 (0.19)** 0.32 (0.11) 83 (180)** 0.58 (0.26)** Females 3.21 (0.68) 0.76 (0.21) 0.30 (0.13) 29 (106) 0.36 (0.26) 70 Males 2.45 (0.38) 0.81 (0.20) 0.26 (0.08) 80 (174)* 0.88 (0.17) Females 2.59 (0.40 0.93 (0.20) 0.26 (0.16) 37 (110) 0.84 (0.21) Note. EA = evasive action; HRTLS = high-risk time left to spare. Sex differences are indicated by *p < .05, **p < .001. Open in new tab Table II. Descriptive Statistics Giving the Mean (SD) for Gap, Start Delay (s), EA and HRTLS (close calls) a Function of Vehicle Speed and Gender Speed (km/hr) . Sex . Gap choice (in seconds) . Start delay . EA . Time car in view (in ms) . HRTLS . 30 Males 2.80 (0.64)** 0.66 (0.19)** 0.32 (0.11) 83 (180)** 0.58 (0.26)** Females 3.21 (0.68) 0.76 (0.21) 0.30 (0.13) 29 (106) 0.36 (0.26) 70 Males 2.45 (0.38) 0.81 (0.20) 0.26 (0.08) 80 (174)* 0.88 (0.17) Females 2.59 (0.40 0.93 (0.20) 0.26 (0.16) 37 (110) 0.84 (0.21) Speed (km/hr) . Sex . Gap choice (in seconds) . Start delay . EA . Time car in view (in ms) . HRTLS . 30 Males 2.80 (0.64)** 0.66 (0.19)** 0.32 (0.11) 83 (180)** 0.58 (0.26)** Females 3.21 (0.68) 0.76 (0.21) 0.30 (0.13) 29 (106) 0.36 (0.26) 70 Males 2.45 (0.38) 0.81 (0.20) 0.26 (0.08) 80 (174)* 0.88 (0.17) Females 2.59 (0.40 0.93 (0.20) 0.26 (0.16) 37 (110) 0.84 (0.21) Note. EA = evasive action; HRTLS = high-risk time left to spare. Sex differences are indicated by *p < .05, **p < .001. Open in new tab Results Pre-Crossing Behaviors Gap Choice Before crossing the road, children are required to make a choice about the size of gap into which they will cross. This choice is a proxy for how dangerous they perceive the traffic to be, that is, a child who selects a larger gap is likely doing so in an effort to mitigate their crossing risk. A repeated-measures ANOVA was conducted with sex (2: male, female) as a between-participant factor, speed (2: 30 km/hr, 70 km/hr) as a within-participant factor, and age as a covariate. A main effect of speed indicated that children selected a significantly smaller gap for 70 km/hr (M = 2.52 s, SD = 0.40 s) than they did for 30 km/hr (M = 2.99 s, SD = 0.69 s), F(1, 123) = 26.23, p < .001, ηp2 = .18. A significant sex x speed interaction [F(1, 123) < 8.36, p = .005, ηp2 = .16] revealed that, in the 30 km/hr condition, girls selected a gap size that was 13% larger (M = 3.21 s, SD = 0.68 s) than that selected by boys (M = 2.80 s, SD = 0.64 s), F(1,123) = 10.98, p < .001, ηp2 = .08. A similar pattern was found for the 70 km/hr condition such that girls selected a gap size that was 5% larger (M = 2.59 s, SD = 0.40 s) than boys did (M = 2.45 s, SD = 0.38 s), though the latter effect was marginally significant, F(1, 123) = 3.73, p < .06, ηp2 = .03. Start Delay Once an inter-vehicle gap has been selected there is an inherent delay between that decision and the person’s movement into the car path. Generally, longer start delays increases injury risk because the approaching car is closer by the time the person enters the street; this is especially true if one has selected a small inter-vehicle gap. A repeated-measures ANOVA was applied to the start delay data, with sex (2: male, female) as a between-participant factor, speed (2: 30 km/hr, 70 km/hr) as a within-participant factor, and age as a covariate. Results revealed a main effect of speed: children were slower to initiate a crossing into the gap at 70 km/hr (M = 0.87 s, SD = 0.21 s) than at 30 km/hr (M = 0.71 s, SD = 0.20 s), F(1, 130) = 5.98 p < .05, ηp2 = .04. When cars appeared further away (70 km/hr), therefore, children were slower in initiating a crossing once they had selected a gap. Results also revealed a 14% greater start delay by girls (M = 0.76 s, SD = 0.21 s) compared to boys (M = 0.66 s, SD = 0.19 s) for the 30 km/hr condition, F(1, 126) = 11.03, p < .001, ηp2 = .08; there was no sex difference for the 70 km/hr condition. Thus, when cars appeared closer, boys initiated a movement into the gap faster than girls; recall that boys selected smaller (riskier) gaps than girls so entering quickly is one way to try and reduce the risk of being hit by the approaching vehicle. Crossing Behaviors Evasive Action Evasive action (i.e., increasing walking speed when in the path of the vehicle) is one way that children can moderate their risk of being hit by an approaching vehicle if they select to cross into a small inter-vehicle gap. A preliminary analysis was conducted to determine if there were sex differences in the velocity at which boys and girls first entered traffic (a potential confounding variable in computing the evasive action score). A repeated-measures ANOVA was conducted, with sex (2: male, female) as a between-participant factor and speed (2: 30 km/hr, 70 km/hr) as a within-participant factor and age as a covariate. The lack of any significant effects confirmed that boys and girls entered into the gap at comparable speed, so this was not a confounding variable in examining evasive action. A repeated-measures ANOVA was applied to the evasive action scores, with sex (2: male, female) as a between-participant factor and speed (2: 30 km/hr, 70 km/hr) as a within participant factor and age as a covariate. Results revealed a significant main effect of speed, F(1, 123) = 3.86, p < .05, ηp2 = .03, with children using more evasive action in the 30 km/hr condition (M = 0.31 m/s, SD = 0.12 m/s) than in the 70 km/hr condition (M = 0.26 m/s, SD = 0.09 m/s). In addition, a sex by speed interaction [F(1, 124) = 3.99, p < .05, ηp2 = .03] with follow-up tests revealed that, in the 30 km/hr condition, there was a marginal but meaningful effect indicating boys were using more (6%) evasive action (M = 0.32 m/s, SD = 0.11 m/s) than girls (M = 0.30 m/s, SD = 0.13 m/s), F(1, 123) = 3.04, p < .07. An increase in evasive action could help boys mitigate the increase in risk that results from their selecting smaller gaps to cross into compared to the girls. High-Risk Time Left to Spare (Hits and Close-Call Crossings) To assess the effectiveness of these strategies and, ultimately, children’s safety when crossing the road, we calculated the proportion of trials wherein risk was elevated on exiting the gap (i.e., high-risk time left to spare); lower scores indicate fewer hits (0 s time left to spare) and close call crossings. Near misses or close calls are most often defined as any crossing wherein the child has 1 s or less (the exact time used varies across studies) between themselves and the approaching vehicle when they exit the vehicle’s path or the lane. Many researchers combine hits and close calls into one risk composite as we did herein (e.g., Schwebel et al., 2014; Stavrinos et al., 2009, 2011). For this study, we operationalized hit and close calls as the proportion of trials that children had 0.75 s or less time left to spare on exiting the car path. A repeated-measures ANOVA on HRTLS scores was applied, with sex (2: male, female) as a between-participant factor, speed (2: 30 km/hr, 70 km/hr) as a within-participant factor, and age as a covariate. Results revealed a significant main effect for speed, F(1, 130) = 16.43, p < .001, ηp2 = .11. In the 30 km/hr condition children had fewer close call trials (M = 0.47 s, SD = 0.38 s) than in the 70 km/hr condition (M = 0.86 s, SD = 0.19 s). This pattern reflects the safety consequences of misjudging the 70 km/hr condition as safer and choosing a smaller gap because cars were further away even though the time to contact was the same in both conditions. In addition, a significant speed x sex interaction also was found. F(1, 130) = 8.44, p < .01, ηp2 = .16. Follow-up tests revealed that females and males did not differ for the 70 km/hr condition (p > .05), but females had fewer hit/close call trials than males in the 30 km/hr condition (M = 0.36 and 0.58, SD = 0.26 and 0.26, respectively), F(1, 130) = 11.36, p < .001, ηp2 = .08. Thus, despite females taking longer to step into the gap and utilizing less evasive action than males as they crossed, they still experienced significantly fewer close calls because they started by selecting larger gaps than their male counterparts. Looking Behaviors Attention to traffic as one crosses yields information about dynamic changes in vehicle speed and distance that is important for making adjustments during a crossing to avoid being hit by approaching cars. Computing the ‘mean time the car was in view while in car path’ for each child (adjusting for the total time the child was in the car path) yielded scores that were badly skewed, and no transformation was able to sufficiently address this problem; an ANOVA, therefore, was a poor choice for analysis of these data. To address this issue, we applied a negative binomial regression analysis and results revealed that attention could be predicted from sex of the child. Incidence rate ratios revealed that boys spent 3.24 times more time attending to cars while in the car path than girls for cars at 30 km/hr (M = 0.12 s and 0.037 s, respectively), after adjustment for total time in car path (as an offset variable), IRR = 3.24, 95% confidence interval (CI; 1.66, 6.33), p < .001. The same pattern emerged for cars approaching at 70 km/hr: boys spent 2.03 times more time attending than girls (M = 0.11 s and 0.05 s, respectively), IRR = 2.03, 95% CI (1.07, 3.82), p < .05. Discussion Injury statistics indicate that boys are at higher risk than girls for pedestrian injuries (Assailly, 1997; DiMaggio & Durkin, 2002; Macpherson et al., 1998). Our understanding of how this differential risk arises, however, has been limited because past research focused solely on the initial decision boys and girls made, namely—what size gap to cross into. In the current study, using a fully immersive virtual reality system with movement tracking allowed us to study how children execute their crossings. The findings revealed other differences that boys and girls show as they cross and that influence their injury risk. The findings also highlight that children are responsive to dynamic perceptual changes that arise as cars approach, though their crossing adjustments are not always adequate to moderate their injury risk. The fact that children selected a smaller gap size when cars moved at faster speeds fits with past research and reflects the fact that children rely predominantly on distance cues when crossing streets (Morrongiello et al., 2015; Simpson et al., 2003). Although cars traveling in the 30 km/hr condition and the 70 km/hr condition would arrive in the child’s path at the same time, cars traveling faster (i.e., 70 km/hr) would need less time to arrive, so they start their approach from a greater distance to produce an equal time to contact in both conditions. In this case, children misjudged the degree of risk and select smaller gaps based on the cars being farther away, failing to realize that this strategy increases risk of injury because of the vehicle’s faster speed (Morrongiello et al., 2015; Wann et al., 2011). Interestingly, girls selected larger gaps than boys consistently, in both speed conditions. Thus, even before they start to cross, boys and girls are utilizing different strategies in their street crossing, with girls being more cautious in their gap choice and proactive about their safety. This cautiousness was particularly evident for the condition that presents as more dangerous because the car appeared closer (30 km/hr). Overall, therefore, the gap selection data confirms past findings that children focus on vehicle distance more than speed and that doing so creates risk of injury as a pedestrian (e.g., Connelly et al., 1998). The findings extend this research, however, by demonstrating that boys select riskier gaps to cross into than girls across varying vehicle speed conditions, which creates greater risk for pedestrian injury before boys even step into the roadway. This places boys on a trajectory for greater risk of injury unless they execute strategies that reduce this risk during a crossing. Varying their start delay and walking speed to take evasive action are two ways that pedestrians can moderate their risk as they cross. The results from start delay data in this study indicate that children are able to vary the haste with which they initiate a crossing and they do so strategically. When cars appear closer to them, they respond with a more expeditious entry into the inter-vehicle gap, whereas when cars are farther away (faster speed condition), they initiate a crossing more slowly. Importantly, boys are faster than girls in initiating a crossing when cars appear closer and they have selected a smaller gap size. Hence, although boys are riskier in the selection of gap size, starting into the gap sooner could help compensate for this. Similar findings were found for evasive action. Children showed more evasive action when cars appeared closer (30 km/hr) than farther away, replicating past findings that children can implement evasive actions during a crossing . In addition, the trend in the data suggests that boys do this more than girls do, presumably in response to their having selected smaller (riskier) gaps initially. It is possible that quicker reaction time by males (see Roivainen, 2011) allows for their more precise timing into the roadway and quicker entry into the gap (Barton & Schwebel, 2007; Hoffrage et al., 2003; Pitcairn & Edlmann, 2000; Stevens et al., 2013). These cumulative effects as boys cross are highly meaningful (see also Plumert & Kearney, 2014). It appears that boys adjust their behavior as they cross in response to their pre-crossing decisions of selecting smaller gaps, essentially trying to compensate for their risky gap choice by entering the gap sooner (short start delay) and accelerating their walking speed (evasive action). Thus, the pattern we see for boys is a ‘reactive’ one in which they select smaller (riskier) gaps to cross into and then react to this by entering into the gap sooner and increasing walking speed when in the path of the vehicle and attending to the approach of the vehicle more. In contrast, girls show a more ‘proactive’ crossing strategy in which they select larger (safer) gaps to cross into, negating their need to start sooner, increase walking speed, or carefully attend to the vehicle as they cross. Importantly, despite their efforts at managing their increased risk from selecting smaller gaps by decreasing start delay and increasing walking speed, boys experienced significantly more hits and close calls than girls. Hence, for boys, starting into traffic sooner and using more evasive action was not sufficient to counteract the heightened risk by their having selected smaller gaps to cross into initially. Past research suggests a variety of contributing factors that may explain why girls are more proactively cautious and select larger gaps to cross into than boys. One possibility is that boys’ heightened activity levels (Eaton & Enns, 1986), coupled with a more impulsive and uncontrolled behavioral style (,Barton & Schwebel, 2007), leads them to select riskier gaps than their female counterparts. Byrnes et al. (1999) found that boys take more risks in the natural environment. As well, boys have been found to behave in riskier ways than girls in pretend road situations, showing more inattention and being less patient before entering the road (,Barton & Schwebel, 2007). Differences in perceptual and/or motor abilities also may contribute to the sex differences in gap selection. Females have been shown to underestimate the time to arrival of oncoming objects compared to males’ estimations (Manser & Hancock, 1996; Neuhoff et al., 2009). Hence, girls would assume a vehicle will arrive sooner than it actually will, which could result in more cautiousness and their selecting a larger inter-vehicle gap size than boys. Research also shows that boys estimate their physical ability and athletic competence to be much greater than girls do (Morrongiello & Dawber, 2004). This may result in boys feeling more confident about their ability to make necessary adjustments mid-crossing and lead them to place less emphasis on exercising caution when making their initial pre-crossing gap selection. Finally, research on children’s injury appraisals reveals that girls, in comparison to boys, are: more concerned about danger and being injured (DeJoy, 1992; Hillier & Morrongiello, 1998; Morrongiello & Dawber, 2004; Peterson et al., 1997); attribute more injuries to their own behaviors than to bad luck (Morrongiello & Rennie, 1998); and are less confident in their ability to cope with danger (Peterson et al., 1997). Each of these appraisals could lead girls to be more cautious and risk avoidant than boys in pedestrian contexts. Hence, a variety of factors may contribute to explain the robust finding that girls are more proactively cautious and select larger inter-vehicle gaps than boys when tasked with crossing streets. Finally, the results indicate that boys and girls are both capable of, and do, vary start delay and evasive action, which creates the opportunity for dynamic adjustments as they cross. Looking behavior, however, is essential for providing the updates in visual information that are necessary to support these behavioral adjustments. This is especially so in real traffic situations in which vehicles might be changing lanes and speed in unpredictable ways. Past studies have only measured attention to traffic while the child is on the curb. This research indicates that girls show more attention to traffic than boys when waiting to cross (Barton & Schwebel, 2007), which is consistent with our conclusion that girls are more proactive and planful in their crossing behavior. In the current study, examining children’s looking behaviors as they crossed revealed important sex differences and the patterns support the differential crossing strategies discussed above and suggest that children tailor their looking as they cross based on their appraisal of injury risk in gap selection. Boys’ selected smaller (riskier) gaps initially and then showed greater looking to traffic as they crossed. Interestingly, this is consistent with an unobtrusive observational study wherein researchers found that boys showed more attention to the surrounding traffic environment while in a crosswalk than girls (Granié, 2007). In contrast, girls in the current study selected larger (safer) gaps initially and then did not view traffic as closely as they crossed. It seems, therefore, that in real traffic environments in which cars may be changing lanes and vehicle speed, if girls do not properly appraise risk at the time of their initial crossing decision, then insufficient looking as they cross could impair their ability to stay safe by making adjustments based on changes in the dynamic flow of traffic. The pattern of these findings, therefore, suggests that pedestrian injury outcomes may arise for boys and girls due to different processes. For boys, selecting risky (small) gaps may be what ultimately leads to being hit by a vehicle if adjustments to start delay and evasive actions are insufficient to counteract the risk from their initial gap decision (i.e., they watch traffic but cannot implement changes sufficiently in response to what they perceive). In contrast, risk of injury to girls as pedestrians may reflect their failing to adequately view traffic as they cross, presumably due to their having selected a safe gap to cross into initially, but then failing to detect changes in the car’s location, speed, and/or distance that has created injury risk as the car is approaching. Further research that focuses on linkages between looking and crossing behaviors is needed, but the current findings suggest there may be important sex differences in these relations that contribute to explaining how risk of pedestrian injury arises differentially for boys and girls. The current findings have important implications for preventing pedestrian injury in children. Given that risk arises differently for boys and girls, any training to improve crossing skills in children would need to incorporate features that address the unique risk factors that apply to boys and to girls. Teaching children to attend to traffic as they cross is essential, and this is particularly important for females to learn. Videos showing the erratic nature of drivers (e.g., changing lanes, varying speed) may be a very effective way to communicate risk to females, particularly because their behavior is often motivated by concerns about getting hurt (Hillier & Morrongiello, 1998). Similarly, selecting large inter-vehicle gaps enhances safety, and this is essential for males to learn. Possibly, communicating to males about the potential severity of pedestrian injury may be an effective way to increase their risk avoidance (e.g., larger gap selection), given that previous research has found that males’ risk decisions are often motivated by judgments about ‘how hurt can I get’ (Hillier & Morrongiello, 1998). Limitations and Future Research Directions Although this research yields important findings, there are some limitations to note and address in future research. First, the sample is fairly homogeneous in demographic characteristics, including ethnicity (mostly Caucasian) and family income (mostly middle class). Extending to include a more diverse sample in future studies is warranted in order to increase generalizability of the conclusions. Second, the findings may not apply to children with different types of traffic experience. For example, children living in urban settings are likely to have heavier traffic and different risks to contend with (e.g., parked cars, greater traffic volume in both directions), and these experiences may lead to different skills and understanding of traffic than children in the current study who have grown up in a suburban traffic setting (McComas et al., 2002). Testing to compare how crossing skills differ for children in urban and suburban settings would be important for the field in order to determine the extent to which children’s traffic exposure experience directs and constrains their emerging crossing skills. Finally, the current study presented children traffic conditions that were not as complex as in the natural environment. For example, car speed did not vary within a trial as the car approached. In future research, it will be important to present children more complex traffic conditions and ascertain how this influences their attention and crossing behaviors. It could be, for example, that if the child saw vehicle speed varying as the car approached, this could evoke more attention to car speed by the child, essentially tutoring the perceptual system about relevant perceptual information. 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TI - Understanding Sex Differences in Children’s Injury Risk as Pedestrians
JF - Journal of Pediatric Psychology
DO - 10.1093/jpepsy/jsaa072
DA - 2020-11-01
UR - https://www.deepdyve.com/lp/oxford-university-press/understanding-sex-differences-in-children-s-injury-risk-as-pedestrians-uXTlz0MZxh
SP - 1144
EP - 1152
VL - 45
IS - 10
DP - DeepDyve
ER -