Testing a Training Program That Uses Virtual Reality Technology to Improve Children's Pedestrian Behaviors: A Randomized Controlled Trial
Phase: NA
Conditions: Child Behavior
Sponsor: University of Guelph

 
 
 
 
 
 
 
 
 
Testing a Training Program That uses Virtual Reality Technology to Improve Children’s 
Pedestrian Behaviors: A Randomized Controlled Trial 
 
#NCT-03960047 
 
 
Sept 1 2019 
 
 

TITLE 
Testing a Training Program That uses Virtual Reality Technology to Improve Children’s Pedestrian 
Behaviors: A Randomized Controlled Trial 
 
NCT-03960047 
 
SETTING 
We focus on midblock crossing on two-lane bidirectional roads, without nearby traffic signals or 
crosswalks. 
For SS-T sessions, a road has been identified (50 km limit) that provides opportunity for training 
in where and how to cross in the same types of road situations to be presented in VR-T. Traffic volume 
was recently measured for 7 days and varied from light (8 cars/min) to heavy (16 cars/min).13,30 This 
range for traffic volume will occur in VR-T.  
For VR-T sessions, these occur in the VR lab at UG campus. 
For pre and post measures, for all groups these occur in two settings: (a) street side (designated as 
“Field” below) with naturally varying traffic (children indicate where and how they will cross but do not 
do so), (b) in the VR lab in which children cross the virtual street in traffic using a gaming controller.  
Note that the pre/post Field measures will be taken at two locations along the same road 
(randomized order, with one used for pre and the other for post), which controls for familiarity and 
minimizes risk of large variation in traffic volume between pre/post measures. The road is nearby the VR 
lab, which allows children to complete pre-test VR and Field measures during the same visit to UG; the 
same will occur for post-test measures. Posted speed is 50 km, which means traffic conditions to be tested 
on are ones that pose risk of severe injury at these ages.13, 57  
  
TESTING PROTOCOL 
  Pre/Post measures for Where and How to Cross   
[Note that pre- and post- measures are the same, and all children, regardless of group, complete all 
measures.] 
Field Measures. For where to cross measures, the child selects the safest location to cross in 3 
different street side situations (parked cars, hill, blind curve). For how to cross measures: the child stands 
1 foot from the curb, with a pressure sensitive step plate in front of him (it connects to a laptop; a clear 
plastic wall prevents entry into the street), and completes a ‘2 step procedure’ (based on observed traffic, 
take 2 steps forward, corresponding to initiating a street crossing;23,58 from this we estimate crossing 
measures.[NOTE: Street side testing is video recorded so traffic and crossing measures can be computed 
later; the PI has used this procedure before.59] 
VR Measures. The same measures as for Field, except completed within a virtual street 
environment. 
  
   Pre-Training Activities & Measures 
 
VR-T. An initial Orientation and Movement-Control Training Module ensures all children are 
practiced with using the controller to navigate in the 3-D world to the same motor skill level, before 
starting pedestrian training. The controller allows them to vary their path, direction, and speed of walking 
and crossing. 
SS-T. Children’s average walking speed for crossing is determined58 and then, in combination 
with video taken street side, is used to estimate crossing measures – so they do not cross in traffic, but 
only give initiation judgements. This is a common approach that the PI has used before.59 
 
 Training Programs [All program decisions were informed by pilot testing.] 
Each includes 6 training sessions of 30 minutes (2% of our pilot sample needed a break) and 
addresses where and how to look and cross. Performance is tracked on a trial-by-trial basis, and errors 
result in repetition of teaching trials, resulting in more training on skills they lack than those they have. 
Difficulty level generally increases over sessions as children acquire skills. 

VR-T. Children experience trials from each of three training modules that are organized logically 
for how one crosses: (1) deciding where to cross (component skills: identify a safe place with parked cars, 
traffic on hills, traffic on blind curves); (2) effective looking (component skills: left-right-left, always left 
as one enters the road); (3) how to cross safely (component skills: check traffic in both directions, select 
larger gaps, start right in when car back bumper passes). On each trial, the computer talks and provides 
immediate feedback on performance so children know if they safely completed the trial (e.g., “Great job”) 
or what they did that was unsafe and why (replays of what the child did are shown and explained), and 
what to do instead. A component skill is tested (with explanatory feedback given for failures) until the 
child achieves at least 80% success over a minimum of 5 trials or completes 15 trials. They then move on 
to test another component skill, with failed component skills retested in a future session. Over sessions, 
they get repeated training on all modules and component skills, but repetition focuses mostly on skills 
they still need to achieve. 
SS-T.  This program is grounded in behavior theory (modeling, reinforcement) and modeled after 
Kerbcraft60 a program shown to improve children’s safe crossing behaviors. The program also includes 
where and how content and is organized similarly to VR-T (modules, teach component skills, immediate 
feedback). Over sessions, they get training on all modules and component skills, with repetition focusing 
on weaker skills.   
 
 
STATISTICAL ANALYSIS PLAN 
The CONSORT flowchart diagram will guide all reporting of results and an intention-to-treat analysis 
will be conducted, as appropriate.  
Covariates 
Summary statistics will be computed for baseline and demographic characteristics. When imbalances 
occur between groups, despite randomization, these variables will be included as covariates. Likely 
covariates include: (a) Child age; (b) Child Pedestrian Experience questionnaire data (parent report), 
which yields average weekly distance walked and other potential covariate indices;68 (c) # of days 
between pre- and post- tests; (d) family income. 
Primary Analyses 
Behaviors. We will document the baseline characteristics of each of the 3 groups (age, 
sex, walking experience, days between pre- and post- tests, family income, and traffic volume for field 
measures). We will produce point estimates and confidence intervals for the mean difference between 
pre- and post-immediate scores (i.e., change scores) for each group and each where (1 score), how (3 
scores), and consequence (2 scores) outcome. We will then use linear regression of change score on group 
adjusting for covariates. We will extend this approach to multivariate (modeling multiple outcomes) 
linear regression incorporating all of the outcomes into one model and employing an adjustment for 
multiple comparisons. We will include interaction terms between group and sex to determine if child sex 
modifies program effects. An intention-to-treat approach will be followed. The goal is to assess for 
immediate gains following program completion in our outcomes (for Sustainability see Secondary 
Analyses).  
Secondary Analyses 
 
 
Behaviors. These will examine pre- to post-immediate changes in Component Skills 
performance (e.g., where: selecting safe location with parked cars) and examine from post-immediate to 
post-2 months later (to assess for sustainability of effects), comparing pre to post scores using the same 
approach as above.