Teaching children pedestrian safety in virtual reality via smartphone: a noninferiority randomized clinical trial.

OBJECTIVE: To evaluate whether child pedestrian safety training in a smartphone-based virtual reality (VR) environment is not inferior to training in a large, semi-immersive VR environment with demonstrated effectiveness. METHODS: Five hundred 7- and 8-year-old children participated; 479 were randomized to one of two conditions: Learning to cross streets in a smartphone-based VR or learning in a semi-immersive kiosk VR. The systems used identical virtual environments and scenarios. At baseline, children's pedestrian skills were assessed in both VR systems and through a vehicle approach estimation task (judging speed/distance of oncoming traffic on monitor). Training in both conditions comprised at least six 30-min sessions in the randomly assigned VR platform and continued for up to 25 visits until adult-level proficiency was obtained. Following training and again 6 months later, children completed pedestrian safety assessments identical to baseline. Three outcomes were considered from assessments in each VR platform: Unsafe crossings (collisions plus close calls), time to contact (shortest time between child and oncoming simulated traffic), and missed opportunities (unselected safe opportunities to cross). RESULTS: Participants achieved adult-level street-crossing skill through VR training. Training in a smartphone-based VR system was generally not inferior to training in a large semi-immersive VR system. There were no adverse effects. CONCLUSIONS: Seven- and 8-year-old children can learn pedestrian safety through VR-based training, including training in a smartphone-based VR system. Combined with recent meta-analytic results, the present findings support broad implementation and dissemination of child pedestrian safety training through VR, including smartphone-based VR systems.

CITIES: Clinical trials with intercurrent events simulator.

Although clinical trials are often designed with randomization and well-controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real-life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data-generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real-life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real-life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real-life trials.

Child pedestrian safety training in virtual reality: How quickly do children achieve adult functioning and what individual differences impact learning efficiency?

INTRODUCTION: Pedestrian injuries represent a leading cause of child death globally. One prevention strategy is teaching children street-crossing skills. Virtual reality (VR) has emerged as a strategy to offer repeated street-crossing practice and overcome ethical barriers of training children in live traffic. This study addressed two questions pertinent to implementation of child pedestrian safety training within VR: (a) how much training do children require to achieve adult street-crossing competency, and (b) what individual differences might facilitate children to acquire that competency more efficiently? METHODS: Five hundred 7- and 8-year-olds were recruited. Children completed pedestrian safety training within VR for up to 25 thirty-minute training sessions until they achieved adult levels of mastery. At baseline, four cognitive-perceptual skills (visual memory, visual perception, processing speed, working memory) and parent-reported externalizing symptomatology were assessed. RESULTS: On average, children achieved adult pedestrian safety competency after 10.0 training sessions (SD = 4.8). Just one child (<1%) failed to achieve adult pedestrian functioning after 25 training sessions. In univariate analyses, boys took slightly longer than girls to achieve adult functioning, and visual memory, visual perception, processing speed, working memory, and fewer externalizing symptoms were all positively associated with shorter time to mastery. In a multivariable model, only child age was a statistically significant predictor. CONCLUSION: Almost all participants achieved adult street-crossing skills competency through VR training, although they required about 10 sessions on average. Analysis of predictor variables confirmed that nearly all 7- and 8-year-olds are trainable. PRACTICAL APPLICATION: Implementation of VR pedestrian safety training is recommended, but must be conducted cautiously to ensure children are not permitted to engage independently in traffic until they are assessed and demonstrate sufficient skills.