Teaching infant car seat installation via interactive visual presence: An experimental trial

<p><b>Objective</b>: A large portion of child restraint systems (car seats) are installed incorrectly, especially when first-time parents install infant car seats. Expert instruction greatly improves the accuracy of car seat installation but is labor intensive and difficult to obtain for many parents. This study was designed to evaluate the efficacy of 3 ways of communicating instructions for proper car seat installation: phone conversation; HelpLightning, a mobile application (app) that offers virtual interactive presence permitting both verbal and interactive (telestration) visual communication; and the manufacturer's user manual.</p> <p><b>Methods</b>: A sample of 39 young adults of child-bearing age who had no previous experience installing car seats were recruited and randomly assigned to install an infant car seat using guidance from one of those 3 communication sources.</p> <p><b>Results</b>: Both the phone and interactive app were more effective means to facilitate accurate car seat installation compared to the user manual. There was a trend for the app to offer superior communication compared to the phone, but that difference was not significant in most assessments. The phone and app groups also installed the car seat more efficiently and perceived the communication to be more effective and their installation to be more accurate than those in the user manual group.</p> <p><b>Conclusions</b>: Interactive communication may help parents install car seats more accurately than using the manufacturer's manual alone. This was an initial study with a modestly sized sample; if results are replicated in future research, there may be reason to consider centralized “call centers” that provide verbal and/or interactive visual instruction from remote locations to parents installing car seats, paralleling the model of centralized Poison Control centers in the United States.</p

How do children learn to cross the street? The process of pedestrian safety training

<p><b>Objective</b>: Pedestrian injuries are a leading cause of child death and may be reduced by training children to cross streets more safely. Such training is most effective when children receive repeated practice at the complex cognitive–perceptual task of judging moving traffic and selecting safe crossing gaps, but there is limited data on how much practice is required for children to reach adult levels of functioning. Using existing data, we examined how children's pedestrian skills changed over the course of 6 pedestrian safety training sessions, each composed of 45 crossings within a virtual pedestrian environment.</p> <p><b>Methods</b>: As part of a randomized controlled trial on pedestrian safety training, 59 children ages 7–8 crossed the street within a semi-immersive virtual pedestrian environment 270 times over a 3-week period (6 sessions of 45 crossings each). Feedback was provided after each crossing, and traffic speed and density were advanced as children's skill improved. Postintervention pedestrian behavior was assessed a week later in the virtual environment and compared to adult behavior with identical traffic patterns.</p> <p><b>Results</b>: Over the course of training, children entered traffic gaps more quickly and chose tighter gaps to cross within; their crossing efficiency appeared to increase. By the end of training, some aspects of children's pedestrian behavior was comparable to adult behavior but other aspects were not, indicating that the training was worthwhile but insufficient for most children to achieve adult levels of functioning.</p> <p><b>Conclusions</b>: Repeated practice in a simulated pedestrian environment helps children learn aspects of safe and efficient pedestrian behavior. Six twice-weekly training sessions of 45 crossings each were insufficient for children to reach adult pedestrian functioning, however, and future research should continue to study the trajectory and quantity of child pedestrian safety training needed for children to become competent pedestrians.</p

Fitted curves between road traffic mortality and per capita motor vehicles based on modified Smeed equation.

Fitted curves between road traffic mortality and per capita motor vehicles based on modified Smeed equation.</p

Road traffic mortality from police data and health data and per capita motor vehicles, China, 1970–2013.

<p>Road traffic mortality from police data and health data and per capita motor vehicles, China, 1970–2013.</p

Model fit of modified Smeed equation using the data of China and 13 selected countries.

<p>Model fit of modified Smeed equation using the data of China and 13 selected countries.</p

Legislative documents concerning social medical insurance reimbursement of injury-induced medical expenses, number (percentage).

Legislative documents concerning social medical insurance reimbursement of injury-induced medical expenses, number (percentage).</p

Decomposed contributions of ASMR, age structure and population size to difference in unintentional fall deaths between the United States and China in 2017, China in 1990 and 2017, and the United States in 1990 and 2017 for different reference population for method III.

Notes: Fig 2A represents the decomposition results between United States and China in 2017; Fig 2B represents the decomposition results between China in 1990 and 2017; Fig 2C represents the decomposition results between United States in 1990 and 2017.</p

Flow chart of searching legislative documents related to the reimbursement for injury-induced medical expense of four basic social medical insurance schemes of China.

CNKI: China National Knowledge Infrastructure. Note: Some invalid documents were searched although “legally valid document” was included as an inclusion criterion.</p

Key results based on the simulation of modified Smeed equation using data from China and 13 selected countries.

<p>Key results based on the simulation of modified Smeed equation using data from China and 13 selected countries.</p

Attributional formulas of three factors for two reference group selection methods, for methods I and II.

Attributional formulas of three factors for two reference group selection methods, for methods I and II.</p