Research In Action
Research In Action
The Center for Child Injury Prevention Studies (CChIPS) is thrilled to announce its 2025-2026 portfolio of 10 new research projects. Co-hosted by the Children's Hospital of Philadelphia (CHOP) Research Institute and The Ohio State University (OSU), CChIPS is a child injury research consortium founded by the National Science Foundation in 2005. CChIPS researchers from CHOP and OSU – with expertise in bioengineering, mechanical engineering, epidemiology, behavioral science, public health, and psychology – work alongside member company representatives on the Industry Advisory Board (IAB) comprised of industry, nonprofit, and government members to conduct translational research that is practical to industry.
The IAB convened in late March in Philadelphia to hear proposal presentations. The 10 newly funded projects, which begin funding on May 1st, span a variety of topics that highlight current challenges in keeping children safer on our roadways. A study led by OSU's Dr. Gretchen Baker will utilize the National Digital Check Form (NDCF) from the National Safety Council which compiles car seat check inspection data representing over 270,000 inspections. Dr. Baker will provide in-depth analysis of the NDCF dataset from 2018-2024 to better understand the characteristics of current child restraint system (CRS) use and how this varies with child and vehicle characteristics. Another project, led by CHOP's Dr. Valentina Graci, looks to address challenges in CRS portability. The Consumer Reports survey of caregivers will be used to gather insights on popular currently used portable CRS models. The most commonly used models will be identified and tested to examine child motion during frontal impacts and compared to traditional CRS such as a high-back booster.
In teen driver safety, a study led by CHOP's Dr. Elizabeth Walshe will utilize the CHOP-developed virtual driving assessment (VDA), which can safely and reliably expose young drivers to common crash scenarios. This CChIPS study seeks to mine the richness of the dynamic VDA data that may provide better prediction of crash risk by utilizing a new dynamic approach which transforms time-based data into meaningful intervals and makes patterns easier to analyze.
