Mehta receives NIOSH grant to revise endurance prediction model for the changing U.S. workforce

With one in three adults considered obese and approximately another 40 percent overweight, accommodating heavier employees has simply become a fact of life in the workplace, from large-scale factories to corporate cubicles.

Injuries from overexertion or fatigue are a significant cause of worker disability, with U.S. employers spending in excess of $200 billion annually on obesity-related health conditions. Existing endurance prediction models provide ergonomists work guidelines to protect workers from injury by measuring the maximum amount of work an individual can perform at different levels of exertion. These models consider many factors, but not obesity.

Ranjana Mehta, Ph.D., assistant professor at the Texas A&M Health Science Center School of Public Health, was recently awarded a research grant from the National Institute for Occupational Safety and Health, a section of the U.S. Centers for Disease Control and Prevention, to develop a revised force-endurance model to accommodate for the changing capacity of the overweight and obese workforce. The $72,750 grant is the first of a two-year research project that will be conducted by researchers at both the Texas A&M School of Public Health and the State University of New York at Buffalo.

“Findings from a Liberty Mutual Research Institute study indicates that obesity is associated with a 25 percent higher risk of work-related injury, independent of all other relevant factors, such as age, work hours, and occupational hazards,” Mehta said. “Americans spend a significant part of our lives in the workplace and this presents an increased injury risk for the majority of the workforce that are overweight and obese.”

Researchers will collect data from individuals in Texas and New York with varying body types – average, overweight and obese. The proposed work will focus on examining individuals’ endurance times at different levels of physical work across three tasks that target commonly injured muscles of the upper body and trunk. The data collected from a diverse, widespread population will be more applicable to the general population and will assist the researchers in developing an accurate revised force-endurance model to reduce workplace injury for all workers in the future.