Hearing impairment is a significant problem facing the US workforce, particularly with the predicted shift in the workforce in coming years. The primary populations most affected by hearing loss are occupational environments and the elderly. However, because of this upcoming shift in the age of the workforce, these two populations begin to overlap significantly. It is estimated that approximately one third of Americans between the ages of 65 and 74 have hearing loss of some kind, and almost half of the population over the age of 75 are affected by it (Olyer, 2013). Additionally, according to the US Census, the effect of the Baby Boomers is having a dramatic effect on the elderly population in the U.S. Data indicate that from 2012-2050 the number of persons over age 65 is expected to double (Ortman, Velkoff, & Hogan, 2014). Prior research indicates strong associations with negative quality of life indicators in the elderly, ranging from communication difficulties and depression to transportation and finance management (Dalton, Cruickshanks, Klein, Klein, Wiley, & Nondahl, 2003; Yueh, Shapiro, MacLean, & Shekelle, 2003; Ciorba, Bianchini, Pelucchi, & Pastore, 2012).
Similarly, high noise occupational environments are high priority research areas. The NIOSH National Occupational Research Agenda (NORA) has identified noise exposures across multiple occupational settings as high priority needs in Research to Practice (r2p) sectors (NIOSH, 2014). It is not surprising that occupationally induced hearing loss is frequently targeted as a major concern for these new employees (Li-Korotoky, 2012). Regardless of the industry, be it rural or urban, public or private, the overall shortfall of qualified audiologists (ASLHA, 2011), the shifts in employed populations, and the increase in the aging population creates the demand for a valid hearing screening methodology.
While audiologists are the gold standard for screening individuals for hearing loss using pure-tone audiometry, other tools exist for screening. These tools, however do have significant limitations producing both false positives and false negatives. Most of these tests are a mixture of whisper tests, handicap inventories, and some technology (Yueh, Shapiro, MacLean, & Shekelle, 2003; Gates, Murphy, Rees, & Fraher, 2003; Cardoso, et al., 2014). It is because of these current limitations that led us to develop a viable user-centric hearing screening application (hEAR) as an alternative to produce quality screening results while increasing access.
