Article Details

Research Database: Article Details

Citation:  Clay, S.L., & Alston, R. (2016). Assistive technology use and veterans: An examination of racial differences between Whites and Blacks using the HAAT model. Journal of Vocational Rehabilitation, 45 (2), 159-171.
Title:  Assistive technology use and veterans: An examination of racial differences between Whites and Blacks using the HAAT model
Authors:  Clay, S.L., & Alston, R.
Year:  2016
Journal/Publication:  Journal of Vocational Rehabilitation
Publisher:  IOS Press
DOI:  https://doi.org/10.3233/JVR-160820
Full text:  http://content.iospress.com/articles/journal-of-vocational-rehabili...   
Peer-reviewed?  Yes
NIDILRR-funded?  No

Structured abstract:

Background:  Many disability researchers have advocated for the use of assistive technology to enhance quality of life for persons with disabilities. However, it has been documented that minorities and veterans are two groups that underutilize the resource.
Purpose:  To use the Human Activity Assistive Technology (HAAT) model to explore assistive technology (AT) use among veterans, specifically examining race, gender, age, socioeconomic determinants (e.g. marital status, educational attainment, employment status, and income), access to health care, general health, and disability status.
Setting:  This study retrospectively examined the 2012 Behavioral Risk Factor Surveillance System (BRFSS) dataset. The first BRFSS was established in 1984 and included only 15 states. Today, it has evolved into one of the largest datasets worldwide incorporating data from all 50 states in the United States, the District of Columbia, American Samoa, Guam, Palau, Puerto Rico, and the U.S. Virgin Islands. Through a collaborative effort between the Centers for Disease and Control (CDC) and state health departments, the BRFSS captures behavioral risk factors on an individual level through a survey containing three parts: the main questionnaire, optional modules, and state-added questions. Data are collected via telephone interviews using landlines and cellular phones, which is new feature that was implemented in 2011. More than 500,000 interviews were conducted in 2011 for the most recent version of the BRFSS (2012). Only the main questionnaire was used in this analysis (www.cdc.gov/brfss).
Study sample:  The original BRFSS assessed several racial groups including Whites, Blacks, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaskan, Other Race, Multiracial, and Hispanic. Consistent with other research exploring the association of AT use and race (Loggins et al., 2013, Alston et al., 2014), the sampling approach included only targeting White and Black respondents from the general population. Further stratification was performed for most analyses to include only White veterans and Black veterans to test our HAAT model. In the general population, there were a total of 402,761 individuals that were either White or Black, with 52,765 (13.1%) being veterans. Of the Black and White veterans, 7,912 (15.0%) individuals used AT. White veterans that used AT accounted for 90.4% (n?=?7,154) and 9.6% (n?=?758) were Black veterans that used AT.
Data collection and analysis:  Data were analyzed from the national 2012 Behavioral Risk Factor Surveillance System (BRFSS). Descriptive statistics, chi-square analyses, and multivariate analyses were performed.
Findings:  Black veterans used AT more than White veterans, which was consistent with the predictions that indicated that Black veterans were 1.3 times more likely to use AT (OR?=?1.30 CI: 1.20–1.42). However, White veterans who used AT had a higher socioeconomic status compared to Black veterans who used AT. More White veterans were married, had higher educational attainment levels, were employed, and had higher income levels. White veterans also had better health coverage, fewer issues with medical costs and better general health. Whereas all of the predictors of AT use were significant for White veterans, only age (p?
Conclusions:  There are differences in AT use between White and Black veterans based on socioeconomic determinants, access to health care, general health, and disability status. Different predictors and differences in magnitude were observed. Racial differences can partially be explained by components of the HAAT model such as the type of activity that the human is engaging in (e.g. employment) and the context (e.g. the environment).

Disabilities served:  Multiple disabilities
Populations served:  Other
Culturally diverse populations (e.g., African Americans, Native Americans, and non-English speaking populations)
Interventions:  Assistive technology