THE DIGITAL DIVIDE: RACIAL AND ETHNIC DIFFERENCES IN U.S. MOBILE PHONE USE TO ACCESS ONLINE HEALTH INFORMATION. Nancy Pontes, PhD, Rutgers University, Camden Manuel Pontes, PhD, Rowan University
The Digital Divide: Access to Internet The Digital Divide: Minorities and Persons with Lower Income are Less Likely to Have Internet Access As A Result, they Have Less Access to Information and Knowledge Minorities and Use of Mobile Devices Univariate Analyses Showed that Minorities are More Likely to Use Mobile Devices to Access Online Health Information (Fox, 2012). Note: Hispanics are Younger than Non Hispanic Whites Greater Mobile Use Among Hispanics May be Confounded with Age (Hispanics More Likely to Use Mobile Devices Because they are Younger)
Pew Internet and Health Survey Pew Internet and American Life Project A series of nationally representative (USA) telephone surveys sponsored by the Pew Foundation. The goal of these surveys is to understand how US adults use the Internet Pew Internet and Health Survey (n=3,014) Nationally representative (USA) survey of adults (18 years or older) Conducted by Princeton Associates Sampling weights provided. Need to use software that incorporates sampling weights for analyses.
Use of R and survey package for analyses R (R Core Team 2015) Open source software for statistical analyses. Thousands of add on packages for specialized analyses Survey package (Lumley 2004, 2014) Used for analyses of survey data. Can be used for data with sampling weights Can be used for data from complex (multistage) sampling designs. R and survey package were used for all analyses
Smartphones and mobile health Smartphones are changing the face of mobile and participatory healthcare (Boulos, M. N., Wheeler, S., Tavares, C., & Jones, R. ,2011). Racial and ethnic minorities have smartphone ownership rates that are comparable to whites. Mobile devices may help bridge the digital divide.
Logistic Regression Model: Independent variables Race/Ethnicity Education Non-Hispanic Black Yes Hispanic No (Ref) Non-Hispanic All Other Age Non-Hispanic White (Ref) 50 years or older Gender 35 – 49 years Male 18 – 34 years (Ref) Female (Ref) Househod Income $50,000 or more $ 0 – 49,000 (Ref)
Logistic Regression Model: Dependent variables Used Smart Phone Accessed Online Health Information Yes (No Use of Mobile Device) No (Ref) Yes Accessed Online Health Information No (Ref) (Any Device) Had Health Apps on Phone Yes Yes No (Ref) No (Ref) Accessed Online Health Information (Mobile Device) Yes No (Ref)
Table 1: Age Distribution of US Cell Phone Users by Race/Ethnicity (2012) NH= Non Hispanic, %=percentage of adult cell phone owners within race/ethnicity, SE=standard error of estimate, significance: *=p<0.05, **=p<0.01.
Table 2A: Logistic Regression – Used Smart Phone % of adult cell phone users who used a smartphone, SE=standard error, t (M)= t statistic, OR=odds ratio, 95% CI=95% confidence interval, significance: *=p<0.05, **=p<0.01
Table 2B: Logistic Regression – Used Smart Phone. % of adult cell phone users who used a smartphone, SE=standard error, t (M)= t statistic, OR=odds ratio, 95% CI=95% confidence interval, significance: *=p<0.05, **=p<0.01 % of adult cell phone users who used a smartphone, SE=standard error, t (M)= t statistic, OR=odds ratio, 95% CI=95% confidence interval, significance: *=p<0.05, **=p<0.01
Table 3A: Logistic Regression – Accessed Online Health Info: Any Device % =percentage of adult cell phone users who accessed online health information, significance: *=p<0.05, **=p<0.01.
Table 3B: Logistic Regression – Accessed Online Health Info: Any Device
Table 4A: Logistic Regression – Accessed Online Health Info: Mobile Device
Table 4B: Logistic Regression – Accessed Online Health Info: Mobile Device
Table 5A: Logistic Regression – Accessed Online Health Info: No Mobile Device Use
Table 5B: Logistic Regression – Accessed Online Health Info: No Mobile Device Use
Table 6A: Logistic Regression – Had Health Apps on Mobile Device
Table 6B: Logistic Regression – Had Health Apps on Mobile Device
Key Findings (Univariate) Hispanics, and other minorites significantly more likely than non Hispanic 1. whites to use smartphones. Hispanics, non-Hispanic blacks, and other minorities significantly more 2. likely than non-Hispanic whites to access online health information through mobile devices. Hispanics, non-Hispanic blacks, and other minorities significantly less likely 3. than non-Hispanic whites to access online health information with no use of mobile devices. Significant age difference (especially 50+ years) in the likelihood that 4. persons access online health info with a mobile device No significant age difference in the likelihood that persons access online 5. health info without any use of mobile deviceHispanics significantly more likely than non Hispanic whites to use smartphones. Note: Hispanics, and other minorities, significantly younger than Non-Hispanic whites
Key Findings (Multivariate) After controlling for age, income and education, and gender Hispanics, non-Hispanic blacks, and other minorites significantly 1. more likely than non Hispanic whites to use smartphones. Hispanics, non-Hispanic blacks, and other minorities significantly 2. more likely than non-Hispanic whites to access online health information through mobile devices. Hispanics, non-Hispanic blacks, and other minorities significantly 3. less likely than non-Hispanic whites to access online health information without no use of mobile devices. Younger adults (18-34 years), significantly more likely than other 4. adults access online health info with a mobile device No significant age difference in the likelihood that persons access 5. online health info without any use of mobile device
Implications for Practice & Future Research Implications for Practice Create Mobile-Friendly Web Sites Websites that recognize whether viewer is using a smart phone (or other mobile device) and format webpage for device. Width of webpage fits device Further research Race/Ethnicity Differences Type of Information Search Extent of Information Search Quality of Information Search
Limitations of Study Self Report Measures of Online Health Information Search Dependent variables were based on self-report by respondents Do not measure amount of information search Do not measure quality of information search
References Boulos, M. N., Wheeler, S., Tavares, C., & Jones, R. (2011). K, et al. “How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomedical engineering online, 10 (1), 24. Fox, S., and Duggan, M. (2012). Mobile health 2012. Washington, DC: Pew Internet A American Life Project. Lumley, T. (2004) Analysis of complex survey samples. Journal of Statistical Software 9(1): 1-19 Lumley, T. (2014) “Survey : analysis of complex survey samples". R package version 3.30. R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
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