OBJECTIVE: To compare mobile health (mHealth) usage by residents of West Virginia with national estimates. METHODS: Pew Research Center data from its Internet and American Life Project were accessed for secondary data analysis. These data, available to the public, are a probability sample of Internet use in the United States, differences in use based on selected variables (eg, education, household income), and how usage affects the lives of Americans. Using SAS software, diagnostics were performed on the data, revealing that the variables of interest were prepared and represented without any need for information. Data were used as is, with categorical and continuous characteristics and stipulations being provided in accompanying documents from the Pew Research Center. RESULTS: The national sample consisted of 509 men and 557 women with an average age of 51.02 years (standard deviation 17.04). The 30 West Virginia residents included 19 women and 11 men (mean for age 48.10, standard deviation 15.30). When controlling for socioeconomic and demographics factors, the odds of a West Virginia resident using an mHealth device were 82% less than the rest of the country, a statistically significant association. Women in West Virginia were 52% more likely to access mHealth information than men, and an increase in age corresponded with increased mHealth usage. CONCLUSIONS: The lack of mHealth use by residents in West Virginia represents an opportunity for clinicians and scientists. The high rates of preventable diseases in the region could be more effectively managed with greater use of these technologies.
OBJECTIVE: To compare mobile health (mHealth) usage by residents of West Virginia with national estimates. METHODS: Pew Research Center data from its Internet and American Life Project were accessed for secondary data analysis. These data, available to the public, are a probability sample of Internet use in the United States, differences in use based on selected variables (eg, education, household income), and how usage affects the lives of Americans. Using SAS software, diagnostics were performed on the data, revealing that the variables of interest were prepared and represented without any need for information. Data were used as is, with categorical and continuous characteristics and stipulations being provided in accompanying documents from the Pew Research Center. RESULTS: The national sample consisted of 509 men and 557 women with an average age of 51.02 years (standard deviation 17.04). The 30 West Virginia residents included 19 women and 11 men (mean for age 48.10, standard deviation 15.30). When controlling for socioeconomic and demographics factors, the odds of a West Virginia resident using an mHealth device were 82% less than the rest of the country, a statistically significant association. Women in West Virginia were 52% more likely to access mHealth information than men, and an increase in age corresponded with increased mHealth usage. CONCLUSIONS: The lack of mHealth use by residents in West Virginia represents an opportunity for clinicians and scientists. The high rates of preventable diseases in the region could be more effectively managed with greater use of these technologies.
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