To compare mobile health (mHealth) usage by residents of West Virginia with national estimates.
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.
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.
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.
Electronic health applications involve the use of a broad range of communication and information technologies, particularly the Internet, to improve health care and monitor patient status.
Although mHealth usage is increasing, some have questioned their efficacy and effectiveness to change behavior.
Another systematic review and consensus statement documented the availability of 7954 and 25,491 mHealth applications on Google Play Store and iTunes, respectively.
The findings reviewed above notwithstanding, we raised questions about the degree to which individuals living in rural areas were represented in the studies in both reviews.
We accessed data in June 2016 from the Internet and American Life Project conducted by the Pew Research Center. This project used a probability sample of survey data to examine Internet use in the United States, differences in Internet usage based on selected variables (eg, education, household income), and how Internet usage impacts the lives of Americans. Pew Research Center reports and associated data are publicly available with appropriate permission. The data were imported into SAS as a comma-separated value file and imported into SAS using the PROCIMPORT command (SAS software, version 9.3, SAS Institute, Cary, NC). For our investigation, we compared the use of mHealth applications between residents of West Virginia and national estimates, controlling for age, education, and income. This nationally representative data consisted of 1066 adult participants, including 30 residents of West Virginia. Probability sampling procedures, such as the one used by Pew, are likely to produce unbiased estimates of population usage of mHealth applications because each member of the population in the United States has an equal probability of being selected as a participant and therefore eliminate potential selection biases.
The data management and analysis for this article were generated using SAS software. No observations were excluded based on the premise that all variables of interest were represented in a reasonable manner. Sampling weights were included with the original data and no other calculations were needed. Three categories of education were used: less than high school, high school and/or some college, and college degree. Annual income was separated into four categories: <$50,000, $50,000 to $100,000, $100,000 to $150,000, and >$150,000. There was a relatively even spread of observations across these categories. A dummy variable was created for West Virginia residential status. Survey-weighted logistic regression models were fitted using the above parameters and the dependent variables of interest using the provided survey weights. All of the analyses were conducted in SAS version 9.3.
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). After controlling for socioeconomic and demographics factors, West Virginia residents were 82% less likely to use mHealth technology compared with the rest of the US population, which was a statistically significant association (odds ratio [OR] 0.182, confidence interval [CI] 0.036–0.932,
The findings of the present study suggest that factors beyond socioeconomic and demographic variables may dictate choices surrounding the use of mHealth applications in West Virginia. These factors may include cultural factors not accounted for or other environmental factors influencing the usage of mHealth. One possibility could be related to the overall use of healthcare resources, because researchers have noted that healthcare usage, in general, is lower in Appalachian communities as compared with non-Appalachian communities.
Another possible explanation for our findings could be related to the lack of broadband and wireless access in rural parts of West Virginia. Further exploration revealed that Internet access can only partially explain our findings. Specifically, one independent source revealed that in 2017, West Virginia ranked 41st in the United States with 66 Internet providers and has an average download speed of 25.5 megabytes per second, which is the 17th fastest in the United States.
The lack of mHealth use by residents in West Virginia represents an opportunity to prevent or mitigate chronic diseases that plague much of the state’s population. These technologies have the potential to reach the entire population of West Virginia and could offer clinicians real-time monitoring of multiple health processes while providing lifestyle management tools for users. Further research should focus on the utilization of mHealth technology during the medical visit and constructing mHealth resources that do not require constant connectivity. The latter issue has been largely addressed with technological advances. For instance, it may be possible for medical professionals to provide tablets or other technologies in the waiting areas for patients to use before visits in primary care or other settings. Additional exposure to mHealth during clinical visits may encourage residents in West Virginia or throughout Appalachia to use these programs, particularly if they are encouraged by physicians or other healthcare professionals. The findings presented here, along with evidence about the efficacy of mHealth applications, could provide a strong rationale for the procurement of public and private support for expanded Internet and wireless access in rural, urban, and community centers throughout West Virginia.
Given these findings and observations, the present study has at least one limitation worth noting. The relatively low sample size of West Virginia residents compared with other states may raise questions about how well these individuals represented West Virginia residents. We contend that the nature of the probability sampling scheme used by the Pew Research Center should significantly mitigate concerns about issues of generalizability and whether the sample is representative of West Virginia as a whole. Another limitation is that the data were self-reported, which is prone to biases related to recall and social desirability.
Finally, future researchers interested in the development and testing of mHealth applications should ensure these technologies be relevant for an Appalachian population by involving end users during the developmental stages. Finally, policies regarding infrastructure and communication improvements within West Virginia may be beneficial for its residents by allowing greater access to mHealth throughout the state.
This publication is a product of the West Virginia Prevention Research Center and was supported by cooperative agreement no. 1-U48-DP-005004 from the Centers for Disease Control and Prevention. Additional support was provided by the National Institute of General Medical Sciences, National Institutes of Health (U54GM104942), and West Virginia IDeA STR. The content and conclusions in this article are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health, the
The authors did not report any financial relationships or conflicts of interest.
Use of mobile health or multiplatform applications has proliferated in recent years.
Secondary analysis of publically available Pew Research Center data showed that residents of West Virginia were 82% less likely than national estimates to use mobile health applications.
Results are possibly the result of cultural ambivalence toward health-seeking behaviors.
Health application usage has the potential to improve the health of West Virginia residents.