JMIR Publications

ABSTRACT

Background:

Interest in and funding for digital health interventions have rapidly grown in recent years. Despite the increasing familiarity with mHealth from regulatory bodies, providers, and patients, overarching research on digital health adoption has been primarily limited to morbidity-specific and non-American samples.

Objective:

To describe uptake and utilization patterns of common digital health tools in Georgia and describe predictors of uptake.

Methods:

Online survey respondents in Georgia over 18 were recruited from mTurk to answer primarily closed-ended questions within the following domains: (1) participant demographics and health consumption background, (2) telehealth, (3) digital health education, (4) prescription management tools, (5) digital mental health services, and (6) doctor finder tools. Multivariate linear and logistic regressions were used to identify predictors of digital health tool usage.

Results:

A total of 362 respondents completed the survey. Private insurance, residence in an urban area, having a primary care provider, fewer urgent ER visits, more ER visits leading to inpatient stays, and chronic condition presence were significantly associated with the number of digital health tools used. The separate logistic regressions exhibited substantial variability with 3.5 statistically significant predictors per model, on average. Age, federal poverty level, number of PCP visits in the past 12 months, number of non-urgent ER visits in the past 12 months, number of urgent ER visits in the past 12 months, number of ER visits leading to inpatient stays in the past 12 months, race, gender, ethnicity, insurance, education, residential area, access to internet, difficulty accessing healthcare, usual source of care, status of PCP, and status of chronic condition all had at least one statistically significant relationship with use of a specific digital health category.

Conclusions:

The results demonstrate that socioeconomically disadvantaged persons who would benefit the most from using certain tools do not adopt them at disproportionately higher rates than more advantaged populations. The variability of digital health adoption necessitates investing in and building a common framework to increase mHealth access. This likely will begin with a coordinated effort to determine barriers to adopting digital health solutions.