Abstract 9298: Associations of Demographic, Socioeconomic, and Cognitive Characteristics With Mobile Health Access Indicate a Digital Divide: Results from the Multi-Ethnic Study of Atherosclerosis (MESA)

Abstract

Background: Mobile health (mHealth) has an emerging role in prevention of cardiovascular disease (CVD), but there is rising concern about inequities in access. This study aimed to evaluate possible divides in mHealth access by demographic, socioeconomic, and cognitive characteristics. Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) is a population-based observational cohort study of adults free of CVD at baseline in 2000-02. Multivariable logistic regression modeling assessed associations of age, sex, race/ethnicity, education level, annual family income, health insurance status, and Cognitive Abilities Screening Instrument (CASI) with three mHealth outcomes in 2019-20. Results: Among 2796 participants (mean [SD] age 75 [9], 55% women, 40% White, 25% Black, 21% Hispanic, 14% Chinese), 63% (n=1756) had internet access (IA), 65% (n=1828) owned a computing device (CD), and 9% (n=248) owned a fitness tracker (FT). There were lower odds of all mHealth outcomes for older age (per 10 years odds ratios (OR) from 0.35 to 0.55), Hispanic ethnicity (vs. White 0.49-0.59), and lower income (<$35,000 vs. ≥$75,000 0.32-0.47) (Figure). For IA and CD, lower odds were seen with Black race (vs. White OR [95% CI] 0.54 [0.41-0.73], 0.66 [0.41-0.73]) and higher odds with higher education (≥Bachelor’s degree vs. <high school 4.75 [3.19-7.12], 3.08 [2.09-4.56]), HMO/private health insurance (vs. other 1.66 [1.31-2.11], 3.08 [2.09-4.56]), and higher CASI score (per 10 points 1.14 [1.02-1.27], 1.15 [1.04-1.28]). There was an inconsistent pattern for Chinese race and sex. Chinese race (vs. White) had lower odds of IA and FT but higher odds of CD, and men (vs. women) had higher odds of CD but lower odds of FT. All results were from a multivariable model. Discussion: All mHealth outcomes varied by major demographic, socioeconomic, and cognitive characteristics, indicating the presence of a digital divide. mHealth interventions should consider individual barriers to access.