Key result
Among older adults, mHealth access varied by demographic factors, with older age (ORs 0.37-0.59 per 10 years) and income <$50,000 (ORs 0.55-0.62) associated with lower odds of all access measures.
Why the study?
Mobile health has an emerging role in cardiovascular disease prevention, but possible inequities in mHealth access among older adults required evaluation.
Are demographic, socioeconomic, and cognitive characteristics associated with mHealth access in older adults?
Cross-Sectional (n=2,796)
Yes
Are demographic, socioeconomic, and cognitive characteristics associated with mHealth access in older adults?
Significant disparities in mHealth access exist among older adults based on age, income, race/ethnicity, and cognitive function, highlighting a digital divide that must be considered in cardiovascular prevention interventions.
mHealth access inequities among older adults may hinder equitable prevention; leaves open whether interventions improve cardiovascular outcomes.
Background Mobile health (mHealth) has an emerging role in the prevention of cardiovascular disease. This study evaluated possible inequities in mHealth access in older adults. Methods and Results mHealth access was assessed from 2019 to 2020 in MESA (Multi-Ethnic Study of Atherosclerosis) telephone surveys of 2796 participants aged 62 to 102 years. A multivariable logistic regression model adjusted for general health status assessed associations of mHealth access measures with relevant demographic, socioeconomic, and cognitive characteristics. There were lower odds of all access measures with older age (odds ratios [ORs], 0.37-0.59 per 10 years) and annual income <$50 000 (versus ≥$50 000 ORs, 0.55-0.62), and higher odds with higher Cognitive Abilities Screening Instrument Score (ORs, 1.22-1.29 per 5 points). Men (versus women) had higher odds of internet access (OR, 1.32 [95% CI,1.05-1.66]) and computing device ownership (OR, 1.31 [95% CI, 1.05-1.63]) but lower fitness tracker ownership odds (OR, 0.70 [95% CI, 0.49-0.89]). For internet access and computing device ownership, we saw lower odds for Hispanic participants (versus White participants OR, 0.61 [95% CI, 0.44-0.85]; OR, 0.69 [95% CI, 0.50-0.95]) and less than a high school education (versus bachelor's degree or higher OR, 0.27 [95% CI, 0.18-0.40]; OR, 0.32 [95% CI, 0.28-0.62]). For internet access, lower odds were seen for Black participants (versus White participants OR, 0.64 [95% CI, 0.47-0.86]) and other health insurance (versus health maintenance organization/private OR, 0.59 [95% CI, 0.47-0.74]). Chinese participants (versus White participants) had lower internet access odds (OR, 0.63 [95% CI, 0.44-0.91]) but higher computing device ownership odds (OR, 1.87 [95% CI, 1.28-2.77]). Conclusions Among older-age adults, mHealth access varied by major demographic, socioeconomic, and cognitive characteristics, suggesting a digital divide. Novel mHealth interventions should consider individual access barriers. Registration URL: https://www.clinicaltrials.gov/; Unique identifier: NCT00005487.
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Patel et al. (2022) conducted a cross-sectional in mHealth access (n=2,796). Demographic, socioeconomic, and cognitive characteristics vs. Reference demographic groups was evaluated on internet access (men vs women) (OR 1.32, 95% CI 1.05-1.66). Among older adults, mHealth access varied by demographic factors, with older age (ORs 0.37-0.59 per 10 years) and income <$50,000 (ORs 0.55-0.62) associated with lower odds of all access measures.
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