IMPORTANCE: Digital health care services expanded with the COVID-19 pandemic. Disparities in telehealth, telemedicine, and telemonitoring use remain understudied. OBJECTIVE: To examine associations between individual-level characteristics and digital health care use and if these associations differ by county-level social vulnerability. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study was an online survey that included a nonprobability sample of US adults aged 18 years or older who resided in 871 counties in the least or most vulnerable quartiles of the Minority Health Social Vulnerability Index (MHSVI), an indicator of county-level social vulnerability. The study was conducted between February and August 2022, and data were analyzed from August 2023 to August 2024. EXPOSURES: Participant characteristics and MHSVI county-level social vulnerability. MAIN OUTCOMES AND MEASURES: Self-reported use of telehealth, telemedicine, and telemonitoring. Multivariable logistic regression models were fit to examine associations between sociodemographic, health, and technology factors and each service use, overall and stratified by MHSVI. RESULTS: Of the 5444 participants who were included in this study, 2927 were female (53.77%), 798 were non-Hispanic Black or African American (14.66%), 838 were Hispanic or Latino (15.39%), 3542 were non-Hispanic White (65.06%); the mean (SE) age was 45.4 (0.2) years. Overall, 2754 participants used telehealth (50.59%), 1609 used telemedicine (29.56%), and 854 used telemonitoring (15.69%). Being English nonproficient (adjusted odds ratio [aOR], 1.54; 95% CI, 1.23-1.92) and having had in-person health care visits (aOR, 4.71; 95% CI, 3.93-5.63) were associated with higher odds of using telehealth, whereas not having a primary care clinician was associated with lower odds (aOR, 0.68; 95% CI, 0.59-0.78). Similar findings were documented for telemedicine and telemonitoring use. Education was associated with higher odds of digital health care use in MHSVI most vulnerable counties (telehealth: aOR, 1.18; 95% CI, 1.06-1.32; telemedicine: aOR, 1.18; 95% CI, 1.05-1.33), whereas individuals who did not self-identify as heterosexual (telehealth: aOR, 1.47; 95% CI, 1.10-1.97; telemedicine: aOR, 1.57; 95% CI, 1.16-2.11; telemonitoring: aOR, 1.54; 95% CI, 1.02-2.31) and those who self-reported fair or poor mental health (telehealth: aOR, 1.29; 95% CI, 1.03-1.61) had higher odds of digital service use in the least vulnerable counties. Self-identifying as Black or African American or Hispanic was associated with high odds of telehealth (Black or African American: aOR, 1.41; 95% CI, 1.17-1.70; Hispanic or Latino: aOR, 1.41; 95% CI, 1.17-1.70), telemedicine (Black or African American: aOR, 1.44; 95% CI, 1.18-1.76; Hispanic or Latino: aOR, 1.27; 95% CI, 1.04-1.54), and telemonitoring (Black or African American: aOR, 1.40; 95% CI, 1.11-1.78; Hispanic or Latino: aOR, 1.46; 95%CI, 1.16-1.84) use overall, but these associations varied across MHSVI strata. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of US adults from MHSVI most and least vulnerable counties, digital health care use varied by participant characteristics. Some traditionally underserved groups self-reported higher use of digital health care. Differing associations between individual-level characteristics and digital health care use by county-level social vulnerability reflect the importance of place-based disadvantage indicators. Eliminating digital health care use disparities is important as it represents a complementary avenue to access health care for underserved populations.