Background: Patient access to their medical records through patient portals (PPs) facilitates information exchange and provision of quality health care. Understanding factors that characterize patients with limited access to and use of PPs is needed. Methods: Data were from the 2017–2018 Health Information National Trends Survey 5, Cycles 1 and 2, a nationally representative survey of US adults ≥ 18 years old (n = 6789). Weighted multivariate logistic regressions modeled the associations between patient characteristics and access to, facilitators of use, and use of PPs and their functions. Results: Individuals without (vs with) a regular doctor (adjusted odds ratio [aOR], 0.4; CI, 0.3-0.5) or health insurance (aOR, 0.4; CI, 0.2-0.7), those with high school (aOR 0.4; CI, 0.3-0.5) or with vocational/some college (aOR, 0.5; CI, 04.-0.7) education (vs college/postgraduate), or those with limited English proficiency (vs those who speak English very well) (aOR, 0.7; CI, 0.5-0.9) were less likely to report accessing their personal medical records. Women (vs men) were more likely to report accessing their medical records (aOR, 1.5; CI, 1.2-1.8). Similar patterns were found for PPs access and facilitators of use. Less consistent associations emerged between patient characteristics and use of PP functionalities. Conclusions: PP access and use are low. Having a primary care clinician, patient’s educational attainment, and being a woman were factors associated with PP access and use, but not race/ethnicity. Once access was achieved, use of PP functionalities was generally uniform across demographic segments. Facilitating PP access and use among all patient populations is warranted.

OBJECTIVES: Examine racial/ethnic differences in smoking susceptibility among US youth nonsmokers over time and age. METHODS: We used nationally representative samples of youths who never tried cigarettes (N = 143 917; age, 9–21, mean, 14.01 years) from National Youth Tobacco Survey, 1999 to 2014. We used time-varying effect modeling to examine nonlinear trends in smoking susceptibility adjusted for demographics, living with smokers, and exposure to tobacco advertising. RESULTS: Compared with non-Hispanic whites (NHWs), Hispanics were more susceptible to smoking from 1999 to 2014 (highest adjusted odds ratio [aOR], 1.67 in 2012). Non-Hispanic blacks were less susceptible to smoking than NHWs from 2000 to 2009 (lowest aOR, 0.80 in 2003–2005). Non-Hispanic Asian Americans were less susceptible to smoking from 2000 to 2009 (aOR, 0.83), after which they did not differ from NHWs. Other non-Hispanics were more susceptible to smoking than NHWs from 2012 to 2014 (highest aOR, 1.40 in 2014). Compared with NHWs, non-Hispanic blacks and other non-Hispanics were more susceptible to smoking at ages 11 to 13 (highest aOR, 1.22 at age 11.5 ) and 12 to 14 (highest aOR, 1.27 at age 12 ), respectively. Hispanics were more susceptible to smoking throughout adolescence peaking at age 12 (aOR, 1.60) and age 16.5 (aOR, 1.46). Non-Hispanic Asian Americans were less susceptible to smoking at ages 11 to 15 (lowest aOR, 0.76 at ages 11–13 ). CONCLUSIONS: Racial/ethnic disparities in smoking susceptibility persisted over time among US youth nonsmokers, especially at ages 11 to 13 . Interventions to combat smoking susceptibility are needed.

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.

Background: Community-based participatory research is an effective tool for improving health outcomes in minority communities. Few community-based participatory research studies have evaluated methods of optimizing smartphone apps for health technology-enabled interventions in African Americans. Objective: This study aimed to utilize focus groups (FGs) for gathering qualitative data to inform the development of an app that promotes physical activity (PA) among African American women in Washington, DC. Methods: We recruited a convenience sample of African American women (N=16, age range 51-74 years) from regions of Washington, DC metropolitan area with the highest burden of cardiovascular disease. Participants used an app created by the research team, which provided motivational messages through app push notifications and educational content to promote PA. Subsequently, participants engaged in semistructured FG interviews led by moderators who asked open-ended questions about participants’ experiences of using the app. FGs were audiorecorded and transcribed verbatim, with subsequent behavioral theory-driven thematic analysis. Key themes based on the Health Belief Model and emerging themes were identified from the transcripts. Three independent reviewers iteratively coded the transcripts until consensus was reached. Then, the final codebook was approved by a qualitative research expert. Results: In this study, 10 main themes emerged. Participants emphasized the need to improve the app by optimizing automation, increasing relatability (eg, photos that reflect target demographic), increasing educational material (eg, health information), and connecting with community resources (eg, cooking classes and exercise groups). Conclusions: Involving target users in the development of a culturally sensitive PA app is an essential step for creating an app that has a higher likelihood of acceptance and use in a technology-enabled intervention. This may decrease health disparities in cardiovascular diseases by more effectively increasing PA in a minority population.