Sociodemographic Factors Associated With Using eHealth for Information Seeking in the United States: Cross-Sectional Population-Based Study With 3 Time Points Using Health Information National Trends Survey Data.

BACKGROUND: Despite the potential benefits of using eHealth, sociodemographic disparities exist in eHealth use, which threatens to further widen health equity gaps. The literature has consistently shown age and education to be associated with eHealth use, while the findings for racial and ethnic disparities are mixed. However, previous disparities may have narrowed as health care interactions shifted to web-based modalities for everyone because of the COVID-19 pandemic. OBJECTIVE: This study aims to provide an updated examination of sociodemographic disparities that contribute to the health equity gap related to using eHealth for information seeking using 3 time points. METHODS: Data for this study came from the nationally representative 2018 (n=3504), 2020 (n=3865), and 2022 (n=6252) time points of the Health Information National Trends Survey. Logistic regression was used to regress the use of eHealth for information seeking on race and ethnicity, sex, age, education, income, health status, and year of survey. Given the consistent association of age with the dependent variable, analyses were stratified by age cohort (millennials, Generation X, baby boomers, and silent generation) to compare individuals of similar age. RESULTS: For millennials, being female, attaining some college or a college degree, and reporting an annual income of US $50,000-$74,999 or >US $75,000 were associated with the use of eHealth for information seeking. For Generation X, being female, having attained some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, better self-reported health, and completing the survey in 2022 (vs 2018; odds ratio [OR] 1.80, 95% CI 1.11-2.91) were associated with the use of eHealth for information seeking. For baby boomers, being female, being older, attaining a high school degree, attaining some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, and completing the survey in 2020 (OR 1.56, 95% CI 1.15-2.12) and 2022 (OR 4.04, 95% CI 2.77-5.87) were associated with the use of eHealth for information seeking. Among the silent generation, being older, attaining some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, and completing the survey in 2022 (OR 5.76, 95% CI 3.05-10.89) were associated with the use of eHealth for information seeking. CONCLUSIONS: Baby boomers may have made the most gains in using eHealth for information seeking over time. The race and ethnicity findings, or lack thereof, may indicate a reduction in racial and ethnic disparities. Disparities based on sex, education, and income remained consistent across all age groups. This aligns with health disparities literature focused on individuals with lower socioeconomic status, and more recently on men who are less likely to seek health care compared to women.

Individualistic Versus Collaborative Learning in an eHealth Literacy Intervention for Older Adults: Quasi-Experimental Study.

BACKGROUND: Older adults tend to have insufficient health literacy, which includes eHealth literacy-the ability to access, assess, and use digital health information. Interventions using methods such as collaborative learning (CL) and individualistic learning (IL) may be effective in addressing older adults' low eHealth literacy, but little is known about the short- and long-term effects of CL versus IL on older adults' eHealth literacy. OBJECTIVE: The objective of this study was to use a 3 × 2 × 3 mixed factorial design to examine older adults' learning with CL versus IL for eHealth literacy. METHODS: Older adults (N=466; mean age 70.5, SD 7.2; range 60-96 years) from diverse racial and ethnic groups were randomly assigned to either the CL or IL group (233/466, 50% in each). The intervention consisted of 4 weeks of training in 2-hour sessions held twice a week. Using ANOVA and multiple regression, we focused on the main effects of learning condition and interaction between learning condition and previous computer experience. Learning method (CL or IL) and previous computer experience (experienced, new, or mixed) were between-subject variables, and time of measurement (pretest measurement, posttest measurement, and 6-month follow-up) was the within-subject variable. Primary outcome variables were eHealth literacy efficacy, computer and web knowledge, basic computer and web operation skills, information-seeking skills, and website evaluation skills. Control variables were age, sex, education, health status, race and ethnicity, income, primary language, and previous health literacy. RESULTS: eHealth literacy efficacy, computer and web knowledge, basic computer and web operation skills, information-seeking skills, and website evaluation skills improved significantly (P<.001 in all cases) from before to after the intervention. From postintervention measurement to 6-month follow-up, there was a significant interaction between learning condition and previous computer experience based on 1 outcome measure, computer and web operation skills (F2,55=3.69; P=.03). To maintain computer and web operation skills 6 months after the intervention, it was more effective for people with little to no previous computer experience to learn individually, whereas for people with more previous computer experience, it was more effective to learn collaboratively. From postintervention measurement to 6-month follow-up, statistically significant decreases were found in 3 of the 5 outcome measures: eHealth literacy efficacy, computer and web knowledge, and basic computer and web operation skills (P<.001 for all 3 cases). CONCLUSIONS: Older adults' eHealth literacy can be improved through effective intervention, and the IL or CL condition may have little effect on short-term outcomes. However, to maintain long-term benefits, it may be best to learn collaboratively with others who have similar previous computer experience. eHealth literacy is multidimensional, with some components retained better over time. Findings suggest a need for resources to provide continuous training or periodic boosting to maintain intervention gains.