{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T10:26:22Z","timestamp":1792146382107,"version":"4.4.0"},"reference-count":69,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Telehealth use surged during the COVID-19 pandemic and has stabilized at levels substantially above prepandemic baselines. However, concerns persist that the digital divide may reproduce or widen disparities in access. Understanding the determinants of telehealth use\u2014and particularly modality choice between video and audio\u2014is essential for designing policies that promote equitable access in the post\u2013public health emergency era.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to identify determinants of telehealth use and modality among US adults in 2022 and quantify the relative contributions of digital, geographic, clinical, and socioeconomic domains.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted a cross-sectional secondary analysis of the sixth cycle of the Health Information National Trends Survey, administered in 2022 by the National Cancer Institute, a nationally representative, 2-stage stratified random probability survey of civilian, noninstitutionalized US adults aged 18 years or older. Sampled households were recruited via mailed invitations, and 1 adult per household was randomly selected using the next birthday method and invited to complete a self-administered questionnaire between February 2022 and November 2022 (N=6252). The primary analytic sample included respondents with nonmissing telehealth modality responses (n=6046, 59.4% female; mean age of 55.1 y). Individual-level data were linked to county-level American Community Survey socioeconomic indicators and broadband availability measures. The primary outcome was telehealth use, categorized as video (n=1641, 27.2%; 95% CI 25.5%-29.1%), audio-only (n=876, 12.1%; 95% CI 10.9%-13.4%), or none (n=3529, 60.7%; 95% CI 58.6%-62.7%). We estimated 4 binary contrasts using survey-weighted linear probability models with jackknife variance estimation, reporting absolute risk differences in percentage points (pp) with 95% CIs. We applied Shorrocks-Shapley decomposition to quantify each predictor domain\u2019s contribution to explained variance.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Nationally, 39.3% (n=2517; 95% CI 37.3%\u201041.4%) reported any telehealth use in the past 12 months. In survey-weighted linear probability models (\n \u03b1<\/jats:italic>\n =.05), significant predictors of any telehealth vs none included: male sex (\u22129.7 pp, 95% CI \u221214.0 to \u22125.4), disability status (+22.5 pp, 95% CI 16.1-28.8), and health app use (+18.4 pp, 95% CI 12.0-24.8). For video vs audio-only telehealth, insurance coverage increased video use (+21.2 pp, 95% CI 13.0-29.3), while basic cell phone only (vs smartphone) decreased video use (\u221220.1 pp, 95% CI \u221233.5 to \u22126.8). Shorrocks-Shapley decomposition revealed that digital access and eHealth behaviors explained 40.4% of variance in video vs audio choice and 33.4% of video vs none; geography explained 40.5% of audio vs none; digital factors (25.7%), geography (19.7%), and health status and needs (15.5%) all contributed substantially to any vs none.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Digital access and eHealth behaviors collectively explain more variance in modality choice than traditional sociodemographic factors. Telehealth uptake reflects a combination of digital factors, geography, and clinical need, whereas video modality specifically hinges on digital readiness. Interventions pairing sustained insurance coverage with targeted investments in device access, affordable high-speed connectivity, and digital literacy training are most likely to narrow persistent telehealth gaps.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/81879","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:50:06Z","timestamp":1769763006000},"page":"e81879-e81879","source":"Crossref","is-referenced-by-count":6,"title":["Telehealth Use and Modality Choice Among US Adults: Shorrocks-Shapley Decomposition of a 2022 Cross-Sectional National Survey"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5782-1844","authenticated-orcid":false,"given":"Corneliu","family":"Bolbocean","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7776-6157","authenticated-orcid":false,"given":"Corey","family":"Hayes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6002-5945","authenticated-orcid":false,"given":"Cari","family":"Bogulski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"R1","unstructured":"What is telehealth? 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