{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T18:03:32Z","timestamp":1791050612155,"version":"4.1.0"},"reference-count":48,"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 Digital health interventions (DHIs), including telemedicine and AI-enabled health tools, are increasingly integrated into health care systems worldwide. While these technologies have the potential to improve access and efficiency, unequal access to resources and health capabilities may create disparities in their use. Evidence remains limited on how social and structural determinants shape population-level entry into DHI use in rapidly digitalizing health systems such as China.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to examine social and structural determinants of broader DHI use among adults in China using the World Health Organization\u2019s (WHO) Social Determinants of Health (SDoH) framework.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This cross-sectional study analyzed data from a national survey conducted in China in 2024 among adults aged 18 years or older. The primary outcome was self-reported ever use of DHIs, including telemedicine, digital health apps, and AI-enabled health tools. Explanatory variables were categorized into 5 SDoH domains: economic stability, education and health-related capabilities, health care access and quality, neighborhood and built environment, and social and community context. Multivariable logistic regression was used to estimate adjusted odds ratios (aORs) and 95% CIs. Adjusted predicted probabilities and subgroup analyses by age and region were conducted as supplementary analyses.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Among 34,672 participants, 14,565 (42.0%) reported ever using a DHI. DHI use was higher among participants with greater socioeconomic resources and health-related capabilities, including higher income, higher educational attainment, and higher health and eHealth literacy (HL, eHL). For example, participants with a monthly income of 6001 CNY (approximately US $889) or higher had higher odds of DHI use than those with an income of 3000 CNY (approximately US $444) or less (aOR 1.370, 95% CI 1.285\u20101.462), and those with a bachelor\u2019s degree or above had higher odds than those with junior high school education or below (aOR 1.487, 95% CI 1.377\u20101.607). Health system engagement also showed substantial differences: participants unaware of family doctor services had lower odds of DHI use than those enrolled in such services (aOR 0.502, 95% CI 0.465\u20100.542). Difficulty paying medical expenses was associated with higher DHI use (aOR 1.314, 95% CI 1.221\u20101.414). Adjusted predicted probabilities showed meaningful absolute differences for education, income, HL, eHL, difficulty paying medical expenses, and family doctor service status, whereas the adjusted urban-rural difference was small.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Broader self-reported DHI use in China is strongly associated with socioeconomic resources, health-related capabilities, and access to health care. Equitable digital health transformation requires more than technological expansion. Strategies to improve digital health equity should strengthen HL and eHL, integrate digital tools with accessible primary care and family doctor services, preserve nondigital support pathways, and incorporate equity-oriented design and governance as AI-enabled health tools become more widely implemented.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/94788","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T20:55:05Z","timestamp":1784840105000},"page":"e94788-e94788","source":"Crossref","is-referenced-by-count":1,"title":["Social Determinants of Digital Health Intervention Use in China: National Cross-Sectional Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7109-1163","authenticated-orcid":false,"given":"Huohuo","family":"Dai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9607-313X","authenticated-orcid":false,"given":"Yibo","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9489-7925","authenticated-orcid":false,"given":"Anke","family":"Versluis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0892-5780","authenticated-orcid":false,"given":"Yiling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8607-9199","authenticated-orcid":false,"given":"Niels","family":"Chavannes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4023-6814","authenticated-orcid":false,"given":"Jiska","family":"Aardoom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"key":"R1","unstructured":"Global strategy on digital health 2020-2025. 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