Digital Health Divide and Its Impact on Access to Preventive Healthcare Services in Urban India

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However, unequal access to digital technologies and digital literacy may create a new form of health inequity—the digital health divide—potentially limiting access to preventive healthcare services. Objective To examine the association between digital health access and utilization of preventive healthcare services among adults in urban Hyderabad, and to identify socio-demographic determinants of poor digital access. Methods A community-based cross-sectional study was conducted among 1,482 adults in Hyderabad using a structured questionnaire. A Digital Health Access Index (DHAI) and a Preventive Healthcare Utilization Index (PHUI) were constructed. Multivariable logistic regression was used to identify predictors of low preventive healthcare utilization and poor digital access. Results Of the 1,482 participants, 58.7% demonstrated high digital health access while 41.3% had low access. Preventive healthcare utilization was significantly lower in the low-access group (32.4% vs 68.9%, p < 0.001). Older age, lower education, lower income, and limited digital literacy were independently associated with poor digital access. After adjustment for confounders, low digital access was associated with reduced odds of adequate preventive healthcare utilization (adjusted OR 0.41, 95% CI 0.33–0.52). Major barriers included lack of digital skills (46.2%), language limitations (38.4%), and privacy concerns (34.1%). Conclusion A substantial digital health divide exists in urban India and is strongly associated with reduced utilization of preventive healthcare services. Public health strategies must integrate digital inclusion, regional language support, community-based digital training, and assisted digital health services to ensure equitable access to preventive care. digital health health equity preventive healthcare urban health telemedicine India Introduction Digital health technologies are increasingly integrated into healthcare delivery systems worldwide, including telemedicine platforms, mobile health applications, and artificial intelligence–enabled clinical decision tools [1–3]. In India, large-scale adoption of digital health has been accelerated by rapid smartphone penetration and national initiatives such as the Ayushman Bharat Digital Mission [4]. These technologies offer potential benefits including early disease detection, improved continuity of care, and reduced burden on overextended urban healthcare facilities [2, 5]. However, access to digital health services is not uniform. Differences in socioeconomic status, education, age, language proficiency, and digital literacy may systematically exclude certain population groups from benefiting from these innovations [6, 7]. This phenomenon, described as the digital health divide , may reinforce existing health inequities by limiting access to preventive services such as routine health screening, vaccination, and lifestyle counseling [8]. Urban India represents a critical context in which advanced digital infrastructure coexists with marked socioeconomic inequality. While high-income and well-educated populations increasingly utilize app-based healthcare services, vulnerable groups may remain dependent on overcrowded physical facilities or forgo preventive care altogether [9]. Despite growing policy attention to digital health expansion, population-level evidence linking digital access to preventive healthcare utilization remains limited in low- and middle-income countries [6, 10]. Quantifying this relationship is essential to inform equitable digital health policy design. This study aimed to evaluate the magnitude of the digital health divide in an urban Indian population and to assess its impact on utilization of preventive healthcare services. Objectives Primary objective To assess the association between digital health access and utilization of preventive healthcare services among adults in urban Hyderabad. Secondary objectives 1. To estimate the prevalence of adequate digital health access. 2. To identify socio-demographic determinants of poor digital access. 3. To examine disparities in preventive healthcare behaviors by digital access level. 4. To identify perceived barriers to digital health use. 5. To generate policy-relevant recommendations for reducing digital health inequity. Hypotheses H1: Low digital health access is associated with lower utilization of preventive healthcare services. H2: Older age, lower education, and lower income independently predict poor digital health access. H3: Digital literacy mediates the relationship between socioeconomic status and preventive healthcare utilization. Methods Study design and setting Community-based cross-sectional study conducted in Hyderabad, Telangana, India (March–July 2026). Study population Adults aged ≥ 18 years residing in Hyderabad for ≥ 6 months. Sample size Target sample: 1,500 participants. Final analyzed sample: 1,482. Sampling method Multisite sampling from outpatient departments, residential colonies, workplaces, public spaces, and online recruitment. Inclusion criteria Adults ≥ 18 years, permanent urban residents, able to provide informed consent. Exclusion criteria Severe cognitive impairment; questionnaires with > 20% missing data. Data collection instrument A structured interviewer-assisted questionnaire was developed based on prior digital health access and technology acceptance literature [ 6 , 10 , 11 ]. The full instrument is provided in Supplementary File 1. Key variables Digital Health Access Index (DHAI) (0–10 scale): smartphone ownership, internet availability, independent app use, frequency of internet use, prior telemedicine use. Preventive Healthcare Utilization Index (PHUI) (0–8 scale): routine health check-up, blood pressure screening, diabetes screening, age-appropriate cancer screening, vaccination status, lifestyle counseling. Outcome definition Adequate preventive healthcare utilization defined as PHUI ≥ 4. Statistical analysis Descriptive statistics, chi-square tests, independent t-tests, and multivariable logistic regression were used. Mediation analysis was performed to examine the role of digital literacy. Statistical significance was set at p < 0.05. Clinical trial number: not applicable. Ethical considerations Institutional ethics committee approval was obtained. Written informed consent was collected. Data were anonymized and stored securely in accordance with ethical guidelines [ 12 ]. Results Sample characteristics Mean age: 37.6 ± 12.9 years; 53.1% male; 44.2% graduates or higher; 96.1% smartphone ownership. The socio-demographic characteristics of the study population are summarized in Table 1 . Table 1 Socio-demographic characteristics (n = 1,482) Variable Category n (%) Age group 18–29 472 (31.8) 30–44 596 (40.2) 45–59 318 (21.5) ≥ 60 96 (6.5) Sex Male 787 (53.1) Female 680 (45.9) Education ≤ 12th 826 (55.8) Graduate+ 656 (44.2) Digital health access High digital access: 58.7% Low digital access: 41.3% Key indicators of digital health access among participants are presented in Table 2 . Table 2 Digital health access indicators Indicator n (%) Smartphone ownership 1424 (96.1) Regular internet access 1296 (87.4) Independent app use 1038 (70.0) Prior telemedicine use 812 (54.8) Preventive healthcare utilization Adequate utilization overall: 54.2% High-access group: 68.9% Low-access group: 32.4% (p < 0.001) Preventive healthcare utilization stratified by digital access level is shown in Table 3 . Table 3 Preventive healthcare utilization by digital access level Preventive service High access (%) Low access (%) Routine check-up 72.4 35.6 BP screening 78.1 44.2 Diabetes screening 69.5 33.1 Vaccination updated 61.3 29.8 Multivariable predictors of low digital access Age ≥ 60 years (aOR 2.41), education ≤ 12th grade (aOR 2.78), monthly income <₹25,000 (aOR 2.12), and low digital literacy (aOR 3.64). Results of the multivariable logistic regression analysis identifying predictors of poor preventive healthcare utilization are presented in Table 4 . Table 4 Multivariable logistic regression for poor preventive care utilization Predictor aOR 95% CI p Low digital access 0.41 0.33–0.52 < 0.001 Age ≥ 60 2.41 1.71–3.39 < 0.001 Education ≤ 12th 2.78 2.09–3.70 < 0.001 Low income 2.12 1.64–2.74 < 0.001 Effect of digital access on preventive care Low digital access was independently associated with reduced preventive care utilization (aOR 0.41, 95% CI 0.33–0.52). Barriers to digital health use Lack of digital skills (46.2%), language limitations (38.4%), privacy concerns (34.1%), cost of devices/data (29.6%), and lack of trust (22.8%). Discussion This study demonstrates a substantial digital health divide in urban India, with over two-fifths of adults experiencing limited access to digital health resources. Individuals with poor digital access were significantly less likely to engage in preventive healthcare activities. The observed association persisted after adjustment for socioeconomic factors, indicating that digital access itself is an independent determinant of preventive care utilization. These findings align with prior evidence from other LMIC settings showing that digital exclusion limits engagement with essential health services [ 6 , 10 , 13 ]. Older adults and socioeconomically disadvantaged populations were disproportionately affected, raising concerns regarding widening health inequities as healthcare delivery becomes increasingly digital. Similar age-related disparities have been reported in digital health adoption studies globally [ 7 , 11 ]. Barriers identified in this study—limited digital skills, language constraints, and privacy concerns—reflect modifiable system-level factors. Strengthening digital literacy and regional language support has been shown to improve digital health engagement in comparable contexts [ 9 , 14 ]. These findings support the integration of assisted digital health services within primary care settings, government hospitals, and community centers, particularly for vulnerable populations. Without targeted interventions, digital health expansion may inadvertently exacerbate existing healthcare disparities. Policy implications ● Establish government-supported digital health kiosks in urban primary care facilities. ● Implement community-based digital literacy programs. ● Mandate regional language interfaces for public digital health platforms. ● Introduce assisted telemedicine services for elderly populations. ● Strengthen data protection frameworks and public communication on privacy safeguards [ 12 , 15 ]. Strengths and limitations Strengths include large sample size, urban community coverage, standardized index construction, and multivariable adjustment. Limitations include cross-sectional design and reliance on self-reported behaviors. Conclusion Digital inequality constitutes a major barrier to preventive healthcare access in urban India. Without targeted inclusion strategies, digital health expansion risks exacerbating existing health disparities. Public health policy must prioritize digital inclusion as a core component of preventive healthcare delivery. Declarations Conflict of interest statement The authors declare no competing interests. Ethics approval and consent to participate All study procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki and its later amendments . Ethical approval for this study was obtained from the Institutional Ethics Committee of Malla Reddy Medical College for Women, Hyderabad, India . Funding statement No external funding was received. Author Contribution N.M. conceived and designed the study, supervised data collection, performed statistical analysis, and drafted the manuscript.S.B. contributed to study design, questionnaire development, data acquisition, and literature review.S.N. assisted with data collection, data cleaning, and preliminary statistical analysis.O.M. contributed to data interpretation, critical revision of the manuscript, and discussion drafting.E.V. contributed to data collection, reference management, manuscript formatting, and final revision.All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. Data Availability Data are available from the corresponding author upon reasonable request. References Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2023;29(1):44–56. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–8. Dwivedi YK, Kshetri N, Hughes L, et al. So what if ChatGPT wrote it? Multidisciplinary perspectives on AI technologies. Int J Inf Manage. 2023;71:102642. Gopalakrishnan S, Ganeshkumar P. Digital health in India: opportunities and challenges. J Fam Med Prim Care. 2022;11(4):1442–8. World Health Organization. Global strategy on digital health 2020–2025. Geneva: WHO; 2021. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Acceptance of artificial intelligence applications in healthcare: systematic review. Int J Med Inf. 2022;165:104828. Choudhury A, Asan O. Trust in artificial intelligence in healthcare: a systematic review. JMIR Med Inf. 2023;11:e46985. van Kessel R, Hrzic R, O’Nuallain E, et al. Digital health paradox: international policy perspectives on health inequities. Lancet Digit Health. 2022;4(10):e709–14. Singh K, Kondal D, Mohan S, et al. Telemedicine in India: challenges and opportunities. BMJ Innov. 2022;8(3):184–90. Kaur S, Kumar R, Sengupta S. Public perception of artificial intelligence in healthcare in India. J Med Syst. 2023;47(2):15. Li J, Dey A, Forlizzi J. Modeling acceptance of AI-based systems: a literature review. ACM Comput Surv. 2022;55(4):1–36. World Health Organization. Ethics and governance of artificial intelligence for health. Geneva: WHO; 2022. Benda NC, Meadors ML, Hettinger AZ, Ratwani RM. Digital health adoption in low-resource settings. NPJ Digit Med. 2021;4:94. Bansal S, Jain V, Malhotra S. Digital literacy and health outcomes in urban India. Int J Public Health. 2023;68:1605432. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Camb Q Healthc Ethics. 2022;31(2):191–202. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor invited by journal 09 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 03 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9004569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612829159,"identity":"d9cfb470-c8c2-4841-8015-57f4586a01f5","order_by":0,"name":"Nishanth Muppa","email":"data:image/png;base64,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","orcid":"","institution":"Malla Reddy VishwaVidyapeeth","correspondingAuthor":true,"prefix":"","firstName":"Nishanth","middleName":"","lastName":"Muppa","suffix":""},{"id":612829160,"identity":"bf7b584e-43b1-4a51-99b7-3df897ce3ad9","order_by":1,"name":"Sowmika Busireddy","email":"","orcid":"","institution":"Malla Reddy VishwaVidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Sowmika","middleName":"","lastName":"Busireddy","suffix":""},{"id":612829161,"identity":"c5e86b7b-2b9f-4dba-b615-74ce3fc1022c","order_by":2,"name":"Sreeja Namilakonda","email":"","orcid":"","institution":"Malla Reddy VishwaVidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Sreeja","middleName":"","lastName":"Namilakonda","suffix":""},{"id":612829162,"identity":"826b898c-dfc5-4149-8d23-1bb79552937c","order_by":3,"name":"Ojal Maddipati","email":"","orcid":"","institution":"Malla Reddy VishwaVidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Ojal","middleName":"","lastName":"Maddipati","suffix":""},{"id":612829163,"identity":"370b569f-9fef-492c-9caf-2d935a3ed22b","order_by":4,"name":"Esha Velaga","email":"","orcid":"","institution":"Malla Reddy VishwaVidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Esha","middleName":"","lastName":"Velaga","suffix":""}],"badges":[],"createdAt":"2026-03-02 01:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9004569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9004569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105728660,"identity":"ddad3101-73c5-4da4-af7e-c67fd2c8bae4","added_by":"auto","created_at":"2026-03-30 11:12:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":797692,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9004569/v1/81bd07de-8388-43a1-8391-565398983cda.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Health Divide and Its Impact on Access to Preventive Healthcare Services in Urban India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDigital health technologies are increasingly integrated into healthcare delivery systems worldwide, including telemedicine platforms, mobile health applications, and artificial intelligence\u0026ndash;enabled clinical decision tools [1\u0026ndash;3]. In India, large-scale adoption of digital health has been accelerated by rapid smartphone penetration and national initiatives such as the Ayushman Bharat Digital Mission [4]. These technologies offer potential benefits including early disease detection, improved continuity of care, and reduced burden on overextended urban healthcare facilities [2, 5].\u003c/p\u003e\n\u003cp\u003eHowever, access to digital health services is not uniform. Differences in socioeconomic status, education, age, language proficiency, and digital literacy may systematically exclude certain population groups from benefiting from these innovations [6, 7]. This phenomenon, described as the \u003cem\u003edigital health divide\u003c/em\u003e, may reinforce existing health inequities by limiting access to preventive services such as routine health screening, vaccination, and lifestyle counseling [8].\u003c/p\u003e\n\u003cp\u003eUrban India represents a critical context in which advanced digital infrastructure coexists with marked socioeconomic inequality. While high-income and well-educated populations increasingly utilize app-based healthcare services, vulnerable groups may remain dependent on overcrowded physical facilities or forgo preventive care altogether [9].\u003c/p\u003e\n\u003cp\u003eDespite growing policy attention to digital health expansion, population-level evidence linking digital access to preventive healthcare utilization remains limited in low- and middle-income countries [6, 10]. Quantifying this relationship is essential to inform equitable digital health policy design.\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the magnitude of the digital health divide in an urban Indian population and to assess its impact on utilization of preventive healthcare services.\u003c/p\u003e\n\u003ch3\u003eObjectives\u003c/h3\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003ePrimary objective\u003c/h2\u003e\n \u003cp\u003eTo assess the association between digital health access and utilization of preventive healthcare services among adults in urban Hyderabad.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary objectives\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1. To estimate the prevalence of adequate digital health access.\u003c/p\u003e\n \u003cp\u003e2. To identify socio-demographic determinants of poor digital access.\u003c/p\u003e\n \u003cp\u003e3. To examine disparities in preventive healthcare behaviors by digital access level.\u003c/p\u003e\n \u003cp\u003e4. To identify perceived barriers to digital health use.\u003c/p\u003e\n \u003cp\u003e5. To generate policy-relevant recommendations for reducing digital health inequity.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eHypotheses\u003c/h3\u003e\n\u003cp\u003eH1: Low digital health access is associated with lower utilization of preventive healthcare services.\u003c/p\u003e\n\u003cp\u003eH2: Older age, lower education, and lower income independently predict poor digital health access.\u003c/p\u003e\n\u003cp\u003eH3: Digital literacy mediates the relationship between socioeconomic status and preventive healthcare utilization.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eCommunity-based cross-sectional study conducted in Hyderabad, Telangana, India (March\u0026ndash;July 2026).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eAdults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years residing in Hyderabad for \u0026ge;\u0026thinsp;6 months.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eTarget sample: 1,500 participants. Final analyzed sample: 1,482.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling method\u003c/h3\u003e\n\u003cp\u003eMultisite sampling from outpatient departments, residential colonies, workplaces, public spaces, and online recruitment.\u003c/p\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eAdults\u0026thinsp;\u0026ge;\u0026thinsp;18 years, permanent urban residents, able to provide informed consent.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExclusion criteria\u003c/h2\u003e \u003cp\u003eSevere cognitive impairment; questionnaires with \u0026gt;\u0026thinsp;20% missing data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData collection instrument\u003c/h2\u003e \u003cp\u003eA structured interviewer-assisted questionnaire was developed based on prior digital health access and technology acceptance literature [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The full instrument is provided in Supplementary File 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eKey variables\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDigital Health Access Index (DHAI)\u003c/b\u003e (0\u0026ndash;10 scale): smartphone ownership, internet availability, independent app use, frequency of internet use, prior telemedicine use.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreventive Healthcare Utilization Index (PHUI)\u003c/b\u003e (0\u0026ndash;8 scale): routine health check-up, blood pressure screening, diabetes screening, age-appropriate cancer screening, vaccination status, lifestyle counseling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOutcome definition\u003c/h2\u003e \u003cp\u003eAdequate preventive healthcare utilization defined as PHUI\u0026thinsp;\u0026ge;\u0026thinsp;4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, chi-square tests, independent t-tests, and multivariable logistic regression were used. Mediation analysis was performed to examine the role of digital literacy. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eInstitutional ethics committee approval was obtained. Written informed consent was collected. Data were anonymized and stored securely in accordance with ethical guidelines [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eMean age: 37.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 years; 53.1% male; 44.2% graduates or higher; 96.1% smartphone ownership.\u003c/p\u003e \u003cp\u003eThe socio-demographic characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics (n\u0026thinsp;=\u0026thinsp;1,482)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e472 (31.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e596 (40.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e318 (21.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e787 (53.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e680 (45.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e826 (55.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduate+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e656 (44.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDigital health access\u003c/h2\u003e \u003cp\u003eHigh digital access: 58.7%\u003c/p\u003e \u003cp\u003eLow digital access: 41.3%\u003c/p\u003e \u003cp\u003eKey indicators of digital health access among participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital health access indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmartphone ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1424 (96.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular internet access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1296 (87.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent app use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1038 (70.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior telemedicine use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e812 (54.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePreventive healthcare utilization\u003c/h2\u003e \u003cp\u003eAdequate utilization overall: 54.2%\u003c/p\u003e \u003cp\u003eHigh-access group: 68.9%\u003c/p\u003e \u003cp\u003eLow-access group: 32.4% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003cp\u003ePreventive healthcare utilization stratified by digital access level is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreventive healthcare utilization by digital access level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive service\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh access (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow access (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoutine check-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccination updated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable predictors of low digital access\u003c/h2\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60 years (aOR 2.41), education \u0026le;\u0026thinsp;12th grade (aOR 2.78), monthly income \u0026lt;₹25,000 (aOR 2.12), and low digital literacy (aOR 3.64).\u003c/p\u003e \u003cp\u003eResults of the multivariable logistic regression analysis identifying predictors of poor preventive healthcare utilization are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression for poor preventive care utilization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow digital access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026ndash;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.71\u0026ndash;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation \u0026le;\u0026thinsp;12th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09\u0026ndash;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64\u0026ndash;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEffect of digital access on preventive care\u003c/h2\u003e \u003cp\u003eLow digital access was independently associated with reduced preventive care utilization (aOR 0.41, 95% CI 0.33\u0026ndash;0.52).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eBarriers to digital health use\u003c/h2\u003e \u003cp\u003eLack of digital skills (46.2%), language limitations (38.4%), privacy concerns (34.1%), cost of devices/data (29.6%), and lack of trust (22.8%).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates a substantial digital health divide in urban India, with over two-fifths of adults experiencing limited access to digital health resources. Individuals with poor digital access were significantly less likely to engage in preventive healthcare activities.\u003c/p\u003e \u003cp\u003eThe observed association persisted after adjustment for socioeconomic factors, indicating that digital access itself is an independent determinant of preventive care utilization. These findings align with prior evidence from other LMIC settings showing that digital exclusion limits engagement with essential health services [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOlder adults and socioeconomically disadvantaged populations were disproportionately affected, raising concerns regarding widening health inequities as healthcare delivery becomes increasingly digital. Similar age-related disparities have been reported in digital health adoption studies globally [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBarriers identified in this study\u0026mdash;limited digital skills, language constraints, and privacy concerns\u0026mdash;reflect modifiable system-level factors. Strengthening digital literacy and regional language support has been shown to improve digital health engagement in comparable contexts [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings support the integration of assisted digital health services within primary care settings, government hospitals, and community centers, particularly for vulnerable populations. Without targeted interventions, digital health expansion may inadvertently exacerbate existing healthcare disparities.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003ePolicy implications\u003c/h2\u003e \u003cp\u003e● Establish government-supported digital health kiosks in urban primary care facilities.\u003c/p\u003e \u003cp\u003e● Implement community-based digital literacy programs.\u003c/p\u003e \u003cp\u003e● Mandate regional language interfaces for public digital health platforms.\u003c/p\u003e \u003cp\u003e● Introduce assisted telemedicine services for elderly populations.\u003c/p\u003e \u003cp\u003e● Strengthen data protection frameworks and public communication on privacy safeguards [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eStrengths include large sample size, urban community coverage, standardized index construction, and multivariable adjustment. Limitations include cross-sectional design and reliance on self-reported behaviors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDigital inequality constitutes a major barrier to preventive healthcare access in urban India. Without targeted inclusion strategies, digital health expansion risks exacerbating existing health disparities. Public health policy must prioritize digital inclusion as a core component of preventive healthcare delivery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest statement\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eAll study procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the \u003cb\u003eDeclaration of Helsinki and its later amendments\u003c/b\u003e. Ethical approval for this study was obtained from the \u003cb\u003eInstitutional Ethics Committee of Malla Reddy Medical College for Women, Hyderabad, India\u003c/b\u003e.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eNo external funding was received.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.M. conceived and designed the study, supervised data collection, performed statistical analysis, and drafted the manuscript.S.B. contributed to study design, questionnaire development, data acquisition, and literature review.S.N. assisted with data collection, data cleaning, and preliminary statistical analysis.O.M. contributed to data interpretation, critical revision of the manuscript, and discussion drafting.E.V. contributed to data collection, reference management, manuscript formatting, and final revision.All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTopol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2023;29(1):44\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwivedi YK, Kshetri N, Hughes L, et al. So what if ChatGPT wrote it? Multidisciplinary perspectives on AI technologies. Int J Inf Manage. 2023;71:102642.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGopalakrishnan S, Ganeshkumar P. Digital health in India: opportunities and challenges. J Fam Med Prim Care. 2022;11(4):1442\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Global strategy on digital health 2020\u0026ndash;2025. Geneva: WHO; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlowais SA, Alghamdi SS, Alsuhebany N, et al. Acceptance of artificial intelligence applications in healthcare: systematic review. Int J Med Inf. 2022;165:104828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhury A, Asan O. Trust in artificial intelligence in healthcare: a systematic review. JMIR Med Inf. 2023;11:e46985.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Kessel R, Hrzic R, O\u0026rsquo;Nuallain E, et al. Digital health paradox: international policy perspectives on health inequities. Lancet Digit Health. 2022;4(10):e709\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh K, Kondal D, Mohan S, et al. Telemedicine in India: challenges and opportunities. BMJ Innov. 2022;8(3):184\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur S, Kumar R, Sengupta S. Public perception of artificial intelligence in healthcare in India. J Med Syst. 2023;47(2):15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Dey A, Forlizzi J. Modeling acceptance of AI-based systems: a literature review. ACM Comput Surv. 2022;55(4):1\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Ethics and governance of artificial intelligence for health. Geneva: WHO; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenda NC, Meadors ML, Hettinger AZ, Ratwani RM. Digital health adoption in low-resource settings. NPJ Digit Med. 2021;4:94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal S, Jain V, Malhotra S. Digital literacy and health outcomes in urban India. Int J Public Health. 2023;68:1605432.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Camb Q Healthc Ethics. 2022;31(2):191\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"digital health, health equity, preventive healthcare, urban health, telemedicine, India","lastPublishedDoi":"10.21203/rs.3.rs-9004569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9004569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRapid expansion of telemedicine, mobile health applications, and AI-based tools has transformed healthcare delivery in urban India. However, unequal access to digital technologies and digital literacy may create a new form of health inequity\u0026mdash;the digital health divide\u0026mdash;potentially limiting access to preventive healthcare services.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo examine the association between digital health access and utilization of preventive healthcare services among adults in urban Hyderabad, and to identify socio-demographic determinants of poor digital access.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional study was conducted among 1,482 adults in Hyderabad using a structured questionnaire. A Digital Health Access Index (DHAI) and a Preventive Healthcare Utilization Index (PHUI) were constructed. Multivariable logistic regression was used to identify predictors of low preventive healthcare utilization and poor digital access.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 1,482 participants, 58.7% demonstrated high digital health access while 41.3% had low access. Preventive healthcare utilization was significantly lower in the low-access group (32.4% vs 68.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Older age, lower education, lower income, and limited digital literacy were independently associated with poor digital access. After adjustment for confounders, low digital access was associated with reduced odds of adequate preventive healthcare utilization (adjusted OR 0.41, 95% CI 0.33\u0026ndash;0.52). Major barriers included lack of digital skills (46.2%), language limitations (38.4%), and privacy concerns (34.1%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA substantial digital health divide exists in urban India and is strongly associated with reduced utilization of preventive healthcare services. Public health strategies must integrate digital inclusion, regional language support, community-based digital training, and assisted digital health services to ensure equitable access to preventive care.\u003c/p\u003e","manuscriptTitle":"Digital Health Divide and Its Impact on Access to Preventive Healthcare Services in Urban India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 21:51:26","doi":"10.21203/rs.3.rs-9004569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-06T15:03:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T10:18:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57945801672397374913451190543477188999","date":"2026-03-28T06:00:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119623639539600688414850267195531550110","date":"2026-03-26T17:07:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271953189583463836550252443001756183840","date":"2026-03-26T06:25:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T05:57:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T09:36:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T04:59:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T03:41:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Health Systems","date":"2026-03-04T03:37:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d54932a-4ae9-4edc-9804-75c17ceaa402","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T21:51:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 21:51:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9004569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9004569","identity":"rs-9004569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}