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Health Literacy and Health Information Technology Adoption: The Potential for a New Digital Divide
Open accessAbstract
BACKGROUND: Approximately one-half of American adults exhibit low health literacy and thus struggle to find and use health information. Low health literacy is associated with negative outcomes including overall poorer health. Health information technology (HIT) makes health information available directly to patients through electronic tools including patient portals, wearable technology, and mobile apps. The direct availability of this information to patients, however, may be complicated by misunderstanding of HIT privacy and information sharing. OBJECTIVE: The purpose of this study was to determine whether health literacy is associated with patients' use of four types of HIT tools: fitness and nutrition apps, activity trackers, and patient portals. Additionally, we sought to explore whether health literacy is associated with patients' perceived ease of use and usefulness of these HIT tools, as well as patients' perceptions of privacy offered by HIT tools and trust in government, media, technology companies, and health care. This study is the first wide-scale investigation of these interrelated concepts. METHODS: Participants were 4974 American adults (n=2102, 42.26% male, n=3146, 63.25% white, average age 43.5, SD 16.7 years). Participants completed the Newest Vital Sign measure of health literacy and indicated their actual use of HIT tools, as well as the perceived ease of use and usefulness of these applications. Participants also answered questions regarding information privacy and institutional trust, as well as demographic items. RESULTS: Cross-tabulation analysis indicated that adequate versus less than adequate health literacy was significantly associated with use of fitness apps (P=.02), nutrition apps (P<.001), activity trackers (P<.001), and patient portals (P<.001). Additionally, greater health literacy was significantly associated with greater perceived ease of use and perceived usefulness across all HIT tools after controlling for demographics. Regarding privacy perceptions of HIT and institutional trust, patients with greater health literacy often demonstrated decreased privacy perceptions for HIT tools including fitness apps (P<.001) and nutrition apps (P<.001). Health literacy was negatively associated with trust in government (P<.001), media (P<.001), and technology companies (P<.001). Interestingly, health literacy score was positively associated with trust in health care (P=.03). CONCLUSIONS: Patients with low health literacy were less likely to use HIT tools or perceive them as easy or useful, but they perceived information on HIT as private. Given the fast-paced evolution of technology, there is a pressing need to further the understanding of how health literacy is related to HIT app adoption and usage. This will ensure that all users receive the full health benefits from these technological advances, in a manner that protects health information privacy, and that users engage with organizations and providers they trust.
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“Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E).”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [53]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Being younger than 44 years was associated with a higher level of uptake and engagement [38,41,42,44,49,53,55,63,64] than older adults.”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [53]
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“Living in an urban area [42,44,55]; having a better education level, such as having high school education or higher [41,42,44,64] and college degree or higher [41,53]; and having a higher income [44] were also associated with better engagement with health and well-being apps.”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [53]
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“Mackert et al [53] also found that adequate heath literacy was associated with increased engagement with fitness and nutrition apps.”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [53]
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“Apps were more frequently downloaded by women than men, with the percentage ranging from 59% to 74% [38,41,49,53,55,63], although 1 study found that being male was associated with using an app to manage alcohol consumption [65].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [53]
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“Advances in digital technologies can unintentionally reinforce or increase existing health disparities [95].”
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Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Trust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635
Health Literacy: A Prescription to End Confusion
Committee on Health Literacy, Board on Neuroscience and Behavioral Health, & Institute of Medicine (2004) · doi:10.17226/10883 · Cited by 3,444
Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., Morton, S. C., & Shekelle, P. G. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-144-10-200605160-00125 · Cited by 3,159
Eysenbach, G. (2001) · Journal of Medical Internet Research · doi:10.2196/jmir.3.2.e20 · Cited by 2,449 · cited 1×
“…and compelling health information is pressing. eHealth (the practice of using the Internet and telecommunication technology to provide health communication and services) [4] presents a powerful tool for bringing health information to low health-literate audiences in ways that are easier to access.”
BackgroundQuick Assessment of Literacy in Primary Care: The Newest Vital Sign
Weiss, B. (2005) · The Annals of Family Medicine · doi:10.1370/afm.405 · Cited by 2,222
Kutner, M. A., Greenburg, E., Jin, Y., & Paulsen, C. A. (2006) · National Center for Education Statistics · Cited by 1,545
Häyrinen, K., Saranto, K., & Nykänen, P. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2007.09.001 · Cited by 1,374
Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011) · Health Affairs · doi:10.1377/hlthaff.2011.0178 · Cited by 1,311
Trust and risk in e-government adoption
Bélanger, F., & Carter, L. (2008) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2007.12.002 · Cited by 1,235
Flagged first, then by citations
Research works printed but not linked: 3
- Eng, D, 2013, Pediatr Diabetes, 14, 1
- Charles, D, 2015, Adoption of Electronic Health Record Systems Among U.S. Non-Federal Acute Care Hospitals 2008-2014. ONC Data Brief, no. 23
- Bahmanziari, T, 2003, J Comput Inform Syst, 43, 46
Cited by
Sorted by citations
Items 401–500 of 506
Inglis-Jassiem, G., Grimmer, K., Conradie, T., & Louw, Q. (2021) · JMIR Research Protocols · doi:10.2196/23174
Novel Validated Index for the Measurement of Disinformation Susceptibility at the County Level
Jin, M. X., Rajan, S., Gary-Bicas, C. E., Hao, M., Dong, L., Mufson, B., & Hafiz, I. (2021) · Cureus · doi:10.7759/cureus.15305
Hübner, U. (2021) · V&R unipress eBooks · doi:10.14220/9783737012027.79
'Healthcare Providers’ Experience Using Telehealth During The COVID-19 Pandemic' (Preprint)Preprint
Hemyari, B. A., Singh, A., Coffey, N., Pfirman, K., Mountjoy, N. J., McElroy, D., & Haider, S. (2021) · doi:10.2196/preprints.32194
Lin, L., Guo, J., Huang, C., Hsu, H., Liao, J., & Cheng-Yu, L. I. N. (2021) · Research Square · doi:10.21203/rs.3.rs-482262/v1
(2020) · Issues in Information Systems · doi:10.48009/1_iis_2020_30-36
Abandoned chip: Investigating abandonment of commercial wearables
Eden-Walker, S. (2020) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2020-14408
Ching, H. (2019) · Health informatics · doi:10.1007/978-3-030-19937-1_23
Lee, M., Kang, D., Yoon, J. H., Shim, S., Kim, I. R., Oh, D., Shin, S.-Y., Hesse, B. W., & Cho, J. (2019) · doi:10.2196/preprints.16110
Um estudo experimental de mensagens de e-saúde com diabéticos:
Nogueira, A., & Damásio, M. J. (2022) · Dispositiva · doi:10.5752/p.2237-9967.2022v11n19p124-144
Lyndgaard, S. F., Tatel, C. E., Kanfer, R., Melkers, J. E., & Sabree, J. (2022) · EdArXiv (OSF Preprints) · doi:10.35542/osf.io/j9tcw
Increased Use of Maps During the COVID-19 Pandemic: An Example from Morocco
Zouhairi, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-94350-9_49
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Mitsuhashi, T. (2023) · JMIR Formative Research · doi:10.2196/47798
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崔, 晓. (2023) · Advances in Social Sciences · doi:10.12677/ass.2023.128584
Wright, J., & O’Connor, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-43633-8_17
Lopes, C. T., & Henriques, M. (2024) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3627508.3638318
Мангушева, М. Т., Карибаева, И. К., & Ауезова, А. М. (2024) · Farmaciâ Kazahstana · doi:10.53511/pharmkaz.2024.88.39.036
Harris, Y., Goldwater, J. C., Swain, M. J., Royston, A., Weidner, H., Bekui, S., Halvensleben, T., Leary, J., Lyles, J., Ouimet, O., et al. (2024) · doi:10.2196/preprints.59907
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Thompson, K. L., & Graf, M. D. (2024) · MCN The American Journal of Maternal/Child Nursing · doi:10.1097/nmc.0000000000001033
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Boonstra, M. D. (2024) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.1073766916
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Velverthi, N. R. (2024) · doi:10.12794/metadc2356214
Godwin, N. U., Mba, A. N. U., & Ogunbewon, P. A. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.24.3.3889
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Brown-Riggs, C., Hintz, A., Huang, H.-H., Jimenez‐Vega, J., Rodríguez, B., Rov-Ikpah, E., & Duker, P. N. (2025) · ADCES in Practice · doi:10.1177/2633559x251321793
Song, X., Tao, J., & Shen, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92710-2_23
Nicol, K. A., Butler-Henderson, K. A., Carlini, J., Tori, K. E., & Lehman, K. (2025) · Journal of Consumer Health on the Internet · doi:10.1080/15398285.2025.2510813
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Theagene, M. S. (2025) · Nutrition and health · doi:10.1007/978-3-031-87441-3_10
Retos de la Documentación en Salud desde la Perspectiva Bibliotecológica
Chávez-Martínez, O. (2025) · Biblioteca Universitaria · doi:10.22201/dgbsdi.0187750xp.2025.1.1582
Mahling, M., McQueeney, A., Wietek, T. F., Masters, K., Zipfel, S., Herrmann‐Werner, A., & Rometsch, C. (2025) · JMIR Formative Research · doi:10.2196/74167
Tayebi, S., Ruano, A. L., Ishchenko, L., Lapii, F., Павлова, Ю., Korzh, O., Belousova, O., & Haque, U. (2026) · International Journal for Equity in Health · doi:10.1186/s12939-026-02845-8
Sengupta, M., & Mandal, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33351-4.00030-1
Gonçalves, A. B. C., Soares, L. S. D. S., Umaña-Rivas, M., Simões, I. G., Arimatea, G. G. Q., da Silva, E. V., & Galato, D. (2025) · Revista Interdisciplinar de Estudos em Saúde · doi:10.33362/ries.v14i2.2661
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Jia, H., & Zhang, L. (2025) · Autex Research Journal · doi:10.1515/aut-2025-0048
Kim, B. H., Nam, H. J., Lee, H., Park, H. N., & Yoon, J. Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332072
Mbanefo, C., & Grobbelaar, S. S. S. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.102206
Evaluating patients’ views of a digital tool designed to streamline clinical trials
Hood, B. (2025) · Cancer Nursing Practice · doi:10.7748/cnp.2025.e1909
Dabbus, M. M., Abdelrahim, D. N., Benajiba, N., Al-Halawani, A. M., Daboul, M. M., & Faris, M. E. (2025) · Journal of the Egyptian Public Health Association · doi:10.1186/s42506-025-00201-2
Alanazi, B. A. H., Almubayrikn, F., Alenazi, A. F. F., Alotaibi, M. G., Bajawi, M. Y. A., Refaee, R. A. A., Showaihy, E. G. A., Alsaedi, M. S. S., Aladeny, F. A. A., Almutairi, O., et al. (2024) · Saudi Journal of Medicine and Public Health · doi:10.64483/202412331
Lin, L.-C., Huang, C.-M., Hsu, H.-P., Liao, J.-Y., Lin, C.-Y., & Guo, J.-L. (2021) · Figshare · doi:10.6084/m9.figshare.16696573
Ratnapradipa, K. L., Wang, R., Kabayundo, J., Lavenant, W. M., Nelson, E., Ahuja, M. M. S., Zhang, Y., & Wang, H. (2024) · Figshare · doi:10.6084/m9.figshare.25347049
Sten-Gahmberg, S., Pedersen, K., Harsheim, I. G., Løyland, H. I., & Abelsen, B. (2024) · Figshare · doi:10.6084/m9.figshare.26693575
Sten-Gahmberg, S., Pedersen, K., Harsheim, I. G., Løyland, H. I., & Abelsen, B. (2024) · Figshare · doi:10.6084/m9.figshare.25414833
Sten-Gahmberg, S., Pedersen, K., Harsheim, I. G., Løyland, H. I., & Abelsen, B. (2024) · Figshare · doi:10.6084/m9.figshare.26693590
Sten-Gahmberg, S., Pedersen, K., Harsheim, I. G., Løyland, H. I., & Abelsen, B. (2024) · Figshare · doi:10.6084/m9.figshare.26693578
Ratnapradipa, K. L., Wang, R., Kabayundo, J., Lavenant, W. M., Nelson, E., Ahuja, M. M. S., Zhang, Y., & Wang, H. (2024) · Figshare · doi:10.6084/m9.figshare.25347253
Sten-Gahmberg, S., Pedersen, K., Harsheim, I. G., Løyland, H. I., & Abelsen, B. (2024) · Figshare · doi:10.6084/m9.figshare.26693581
Ratnapradipa, K. L., Wang, R., Kabayundo, J., Lavenant, W. M., Nelson, E., Ahuja, M. M. S., Zhang, Y., & Wang, H. (2024) · Figshare · doi:10.6084/m9.figshare.26689304
Sten-Gahmberg, S., Pedersen, K., Harsheim, I. G., Løyland, H. I., & Abelsen, B. (2024) · Figshare · doi:10.6084/m9.figshare.25415021
Sten-Gahmberg, S., Pedersen, K., Harsheim, I. G., Løyland, H. I., & Abelsen, B. (2024) · Figshare · doi:10.6084/m9.figshare.26693587
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Martins, N. L. M., Duarte, P., & Pinho, J. C. M. (2021) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.1590/s0034-759020210403x
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Raynor, E. M. (2025) · Springer eBooks · doi:10.1007/978-3-032-10492-2_4
Hermansson, O., Dümig, P., Witzel, B., Weinreich, L., Volkmer, S., Schulte‐Körne, G., & Moll, K. (2026) · doi:10.2196/preprints.91824
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Abeş, D., Tuhanioğlu, B., & Demirtaş, M. S. (2026) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-025-09834-7
Gülbetekin, E., & Can, F. G. (2026) · Psychology Health & Medicine · doi:10.1080/13548506.2026.2632397
Zhu, X., Zeng, J., Cao, S., Jia, Y., Zhou, S., Zhang, X., Shi, H., Cui, Y., Xu, J., & Peng, Q. (2026) · Informatics and Health · doi:10.1016/j.infoh.2025.11.002
Mani, K., Nandal, S., Scharfenberger, T., Veldhuis, M., Tang, K., Gad, B. V., & Jariwala, S. P. (2026) · Applied Clinical Informatics · doi:10.1055/a-2830-2591
Saravana Selvi., C. (2026) · Research Square · doi:10.21203/rs.3.rs-8774156/v1
Cho, S., Park, C., & Seo, D. (2026) · Journal of Global Information Technology Management · doi:10.1080/1097198x.2026.2646732
Montes, Ó., Gogeascoechea-Trejo, M. D. C., & Bermúdez‐Tamayo, C. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15062465
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Sferratore, J., Patel, T., Penza, A., Jovanovic, E., Blum, L., & Seshadri, D. R. (2026) · Human Factors in Healthcare · doi:10.1016/j.hfh.2026.100136
Krishnamurti, T., Rodriguez, S., Cope, L., Lemon, L. S., Gopalan, P. R., Nikolajski, C., Simhan, H. N., & Williams, K. R. (2026) · JMIR Human Factors · doi:10.2196/81638
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Thomas, E., Tanaka, A., Mackridge, A. J., & Payne, R. (2026) · International Journal of Pharmacy Practice · doi:10.1093/ijpp/riag056
Lu, X., Hu, D., Nguyen, A. T., Morse, B., Schilling, L. M., Zheng, K., Keller, M. S., Lucila Ohno-machado, A., & Chen, Y. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3788079
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Tazkia, S., Nuraini, T., Wildani, A. A., & Kuntarti, K. (2026) · Jurnal Persatuan Perawat Nasional Indonesia (JPPNI) · doi:10.32419/jppni.v11i1.715
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Beccia, F., Causio, F. A., Porcelli, M., Proto, L., Calabrò, G. E., van El, C. G., Boccia, S., & Pastorino, R. (2026) · Public Health · doi:10.1016/j.puhe.2026.106381
Zaghloul, H. B., Ahdab, F., Tag, Z. M., Varghese, S., Arabi, B., Al-Ani, M., Abdullah, A., Omar, S., Arabi, M., Ahmed, L., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1809014
Dani, A. H., Herawati, C., Arofah, N., & Alimmuddin, A. (2026) · JUKEJ Jurnal Kesehatan Jompa · doi:10.57218/jkj.vol5.iss2.2722
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Panesar, A., & Kant, S. (2026) · Discover Public Health · doi:10.1186/s12982-026-02750-z
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Önal, Ö., Baser, G., BATMAZ, K., Çoban, B., Kişioğlu, A. N., & Uşkun, E. (2026) · Anadolu Kliniği Tıp Bilimleri Dergisi · doi:10.21673/anadoluklin.1846795
Gao, Y., Jiang, L., & Gao, Y. (2026) · The Journal of Medicine Humanity and Media · doi:10.62787/jmhm.v4i3.395
Tang, G. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2739765
Santos, E., de Pinho, L. G., & do Arco, H. M. D. S. L. R. (2026) · Portuguese Journal of Public Health · doi:10.1159/pjp/aekag006
Chen, X., & Zhang, Z. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1934669
Дмитриевич, Г. Л. (2026) · City Healthcare · doi:10.47619/2713-2617.zm.2026.v.7i3;189-197
Lyndgaard, S. F., Tatel, C. E. T. C. E., Kanfer, R. K. R., Melkers, J. E., & Sabree, J. C. S. J. C. (2022) · Computer-Based Learning in Context · doi:10.35510/cblc.2022.4.1.2
Metadata
- Authors
- Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E., & Pounders, K.
- Affiliations
- The University of Texas at Austin (US)
- University of North Texas (US)
- Corresponding author
- Mackert, Michael
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2016-10-04
- Volume
- 18
- Issue
- 10
- Pages
- e264
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5069402
- OpenAlex
- W2528685687
- Cited by
- 503
- References
- 31
- Citations by year
- 2026: 57 · 2025: 60 · 2024: 63 · 2023: 46 · 2022: 46 · 2021: 74 · 2020: 68 · 2019: 40 · 2018: 24 · 2017: 25
- FWCI
- 55.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of North Texas Digital Library (University of North Texas) (submittedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- literacy, health information technology adoption, digital divide, patient portals, fitness apps, nutrition apps, wearable activity trackers, perceived ease of use, perceived usefulness, institutional trust, public trust in government, trust in healthcare, wearable devices, mobile health apps, Newest Vital Sign, health disparities, patient empowerment, health information access, health equity
- MeSH terms
- Adult, Female, Humans, Male, Medical Informatics, Health Records, Personal, Health Literacy, Mobile Applications, Digital Divide
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