eHealth Literacy: Essential Skills for Consumer Health in a Networked World.
Open accessAbstract
Electronic health tools provide little value if the intended users lack the skills to effectively engage them. With nearly half the adult population in the United States and Canada having literacy levels below what is needed to fully engage in an information-rich society, the implications for using information technology to promote health and aid in health care, or for eHealth, are considerable. Engaging with eHealth requires a skill set, or literacy, of its own. The concept of eHealth literacy is introduced and defined as the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem. In this paper, a model of eHealth literacy is introduced, comprised of multiple literacy types, including an outline of a set of fundamental skills consumers require to derive direct benefits from eHealth. A profile of each literacy type with examples of the problems patient-clients might present is provided along with a resource list to aid health practitioners in supporting literacy improvement with their patient-clients across each domain. Facets of the model are illustrated through a set of clinical cases to demonstrate how health practitioners can address eHealth literacy issues in clinical or public health practice. Potential future applications of the model are discussed.
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DHL varies among people depending on literacy levels, socioeconomic status, presenting health issues, motivation to seek online information, and familiarity with technology and electronic gadgets.
“DHL varies among people depending on their literacy levels and socioeconomic status, presenting health issues, motivation to seek online information, and familiarity with technology and electronic gadgets [11, 13].”
From Digital health literacy, online information-seeking behaviour, and satisfaction of Covid-19 information among the university students of East and South-East Asia (2022) · cites this paper as [11]
The Lily Model section says eHealth literacy is influenced by a person’s presenting health issue, educational background, health status, motivation for seeking information, and technologies used. The Access Barriers and Computer Literacy sections also discuss literacy, income, and access to technology, but the paper does not specifically establish socioeconomic status as a determinant of eHealth literacy. (LLM, conf=0.84)
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Sarcone, A., Pileggi, C., Nobile, C., Simeone, S., & Mercuri, C. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1931903
- Authors
- Norman, C. D., & Skinner, H. A.
- Affiliations
- University Health Network (CA)
- University of Toronto (CA)
- Corresponding author
- Norman, Cameron D
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2006-06-16
- Volume
- 8
- Issue
- 2
- Pages
- e9
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC1550701
- OpenAlex
- W2150353123
- Cited by
- 2,643
- References
- 3
- Citations by year
- 2026: 482 · 2025: 344 · 2024: 280 · 2023: 259 · 2022: 268 · 2021: 184 · 2020: 167 · 2019: 128 · 2018: 100 · 2017: 96 · 2016: 75 · 2015: 60 · 2014: 43 · 2013: 29 · 2012: 40
- FWCI
- 2.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TSpace (University of Toronto) (submittedVersion, cc-by)
- Funders
- Health Canada
- Canadian Institutes of Health Research
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Demography › Technology Use by Older Adults
- Physical Sciences › Computer Science › Information Systems › ICT in Developing Communities
- Keywords
- digital health literacy, health information literacy, consumer health, health information seeking, health information appraisal, patient-clinician communication, clinical case, public health practice
- MeSH terms
- Computer Communication Networks, Computer Literacy, Health Education, Health Promotion, Humans, Internet, Information Dissemination
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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