Cited by 125Health Literacy and Information AccessibilityMobile Health and mHealth ApplicationsMobile Learning in Education

Mobile Health Application and E-Health Literacy: Opportunities and Concerns for Cancer Patients and Caregivers

Kim, Hyunmin, Goldsmith, Joy V., Sengupta, Soham, Mahmood, Asos, Powell, M. Paige, Bhatt, Jay, Chang, Cyril F., Bhuyan, Soumitra S.·2019·Journal of Cancer Education

Abstract

Health literacy is critical for cancer patients as they must understand complex procedures or treatment options. Caregivers' health literacy also plays a crucial role in caring for cancer patients. Low health literacy is associated with low adherence to medications, poor health status, and increased health care costs. There is a growing interest in the use of mobile health applications (apps) to improve health literacy. Mobile health apps can empower underserved cancer patients and their caregivers by providing features or functionalities to enhance interactive patient-provider communication and to understand medical information more readily. Despite the potentiality of improving health literacy through mobile health apps, there exist several related concerns: no equal access to mobile technology, no familiarity or knowledge of using mobile health apps, and privacy and security concerns. These elements should be taken into account for health policy making and mobile apps design and development. Importantly, mobile apps should be developed with the goal of achieving a high range of user access by considering all health literacy level and various cultural and linguistic needs.

Integrity signals

Registry

Crossref · 10.1007/s13187-017-1293-5

Cites

4 of 4 references verified0 citation errors · 0 not checkable

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Recomputing p-values needs the paper’s full text.

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This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Animation, text or social media promotion, or apps help patients acquire health information more effectively.

    “For example, animation [357,358], text or social media promotion [359,360,361], or apps [362,363] help patients acquire health information more effectively [364].”

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Authors
Kim, H., Goldsmith, J. V., Sengupta, S., Mahmood, A., Powell, M. P., Bhatt, J., Chang, C. F., & Bhuyan, S. S.
Affiliations
  • University of Memphis (US)
  • American Hospital Association (US)
  • Methodist Le Bonheur Healthcare (US)
Corresponding author
Bhuyan, Soumitra S.
Venue
Journal of Cancer Education
Published
2017-11-14
Volume
34
Issue
1
Pages
3-8
ISSN
  • 0885-8195 (ISSN-L)
  • 1543-0154
OpenAlex
W2769152010
Cited by
125
References
4
Citations by year
2026: 10 · 2025: 25 · 2024: 24 · 2023: 27 · 2022: 13 · 2021: 13 · 2020: 9 · 2019: 3 · 2018: 1
FWCI
5.74
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Cancer Education (publishedVersion)
  • PubMed (publishedVersion)
  • Rutgers University Community Repository (Rutgers University) (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
  • Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
Keywords
mobile health apps, digital health literacy, cancer patients, cancer caregivers, health literacy, medication adherence, patient-provider communication, digital divide, privacy and security concerns, underserved populations, mobile application design
MeSH terms
Humans, Health Knowledge, Attitudes, Practice, Neoplasms, Patients, Caregivers, Telemedicine, Privacy, Health Literacy, Mobile Applications

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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.

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