Cited by 61Mobile Health and mHealth ApplicationsHealth, Technology, Consumer BehaviorTechnology Use by Older Adults

Use of Mobile Health (mHealth) Tools by Primary Care Patients in the WWAMI Region Practice and Research Network (WPRN)

Open access
Bauer, A. M., Rue, T., Keppel, G. A., Cole, A. M., Baldwin, L.-M., Katon, W.·2014·The Journal of the American Board of Family Medicine

Abstract

PURPOSE: The purpose of this study was to determine the prevalence of mobile health (mHealth) use among primary care patients and examine demographic and clinical correlates. METHODS: Adult patients who presented to 1 of 6 primary care clinics in a practice-based research network in the northwest United States during a 2-week period received a survey that assessed smartphone ownership; mHealth use; sociodemographic characteristics (age, sex, race/ethnicity, health literacy); chronic conditions; and depressive symptoms (2-item Patient Health Questionnaire). Data analysis used descriptive statistics and mixed logistic regression. RESULTS: Of 918 respondents (estimated response rate, 67.4%), 55% owned a smartphone, among whom 70% were mHealth users. In multivariate analyses, smartphone ownership and mHealth use were not associated with health literacy, chronic conditions, or depression but were less common among adults >45 years old (adjusted odds ratio, 0.07-0.39; P < .001). Only 10% of patients learned about mHealth tools from their physician, and few (31%) prioritized their provider's involvement. CONCLUSIONS: Use of mHealth technologies is lower among older adults but otherwise is common among primary care patients, including those with limited health literacy and those with chronic conditions. Findings support the potential role of mHealth in improving disease management among certain groups in need; however, greater involvement of health care providers may be important for realizing this potential.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3122/jabfm.2014.06.140108

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 18 · Cited by 61

Authors
Bauer, A. M., Rue, T., Keppel, G. A., Cole, A. M., Baldwin, L.-M., & Katon, W.
Affiliations
University of Washington (US)
Venue
The Journal of the American Board of Family Medicine
Published
2014-11-01
Volume
27
Issue
6
Pages
780-788
ISSN
  • 1557-2625 (ISSN-L)
  • 1558-7118
OpenAlex
W2162453520
Cited by
61
References
18
Citations by year
2026: 1 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 6 · 2019: 7 · 2018: 13 · 2017: 5 · 2016: 7 · 2015: 5 · 2014: 2
FWCI
11.47
Type
journal article
Language
en
Open access
Locations
  • The Journal of the American Board of Family Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Georgia Clinical and Translational Science Alliance
  • National Institutes of Health (UL1TR000423, KL2TR000421)
  • National Center for Advancing Translational Sciences
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
  • Social Sciences › Demography › Technology Use by Older Adults
Keywords
mobile health, smartphone ownership, primary care patients, health literacy, chronic disease, depressive symptoms, older adults, practice-based research networks
MeSH terms
Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Primary Health Care, Telemedicine, Cell Phone, Young Adult

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 ›

Are you an author, or do you see a problem with this page? Request a review