Cited by 46Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsMobile Learning in Education

Use of mobile technology in a community mental health setting

Glick, Gretl, Druss, Benjamin, Pina, Jamie, Lally, Cathy, Conde, Mark·2016·Journal of Telemedicine and Telecare

Abstract

Introduction: mHealth holds promise in transforming care for people with serious mental illness (SMI) and other disadvantaged populations. However, information about the rates of smartphone ownership and usage of mobile health apps among people with SMI is limited. The objective of this research is to examine the current ownership, usage patterns, and existing barriers to mobile health interventions for people with SMI treated in a public sector community mental health setting and to compare the findings with national usage patterns from the general population. Methods: A survey was conducted to determine rates of ownership of smartphone devices among people with SMI. Surveys were administered to 100 patients with SMI at an outpatient psychiatric clinic. Results were compared with respondents to the 2012 Pew Survey of mobile phone usage. Results: A total of 85% of participants reported that they owned a cell phone; of those, 37% reported that they owned a smartphone, as compared with 53% of respondents to the Pew Survey and 44% of socioeconomically disadvantaged respondents to the Pew Survey. Discussion: While cell phone ownership is common among people with SMI, their adoption of smartphone technology lags behind that of the general population primarily due to cost barriers. Efforts to use mHealth in these populations need to recognize current mobile ownership patterns while planning for anticipated expansion of new technologies to poor populations as cost barriers are reduced in the coming years.

Integrity signals

Registry

Crossref · 10.1177/1357633x15613236

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

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

Claims the paper makes

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?

  • Individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones.

    “Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones”

    From Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice (2020) · cites this paper as Glick, Druss, Pina, Lally, & Conde, 2016

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Glick, G., Druss, B., Pina, J., Lally, C., & Conde, M.
Affiliations
  • Emory University (US)
  • RTI International (US)
Corresponding author
Glick, Gretl
Venue
Journal of Telemedicine and Telecare
Published
2016-07-08
Volume
22
Issue
7
Pages
430-435
ISSN
  • 1357-633X (ISSN-L)
  • 1758-1109
OpenAlex
W2175411808
Cited by
46
References
12
Citations by year
2025: 1 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 6 · 2020: 5 · 2019: 3 · 2018: 10 · 2017: 5 · 2016: 4
FWCI
11.55
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Telemedicine and Telecare (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
Keywords
mobile health, severe mental illness, smartphone ownership, technology adoption barriers, cost barriers, community mental health services, mobile phone use, socioeconomic deprivation
MeSH terms
Mental Disorders, Community Mental Health Centers, Female, Georgia, Humans, Male, Middle Aged, Surveys and Questionnaires, Telemedicine, Cell Phone, Mobile Applications

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