Cited by 201Digital Mental Health InterventionsMobile Health and mHealth ApplicationsHealth, Technology, Consumer Behavior

Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: comparative survey study

Open access
Torous, John, Wisniewski, Hannah, Liu, Gang, Keshavan, Matcheri·2018·JMIR Mental Health

Published version of a preprint

Abstract

Background: Despite the popularity of mental health apps, it is unknown if they are actually used by those with mental illness. This study assessed whether differences in clinic setting may influence the use of mental health apps and which factors influence patient perception of apps. Objective: The objective of this study was to gain an understanding of how individuals with mental illness use their mobile phones by exploring their access to mobile phones and their use of mental health apps. Methods: A single time point survey study was conducted over a 2-week period in February 2018 at two nearby outpatient psychiatry clinics: one serving largely mood and anxiety disorder patients with private insurance staffed by both faculty and residents and the other serving largely psychotic disorder patients in a state Department of Mental Health (DMH) setting. A total of 25 patients at the state DMH clinic also consented for a single time point observation of apps currently installed on their personal mobile phone. Results: A total of 113 patients at the private insurance clinic and 73 at the state DMH clinic completed the survey. Those in the private insurance clinic were more likely to download a mental health app compared to the state DMH clinic, but actual rates of reported current app usage were comparable at each clinic, approximately 10%. Verifying current apps on patients’ mobile phones at the state DMH clinic confirmed that approximately 10% had mental health apps installed. Patients at both clinics were most concerned about privacy of mental health apps, although those at the state DMH clinic viewed cost savings as the greatest benefit while those at the private clinic reported time as the greatest benefit. Conclusions: High interest in mental health apps does not automatically translate into high use. Our results of low but similar rates of mental health app use at diverse clinics suggests DMH patients with largely psychotic disorders are as interested and engaged with apps as those in a private insurance clinic treating largely mood and anxiety disorders. Results from our study also highlight the importance of understanding how actual patients are using apps instead of relying on internet-based samples, which often yield higher results due to their likelihood of being selected.

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 · 10.2196/11715

Cites

24 of 24 references verified0 citation errors · 0 not checkable

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

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?

  • The COINCIDE trial found that collaborative care for people with depression and diabetes or cardiovascular disease improved depression outcomes and increased self-management, with modest effect sizes, in routine practice in areas of high deprivation.

    “The intervention improved depression outcomes and increased self-management. Effect sizes were modest, but the intervention was delivered in the context of routine practice in areas of high deprivation. 613”
  • Multiple studies show that digital biomarkers including movement intensity, light exposure, and smartphone usage patterns can forecast the diagnostic status of individuals with mood disorders.

    “Multiple studies have showcased the promise of digital biomarkers, encompassing factors like movement intensity, light exposure, and smartphone usage patterns, in forecasting the diagnostic status of individuals grappling with mood disorders (19, 22, 38).”
  • Wearable technologies such as smartwatches and novel sensors can generate valuable digital biomarkers of depression in real-world settings.

    “Wearable technologies, such as smartwatches and novel sensors, can generate valuable digital biomarkers of depression in real-world settings (22)”
  • Several digital biomarkers have been investigated for MDD characterization and diagnosis, including physical activity patterns, voice features, light exposure measurements, mobile phone GPS, and normal smartphone usage duration and frequency.

    “In the recent years, several digital biomarkers have been investigated for MDD characterization and diagnosis such as measure patterns of physical activity (24, 25) features from voice samples (26, 27), light exposure measurements (25), mobile phone global positioning systems (GPS) and normal usage of smartphones such as usage duration and frequency (22).”

Self-check

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

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

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Torous, J., Wisniewski, H., Liu, G., & Keshavan, M.
Affiliations
  • Beth Israel Deaconess Medical Center (US)
  • Harvard University (US)
Corresponding author
Torous, John
Venue
JMIR Mental Health
Published
2018-11-16
Volume
5
Issue
4
Pages
e11715
ISSN
2368-7959 (ISSN-L)
OpenAlex
W2896957784
Cited by
201
References
24
Citations by year
2026: 24 · 2025: 24 · 2024: 29 · 2023: 21 · 2022: 24 · 2021: 30 · 2020: 30 · 2019: 17 · 2018: 1 · 2017: 1
FWCI
24.59
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Mental Health (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
National Institute of Mental Health (K23MH116130)
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
Keywords
mental health apps, psychiatric outpatients, mood and anxiety disorders, psychosis, state mental health services

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