CoChat
Sign Up
Cited by 231Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMental Health Research Topics

The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.

Engagement in mobile phone app for self-monitoring of emotional wellbeing predicts changes in mental health: MoodPrism

Bakker, David, Rickard, Nikki·2018·Journal of Affective Disorders
DOI 10.1016/j.jad.2017.11.016
Open this paper in CoChat — free account

Abstract

Background Mobile apps are being used increasingly for mental health purposes, but evidence of their efficacy remains limited. The mechanisms underlying any effects of such apps are also unclear. This study examined the effectiveness of a self-monitoring mobile phone app by investigating the relationships between app engagement and mental health outcomes.Method Participants downloaded the MoodPrism app from the iOS and Android app stores, completing in-app assessments at start of use and again 30days later. The app prompted participants daily to complete a short mood questionnaire and formulated their responses into a mood diary. Data from 234 assessment completers (73% female; M age = 34.8 years) were analysed via hierarchical and mediation regressions.Results In this community sample, app engagement ratings predicted decreases in depression and anxiety, and increases in mental well-being. These effects were mediated by increases in emotional self-awareness, but only for participants who were clinically depressed or anxious at the time of the baseline assessment. Mental health literacy and coping self-efficacy did not play mediating roles.Limitations Findings suggest that other influential mediators may have not been measured, and future studies could verify the findings by using alternative methodologies, such as comparison with a control group.Conclusions Engaging with an emotional wellbeing self-monitoring app may reduce depressive and anxious symptoms, and increase mental well-being. Increases in emotional self-awareness may mediate these changes in clinical populations, and further research is needed to reveal other mechanisms that mental health apps can utilize.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.jad.2017.11.016

Cites

68 of 68 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

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?

Citations

Cites

Flagged first, then by citations

Cited by

Sorted by citations

See all 231

Related

See all

Metadata

Authors
Bakker, D., & Rickard, N.
Affiliations
  • Monash University (AU)
  • The University of Melbourne (AU)
Corresponding author
Bakker, David
ORCID
Venue
Journal of Affective Disorders
Publisher
Elsevier BV
Published
2018-02
Volume
227
Pages
432-442
ISSN
0165-0327 (ISSN-L)
DOI
10.1016/j.jad.2017.11.016
OpenAlex
W2767558581
Cited by
231
References
68
Citations by year
2026: 16 · 2025: 47 · 2024: 28 · 2023: 30 · 2022: 37 · 2021: 29 · 2020: 26 · 2019: 14 · 2018: 4
FWCI
13.76
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of Affective Disorders (publishedVersion)
  • PubMed (publishedVersion)
Funders
beyondblue
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
mental health apps, app engagement, self-monitoring, emotional well-being, mood tracking, emotional self-awareness, depression, anxiety, psychological well-being, mental health literacy, coping self-efficacy, mediation
MeSH terms
Adult, Anxiety Disorders, Awareness, Depressive Disorder, Emotions, Female, Follow-Up Studies, Humans, Male, Psychometrics, Quality of Life, Surveys and Questionnaires, Self Care, Software, Therapy, Computer-Assisted, Cell Phone, Young Adult, 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