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Cited by 24Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMental Health Research Topics

Effectiveness of using mental health mobile apps as digital antidepressants for reducing anxiety and depression: protocol for a multiple baseline across-individuals design

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Marshall, Jamie M, Dunstan, Debra A, Bartik, Warren·2020·JMIR Research Protocols
DOI 10.2196/17159PMC PMC7381081

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Abstract

Background: The use of mental health mobile apps to treat anxiety and depression is widespread and growing. Several reviews have found that most of these apps do not have published evidence for their effectiveness, and existing research has primarily been undertaken by individuals and institutions that have an association with the app being tested. Another reason for the lack of research is that the execution of the traditional randomized controlled trial is time prohibitive in this profit-driven industry. Consequently, there have been calls for different methodologies to be considered. One such methodology is the single-case design, of which, to the best of our knowledge, no peer-reviewed published example with mental health apps for anxiety and/or depression could be located. Objective: The aim of this study is to examine the effectiveness of 5 apps (Destressify, MoodMission, Smiling Mind, MindShift, and SuperBetter) in reducing symptoms of anxiety and/or depression. These apps were selected because they are publicly available, free to download, and have published evidence of efficacy. Methods: A multiple baseline across-individuals design will be employed. A total of 50 participants will be recruited (10 for each app) who will provide baseline data for 20 days. The sequential introduction of an intervention phase will commence once baseline readings have indicated stability in the measures of participants’ mental health and will proceed for 10 weeks. Postintervention measurements will continue for a further 20 days. Participants will be required to provide daily subjective units of distress (SUDS) ratings via SMS text messages and will complete other measures at 5 different time points, including at 6-month follow-up. SUDS data will be examined via a time series analysis across the experimental phases. Individual analyses of outcome measures will be conducted to detect clinically significant changes in symptoms using the statistical approach proposed by Jacobson and Truax. Participants will rate their app on several domains at the end of the intervention. Results: Participant recruitment commenced in January 2020. The postintervention phase will be completed by June 2020. Data analysis will commence after this. A write-up for publication is expected to be completed after the follow-up phase is finalized in January 2021. Conclusions: If the apps prove to be effective as hypothesized, this will provide collateral evidence of their efficacy. It could also provide the benefits of (1) improved access to mental health services for people in rural areas, lower socioeconomic groups, and children and adolescents and (2) improved capacity to enhance face-to-face therapy through digital homework tasks that can be shared instantly with a therapist. It is also anticipated that this methodology could be used for other mental health apps to bolster the independent evidence base for this mode of treatment. International Registered Report Identifier (IRRID): PRR1-10.2196/17159

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  • The acceptability of digital interventions is closely linked to their interactivity, enjoyment, and feedback mechanisms.

    “Research indicates that the acceptability of digital interventions is closely linked to their interactivity, enjoyment, and feedback mechanisms (83).”

    From Recent developments in bibliotherapy for adolescent depression (2025) · cites this paper as [83]

    The Discussion (Potential Added Value for Clinicians and Consumers; page unavailable) says consumers may react differently to app features and identifies gamification, aesthetics, and usability/interface as possible contributors to effectiveness. This partly supports the importance of enjoyable and interactive features, but it does not establish a close link to acceptability or identify feedback mechanisms as a factor. (LLM, conf=0.78)

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Sorted by citations

Authors
Marshall, J. M., Dunstan, D. A., & Bartik, W.
Affiliations
University of New England (AU)
Corresponding author
Marshall, Jamie M
ORCID
Venue
JMIR Research Protocols
Publisher
JMIR Publications Inc.
Published
2020-07-05
Volume
9
Issue
7
Pages
e17159
ISSN
1929-0748 (ISSN-L)
DOI
10.2196/17159
PMC
PMC7381081
OpenAlex
W3039529940
Cited by
24
References
25
Citations by year
2026: 1 · 2025: 8 · 2024: 5 · 2023: 1 · 2022: 5 · 2021: 4
FWCI
2.26
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Research Protocols (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RUNE (Research UNE) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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
anxiety, depression, single-case experimental design, subjective units of distress, app efficacy, digital mental health interventions, time series analysis

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