Authors | Institution | |
David Wiljer Andrew Johnson Michelle Hamilton-Page Jackie Bender Michael-Jane Levitan Nelson Shen Alejandro R. Jadad |
Centre for Addiction and Mental Health Princess Margaret Hospital |
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A Systematic Review of Mobile Applications for Mental Health Education |
Take-home Messages
- The lack of formal evaluations for mobile health, or “mHealth,” applications (apps) creates uncertainty around medical validity and efficacy (Luxton et al., 2011).
- There is a critical need to assess the landscape of available apps to better understand their role in mental health education.
- A wide range of mobile apps found through the search term “depression” were not specific to clinical depression.
- There was a lack of detailed information related to data source or affiliation of reviewed apps.
- A low barrier of entry for app developers may result in market saturation of low quality apps.
Background
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The recent explosion of mHealth apps hints at a rapidly growing market that has revolutionized health care practice.
- Thousands of health care apps offering user-friendly clinical assessments, encyclopedias and real-time symptom trackers have surfaced to cater to nearly six billion mobile subscribers worldwide (Ben-Zeev, 2012).
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Apps for a range of mental health disorders comprise a significant proportion of the available health care apps with over 700 apps offered on Apple alone (Proudfoot, 2013).
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This systematic review is designed to inform a conceptual framework for the assessment of mobile tools and applications in mental health.
Summary of Work
- A systematic review of applications on five main mobile platforms was generated from the search term “depression.”
- Two reviewers iteratively developed selection criterion that was used to independently assess the eligibility of the apps.
- Apps targeting public audiences in need of support for depression were included while apps intended exclusively for health care professionals were excluded.
- Each reviewer then analyzed included apps based only on store descriptions and applied a verified coding scheme to available information. Characteristic data was extracted on commercial information, app developer, affiliation, purpose, functionality, audience and popularity.
- The average inter-rater reliability (k = .73) indicated substantial agreement between the two reviewers. The kappas, ranging from 0.53 to 0.89, were significant at p < .01.
- Differences between the two raters were resolved for the two variables that did not meet the threshold kappa of 0.7, and a third reviewer was consulted when consensus could not be met.
Summary of Results
- The search generated 1,054 apps, with 243 apps meeting the inclusion criteria.
- Google (53.5%) and Apple (37.0%) market places accounted for the majority of eligible apps included in the review.
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Only 4.5% of the apps clearly indicated affiliations to institutions (2.9%), medical centres (0.8%), or universities (0.8%) in the store description of their apps.
- 61.7% of apps did not provide sufficient information pertaining to the source of information/intervention. Of the apps that did provide this information, the majority of the apps cited an external (17.7%) or expert (14.0%) source while only 16 apps that were sourced from patient experts (4.5%) and laypersons (2.1%).
- Approximately two-thirds of the apps focused on providing therapeutic treatment (33.7%) and psychosocial education (32.1%). A quarter of the apps provided functions that facilitated medical assessment (16.9%) and symptom management (8.2%). Only four apps were focused on providing users with supportive resources (1.6%). Eighteen of the apps had multiple functions (7.4%).
- Over half of the apps were text-based (51.9%). Sixteen percent of the apps utilized audio as the primary media in the app while 14.4% incorporated a visualization of information (i.e., graphs and charts) that users could generate.
Conclusion
- Despite yielding over 1000 applications, less than a quarter of the applications met the criteria for a depression-focused app.
- A majority of the descriptions did not indicate any academic or institutional affiliation in its development or a credible source for the intervention.
- The lack of producer credibility could be problematic if the depression apps are not vetted to ensure its relevance, reliability and quality.
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Further research is needed on downloaded apps to closely appraise quality and develop a framework to help users better navigate and assess the appropriateness and reliability of emerging mHealth resources.
References
Ben-Zeev, D. (2012). Mobile technologies in the study, assessment, and treatment of schizophrenia. Schizophrenia Bulletin, 38(3), 384–385.
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42(6), 505.
Proudfoot, J. (2013). The future is in our hands: The role of mobile phones in the prevention and management of mental disorders. Australian and
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