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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
Theme
eLearning
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
  • 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).

  • 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).
  • 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. 

Flow diagram of inclusion and exclusion of depression apps

  • Google (53.5%) and Apple (37.0%) market places accounted for the majority of eligible apps included in the review.  

Breakdown of included apps by marketplace

  • 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.   
  • 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), 384385.

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 New Zealand Journal of Psychiatry, 47(2), 111113.


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Take-home Messages
Background

Evidence is slowly emerging to suggest that mHealth apps can have strong implications for the self-management of chronic conditions. However, some researchers are skeptical of the efficacy of apps that stem from largely unregulated, non-standardized markets. The risk of misinformation can be especially deleterious for users with stigmatizing mental health conditions that face barriers in otherwise seeking health advice from medical professionals.

Summary of Work
Summary of Results

 

  

 

Conclusion
References
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