Recommendation 23 Literature Review Summary

Key Points

  • Mobile Health (mHealth) can complement existing mental health services by providing health information, offering tools and exercises that correspond with in-person treatments, and increasing access to interventions.
  • The evidence surrounding the clinical effectiveness and long-term engagement with mHealth is mixed.
  • Gamification techniques, skill-building exercises, and social interactivity are promising practices.
  • There are a variety of issues unique to mHealth, such as privacy concerns, app design and functionality.
  • Evaluation frameworks have been developed, although they vary in reliability and validity.

Literature Review Findings

mHealth can fit into the spectrum of mental health services at many levels. Health promotion and prevention can be improved by tools that deliver health information and education, as well as facilitating healthy behaviours (e.g. nutrition, physical activity, etc.). Care providers can also make use of apps that offer complementary homework packages or skill-building exercises. For individuals waiting for in-person services, as well as those unlikely to access any in-person service, mHealth represents an opportunity to provide care.

The clinical effectiveness of apps is inconsistent. While some studies report improvements in symptoms related to depression, substance use, and anxiety, others have reported no substantial changes over time. The majority of apps available in the consumer market have not undergone rigorous empirical testing, and so users and clinicians must be wary of which tools they use. Engagement over time also varies by study. Literature reviews found that apps incorporating elements of gamification, skill-building, user-friendly design, and social interactions can facilitate engagement and improve outcomes.

Other issues unique to mHealth are concerns about privacy and confidentiality, user-design, and functionality. Since information stored on computers or phones can be lost, stolen, or sold to third-party organizations, users and clinicians need to select apps that provide detailed information about security. Apps that were not developed with input from users tended to lack features that would engage users over time. Additionally, users do not want to manually input data into an isolated app; a central system that incorporates data from different sources leads to better uptake.

While there is no gold standard for evaluating mHealth currently, a number of frameworks have been developed. Inter-rater reliability, internal consistency, and content validity vary across methods; the user-version of the Mobile Application Rating Scale (uMARS) has undergone RCT testing. The application of these different systems in real-world contexts is needed to provide further evidence of their utility, reliability, and validity.

Implications for Practice

Technological innovations and wide-scale accessibility of mHealth options mean that they are desirable to many users, and so they should be integrated into the health system to fill in existing service gaps and complement in-person therapy. However, the rate at which apps are developed is far greater than the accompanying evidence base; users and clinicians must be cautious when selecting apps for treatment.

Before apps receive endorsement from a clinician or organization, they need to be comprehensively evaluated. However, there is no obvious choice of framework for assessing apps, and so consensus among existing options will be necessary.