Using science to sell apps: Evaluation of mental health app store quality claims

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Abstract

Despite the emergence of curated app libraries for mental health apps, personal searches by consumers remain a common method for discovering apps. App store descriptions therefore represent a key channel to inform consumer choice. This study examined the claims invoked through these app store descriptions, the extent to which scientific language is used to support such claims, and the corresponding evidence in the literature. Google Play and iTunes were searched for apps related to depression, self-harm, substance use, anxiety, and schizophrenia. The descriptions of the top-ranking, consumer-focused apps were coded to identify claims of acceptability and effectiveness, and forms of supporting statement. For apps which invoked ostensibly scientific principles, a literature search was conducted to assess their credibility. Seventy-three apps were coded, and the majority (64%) claimed effectiveness at diagnosing a mental health condition, or improving symptoms, mood or self-management. Scientific language was most frequently used to support these effectiveness claims (44%), although this included techniques not validated by literature searches (8/24 = 33%). Two apps described low-quality, primary evidence to support the use of the app. Only one app included a citation to published literature. A minority of apps (14%) described design or development involving lived experience, and none referenced certification or accreditation processes such as app libraries. Scientific language was the most frequently invoked form of support for use of mental health apps; however, high-quality evidence is not commonly described. Improved knowledge translation strategies may improve the adoption of other strategies, such as certification or lived experience co-design.

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APA

Larsen, M. E., Huckvale, K., Nicholas, J., Torous, J., Birrell, L., Li, E., & Reda, B. (2019). Using science to sell apps: Evaluation of mental health app store quality claims. Npj Digital Medicine, 2(1). https://doi.org/10.1038/s41746-019-0093-1

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