Research investigating commercial mobile applications for depression have shown a range of concerns from limited research evidence, poor treatment fidelity, and issues with privacy and data security. This study advances this work through a content analysis and ethical review of app store listings of apps for depression. Whilst past content analyses and app reviews have highlighted some ethical and safety concerns, there has been no focussed ethical review to consider how these issues may present to potential users who seek to find help and support through the app stores.
We conducted search of the Google Play Store and Apple iOS App Store in October and November 2018. Apps were included in the review if their description mentioned use for depression. Apps were reviewed for treatment information and ethical issues.
We identified 353 eligible depression apps. Treatment approach varied across apps, with 24 different treatment approaches being described by developers. Treatment strategies also varied, with 34 different strategies being listed. The review showed the use of several non-evidence-based approaches and strategies. Also evident was the continued lack of research evidence for most apps (314/353) and a general lack of transparency in the information provided to potential users. These ethical issues were further explored within the framework of psychological ethical principles, with the review highlighting issues in areas of beneficence/nonmaleficence, fidelity and responsibility, integrity, justice, and respect of person’s rights and dignity.
Despite advances in mobile mental health, commercial mental health apps continue to trail in evidence and practice. There is need for greater research into the efficacy and outcomes of treatment strategies and combinations of approaches. There is also great need for increased transparency of information to help users to make informed and safe choices. Many of the ethical issues discussed can be addressed by presenting potential users with clear and accurate information.
This research is part of a PhD research project under the AffecTech ITN. AffecTech is a multidisciplinary research and development project funded by the Horizon 2020 Innovative Training Network of the European Union under the Marie Skłodowska-Curie grant agreement no. 722022.
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