Abstract
Background: It is unclear why some smartphone applications designed for people with psychosis are engaging, while others are not. One possible explanation is the apps’ persuasive features, and the operationalization and implementation of these features.
Objective: This systematic review set out to quantify and describe the persuasive features used in smartphone applications for psychosis, investigate whether there was any association between persuasive features and attrition or adherence rates and document the quality of the included apps.
Methods: We searched electronic databases PsycINFO, Pubmed and Google Scholar for eligible papers published between the years of 2013 and 2025. Hand searches of reference lists were completed. Apps were selected if they were designed for people with psychosis and there were published empirical studies investigating the apps. Two reviewers independently screened papers and extracted data on adherence and attrition, as well as coded papers for evidence of persuasive features according to the Persuasive Systems Design model. Available data was synthesized descriptively and narratively. We attempted to access apps via app stores, or by correspondence with the research team.
Results: We found 22 apps for psychosis, with 30 associated published papers. The persuasive features were as follows: personalization (18 apps), reminders (15 apps), suggestion (11 apps) , tunnelling and self-monitoring (10 apps), reduction (9 apps), liking (8 apps), social role (6 apps), rehearsal, praise and similarity (5 apps), rewards (4 apps), simulation, real world feel and social learning (3 apps), surface credibility and normative influence (2 apps), trustworthiness, social comparison and social facilitation (1 app). Expertise, authority, 3rd party endorsements, verifiability, cooperation, competition and recognition were present in zero apps. Features in the categories of primary task support and dialogue support were well represented, while social support and system credibility support were underutilized. It was found that there was no association between the number of persuasive features and attrition; and association between persuasive features and adherence could not be assessed. The quality of the apps could not be judged due to 20 of the 22 apps being inaccessible either through the research papers’ authors, or through app stores.
Conclusions: Our findings indicate that in psychosis apps there is potential to include a broader range of persuasive features which might maximize engagement. Psychosis apps may benefit from incorporating more features which leverage the persuasive impact of having users interact (social support) and incorporating features that emphasize system credibility and trustworthiness. Further studies could determine whether an increase in number of persuasive features will impact app engagement, and which features are most impactful in this context.
Objective: This systematic review set out to quantify and describe the persuasive features used in smartphone applications for psychosis, investigate whether there was any association between persuasive features and attrition or adherence rates and document the quality of the included apps.
Methods: We searched electronic databases PsycINFO, Pubmed and Google Scholar for eligible papers published between the years of 2013 and 2025. Hand searches of reference lists were completed. Apps were selected if they were designed for people with psychosis and there were published empirical studies investigating the apps. Two reviewers independently screened papers and extracted data on adherence and attrition, as well as coded papers for evidence of persuasive features according to the Persuasive Systems Design model. Available data was synthesized descriptively and narratively. We attempted to access apps via app stores, or by correspondence with the research team.
Results: We found 22 apps for psychosis, with 30 associated published papers. The persuasive features were as follows: personalization (18 apps), reminders (15 apps), suggestion (11 apps) , tunnelling and self-monitoring (10 apps), reduction (9 apps), liking (8 apps), social role (6 apps), rehearsal, praise and similarity (5 apps), rewards (4 apps), simulation, real world feel and social learning (3 apps), surface credibility and normative influence (2 apps), trustworthiness, social comparison and social facilitation (1 app). Expertise, authority, 3rd party endorsements, verifiability, cooperation, competition and recognition were present in zero apps. Features in the categories of primary task support and dialogue support were well represented, while social support and system credibility support were underutilized. It was found that there was no association between the number of persuasive features and attrition; and association between persuasive features and adherence could not be assessed. The quality of the apps could not be judged due to 20 of the 22 apps being inaccessible either through the research papers’ authors, or through app stores.
Conclusions: Our findings indicate that in psychosis apps there is potential to include a broader range of persuasive features which might maximize engagement. Psychosis apps may benefit from incorporating more features which leverage the persuasive impact of having users interact (social support) and incorporating features that emphasize system credibility and trustworthiness. Further studies could determine whether an increase in number of persuasive features will impact app engagement, and which features are most impactful in this context.
| Original language | English |
|---|---|
| Journal | JMIR Human Factors |
| Early online date | 8 May 2026 |
| DOIs | |
| Publication status | Published - 8 May 2026 |
Data Availability Statement
The datasets generated or analyzed during this study are available in the Open Science Framework repositoryAcknowledgements
A machine tool (Claude Opus 3) was used to suggest language to improve within the manuscript, for example, grammar, and spell check on content written by the research team. Responsibility for the final manuscript lies entirely with the authors. Generative AI tools are not listed as authors and do not bear responsibility for the final outcomes. There were no funders or sponsors for this research.Funding
The authors declared no financial support was received for this work.
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