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Abstract
Background: Paranoia is a highly debilitating mental health condition. One novel intervention for paranoia is cognitive bias modification for paranoia (CBM-pa). CBM-pa comes from a class of interventions that focus on manipulating interpretation bias. Here, we aimed to develop and evaluate new therapy content for CBM-pa for later use in a self-administered digital therapeutic for paranoia called STOP ("Successful Treatment of Paranoia").
Objective: This study aimed to (1) take a user-centered approach with input from living experts, clinicians, and academics to create and evaluate paranoia-relevant item content to be used in STOP and (2) engage with living experts and the design team from a digital health care solutions company to cocreate and pilot-test the STOP mobile app prototype.
Methods: We invited 18 people with living or lived experiences of paranoia to create text exemplars of personal, everyday emotionally ambiguous scenarios that could provoke paranoid thoughts. Researchers then adapted 240 suitable exemplars into corresponding intervention items in the format commonly used for CBM training and created 240 control items for the purpose of testing STOP. Each item included newly developed, visually enriching graphics content to increase the engagement and realism of the basic text scenarios. All items were then evaluated for their paranoia severity and readability by living experts (n=8) and clinicians (n=7) and for their item length by the research team. Items were evenly distributed into six 40-item sessions based on these evaluations. Finalized items were presented in the STOP mobile app, which was co-designed with a digital health care solutions company, living or lived experts, and the academic team; user acceptance was evaluated across 2 pilot tests involving living or lived experts. Results: All materials reached predefined acceptable thresholds on all rating criteria: paranoia severity (intervention items: ≥1; control items: ≤1, readability: ≥3, and length of the scenarios), and there was no systematic difference between the intervention and control group materials overall or between individual sessions within each group. For item graphics, we also found no systematic differences in users' ratings of complexity (P=.68), attractiveness (P=.15), and interest (P=.14) between intervention and control group materials. User acceptance testing of the mobile app found that it is easy to use and navigate, interactive, and helpful.
Conclusions: Material development for any new digital therapeutic requires an iterative and rigorous process of testing involving multiple contributing groups. Appropriate user-centered development can create user-friendly mobile health apps, which may improve face validity and have a greater chance of being engaging and acceptable to the target end users.
Original language | English |
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Article number | e45453 |
Journal | JMIR Human Factors |
Volume | 10 |
Issue number | 1 |
DOIs | |
Publication status | Published - 8 Dec 2023 |
Funding
This work was supported by the Medical Research Council Biomedical Catalyst: Developmental Pathway Funding scheme (reference MR/V027484/1). We also would like to express our gratitude to the National Institute for Health and Care Research Biomedical Research Centre hosted at South London and Maudsley National Health Service Foundation Trust in partnership with King's College London. The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health and Care Research, the Department of Health and Social Care, the Economic and Social Research Council (ESRC), or King’s College London. The early stages of this work were funded by the National Institute for Health and Care Research) under its Research for Patient Benefit Programme (grant PB-PG-0214-33007). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. We acknowledge with thanks the contributions made to this work by the STOP study LEAP supported by The McPin Foundation and in particular Alex Kenny and Thomas Kabir. We thank digital health care company Avegen for implementing the design and setup of STOP on their proprietary product development platform HealthMachine. Avegen is General Data Protection Regulation (GDPR)– and ISO13485-compliant and supports the delivery of customized, regulated Digital Health apps. For the purposes of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Accepted Author Manuscript version arising from this submission.
Funders | Funder number |
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McPin Foundation | |
Medical Research Council | MR/V027484/1 |
National Institute for Health and Care Research | PB-PG-0214-33007 |
Keywords
- cognitive bias modification
- content specificity
- delusions
- digital therapeutic
- megalomania
- mental health
- mhealth
- mobile app
- monomania
- obsession
- paranoia
- paranoid
- persecution
- persecution complex
- psychosis
- psychotic
- user
- user-centered development
- user-friendly app
ASJC Scopus subject areas
- Human Factors and Ergonomics
- Health Informatics
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