A Digital Mental Health Intervention for Paranoia (the STOP App): Qualitative Study on User Acceptability
Bibliographic record
Abstract
BACKGROUND: Cognitive bias modification for interpretation (CBM-I) is a technique to modify interpretation and is used to reduce unhelpful negative biases. CBM-I has been extensively studied in anxiety disorders where interpretation bias has been shown to play a causal role in maintaining the condition. Successful Treatment of Paranoia (STOP) is a CBM-I smartphone app targeting interpretation bias in paranoia. It has been developed following research on the feasibility and acceptability of a computerized version. This qualitative study extended that research by investigating the acceptability of STOP in individuals with paranoia. The study design and implementation were informed by the Evidence Standards Framework for Digital Health Technologies (DHTs) published by the UK National Institute for Health and Care Excellence (NICE). OBJECTIVE: The aim of the study was to involve service users in the design, development, and testing of STOP and understand the degree of satisfaction with the product. We aimed to establish the extent to which STOP met the NICE minimum and best practice standards for DHTs, specifically its acceptability to intended end users. METHODS: In total, 12 participants experiencing mild to moderate levels of paranoia were recruited to complete 6 weekly sessions of STOP before being invited to a feedback interview to share their experiences. Interview questions revolved around the acceptability of the app, its perceived usefulness, and barriers to the intervention, as well as practicality and views on the use of a digital intervention in principle. Interviews were coded and analyzed using the framework analysis method, combining both deductive and inductive approaches. RESULTS: Framework analysis yielded 6 themes: independent use and personal fit; digital versus traditional approaches; user reactions and emotional impact; impact on thinking, awareness, and well-being; design, engagement, and usability; and intervention relevance and practical fit. CONCLUSIONS: STOP was found to be a broadly acceptable intervention and was positively received by most participants. The study findings are in line with the NICE Evidence Standards Framework for DHTs, as intended end users were involved in the development, design, and testing of STOP and were mostly satisfied with it. These findings will contribute to the further iterative development of this intervention that targets interpretation bias in paranoia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-024-08570-3.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".