Transdiagnostic App–Based Cognitive Bias Modification Intervention for Paranoia (Successful Treatment of Paranoia; STOP): Protocol for a Mixed Methods Process Evaluation Embedded in a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Paranoia (unfounded concerns that other people are deliberately trying to harm you) can be a distressing experience that impacts day-to-day functioning for many people. Digitally delivered interventions are a promising mode of treatment that can increase access to support and reach populations underserved by current provision. One such intervention is the Successful Treatment of Paranoia (STOP) intervention, which is a transdiagnostic self-administered smartphone app for paranoia that was evaluated in England for efficacy in a large, multisite randomized controlled trial. Although there is growing evidence regarding how STOP may work, little is known about the factors that influence its implementation during or after a trial. Understanding these factors is critical for supporting the future adoption of STOP and may inform the implementation of other digital mental health interventions. OBJECTIVE: This paper aims to describe the protocol for a process evaluation that will explore intervention implementation to understand how much, by whom, and under what circumstances STOP was used in a trial context and what factors might influence future implementation in routine practice. METHODS: We will conduct a mixed methods process evaluation informed by guidance published by the Medical Research Council in the United Kingdom and embedded in the main STOP efficacy randomized controlled trial, with the aim of understanding who agreed to try STOP, how they used STOP, and users' and health care professionals' views on factors that will affect future implementation, including barriers and facilitators. Process evaluation participants will include three samples: (1) STOP trial participants (randomized participants and nonrandomized referrals who agreed to try STOP), (2) individuals experiencing paranoia who participated in the Adult Psychiatric Morbidity Survey in England, and (3) health care professionals in England. We will use mixed methods data collected in the STOP trial (demographics, app use, recruitment data, and qualitative data exploring intervention acceptability from the perspectives of users and health care professionals) and demographic data from another paranoia population collected in the Adult Psychiatric Morbidity Survey. RESULTS: The STOP trial was completed in December 2024, having recruited 274 participants. The process evaluation received funding in winter 2023. As of December 2025, data analysis for process evaluation studies 2 and 3 is ongoing following preregistration in July 2025. Results are expected to be completed and submitted for publication by February 2027. CONCLUSIONS: Findings from this process evaluation will help develop an evidence-based understanding of implementing STOP, including identifying suitable implementation strategies for posttrial delivery both inside and outside mental health services. Our findings will inform future iterations and the upscaling of STOP and may inform implementation of other self-administered digital mental health interventions. TRIAL REGISTRATION: International Standard Registered Clinical/Social Study Number ISRCTN17754650; https://www.isrctn.com/ISRCTN17754650. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/81167.
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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 | 0.092 | 0.085 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.076 | 0.017 |
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".