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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,092 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,005 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,012 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,076 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».