Improving Outpatient Psychotherapy for Adults With Major Depressive and Anxiety Disorders Using Web-Based High-Frequency Monitoring and Feedback in Autosystemic Hypnotherapy: Protocol for a Two-Arm ABAB Crossed-Therapist Randomized Clinical Implementation Trial
Notice bibliographique
Résumé
BACKGROUND: In recent years, routine outcome monitoring has been increasingly complemented by routine process monitoring in psychotherapy and other health care settings. Various approaches to therapy feedback exist, differing in assessment frequency, integration into the therapeutic process, and degree of personalization. In this study, we will use a procedure of high-frequency assessment through daily self-ratings, a standard process questionnaire, alongside a personalized questionnaire derived from case formulation, and frequent feedback interviews using visual diagrams to mirror the ongoing therapeutic processes. OBJECTIVE: This study aims to investigate the effectiveness of combining routine process monitoring with hypno-psychotherapy (autosystemic hypnotherapy) by comparing it to autosystemic hypnotherapy without process feedback in the outpatient treatment of mood disorders. It also seeks to examine process-outcome relationships and mechanisms of change through high-frequency self-assessments and session-based feedback. METHODS: This study is a randomized controlled trial with 2 arms, using within-therapist randomization (ABAB design) in outpatient psychotherapy. Participants are recruited offline via routine intake procedures. A total of 100 patients will be randomly assigned to one of the two conditions following a waiting period. The inclusion criterion is the existence of any mood disorder (major depressive disorder or anxiety disorder), assessed via a clinical interview. Each therapist treats patients in both conditions. Outcomes will be measured at 4 time points: after diagnosis confirmation, postwaiting period, posttreatment, and a 6-month follow-up. Primary and secondary outcomes, including symptom severity, will be assessed using questionnaires. Data collection also includes patient and therapist session evaluations using the Bern Patient and Therapist Session Questionnaire. In the feedback condition, therapists conduct frequent interviews using time-series data generated from daily self-assessments using the synergetic navigation system, including the Therapy Process Questionnaire and an individualized measure based on case conceptualization. RESULTS: While this study is ongoing, the primary aim is to assess the effects of the feedback condition on therapeutic outcomes, including symptom reduction and patient motivation. This study will also explore how dynamic monitoring and feedback influence the therapeutic alliance and session-level improvements. It is expected that the feedback condition will lead to improvements in symptom severity and therapeutic engagement compared to the nonfeedback condition. Recruitment is ongoing, with 22 participants enrolled. The training of therapists and the data collection began in 2022. Data collection will end and study findings will be published in 2027. The German Society for Auto-Systemic Hypnotherapy is funding these training courses. CONCLUSIONS: This study combines effect and process measures within a feedback condition, compared to a nonfeedback condition. It incorporates dynamic process assessment to explore change mechanisms by analyzing patterns of time-series data and session ratings by patients and therapists. The approach provides insights into how continuous feedback and tailored monitoring influence therapeutic progress and outcomes. TRIAL REGISTRATION: OSF Registries osf.io/z2efy; https://doi.org/10.17605/OSF.IO/Z2EFY. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/78166.
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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 | 0,016 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,006 |
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 ».