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
Bibliographic record
Abstract
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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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".