Evidence map›Paper›PMID 41499163›Full record

ArticleJMIR research protocols2026

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.

Günter Schiepek, Stephanie Wackernagel

Abstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Günter SchiepekInstitute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria.ORCID 0000-0003-3482-801X
Stephanie WackernagelFaculty of Psychology and Education, Ludwig-Maximilians-Universität München, Munich, Germany.ORCID 0000-0001-8711-3857

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn 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.

objectiveThis 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.

methodsThis 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.

resultsWhile 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.

conclusionsThis 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 registrationOSF Registries osf.io/z2efy; https://doi.org/10.17605/OSF.IO/Z2EFY. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/78166.

Indexed as

Anxiety DisordersHypnosisMajor Depressive DisorderPsychotherapyAdultAmbulatory CareFeedbackFemaleHumansInternetMaleOutpatientsRandomized Controlled Trials as TopicSurveys and QuestionnairesTreatment Outcomeanxiety disordersautosystemic hypnotherapyfeedback effectshigh-frequency monitoringmajor depressive disordermobile phoneoutpatient psychotherapyrandomized clinical trialroutine process monitoringsynergetic navigation systemtherapist effects

Identifiers

PMID41499163
PMCPMC12824570

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.