REMOTION Blended Transdiagnostic Intervention for Symptom Reduction and Improvement of Emotion Regulation in an Outpatient Psychotherapeutic Setting: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Emotion regulation has been identified as an important transdiagnostic factor relevant to the treatment of mental health disorders. Many empirically validated psychotherapeutic treatments incorporate elements targeting emotion regulation. Most of these treatment approaches are conceptualized as standard face-to-face treatments not as blended treatments, which include an internet-based intervention. OBJECTIVE: The aim of this study is to examine, for the first time, a new internet-based intervention-REMOTION-that will be provided transdiagnostically, as an add-on to psychotherapy, to provide a blended treatment format. METHODS: A total of 70 participants will be assigned (1:1 allocation ratio) to either the intervention group (REMOTION + psychotherapy) or the treatment-as-usual group that receives psychotherapy alone. To maximize external validity, a typical outpatient treatment sample of patients diagnosed with a range of disorders such as depression, anxiety disorders, and adjustment disorder will be recruited from a university outpatient clinic. Patients with bipolar disorder, psychotic disorders, or acute suicidality will be excluded from the study. The feasibility and potential effectiveness of the intervention will be examined by assessing data at baseline, 6 weeks (post), and 12 weeks (follow-up). The primary outcome is general symptom severity, assessed with the Brief Symptom Inventory. Secondary outcomes are emotion regulation, depressive symptoms, anxiety symptoms, health related quality of life, well-being, and a variety of feasibility parameters. Quantitative data will be analyzed on an intention-to-treat basis. RESULTS: Participant recruitment and data collection started in February 2020, and as of November 2020, are ongoing. Results for the study are expected in 2022. CONCLUSIONS: This pilot randomized controlled trial will inform future studies using transdiagnostic blended treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT04262726; http://clinicaltrials.gov/ct2/show/NCT04262726. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/20936.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.010 |
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".