Decreased Emotional Dysregulation Following Multi-Modal Motion-Assisted Memory Desensitization and Reconsolidation Therapy (3MDR): Identifying Possible Driving Factors in Remediation of Treatment-Resistant PTSD
Bibliographic record
Abstract
Multi-modal motion-assisted memory desensitization and reconsolidation therapy (3MDR), an interactive, virtual reality-assisted, exposure-based intervention for PTSD, has shown promising results for treatment-resistant posttraumatic stress disorder (TR-PTSD) among military members (MMs) and veterans in randomized controlled trials (RCT). Previous research has suggested that emotional regulation (ER) and emotional dysregulation (ED) may be factors which are correlated with symptom severity and maintenance of TR-PTSD. This embedded mixed-methods pilot study (n = 9) sought to explore the impact of 3MDR on ER and ED of MMs and veterans. Difficulties in Emotional Regulation Scale (DERS-18) data were collected at baseline, prior to each session, and at one week, one month, and three months postintervention and analyzed. Qualitative data collected from sessions, debriefs, and follow-up interviews were transcribed and descriptively analyzed. Results demonstrated statistically significant decreases in DERS-18 scores from preintervention to postintervention at each timepoint. Qualitatively, participants perceived improvements in ER within specified DERS-18 domains. We describe how 3MDR’s unique and novel approach addresses ED through cognitive–motor stimulation, narration, divergent thinking, reappraisal of aversive stimuli, dual-task processing, and reconsolidation of traumatic memories. More studies are needed to better understand the underlying neurobiological mechanisms by which 3MDR addresses ER and PTSD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".