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Record W6939019448 · doi:10.60692/cx194-nf892

The effect of motor interference therapy on traumatic memories: A randomized, double blind, controlled study

2022· article· en· W6939019448 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of AlbertaDouglas College
Fundersnot available
KeywordsAnalysis of varianceTraumatic stressRelaxation (psychology)Repeated measures designPsychological interventionSession (web analytics)Visual analogue scale

Abstract

fetched live from OpenAlex

Abstract Introduction: Traumatic memories are a core symptom of PTSD and stress-related disorders, as well as a transdiagnostic symptom found in many different mental disorders. There are effective psychological treatments for PTSD symptoms, but access to these specialized treatments can be difficult and expensive. One potential for treatment is the use of visuospatial tasks to interrupt memory reconsolidation processes. The aim of this pilot study was to determine the usefulness of Motor Interference Therapy (MIT), which consists of a visuospatial task verbally directed through an audio, for the treatment of traumatic memories. Methods: We conducted a randomized, double blind, controlled study. 28 participants with at least one traumatic memory causing distress were randomized to receive either MIT or an abbreviated version of Jacobson´s Progressive Muscle Relaxation Technique (PMR). Both interventions were administered twice for a total duration of 30 minutes. The assessment scales (PTSD Symptom Severity Scale-Revised, visual-analog scale (EQ-VAS) from EuroQol 5D, and a visual analogue scale of traumatic memory distress) were administered by a blinded researcher to the treatment group in three times: before the intervention, one week after the intervention and one month later. Only the visual-analog scale that rated the level of stress provoked by the traumatic memory was also applied immediately after the intervention. For each dependent variable a Group (PMR, MIT) x Session analysis of variance was conducted with repeated measures on the second variable. Critical Group x Session interactions were analyzed further with pairwise comparisons. Analyses of covariance were conducted to evaluate posttest scores adjusted for any pretest differences. Results: Mean scores improved from pretest to posttests for both interventions on all seven measures, and these improvements were statistically significant in all seven cases for MIT and in five of seven cases for PMR. Significant statistical differences were observed between groups on the visual analog scale for traumatic memories: MR scores declined from pretest to the immediate posttest ( p = .002) but showed no further decline. MIT scores also declined from pretest to immediate posttest ( p < .001), but they continued to improve over the subsequent week (p = .002) and were sustained one month following treatment. Mean MIT scores were lower than mean PMR scores at one week and one month ( p s < .002). no adverse events were reported in either group. Conclusion: MIT is an easy to apply technique that requires few resources and little training. The results strongly suggests that MIT could be a useful tool in the treatment of traumatic memories and yields proof-of-principle support for conducting future research with a large cohort, properly powered to stablish efficacy. ClinicalTrials.gov Identifier: NCT03627078

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.082
GPT teacher head0.314
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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