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Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention
Ramineni, Varsha
, Millroth, Philip, Iyadurai, Lalitha, Jaki, Thomas
, Kingslake, Jonathan
, Highfield, Julie, Summers, Charlotte
, Bonsall, Michael B. and Holmes, Emily A.
(2023)
Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention.
Molecular Psychiatry 28, pp. 2985-2994.
Date of publication of this fulltext: 18 Mar 2025 10:07
Article
DOI to cite this document: 10.5283/epub.75903
Abstract
Intensive care unit (ICU) staff continue to face recurrent work-related traumatic events throughout the COVID-19 pandemic. Intrusive memories (IMs) of such traumatic events comprise sensory image-based memories. Harnessing research on preventing IMs with a novel behavioural intervention on the day of trauma, here we take critical next steps in developing this approach as a treatment for ICU staff ...
Intensive care unit (ICU) staff continue to face recurrent work-related traumatic events throughout the COVID-19 pandemic. Intrusive memories (IMs) of such traumatic events comprise sensory image-based memories. Harnessing research on preventing IMs with a novel behavioural intervention on the day of trauma, here we take critical next steps in developing this approach as a treatment for ICU staff who are already experiencing IMs days, weeks, or months post-trauma. To address the urgent need to develop novel mental health interventions, we used Bayesian statistical approaches to optimise a brief imagery-competing task intervention to reduce the number of IMs. We evaluated a digitised version of the intervention for remote, scalable delivery. We conducted a two-arm, parallel-group, randomised, adaptive Bayesian optimisation trial. Eligible participants worked clinically in a UK NHS ICU during the pandemic, experienced at least one work-related traumatic event, and at least three IMs in the week prior to recruitment. Participants were randomised to receive immediate or delayed (after 4 weeks) access to the intervention. Primary outcome was the number of IMs of trauma during week 4, controlling for baseline week. Analyses were conducted on an intention-to-treat basis as a between-group comparison. Prior to final analysis, sequential Bayesian analyses were conducted (n = 20, 23, 29, 37, 41, 45) to inform early stopping of the trial prior to the planned maximum recruitment (n = 150). Final analysis (n = 75) showed strong evidence for a positive treatment effect (Bayes factor, BF = 1.25 x 10(6)): the immediate arm reported fewer IMs (median = 1, IQR = 0-3) than the delayed arm (median = 10, IQR = 6-16.5). With further digital enhancements, the intervention (n = 28) also showed a positive treatment effect (BF = 7.31). Sequential Bayesian analyses provided evidence for reducing IMs of work-related trauma for healthcare workers. This methodology also allowed us to rule out negative effects early, reduced the planned maximum sample size, and allowed evaluation of enhancements. Trial Registration NCT04992390 (www.clinicaltrials.gov).
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Details
| Item type | Article | ||||
| Journal or Publication Title | Molecular Psychiatry | ||||
| Publisher: | Springer Nature | ||||
|---|---|---|---|---|---|
| Place of Publication: | LONDON | ||||
| Volume: | 28 | ||||
| Page Range: | pp. 2985-2994 | ||||
| Date | 26 April 2023 | ||||
| Institutions | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Computational Statistics (Prof. Dr. Thomas Jaki) | ||||
| Identification Number |
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| Keywords | POSTTRAUMATIC-STRESS-DISORDER; COMPUTER GAME PLAY; PSYCHOLOGICAL TREATMENTS; MENTAL-HEALTH; | ||||
| Dewey Decimal Classification | 000 Computer science, information & general works > 004 Computer science 600 Technology > 610 Medical sciences Medicine | ||||
| Status | Published | ||||
| Refereed | Yes, this version has been refereed | ||||
| Created at the University of Regensburg | Partially | ||||
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-759037 | ||||
| Item ID | 75903 |
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