Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention
Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention
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 × 106): 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.
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Ramineni, Varsha
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Millroth, Philip
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Iyadurai, Lalitha
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Jaki, Thomas
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Kingslake, Jonathan
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Highfield, Julie
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Summers, Charlotte
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Bonsall, Michael B.
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Holmes, Emily A.
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Ramineni, Varsha
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Millroth, Philip
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Iyadurai, Lalitha
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Jaki, Thomas
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Kingslake, Jonathan
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Highfield, Julie
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Summers, Charlotte
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Bonsall, Michael B.
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Holmes, Emily A.
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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, .
(doi:10.1101/2022.10.06.22280777).
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 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 × 106): 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.
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Accepted/In Press date: 28 March 2023
e-pub ahead of print date: 26 April 2023
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Local EPrints ID: 507821
URI: http://eprints.soton.ac.uk/id/eprint/507821
ISSN: 1359-4184
PURE UUID: a5ad2af1-8609-47d0-8cce-efd45f232e65
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Date deposited: 06 Jan 2026 17:58
Last modified: 10 Jan 2026 05:07
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Author:
Varsha Ramineni
Author:
Philip Millroth
Author:
Lalitha Iyadurai
Author:
Thomas Jaki
Author:
Jonathan Kingslake
Author:
Julie Highfield
Author:
Charlotte Summers
Author:
Michael B. Bonsall
Author:
Emily A. Holmes
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