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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
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.
1359-4184
2985–2994
Ramineni, Varsha
a7b2741e-7773-48cc-bf35-0281f7a898ec
Millroth, Philip
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Iyadurai, Lalitha
daf0f3ec-9224-4565-b16d-c93b1ec23293
Jaki, Thomas
efdca16f-300c-4d9c-9b05-54212d077635
Kingslake, Jonathan
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Highfield, Julie
fce223d1-1632-4ff0-b1f4-b73e91c719c9
Summers, Charlotte
6382a88f-4bde-43e2-8f73-bc01cb9e1b43
Bonsall, Michael B.
d0b21c0f-ede4-40e9-91a2-4fe41a06d3c6
Holmes, Emily A.
a6379ab3-b182-45f8-87c9-3e07e90fe469
Ramineni, Varsha
a7b2741e-7773-48cc-bf35-0281f7a898ec
Millroth, Philip
d29ab817-ab79-4315-bfc9-2ce06b0c8a23
Iyadurai, Lalitha
daf0f3ec-9224-4565-b16d-c93b1ec23293
Jaki, Thomas
efdca16f-300c-4d9c-9b05-54212d077635
Kingslake, Jonathan
97f6013b-8fe2-41d6-905c-2c556703a90f
Highfield, Julie
fce223d1-1632-4ff0-b1f4-b73e91c719c9
Summers, Charlotte
6382a88f-4bde-43e2-8f73-bc01cb9e1b43
Bonsall, Michael B.
d0b21c0f-ede4-40e9-91a2-4fe41a06d3c6
Holmes, Emily A.
a6379ab3-b182-45f8-87c9-3e07e90fe469

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, 2985–2994. (doi:10.1101/2022.10.06.22280777).

Record type: Article

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

Identifiers

Local EPrints ID: 507821
URI: http://eprints.soton.ac.uk/id/eprint/507821
ISSN: 1359-4184
PURE UUID: a5ad2af1-8609-47d0-8cce-efd45f232e65
ORCID for Emily A. Holmes: ORCID iD orcid.org/0000-0001-7319-3112

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Date deposited: 06 Jan 2026 17:58
Last modified: 10 Jan 2026 05:07

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Contributors

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 ORCID iD

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