Pub Date : 2026-03-11DOI: 10.1016/s2215-0366(26)00032-5
Sunniva Haynes, Carina Andrews, Ashley Nsimbi, Shizana Arshad, Annabel E L Walsh
{"title":"Lived experience perspectives on the development of a Psychosis Metabolic Risk Calculator (PsyMetRiC)","authors":"Sunniva Haynes, Carina Andrews, Ashley Nsimbi, Shizana Arshad, Annabel E L Walsh","doi":"10.1016/s2215-0366(26)00032-5","DOIUrl":"https://doi.org/10.1016/s2215-0366(26)00032-5","url":null,"abstract":"","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"105 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-11DOI: 10.1016/s2215-0366(25)00398-0
Benjamin I Perry, Emanuele F Osimo, Shuqing Si, Karla V B Hitchins, Clara Lewis, Ben Laws, Simon J Griffin, Golam M Khandaker, Graham K Murray, David Shiers, Carolyn A Chew-Graham, Peter B Jones, Alastair K Denniston, Marco Bardus, Sue Jowett, Annabel E L Walsh, Shizana Arshad, Tomas Formanek, Toby Pillinger, Robert A McCutcheon, Gerardo A Zavala
<h3>Background</h3>Young people with psychosis spectrum disorders are at a high risk of cardiometabolic morbidity and subsequent premature mortality, but there are no accurate clinic-ready prediction models for this group. We aimed to collaboratively refine, extend, and validate the Psychosis Metabolic Risk Calculator (PsyMetRiC) prediction models for accuracy, clinical usefulness, and acceptability, and to translate the models into a regulated, clinically available medical device.<h3>Methods</h3>In this retrospective, multicohort clinical prediction model study, we used primary care (Clinical Practice Research Datalink and QResearch) and secondary care (South London and Maudsley NHS Foundation Trust) datasets. Individuals from primary care sources were aged 16–35 years when they received a first recorded diagnosis of a psychosis-spectrum disorder between Jan 1, 2005, and Dec 31, 2015, with follow-up to Dec 31, 2020. Individuals from the secondary care source were enrolled in the psychosis early intervention service between Jan 1, 2012, and Dec 31, 2024. We developed models for a binary outcome of metabolic syndrome within 1–6 years using logistic regression; a time-to-event outcome of type 2 diabetes within 10 years using Weibull regression; and a binary outcome of clinically significant weight gain within 1 year using logistic regression. We revised existing predictors (hereafter referred to as the PsyMetRiC1 models) for finer detail and added new predictors: a family history of cardiometabolic disorder, antidepressant prescription, systolic blood pressure, and HbA<sub>1C</sub> (hereafter PsyMetRiC2 models). Refinement and external validation were performed for metabolic syndrome models (PsyMetRiC1-MetS and PsyMetRiC2-MetS), and development and external validation were performed for the type 2 diabetes model (PsyMetRiC2-T2D). Development and internal validation were performed for the clinically significant weight gain model (PsyMetRiC2-WG), but external validation was not possible due to data availability. Partial versions without biochemical results were also developed for weight gain and metabolic syndrome models. We involved stakeholders including people with lived experience; and implemented the models in a web application compliant with regulatory standards in Great Britain.<h3>Findings</h3>In total, we included 25 850 individuals (male, n=13 614 [52·7%]; female, n=12 236 [47·3%]; White European, 16 445 [63·6%]; Black African or Caribbean, south Asian, mixed, and east Asian or other n=9405 [36·3%]; and mean age 26·7 years [SD=5·4]). For primary care, we included 3989 individuals for development and 4347 individuals for external validation of metabolic syndrome outcomes; and 9181 individuals for development and 7487 individuals for external validation of type 2 diabetes outcomes. For secondary care, we included 846 individuals for development and internal validation of weight gain outcomes. For metabolic syndrome, the performance of PsyMetR
{"title":"Cardiometabolic prediction models for young people with psychosis spectrum disorders in the UK (PsyMetRiC 2.0): a retrospective, multicohort clinical prediction model study","authors":"Benjamin I Perry, Emanuele F Osimo, Shuqing Si, Karla V B Hitchins, Clara Lewis, Ben Laws, Simon J Griffin, Golam M Khandaker, Graham K Murray, David Shiers, Carolyn A Chew-Graham, Peter B Jones, Alastair K Denniston, Marco Bardus, Sue Jowett, Annabel E L Walsh, Shizana Arshad, Tomas Formanek, Toby Pillinger, Robert A McCutcheon, Gerardo A Zavala","doi":"10.1016/s2215-0366(25)00398-0","DOIUrl":"https://doi.org/10.1016/s2215-0366(25)00398-0","url":null,"abstract":"<h3>Background</h3>Young people with psychosis spectrum disorders are at a high risk of cardiometabolic morbidity and subsequent premature mortality, but there are no accurate clinic-ready prediction models for this group. We aimed to collaboratively refine, extend, and validate the Psychosis Metabolic Risk Calculator (PsyMetRiC) prediction models for accuracy, clinical usefulness, and acceptability, and to translate the models into a regulated, clinically available medical device.<h3>Methods</h3>In this retrospective, multicohort clinical prediction model study, we used primary care (Clinical Practice Research Datalink and QResearch) and secondary care (South London and Maudsley NHS Foundation Trust) datasets. Individuals from primary care sources were aged 16–35 years when they received a first recorded diagnosis of a psychosis-spectrum disorder between Jan 1, 2005, and Dec 31, 2015, with follow-up to Dec 31, 2020. Individuals from the secondary care source were enrolled in the psychosis early intervention service between Jan 1, 2012, and Dec 31, 2024. We developed models for a binary outcome of metabolic syndrome within 1–6 years using logistic regression; a time-to-event outcome of type 2 diabetes within 10 years using Weibull regression; and a binary outcome of clinically significant weight gain within 1 year using logistic regression. We revised existing predictors (hereafter referred to as the PsyMetRiC1 models) for finer detail and added new predictors: a family history of cardiometabolic disorder, antidepressant prescription, systolic blood pressure, and HbA<sub>1C</sub> (hereafter PsyMetRiC2 models). Refinement and external validation were performed for metabolic syndrome models (PsyMetRiC1-MetS and PsyMetRiC2-MetS), and development and external validation were performed for the type 2 diabetes model (PsyMetRiC2-T2D). Development and internal validation were performed for the clinically significant weight gain model (PsyMetRiC2-WG), but external validation was not possible due to data availability. Partial versions without biochemical results were also developed for weight gain and metabolic syndrome models. We involved stakeholders including people with lived experience; and implemented the models in a web application compliant with regulatory standards in Great Britain.<h3>Findings</h3>In total, we included 25 850 individuals (male, n=13 614 [52·7%]; female, n=12 236 [47·3%]; White European, 16 445 [63·6%]; Black African or Caribbean, south Asian, mixed, and east Asian or other n=9405 [36·3%]; and mean age 26·7 years [SD=5·4]). For primary care, we included 3989 individuals for development and 4347 individuals for external validation of metabolic syndrome outcomes; and 9181 individuals for development and 7487 individuals for external validation of type 2 diabetes outcomes. For secondary care, we included 846 individuals for development and internal validation of weight gain outcomes. For metabolic syndrome, the performance of PsyMetR","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"53 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1016/s2215-0366(26)00063-5
Polly Waite, Bridget Bradley, Clare E Mackay
{"title":"Stigmatising language in research and clinical care for body-focused repetitive behaviours","authors":"Polly Waite, Bridget Bradley, Clare E Mackay","doi":"10.1016/s2215-0366(26)00063-5","DOIUrl":"https://doi.org/10.1016/s2215-0366(26)00063-5","url":null,"abstract":"","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"44 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147392478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1016/s2215-0366(26)00058-1
Atefeh Zandifar, Rahim Badrfam
No Abstract
没有抽象的
{"title":"Addressing mental health problems among Iranian adolescents and youth in times of crisis","authors":"Atefeh Zandifar, Rahim Badrfam","doi":"10.1016/s2215-0366(26)00058-1","DOIUrl":"https://doi.org/10.1016/s2215-0366(26)00058-1","url":null,"abstract":"No Abstract","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"8 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-06DOI: 10.1016/s2215-0366(26)00051-9
Marisa Casanova Dias,Minne Van Den Noortgate,Emma Sofie Høgsted,Kate Womersley,Angelina Spicer,Prabha S Chandra,Marion Leboyer,Livia De Picker
{"title":"Announcing the Lancet Psychiatry Commission on Women's Mental Health: a new era for mental health.","authors":"Marisa Casanova Dias,Minne Van Den Noortgate,Emma Sofie Høgsted,Kate Womersley,Angelina Spicer,Prabha S Chandra,Marion Leboyer,Livia De Picker","doi":"10.1016/s2215-0366(26)00051-9","DOIUrl":"https://doi.org/10.1016/s2215-0366(26)00051-9","url":null,"abstract":"","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"76 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1016/s2215-0366(25)00396-7
Hamilton Morrin,Luke Nicholls,Michael Levin,Jenny Yiend,Udita Iyengar,Francesca DelGuidice,Sagnik Bhattacharya,Stefania Tognin,James MacCabe,Ricardo Twumasi,Ben Alderson-Day,Thomas A Pollak
Large language models (LLMs) are poised to become a ubiquitous feature of everyday life, mediating communication, decision making, and information curation across nearly every domain. Within psychiatry and psychology, the attention has largely been on bespoke therapeutic applications, sometimes narrowly focused and often diagnostically siloed, rather than on the broader reality that individuals with mental illness will increasingly engage in agential interactions with artificial intelligence (AI) systems. Although the capacity of these systems to model therapeutic dialogue, provide companionship at any hour of the day, and assist with cognitive support has sparked understandable enthusiasm, these same systems might contribute to the onset or exacerbation of psychotic symptoms. Emerging evidence indicates that agential AI might validate or amplify delusional or grandiose content, particularly in users already vulnerable to psychosis, although it is not clear whether these interactions can result in the emergence of de novo psychosis in the absence of pre-existing vulnerability. Some individuals might benefit from AI interactions, for example, where the AI agent functions as a benign and predictable conversational anchor, but there is a growing concern that these agents could reinforce epistemic instability and blur reality boundaries. In this Personal View, we outline the emerging risks, possible mechanisms of delusion co-creation, and safeguarding strategies for agential AI for people with psychotic disorders. We propose a framework of AI-informed care, involving personalised instruction protocols, reflective check-ins, digital advance statements, and escalation safeguards to support epistemic security in vulnerable users. These tools reframe the AI agent as an epistemic ally (as opposed to a therapist or a friend), which functions as a partner in relapse prevention and cognitive containment. Given the rapid adoption of LLMs across all domains of digital life, these protocols must be urgently co-designed with service users and clinicians and tested in clinical trials.
{"title":"Artificial intelligence-associated delusions and large language models: risks, mechanisms of delusion co-creation, and safeguarding strategies.","authors":"Hamilton Morrin,Luke Nicholls,Michael Levin,Jenny Yiend,Udita Iyengar,Francesca DelGuidice,Sagnik Bhattacharya,Stefania Tognin,James MacCabe,Ricardo Twumasi,Ben Alderson-Day,Thomas A Pollak","doi":"10.1016/s2215-0366(25)00396-7","DOIUrl":"https://doi.org/10.1016/s2215-0366(25)00396-7","url":null,"abstract":"Large language models (LLMs) are poised to become a ubiquitous feature of everyday life, mediating communication, decision making, and information curation across nearly every domain. Within psychiatry and psychology, the attention has largely been on bespoke therapeutic applications, sometimes narrowly focused and often diagnostically siloed, rather than on the broader reality that individuals with mental illness will increasingly engage in agential interactions with artificial intelligence (AI) systems. Although the capacity of these systems to model therapeutic dialogue, provide companionship at any hour of the day, and assist with cognitive support has sparked understandable enthusiasm, these same systems might contribute to the onset or exacerbation of psychotic symptoms. Emerging evidence indicates that agential AI might validate or amplify delusional or grandiose content, particularly in users already vulnerable to psychosis, although it is not clear whether these interactions can result in the emergence of de novo psychosis in the absence of pre-existing vulnerability. Some individuals might benefit from AI interactions, for example, where the AI agent functions as a benign and predictable conversational anchor, but there is a growing concern that these agents could reinforce epistemic instability and blur reality boundaries. In this Personal View, we outline the emerging risks, possible mechanisms of delusion co-creation, and safeguarding strategies for agential AI for people with psychotic disorders. We propose a framework of AI-informed care, involving personalised instruction protocols, reflective check-ins, digital advance statements, and escalation safeguards to support epistemic security in vulnerable users. These tools reframe the AI agent as an epistemic ally (as opposed to a therapist or a friend), which functions as a partner in relapse prevention and cognitive containment. Given the rapid adoption of LLMs across all domains of digital life, these protocols must be urgently co-designed with service users and clinicians and tested in clinical trials.","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"14 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1016/s2215-0366(26)00065-9
Gergel T. Lived experience research: recognition of dual expertise. Lancet Psychiatry 2026; published online March 2. https://doi.org/10.1016/S2215-0366(26)00057-X—In this Comment, the first sentence of the sixth paragraph should have read “Dual expertise should be seen as knowledge that can complement, not supplant, other forms of lived experience scholarship”. This correction has been made to the online version as of March 5, 2026, and will be made to the printed version.
{"title":"Correction to Lancet Psychiatry 2026; published online March 2. https://doi.org/10.1016/S2215-0366(26)00057-X","authors":"","doi":"10.1016/s2215-0366(26)00065-9","DOIUrl":"https://doi.org/10.1016/s2215-0366(26)00065-9","url":null,"abstract":"<em>Gergel T. Lived experience research: recognition of dual expertise</em>. Lancet Psychiatry <em>2026; published online March 2. https://doi.org/10.1016/S2215-0366(26)00057-X</em>—In this Comment, the first sentence of the sixth paragraph should have read “Dual expertise should be seen as knowledge that can complement, not supplant, other forms of lived experience scholarship”. This correction has been made to the online version as of March 5, 2026, and will be made to the printed version.","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"52 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-03DOI: 10.1016/s2215-0366(26)00055-6
Zinnatul Borak, Shamsul Haque
No Abstract
没有抽象的
{"title":"Rohingya refugees in Bangladesh should be allowed to work","authors":"Zinnatul Borak, Shamsul Haque","doi":"10.1016/s2215-0366(26)00055-6","DOIUrl":"https://doi.org/10.1016/s2215-0366(26)00055-6","url":null,"abstract":"No Abstract","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"23 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01DOI: 10.1016/S2215-0366(25)00397-9
Amy C Beckenstrom, Michael B Bonsall, Alfred Markham, Owen Slade, Varsha Ramineni, Lalitha Iyadurai, Zunaid Islam, Julie Highfield, Thomas Jaki, Guy M Goodwin, Rebecca Dias, Rebecca Daniels, Asad Malik, Charlotte Summers, Jonathan Kingslake, Emily A Holmes
<p><strong>Background: </strong>Psychological trauma, such as witnessing an untimely or gruesome death, commonly provokes intrusive memories that might persist for days to years with adverse effects on individual mental and physical health and functioning. Despite the global prevalence of trauma, scalable evidence-based interventions are absent. Reducing the impact of intrusive memories is crucial for people frequently exposed to trauma, such as health-care workers. This study aimed to determine whether a brief digital imagery-competing task intervention (ICTI) reduced intrusive memory frequency after 4 weeks. Harms were also assessed.</p><p><strong>Methods: </strong>The GAINS-02 decentralised digital, parallel-group Bayesian adaptive randomised controlled trial tested a brief ICTI against an active control and treatment as usual to determine the effect on reducing intrusive memory frequency. Health-care workers in facilities that admitted patients with COVID-19 during the pandemic, who had experienced one or more traumatic events and reported at least three intrusive memories in the week before screening were randomised 2:2:1 (ICTI to active control to treatment as usual) via block randomisation (web-based). ICTI and active control participants were masked to treatment allocation, and both had one guided session then optional self-use. ICTI involved image-based memory retrieval then Tetris computer gameplay with mental rotation. The active control involved a music-listening task. Study statisticians were masked to ICTI and active control group. The primary outcome was the number of intrusive memories in week 4 (controlling for baseline), which was evaluated on an intention-to-treat basis. Treatment effects for the intervention group versus the comparator groups were assessed using Bayes regression analyses. Harms were assessed through adverse event reporting and interim analyses on primary outcome. People with lived experience were involved from study conception and throughout the research and writing process. The trial was pre-registered at clinicaltrials.gov (NCT05616676) and is completed.</p><p><strong>Findings: </strong>Between Dec 8, 2022, and Sept 15, 2023, 176 participants were screened and 99 included (ICTI n=40, active control n=39, treatment as usual n=20) with mean age 41·2 years (SD 10·2; range 21-62). Of these 99 participants, 85 (86%) self-identified as women and 89 (90%) as White. Bayesian analyses gave robust evidence that ICTI reduced intrusive memories at week 4: ICTI participants reported fewer intrusive memories (median 0·5 [IQR 0·0-5·0]) compared with the active control (active control 5·0 [3·0-11·5]; Bayes factor [BF]<sub>active control>ICTI vs active control=ICTI</sub> 114·1; β<sub>active control>ICTI</sub> 1·29 [95% CrI 0·64-2·00]) and treatment as usual (median 5·0 [IQR 2·5-8·0]; BF<sub>treatment as usual>ICTI vs treatment as usual=ICTI</sub>=15·8; β<sub>treatment as usual>ICTI</sub> 1·21 [95% CrI 0·49-1·98]) groups. No
{"title":"A digital imagery-competing task intervention for stopping intrusive memories in trauma-exposed health-care staff during the COVID-19 pandemic in the UK: a Bayesian adaptive randomised clinical trial.","authors":"Amy C Beckenstrom, Michael B Bonsall, Alfred Markham, Owen Slade, Varsha Ramineni, Lalitha Iyadurai, Zunaid Islam, Julie Highfield, Thomas Jaki, Guy M Goodwin, Rebecca Dias, Rebecca Daniels, Asad Malik, Charlotte Summers, Jonathan Kingslake, Emily A Holmes","doi":"10.1016/S2215-0366(25)00397-9","DOIUrl":"10.1016/S2215-0366(25)00397-9","url":null,"abstract":"<p><strong>Background: </strong>Psychological trauma, such as witnessing an untimely or gruesome death, commonly provokes intrusive memories that might persist for days to years with adverse effects on individual mental and physical health and functioning. Despite the global prevalence of trauma, scalable evidence-based interventions are absent. Reducing the impact of intrusive memories is crucial for people frequently exposed to trauma, such as health-care workers. This study aimed to determine whether a brief digital imagery-competing task intervention (ICTI) reduced intrusive memory frequency after 4 weeks. Harms were also assessed.</p><p><strong>Methods: </strong>The GAINS-02 decentralised digital, parallel-group Bayesian adaptive randomised controlled trial tested a brief ICTI against an active control and treatment as usual to determine the effect on reducing intrusive memory frequency. Health-care workers in facilities that admitted patients with COVID-19 during the pandemic, who had experienced one or more traumatic events and reported at least three intrusive memories in the week before screening were randomised 2:2:1 (ICTI to active control to treatment as usual) via block randomisation (web-based). ICTI and active control participants were masked to treatment allocation, and both had one guided session then optional self-use. ICTI involved image-based memory retrieval then Tetris computer gameplay with mental rotation. The active control involved a music-listening task. Study statisticians were masked to ICTI and active control group. The primary outcome was the number of intrusive memories in week 4 (controlling for baseline), which was evaluated on an intention-to-treat basis. Treatment effects for the intervention group versus the comparator groups were assessed using Bayes regression analyses. Harms were assessed through adverse event reporting and interim analyses on primary outcome. People with lived experience were involved from study conception and throughout the research and writing process. The trial was pre-registered at clinicaltrials.gov (NCT05616676) and is completed.</p><p><strong>Findings: </strong>Between Dec 8, 2022, and Sept 15, 2023, 176 participants were screened and 99 included (ICTI n=40, active control n=39, treatment as usual n=20) with mean age 41·2 years (SD 10·2; range 21-62). Of these 99 participants, 85 (86%) self-identified as women and 89 (90%) as White. Bayesian analyses gave robust evidence that ICTI reduced intrusive memories at week 4: ICTI participants reported fewer intrusive memories (median 0·5 [IQR 0·0-5·0]) compared with the active control (active control 5·0 [3·0-11·5]; Bayes factor [BF]<sub>active control>ICTI vs active control=ICTI</sub> 114·1; β<sub>active control>ICTI</sub> 1·29 [95% CrI 0·64-2·00]) and treatment as usual (median 5·0 [IQR 2·5-8·0]; BF<sub>treatment as usual>ICTI vs treatment as usual=ICTI</sub>=15·8; β<sub>treatment as usual>ICTI</sub> 1·21 [95% CrI 0·49-1·98]) groups. No ","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"13 3","pages":"233-247"},"PeriodicalIF":24.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12916470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：