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Fairly evaluating the performance of normative models 公平评估规范模型的性能。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 10.1016/S2589-7500(24)00200-0
Andre Marquand , Saige Rutherford , Richard Dinga
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引用次数: 0
Fairly evaluating the performance of normative models – Authors' reply 公平评估规范模型的性能 - 作者的答复。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 10.1016/S2589-7500(24)00199-7
Ruiyang Ge , Yuetong Yu , Denghuang Zhan , Sophia Frangou
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引用次数: 0
Lifting the veil on health datasets 揭开健康数据集的面纱。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 10.1016/S2589-7500(24)00221-8
The Lancet Digital Health
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引用次数: 0
Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge 在深度学习辅助的泛癌症腹部器官量化中释放无标记数据的优势:FLARE22 挑战赛。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 10.1016/S2589-7500(24)00154-7
Jun Ma PhD , Yao Zhang PhD , Song Gu MSc , Cheng Ge MSc , Shihao Mae BSc , Adamo Young MSc , Cheng Zhu PhD , Prof Xin Yang PhD , Prof Kangkang Meng PhD , Ziyan Huang BSc , Fan Zhang MSc , Yuanke Pan MSc , Shoujin Huang BSc , Jiacheng Wang PhD , Mingze Sun PhD , Prof Rongguo Zhang PhD , Dengqiang Jia PhD , Jae Won Choi MD , Natália Alves MSc , Bram de Wilde PhD , Prof Bo Wang PhD
Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To address these limitations, we organised the FLARE 2022 challenge to benchmark fast, low-resource, and accurate abdominal organ segmentation algorithms. We first constructed an intercontinental abdomen CT dataset from more than 50 clinical research groups. We then independently validated that deep learning algorithms achieved a median dice similarity coefficient (DSC) of 90·0% (IQR 87·4–91·3%) by use of 50 labelled images and 2000 unlabelled images, which can substantially reduce manual annotation costs. The best-performing algorithms successfully generalised to holdout external validation sets, achieving a median DSC of 89·4% (85·2–91·3%), 90·0% (84·3–93·0%), and 88·5% (80·9–91·9%) on North American, European, and Asian cohorts, respectively. These algorithms show the potential to use unlabelled data to boost performance and alleviate annotation shortages for modern artificial intelligence models.
深度学习在腹部器官自动分割和量化方面显示出巨大的潜力。然而,大多数现有算法都依赖于专家注释,并没有在真实世界的多国环境中进行全面评估。为了解决这些局限性,我们组织了 FLARE 2022 挑战赛,以对快速、低资源和准确的腹部器官分割算法进行基准测试。我们首先构建了一个来自 50 多个临床研究小组的洲际腹部 CT 数据集。然后,我们独立验证了深度学习算法通过使用 50 张标注图像和 2000 张未标注图像,达到了 90-0%(IQR 87-4-91-3%)的中位数骰子相似系数(DSC),这可以大大降低人工标注成本。表现最好的算法成功地推广到了外部验证集,在北美、欧洲和亚洲队列中的 DSC 中值分别达到了 89-4%(85-2-91-3%)、90-0%(84-3-93-0%)和 88-5%(80-9-91-9%)。这些算法显示了使用无标签数据提高性能和缓解现代人工智能模型注释不足的潜力。
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引用次数: 0
Effect of the i2TransHealth e-health intervention on psychological distress among transgender and gender diverse adults from remote areas in Germany: a randomised controlled trial. i2TransHealth 电子健康干预对德国偏远地区变性和性别多元化成年人心理困扰的影响:随机对照试验。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-16 DOI: 10.1016/S2589-7500(24)00192-4
Timo O Nieder, Janis Renner, Susanne Sehner, Amra Pepić, Antonia Zapf, Martin Lambert, Peer Briken, Arne Dekker
<p><strong>Background: </strong>Transgender and gender diverse (TGD) people in remote areas face challenges accessing health-care services, including mental health care and gender-affirming medical treatment, which can be associated with psychological distress. In this study, we aimed to evaluate the effectiveness of a 4-month TGD-informed e-health intervention to improve psychological distress among TGD people from remote areas in northern Germany.</p><p><strong>Methods: </strong>In a randomised controlled trial done at a single centre in Germany, adults (aged ≥18 years) who met criteria for gender incongruence or gender dysphoria and who lived at least 50 km outside of Hamburg in one of the northern German federal states were recruited and randomly assigned (1:1) to i<sup>2</sup>TransHealth intervention or a wait list control group. Randomisation was performed with the use of a computer-based code. Due to the nature of the intervention, study participants and clinical staff were aware of treatment allocation, but researchers responsible for data analysis were masked to allocation groups. Study participants in the intervention group (service users) started the i<sup>2</sup>TransHealth intervention immediately after completing the baseline survey after enrolment. Participants assigned to the control group waited 4 months before they were able to access i<sup>2</sup>TransHealth services or regular care. The primary outcome was difference in the Brief Symptom Inventory (BSI)-18 summary score between baseline and 4 months, assessed using a linear model analysis. The primary outcome was assessed in the intention-to-treat (ITT) population, which included all randomly assigned participants. The trial was registered with ClinicalTrials.gov, NCT04290286.</p><p><strong>Findings: </strong>Between May 12, 2020, and May 2, 2022, 177 TGD people were assessed for eligibility, of whom 174 were included in the ITT population (n=90 in the intervention group, n=84 in the control group). Six participants did not provide data for the primary outcome at 4 months, and thus 168 people were included in the analysis population (88 participants in the intervention group and 80 participants in the control group). At 4 months, in the intervention group, the adjusted mean change in BSI-18 from baseline was -0·65 (95% CI -2·25 to 0·96; p=0·43) compared with 2·34 (0·65 to 4·02; p=0·0069) in the control group. Linear model analysis identified a significant difference at 4 months between the groups with regard to change in BSI-18 summary scores from baseline (between-group difference -2·98 [95% CI -5·31 to -0·65]; p=0·012). Adverse events were rare: there were two suicide attempts and one participant was admitted to hospital in the intervention group, and in the control group, there was one case of self-harm and one case of self-harm followed by hospital admission.</p><p><strong>Interpretation: </strong>The intervention was clinically significant in averting worsening psychologi
背景:偏远地区的变性者和性别多元化者(TGD)在获得医疗保健服务(包括心理保健和性别确认医疗)方面面临挑战,这可能与心理困扰有关。在这项研究中,我们旨在评估为期 4 个月的以 TGD 为基础的电子健康干预对改善德国北部偏远地区 TGD 患者心理困扰的有效性:在德国的一个单一中心进行的随机对照试验中,我们招募了符合性别不协调或性别焦虑标准的成年人(年龄≥18 岁),他们居住在德国北部联邦州之一、汉堡以外至少 50 公里的地方,并随机分配(1:1)到 i2TransHealth 干预组或候补对照组。随机分配是通过计算机代码进行的。由于干预措施的性质,研究参与者和临床工作人员都知道治疗分配,但负责数据分析的研究人员对分配组别进行了屏蔽。干预组的研究参与者(服务使用者)在完成注册后的基线调查后,立即开始接受 i2TransHealth 干预。被分配到对照组的参与者则要等待 4 个月后才能获得 i2TransHealth 服务或常规护理。主要结果是基线和 4 个月之间简短症状量表 (BSI)-18 总分的差异,采用线性模型分析法进行评估。主要结果在意向治疗(ITT)人群中进行评估,意向治疗人群包括所有随机分配的参与者。该试验已在ClinicalTrials.gov注册,编号为NCT04290286.研究结果:2020年5月12日至2022年5月2日期间,共有177名TGD患者接受了资格评估,其中174人被纳入ITT人群(干预组90人,对照组84人)。有 6 名参与者没有提供 4 个月时的主要结果数据,因此有 168 人被纳入分析人群(干预组 88 人,对照组 80 人)。4个月时,干预组的BSI-18与基线相比的调整后平均变化为-0-65(95% CI -2-25至0-96;p=0-43),而对照组为2-34(0-65至4-02;p=0-0069)。线性模型分析表明,4个月后,两组间的BSI-18总评分与基线相比有显著差异(组间差异为-2-98 [95% CI -5-31至-0-65];P=0-012)。不良事件很少发生:干预组有两例自杀未遂,一例入院治疗;对照组有一例自残,一例自残后入院治疗:干预在避免服务对象心理压力恶化方面具有重要临床意义,其效果优于等待名单对照组。这些研究结果支持电子健康服务在TGD医疗保健中的有效性,特别是对偏远地区人群的有效性:资金来源:联邦联合委员会创新委员会:摘要德文译文见补充材料部分。
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引用次数: 0
Correction to Lancet Digit Health 2024; published online Sept 17. https://doi.org/10.1016/S2589-7500(24)00143-2 https://doi.org/10.1016/S2589-7500(24)00143-2.
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-09 DOI: 10.1016/S2589-7500(24)00220-6
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引用次数: 0
Combating medical misinformation and rebuilding trust in the USA 在美国打击医疗误导,重建信任。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-07 DOI: 10.1016/S2589-7500(24)00197-3
Clara E Tandar , John C Lin , Fatima Cody Stanford
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引用次数: 0
Emotional competence self-help mobile phone app versus cognitive behavioural self-help app versus self-monitoring app to promote mental wellbeing in healthy young adults (ECoWeB PROMOTE): an international, multicentre, parallel, open-label, randomised controlled trial. 情绪能力自助手机应用与认知行为自助应用和自我监控应用对比,以促进健康年轻人的心理健康(ECoWeB PROMOTE):一项国际、多中心、平行、开放标签、随机对照试验。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-04 DOI: 10.1016/S2589-7500(24)00149-3
Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor
<p><strong>Background: </strong>Based on evidence that mental health is more than an absence of mental disorders, there have been calls to find ways to promote flourishing at a population level, especially in young people, which requires effective and scalable interventions. Despite their potential for scalability, few mental wellbeing apps have been rigorously tested in high-powered trials, derived from models of healthy emotional functioning, or tailored to individual profiles. We aimed to test a personalised emotional competence self-help app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to promote mental wellbeing in healthy young people.</p><p><strong>Methods: </strong>This international, multicentre, parallel, open-label, randomised controlled trial within a cohort multiple randomised trial (including a parallel trial of depression prevention) was done at four university trial sites in four countries (the UK, Germany, Spain, and Belgium). Participants were recruited from schools and universities and via social media from the four respective countries. Eligible participants were aged 16-22 years with well adjusted emotional competence profiles and no current or past diagnosis of major depression. Participants were randomised (1:1:1) to usual practice plus either the emotional competence app, the CBT app or the self-monitoring app, by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. The primary outcome was mental wellbeing (indexed by the Warwick-Edinburgh Mental Well Being Scale [WEMWBS]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. Outcome assessors were masked to group allocation. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.</p><p><strong>Findings: </strong>Between Oct 15, 2020, and Aug 3, 2021, 2532 participants were enrolled, and 847 were randomly assigned to the emotional competence app, 841 to the CBT app, and 844 to the self-monitoring app. Mean age was 19·2 years (SD 1·8). Of 2532 participants self-reporting gender, 1896 (74·9%) were female, 613 (24·2%) were male, 16 (0·6%) were neither, and seven (0·3%) were both. 425 participants in the emotional competence app group, 443 in the CT app group, and 447 in the self-monitoring app group completed the follow-up assessment at 3 months. There was no difference in mental wellbeing between the groups at 3 months (global p=0·47). The emotional competence app did not differ from the CBT app (mean difference in WEMWBS -0·21 [95% CI -1·08 to 0·66]) or the self-monitoring app (0·32 [-0·54 to 1·19]) and the CBT app did not differ from the self-monitoring app (0·53 [-0·33 to 1·39]). 14 of 1315 participants were admitted to or treated in hospital (or both) for mental health-related reasons, which were considered unrelated to the interventions (five participants in the emotional competence
背景:有证据表明,心理健康不仅仅是没有精神障碍,因此,人们一直呼吁找到促进人群(尤其是年轻人)心理健康的方法,这需要有效且可扩展的干预措施。尽管心理健康应用程序具有可扩展性的潜力,但很少有心理健康应用程序经过高功率试验的严格测试,这些应用程序源自健康的情绪功能模型,或根据个人情况量身定制。我们旨在测试个性化情绪能力自助应用程序与认知行为疗法(CBT)自助应用程序和自我监控应用程序的对比,以促进健康年轻人的心理健康:在四个国家(英国、德国、西班牙和比利时)的四所大学的试验点进行了这项国际多中心、平行、开放标签、随机对照试验(包括一项预防抑郁症的平行试验)。参与者从四个国家的学校、大学以及社交媒体招募。符合条件的参与者年龄在 16-22 岁之间,具有良好的情绪能力,目前或过去未被诊断出患有重度抑郁症。参与者通过一个独立的计算机系统被随机分配(1:1:1)到通常做法加情绪能力应用程序、CBT 应用程序或自我监控应用程序,最小化国家、年龄和自我报告的性别,并在随机分配后随访 12 个月。主要结果是3个月随访时的心理健康(以沃里克-爱丁堡心理健康量表[WEMWBS]为指标),对完成3个月随访评估的参与者进行分析。结果评估人员对组别分配进行了屏蔽。该研究已在 ClinicalTrials.gov 注册,编号为 NCT04148508,现已结束:2020年10月15日至2021年8月3日期间,共有2532名参与者注册,其中847人被随机分配到情绪能力应用程序,841人被随机分配到CBT应用程序,844人被随机分配到自我监控应用程序。平均年龄为 19-2 岁(SD 1-8)。在 2532 名自我报告性别的参与者中,1896 人(74-9%)为女性,613 人(24-2%)为男性,16 人(0-6%)两者都不是,7 人(0-3%)两者都是。情绪能力应用程序组有 425 人、CT 应用程序组有 443 人、自我监控应用程序组有 447 人完成了 3 个月的跟踪评估。在 3 个月时,各组之间的心理健康状况没有差异(总体 p=0-47)。情绪能力应用程序与 CBT 应用程序(WEMWBS 平均差异-0-21 [95% CI -1-08 to 0-66])或自我监控应用程序(0-32 [-0-54 to 1-19])无差异,CBT 应用程序与自我监控应用程序(0-53 [-0-33 to 1-39])无差异。1315名参与者中有14人因精神健康相关原因入院或住院治疗(或两者兼有),这些原因被认为与干预措施无关(情绪能力应用程序组5人,CBT应用程序组8人,自我监控应用程序组1人)。没有人死亡:解释:情绪能力应用程序和 CBT 应用程序在促进健康青少年心理健康方面的益处有限。这一发现可能反映了这些干预措施的强度较低,以及在低风险人群中通过普及数字干预措施改善心理健康的难度:欧盟委员会。
{"title":"Emotional competence self-help mobile phone app versus cognitive behavioural self-help app versus self-monitoring app to promote mental wellbeing in healthy young adults (ECoWeB PROMOTE): an international, multicentre, parallel, open-label, randomised controlled trial.","authors":"Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor","doi":"10.1016/S2589-7500(24)00149-3","DOIUrl":"10.1016/S2589-7500(24)00149-3","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Based on evidence that mental health is more than an absence of mental disorders, there have been calls to find ways to promote flourishing at a population level, especially in young people, which requires effective and scalable interventions. Despite their potential for scalability, few mental wellbeing apps have been rigorously tested in high-powered trials, derived from models of healthy emotional functioning, or tailored to individual profiles. We aimed to test a personalised emotional competence self-help app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to promote mental wellbeing in healthy young people.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This international, multicentre, parallel, open-label, randomised controlled trial within a cohort multiple randomised trial (including a parallel trial of depression prevention) was done at four university trial sites in four countries (the UK, Germany, Spain, and Belgium). Participants were recruited from schools and universities and via social media from the four respective countries. Eligible participants were aged 16-22 years with well adjusted emotional competence profiles and no current or past diagnosis of major depression. Participants were randomised (1:1:1) to usual practice plus either the emotional competence app, the CBT app or the self-monitoring app, by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. The primary outcome was mental wellbeing (indexed by the Warwick-Edinburgh Mental Well Being Scale [WEMWBS]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. Outcome assessors were masked to group allocation. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Between Oct 15, 2020, and Aug 3, 2021, 2532 participants were enrolled, and 847 were randomly assigned to the emotional competence app, 841 to the CBT app, and 844 to the self-monitoring app. Mean age was 19·2 years (SD 1·8). Of 2532 participants self-reporting gender, 1896 (74·9%) were female, 613 (24·2%) were male, 16 (0·6%) were neither, and seven (0·3%) were both. 425 participants in the emotional competence app group, 443 in the CT app group, and 447 in the self-monitoring app group completed the follow-up assessment at 3 months. There was no difference in mental wellbeing between the groups at 3 months (global p=0·47). The emotional competence app did not differ from the CBT app (mean difference in WEMWBS -0·21 [95% CI -1·08 to 0·66]) or the self-monitoring app (0·32 [-0·54 to 1·19]) and the CBT app did not differ from the self-monitoring app (0·53 [-0·33 to 1·39]). 14 of 1315 participants were admitted to or treated in hospital (or both) for mental health-related reasons, which were considered unrelated to the interventions (five participants in the emotional competence","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378382","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}
引用次数: 0
Emotional competence self-help app versus cognitive behavioural self-help app versus self-monitoring app to prevent depression in young adults with elevated risk (ECoWeB PREVENT): an international, multicentre, parallel, open-label, randomised controlled trial. 情绪能力自助应用程序与认知行为自助应用程序和自我监控应用程序对比,以预防风险较高的年轻人患抑郁症(ECoWeB PREVENT):一项国际、多中心、平行、开放标签、随机对照试验。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-04 DOI: 10.1016/S2589-7500(24)00148-1
Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor
<p><strong>Background: </strong>Effective, scalable interventions are needed to prevent poor mental health in young people. Although mental health apps can provide scalable prevention, few have been rigorously tested in high-powered trials built on models of healthy emotional functioning or tailored to individual profiles. We aimed to test a personalised emotional competence app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to prevent an increase in depression symptoms in young people.</p><p><strong>Methods: </strong>This multicentre, parallel, open-label, randomised controlled trial, within a cohort multiple randomised trial (including a parallel trial of wellbeing promotion) was done at four university trial sites in the UK, Germany, Spain, and Belgium. Participants were recruited from schools, universities, and social media from the four respective countries. Eligible participants were aged 16-22 years with increased vulnerability indexed by baseline emotional competence profile, without current or past diagnosis of major depression. Participants were randomly assigned (1:1:1) to usual practice plus either the personalised emotional competence self-help app, the generic CBT self-help app, or the self-monitoring app by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. Outcome assessors were masked to group allocation. The primary outcome was depression symptoms (according to Patient Health Questionnaire-9 [PHQ-9]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.</p><p><strong>Findings: </strong>Between Oct 15, 2020, and Aug 3, 2021, 1262 participants were enrolled, including 417 to the emotional competence app, 423 to the CBT app, and 422 to the self-monitoring app. Mean age was 18·8 years (SD 2·0). Of 1262 participants self-reporting gender, 984 (78·0%) were female, 253 (20·0%) were male, 15 (1·2%) were neither, and ten (0·8%) were both. 178 participants in the emotional competence app group, 191 in the CBT app group, and 199 in the self-monitoring app group completed the follow-up assessment at 3 months. At 3 months, depression symptoms were lower with the CBT app than the self-monitoring app (mean difference in PHQ-9 -1·18 [95% CI -2·01 to -0·34]; p=0·006), but depression symptoms did not differ between the emotional competence app and the CBT app (0·63 [-0·22 to 1·49]; p=0·15) or the self-monitoring app and emotional competence app (-0·54 [-1·39 to 0·31]; p=0·21). 31 of the 541 participants who completed any of the follow-up assessments received treatment in hospital or were admitted to hospital for mental health-related reasons considered unrelated to interventions (eight in the emotional competence app group, 15 in the CBT app group, and eight in the self-monitoring app group). No deaths o
背景:需要有效的、可扩展的干预措施来预防青少年的不良心理健康。虽然心理健康应用程序可以提供可扩展的预防措施,但很少有应用程序在建立在健康情绪功能模型基础上或根据个人情况量身定制的高功率试验中接受过严格测试。我们的目标是测试个性化情绪能力应用程序与认知行为疗法(CBT)自助应用程序和自我监控应用程序的对比,以防止青少年抑郁症状的增加:这项多中心、平行、开放标签、随机对照试验是在英国、德国、西班牙和比利时的四个大学试验点进行的,属于队列多重随机试验(包括一项促进健康的平行试验)的一部分。参与者分别从四个国家的学校、大学和社交媒体招募。符合条件的参与者年龄在 16-22 岁之间,根据基线情绪能力档案,他们的脆弱性有所提高,但目前或过去未被诊断出患有重度抑郁症。参与者通过一个独立的计算机系统被随机分配(1:1:1)到常规实践加个性化情绪能力自助应用程序、通用 CBT 自助应用程序或自我监控应用程序中,最小化国家、年龄和自我报告的性别,并在随机分配后随访 12 个月。结果评估人员对组别分配进行了屏蔽。主要结果是随访3个月时的抑郁症状(根据患者健康问卷-9 [PHQ-9]),对完成3个月随访评估的参与者进行分析。该研究已在 ClinicalTrials.gov 注册,编号为 NCT04148508,现已结束:2020年10月15日至2021年8月3日期间,共有1262名参与者注册,其中417人使用情绪能力应用程序,423人使用CBT应用程序,422人使用自我监控应用程序。平均年龄为 18-8 岁(SD 2-0)。在 1262 名自我报告性别的参与者中,984 人(78-0%)为女性,253 人(20-0%)为男性,15 人(1-2%)两者都不是,10 人(0-8%)两者都是。情绪能力应用程序组的 178 名参与者、CBT 应用程序组的 191 名参与者和自我监控应用程序组的 199 名参与者完成了 3 个月的跟踪评估。3 个月时,CBT 应用程序的抑郁症状低于自我监控应用程序(PHQ-9 的平均差异为 -1-18 [95% CI -2-01 to -0-34];p=0-006),但情绪能力应用程序与 CBT 应用程序(0-63 [-0-22 to 1-49];p=0-15)或自我监控应用程序与情绪能力应用程序(-0-54 [-1-39 to 0-31];p=0-21)之间的抑郁症状没有差异。在完成任何一项后续评估的 541 名参与者中,有 31 人接受了住院治疗,或因与干预无关的精神健康相关原因入院治疗(情绪能力应用程序组 8 人,CBT 应用程序组 15 人,自我监控应用程序组 8 人)。没有人死亡:与自我监控应用程序相比,CBT 应用程序延迟了高危青少年抑郁症状的增加,尽管这种益处在 12 个月后逐渐消失。与假设相反,情绪能力应用程序在减少抑郁症状方面并不比自我监控应用程序更有效。鉴于CBT自助应用程序的可扩展性、非消耗性和可负担性,它可能是针对年轻人的有价值的公共心理健康干预措施:欧盟委员会。
{"title":"Emotional competence self-help app versus cognitive behavioural self-help app versus self-monitoring app to prevent depression in young adults with elevated risk (ECoWeB PREVENT): an international, multicentre, parallel, open-label, randomised controlled trial.","authors":"Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor","doi":"10.1016/S2589-7500(24)00148-1","DOIUrl":"10.1016/S2589-7500(24)00148-1","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Effective, scalable interventions are needed to prevent poor mental health in young people. Although mental health apps can provide scalable prevention, few have been rigorously tested in high-powered trials built on models of healthy emotional functioning or tailored to individual profiles. We aimed to test a personalised emotional competence app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to prevent an increase in depression symptoms in young people.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This multicentre, parallel, open-label, randomised controlled trial, within a cohort multiple randomised trial (including a parallel trial of wellbeing promotion) was done at four university trial sites in the UK, Germany, Spain, and Belgium. Participants were recruited from schools, universities, and social media from the four respective countries. Eligible participants were aged 16-22 years with increased vulnerability indexed by baseline emotional competence profile, without current or past diagnosis of major depression. Participants were randomly assigned (1:1:1) to usual practice plus either the personalised emotional competence self-help app, the generic CBT self-help app, or the self-monitoring app by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. Outcome assessors were masked to group allocation. The primary outcome was depression symptoms (according to Patient Health Questionnaire-9 [PHQ-9]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Between Oct 15, 2020, and Aug 3, 2021, 1262 participants were enrolled, including 417 to the emotional competence app, 423 to the CBT app, and 422 to the self-monitoring app. Mean age was 18·8 years (SD 2·0). Of 1262 participants self-reporting gender, 984 (78·0%) were female, 253 (20·0%) were male, 15 (1·2%) were neither, and ten (0·8%) were both. 178 participants in the emotional competence app group, 191 in the CBT app group, and 199 in the self-monitoring app group completed the follow-up assessment at 3 months. At 3 months, depression symptoms were lower with the CBT app than the self-monitoring app (mean difference in PHQ-9 -1·18 [95% CI -2·01 to -0·34]; p=0·006), but depression symptoms did not differ between the emotional competence app and the CBT app (0·63 [-0·22 to 1·49]; p=0·15) or the self-monitoring app and emotional competence app (-0·54 [-1·39 to 0·31]; p=0·21). 31 of the 541 participants who completed any of the follow-up assessments received treatment in hospital or were admitted to hospital for mental health-related reasons considered unrelated to interventions (eight in the emotional competence app group, 15 in the CBT app group, and eight in the self-monitoring app group). No deaths o","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378381","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}
引用次数: 0
Demographic reporting in biosignal datasets: a comprehensive analysis of the PhysioNet open access database 生物信号数据集的人口统计学报告:对开放存取的 PhysioNet 数据库的综合分析。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-01 DOI: 10.1016/S2589-7500(24)00170-5
Sarah Jiang , Perisa Ashar , Md Mobashir Hasan Shandhi , Jessilyn Dunn
The PhysioNet open access database (PND) is one of the world's largest and most comprehensive repositories of biosignal data and is widely used by researchers to develop, train, and validate algorithms. To contextualise the results of such algorithms, understanding the underlying demographic distribution of the data is crucial—specifically, the race, ethnicity, sex or gender, and age of study participants. We sought to understand the underlying reporting patterns and characteristics of the demographic data of the datasets available on PND. Of the 181 unique datasets present in the PND as of July 6, 2023, 175 involved human participants, with less than 7% of studies reporting on all four of the key demographic variables. Furthermore, we found a higher rate of reporting sex or gender and age than race and ethnicity. In the studies that did include participant sex or gender, the samples were mostly male. Additionally, we found that most studies were done in North America, particularly in the USA. These imbalances and poor reporting of representation raise concerns regarding potential embedded biases in the algorithms that rely on these datasets. They also underscore the need for universal and comprehensive reporting practices to ensure equitable development and deployment of artificial intelligence and machine learning tools in medicine.
PhysioNet 开放存取数据库 (PND) 是世界上最大、最全面的生物信号数据存储库之一,被研究人员广泛用于开发、训练和验证算法。要使这些算法的结果符合实际情况,了解数据的基本人口分布至关重要,特别是研究参与者的种族、民族、性别和年龄。我们试图了解 PND 数据集人口统计数据的基本报告模式和特征。截至 2023 年 7 月 6 日,PND 上有 181 个独特的数据集,其中 175 个涉及人类参与者,只有不到 7% 的研究报告了所有四个关键人口统计学变量。此外,我们发现报告性别和年龄的比例高于报告种族和民族的比例。在包含参与者性别的研究中,样本大多为男性。此外,我们发现大多数研究都是在北美进行的,尤其是美国。这些不平衡和代表性报告的不足引起了人们对依赖于这些数据集的算法中潜在的嵌入式偏见的担忧。它们还强调了普遍和全面报告实践的必要性,以确保医学中人工智能和机器学习工具的公平开发和部署。
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Lancet Digital Health
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