Bjurner Pontus, Isacsson Nils Hentati, Abdesslem Fehmi Ben, Boman Magnus, Forsell Erik, Kaldo Viktor
Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Health Care Services, Region Stockholm, Sweden.
Department of Computer Science, RISE Research Institutes of Sweden, Stockholm, Sweden.
Internet Interv. 2025 Mar 3;40:100816. doi: 10.1016/j.invent.2025.100816. eCollection 2025 Jun.
Therapist-supported internet-based Cognitive Behavioural Therapy (ICBT) has strong scientific support, but all patients are not helped, and further improvements are needed. Personalized medicine could enhance ICBT. One promising approach uses a Machine learning (ML) based predictive decision support tool (DST) to help therapists identify patients at risk of treatment failure and adjust their treatments accordingly. ICBT is a suitable clinical context for developing and testing such predictive DST's, since its delivery is quite flexible and can quickly be adapted for probable non-responders, for example by increasing the level and nature of therapist support, to avoid treatment failures and improve overall outcomes. This type of strategy has never been tested in a triple-blind randomised controlled trial (RCT) and has rarely been studied in ICBT.The aim of this protocol is to expand on previous registered protocols with more detailed descriptions of methods and analyses before analyses is being conducted.
A triple blind RCT comparing ICBT with a DST (DST condition), to ICBT as usual (TAU condition). The primary objective is to evaluate if the DST condition is superior to the TAU condition in decreasing diagnose-specific symptoms among patients identified to be at risk of failure. Secondary objectives are to evaluate if the DST improves functioning, interaction, adherence, patient satisfaction, and therapist time efficiency and decreases the number of failed treatments. Additionally, we will investigate the therapists' experience of using the DST.Patients and therapists have been recruited nationally. They were randomised and given a sham rationale for the trial to ensure allocation blindness. The total number of patients included was 401, and assessments were administered pre-treatment, weekly during treatment, at post-treatment and at 12-month follow-up. Primary outcome is one of the three diagnosis-specific symptom rating scales for respective treatment and primary analysis is difference in change from pre- to post-treatment for at-risk patients on these scales.
Informed consent to participate in the study was obtained from all participants. Both therapists and patients are participants in this trial. For patients, informed consent to participate in the study was obtained when they registered interest for the study via the study's secure web platform and carried out initial screening before the diagnostic and fit for treatment assessment, they first received the research subject information and were asked for consent by digitally signing that they had read and understood the information. For therapists who were part of the study, consent was requested after they had registered their interest. Therapists then received an email with a link to the study's secure web platform with the research person's information and were asked for consent by digitally signing that they had read and understood the information. All documents are stored in secure, locked filing cabinets on the clinic's premises or on a secure digital consent database.
Approved by the Swedish Ethical Review Authority (SERA), record number 2020-05772.
由治疗师支持的基于互联网的认知行为疗法(ICBT)有强有力的科学依据,但并非所有患者都能从中受益,仍需进一步改进。个性化医疗可能会提升ICBT的效果。一种有前景的方法是使用基于机器学习(ML)的预测性决策支持工具(DST),帮助治疗师识别有治疗失败风险的患者,并据此调整治疗方案。ICBT是开发和测试此类预测性DST的合适临床环境,因为其实施相当灵活,能够迅速针对可能无反应者进行调整,例如增加治疗师支持的程度和性质,以避免治疗失败并改善总体结果。这种策略从未在三盲随机对照试验(RCT)中进行过测试,在ICBT中也很少被研究。本方案的目的是在之前注册的方案基础上进行扩展,在进行分析之前更详细地描述方法和分析过程。
一项三盲RCT,将ICBT与DST(DST组)进行比较,与常规ICBT(常规治疗组)进行比较。主要目标是评估在降低被确定有失败风险的患者的诊断特异性症状方面,DST组是否优于常规治疗组。次要目标是评估DST是否能改善功能、互动、依从性、患者满意度和治疗师的时间效率,并减少治疗失败的次数。此外,我们将调查治疗师使用DST的体验。患者和治疗师已在全国范围内招募。他们被随机分组,并被告知该试验的虚假理由以确保分配的盲法。纳入的患者总数为401名,在治疗前、治疗期间每周、治疗后和12个月随访时进行评估。主要结局是各治疗的三种诊断特异性症状评定量表之一,主要分析是有风险患者在这些量表上从治疗前到治疗后的变化差异。
所有参与者均获得了参与本研究的知情同意。治疗师和患者都是本试验的参与者。对于患者,当他们通过研究的安全网络平台登记对研究的兴趣并在诊断和适合治疗评估之前进行初步筛查时,获得了参与研究的知情同意,他们首先收到研究对象信息,并被要求通过数字签名表示已阅读并理解该信息来给予同意。对于参与研究的治疗师,在他们登记兴趣后被要求给予同意。然后治疗师收到一封带有研究安全网络平台链接以及研究人员信息的电子邮件,并被要求通过数字签名表示已阅读并理解该信息来给予同意。所有文件都存储在诊所场所的安全锁柜中或安全的数字同意数据库中。
经瑞典伦理审查局(SERA)批准,记录编号2020 - 05772。