Study protocol for a triple-blind randomised controlled trial evaluating a machine learning-based predictive clinical decision support tool for internet-delivered cognitive behaviour therapy (ICBT) for depression and anxiety

IF 3.6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Pontus Bjurner , Nils Hentati Isacsson , Fehmi Ben Abdesslem , Magnus Boman , Erik Forsell , Viktor Kaldo
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引用次数: 0

Abstract

Introduction

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.

Methods and analysis

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.

Human ethics and consent to participate

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.

Approval committee

Approved by the Swedish Ethical Review Authority (SERA), record number 2020–05772.
一项三盲随机对照试验的研究方案,评估基于机器学习的预测临床决策支持工具,用于互联网提供的抑郁症和焦虑症认知行为疗法(ICBT)
治疗师支持的基于互联网的认知行为疗法(ICBT)有强有力的科学支持,但并不是所有的患者都得到了帮助,需要进一步的改进。个性化医疗可以增强ICBT。一种很有前景的方法是使用基于机器学习(ML)的预测决策支持工具(DST)来帮助治疗师识别有治疗失败风险的患者,并相应地调整治疗方法。ICBT是开发和测试这种预测性DST的合适临床环境,因为它的交付非常灵活,可以快速适应可能无反应的患者,例如通过提高治疗师支持的水平和性质,以避免治疗失败并改善总体结果。这种类型的策略从未在三盲随机对照试验(RCT)中进行过测试,也很少在ICBT中进行研究。本议定书的目的是在先前已注册的议定书的基础上,在进行分析之前对方法和分析进行更详细的描述。方法和分析采用三盲随机对照试验,比较ICBT与DST (DST状态)和常规ICBT (TAU状态)。主要目的是评估在确定有失败风险的患者中,DST条件在减少诊断特异性症状方面是否优于TAU条件。次要目标是评估DST是否能改善功能、相互作用、依从性、患者满意度和治疗师时间效率,并减少治疗失败的数量。此外,我们将调查治疗师使用DST的经验。患者和治疗师在全国范围内招募。他们是随机分配的,并给出了一个虚假的试验理由,以确保分配盲性。纳入的患者总数为401例,并在治疗前、治疗期间、治疗后和12个月随访时进行评估。主要转归是针对各自治疗的三个诊断特异性症状评定量表之一,主要分析是高危患者在这些量表上治疗前后的变化差异。人类伦理和参与者的同意所有参与者都获得了参与研究的知情同意。治疗师和患者都是这项试验的参与者。对于患者,当他们通过研究的安全网络平台注册对研究感兴趣,并在诊断和适合治疗评估前进行初步筛选时,即获得参与研究的知情同意,他们首先收到研究受试者信息,并通过数字签名要求他们同意阅读并理解信息。对于参与研究的治疗师,在他们登记了他们的兴趣后,他们被要求同意。然后,治疗师会收到一封电子邮件,其中有研究人员信息的安全网络平台的链接,并通过数字签名要求他们同意阅读并理解这些信息。所有文件都存储在诊所场所的安全、上锁的文件柜中,或者存储在安全的数字同意数据库中。经瑞典伦理审查局(SERA)批准,备案号2020-05772。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
9.30%
发文量
94
审稿时长
6 weeks
期刊介绍: Official Journal of the European Society for Research on Internet Interventions (ESRII) and the International Society for Research on Internet Interventions (ISRII). The aim of Internet Interventions is to publish scientific, peer-reviewed, high-impact research on Internet interventions and related areas. Internet Interventions welcomes papers on the following subjects: • Intervention studies targeting the promotion of mental health and featuring the Internet and/or technologies using the Internet as an underlying technology, e.g. computers, smartphone devices, tablets, sensors • Implementation and dissemination of Internet interventions • Integration of Internet interventions into existing systems of care • Descriptions of development and deployment infrastructures • Internet intervention methodology and theory papers • Internet-based epidemiology • Descriptions of new Internet-based technologies and experiments with clinical applications • Economics of internet interventions (cost-effectiveness) • Health care policy and Internet interventions • The role of culture in Internet intervention • Internet psychometrics • Ethical issues pertaining to Internet interventions and measurements • Human-computer interaction and usability research with clinical implications • Systematic reviews and meta-analysis on Internet interventions
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