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针对抑郁和焦虑的基于互联网的认知行为疗法反应的人口统计学和临床预测因素。

Demographic and clinical predictors of response to internet-enabled cognitive-behavioural therapy for depression and anxiety.

作者信息

Catarino Ana, Bateup Sarah, Tablan Valentin, Innes Katherine, Freer Stephen, Richards Andy, Stott Richard, Hollon Steven D, Chamberlain Samuel Robin, Hayes Ann, Blackwell Andrew D

机构信息

Senior Scientist, Clinical Science Laboratory at Ieso, Ieso Digital Health, UK.

Chief Clinical Officer, Clinical Science Laboratory at Ieso, Ieso Digital Health, UK.

出版信息

BJPsych Open. 2018 Oct 2;4(5):411-418. doi: 10.1192/bjo.2018.57. eCollection 2018 Sep.

Abstract

BACKGROUND

Common mental health problems affect a quarter of the population. Online cognitive-behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear.

AIMS

This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet.

METHOD

Real-world clinical outcomes data were collected from 2211 NHS England patients completing a course of CBT delivered by a trained clinician via the internet. Logistic regression analyses were performed using patient and service variables to identify significant predictors of response to treatment.

RESULTS

Multiple patient variables were significantly associated with positive response to treatment including older age, absence of long-term physical comorbidities and lower symptom severity at start of treatment. Service variables associated with positive response to treatment included shorter waiting times for initial assessment and longer treatment durations in terms of the number of sessions.

CONCLUSIONS

Knowledge of which patient and service variables are associated with good clinical outcomes can be used to develop personalised treatment programmes, as part of a quality improvement cycle aiming to drive up standards in mental healthcare. This study exemplifies translational research put into practice and deployed at scale in the National Health Service, demonstrating the value of technology-enabled treatment delivery not only in facilitating access to care, but in enabling accelerated data capture for clinical research purposes.

DECLARATION OF INTEREST

A.C., S.B., V.T., K.I., S.F., A.R., A.H. and A.D.B. are employees or board members of the sponsor. S.R.C. consults for Cambridge Cognition and Shire. Keywords: Anxiety disorders; cognitive behavioural therapies; depressive disorders; individual psychotherapy.

摘要

背景

常见心理健康问题影响着四分之一的人口。在线认知行为疗法(CBT)的使用日益广泛,但调节对这种治疗方式反应的因素仍不明确。

目的

本研究旨在探讨通过互联网提供的一对一CBT治疗反应的人口统计学和临床预测因素。

方法

从2211名完成由经过培训的临床医生通过互联网提供的CBT疗程的英国国民保健服务(NHS)患者中收集真实世界的临床结果数据。使用患者和服务变量进行逻辑回归分析,以确定治疗反应的显著预测因素。

结果

多个患者变量与治疗的积极反应显著相关,包括年龄较大、无长期身体合并症以及治疗开始时症状严重程度较低。与治疗积极反应相关的服务变量包括初始评估等待时间较短以及就疗程数量而言治疗持续时间较长。

结论

了解哪些患者和服务变量与良好的临床结果相关,可用于制定个性化治疗方案,作为旨在提高精神卫生保健标准的质量改进周期的一部分。本研究例证了转化研究在实践中的应用并在国家卫生服务体系中大规模部署,证明了技术支持的治疗方式不仅在促进获得护理方面有价值,而且在为临床研究目的加速数据采集方面也有价值。

利益声明

A.C.、S.B.、V.T.、K.I.、S.F.、A.R.、A.H.和A.D.B.是赞助商的员工或董事会成员。S.R.C.为剑桥认知公司和夏尔公司提供咨询服务。关键词:焦虑症;认知行为疗法;抑郁症;个体心理治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0dfd/6171334/87d3badf4db5/S2056472418000571_fig1.jpg

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