Background

Depression is one of the most disabling disorders worldwide, yet it often remains undetected. One promising approach to address both early detection and disease burden is depression screening followed by direct feedback to patients. Evidence suggests that individuals often seek information regarding mental health on the internet. Thus, internet-based screening with automated feedback has great potential to address individuals with undetected depression.

Objectives

To determine whether automated feedback after internet-based depression screening reduces depression severity as compared to no feedback.

Methods

The internet-based, observer-blinded DISCOVER RCT aims to recruit a total of 1074 individuals. Participants will be screened for depression using the Patient Health Questionnaire (PHQ-9). In case of a positive screening result (PHQ-9 ≥ 10), participants with undetected depression will be randomised into one of three balanced study arms to receive either (a) no feedback (control arm), (b) standard feedback, or (c) tailored feedback on their screening result. The tailored feedback version will be adapted to participants' characteristics, i.e. symptom profile, preferences, and demographic characteristics. The primary hypothesis is that feedback reduces depression severity six months after screening compared to no feedback. The secondary hypothesis is that tailored feedback is more efficacious compared to standard feedback. Further outcomes are depression care, help-seeking behaviour, health-related quality of life, anxiety, somatic symptom severity, intervention acceptance, illness beliefs, adverse events, and a health economic evaluation. Follow-ups will be conducted one month and six months after screening by self-report questionnaires and clinical interviews. According to a statistical analysis plan, the primary outcome will be analysed on an intention-to-treat basis applying multilevel modelling.

Discussion

The results of the DISCOVER RCT will inform about how automated feedback after internet-based screening could improve early detection and resolution of depression. Ways of dissemination and how the trial can contribute to an understanding of help-seeking behaviour processes will be discussed. If the results show that automated feedback after internet-based depression screening can reduce depression severity, the intervention could be easily implemented and might substantially reduce the disease burden of individuals with undetected depression.

Ethical approval

The study is approved by the Ethics Committee of the Hamburg Medical Association.

Trial registration

The trial was registered at ClinicalTrials.gov in November 2020 (identifier: NCT04633096).

中文翻译：

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基于互联网的抑郁症筛查后自动反馈的功效：德国三臂随机对照试验的研究方案

背景

抑郁症是全世界最致残的疾病之一，但它常常未被发现。解决早期发现和疾病负担的一种有希望的方法是抑郁症筛查，然后直接反馈给患者。有证据表明，个人经常在互联网上寻找有关心理健康的信息。因此，基于互联网的自动反馈筛查在解决未检测到的抑郁症患者方面具有巨大潜力。

目标

确定基于互联网的抑郁症筛查后的自动反馈与没有反馈相比是否会降低抑郁症的严重程度。

方法

基于互联网的、观察者盲的 DISCOVER RCT 旨在招募 1074 人。将使用患者健康问卷 (PHQ-9) 对参与者进行抑郁症筛查。如果筛查结果为阳性（PHQ-9 ≥ 10），未检测到抑郁症的参与者将被随机分配到三个平衡研究组之一，接受（a）无反馈（控制组）、（b）标准反馈或（ c) 对其筛选结果的定制反馈。量身定制的反馈版本将适应参与者的特征，即症状概况、偏好和人口统计特征。主要假设是，与没有反馈相比，筛查后六个月，反馈降低了抑郁症的严重程度。第二个假设是，与标准反馈相比，定制反馈更有效。进一步的结果是抑郁症护理、求助行为、与健康相关的生活质量、焦虑、躯体症状严重程度、干预接受度、疾病信念、不良事件和健康经济评估。通过自我报告问卷和临床访谈筛选后，将进行1个月和6个月的随访。根据统计分析计划，主要结果将在意向治疗的基础上应用多级建模进行分析。

讨论

DISCOVER RCT 的结果将告知基于互联网的筛查后的自动反馈如何改善抑郁症的早期发现和解决。将讨论传播方式以及试验如何有助于理解寻求帮助的行为过程。如果结果表明基于互联网的抑郁症筛查后的自动反馈可以降低抑郁症的严重程度，那么干预可以很容易地实施，并可能大大减轻未检测到抑郁症患者的疾病负担。

道德批准

该研究得到了汉堡医学协会伦理委员会的批准。

试用注册

该试验于 2020 年 11 月在 ClinicalTrials.gov 注册（标识符：NCT04633096）。