Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/56, 8006, Zürich, Switzerland.
Department of Psychology, Experimental Psychopathology and Psychotherapy, Binzmühlestrasse 14, Box 8, 8050, Zürich, Switzerland.
BMC Psychol. 2023 Jun 22;11(1):186. doi: 10.1186/s40359-023-01215-1.
Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression).
With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression.
Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives.
Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022).
抑郁症仍是一个全球性的健康问题，其患病率在全球范围内呈上升趋势。数字生物标志物越来越多地被用于针对抑郁症的可扩展干预措施的启动和定制。由于新病例的不断涌入，仅关注治疗是不够的；学术界和从业者需要专注于抑郁症的预防（即，解决亚临床抑郁症）。
通过我们的研究，我们旨在：（i）开发亚临床抑郁症症状的数字生物标志物，（ii）开发亚临床抑郁症严重程度的数字生物标志物，以及（iii）研究数字干预在减轻亚临床抑郁症症状和严重程度方面的疗效。
参与者将与数字干预措施 BEDDA 互动，该干预措施包括一个脚本化的对话代理、慢节奏呼吸训练 Breeze 以及针对不同症状的可操作建议。干预措施包括 30 次每日互动，需在不到 45 天的时间内完成。我们将收集关于情绪、激动、快感缺失（近端结果；第一个目标）、抑郁严重程度自我报告（主要远端结果；第二个和第三个目标）、焦虑严重程度（次要远端结果；第二个和第三个目标）、压力（次要远端结果；第二个和第三个目标）、声音和呼吸的自我报告。参与者的 25%的样本将使用智能手表记录生理数据（例如，心率、心率变异性），这些数据将用于所有三个目标的分析。
基于数字语音和呼吸的生物标志物可以通过提供一种非侵入性的、补充或替代自我报告的评估方式，改善诊断、预防和护理。此外，我们的结果可能会增进我们对亚临床抑郁症中潜在心理生理变化的理解。我们的研究还提供了关于独立数字健康干预措施预防抑郁症的疗效的进一步证据。
ETH 苏黎世伦理委员会（EK-2022-N-31）提供了伦理批准，该研究已在 ISRCTN 注册处注册（注册号：ISRCTN38841716，提交日期：2022 年 8 月 20 日）。
