Mofsen Aaron M, Rodebaugh Thomas L, Nicol Ginger E, Depp Colin A, Miller J Philip, Lenze Eric J
Department of Psychiatry, School of Medicine, Washington University in St Louis, St Louis, MO, United States.
Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, United States.
JMIR Ment Health. 2019 Apr 21;6(5):e11845. doi: 10.2196/11845.
A major problem in mental health clinical trials, such as depression, is low assay sensitivity in primary outcome measures. This has contributed to clinical trial failures, resulting in the exodus of the pharmaceutical industry from the Central Nervous System space. This reduced assay sensitivity in psychiatry outcome measures stems from inappropriately broad measures, recall bias, and poor interrater reliability. Limitations in the ability of traditional measures to differentiate between the trait versus state-like nature of individual depressive symptoms also contributes to measurement error in clinical trials. In this viewpoint, we argue that ecological momentary assessment (EMA)-frequent, real time, in-the-moment assessments of outcomes, delivered via smartphone-can both overcome these psychometric challenges and reduce clinical trial failures by increasing assay sensitivity and minimizing recall and rater bias. Used in this manner, EMA has the potential to further our understanding of treatment response by allowing for the assessment of dynamic interactions between treatment and distinct symptom response.
心理健康临床试验（如抑郁症试验）中的一个主要问题是主要结局指标的检测灵敏度较低。这导致了临床试验的失败，使得制药行业纷纷撤离中枢神经系统领域。精神病学结局指标检测灵敏度降低的原因包括测量指标过于宽泛、回忆偏差以及评分者间信度较差。传统测量方法在区分个体抑郁症状的特质性与状态性方面能力有限，这也导致了临床试验中的测量误差。在本文观点中，我们认为生态瞬时评估（EMA）——通过智能手机进行的对结局的频繁、实时、即时评估——既能克服这些心理测量学挑战，又能通过提高检测灵敏度以及将回忆偏差和评分者偏差降至最低来减少临床试验失败。以这种方式使用时，EMA有潜力通过评估治疗与不同症状反应之间的动态相互作用，进一步加深我们对治疗反应的理解。
