Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
J Med Internet Res. 2024 Aug 5;26:e59826. doi: 10.2196/59826.
Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the "Decade of the Brain" was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise development and implementation approach, similar to the one that has been successful in the prediction and prevention of cardiovascular disease, to achieve clinically actionable predictions in psychiatry.
一些精神障碍或行为的模型（例如，自杀）已经成功开发出来，可以在人群水平上进行预测。然而，目前的人口统计学和临床变量既不敏感也不特异，无法做出个体可操作的临床预测。“大脑十年”的主要希望是生物测量（生物标志物）将解决这些问题，并导致精准精神病学。然而，由于这些模型是基于社会人口统计学和临床数据的，即使这些生物标志物在患者和对照组之间存在显著差异，它们仍然不够敏感和特异，无法应用于个体患者。过去十年的技术进步提供了一种很有前途的方法，基于新的测量方法，这些方法可能对理解精神障碍和预测其轨迹至关重要。一些新的工具使我们能够连续监测客观的行为测量（例如，睡眠时间）和密集采样主观测量（例如，情绪）。这种被称为数字表型的方法的前景在近十年前就已经被认识到，它对精神病学的潜在影响被比作显微镜对生物科学的影响。然而，尽管人们直观地认为，收集密集采样的数据（大数据）可以改善临床结果，但最近的临床试验表明，将数字表型纳入其中并不能改善临床结果。这一观点提供了一种逐步的开发和实施方法，类似于在预测和预防心血管疾病方面取得成功的方法，以实现精神病学中的临床可操作预测。
