Chronic stress is a major underlying origin of the top leading causes of death, globally. Yet, the mechanistic explanation of the association between stress and disease is poorly understood. This stems from the inability to adequately measure stress in its naturally occurring state and the extreme heterogeneity by inter and intraindividual characteristics. The growth and availability of digital technologies involving wearable devices and mobile phone apps afford the opportunity to dramatically improve measurement of the biological stress response in real time. In parallel, the advancement and capabilities of artificial intelligence (AI) and machine learning could discern heterogeneous, multidimensional information from individual signs of stress, and possibly inform how these signs forecast the downstream consequences of stress in the form of end-organ damage. The marriage of these tools could dramatically enhance the field of stress research contributing to impactful and empowering interventions for individuals bridging knowledge to practice, and intervention to real-world use. Here we discuss this potential, anticipated challenges, and emerging opportunities.

中文翻译：

---

利用数字技术释放压力并预测其后果

慢性应激是全球范围内主要死亡原因的主要潜在来源。然而，对压力与疾病之间关系的机械解释了解甚少。这是由于无法通过内部和个体内的特征来适当地测量其自然状态下的应力以及极端的异质性。涉及可穿戴设备和移动电话应用程序的数字技术的增长和可用性为实时改善对生物压力反应的测量提供了机会。同时，人工智能（AI）和机器学习的进步和能力可以从压力的个体迹象中识别出异质的多维信息，并可能以这些信号如何以终末器官损害的形式预测压力的下游后果。这些工具的结合可以极大地增强压力研究的领域，从而为具有影响力和赋权的干预措施做出贡献，从而使个人将知识转化为实践，并将干预措施应用于现实世界。在这里，我们讨论了这种潜在的，预期的挑战和新出现的机会。