Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.

目前的医疗保健实践是被动的，并且基于数月或数年收集的有限生理信息。通过使患者和健康消费者能够持续测量健康状况，可穿戴设备和数字医疗可以实现高度个性化和预防性护理。然而，当前大多数数字技术提供的信息仅限于一组有限的生理特征，例如心率和步数，仅凭这些特征无法深入了解大多数疾病的病因。在这里，我们建议将来自生物健康智能手机应用程序的数据与源自尿液代谢物的连续代谢表型相整合。分子表型与生活方式定量测量的结合实时反映了人类行为的生物学后果。我们提供了一项涉及两名健康受试者的观察性研究的数据，并讨论了将这一新的生理信息层整合到数字医学中的挑战、机遇和影响。尽管我们的数据集仅限于两个受试者，但我们的分析（也可以通过基于网络的交互式可视化工具进行）提供了一个初始框架来监测生活方式因素，例如营养、药物代谢、运动和使用尿液代谢物的睡眠。