PatientsLikeMe, 160 Second Street, Cambridge, MA, 02142, USA.
Biomed Eng Online. 2018 Sep 6;17(1):119. doi: 10.1186/s12938-018-0553-x.
The use of information and communication technologies for health (eHealth) delivered via mobile-based or digitally enhanced solutions (mHealth) have rapidly evolved. When used together across various mobile applications and devices eHealth and mHealth technologies have the ability to passively monitor behavior as an indicator of socialization and mood; accumulate a range of biomedical data such as weight and heart rate; and track metrics associated with activities including steps taken and hours slept. Yet, these technologies are insufficient for measuring the full array of data about an individual and the impact of that data on a person's current and future health. Digital health converges eHealth and mHealth with patient data about their health, healthcare, living, and environment with genomics. An innovative opportunity to unravel the complexities of disease and aging is increasingly possible with an integrative multi-omics approach informed by multidisciplinary sciences including medicine, design, biomedical informatics and engineering. The digitization of individual level data from all available sources makes possible the development of DigitalMe™, a personalized virtual avatar of a real person. The combination of longitudinally collected person generated data and molecular data derived from biospecimens offers researchers unique opportunities to better understand the mechanisms of disease while advancing person-centric hypotheses generation related to treatments, diagnostics, and prognostics.
移动医疗(mHealth)是通过基于移动设备或数字化增强的解决方案来提供的医疗保健信息和通信技术(eHealth),其应用已迅速发展。当各种移动应用程序和设备上共同使用 eHealth 和 mHealth 技术时,它们能够被动地监测行为,作为社交和情绪的指标;积累一系列生物医学数据,如体重和心率;并跟踪与活动相关的指标,包括所走的步数和睡眠时间。然而,这些技术不足以全面测量个体的数据以及这些数据对个人当前和未来健康的影响。数字健康将患者的健康、医疗、生活和环境数据与基因组学结合起来,融合了 eHealth 和 mHealth。通过多组学方法,包括医学、设计、生物医学信息学和工程学等多学科的综合信息,可以为疾病和衰老的复杂性提供新的见解。从所有可用来源数字化个体水平数据使得开发 DigitalMe™ 成为可能,这是一个真实人的个性化虚拟化身。个人生成数据和生物样本衍生的分子数据的纵向收集为研究人员提供了独特的机会,可以更好地了解疾病的机制,同时推进与治疗、诊断和预后相关的以人为本的假说生成。