Spruijt-Metz Donna, Hekler Eric, Saranummi Niilo, Intille Stephen, Korhonen Ilkka, Nilsen Wendy, Rivera Daniel E, Spring Bonnie, Michie Susan, Asch David A, Sanna Alberto, Salcedo Vicente Traver, Kukakfa Rita, Pavel Misha
University of Southern California, 635 Downey Way, Suite 305 Building Code: VPD 3332, Los Angeles, CA 90089-3332 USA.
Arizona State University, Tempe, AZ USA.
Transl Behav Med. 2015 Sep;5(3):335-46. doi: 10.1007/s13142-015-0324-1.
Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.
不良和次优的健康行为及习惯除了会对生活质量和经济产生不利影响外，还导致了约40%的可预防死亡。我们目前对人类行为的理解很大程度上基于人类行为的静态“快照”，而非针对不断变化的生物、社会、个人和环境状态所做出的持续、动态的行为反馈循环。本文首先讨论新技术（即移动传感器、智能手机、普适计算和基于云的处理/计算）以及新兴的系统建模技术如何推动新型动态实证人类行为模型的开发，这些模型有助于实现即时自适应、可扩展的干预措施。接着，本文描述了创建强大的动态行为数学模型的具体步骤，包括：（1）建立“金标准”测量方法；（2）创建行为本体，作为共享语言和理解工具，以促进跨学科的动态理论构建；（3）开发数据共享资源；（4）推动数学模型和工具的更好共享，以支持模型的快速整合。我们在结论部分讨论了可纳入“知识共享”的内容，这有助于将这些不同的活动整合到一个统一的系统和结构中，用于组织有关行为的知识。
