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"Agile science is: people-centered as it is grounded in the real-world needs of individuals.   success-oriented as we ground all evaluation and optimization on clearly defined criteria of success.   efficiently  rigorous via the classic scientific concept of triangulation. We strive to clarify the most important assumption being made at a given moment and then evaluate it as efficiently and rigorously as possible. We then iterate and test again, often with a complementary but also efficient method for the stage of the process to balance strengths/limitations of the approaches." Eric Hekler, Ph.D. Associate Professor Department of Family Medicine & Public Health Director, Center for Wireless & Population Health Systems

"Evidence-based medicine is increasingly the primary way in which individuals are receiving care and support. Advocates for the approach (Sackets et al 1996) defined evidence-based medicine as: "

"Camille Nebeker, Bethany Weisberg, Eric Hekler, and Michael Kurisu Frontiers in Digital Health - This paper is also posted on our PHE Website

"prof m.c. schraefel, phd,fbcs, ceng, cscs (lower case deliberate). Professor of Computer Science and Human Performance, Fellow, british computer society, Research Chair, Royal Academy of Engineering, Chartered Engineer. Head, Agents Interaction and Complexity Group And now a wee bit more: Deputy Head of Department, Research, and current REF champion (for those in the UK) for CS."

"In my opinion, the fact that expectation does not match reality is the ultimate reason why many data scientists leave. There are many reasons for this and I can't possibly come up with an exhaustive list but this post is essentially a list of some of the reasons that I encountered."

"There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various 'big data' efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary 'small data' paradigm that can function both autonomously from and in collaboration with big data is also needed. By 'small data' we build on Estrin's formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit."