S. M. Nusser, S. S. Intille, and R. Maitra, "Emerging technologies and next generation intensive longitudinal data collection," in Models for Intensive Longitudinal Data, T. Walls and J.L. Schafer, Eds. New York: Oxford, 2006.   


In this chapter, we consider newly emerging measurement technologies for intensive monitoring of individual behaviors and physiological responses in a wide range of settings. Our goal is to introduce these wearable assessment systems and the algorithms being used to extract meaningful data summaries from massive amounts of raw multidimensional sensor data. Because the social science community is largely unfamiliar with this new class of longitudinal data, we focus on the opportunities and limitations associated with the intensive longitudinal data generated by these technologies, how they impact study design and subsequent analyses, as well as statistical issues associated with processing such voluminous datasets into meaningful forms.


Context-sensitive ecological momentary assessment, context-aware experience sampling, longitudinal data, pattern recognition.


The authors wish to thank reviewers of an earlier version of this manuscript for their insightful comments. S. M. Nusser is supported in part by National Science Foundation Digital Government Grant #9983289 and #0306855. S. S. Intille is supported, in part, by National Science Foundation ITR grant #0112900 and the Changing Places/House n Consortium. R. Maitra is supported in part by National Science Foundation CAREER award # DMS-0437555.