All people are uniquely endowed at birth by genetic and environmental conditions; by the time they enter their last decades, they have a lifetime of differentiation that determines their state of health and response to new events and conditions. This cumulative differentiation creates substantial intraindividual variability in the rate of aging as well as the extent of resistance and resilience to pathological insults. Therefore, applying normative group data such as group means or median thresholds often fails to accurately identify and predict an individual's clinical state and prognosis. There are two ways to cope with this high intraindividual variability. One is to use "big data," which consists of a large number of subjects to improve the prediction algorithm. Another is to use each subject as their own universe to identify subtle changes or deviations from their premorbid stage. Rich temporal data from a single person-what we call "small data"-can be used for the individual's tailored diagnosis, disease management, and health behavior. Using such data for patient care, self-care, sustained independence, and research involves access to, processing, and interpretive use of an individual's combined data streams over time.

Hiroko H Dodge

Making Sense of Aging with Data Big and Small.

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