Abstract
Despite clear links between genes and smoking, effective public policy requires far richer measurement of the feedback between biological, behavioral, and environmental factors. The Kavli HUMAN Project (KHP) plans to exploit the plummeting costs of data gathering and to make creative use of new technologies to construct a longitudinal panel data set that would compare favorably to existing longitudinal surveys, both in terms of the richness of the behavioral measures and the cost-effectiveness of the data collection. By developing a more comprehensive approach to characterizing behavior than traditional methods, KHP will allow researchers to paint a much richer picture of an individual's life-cycle trajectory of smoking, alcohol, and drug use, and interactions with other choices and environmental factors. The longitudinal nature of KHP will be particularly valuable in light of the increasing evidence for how smoking behavior affects physiology and health. The KHP could have a transformative impact on the understanding of the biology of addictive behaviors such as smoking, and of a rich range of prevention and amelioration policies.
Original language | English (US) |
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Pages (from-to) | 198-202 |
Number of pages | 5 |
Journal | Big Data |
Volume | 3 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1 2015 |
Keywords
- deep phenotyping
- genetics
- smoking
- smoking cessation
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Information Systems and Management