Intensive Longitudinal Health Behaviors Network (ILHBN)

This Working Group wiki page is created to serve the NIH ILHBN Consortium.

The purpose of the Longitudinal Health Behaviors initiative is to establish a cooperative agreement network of U01 projects and 1 U24 Research Coordinating Center (RCC), to collaboratively study factors that influence key health behaviors in the dynamic environment of individuals, using intensive longitudinal data collection and analytic methods. The network will also assess how study results can be leveraged to introduce innovations into longstanding behavioral theories to advance the field of theory-driven behavior change interventions. The knowledge gained will inform the development of personalized prevention strategies and best implementation strategies for communities, including health disparity populations, towards the goal of reducing disease risk and maintaining ideal health.

Behavioral science places strong emphasis on theoretical models to systematically explain and predict behaviors and events influencing health outcomes. Although these theories are useful frameworks for developing behavioral change interventions, their ability to explain and predict behavior has been only modestly successful.  The research funded by this initiative will examine theoretical constructs and health behaviors from a different scientific perspective and approach than has been traditionally used and is critical for moving health behavior science towards more effective health behavior interventions for reducing disease. 

Health behavior theories have developed and been evaluated primarily from a between-person perspective, attempting to explain why some people engage in health behaviors while others do not.  While such questions remain important, this between-person focus has contributed to theoretical research that is predominately cross-sectional in nature and that emphasizes dispositional variables such as attitudes and normative beliefs which are relatively static over time and more trait-like in nature.  In contrast, a within-person approach to health behavior theory research seeks to explain why a given individual engages in healthy or risky behaviors at one time versus another.  Within-person analysis of intensive longitudinal data is likely to provide insight into the dynamic factors in the physical, social, and/or built environment that facilitate or hinder engaging in certain behaviors at specific points in time, in addition to the interaction between factors.

U01 FOA,

R24 FOA,

ILHBN Website:  :


Goal of THIS WG:  Share best practices for computational modeling across the network, develop appropriate adaptations to existing approaches for social and behavioral phenomena, and develop and test innovative approaches to computational modeling in this space.


1/4/19, Grace Peng - please post your model descriptions here (methodologies, data types, end users, context of use)


Project Name/ Focus ILHBN Project PI's Theorists/Modelers emails
Research Coordinating Center Sy-Miin Chow Sy-Miin Chow, Nilam Ram, Peter Molenaar, Timothy Brick, Zita Oravecz, Guangching qhi symiin AT
  Nicholas Allen and Randy Auerbach Louis-Phillipe Morency, Jeff Cohn, David Brent

nallen3 AT

randy.auerbach AT

  Matthew Nock Evan Kleiman, John Torous, JP Onnela, Rosaline Picard, Tyler VanderWeele nock AT
  Justin Baker and Scott Rauch Ian Barnett, Louis-Phillipe Morency


ibarnett AT 

  Genevieve Dunton and Stephen Intille

Stephen Intille

Don Hedeker

dunton AT

s.intille AT

  Donna Spruijt-Metz, Ben Marlin, and Pedgrag  Benjamin Marlin, Daniel Rivera, Misha Pavel

m.pavel AT

dmetz AT

  Inbal (Billie) Nahum-Shani and David Wetter Jim Rehg, Susan Murphy inbal AT
  Scott Vrieze and Naomi Friedman Scott Vrieze, Naomi Friedman

vrie0006 AT



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This wiki was originally created for the Multiscale Modeling (MSM) Consortium, so in the Signup entries for MSM grant title, grant number, and project collaborators, please enter your U19 grant title, number and collaborators.  Look for “Other - Intensive Longitudinal Health Behaviors Network (ILHBN)” in the WG involvement menu. 

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