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Grant Details

Grant Number: 5U24AA027684-03 Interpret this number
Primary Investigator: Chow, Sy-Miin
Organization: Pennsylvania State University-Univ Park
Project Title: The Center for Innovation in Intensive Longitudinal Studies (CIILS)
Fiscal Year: 2020


Abstract

PROJECT SUMMARY Significance. The Intensive Longitudinal Behavior Network (ILHBN) provides an unprecedented opportunity to advance and shape the future landscape of health behavior science and related intervention practice. The proposed Research Coordinating Center, the Center for Innovation in Intensive Longitudinal Studies (CIILS), housed at the Pennsylvania State University (Penn State), will bring together an interdisciplinary team to synergistically support and coordinate research activities across a diverse portfolio of anticipated U01 projects to accomplish the Network’s larger goal of sustained innovation in the use of intensive longitudinal data (ILD) and associated methods in the study of health behavior change, and in informing prevention and intervention designs. Innovation. The proposed organizational structure of the ILHBN as a small-world network is motivated by our team’s collective decades of experience with multidisciplinary and multi-site collaborations, and is designed to facilitate information flow, collective decision making, and coordination of goals and effort within the ILHBN. Approach. CIILS consists of five Cores with expertise in management of multi-site projects and coordinating centers (Administrative Core); development of novel methods for analysis of ILD (Methods Core); ILD collection, harmonization, sharing, security, as well as collection of digital footprints (Data Core); ILD design, harmonization and instrumentation support (Design Core); and integration of health behavior theories, translation, and implementation of within-person health preventions/interventions (Theory Core). Key personnel with rich and complementary expertise are supported by a roster of advisory Co-Is at Penn State and distributed consultants who are leaders and innovators in their respective fields. Institutional support and contributed staff time by Penn State provide robust infrastructure, expertise, and “boots on the ground” to support the operation and coordination activities of ILHBN; and a wealth of additional resources to elevate and broaden the collective impacts of the Network.



Publications

Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study.
Authors: Brick T.R. , Mundie J. , Weaver J. , Fraleigh R. , Oravecz Z. .
Source: JMIR formative research, 2020-06-17; 4(6), p. e16072.
EPub date: 2020-06-17.
PMID: 32554373
Related Citations

A Person- and Time-Varying Vector Autoregressive Model to Capture Interactive Infant-Mother Head Movement Dynamics.
Authors: Chen M. , Chow S.M. , Hammal Z. , Messinger D.S. , Cohn J.F. .
Source: Multivariate behavioral research, 2020-06-12; , p. 1-29.
EPub date: 2020-06-12.
PMID: 32530313
Related Citations

Efficient Algorithms towards Network Intervention.
Authors: Hung H.J. , Shen C.Y. , Lee W.C. , Lei Z. , Yang D.N. , Chow S.M. .
Source: Proceedings of the ... International World-Wide Web Conference. International WWW Conference, 2020 Apr; 2020, p. 2021-2031.
PMID: 32685939
Related Citations

A Diagnostic Procedure for Detecting Outliers in Linear State-Space Models.
Authors: You D. , Hunter M. , Chen M. , Chow S.M. .
Source: Multivariate behavioral research, 2020 Mar-Apr; 55(2), p. 231-255.
EPub date: 2019-07-02.
PMID: 31264463
Related Citations

A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes.
Authors: Ji L. , Chen M. , Oravecz Z. , Cummings E.M. , Lu Z.H. , Chow S.M. .
Source: Structural equation modeling : a multidisciplinary journal, 2020; 27(3), p. 442-467.
PMID: 32601517
Related Citations

Exploring Sleep Dynamic of Mother-Infant Dyads Using a Regime-Switching Vector Autoregressive Model.
Authors: Ji L. , Chow S.M. , Crosby B. , Teti D.M. .
Source: Multivariate behavioral research, 2019-12-03; , p. 1.
EPub date: 2019-12-03.
PMID: 31793805
Related Citations

Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models.
Authors: Chow S.M. .
Source: Multivariate behavioral research, 2019 Sep-Oct; 54(5), p. 690-718.
EPub date: 2019-04-05.
PMID: 30950646
Related Citations

The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data.
Authors: Jacobson N.C. , Chow S.M. , Newman M.G. .
Source: Behavior research methods, 2019 02; 51(1), p. 295-315.
PMID: 30120682
Related Citations

dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling.
Authors: Li Y. , Ji L. , Oravecz Z. , Brick T.R. , Hunter M.D. , Chow S.M. .
Source: World academy of science, engineering and technology, 2019; 13(5), p. 302-311.
PMID: 31431819
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