Leveraging intensive longitudinal data to better understand health behaviors

Kevin P. Timms, Cesar A. Martin, Daniel Rivera, Eric B. Hekler, William Riley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Behavioral scientists have historically relied on static modeling methodologies. The rise in mobile and wearable sensors has made intensive longitudinal data (ILD) - behavioral data measured frequently over time - increasingly available. Consequently, analytical frameworks are emerging that seek to reliably quantify dynamics reflected in these data. Employing an input-output perspective, dynamical systems models from engineering can characterize time-varying behaviors as processes of change. Specifically, ILD and parameter estimation routines from system identification can be leveraged together to offer parsimonious and quantitative descriptions of dynamic behavioral constructs. The utility of this approach for facilitating a better understanding of health behaviors is illustrated with two examples. In the first example, dynamical systems models are developed for Social Cognitive Theory (SCT), a prominent concept in behavioral science that considers interrelationships between personal factors, the environment, and behaviors. Estimated models are then obtained that explore the role of SCT in a physical activity intervention. The second example uses ILD to model day-to-day changes in smoking levels as a craving-mediated process of behavior change.

Original languageEnglish (US)
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6888-6891
Number of pages4
ISBN (Print)9781424479290
DOIs
StatePublished - Nov 2 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

Fingerprint

Health Behavior
Health
Behavioral Sciences
Dynamical systems
Smoking
Parameter estimation
Identification (control systems)
Social Theory

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

Cite this

Timms, K. P., Martin, C. A., Rivera, D., Hekler, E. B., & Riley, W. (2014). Leveraging intensive longitudinal data to better understand health behaviors. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 6888-6891). [6945211] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6945211

Leveraging intensive longitudinal data to better understand health behaviors. / Timms, Kevin P.; Martin, Cesar A.; Rivera, Daniel; Hekler, Eric B.; Riley, William.

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 6888-6891 6945211.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Timms, KP, Martin, CA, Rivera, D, Hekler, EB & Riley, W 2014, Leveraging intensive longitudinal data to better understand health behaviors. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6945211, Institute of Electrical and Electronics Engineers Inc., pp. 6888-6891, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 8/26/14. https://doi.org/10.1109/EMBC.2014.6945211
Timms KP, Martin CA, Rivera D, Hekler EB, Riley W. Leveraging intensive longitudinal data to better understand health behaviors. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 6888-6891. 6945211 https://doi.org/10.1109/EMBC.2014.6945211
Timms, Kevin P. ; Martin, Cesar A. ; Rivera, Daniel ; Hekler, Eric B. ; Riley, William. / Leveraging intensive longitudinal data to better understand health behaviors. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 6888-6891
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