Functional data analysis for dynamical system identification of behavioral processes

Jessica B. Trail, Linda Marie Collins, Daniel E. Rivera, Runze Li, Megan E. Piper, Timothy B. Baker

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input- output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.

Original languageEnglish (US)
Pages (from-to)175-187
Number of pages13
JournalPsychological Methods
Volume19
Issue number2
DOIs
StatePublished - Jan 1 2014

Fingerprint

Substance Withdrawal Syndrome
Smoking Cessation
Nicotine
Tobacco
Smoking
Research Personnel
Clinical Trials
Technology
Population
Craving

All Science Journal Classification (ASJC) codes

  • Psychology (miscellaneous)

Cite this

Trail, Jessica B. ; Collins, Linda Marie ; Rivera, Daniel E. ; Li, Runze ; Piper, Megan E. ; Baker, Timothy B. / Functional data analysis for dynamical system identification of behavioral processes. In: Psychological Methods. 2014 ; Vol. 19, No. 2. pp. 175-187.
@article{637aa130043642208e6e4ab7133955da,
title = "Functional data analysis for dynamical system identification of behavioral processes",
abstract = "Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input- output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.",
author = "Trail, {Jessica B.} and Collins, {Linda Marie} and Rivera, {Daniel E.} and Runze Li and Piper, {Megan E.} and Baker, {Timothy B.}",
year = "2014",
month = "1",
day = "1",
doi = "10.1037/a0034035",
language = "English (US)",
volume = "19",
pages = "175--187",
journal = "Psychological Methods",
issn = "1082-989X",
publisher = "American Psychological Association Inc.",
number = "2",

}

Functional data analysis for dynamical system identification of behavioral processes. / Trail, Jessica B.; Collins, Linda Marie; Rivera, Daniel E.; Li, Runze; Piper, Megan E.; Baker, Timothy B.

In: Psychological Methods, Vol. 19, No. 2, 01.01.2014, p. 175-187.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Functional data analysis for dynamical system identification of behavioral processes

AU - Trail, Jessica B.

AU - Collins, Linda Marie

AU - Rivera, Daniel E.

AU - Li, Runze

AU - Piper, Megan E.

AU - Baker, Timothy B.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input- output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.

AB - Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input- output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.

UR - http://www.scopus.com/inward/record.url?scp=84902518847&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902518847&partnerID=8YFLogxK

U2 - 10.1037/a0034035

DO - 10.1037/a0034035

M3 - Article

C2 - 24079929

AN - SCOPUS:84902518847

VL - 19

SP - 175

EP - 187

JO - Psychological Methods

JF - Psychological Methods

SN - 1082-989X

IS - 2

ER -