Supplemental Material for A Time-Varying Effect Model for Intensive Longitudinal Data

2011 ◽  
Keyword(s):  
Longitudinal Data ◽  
Time Varying ◽  
Intensive Longitudinal Data ◽  
Effect Model

scholarly journals A time-varying effect model for intensive longitudinal data.

2012 ◽  
Vol 17 (1) ◽  
pp. 61-77 ◽  
Author(s):  
Xianming Tan ◽  
Mariya P. Shiyko ◽  
Runze Li ◽  
Yuelin Li ◽  
Lisa Dierker
Keyword(s):  
Longitudinal Data ◽  
Time Varying ◽  
Intensive Longitudinal Data ◽  
Effect Model

scholarly journals Determining Timing Effects of Microrandomized Trials Using Intensive Longitudinal Data and the Differential Time-Varying Effect Model

2020 ◽  
Author(s):  
Kejin Wu ◽  
Jason R. McFadden ◽  
Nicholas C. Jacobson
Keyword(s):  
Longitudinal Data ◽  
Mixed Models ◽  
Optimal Time ◽  
Controlled Trials ◽  
Type I ◽  
Time Lags ◽  
Time Varying ◽  
Intervention Effects ◽  
Intensive Longitudinal Data ◽  
Effect Model

In the areas of behavioral sciences, methods for delivering interventions within the context of daily life are developing rapidly, fueled by the development of microrandomized controlled trials and ecological momentary interventions. Although intensive longitudinal data are often collected to evaluate the immediate effects of these interventions, the timing of these interventions on behavioral has been given limited attention. Given this, the field could benefit from an accessible and efficient tool to detect and estimate the time in which interventions have an impact on their respective outcomes. Nevertheless, existing tools have difficulty in estimating the timing of interventions. Consequently, in this paper, we propose an extension of the Differential Time-Varying Effect Model (DTVEM) which attempts to detect the timing of interventions on outcomes by trying to detect the lag intervals between exogenous variables (i.e. intervention delivery) and outcomes. Within this paper, we extend the DTVEM by pairing generalized additive mixed models with linear mixed models to identify optimal time lags and intervention effects, respectively. By intensive simulations based on, the efficiency of the DTVEM with additional stage is tested, and the results showed promising power and point estimates, and low type I error. Consequently, the extended DTVEM allows researchers to perform power analyses regarding timing of intervention effects and also detect timing of intervention effects using intensive longitudinal data and microrandomized controlled trials.

scholarly journals Modeling intensive longitudinal data with mixtures of nonparametric trajectories and time-varying effects.

2015 ◽  
Vol 20 (4) ◽  
pp. 444-469 ◽  
Author(s):  
John J. Dziak ◽  
Runze Li ◽  
Xianming Tan ◽  
Saul Shiffman ◽  
Mariya P. Shiyko
Keyword(s):  
Longitudinal Data ◽  
Time Varying ◽  
Intensive Longitudinal Data

scholarly journals How intensive longitudinal data can stimulate advances in health behavior maintenance theories and interventions

2019 ◽  
Author(s):  
Genevieve F Dunton ◽  
Alexander J Rothman ◽  
Adam M Leventhal ◽  
Stephen S Intille
Keyword(s):  
Health Behavior ◽  
Longitudinal Data ◽  
Research Methods ◽  
Health Behavior Change ◽  
Noncommunicable Disease ◽  
Time Activity ◽  
Intensive Longitudinal Data ◽  
Reflective Systems ◽  
Behavior Maintenance

Abstract Interventions that promote long-term maintenance of behaviors such as exercise, healthy eating, and avoidance of tobacco and excessive alcohol are critical to reduce noncommunicable disease burden. Theories of health behavior maintenance tend to address reactive (i.e., automatic) or reflective (i.e., deliberative) decision-making processes, but rarely both. Progress in this area has been stalled by theories that say little about when, why, where, and how reactive and reflective systems interact to promote or derail a positive health behavior change. In this commentary, we discuss factors influencing the timing and circumstances under which an individual may shift between the two systems such as (a) limited availability of psychological assets, (b) interruption in exposure to established contextual cues, and (c) lack of intrinsic or appetitive motives. To understand the putative factors that regulate the interface between these systems, research methods are needed that are able to capture properties such as (a) fluctuation over short periods of time, (b) change as a function of time, (c) context dependency, (d) implicit and physiological channels, and (e) idiographic phenomenology. These properties are difficult to assess with static, cross-sectional, laboratory-based, or retrospective research methods. We contend that intensive longitudinal data (ILD) collection and analytic strategies such as smartphone and sensor-based real-time activity and location monitoring, ecological momentary assessment (EMA), machine learning, and systems modeling are well-positioned to capture and interpret within-person shifts between reactive and reflective systems underlying behavior maintenance. We conclude with examples of how ILD can accelerate the development of theories and interventions to sustain health behavior over the long term.

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