Using the Time-Varying Effect Model (TVEM) to Examine Dynamic Associations between Negative Affect and Self Confidence on Smoking Urges: Differences between Successful Quitters and Relapsers
- 928 Downloads
With technological advances, collection of intensive longitudinal data (ILD), such as ecological momentary assessments, becomes more widespread in prevention science. In ILD studies, researchers are often interested in the effects of time-varying covariates (TVCs) on a time-varying outcome to discover correlates and triggers of target behaviors (e.g., how momentary changes in affect relate to momentary smoking urges). Traditional analytical methods, however, impose important constraints, assuming a constant effect of the TVC on the outcome. In the current paper, we describe a time-varying effect model (TVEM) and its applications to data collected as part of a smoking-cessation study. Differentiating between groups of short-term successful quitters (N = 207) and relapsers (N = 40), we examine the effects of momentary negative affect and abstinence self-efficacy on the intensity of smoking urges in each subgroup in the 2 weeks following a quit attempt. Successful quitters demonstrated a rapid reduction in smoking urges over time, a gradual decoupling of the association between negative affect and smoking urges, and a consistently strong negative effect of self-efficacy on smoking urges. In comparison, relapsers exhibited a high level of smoking urges throughout the post-quit period, a time-varying and, generally, weak effect of self-efficacy on smoking urges, and a gradual reduction in the strength of the association between negative affect and smoking urges. Implications of these findings are discussed. The TVEM is made available to applied prevention researchers through a SAS macro.
KeywordsIntensive longitudinal data Time-varying covariates Ecological momentary assessments Modeling Multilevel modeling
The authors would like to thank Linda Collins, John Dziak, Charu Mathur, C.J. Powers, and Violet (Shu) Xu for comments on earlier drafts of this manuscript, and Amanda Applegate for her editorial suggestions. The work of Shiyko, Lanza, Tan, & Li was supported by the National Institute on Drug Abuse grant P50 DA010075-14 and R21 DA024260. The work of Shiffman was supported by the National Institute on Drug Abuse grant DA06084. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health.
- Baker, T. B., Morse, E., & Sherman, J. E. (1987). The motivation to use drugs: A psychobiological analysis of urges. In P. C. Rivers (Ed.), The Nebraska symposium on motivation: Alcohol use and abuse (pp. 257–232). Lincoln: University of Nebraska Press.Google Scholar
- Hastie, T., & Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society. Series B (Methodological), 55, 757–779.Google Scholar
- Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Social and Behavioral Science, 15, 41–56.Google Scholar
- Marlatt, G. A., & Gordon, J. R. (1985). Relapse prevention: Maintenance strategies in the treatment of addictive behaviors. New York: Guilford Press.Google Scholar
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
- Schwartz, J. E., & Stone, A. A. (2007). The analysis of real-time momentary data: A practical guide. In A. A. Stone, S. Shiffman, A. A. Atienza, & L. Nebeling (Eds.), The science of real-time data capture: Self-reports in health research (pp. 76–113). New York: Oxford University Press.Google Scholar
- Shiffman, S., Hickcox, M., Paty, J. A., Gnys, M., Kassel, J. D., & Richards, T. J. (1996). Progression from a smoking lapse to relapse: Prediction from abstinence violation effects, nicotine dependence, and lapse characteristics. Health Psychology, 64, 993–1002.Google Scholar
- Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.Google Scholar
- Stone, A. A., & Shiffman, S. (1994). Ecological momentary assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine, 16, 199–202.Google Scholar
- Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2010). Intensive longitudinal data and model with varying effects (Technical Report No. 10–106). University Park, PA: The Methodology Center, The Pennsylvania State University.Google Scholar
- Walls, T. A., & Schafer, J. L. (Eds.) (2006). Modeling for intensive longitudinal data. New York, NY: Oxford University Press.Google Scholar