Across several studies, it has been shown that the most common outcome following a behavior change attempt is a return to engaging in the undesired behavior (Polivy & Herman, 2002
). For example, it has been shown that more than 60% of individuals will have at least one drinking episode (i.e., “lapse”) in the first year following alcohol treatment (Maisto, Pollock, Cornelius, Lynch, & Martin, 2003
; Whitford, Widner, Mellick, & Elkins, 2009
). Several authors have proposed that high rates of lapsing might be partially explained by the complexity of the addictive behavior change process (Connor, Symons, Feeney, Young, & Wiles, 2007
; Donovan, 1996
; Niaura, 2000
; Skinner, 1989
; Warren, Hawkins, & Sprott, 2003
) and many models of relapse have been developed that attempt to characterize the antecedents of lapse events (Annis, 1986
; Cronkite & Moos, 1980
; Litman, 1986
; Ludwig & Wikler, 1974
; Marlatt & Gordon, 1985
; Sanchez-Craig, 1976
; see Connors, Maisto, & Donovan, 1996
for a review).
One of the most widely cited models of relapse, the cognitive-behavioral model, was first proposed by Marlatt and Gordon (1985)
in their influential text on relapse prevention. The cognitive-behavioral model of relapse, which combines cognitive (e.g., beliefs about one’s ability to abstain), behavioral (e.g., coping responses), and situational/environmental antecedents of substance use lapses, was largely derived from a taxonomy of relapse situations developed by Marlatt and Gordon (1980)
. The relapse taxonomy, which was based on qualitative interviews with clients who had experienced drinking lapses following treatment, consisted of three hierarchically arranged levels that distinguished between the intrapersonal and interpersonal precipitants (level 1); eight categories of antecedents within the level 1 precipitants (level 2); and specific subdivisions for five of the eight level 2 categories. The eight subdivisions within the two level 1 categories, included coping with negative emotional states, coping with negative physical-psychological states, enhancement of positive-emotional states, testing personal control, and giving in to temptations and urges; and coping with interpersonal conflict, social pressure, and enhancement of positive emotional states. Five of these subdivisions were further divided on level 3 (e.g., Coping with negative emotional states was segregated into Coping with frustration and/or anger and Coping with other negative emotional states).
The relapse taxonomy and cognitive behavioral model of relapse have been very influential in the field of addiction, making significant contributions to clinical practice and stimulating the development of relapse prevention strategies. Due to the widespread popularity of Marlatt’s model, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) funded a large scale study (the Relapse Replication and Extension Project (RREP)) to test the reliability and validity of the taxonomic system for classifying relapse episodes. Investigators in the RREP recruited 563 clients with alcohol dependence from alcohol treatment programs in three geographically distinct areas in the United States and conducted bimonthly prospective assessments of drinking and potential relapse antecedents for one year. Results from the RREP raised significant methodological issues concerning the reliability (Longabaugh, Rubin, Stout, Zywiak, & Lowman, 1996
), construct validity (Maisto, Connors & Zywiak, 1996
), and predictive validity (Stout, Longabaugh, & Rubin, 1996
) of Marlatt’s model. Based on the findings in the RREP, a major re-conceptualization of the relapse taxonomy was suggested (Donovan, 1996
Following up on this recommendation, Witkiewitz and Marlatt (2004)
proposed a re-conceptualization of the cognitive-behavioral model of relapse as a nonlinear dynamic system. The dynamic model of relapse builds upon several previous studies of relapse risk factors (Connors, Maisto, & Zywiak, 1996
; Lowman, Allen, & Miller, 1996
; Miller, Westerberg, Harris, & Tonigan, 1996
; Shiffman, Balabanis, Paty, Engberg, Gwaltney, Liu et al., 2000
) by incorporating the characterization of distal and proximal risk factors proposed by Shiffman (1989; see also Donovan, 1996
). Distal risks, which are thought to increase the probability of relapse, include background variables (e.g., alcohol dependence) and relatively stable pre-treatment characteristics (e.g., expectancies). Proximal risks actualize, or complete, the distal predispositions and include transient lapse precipitants (e.g., stressful situations) and dynamic individual characteristics (e.g., negative affect). Combinations of precipitating and predisposing risk factors are innumerable for any particular individual and may create a complex system in which the probability of relapse is greatly increased. The system is further characterized using the temporal definitions of tonic and phasic processes, where tonic processes represent stable factors and phasic processes represent transient risk precipitants (Grace, 2000
). Risk factors may operate within either tonic or phasic processes. For example, momentary self-efficacy (i.e., phasic) has been shown to predict smoking lapses above and beyond that which is predicted by baseline (i.e., tonic) self-efficacy (Shiffman et al, 2000
The dynamic model of relapse has generated enthusiasm among researchers and clinicians who have observed these processes in their data and their clients (see Ashton, n.d.
; Brandon, Vidrine, & Litvin, 2007
; Cohen & Sutker, 2006
; Hunter-Reel, McCrady, & Hilderbrandt, 2009
; McCarthy, Piasecki, Fiore, & Baker, 2006
; Stanton, 2005
). Yet, the dynamic model of relapse is a theoretical model and has not yet been subjected to a rigorous empirical test. Empirical analyses of the RREP data by Miller and colleagues (1996)
and Connors and colleagues (Connors, Maisto, & Zywiak, 1996
) partially inspired the development of the theoretical dynamic model of relapse (Witkiewitz & Marlatt, 2004
) and many aspects of the model are supported by these early empirical studies. Miller and colleagues (1996)
used data from the Albuquerque New Mexico site of the RREP in the estimation of prospective relapse models that incorporated six domains of relapse risk factors, life events (e.g., interpersonal stress), cognitive variables (e.g., self-efficacy), coping resources, craving, and pre-treatment characteristics (e.g., alcohol dependence), as predictors of drinking outcomes at the six month assessment. Pre-treatment characteristics were assessed at baseline and considered “static antecedents” (p. 156), whereas proximal risk factors (life events, cognitions, coping, and craving) were measured at the four month assessment point and described as “dynamic antecedents” (p. 156). Results indicated that, with the exception of life events, all of the proximal risk factors were significantly associated with six month outcomes and proximal risk factors were stronger predictors of relapse than the distal risk factors.
Using data from the Buffalo NY site of the RREP, Connors and colleagues (Connors, Maisto, & Zywiak, 1996
) estimated a path model of drinking outcomes (frequency, intensity, and drinking consequences) in months seven through twelve following treatment predicted by five relapse risk factor domains: background characteristics (e.g., psychiatric symptoms), alcohol involvement (e.g., alcohol dependence), treatment factors (e.g., treatment satisfaction), coping skills, and stressors. The model incorporated direct effects of baseline background characteristics and alcohol involvement, the six month assessments of the treatment factors, coping skills, and stressors (i.e., proximal influences), as well as the indirect effects of distal influences on drinking outcomes via the proximal influences. Background characteristics, alcohol involvement, treatment, and coping skills were all significant predictors of drinking frequency and intensity, and both alcohol involvement and coping skills were significantly associated with drinking consequences. The indirect effects were relatively small in magnitude, although the authors did note the somewhat larger effect of background characteristics impacting outcomes via treatment factors and treatment factors impacting outcomes via coping skills.
Analyses by Miller and colleagues (1996)
and Connors and colleagues (Connors, Maisto, & Zywiak, 1996
) provided partial support for some of the hypotheses of the dynamic model of relapse. Namely, both studies showed that distal and proximal influences play an important role in predicting post-treatment drinking outcomes and lapse events. There are also several aspects of the dynamic model that were not addressed in either study. Both studies focused on drinking outcomes at either a single time-point (Miller et al., 1996
) or averaged across time (Connors, Maisto, & Zywiak, 1996
) and neither study examined the potential reciprocal effects of drinking on proximal risk factors; thus ignoring temporal associations between risk factors and drinking. Likewise, both studies focused on post-treatment associations between risk and alcohol lapses, and did not address whether proximal risk factors, which are modifiable, influenced alcohol use during treatment. Both studies also relied on composites of risk domains and used statistical techniques that did not take into account composite measurement error. If there was a high degree of measurement error in the composites, then meaningful risk factor effects could have been obscured (Jaccard & Wan, 1995
). In addition, the sample sizes for both studies (n=122; n=142) were rather small for testing complex models.
The goal of the current study was to address the limitations of previous studies (Miller et al., 1996
; Connors et al., 1996
) using data from the COMBINE study (COMBINE Study Research Group, 2003
). The current study was designed to examine changes in proximal risk and drinking across multiple time points in a large sample of individuals (n
= 1383) and estimate reciprocal associations between proximal risk and heavy drinking using statistical techniques that take into account measurement error. Specifically, the current study used latent variable modeling techniques to examine the static and dynamic associations between distal risk factors (including alcohol dependence, marital status, pre-treatment psychiatric problems, and pre-treatment self-efficacy), proximal risk factors (including craving, perceived stress, and negative mood), and frequency of heavy drinking during the course of treatment and up to one year following treatment.