An empirical typology of subjects within stage of change

doi:10.1016/0306-4603(94)00069-B

Addictive Behaviors

Volume 20, Issue 3, May–June 1995, Pages 299-320

https://doi.org/10.1016/0306-4603(94)00069-B Get rights and content

Abstract

The transtheoretical model of behavior change postulates five distinct, welldefined stages of change: Precontemplation, Contemplation, Preparation, Action, and Maintenance. Each stage has been regarded as reflecting a motivational posture and treated as if it is homogenous with respect to membership. This paper reports the results of four cluster analytic studies, one within each of the first four stages of change. The cluster analysis was based on three constructs of the model. Four distinct subtypes were found within Contemplation, Preparation, and Action, and three subtypes were found within Precontemplation. External validation for the clusters was provided using the 10 Processes of Change and 8 behavioral measures as dependent measures. At least one subtype corresponded to the existing stage definition but the other subtypes suggested alternative intervention strategies.

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Il est important de ne pas négliger la recherche de troubles co-occurrents, psychiatriques et addictologiques, qui sont fréquents.
Only few subjects with addictive disorders seek treatment.
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Replicating cluster subtypes for the prevention of adolescent smoking and alcohol use
2015, Addictive Behaviors
Citation Excerpt :
The procedure groups individuals into categories based on the level, scatter, and shape of the patterns (Cronbach & Gleser, 1953). Cluster analyses have been utilized to explore data from TTM-based interventions in past studies; multiple studies (Anatchkova, Velicer, & Prochaska, 2005, 2006a,b; Norman, Velicer, Fava, & Prochaska, 2000; Velicer, Hughes, Fava, Prochaska, & DiClemente, 1995) have found replicable subtypes of smokers within different TTM stages of change for smoking cessation. These studies identified clusters using three key measures from the TTM: Pros, Cons, and Self-efficacy/Temptations.
Substance abuse interventions tailored to the individual level have produced effective outcomes for a wide variety of behaviors. One approach to enhancing tailoring involves using cluster analysis to identify prevention subtypes that represent different attitudes about substance use. This study applied this approach to better understand tailored interventions for smoking and alcohol prevention.
Analyses were performed on a sample of sixth graders from 20 New England middle schools involved in a 36-month tailored intervention study. Most adolescents reported being in the Acquisition Precontemplation (aPC) stage at baseline: not smoking or not drinking and not planning to start in the next six months. For smoking (N = 4059) and alcohol (N = 3973), each sample was randomly split into five subsamples. Cluster analysis was performed within each subsample based on three variables: Pros and Cons (from Decisional Balance Scales), and Situational Temptations.
Across all subsamples for both smoking and alcohol, the following four clusters were identified: (1) Most Protected (MP; low Pros, high Cons, low Temptations); (2) Ambivalent (AM; high Pros, average Cons and Temptations); (3) Risk Denial (RD; average Pros, low Cons, average Temptations); and (4) High Risk (HR; high Pros, low Cons, and very high Temptations).
Finding the same four clusters within aPC for both smoking and alcohol, replicating the results across the five subsamples, and demonstrating hypothesized relations among the clusters with additional external validity analyses provide strong evidence of the robustness of these results. These clusters demonstrate evidence of validity and can provide a basis for tailoring interventions.
Risk groups and predictors of short-term abstinence from smoking in patients with coronary heart disease
2012, Heart and Lung: Journal of Acute and Critical Care
We sought to identify risk groups among smoking cardiac patients from their social cognitive profiles, and to assess predictors of smoking abstinence shortly after discharge.
Smoking cardiac patients (n = 133) completed questionnaires at hospital admission and 1 month after discharge. Hierarchical cluster analysis was used to detect risk groups of smokers, based on baseline scores for smoking-related social cognitions. Regression analyses were used to identify predictors of the intention to abstain from smoking and smoking abstinence 1 month after discharge.
Three groups of smokers were distinguished that differed significantly on the pros of nonsmoking, self-efficacy expectancies toward nonsmoking, social support, social modeling, and smoking behavior. Abstinence from smoking 1 month after discharge was predicted by group membership and a stronger intention to quit. A previous hospital admission because of a cardiac event significantly decreased the likelihood of abstinence.
One third of cardiac patients are at high risk of continuing smoking after hospital discharge because of an unfavorable smoking and disease history and a poor social cognitive profile. Interventions for cardiac patients should address risk profiles to achieve long-term abstinence. The implications of nursing practices in smoking cessation treatments are discussed.
Cluster subtypes appropriate for preventing postpartum smoking relapse
2012, Addictive Behaviors
Citation Excerpt :
In the simultaneous case, the feedback is based on the profile of the individual across different TTM variables (Levesque, Driskell, & Prochaska, 2008). For smoking cessation, a series of cluster analysis studies starting with Velicer, Hughes, Fava, Prochaska, and DiClemente (1995) have found consistent subtypes within each stage of change. The subtypes have been replicated across multiple samples for smoking cessation (Anatchkova, Velicer, & Prochaska, 2005, 2006a, 2006b; Dijkstra, Bakker, & deVries, 1997; Kremers, Mudde, & deVries, 2001; Norman, Velicer, Fava, & Prochaska, 2000).
While the majority of women quit smoking either before or during pregnancy, 60 to 80% relapse in the postpartum period. The objective of this research was to examine postpartum women who quit smoking during their pregnancies and to determine the predictive factors for relapse in the postpartum period by identifying different subgroups that predict risk of relapse.
One hundred forty four postpartum women who were abstinent at the time of delivery were recruited. Data regarding the Acquisition Stage of Change, Decisional Balance and Situational Temptations to Smoke were assessed in the immediate postpartum period. Based on their intention to remain abstinent, 121 women identified in the acquisition-Precontemplation (aPC) group comprised the study sample. Smoking status was assessed again at 2 months postpartum.
A cluster analysis was performed to idenitfy subgroups of the acquisition-Precontemplation (aPC) group. Four subgroups were identified and were labeled Most Protected, Ambivalent, Risk Denial, and High Risk. Logistic regression was performed to establish external validity of the clusters. The clusters and exclusive breastfeeding were the only statistically significant variables associated with relapse at 2 months postpartum.
The results confirmed the clusters identified in previous prevention research with both adolescents and postpartum women, The cluster profiles can serve to guide the development of a tailored intervention program.
Identifying subtypes of dual alcohol and marijuana users: A methodological approach using cluster analysis
2012, Addictive Behaviors
Citation Excerpt :
This procedure consists of identifying cases, variable selection, determining distance metric, choosing a hierarchical algorithm, deciding on the number of clusters, cluster interpretation, and the internal and external validation of clusters (Rapkin & Luke, 1993). This method has been widely applied in community and health psychology research (Babor et al., 1992; Humphreys & Rosenheck, 1995; Maibach, Maxfield, Ladin, & Slater, 1996; Norman & Velicer, 2003; Shaw, Shah, Jolly, & Wylie, 2008; Velicer, Hughes, Fava, Prochaska, & DiClemente, 1995). In the current study, we propose to 1) establish the type of association between alcohol and marijuana use, hypothesizing that there is multidimensional relationship between these two substances; 2) identify distinct patterns of dual alcohol and marijuana use; and 3) examine differences in the negative consequences among different patterns of dual substance use.
Alcohol is the most common psychoactive substance used with marijuana. However, little is known about the potential impact of different levels of use of both alcohol and marijuana and their influence on risky behaviors, injuries and psychosocial functioning. A systematic approach to identifying patterns of alcohol and marijuana use associated with increased risks has not yet been identified in the literature.
We report on the secondary analysis of data collected from a RCT conducted in a busy urban emergency department. Cluster analysis was performed on the patterns of past 30-day alcohol and marijuana use in two random subsamples N₁ = 210 and N₂ = 217. Four distinct subtypes of those who use both alcohol and marijuana were identified: (1) Daily Marijuana and Weekly Alcohol users; (2) Weekly Alcohol and Weekly Marijuana users; (3) Daily Alcohol and Daily Marijuana users; and (4) Daily Alcohol, Weekly Marijuana users. The four subtypes were replicated in both subsamples and examination of the external validity using ANOVA to determine cluster differences on psychosocial and behavioral variables confirmed the theoretical relevance of different patterns of alcohol and marijuana use. There were significantly different psychosocial negative consequences and related risky behaviors among subtypes.
We found that Daily Alcohol and Daily Marijuana users are at the highest risk to experience more negative consequences and engage in a broader spectrum of risky behaviors related to both substances, than the other three types of alcohol and marijuana users.
Motivational profiles of clients seeking methadone maintenance therapy in China
2011, Drug and Alcohol Dependence
This study applied a stage-of-change model to examine the motivational profiles of clients seeking methadone maintenance therapy (MMT) in China.
Face-to-face interviews were conducted with a total of 179 clients from six MMT clinics. The University of Rhode Island Change Assessment (URICA) scale was used to measure the participants’ motivation and readiness to change. Cluster analysis was performed to classify the sample into subgroups with respect to their change dimensions.
The study sample was allocated into five distinct clusters: uninvolved, denial, pre-participation, ambivalent, and participation. Participants who were classified in the denial cluster were older than those in the pre-participation and participation clusters. A higher level of motivation to change was positively associated with continued heroin use and more severe drug problems.
It would be beneficial to evaluate motivational profiles of individual clients in the treatment planning process and provide tailored interventions for sustained treatment retention and outcomes.

View all citing articles on Scopus

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This research was partially supported by Grants CA 27021 and CA 50087 from the National Cancer Institute.

Early versions of the paper were presented at the Eleventh Annual Meeting of the Society for Behavioral Medicine, April, 1990, Chicago; the American Psychological Association Meeting, August, 1990, Boston, and the American Psychological Association meeting, August, 1991, San Francisco; and the 25th International Congress of Psychology, Brussels, July, 1992.

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An empirical typology of subjects within stage of change☆

Abstract

Addictive Behaviors

Addictive Behaviors

Addictive Behaviors

Addictive Behaviors

Addictive Behaviors

Cluster analysis

Self-efficacy: Toward a unifying theory of behavioral change

Psychological Review

Self-efficacy mechanism in human agency

American Psychologist

Understanding and preventing relapse

American Psychologist

Assessing similarities between profiles

Psychological Bulletin

The process of smoking cessation: An analysis of precontemplation, contemplation, and preparation stages of change

Journal of Consulting and Clinical Psychology

Self-efficacy and the stages of self-change of smoking

Cognitive Therapy and Research

Comparing the accuracy of hierarchal clustering algorithms: The problem of classifying everybody

Multivariate Behavioral Research

Cluster analysis

Under what conditions does theory obstruct research progress?

Psychological Review

Foreword

A model for the study of personal choice health behavior

International Journal of Health Education

Some dimensions of a model for smoking behavior change

American Journal of Public Health

Decision making: A psychological analysis of conflict, choice, an commitment

The smoking problem: A review of the research and theory in behavioral risk modification

Psychological Bulletin

Cluster analysis for social scientists

An examination of the effect of six types of error perturbation on fifteen clustering algorithms

Psychometrika