Steven F. Babbin

Treated individuals who progress to action or maintenance for one behavior are more likely to make similar progress on another behavior: Coaction results of a pooled data analysis of three trials

OBJECTIVE This study compared, in treatment and control groups, the phenomena of coaction, which is the probability that taking effective action on one behavior is related to taking effective action on a second behavior. METHODS Pooled data from three randomized trials of Transtheoretical Model (TTM) tailored interventions (n=9461), completed in the U.S. in 1999, were analyzed to assess coaction in three behavior pairs (diet and sun protection, diet and smoking, and sun protection and smoking). Odds ratios (ORs) compared the likelihood of taking action on a second behavior compared to taking action on only one behavior. RESULTS Across behavior pairs, at 12 and 24 months, the ORs for the treatment group were greater on an absolute basis than for the control group, with two being significant. The combined ORs at 12 and 24 months, respectively, were 1.63 and 1.85 for treatment and 1.20 and 1.10 for control. CONCLUSIONS The results of this study with addictive, energy balance and appearance-related behaviors were consistent with results found in three studies applying TTM tailoring to energy balance behaviors. Across studies, there was more coaction within the treatment group. Future research should identify predictors of coaction in more multiple behavior change interventions.

Prevention of alcohol use in middle school students: Psychometric assessment of the decisional balance inventory

A measurement model should be equivalent across the different subgroups of a target population. The Decisional Balance Inventory for the Prevention of Alcohol Use is a 2-factor correlated model with 3 items for Pros of alcohol use and 3 items for Cons. The measure is part of a tailored intervention for middle school students. This study evaluated the important psychometric assumptions of factorial invariance and scale reliability with a large sample of sixth grade students (N=3565) from 20 schools. A measure is factorially invariant when the model is the same across subgroups. Three levels of invariance were assessed, from least restrictive to most restrictive: 1) Configural Invariance (unconstrained nonzero factor loadings); 2) Pattern Identity Invariance (equal factor loadings); and 3) Strong Factorial Invariance (equal factor loadings and measurement errors). Structural equation modeling was used to assess invariance over two levels of gender (male and female), race (white and black), ethnicity (Hispanic and non-Hispanic), and school size (large, indicating >200 students per grade, or small). The strongest level of invariance, Strong Factorial Invariance, was a good fit for the model across all of the subgroups: gender (CFI: 0.94), race (CFI: 0.96), ethnicity (CFI: 0.93), and school size (CFI: 0.97). Coefficient alpha was 0.61 for the Pros and 0.67 for Cons. Together, invariance and reliability provide strong empirical support for the validity of the measure.

Psychometric Assessment of the Temptations to Try Alcohol Scale

Effective interventions require an understanding of the behaviors and cognitions that facilitate positive change as well as the development of psychometrically sound measures. This paper reports on the psychometric properties of the Temptations to Try Alcohol Scale (TTAS), including factorial invariance across different subgroups. Data were collected from 3565 6th grade RI middle school students. Structural equation modeling was used to determine the appropriate factorial invariance model for the 9-item TTAS. The measure consists of three correlated subscales: Social Pressure, Social Anxiety, and Opportunity. Three levels of invariance, ranging from the least to the most restrictive, were examined: Configural Invariance, which constrains only the factor structure and zero loadings; Pattern Identity Invariance, which requires factor loadings to be equal across the groups; and Strong Factorial Invariance, which requires factor loadings and error variances to be constrained. Separate analyses evaluated the invariance across two levels of gender (males vs. females), race (white vs. black) ethnicity (Hispanic vs. Non-Hispanic) and school size (small, meaning <200 6th graders, or large). The highest level of invariance, Strong Factorial Invariance, provided a good fit to the model for gender (CFI: .95), race (CFI: .94), ethnicity (CFI: .94), and school size (CFI: .97). Coefficient Alpha was .90 for Social Pressure, .81 for Social Anxiety, and .82 for Opportunity. These results provide strong empirical support for the psychometric structure and construct validity of the TTAS in middle school students.

Prevention of Smoking in Middle School Students: Psychometric Assessment of the Temptations to Try Smoking Scale

Establishment of psychometrically sound measures is critical to the development of effective interventions. The current study examined the psychometric properties, including factorial invariance, of a six item Temptations to Try Smoking Scale on a sample of middle school students. The sample of 6th grade students (N=3527) was from 20 Rhode Island middle schools and was 52% male and 84% white. The Temptations to Try Smoking Scale consisted of two correlated subscales: Positive Social and Curiosity/Stress. Structural equation modeling was implemented to evaluate the factorial invariance across four different subgroups defined by gender (male/female), race (white/black), ethnicity (Hispanic/Non-Hispanic), and school size (<200/ >200 6th graders). A model is factorially invariant when the measurement model is the same in each of the subgroups. Three levels of invariance were examined in sequential order: 1) Configural Invariance (unconstrained nonzero factor loadings); 2) Pattern Identity Invariance (equal factor loadings); and 3) Strong Factorial Invariance (equal factor loadings and measurement errors). Strong Factorial Invariance provided a good fit to the model across gender (CFI=.96), race (CFI=.96), ethnicity (CFI=.94), and school size (CFI=.97). Coefficient Alphas for the two subscales, Positive Social and Curiosity/Stress, were .87 and .86, respectively. These findings provide empirical support for the construct validity of the Temptations to Try Smoking Scale in middle school students.

Replicating cluster subtypes for the prevention of adolescent smoking and alcohol use.

INTRODUCTION 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. METHODS 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. RESULTS 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). CONCLUSIONS 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.

Psychometric assessment of the processes of change scale for sun protection

Abstract The fourteen-factor Processes of Change Scale for Sun Protection assesses behavioral and experiential strategies that underlie the process of sun protection acquisition and maintenance. Variations of this measure have been used effectively in several randomized sun protection trials, both for evaluation and as a basis for intervention. However, there are no published studies, to date, that evaluate the psychometric properties of the scale. The present study evaluated factorial invariance and scale reliability in a national sample (N = 1360) of adults involved in a Transtheoretical model tailored intervention for exercise and sun protection, at baseline. Invariance testing ranged from least to most restrictive: Configural Invariance (constraints only factor structure and zero loadings); Pattern Identity Invariance (equal factor loadings across target groups); and Strong Factorial Invariance (equal factor loadings and measurement errors). Multi-sample structural equation modeling tested the invariance of the measurement model across seven subgroups: age, education, ethnicity, gender, race, skin tone, and Stage of Change for Sun Protection. Strong factorial invariance was found across all subgroups. Internal consistency coefficient Alpha and factor rho reliability, respectively, were .83 and .80 for behavioral processes, .91 and .89 for experiential processes, and .93 and .91 for the global scale. These results provide strong empirical evidence that the scale is consistent, has internal validity and can be used in research interventions with population-based adult samples.

Identifying treatment response subgroups for adolescent cannabis use

INTRODUCTION Outpatient treatments for adolescent substance use demonstrate clinically meaningful reductions in substance use, but effect sizes are often low, relapse rates are high, and response to treatment is heterogeneous across participants. The present study utilized cluster analysis to identify subgroups of treatment response among adolescents from three randomized clinical trials evaluating behavioral treatments for substance use. METHODS Analyses were performed on a sample of 194 adolescents (average age=15.8, 81.4% male) who reported cannabis use during the past 30days or had a cannabis-positive urine test. Clustering was based on percent days cannabis use at 5 time periods (intake, end of treatment, 3, 6, and 9months post-treatment). Participants in the identified subgroups were then compared across a number of variables not involved in the clustering (e.g., substance use, demographics, and psychopathology) to test for predictors of cluster membership. RESULTS Four clusters were identified based on statistical indices and visual inspection of the resulting cluster profiles: Low Use Responders (n=109, low baseline level, sustained decrease); High Use Responders (n=45, high baseline level, sustained decrease); Relapsers (n=25, medium baseline level, decrease, rapid increase post-treatment); and Non-Responders (n=15; consistently high level of use). Cannabis dependence, mean cannabis uses per day, and socioeconomic status were predictive of cluster membership. CONCLUSIONS Cluster analysis empirically identified different patterns of treatment response over time for adolescent outpatients. Investigating homogenous subgroups of participants provides insight into study outcomes, and variables associated with clusters have potential utility to identify participants that may benefit from more intensive treatment.

Identifying Longitudinal Patterns for Individuals and Subgroups: An Example with Adherence to Treatment for Obstructive Sleep Apnea

To improve complex behaviors such as adherence to medical recommendations, a better understanding of behavior change over time is needed. The focus of this study was adherence to treatment for obstructive sleep apnea (OSA). Adherence to the most common treatment for OSA is poor. This study involved a sample of 161 participants, each with approximately 180 nights of data. First, a time series analysis was performed for each individual. Time series parameters included the mean (average hours of use per night), level, slope, variance, and autocorrelation. Second, a dynamic cluster analysis was performed to find homogenous subgroups of individuals with similar adherence patterns. A four-cluster solution was found, and the subgroups were labeled: Great Users (17.2%; high mean and level, no slope), Good Users (32.8%; moderate mean and level, no slope), Low Users (22.7%; low mean and level, negative slope), and Slow Decliners (moderate mean and level, negative slope, high variance). Third, participants in the identified subgroups were compared to establish external validity. These steps represent a Typology of Temporal Patterns (TTP) approach. Combining time series analysis and dynamic cluster analysis is a useful way to evaluate longitudinal patterns at both the individual level and subgroup level.

Validity and Stability of the Decisional Balance for Sun Protection Inventory

The 8-item Decisional Balance for sun protection inventory (SunDB) assesses the relative importance of the perceived advantages (Pros) and disadvantages (Cons) of sun protective behaviors. This study examined the psychometric properties of the SunDB measure, including invariance of the measurement model, in a population-based sample of N = 1336 adults. Confirmatory factor analyses supported the theoretically based 2-factor (Pros, Cons) model, with high internal consistencies for each subscale (α ≥ .70). Multiple-sample CFA established that this factor pattern was invariant across multiple population subgroups, including gender, racial identity, age, education level, and stage of change subgroups. Multivariate analysis by stage of change replicated expected patterns for SunDB (Pros η 2 = .15, Cons η 2 = .02). These results demonstrate the internal and external validity and measurement stability of the SunDB instrument in adults, supporting its use in research and intervention.

Identifying longitudinal patterns of adherence to treatment for obstructive sleep apnea

Increasing adherence to medical recommendations is crucial for improving health outcomes and reducing costs of health care. To improve adherence, we have to better understand behavior change over time. The focus of this study was adherence to treatment for obstructive sleep apnea (OSA). Adherence to positive airway pressure (PAP), the most common treatment for OSA, is poor. This study involved an international sample of 161 participants, each with approximately 180 nights of data, and had three phases. First, a separate time series analysis was performed for each individual. Time series parameters included the mean (average hours of use per night), level (the intercept), slope (the rate of change over time), variance (variability in use), and autocorrelation (a measure of dependency). Second, a dynamic cluster analysis was performed to find homogenous subgroups of individuals with similar adherence patterns. A four-cluster solution was found, and the subgroups were labeled (see Figure 1): Great Users (17.2%; high mean and level, no slope), Good Users (32.8%; moderate mean and level, no slope), Poor Users (22.7%; low mean and level, negative slope), and Slow Decliners (moderate mean and level, negative slope, high variance). Third, participants in the identified subgroups were compared on a number of variables that were not involved in the clustering to establish external validity. Some notable findings at later time points include the following: Great Users reported the most self-efficacy (confidence to use PAP), Poor Users reported the most sleepiness, and Great Users reported the highest quality of sleep. Combining time series analysis and dynamic cluster analysis is a useful way to evaluate adherence patterns at both the individual level and subgroup level. Psychological variables relevant to adherence patterns, such as self-efficacy, could be the focus of interventions to increase PAP usage.