Building Better Adaptive Interventions by Expanding SMART

June 27, 2019:

John DziakBehavioral interventions for prevention and treatment are an important part of the fight against drug abuse and HIV/AIDS. Among the challenges faced by scientists is how and when to alter the course of treatment for participants in the intervention. Adaptive interventions change based on evidence about what is best for the participant at a given time.

For over a decade, Methodology Center researchers have developed and applied sequential, multiple assignment, randomized trials (SMARTs), which are experimental designs that can be used to build adaptive interventions that address a variety of health and behavioral challenges, such as substance abuse abstinence, weight loss, ADHD management, and language acquisition. Recently, researchers have begun developing methods to evaluate SMARTs by using multiple measures of the outcome over time rather than only considering the outcome at the end of the study. For example, a researcher who is developing an adaptive intervention to promote abstinence from alcohol may want to consider alcohol usage rates every month for six months to decide how to construct the intervention. In a recent article in Multivariate Behavioral Research by Methodology Center Investigator John Dziak, Methodology Center Affiliates Daniel Almirall and Inbal “Billie” Nahum-Shani, and others, the authors develop and demonstrate a new method for evaluating a SMART using repeated measures of a binary outcome (such as substance use versus nonuse).

The authors apply their method to the ENGAGE SMART study, which was conducted to help develop an adaptive intervention for promoting treatment engagement among cocaine- and alcohol-dependent individuals. The authors found that certain designs correlated to increased abstinence rates during the first two months but abstinence rates that were equivalent to other designs by the end of the study. Had the investigators measured relapse solely at six months, they would not have observed the relapse differences during the early months, which may have practical or clinical significance. The authors go on to provide guidelines for using multiple binary measurements of the outcome while analyzing data from a SMART.

Lead author John Dziak discussed the importance of the study. “SMART is a valuable method because conditions such as addiction and many other health problems, are chronic and often need treatment over time. In many cases, the appropriate treatment could change depending on the individual’s experiences. SMART trials can help scientists decide which set of adaptive treatment rules will work the best. In a lot of the past SMART literature, ‘work the best’ just meant having the best expected outcome at the end of the study.  But considering short-term and long-term effects together might help clinicians make better decisions to fit an individual’s  goals.  Also, it allows scientists to study delayed effects, where an early treatment choice affects how well later treatments work, and that could render theoretical insight into the treatments.”


Dziak, J. J., Yap, J. R., Almirall, D., McKay, J. R., Lynch, K. G., & Nahum-Shani, I. (2019). A data analysis method for using longitudinal binary outcome data from a SMART to compare adaptive interventions. Multivariate Behavioral Research, 1-24.

Featured Article: Counting Drinks to Understand Alcohol Use Disorder

November 27, 2018:
ALC - square

Alcohol use disorder (AUD) occurs more frequently among young adults than other age groups. Heavy drinking, which is a strong predictor of whether someone will experience an AUD, is common among young adults. The generally accepted guideline for “heavy episodic drinking” or a “binge” is four (for women) or five (for men) or more drinks during a drinking occasion or within a two-hour period. In a forthcoming article by Methodology Center researchers Ashley Linden-Carmichael, Michael Russell, and Stephanie Lanza, the authors used time-varying effect modeling (TVEM) to examine whether these drink thresholds provide the best picture of who is at risk for AUD.

The authors analyzed a sample of more than 6,000 young adult drinkers from the National Epidemiologic Survey on Alcohol and Related Conditions-III. Among other questions, the authors examined whether consuming different average numbers of drinks on each drinking occasion was associated with higher prevalence of AUD. They found that rates of AUD for women increased until women reached about nine drinks per drinking session, when AUD rates plateaued at about 80%. AUD rates plateaued at 80% for men when they consumed about 12 drinks. This study suggests that defining a binge as four or five drinks and focusing prevention messages around that threshold neither matches young adult behavior nor does it enable us to understand the full scope of risky drinking.

Lead author Ashley Linden-Carmichael spoke about the implications of the findings and what further questions remain. “Alcohol clinical trials often use percentage of no-binge-drinking days as a marker of the trial’s efficacy. Our results suggest that focusing on reducing the number of drinks rather than whether they surpassed a threshold may be a better measure of treatment success.” Realistic and useful standards for what constitutes risky drinking could serve as an important tool in the effort to curb young-adult drinking to safer levels.

For a pre-print copy of the article, please email



Linden-Carmichael, A. N., Russell, M. A., & Lanza S. T. (In press). Flexibly modeling alcohol use disorder risk: How many drinks should we count? Psychology of Addictive Behaviors.

Featured Article: Sexual Abuse as a Unique Predictor of Issues in Adolescence

July 30, 2018:kateg

In a recent article in Journal of Adolescence, Kate Guastaferro, assistant research professor at The Methodology Center, and her collaborators examined whether sexual abuse is a unique predictor of adolescent sexual behaviors, pregnancy, and motherhood. Previous research indicated that maltreatment is a risk factor for many negative outcomes but did not adequately examine the impacts of different types of maltreatment (e.g., sexual abuse, physical abuse, or neglect). If differences exist between types of maltreatment, then failure to detect those differences means that prevention and treatment interventions might not be designed properly. In this article, the authors analyzed longitudinal data from a cohort of 275 maltreated females (aged 14-19) in the Female Adolescent Developmental Study to examine whether the risk behaviors exhibited during adolescence were different for the different types of maltreatment. After controlling for other risk factors (e.g., attitudes and beliefs, non-sexual risk behaviors, psychosocial functioning and contextual antecedents), the authors found that sexual abuse uniquely predicts adolescent motherhood.

When asked about how this research can lead to better outcomes for sexually abused adolescents, Kate said, “There may be ways to streamline funding for prevention programs in order to target behaviors associated with a specific exposure. In other words, the prevention programming offered to individuals who have experienced maltreatment should potentially differ depending upon the type of maltreatment experienced. Females with a history of sexual abuse should be offered more programming around the prevention of risky sexual behaviors and adolescent motherhood, whereas those who experience physical abuse may benefit from only the risky sexual behavior prevention programming. This approach would enable prevention scientists to be more effective and more efficient at the same time.”

Open the article.


Noll, J. G., Guastaferro, K., Beal, S. J., Schreier, H. M., Barnes, J., Reader, J. M., & Font, S. A. (2018). Is sexual abuse a unique predictor of sexual risk behaviors, pregnancy, and motherhood in adolescence? Journal of research on adolescence.

Article: Are Drinking and Sexual Behaviors Different for People Who Go to College?

October 18, 2017:sv home

Despite the media coverage and research that have been devoted to risky behavior among young adults, many questions remain about which populations are at risk. In a recent article in Journal of Research on Adolescence, Methodology Center Investigators Sara Vasilenko, Ashley Linden-Carmichael, Stephanie Lanza, and Methodology Center Affiliate Megan Patrick examine differences between college attenders and their non-attending peers in terms of drinking behavior, sexual behavior, and the co-occurrence of heavy drinking and sex. This article also provides a thorough introduction to weighted time-varying-effect modeling (TVEM) and moderation in TVEM.

The authors analyzed data from 11,848 participants in The National Longitudinal Survey of Adolescent to Adult Health (Add Health). They examined whether—and how strongly—sexual behavior was predicted by frequent heavy episodic drinking (HED—occasions when a person has five or more drinks in a row) at different ages from 14-24. As noted above, they also examined whether there was a difference in behavior between college attenders and college non-attenders. They found that the association between HED and sexual behaviors is stronger for college attenders than it is for non-attenders both early in adolescence and during the early college years.

Lead author Sara Vasilenko said, “What I found interesting about this research is that, despite the popular belief that college is a hotbed of risky sexual behavior, college attenders were less likely to engage in risky sexual behavior than their non-attending peers. That said, the association between risky sexual behavior and HED is stronger for attenders than non-attenders from ages 18 to 20. So, while attending college does not appear to lead to greater levels of risky sexual behavior overall, college may be a unique environment for the co-occurrence of drinking and sex.

“In addition to the substantive contributions, this paper demonstrates two different ways to perform moderation in TVEM, and the article includes sample code. This will be useful to researchers who want to learn if time-varying effects differ for different types of people. In this example, we examined the differences between college attenders and non-attenders, but the same technique for dynamic moderation could be applied to any type of moderator.”

Open the article. (Journal access required.)


Vasilenko, S. A., Linden-Carmichael, A., Lanza, S. A. & Patrick. M. E. (in press). Sexual behavior and heavy episodic drinking across the transition to adulthood: Differences by college attendance. Journal of Research on Adolescence.

Stress-Related Drinking in College Linked to Future Alcohol Problems

August 24, 2017:Mike Russell

Many people consume alcohol at the end of a stressful day, but there are questions about the long-term consequences of this type of drinking. In a new article in Psychology of Addictive Behaviors, Methodology Center Investigator Michael Russell and his collaborators David Almeida and Jennifer Maggs analyzed intensive longitudinal data (ILD) from a daily diary study to determine what links may exist between stress-related drinking and future problems with alcohol use.

The authors analyzed data from the University Life Study (PI: Jennifer Maggs), which collected daily diary data on 744 college students during the first four years of their university education. Using multi-level models, the authors examined the relationship between stress and drinking in daily life across more than 49,000 days (totaling all days from all students), comparing students’ likelihood of drinking on high-stress days to their likelihood of drinking on low-stress days. The authors found that, compared to themselves, students were somewhat more likely to drink on a high-stress day than on a low-stress day, but that this likelihood varied greatly between students. Next, they examined how an individual’s tendency to drink when he/she is under increased stress might impact the likelihood that he/she may have indicators of a drinking problem later in college. The authors found that students whose drinking was more reactive to stressors—that is, students whose drinking increased more sharply on high- versus low-stress days—were at greater risk for alcohol problems during their fourth year of college than students whose drinking was less reactive to stressors.

This article demonstrates how within-person slopes from multilevel models, which characterize the relationship between dynamic factors such as stress and drinking for each individual, can be useful in predicting risk for public health-relevant outcomes, such as risk for alcohol problems in university students. Future research is needed to determine whether interventions focusing on stress management could help reduce rates of problem drinking in young adult populations.

Open the article. (Journal access required.)


Russell, M. A., Almeida, D., & Maggs, J. L. (in press). Stressor-related drinking and future alcohol problems among university students. Psychology of Addictive Behaviors.

Featured Article: LCA of Why Young Adults Use Marijuana and Why Their Reasons Matters

March 9, 2017:smoke--marijuana

As state laws regarding marijuana change around the nation, legislators and the public need information about the impacts of marijuana use. Research has shown that smoking marijuana in order to cope with problems is associated with later marijuana-related problems (Fox et al., 2011). In a recent article in Journal of Studies on Alcohol and Drugs, a team of researchers including Methodology Center Associate Director Bethany Bray examined data on self-reported motives for using marijuana during young adulthood and then determined which motivational profiles were associated with later marijuana use and problems.

In the article, “Reasons for marijuana use among young adults and long-term associations with marijuana use and problems,” the authors applied latent class analysis (LCA) to a sample from the long-running Monitoring the Future study. The sub-sample included participants who provided marijuana use data at age 19 or 20 and again at age 35. The analysis revealed five classes of motives for using marijuana in young adulthood: Experimental Reasons, Get High and Relax Reasons, Typical Reasons, Typical and Escape Reasons, and Coping and Drug Effect Reasons. “Typical” reasons included “to feel good or get high,” “to have a good time with my friends,” “to experiment,” and “to relax.” When looking at the association between class membership in young adulthood and later problems, the authors found that members of the Experimental Reasons class were the least likely to use marijuana at age 35. Members of the Get High and Relax Reasons and Coping and Drug Effect Reasons classes were most likely to use marijuana and have problems with marijuana use at age 35.

Co-author Bethany Bray said, “Because individuals may hold multiple reasons for marijuana use simultaneously, methods like LCA can provide unique information about how reasons cluster within individuals and which patterns confer the greatest risk for later problems. Our findings support the need for motivation-based interventions that can be targeted and adapted based on salient reasons for use among young adults.”

Open the article (Journal access required)


Patrick, M. E., Bray, B. C., & Berglund, P. A. (2016). Reasons for marijuana use among young adults and long-term associations with marijuana use and problems. Journal of Studies on Alcohol and Drugs, 77(6), 881-888.

Fox, C. L., Towe, S. L., Stephens, R. S., Walker, D. D., & Roffman, R. A. (2011). Motives for cannabis use in high-risk adolescent users. Psychology of Addictive Behaviors, 25, 492–500. doi:10.1037/a0024331

Featured Article: Building Optimized Adaptive Interventions

January 18, 2017:

Every year in the United States, 800,000 deaths are directly attributable to behavioral factors like smoking and alcohol use.mostfig Interventions that help people modify their risky behavior could save many lives. Because adaptive interventions (also called dynamic treatment regimens) adjust based on participant need or preference, they have the capacity to increase intervention effectiveness and/or decrease cost and patient burden.

Two research projects at The Methodology Center develop methods for optimizing interventions and methods for adaptive interventions. These projects were developed from a common research agenda, and they remain deeply connected. The multiphase optimization strategy (MOST) for optimizing interventions sometimes employs factorial experiments to select what should be included in an intervention. The sequential, multiple assignment, randomized trial (SMART) is a special case of the factorial experiment. In the 2014 article “Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART)” in Clinical Trials, Linda Collins and SMART researchers Inbal “Billie” Nahum-Shani and Daniel Almirall describe how to develop optimized adaptive interventions using a SMART.

Linda thinks interested researchers should understand the connection between SMART and MOST. “MOST is a comprehensive framework for development, optimization, and evaluation of all types of behavioral and biobehavioral interventions. The appropriate strategy for experimentation in the optimization phase depends on the type of intervention being developed. SMARTs are an excellent strategy for gathering the scientific information needed to optimize a time-varying adaptive intervention.” For researchers interested in SMART, MOST, or both, The Methodology Center provides advice about how to integrate MOST into a grant proposal and a list of funding opportunities that solicit the use of MOST and/or SMART.

Open the article.

Collins, L. M., Nahum-Shani, I., & Almirall, D. (2014). Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART). Clinical Trials, 11, 426-434.

Article: Do Social Networks Change After Quitting Smoking?

December 1, 2016:

A common fear of many smokers who want to quit is that they will lose many people in their social network―family, friends or co-workers―when they quit.Kids-smoking In a recent article in the journal Nicotine and Tobacco Research, researchers applied latent transition analysis to examine the changes in the social networks of smokers who are quitting. The authors identified five types of networks and found that people who successfully quit are actually likely to increase the size of their social networks.

The results are based on a sample from a smoking-cessation trial conducted by researchers at the University of Wisconsin Center for Tobacco Research (UW-CTRI). In the study, UW-CTRI researchers interviewed 691 adult smokers about their friends and associates.

This analysis was led by Methodology Center Associate Director Bethany Bray. The authors analyzed how participants’ social networks changed during the three years after quitting. Smokers who quit were more likely to transition to larger social networks, especially amongst participants who had the highest levels of exposure to other smokers before quitting. In other words, quitting was associated with an increase in the number of people, especially non-smokers, in people’s social networks.

“Clinicians can use results from this study to reassure smokers that quitting tends to increase, not decrease, the size of social networks,” said co-author and smoking expert Megan Piper. “Many smokers tell us cigarettes are their best friend, and that doesn’t have to be the case. We found our patients who quit expanded their social networks and developed meaningful relationships after they quit smoking.”

Open the article. (Journal access required.)

Latent Classes of Quitting Smokers: Who Will Relapse?

stl_savOctober 25, 2016:

Research indicates that withdrawal is one of the primary reasons that people do not quit smoking (Piper, 2015). Improving our understanding of withdrawal may allow us to better support people who wish to quit smoking. In a new article in Addiction, “What a difference a day makes:  Differences in initial abstinence response during a smoking cessation attempt,” the authors present a latent class analysis (LCA) that identifies four types of smokers based on their withdrawal symptoms on the day they quit. They found that a subset of quitting smokers reported extreme craving or extreme negative affect, and that this predicted earlier relapse.

The authors analyzed data from 1236 participants in a smoking cessation trial who provided ecological momentary assessments (EMA) of their withdrawal symptoms on the day that they quit smoking.  The LCA model incorporated participant reports of hunger, poor concentration, negative mood, cigarette craving, and anhedonia (the inability to feel pleasure). The authors identified four classes of smokers: High-Craving Anhedonia, Moderate Withdrawal, Affective Withdrawal, and Hunger. The Moderate Withdrawal class comprised 64% of the sample and was characterized by lower symptom levels. The High-Craving Anhedonia class comprised 8% of the sample and was characterized by high levels of craving and anhedonia. The Affective Withdrawal class comprised 13% of the sample and was characterized by high levels of poor concentration and negative mood. The Hunger class comprised 15% of the sample and was characterized by high levels of hunger but low levels of the other measures.

Among other results, the authors found that members of the High-Craving Anhedonia and Affective Withdrawal classes returned to regular smoking sooner than members of the Moderate Withdrawal class.

Methodology Center investigators Sara Vasilenko and Stephanie Lanza conducted this research with Megan Piper and Jessica Cook of The University of Wisconsin’s Center for Tobacco Research and Intervention. This LCA in this article is not especially complex and could be understood by people who are relatively new to the method.  The article does not, however, describe LCA extensively and is not intended as an introduction to the method. For an introduction to LCA, please see our recommended reading list.

Read the article. (Journal access required.)



Piper, M. E., Vasilenko, S. A., Cook, J. W., & Lanza, S. T. (2016). What a difference a day makes: Differences in initial abstinence response during a smoking cessation attempt. Addiction. Advance online publication. doi: 10.1111/add.13613

Piper, M.E. (2015). Withdrawal: expanding a key addiction construct. Nicotine and Tobacco Research. 17(12), 1405-15.

Special Issue: Adaptive Interventions for Children’s Mental Health

September 8, 2016:boy sits in field

There are vast individual differences in youth presenting for mental health treatment. Youth vary in their initial clinical presentation; their contextual risk and protective factors; and their engagement, adherence and response to evidence-based treatments. For this reason, adaptive interventions, which are individually tailored to each person, are valuable tools in the treatment and prevention of child and adolescent mental health (CAMH) disorders. Methodology Center Investigator Daniel Almirall co-edited a recent special issue of the Journal of Clinical Child & Adolescent Psychology that showcases recent applications and innovations of adaptive interventions for addressing CAMH disorders.

To introduce the issue, Daniel and his co-editor Andrea Chronis-Tuscano wrote an article that introduces adaptive interventions and the use of the sequential, multiple assignment, randomized trial (SMART) for the development of evidence-based adaptive interventions. The article also gives an overview of research using adaptive interventions for CAMH disorders and describes future directions for this research.

The special issue includes articles on using adaptive interventions to treat ADHD, autism spectrum disorder, depression, conduct problems and more.

Read the article.

Open the special issue.



Almirall, D., & Chronis-Tuscano, A. (2016). Adaptive interventions in child and adolescent mental health. Journal of Clinical Child & Adolescent Psychology, 45(4), 383-395.

Featured Articles: Two Types of TVEM

Time-varying effect modeling (TVEM) is a flexible approach that can be used to answer different types of questions using different types of data. Two articles in a recent issue of Drug and Alcohol Dependence demonstrate the range of possibilities for TVEM. In one, a group of researchers led by Michael Mason, associate professor of psychiatry and director of the Commonwealth Institute for Child & Family Studies at Virginia Commonwealth University, examined the time-varying effects of a smoking intervention using ecological momentary assessment (EMA). In the other, Megan Schuler, Marshall J. Seidman Fellow in Health Care Policy at Harvard Medical School, and her co-authors use data from Add Health to examine how the relationship between depression and substance use changes across adolescence and young adulthood. Both articles use The Methodology Center’s TVEM SAS macro for analyses.

In the first article, “Time-Varying Effects of a Text-Based Smoking Cessation Intervention for Urban Adolescents,” the authors examined EMA data from a six-month intervention that sent text messages to 200 urban adolescents who were trying to quit smoking. The authors used TVEM to examine whether the intervention had an impact on the association between stress and craving over time. They found that, during the second and third months, the association between stress and craving was weaker for the intervention group than it was for the control group. They also found that the intervention helped steadily reduce craving over time.  The authors of the article include Methodology Center Investigators Stephanie Lanza and Michael Russell.

Open the “smoking cessation intervention” article. (Journal access required.)

In the second article, “Age-Varying Associations Between Substance Use Behaviors And Depressive Symptoms During Adolescence And Young Adulthood,” the authors used TVEM to examine how heavy episodic drinking (HED), daily smoking, and marijuana use related to depressive symptoms from ages 12 to age 31. HED was associated with depressive symptoms during adolescence, but not afterwards. Both marijuana use and daily smoking were associated with depressive symptoms at most ages from 12 to 31. Megan Schuler, a former postdoctoral fellow in the Prevention and Methodology Training (PAMT) program, worked with Methodology Center Investigators Sara Vasilenko and Stephanie Lanza on the article.

Open the “age-varying associations” article. (Journal access required.)


Mason, M., Mennis, J., Way, T., Lanza, S., Russell, M., & Zaharakis, N. (2015). Time-varying effects of a text-based smoking cessation intervention for urban adolescents. Drug and Alcohol Dependence157, 99-105. doi: 10.1016/j.drugalcdep.2015.10.016

Schuler, M. S., Vasilenko, S. A., & Lanza, S. T. (2015). Age-varying associations between substance use behaviors and depressive symptoms during adolescence and young adulthood. Drug and Alcohol Dependence,157, 75-82. doi: 10.1016/j.drugalcdep.2015.10.005

Featured Article: LCA of Substance Use Among College Students

February 11, 2016:lcasubstanceuse

By understanding drug use profiles among college students, intervention designers may be able to target substance-use prevention efforts more effectively. In a new article in Addictive Behaviors, three Penn State researchers, Rebecca Evans-Polce and Stephanie Lanza of The Methodology Center, and Jennifer Maggs of the Department of Human Development and Family Studies, use latent class analysis (LCA) to examine use profiles among fourth-year college students of a broad variety of substances, including alcohol, marijuana, prescription drugs, and traditional and alternative forms of tobacco. They identified some unexpected classes and found that information about a student’s age, gender, and activities predicted their class membership.

Lead author Rebecca Evans-Polce said, “We know that individuals, if they are going to use substances, often use more than one type of substance. This study allowed us to examine important subgroups of individuals in terms of the types of substances they use. We discovered a lot of heterogeneity in terms of the patterns of substance use behaviors among individuals and in terms of what predicted who would be in one subgroup versus another. Some of these subgroups do not fit the traditional stereotype of a ‘substance user’ and thus may be under-recognized in terms of substance-use-prevention intervention efforts. In the future, we hope to examine how an individual might progress from one class to another or how health and education outcomes might differ across the latent classes.”

The authors examined a sample of 608 fourth-year college students from the 2011 University Life Study. They examined seven substance use measures: extreme heavy episodic drinking (HED), cigarettes, cigars, smokeless tobacco, hookahs, marijuana, and non-medical prescription drugs. Their analysis indicated a five-class model. The classes identified were Non/Low Users, Non-Hookah Tobacco Users, Extreme HED and Marijuana Users, Hookah and Marijuana Users, and Poly-Substance Users.



Evans-Polce, R. E., Lanza, S. T., & Maggs, J. L. (2016). Heterogeneity of alcohol, tobacco, and other substance use behaviors in US college students: A latent class analysis. Addictive Behaviors, 53, 80-85.

Featured Article: Getting the Most from HIV/AIDS Interventions

January 28, 2016:Kari KuglerLinda Collins

A new article in AIDS and Behavior introduces to HIV and AIDS researchers the multiphase optimization strategy (MOST), a framework for developing and evaluating optimized interventions. Methodology Center researchers Linda Collins and Kari Kugler and their collaborator Marya Gwadz of New York University wrote the article, “Optimization of Multicomponent Behavioral and Biobehavioral Interventions for the Prevention and Treatment of HIV/AIDS.” The authors explain the benefits of MOST within the context of a hypothetical intervention targeting people who live with HIV/AIDS and drink alcohol at hazardous levels. The authors explore MOST’s potential for answering questions that a traditional approach to intervention development cannot address.
Linda explained the rationale for using MOST: “MOST can be used to develop optimized behavioral and biobehavioral interventions in the HIV field and other areas. It enables scientists to engineer interventions to meet specific standards of effectiveness, efficiency, economy, and scalability. This is accomplished by looking inside the ‘black box’ of multicomponent interventions by using highly efficient randomized experiments.”

MOST is a comprehensive, principled, engineering-inspired framework. MOST includes a randomized controlled trial (RCT) for intervention evaluation, but also includes other phases of research before the RCT, unlike the standard approach to intervention development. The three phases of MOST—preparation, optimization, and evaluation—are aimed at intervention optimization using criteria selected by the scientist. The goal may be to develop a cost-effective intervention, an intervention that achieves a specified level of effectiveness, the briefest intervention that achieves a minimum level of effectiveness, or any other reasonable and explicitly operationalized goal.

Open the article.

Collins, L. M., Kugler, K. C., & Gwadz, M. V. (2016).  Optimization of multicomponent behavioral and biobehavioral interventions for the prevention and treatment of HIV/AIDS.  AIDS and Behavior, 20, 197-214.

Featured Article: Nicotine Dependence Diminishes the Effect of Smoking on Mood

November 11, 2015:
women smokerResearch has shown that some adolescents experience nicotine dependence at low levels of smoking (DiFranza et al., 2000; O’Loughlin et al., 2003). Other results indicate that early nicotine dependence strongly predicts future smoking (Dierker & Mermelstein, 2010; DiFranza et al., 2002). A recent paper in the journal Addictive Behavior, “Nicotine-dependence-varying effects of smoking events on momentary mood changes among adolescents,” provides insight into the mechanisms that encourage and maintain nicotine dependence. In this paper, the authors apply time-varying effect modeling (TVEM) and other methods to examine the association between nicotine dependence and the impact of smoking on mood. Authors include Methodology Center investigators, affiliates, and collaborators Arielle Selya, Nicole Updegrove, Jennifer Rose, Lisa Dierker, Xianming Tan, Donald Hedeker, Runze Li, and Robin Mermelstein.

The authors recruited a subsample from the Social and Emotional Contexts of Adolescent Smoking Patterns Study who were classified as former experimental smokers, current experimental smokers, or regular smokers. Participants were followed for 24 months using several waves of questionnaires and week-long ecological momentary assessments (EMA). Some of their findings validated current theory: adolescents with low nicotine dependence experienced improved mood when smoking, but this same improvement in mood was not observed in adolescents with higher levels of nicotine dependence. In other words, smoking more only improved mood among individuals with low levels of nicotine dependence.

Interestingly, the findings did not support other aspects of current theories, which postulate that nicotine withdrawal generates negative reinforcement that is responsible for maintaining dependence (Tiffany et al., 2004). In the current study the correlation between negative affect and amount smoked was NOT significant at higher levels of nicotine dependence.

Lead author Arielle Selya, assistant professor of family and community medicine at The University of North Dakota, is excited about the prospect of untangling the factors that lead to and maintain nicotine dependence. “These findings support theories that positive reinforcement is important at early stages in the addiction process, but we were surprised to see no support for the role of negative reinforcement in maintaining more severe levels of addiction. Perhaps smokers are pre-emptively smoking before they experience withdrawal symptoms. Perhaps social and situational factors are stronger drivers of the average individual smoking event than are nicotine dependence symptoms. Future research can tease apart these different factors and help us to better understand why smokers keep smoking.

Open the article. (Journal access required)


Selya, A. S., Updegrove, N., Rose, J. S., Dierker, L., Tan, X., Hedeker, D., … & Mermelstein, R. J. (2015). Nicotine-dependence-varying effects of smoking events on momentary mood changes among adolescents. Addictive Behaviors, 41, 65-71. doi: 10.1016/j.addbeh.2014.09.028

Dierker, L., & Mermelstein, R. J. (2010). Early emerging nicotine-dependence symptoms: A signal of propensity for chronic smoking behavior in adolescents. The Journal of Pediatrics, 156(5), 818–822. doi: 10.1016/j.jpeds.2009.11.044

DiFranza, J. R., Rigotti, N. A., McNeill, A.D., Ockene, J. K., Savageau, J. A., St Cyr, D., & Coleman, M. (2000). Initial symptoms of nicotine dependence in adolescents.Tobacco Control, 9(3), 313–319. doi: 10.1136/tc.9.3.313

DiFranza, J. R., Savageau, J. A., Rigotti, N. A., Fletcher, K., Ockene, J. K., McNeill, A.D., … & Wood, C. (2002). Development of symptoms of tobacco dependence in youths: 30 month follow up data from the DANDY study. Tobacco Control, 11(3), 228–235. doi: 10.1136/tc.11.3.228

O’Loughlin, J., DiFranza, J., Tyndale, R. F., Meshefedjian, G., McMillan-Davey, E., Clarke, P. B., … & Paradis, G. (2003). Nicotine-dependence symptoms are associated with smoking frequency in adolescents. American Journal of Preventive Medicine, 25(3), 219–225. doi: 10.1016/S0749379703001983

Tiffany, S. T., Conklin, C. A., Shiffman, S., & Clayton, R. R. (2004). What can dependence theories tell us about assessing the emergence of tobacco dependence?Addiction, 99(Suppl. 1), 78–86. doi: 10.1111/j.1360-0443.2004.00734.x

Featured Article: Which Young Smokers Will End Up Addicted?

October 12, 2015:
Stephanie LanzaSV

Early milestones in the development of smoking, such as first cigarette, experimental smoking, and onset of regular smoking, are key risk factors for later nicotine dependence (Dierker et al., 2008). The risk associated with age of smoking initiation has been studied widely, but less research has examined the link between the age of onset of regular smoking and later dependence. In a new article to appear in Addictive Behaviors, Methodology Center Investigators Stephanie Lanza and Sara Vasilenko apply time varying effect modeling (TVEM) to explore the link between age of onset of regular smoking and adult nicotine dependence.

This brief article is the first to apply TVEM to explore the complex association between the age of onset of a risky behavior and later diagnosis.

Using a sample of 15,748 adults from the National Epidemiologic Survey on Alcohol and Related Conditions, the authors applied TVEM to model nicotine dependence in adulthood as a flexible function of age of first regular smoking. They found that adult nicotine dependence is highest for people who began to smoke regularly around age 10. The risk for adult dependence decreased steadily to about half for people who initiated at age 18. The association between age of onset of regular smoking and adult dependence was stronger for females. People who initiated regular smoking in adulthood had lower rates of nicotine dependence than those who initiated at any time during adolescence.

Open the article. (Journal access required)


Lanza, S. T., & Vasilenko, S. A. (2015). New methods shed light on age of onset as a risk factor for nicotine dependence. Addictive Behaviors50, 161-164.

Dierker L., He J., Kalaydjian A., Swendsen J., Degenhardt L., Glantz, M., et al. (2008). The importance of timing of transitions for risk of regular smoking and nicotine dependence. Annals of Behavioral Medicine, 36, 87-92.

Featured Article: Does High Self-Efficacy in Sexual Situations Reduce the Risk of Rape?

August 28, 2015:

DonnaCoffmanTwenty percent of South African youth report having experienced forced sex, or rape. Compounding this problem is the fact that youth who have been raped are more likely to engage in risky sexual behavior (e.g., sex without a condom) in the future. Previous research established that youth in the United States who believe in their ability to control a situation (i.e., youth with high self-efficacy) are less likely to experience rape by a peer or date (Walsh & Foshee, 1998). A new article, “Forced Sexual Experiences and Sexual Situation Self-Efficacy Among Youth,” coauthored by former PAMT trainee Jacqui Miller, Methodology Center Investigator Donna Coffman and several investigators on the HealthWise South Africa project, examines whether this correlation holds true for South African youth.

The authors applied multilevel models to a subset of data (N=10,379) from the HealthWise project. Heathwise South Africa is a school-based substance use and HIV prevention program in the area around Cape Town, South Africa. Their findings supported the importance of sexual situation self-efficacy for reducing the risk of rape. The impact of sexual self-efficacy was observed regardless of whether the teen had previously been a victim of rape. The authors also found that self-efficacy varies across time, which can lead to different levels of risk at different times.
Open the article. (Journal access required)

Miller, J. A., Smith, E. A., Coffman, D., Mathews, C., & Wegner, L. (2015). Forced sexual experiences and sexual situation self‐efficacy among South African youth.Journal of Research on Adolescence. Advance online publication. doi: 10.1111/jora.12217

Health Education Research: Theory and Practice, 13, 139–144. doi: 10.1093/her/13.1.139