Linda Collins
Linda Collins
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Professor of Social and Behavioral Sciences
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Professional overview
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Linda M. Collins is Professor of Global Public Health in the Department of Social and Behavioral Sciences, with a secondary appointment in the Department of Biostatistics. She earned her B.A. in Psychology at the University of Connecticut and her Ph.D. in Quantitative Psychology at the University of Southern California.
Collins’ research interests are focused on the development, dissemination, and application of the multiphase optimization strategy (MOST), a framework for the optimization of behavioral, biobehavioral, and social-structural interventions. The objective of MOST is to improve intervention effectiveness, efficiency, economy, and scalability. She is currently collaborating on research applying MOST in the areas of smoking cessation, the prevention of excessive drinking and risky sex in college students, and HIV services.
Collins’ research has been funded by the National Institute on Drug Abuse, the National Institute on Alcohol Abuse and Alcoholism, and the National Science Foundation, among others. She has given more than 150 presentations on MOST around the world, and her publications have appeared in journals in the fields of behavioral science, quantitative methodology, medicine, and engineering.
Collins has held tenured faculty positions at the University of Southern California and at Penn State University, where she was Distinguished Professor of Human Development and Family Studies and Director of The Methodology Center. She is a Fellow of the American Psychological Association, the Association for Psychological Science, the Society of Behavioral Medicine, and is a past president of the Society of Multivariate Experimental Psychology and the Society for Prevention Research.
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Education
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BA, Psychology, University of Connecticut, Storrs, CTPhD, Quantitative Psychology, University of Southern California, Los Angeles, CA
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Honors and awards
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Fulbright Specialist, National University of Ireland Galway (2018)Pauline Schmitt Russell Distinguished Career Award, Pennsylvania State University’s College of Health and Human Development (2017)Evan G. and Helen G. Pattishall Outstanding Research Achievement Award, Pennsylvania State University’s College of Health and Human Development (2011)President’s Award, Society for Prevention Research (2004)Faculty Scholar Medal for the Social and Behavioral Sciences, Pennsylvania State University (2000)Psychology Department Teacher of the Year, University of Southern California (1992)Psychology Department Mentorship Award, University of Southern California (1991)Society of Multivariate Experimental Psychology Award for Distinguished Early Career Contributions to Multivariate Behavioral Research (1991)
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Areas of research and study
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Behavioral ScienceCost EffectivenessCost-effective Health Programs and PoliciesDissemination and Implementation of Evidence-based Programs
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Publications
Publications
A dynamical systems approach to understanding self-regulation in smoking cessation behavior change
AbstractTimms, K. P., Rivera, D. E., Collins, L., & Piper, M. E. (n.d.).Publication year
2014Journal title
Nicotine and Tobacco ResearchVolume
16Issue
SUPPL2Page(s)
S159-S168AbstractIntroduction: Self-regulation, a key component of the addiction process, has been challenging to model precisely in smoking cessation settings, largely due to the limitations of traditional methodological approaches in measuring behavior over time. However, increased availability of intensive longitudinal data (ILD) measured through ecological momentary assessment facilitates the novel use of an engineering modeling approach to better understand self-regulation. Methods: Dynamical systems modeling is a mature engineering methodology that can represent smoking cessation as a selfregulation process. This article shows how a dynamical systems approach effectively captures the reciprocal relationship between day-to-day changes in craving and smoking. Models are estimated using ILD from a smoking cessation randomized clinical trial. Results: A system of low-order differential equations is presented that models cessation as a self-regulatory process. It explains 87.32% and 89.16% of the variance observed in craving and smoking levels, respectively, for an active treatment group and 62.25% and 84.12% of the variance in a control group. The models quantify the initial increase and subsequent gradual decrease in craving occurring postquit as well as the dramatic quit-induced smoking reduction and postquit smoking resumption observed in both groups. Comparing the estimated parameters for the group models suggests that active treatment facilitates craving reduction and slows postquit smoking resumption. Conclusions: This article illustrates that dynamical systems modeling can effectively leverage ILD in order to understand selfregulation within smoking cessation. Such models quantify group-level dynamic responses in smoking cessation and can inform the development of more effective interventions in the future.A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention
AbstractTimms, K. P., Rivera, D. E., Piper, M. E., & Collins, L. (n.d.).Publication year
2014Page(s)
2389-2394AbstractThe chronic, relapsing nature of tobacco use represents a major challenge in smoking cessation treatment. Recently, novel intervention paradigms have emerged that seek to adjust treatments over time in order to meet a patient's changing needs. This article demonstrates that Hybrid Model Predictive Control (HMPC) offers an appealing framework for designing these optimized, time-varying smoking cessation interventions. HMPC is a particularly appropriate approach as it recognizes that intervention doses must be assigned in predetermined, discrete units while retaining receding-horizon, constraint-handling, and combined feedback and feedforward capabilities. Specifically, an intervention algorithm is developed here in which counseling and two pharmacotherapies are manipulated to reduce daily smoking and craving levels. The potential usefulness of such an intervention is illustrated through simulated treatment of a quit attempt in a hypothetical patient, which highlights that prioritizing reduction in craving over total daily smoking levels significantly reduces craving levels, suppresses relapse, and successfully rejects time-varying disturbances such as stress, all while adhering to several practical operational constraints and resource use considerations.Continuous-time system identification of a smoking cessation intervention
AbstractTimms, K. P., Rivera, D. E., Collins, L., & Piper, M. E. (n.d.).Publication year
2014Journal title
International Journal of ControlVolume
87Issue
7Page(s)
1423-1437AbstractCigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.Europe Needs a Central, Transparent, and Evidence-Based Approval Process for Behavioural Prevention Interventions
AbstractFaggiano, F., Allara, E., Giannotta, F., Molinar, R., Sumnall, H., Wiers, R., Michie, S., Collins, L., & Conrod, P. (n.d.).Publication year
2014Journal title
PLoS MedicineVolume
11Issue
10Abstract~Evaluating individual intervention components : making decisions based on the results of a factorial screening experiment
AbstractCollins, L., Trail, J. B., Kugler, K. C., Baker, T. B., Piper, M. E., & Mermelstein, R. J. (n.d.).Publication year
2014Journal title
Translational Behavioral MedicineVolume
4Issue
3Page(s)
238-251AbstractThe multiphase optimization strategy (MOST) is a framework for not only evaluating but also optimizing behavioral interventions. A tool critical for MOST is the screening experiment, which enables efficient gathering of information for deciding which components to include in an optimized intervention. This article outlines a procedure for making decisions based on data from a factorial screening experiment. The decision making procedure is illustrated with artificial data generated to resemble empirical data. The illustration suggests that this approach is useful for selecting intervention components and settings based on the results of a factorial screening experiment. It is important to develop methods for making decisions based on factorial screening experiments. The approach demonstrated here is potentially useful, but has limited generalizability. Future research should develop additional decision making procedures for a variety of situations.Factorial experiments : Efficient tools for evaluation of intervention components
AbstractCollins, L., Dziak, J. J., Kugler, K. C., & Trail, J. B. (n.d.).Publication year
2014Journal title
American journal of preventive medicineVolume
47Issue
4Page(s)
498-504AbstractBackground: An understanding of the individual and combined effects of a set of intervention components is important for moving the science of preventive medicine interventions forward. This understanding can often be achieved in an efficient and economical way via a factorial experiment, in which two or more independent variables are manipulated. The factorial experiment is a complement to the RCT; the two designs address different research questions. Purpose: To offer an introduction to factorial experiments aimed at investigators trained primarily in the RCT. Methods: The factorial experiment is compared and contrasted with other experimental designs used commonly in intervention science to highlight where each is most efficient and appropriate. Results: Several points are made: factorial experiments make very efficient use of experimental subjects when the data are properly analyzed; a factorial experiment can have excellent statistical power even if it has relatively few subjects per experimental condition; and when conducting research to select components for inclusion in a multicomponent intervention, interactions should be studied rather than avoided. Conclusions: Investigators in preventive medicine and related areas should begin considering factorial experiments alongside other approaches. Experimental designs should be chosen from a resource management perspective, which states that the best experimental design is the one that provides the greatest scientific benefit without exceeding available resources.Functional data analysis for dynamical system identification of behavioral processes
AbstractTrail, J. B., Collins, L., Rivera, D. E., Li, R., Piper, M. E., & Baker, T. B. (n.d.).Publication year
2014Journal title
Psychological MethodsVolume
19Issue
2Page(s)
175-187AbstractEfficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input- output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.Moving beyond the treatment package approach to developing behavioral interventions : addressing questions that arose during an application of the Multiphase Optimization Strategy (MOST)
AbstractWyrick, D. L., Rulison, K. L., Fearnow-Kenney, M., Milroy, J. J., & Collins, L. (n.d.).Publication year
2014Journal title
Translational Behavioral MedicineVolume
4Issue
3Page(s)
252-259AbstractGiven current pressures to increase the public health contributions of behavioral interventions, intervention scientists may wish to consider moving beyond the classical treatment package approach that focuses primarily on achieving statistical significance. They may wish also to focus on goals directly related to optimizing public health impact. The Multiphase Optimization Strategy (MOST) is an innovative methodological framework that draws on engineering principles to achieve more potent behavioral interventions. MOST is increasingly being adopted by intervention scientists seeking a systematic framework to engineer an optimized intervention. As with any innovation, there are challenges that arise with early adoption. This article describes the solutions to several critical questions that we addressed during the first-ever iterative application of MOST. Specifically, we describe how we have applied MOST to optimize an online program (myPlaybook) for the prevention of substance use among college student-athletes. Our application of MOST can serve as a blueprint for other intervention scientists who wish to design optimized behavioral interventions. We believe using MOST is feasible and has the potential to dramatically improve program effectiveness thereby advancing the public health impact of behavioral interventions.Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART)
AbstractCollins, L., Nahum-Shani, I., & Almirall, D. (n.d.).Publication year
2014Journal title
Clinical TrialsVolume
11Issue
4Page(s)
426-434AbstractBackground and purpose A behavioral intervention is a program aimed at modifying behavior for the purpose of treating or preventing disease, promoting health, and/or enhancing well-being. Many behavioral interventions are dynamic treatment regimens, that is, sequential, individualized multicomponent interventions in which the intensity and/or type of treatment is varied in response to the needs and progress of the individual participant. The multiphase optimization strategy (MOST) is a comprehensive framework for development, optimization, and evaluation of behavioral interventions, including dynamic treatment regimens. The objective of optimization is to make dynamic treatment regimens more effective, efficient, scalable, and sustainable. An important tool for optimization of dynamic treatment regimens is the sequential, multiple assignment, randomized trial (SMART). The purpose of this article is to discuss how to develop optimized dynamic treatment regimens within the MOST framework. Methods and results The article discusses the preparation, optimization, and evaluation phases of MOST. It is shown how MOST can be used to develop a dynamic treatment regimen to meet a prespecified optimization criterion. The SMART is an efficient experimental design for gathering the information needed to optimize a dynamic treatment regimen within MOST. One signature feature of the SMART is that randomization takes place at more than one point in time. Conclusion MOST and SMART can be used to develop optimized dynamic treatment regimens that will have a greater public health impact.Optimization of remotely delivered intensive lifestyle treatment for obesity using the Multiphase Optimization Strategy : Opt-IN study protocol
AbstractPellegrini, C. A., Hoffman, S. A., Collins, L., & Spring, B. (n.d.).Publication year
2014Journal title
Contemporary Clinical TrialsVolume
38Issue
2Page(s)
251-259AbstractBackground: Obesity-attributable medical expenditures remain high, and interventions that are both effective and cost-effective have not been adequately developed. The Opt-IN study is a theory-guided trial using the Multiphase Optimization Strategy (MOST) to develop an optimized, scalable version of a technology-supported weight loss intervention. Objective: Opt-IN aims to identify which of 5 treatment components or component levels contribute most meaningfully and cost-efficiently to the improvement of weight loss over a 6. month period. Study design: Five hundred and sixty obese adults (BMI 30-40kg/m2) between 18 and 60years old will be randomized to one of 16 conditions in a fractional factorial design involving five intervention components: treatment intensity (12 vs. 24 coaching calls), reports sent to primary care physician (No vs. Yes), text messaging (No vs. Yes), meal replacement recommendations (No vs. Yes), and training of a participant's self-selected support buddy (No vs. Yes). During the 6-month intervention, participants will monitor weight, diet, and physical activity on the Opt-IN smartphone application downloaded to their personal phone. Weight will be assessed at baseline, 3, and 6months. Significance: The Opt-IN trial is the first study to use the MOST framework to develop a weight loss treatment that will be optimized to yield the best weight loss outcome attainable for $500 or less.Youths' Substance Use and Changes in Parental Knowledge-Related Behaviors During Middle School : A Person-Oriented Approach
AbstractLippold, M. A., Greenberg, M. T., & Collins, L. (n.d.).Publication year
2014Journal title
Journal of Youth and AdolescenceVolume
43Issue
5Page(s)
729-744AbstractParental knowledge is a key protective factor for youths' risky behavior. Little is known about how longitudinal combinations of knowledge-related behaviors are associated with youths' substance use. This longitudinal study uses Latent Transition Analysis to identify latent patterns of parental knowledge-related behaviors occurring in mother-youth dyads during middle school and to investigate how changes in knowledge-related patterns are associated with youths' substance use in Grade 6 and the initiation of substance use from Grade 6 to 8. Using a sample of 536 rural dyads (53 % female, 84 % White), we assessed mother and youths' reports of parental knowledge, active parental monitoring efforts, youth disclosure, and parent-youth communication to identify six latent patterns of knowledge-related behaviors: High Monitors, Low Monitors, Communication-Focused, Supervision-Focused, Maternal Over-Estimators, and Youth Over-Estimators. Fifty percent or more of dyads in the High Monitors, Communication-Focused and Youth Over-Estimators were in the same status in both 6th and 8th grade: 98 % of Low Monitors in Grade 6 were also in this status in Grade 8. The initiation of alcohol, smoking, and marijuana was associated significantly with transitions between patterns of knowledge-related behaviors. The initiation of alcohol and smoking were associated with increased odds of transitions into the Low Monitors from the Communication-Focused, Supervision-Focused, and Maternal Over-Estimators. However, the initiation of substance use was associated with decreased odds of transitions from the High Monitors to the Low Monitors and with increased odds of transitions from High Monitors to Supervision-Focused. The discussion focuses on the value of using a person-oriented dyadic approach with multiple reporters to study changes in knowledge-related behaviors over the middle school period.Control systems engineering for understanding and optimizing smoking cessation interventions
AbstractTimms, K. P., Rivera, D. E., Collins, L., & Piper, M. E. (n.d.).Publication year
2013Page(s)
1964-1969AbstractCigarette smoking remains a major public health issue. Despite a variety of treatment options, existing intervention protocols intended to support attempts to quit smoking have low success rates. An emerging treatment framework, referred to as adaptive interventions in behavioral health, addresses the chronic, relapsing nature of behavioral health disorders by tailoring the composition and dosage of intervention components to an individual's changing needs over time. An important component of a rapid and effective adaptive smoking intervention is an understanding of the behavior change relationships that govern smoking behavior and an understanding of intervention components' dynamic effects on these behavioral relationships. As traditional behavior models are static in nature, they cannot act as an effective basis for adaptive intervention design. In this article, behavioral data collected daily in a smoking cessation clinical trial is used in development of a dynamical systems model that describes smoking behavior change during cessation as a self-regulatory process. Drawing from control engineering principles, empirical models of smoking behavior are constructed to reflect this behavioral mechanism and help elucidate the case for a control-oriented approach to smoking intervention design.Hybrid model predictive control for optimizing gestational weight gain behavioral interventions
AbstractDong, Y., Rivera, D. E., Downs, D. S., Savage, J. S., Thomas, D. M., & Collins, L. (n.d.).Publication year
2013Page(s)
1970-1975AbstractExcessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.Parental Knowledge and Youth Risky Behavior : A Person Oriented Approach
AbstractLippold, M. A., Greenberg, M. T., & Collins, L. (n.d.).Publication year
2013Journal title
Journal of Youth and AdolescenceVolume
42Issue
11Page(s)
1732-1744AbstractMost studies isolate the effects of one knowledge-related behavior on youth outcomes. This study explores the relationship between subgroups of mother-youth dyads that use specific combinations of parental knowledge-related behaviors and youth risky behavior. Using a sample of 796 rural 6th graders (53 % female), we assessed mother and youth reports of maternal knowledge, active parent monitoring efforts, youth disclosure, parental supervision, and the amount of parent-youth communication to identify five knowledge latent classes: High-Monitors, Maternal Over-Estimators, Low-Monitors, Communication-Focused, and Supervision-Focused. Delinquency, antisocial peers, and substance use were associated with increased odds of membership in the Supervision-Focused class, relative to the High Monitors. Membership in the Low Monitors and Maternal Over-Estimators classes was associated with unhealthy attitudes towards substances and for Low Monitors, substance use. The discussion focuses on the value of using a person-oriented approach to understand parental knowledge and risky behavior during early adolescence and intervention implications.Recruiting and engaging smokers in treatment in a primary care setting : Developing a chronic care model implemented through a modified electronic health record
AbstractPiper, M. E., Baker, T. B., Mermelstein, R., Collins, L., Fraser, D. L., Jorenby, D. E., Smith, S. S., Christiansen, B. A., Schlam, T. R., Cook, J. W., Oguss, M., & Fiore, M. C. (n.d.).Publication year
2013Journal title
Translational Behavioral MedicineVolume
3Issue
3Page(s)
253-263AbstractAlmost 35 million U.S. smokers visit primary care clinics annually, creating a need and opportunity to identify such smokers and engage them in evidence-based smoking treatment. The purpose of this study is to examine the feasibility and effectiveness of a chronic care model of treating tobacco dependence when it is integrated into primary care systems using electronic health records (EHRs). The EHR prompted primary care clinic staff to invite patients who smoked to participate in a tobacco treatment program. Patients who accepted and were eligible were offered smoking reduction or cessation treatment. More than 65 % of smokers were invited to participate, and 12.4 % of all smokers enrolled in treatment-30 % in smoking reduction and 70 % in cessation treatment. The chronic care model developed for treating tobacco dependence, integrated into the primary care system through the EHR, has the potential to engage up to 4.3 million smokers in treatment a year.Some methodological considerations in theory-based health behavior research
AbstractCollins, L., MacKinnon, D. P., & Reeve, B. B. (n.d.).Publication year
2013Journal title
Health PsychologyVolume
32Issue
5Page(s)
586-591AbstractAs this special issue shows, much research in social and personality psychology is directly relevant to health psychology. In this brief commentary, we discuss three topics in research methodology that may be of interest to investigators involved in health-related psychological research. The first topic is statistical analysis of mediated and moderated effects. The second is measurement of latent constructs. The third is the Multiphase Optimization Strategy, a framework for translation of innovations from social and personality psychology into behavioral interventions.A dynamical systems model for improving gestational weight gain behavioral interventions
AbstractDong, Y., Rivera, D. E., Thomas, D. M., Navarro-Barrientos, J. E., Downs, D. S., Savage, J. S., & Collins, L. (n.d.).Publication year
2012Page(s)
4059-4064AbstractExcessive gestational weight gain (GWG) represents a major public health concern. In this paper, we present a dynamical systems model that describes how a behavioral intervention can influence weight gain during pregnancy. The model relies on the integration of a mechanistic energy balance with a dynamical behavioral model. The behavioral model incorporates some well-accepted concepts from psychology: the Theory of Planned Behavior (TPB) and the principle of self-regulation which describes how internal processes within the individual can serve to reinforce the positive outcomes of an intervention. A hypothetical case study is presented to illustrate the basic workings of the model and demonstrate how the proper design of the intervention can counteract natural trends towards declines in healthy eating and reduced physical activity during the course of pregnancy. The model can be used by behavioral scientists to evaluate decision rules for adaptive time-varying behavioral interventions, or as the open-loop model for hybrid model predictive control algorithms acting as decision frameworks for such interventions.Dynamic energy-balance model predicting gestational weight gain
AbstractThomas, D. M., Navarro-Barrientos, J. E., Rivera, D. E., Heymsfield, S. B., Bredlau, C., Redman, L. M., Martin, C. K., Lederman, S. A., Collins, L., & Butte, N. F. (n.d.).Publication year
2012Journal title
American Journal of Clinical NutritionVolume
95Issue
1Page(s)
115-122AbstractBackground: Gestational weight gains (GWGs) that exceed the 2009 Institute of Medicine recommended ranges increase risk of long-term postpartum weight retention; conversely, GWGs within the recommended ranges are more likely to result in positive maternal and fetal outcomes. Despite this evidence, recent epidemiologic studies have shown that the majority of pregnant women gain outside the target GWG ranges. A mathematical model that predicts GWG and energy intake could provide a clinical tool for setting precise goals during early pregnancy and continuous objective feedback throughout pregnancy. Objective: The purpose of this study was to develop and validate a differential equation model for energy balance during pregnancy that predicts GWG that results from changes in energy intakes. Design: A set of prepregnancy BMI - dependent mathematical models that predict GWG were developed by using data from a longitudinal study that measured gestational-changes in fat-free mass, fat mass, total body water, and total energy expenditure in 63 subjects. Results: Mathematical models developed for women with low, normal, and high prepregnancy BMI were shown to fit the original data. In 2 independent studies used for validation, model predictions of fat-free mass, fat mass, and total body water matched actual measurements within 1 kg. Conclusions: Our energy-balance model provides plausible predictions of GWG that results from changes in energy intakes. Because the model was implemented as a Web-based applet, it can be widely used by pregnant women and their health care providers.Multilevel factorial experiments for developing behavioral interventions : Power, sample size, and resource considerations
AbstractDziak, J. J., Nahum-Shani, I., & Collins, L. (n.d.).Publication year
2012Journal title
Psychological MethodsVolume
17Issue
2Page(s)
153-175AbstractFactorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions by helping investigators to screen several candidate intervention components simultaneously and to decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or when employees are nested within organizations). In this article, we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel, multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements-such as the number of clusters, the number of lower-level units, and the intraclass correlation-affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation.System identification modeling of a smoking cessation intervention
AbstractTimms, K. P., Rivera, D. E., Collins, L., & Piper, M. E. (n.d.). (PART 1).Publication year
2012Page(s)
786-791AbstractThis paper examines the use of system identification to describe time-varying phenomena in a smoking cessation intervention. The analysis is facilitated by the availability of intensive longitudinal data that enables the application of system identification techniques. Two model structures are considered; one involves the concept of statistical mediation, while the other describes a feedback mechanism. In fitting these models to intensive longitudinal data from a University of Wisconsin clinical trial that studied bupropion and counseling as smoking cessation aids, we focus on the relationship between craving and smoking. Here, we find craving features inverse response and smoking behavior features a dramatic reduction on the quit date, followed by a resumption in smoking. Analyzing the resulting models, we find that they differ in how they describe smoking resumption, and the case is made that the feedback mechanism more appropriately describes the relationship between craving and smoking.Translational Research in South Africa : Evaluating Implementation Quality Using a Factorial Design
AbstractCaldwell, L. L., Smith, E. A., Collins, L., Graham, J. W., Lai, M., Wegner, L., Vergnani, T., Matthews, C., & Jacobs, J. (n.d.).Publication year
2012Journal title
Child and Youth Care ForumVolume
41Issue
2Page(s)
119-136AbstractBackground: HealthWise South Africa: Life Skills for Adolescents (HW) is an evidence-based substance use and sexual risk prevention program that emphasizes the positive use of leisure time. Since 2000, this program has evolved from pilot testing through an efficacy trial involving over 7,000 youth in the Cape Town area. Beginning in 2011, through 2015, we are undertaking a new study that expands HW to all schools in the Metro South Education District. Objective: This paper describes a research study designed in partnership with our South African collaborators that examines three factors hypothesized to affect the quality and fidelity of HW implementation: enhanced teacher training; teacher support, structure and supervision; and enhanced school environment. Methods: Teachers and students from 56 schools in the Cape Town area will participate in this study. Teacher observations are the primary means of collecting data on factors affecting implementation quality. These factors address the practical concerns of teachers and schools related to likelihood of use and cost-effectiveness, and are hypothesized to be "active ingredients" related to high-quality program implementation in real-world settings. An innovative factorial experimental design was chosen to enable estimation of the individual effect of each of the three factors. Results: Because this paper describes the conceptualization of our study, results are not yet available. Conclusions: The results of this study may have both substantive and methodological implications for advancing Type 2 translational research.A dynamical model for describing behavioural interventions for weight loss and body composition change
AbstractNavarro-Barrientos, J. E., Rivera, D. E., & Collins, L. (n.d.).Publication year
2011Journal title
Mathematical and Computer Modelling of Dynamical SystemsVolume
17Issue
2Page(s)
183-203AbstractWe present a dynamical model incorporating both physiological and psychological factors that predict changes in body mass and composition during the course of a behavioural intervention for weight loss. The model consists of a three-compartment energy balance integrated with a mechanistic psychological model inspired by the Theory of Planned Behaviour. This describes how important variables in a behavioural intervention can influence healthy eating habits and increased physical activity over time. The novelty of the approach lies in representing the behavioural intervention as a dynamical system and the integration of the psychological and energy balance models. Two simulation scenarios are presented that illustrate how the model can improve the understanding of how changes in intervention components and participant differences affect outcomes. Consequently, the model can be used to inform behavioural scientists in the design of optimized interventions for weight loss and body composition change.A risk-based model predictive control approach to adaptive interventions in behavioral health
AbstractZafra-Cabeza, A., Rivera, D. E., Collins, L., Ridao, M. A., & Camacho, E. F. (n.d.).Publication year
2011Journal title
IEEE Transactions on Control Systems TechnologyVolume
19Issue
4Page(s)
891-901AbstractThis brief examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based model predictive control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this brief can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm.Commentaries on Replication in Prevention Science : A Rejoinder
AbstractValentine, J. C., Biglan, A., Boruch, R. F., Castro, F. G., Collins, L., Flay, B. R., Kellam, S., Mościcki, E. K., & Schinke, S. P. (n.d.).Publication year
2011Journal title
Prevention ScienceVolume
12Issue
2Page(s)
123-125Abstract~Erratum : New methods for tobacco dependence treatment research (Annals of Behavioral Medicine (2011) 41:2 (192-207) DOI: 10.1007/s12160-010-9252-y)
AbstractBaker, T. B., Mermelstein, R. J., Collins, L., Piper, M. E., Jorenby, D. E., Smith, S. S., Christiansen, B. A., Schlam, T. R., Cook, J. W., & Fiore, M. C. (n.d.).Publication year
2011Journal title
Annals of Behavioral MedicineVolume
42Issue
1Page(s)
139Abstract~