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
Latent transition analysis and how it can address prevention research questions
AbstractCollins, L., Collins, L. M., Graham, J. W., Rousculp, S. S., Fidler, P. L., Pan, J., & Hansen, W. B. (n.d.).Publication year
1994Journal title
NIDA Research Monograph SeriesIssue
142Page(s)
81-111AbstractThe objective of this chapter is to introduce latent transition analysis (LTA) to the substance use prevention research community. LTA is a new methodological technique for testing stage-sequential models, such as models of substance use onset. LTA estimates several different sets of parameters. One of these sets is the transition probability matrix, which contains information about the probability of movement between stages in the model. LTA can be used to evaluate the effectiveness of prevention intervention programs by comparing the transition probability matrices of the program and control groups. If the prevention program is successful, the transition probability matrices will indicate that the probability of moving to a more advanced stage of drug use is lower for the program participants than for the control group. An advantage of taking a stage-sequential approach is that examining the transition probability matrix reveals how effective a program is for individuals entering the program with different levels and types of substance use experience.Latent transition analysis for longitudinal data
AbstractCollins, L., Velicer, W. F., Martin, R. A., & Collins, L. M. (n.d.).Publication year
1996Journal title
AddictionVolume
91Issue
SUPPL.Page(s)
197-210AbstractAssessing outcome is a critical problem for the study of addictive behaviors. Traditional approaches often lack power and sensitivity. Latent Transition Analysis is an alternative procedure that is applicable to categorical latent variable models such as stage models. The method involves four different types of parameters, each of which may be relevant to different research questions. Two examples that employ the Stages of Change construct are used to illustrate the method. In the first example, three different models of longitudinal change are compared. In the second example, the effects of an expert system intervention for smoking is compared to a control condition. The method permits the investigation of a series of specific comparisons: (1) the effectiveness of the intervention for individuals in different stages can be assessed; (2) the effectiveness of the intervention can be evaluated for different time intervals; and (3) the effects of intervention on both progression through the stages and regression through the stages or relapse can be assessed. Other potential applications of the method are also discussed.Measurement and design issues in tobacco and drug use research
AbstractCollins, L., Flaherty, B. P., & Colby, S. M. (n.d.).Publication year
2002Journal title
Drug and alcohol dependenceVolume
68Issue
SUPPL.Page(s)
S1-S2Abstract~Methodological Considerations in Prevention Research
AbstractCollins, L., & Flaherty, B. P. (n.d.).Publication year
2006Page(s)
557-573AbstractThis chapter discussed a number of methodological considerations that face prevention research. It examined the central importance of theory in design and analysis of prevention studies. It considered the role of factorial invariance in developing culture-specific measures. It discussed the importance of statistical power and how it is dependent on factors other than sample size. It considered growth curve models, survival analysis, and lta, all relatively new procedures for dealing with change over time. It also discussed two approaches to the trait-state distinction and looked at missing data procedures and how important they are for prevention research. Finally, it discussed two general types of models that frequently arise in prevention research, mediation models and models of reciprocal causation, and how the customary ways of testing these models should perhaps be reconsidered.Mining health app data to find more and less successful weight loss subgroups
AbstractSerrano, K. J., Yu, M., Coa, K. I., Collins, L., & Atienza, A. A. (n.d.).Publication year
2016Journal title
Journal of medical Internet researchVolume
18Issue
6AbstractBackground: More than half of all smartphone app downloads involve weight, diet, and exercise. If successful, these lifestyle apps may have far-reaching effects for disease prevention and health cost-savings, but few researchers have analyzed data from these apps. Objective: The purposes of this study were to analyze data from a commercial health app (Lose It!) in order to identify successful weight loss subgroups via exploratory analyses and to verify the stability of the results. Methods: Cross-sectional, de-identified data from Lose It! were analyzed. This dataset (n=12,427,196) was randomly split into 24 subsamples, and this study used 3 subsamples (combined n=972,687). Classification and regression tree methods were used to explore groupings of weight loss with one subsample, with descriptive analyses to examine other group characteristics. Data mining validation methods were conducted with 2 additional subsamples. Results: In subsample 1, 14.96% of users lost 5% or more of their starting body weight. Classification and regression tree analysis identified 3 distinct subgroups: "the occasional users" had the lowest proportion (4.87%) of individuals who successfully lost weight; "the basic users" had 37.61% weight loss success; and "the power users" achieved the highest percentage of weight loss success at 72.70%. Behavioral factors delineated the subgroups, though app-related behavioral characteristics further distinguished them. Results were replicated in further analyses with separate subsamples. Conclusions: This study demonstrates that distinct subgroups can be identified in "messy" commercial app data and the identified subgroups can be replicated in independent samples. Behavioral factors and use of custom app features characterized the subgroups. Targeting and tailoring information to particular subgroups could enhance weight loss success. Future studies should replicate data mining analyses to increase methodology rigor.Modeling multidimensional sexual risk behavior using latent class analysis
AbstractLanza, S. T., & Collins, L. (n.d.).Publication year
2011Page(s)
119-124AbstractUnderstanding the intersection of various dimensions of sexual risk behavior in a population is critical for effective prevention. For example, frequency of sexual intercourse, the number of sexual partners, and inconsistent condom use are three dimensions of behavior that relate to the acquisition of sexually transmitted infections (STI's). Although estimating the differential risk posed by each dimension of behavior can be informative, taking a person-centered approach to modeling sexual risk behavior that incorporates multiple dimensions simultaneously can provide an intuitive and more complete picture of different profiles of behavior that are common in the population. Further, the identification of individual characteristics that predict membership in groups characterized by a profile of high-risk behavior can inform how to target intervention resources. Latent class analysis (LCA) is a latent variable model that can be used to identify risk profiles in empirical data. LCA measures an underlying, or latent, variable using a set of observed variables. The latent variable is made up of subgroups, or latent classes, that account for population heterogeneity. The variables measure dimensions of the latent classes. When applied to sexual risk behavior, each latent class represents a different multidimensional profile of risk. For example, Lanza and Collins (2008) used LCA to identify five latent classes of adolescents in the United States defined by common patterns of sexual risk behavior: Non-daters, Daters, Monogamous, Multipartner Safe, and Multipartner Exposed. A longitudinal extension of this approach, where transitions to more risky stages are estimated, can provide information on stability and change in sexual risk behavior profiles over time. Such a model would provide important insight about sexual risk behavior subgroups that are most at-risk of making a transition to high-risk behavior in the future. In addition, individual characteristics can be incorporated in the model to predict transitions to risky behavior. Identification of risk behavior profiles, modeling transitions between profiles over time, and predicting profile membership and transitions between profiles all have direct implications for prevention of HIV/AIDS and other STI's.Modeling Transitions in Latent Stage-Sequential Processes : A Substance Use Prevention Example
AbstractGraham, J. W., Collins, L., Wugalter, S. E., Chung, N. K., & Hansen, W. B. (n.d.).Publication year
1991Journal title
Journal of consulting and clinical psychologyVolume
59Issue
1Page(s)
48-57AbstractThis article illustrates the use of latent transition analysis (LTA), a methodology for testing stage-sequential models of individual growth. LTA is an outgrowth of latent class theory and is a particular type of latent Markov model emphasizing the use of multiple manifest indicators. LTA is used to compare the fit of two models of early adolescent substance use onset and to assess the effects of a school-based substance use prevention program on Ss measured in 7th grade and again in 8th grade. Several interesting findings emerged. First, a model of substance use onset including both alcohol and tobacco use as possible starting points fit better than a model that included alcohol use as the only starting point. Second, Ss who had tried tobacco but not alcohol in 7th grade seemed to be on an accelerated onset trajectory. Third, the normative education prevention program was generally successful, except for the students who had tried only tobacco in 7th grade.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.Multilevel factorial designs with experiment-induced clustering
AbstractNahum-Shani, I., Dziak, J. J., & Collins, L. (n.d.).Publication year
2018Journal title
Psychological MethodsVolume
23Issue
3Page(s)
458-479AbstractFactorial experimental designs have many applications in the behavioral sciences. In the context of intervention development, factorial experiments play a critical role in building and optimizing high-quality, multicomponent behavioral interventions. One challenge in implementing factorial experiments in the behavioral sciences is that individuals are often clustered in social or administrative units and may be more similar to each other than to individuals in other clusters. This means that data are dependent within clusters. Power planning resources are available for factorial experiments in which the multilevel structure of the data is due to individuals' membership in groups that existed before experimentation. However, in many cases clusters are generated in the course of the study itself. Such experiment-induced clustering (EIC) requires different data analysis models and power planning resources from those available for multilevel experimental designs in which clusters exist prior to experimentation. Despite the common occurrence of both experimental designs with EIC and factorial designs, a bridge has yet to be built between EIC and factorial designs. Therefore, resources are limited or nonexistent for planning factorial experiments that involve EIC. This article seeks to bridge this gap by extending prior models for EIC, developed for single-factor experiments, to factorial experiments involving various types of EIC. We also offer power formulas to help investigators decide whether a particular experimental design involving EIC is feasible. We demonstrate that factorial experiments can be powerful and feasible even with EIC. We discuss design considerations and directions for future research. (PsycINFO Database RecordMultilevel 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.New methods for tobacco dependence treatment research
AbstractBaker, T. B., Mermelstein, R., 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
41Issue
2Page(s)
192-207AbstractIntroduction: Despite advances in tobacco dependence treatment in the past two decades, progress has been inconsistent and slow. This paper reviews pervasive methodological issues that may contribute to the lack of timely progress in tobacco treatment science including the lack of a dynamic model or framework of the cessation process, inefficient study designs, and the use of distal outcome measures that poorly index treatment effects. The authors then present a phase-based cessation framework that partitions the cessation process into four discrete phases based on current theories of cessation and empirical data. These phases include: (1) Motivation, (2) Precessation, (3) Cessation, and (4) Maintenance. Discussion: Within this framework, it is possible to identify phase-specific challenges that a smoker would encounter while quitting smoking, intervention components that would address these phase-specific challenges, mechanisms via which such interventions would exert their effects, and optimal outcome measures linked to these phase-specific interventions. Investigation of phase-based interventions can be accelerated by using efficient study designs that would permit more timely development of an optimal smoking cessation treatment package.New statistical methods for substance use prevention research
AbstractCollins, L., Collins, L. M., & Seitz, L. A. (n.d.).Publication year
1994Journal title
NIDA Research Monograph SeriesIssue
142Page(s)
1-12Abstract~Omega : A General Formulation of the Rand Index of Cluster Recovery Suitable for Non-disjoint Solutions
AbstractCollins, L., & Dent, C. W. (n.d.).Publication year
1988Journal title
Multivariate Behavioral ResearchVolume
23Issue
2Page(s)
231-242AbstractCluster recovery indices are more important than ever, because of the necessity for comparing the large number of clustering procedures available today. Of the cluster recovery indices prominent in contemporary literature, the Hubert and Arabie (1985) adjustment to the Rand index (1971) has been demonstrated to have the most desirable properties (Milligan & Cooper, 1986). However, use of the Hubert and Arabie adjustment to the Rand index is limited to cluster solutions involving non-overlapping, or disjoint, clusters. The present paper introduces a generalization of the Hubert and Arabie adjusted Rand index. This generalization, called the Omega index, can be applied to situations where both, one, or neither of the solutions being compared is non-disjoint. In the special case where both solutions are disjoint, the Omega index is equivalent to the Hubert and Arabie adjusted Rand index.One view of the next decade of research on behavioral and biobehavioral approaches to cancer prevention and control : Intervention optimization
AbstractCollins, L., Strayhorn, J. C., & Vanness, D. J. (n.d.).Publication year
2021Journal title
Translational Behavioral MedicineVolume
11Issue
11Page(s)
1998-2008Abstract~Optimization of a technology-supported physical activity intervention for breast cancer survivors : Fit2Thrive study protocol
AbstractPhillips, S. M., Collins, L., Penedo, F. J., Courneya, K. S., Welch, W., Cottrell, A., Lloyd, G. R., Gavin, K., Cella, D., Ackermann, R. T., Siddique, J., & Spring, B. (n.d.).Publication year
2018Journal title
Contemporary Clinical TrialsVolume
66Page(s)
9-19AbstractFit2Thrive is a theory-guided physical activity promotion trial using the Multiphase Optimization Strategy (MOST) to test efficacy for improving physical activity of five technology-supported physical activity promotion intervention components among breast cancer survivors. This trial will recruit 256 inactive breast cancer survivors nationwide. All participants will receive the core intervention which includes a Fitbit and standard self-monitoring Fit2Thrive smartphone application which will be downloaded to their personal phone. Women will be randomized to one of 32 conditions in a factorial design involving five factors with two levels: support calls (No vs. Yes), app type (standard vs. deluxe), text messaging (No vs. Yes), online gym (No vs. Yes) and Fitbit Buddy (No vs. Yes). The proposed trial examines the effects of the components on physical activity at 12 and 24 weeks. Results will support the selection of a final package of intervention components that has been optimized to maximize physical activity and is subject to an upper limit of cost. The optimized intervention will be tested in a future trial. Fit2Thrive is the first trial to use the MOST framework to develop and test a physical activity promotion intervention in breast cancer survivors and will lead to an improved understanding of how to effectively change survivors' physical activity. These findings could result in more scalable, effective physical activity interventions for breast cancer survivors, and, ultimately, improve health and disease outcomes.Optimization of a technology-supported physical activity promotion intervention for breast cancer survivors : Results from Fit2Thrive
AbstractPhillips, S. M., Penedo, F. J., Collins, L., Solk, P., Siddique, J., Song, J., Cella, D., Courneya, K. S., Ackermann, R. T., Welch, W. A., Auster-Gussman, L. A., Whitaker, M., Cullather, E., Izenman, E., & Spring, B. (n.d.).Publication year
2022Journal title
CancerVolume
128Issue
5Page(s)
1122-1132AbstractBackground: The benefits of moderate to vigorous physical activity (MVPA) for breast cancer survivors are well established. However, most are insufficiently active. Fit2Thrive used the Multiphase Optimization Strategy methodology to determine the effect of 5 intervention components on MVPA in this population. Methods: Two hundred sixty-nine participants (mean age, 52.5 years; SD, 9.9 years) received a core intervention (the Fit2Thrive self-monitoring app and Fitbit) and were randomly assigned to 5 intervention components set to on/off in a full factorial experiment: support calls, deluxe app, buddy, online gym, and text messages. The intervention was delivered over 12 weeks with a 12-week follow-up. MVPA was measured via accelerometry at the baseline (T1), at 12 weeks (T2), and at 24 weeks (T3). The main effects and interaction effects at each time point were examined for all components. Results: Trial retention was high: 91.8% had valid accelerometer data at T2 or T3. Across all conditions, there were significant increases in MVPA (+53.6 min/wk; POptimization 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 Multicomponent Behavioral and Biobehavioral Interventions for the Prevention and Treatment of HIV/AIDS
AbstractCollins, L., Kugler, K. C., & Gwadz, M. V. (n.d.).Publication year
2016Journal title
AIDS and BehaviorVolume
20Page(s)
197-214AbstractTo move society toward an AIDS-free generation, behavioral interventions for prevention and treatment of HIV/AIDS must be not only effective, but also cost-effective, efficient, and readily scalable. The purpose of this article is to introduce to the HIV/AIDS research community the multiphase optimization strategy (MOST), a new methodological framework inspired by engineering principles and designed to develop behavioral interventions that have these important characteristics. Many behavioral interventions comprise multiple components. In MOST, randomized experimentation is conducted to assess the individual performance of each intervention component, and whether its presence/absence/setting has an impact on the performance of other components. This information is used to engineer an intervention that meets a specific optimization criterion, defined a priori in terms of effectiveness, cost, cost-effectiveness, and/or scalability. MOST will enable intervention science to develop a coherent knowledge base about what works and does not work. Ultimately this will improve behavioral interventions systematically and incrementally.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.Optimization of Smoking Cessation Interventions via Multiphase Optimization STrategy (MOST): Basic Concepts, Practical Considerations and New Developments
AbstractCollins, L., Guastaferro, K., Strayhorn, J., Cantrell, J., Kimber, C., & Piper, M. (n.d.).Publication year
2024Abstract~Optimizing a Positive Psychology Intervention to Promote Health Behaviors after an Acute Coronary Syndrome : The Positive Emotions after Acute Coronary Events III (PEACE-III) Randomized Factorial Trial
AbstractCelano, C. M., Albanese, A. M., Millstein, R. A., Mastromauro, C. A., Chung, W. J., Campbell, K. A., Legler, S. R., Park, E. R., Healy, B. C., Collins, L., Januzzi, J. L., & Huffman, J. C. (n.d.).Publication year
2018Journal title
Psychosomatic MedicineVolume
80Issue
6Page(s)
526-534AbstractObjective Despite the clear benefits of physical activity and related behaviors on prognosis, most patients experiencing an acute coronary syndrome (ACS) remain nonadherent to these behaviors. Deficits in positive psychological constructs (e.g., optimism) are linked to reduced participation in health behaviors, supporting the potential utility of a positive psychology (PP)-based intervention in post-ACS patients. Accordingly, we aimed to identify optimal components of a PP-based intervention to promote post-ACS physical activity. Methods As part of a multiphase optimization strategy, we completed a randomized factorial trial with eight conditions in 128 post-ACS patients to efficiently identify best-performing intervention components. All participants received a PP-based intervention, with conditions varying in duration (presence/absence of booster sessions), intensity (weekly/daily PP exercises), and content (PP alone or combined with motivational interviewing), allowing three concurrent comparisons within the trial. The study aims included assessments of the overall feasibility, acceptability, and impact of the intervention, along with the primary aim of determining which components were associated with objectively measured physical activity and self-reported health behavior adherence at 16 weeks, assessed using longitudinal models. Results The intervention was well accepted and associated with substantial improvements in behavioral and psychological outcomes. Booster sessions were associated with greater activity to a nearly significant degree (β = 8.58, 95% confidence interval =-0.49-17.65, effect size difference =.43, p =.064), motivational interviewing was associated with overall adherence (β = 0.95, 95% confidence interval = 0.02-1.87, effect size difference =.39, p =.044), and weekly exercise completion was generally superior to daily. Conclusions These findings will enable optimization of the PP-based intervention in preparation for a well-powered controlled trial. Clinical Trial Registration Clinicaltrials.gov, NCT02754895.Optimizing Interventions for Equitability: Some Initial Ideas
AbstractCollins, L., Strayhorn, J. C., Vanness, D. J., & Collins, L. M. (n.d.).Publication year
2024Journal title
Prev SciAbstractInterventions (including behavioral, biobehavioral, biomedical, and social-structural interventions) hold tremendous potential not only to improve public health overall but also to reduce health disparities and promote health equity. In this study, we introduce one way in which interventions can be optimized for health equity in a principled fashion using the multiphase optimization strategy (MOST). Specifically, we define intervention equitability as the extent to which the health benefits provided by an intervention are distributed evenly versus concentrated among those who are already advantaged, and we suggest that, if intervention equitability is acknowledged to be a priority, then equitability should be a key criterion that is balanced with other criteria (effectiveness overall, as well as affordability, scalability, and/or efficiency) in intervention optimization. Using a hypothetical case study and simulated data, we show how MOST can be applied to achieve a strategic balance that incorporates equitability. We also show how the composition of an optimized intervention can differ when equitability is considered versus when it is not. We conclude with a vision for next steps to build on this initial foray into optimizing interventions for equitability.Optimizing Interventions for Equitability: Some Initial Ideas
AbstractStrayhorn, J. C., Vanness, D. J., & Collins, L. (n.d.).Publication year
2024Journal title
Prevention ScienceAbstractInterventions (including behavioral, biobehavioral, biomedical, and social-structural interventions) hold tremendous potential not only to improve public health overall but also to reduce health disparities and promote health equity. In this study, we introduce one way in which interventions can be optimized for health equity in a principled fashion using the multiphase optimization strategy (MOST). Specifically, we define intervention equitability as the extent to which the health benefits provided by an intervention are distributed evenly versus concentrated among those who are already advantaged, and we suggest that, if intervention equitability is acknowledged to be a priority, then equitability should be a key criterion that is balanced with other criteria (effectiveness overall, as well as affordability, scalability, and/or efficiency) in intervention optimization. Using a hypothetical case study and simulated data, we show how MOST can be applied to achieve a strategic balance that incorporates equitability. We also show how the composition of an optimized intervention can differ when equitability is considered versus when it is not. We conclude with a vision for next steps to build on this initial foray into optimizing interventions for equitability.Optimizing self-monitoring in a digital weight loss intervention (Spark): Protocol of a factorial randomized trial
AbstractPatel, M. L., King, A. C., Bennett, G. G., Collins, L., Gallis, J. A., Zeitlin, A. B., Talreja, P. S., Crosthwaite, P. C., Collins, K. A., Lim, A., Kim, T. S., & Rosas, L. G. (n.d.).Abstract~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.