Jillian Strayhorn
Jillian Strayhorn
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Assistant Professor of Social and Behavioral Sciences
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Professional overview
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Jillian C. Strayhorn, PhD is an Assistant Professor in the Department of Social and Behavioral Sciences at GPH and Associate Director of its Center for the Advancement and Dissemination of Intervention Optimization (cadio). She is a quantitative methodologist and decision scientist whose research focuses on the complex multi-criteria decision-making that goes into optimizing multicomponent interventions to achieve public health impact.
Dr. Strayhorn is an expert on the multiphase optimization strategy (MOST), a framework for optimizing behavioral, biobehavioral, and social-structural interventions. Her work in intervention optimization is highly interdisciplinary, bringing together ideas and methods from Bayesian statistics, health economics and multi-criteria decision analysis. The driving mission of this work is to enable more successful identification and advancement of high-value interventions capable of accomplishing complex objectives, including objectives that involve multiple outcomes, efficiency of resource use, or health equity. Dr. Strayhorn collaborates on applications of MOST across various areas of public health, including cancer risk reduction, smoking cessation, HIV, substance misuse, and mental health, among others.
Dr. Strayhorn earned her BA in Psychology, summa cum laude with distinction in all subjects, at Cornell University, and her PhD in Human Development and Family Studies at Pennsylvania State University, where she was the recipient of a Ruth L. Kirschstein NRSA predoctoral award (F31) from the National Institute on Drug Abuse . Her latest work has been published in Psychological Methods, Health Psychology, and Translational Behavioral Medicine.
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Education
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BA, Psychology, Cornell University, Ithaca, NYMS, Human Development and Family Studies, Pennsylvania State University, University Park, PAPhD, Human Development and Family Studies, Pennsylvania State University, University Park, PA
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Honors and awards
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Alumni Association Dissertation Award, Pennsylvania State University (2022)Student Optimization of Behavioral and Biobehavioral Interventions Research Award, Society of Behavioral Medicine (2021)Merrill Presidential Scholar Award, Cornell University (2014)Phi Beta Kappa Junior Inductee, Cornell University (2013)Robinson-Appel Humanitarian Award, Cornell University (2013)
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Publications
Publications
A pilot trial of 'HypoPals': Assessing trial procedures feasibility and intervention acceptability for a prospective digital hypoglycemia behavioral intervention study
AbstractLin, Y. K. K., Hepworth, E., Lucien, J., Agni, A., Martin-Schwarze, A., Newman, S., Marinec, N., Aikens, J. E., Spino, C., Pop-Busui, R., Strayhorn, J., DeJonckheere, M., de Zoysa, N., Amiel, S. A., & Piette, J. D. (n.d.).Publication year
2026Journal title
Contemporary clinical trialsVolume
163Page(s)
108261AbstractDespite advances in diabetes technology, hypoglycemia remains a major problem for people with type 1 diabetes (T1D). We developed HypoPals, a scalable digital behavioral intervention program comprised of two intervention components, (i) hypoglycemia symptom detection training ('SDT') and (ii) psychoeducation to address unhelpful hypoglycemia beliefs (Education Plus, 'ED+'), to improve hypoglycemia self-management in adults with T1D.Selecting optimized behavioral interventions from an optimization randomized controlled trial on increasing COVID-19 testing for African American/Black and Latino frontline essential workers not up-to-date on COVID-19 vaccination.
AbstractHeng, S., Ye, X., Strayhorn, J., Cleland, C., Parameswaran, L., & Gwadz, M. (n.d.). (1st ed.).Publication year
2026Volume
60Abstract~An Optimization Randomized Clinical Trial to Identify an Effective, Efficient Smoking Cessation Intervention in the Context of Lung Cancer Screening: Cessation and Screening to Save Lives (CASTL).
AbstractOstroff, J., Shelley, D., Chichester, L., Schofield, E., Li, Y., Collins, L., Elkin, E., Strayhorn, J., Ciupek, A., King, J., & Barry, A. (n.d.).Publication year
2025Journal title
CHESTAbstract~Bayesian Multicriteria Decision Analysis Methods for Optimizing Multicomponent Interventions: The Effect of Value Function Misspecification
AbstractStrayhorn, J., & Vanness, D. (n.d.).Publication year
2025Abstract~Decision Analysis for Intervention Value Efficiency (DAIVE): A Tool for Cost-Informed Decision-Making in Intervention Optimization.
AbstractStrayhorn, J., Cantrell, J., Collins, L., Vanness, D., Fiore, M., Baker, T., & Piper, M. (n.d.).Publication year
2025AbstractSymposium presentation offered as part of the session, “Form Follows Function: Choosing Economic Methods to Match Implementation Goals”; 2025 Science of Dissemination and Implementation Conference; National Harbor, MD.Effects of behavioral intervention components to increase COVID-19 testing for African American/Black and Latine frontline essential workers not up-to-date on COVID-19 vaccination: Results of an optimization randomized controlled trial
AbstractGwadz, M., Heng, S., Cleland, C. M., Strayhorn, J., Robinson, J. A., Serrano, F. G. B., Wang, P., Parameswaran, L., & Chero, R. (n.d.).Publication year
2025Journal title
Journal of behavioral medicineAbstractRacial/ethnic disparities in COVID-19, including incidence, hospitalization, and death rates, are serious and persistent. Among those at highest risk for COVID-19 and its adverse effects are African American/Black and Latine (AABL) frontline essential workers in public-facing occupations (e.g., food services, retail). Testing for COVID-19 in various scenarios (when exposed or symptomatic, regular screening testing) is an essential component of the COVID-19 control strategy in the United States. However, AABL frontline workers have serious barriers to COVID-19 testing at the individual (insufficient knowledge, distrust, cognitive biases), social (norms), and structural levels of influence (access). Thus, testing rates are insufficient and interventions are needed. The present study is grounded in the multiphase optimization strategy (MOST) framework. It tests the main and interaction effects of a set of candidate behavioral intervention components to increase COVID-19 testing rates in this population. The study enrolled adult AABL frontline essential workers who were not up-to-date on COVID-19 vaccination nor recently tested for COVID-19. It used a factorial design to examine the effects of candidate behavioral intervention components, where each component was designed to address a specific barrier to COVID-19 testing. All participants received a core intervention comprised of health education. The candidate components were motivational interviewing counseling (MIC), a behavioral economics intervention (BEI), peer education (PE), and access to testing (either self-test kits [SK] or a navigation meeting [NM]). The primary outcome was COVID-19 testing in the follow-up period. Participants were assessed at baseline, randomly assigned to one of 16 experimental conditions, and assessed six- and 12-weeks later. The study was carried out in English and Spanish. We used a logistic regression model and multiple imputation to examine the main and interaction effects of the four factors (representing components): MIC, BEI, PE, and Access. We also conducted a sensitivity analysis using the complete case analysis. Participants (N = 438) were 35 years old on average (SD = 10). Half identified as men/male (52%), and 48% as women/female/other. Almost half (49%) were African American/Black, and 51% were Latine/Hispanic (12% participated in Spanish). A total of 32% worked in food services. Attendance in components was very high (~ 99%). BEI had positive effect on the outcome (OR = 1.543; 95% CI: [0.977, 2.438]; p-value = 0.063) as did Access, in favor of SK (OR = 1.351; 95% CI: [0.859, 2.125]; p-value = 0.193). We found a three-way interaction among MIC*PE*Access (OR: 0.576; 95% CI: [0.367, 0.903]; p-value = 0.016): when MIC was present, SK tended to increase COVID testing when PE was not present. The study advances intervention science and takes the first step toward creating an efficient and effective multi-component intervention to increase COVID-19 testing rates in AABL frontline workers.Explaining the effects of treatment components in internet-based cognitive-behavioral treatment for social anxiety disorder: A factorial mediation analysis
AbstractLopes, R. C. T., Šipka, D., Strayhorn, J., Fernández Álvarez, J., Krieger, T., Klein, J. P. P., & Berger, T. (n.d.).Publication year
2025Journal title
Behaviour research and therapyVolume
193Page(s)
104816AbstractMore understanding of how internet-based cognitive-behavioral programs (ICBT) for social anxiety disorder (SAD) work could help further optimize treatment. This paper examines the mediational role of knowledge of SAD, negative social cognitions, self-focused attention, avoidance of social situations, and use of safety behaviors in explaining the main components of ICBT for SAD (psychoeducation, cognitive restructuring, attention training, and exposure).Intervention optimization: A new generation of Behavioral Interventions.
AbstractStrayhorn, J., & Merdjanoff, A. A. (n.d.).Publication year
2025Abstract~Justifying the sample size for a factorial trial
AbstractStrayhorn, J. (n.d.).Publication year
2025Abstract~Optimizing Interventions for Equitability: Some Initial Ideas.
AbstractStrayhorn, J., & Collins, L. (n.d.).Publication year
2025Abstract~Value efficiency in intervention optimization
AbstractStrayhorn, J., Williams, A., Eaton, S., McKay, J., & Vanness, D. (n.d.).Publication year
2025Journal title
Annals of Behavioral MedicineAbstract~A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science
AbstractStrayhorn, J., Collins, L. M., & Vanness, D. J. (n.d.).Publication year
2024Journal title
Psychological methodsAbstractIn current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2 factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then base decisions about component selection on these important effects. We propose an alternative posterior expected value approach based on Bayesian decision theory. This new approach aims to be easier to apply and more readily extensible to a variety of intervention optimization problems. We used Monte Carlo simulation to evaluate the performance of a posterior expected value approach and CSA (automated for simulation purposes) relative to two benchmarks: random component selection, and the classical treatment package approach. We found that both the posterior expected value approach and CSA yielded substantial performance gains relative to the benchmarks. We also found that the posterior expected value approach outperformed CSA modestly but consistently in terms of overall accuracy, sensitivity, and specificity, across a wide range of realistic variations in simulated factorial optimization trials. We discuss implications for intervention optimization and promising future directions in the use of posterior expected value to make decisions in MOST. (PsycInfo Database Record (c) 2023 APA, all rights reserved).Decision-making in factorial optimization trials with multiple outcomes: A posterior expected value approach.
AbstractStrayhorn, J., Collins, L., & Vanness, D. (n.d.).Publication year
2024Journal title
Medical Decision MakingAbstract~Decision-making in the multiphase optimization strategy (MOST): Applying decision analysis for intervention value efficiency (DAIVE) to optimize an information leaflet to support medication adherence.
AbstractGreen, S. M., Smith, S. G., Collins, L., & Strayhorn, J. (n.d.).Publication year
2024Journal title
Translational Behavioral MedicineAbstract~Decision-making in the multiphase optimization strategy: application of a posterior expected value approach.
AbstractGreen, S., Smith, S., Collins, L., & Strayhorn, J. (n.d.).Publication year
2024AbstractBackground: Intervention optimization using the multiphase optimization strategy (MOST) involves strategic decision-making about the composition of the optimized intervention, based on empirical results from an optimization trial. The initial component screening approach (CSA) to optimization decision-making had limitations, including reliance on arbitrary significance thresholds and inability to incorporate multiple outcome variables. Recent advances in MOST have suggested an alternative posterior expected value (PEV) approach, which does not have the limitations of a CSA and showedsuperior decision-making performance, relative to a CSA, in simulation. We previously used a CSA to select, based on a single primary outcome, an optimized version of an information leaflet designed to support medication beliefs in women with breast cancer. Here we apply the innovative PEV approach to select an optimized leaflet based on multiple valued outcomes.Methods: We used data from a 2 5 factorial trial (n=1604) involving five candidate intervention components: 1) diagrams about the medication, 2) medication benefits, 3) side-effect information, 4) answers to common concerns, and 5) quotes from breast cancer survivors. Components were hypothesized to contribute to three outcomes: beliefs about medication (BMQ; primary), satisfaction with information about medication (secondary), and knowledge about the medication (secondary). To apply a PEV approach, we used Bayesian factorial analysis of variance to estimate expected outcomesfor each unique intervention (32 total) on each of the three valued outcomes. We put outcomes on the same 0-1 scale and combined them using a linear value function, which gave more importance to the primary outcome than the secondary outcomes, yielding an expected value for each intervention. We identified the intervention with the largest expected value as the optimized leaflet.Results: When performance on all three outcomes was considered, the optimized leaflet contained two components: side-effect information and quotes from breast cancer survivors. The remaining three candidate components (diagrams, benefits and common concerns) were not included. When the CSA was used with a single primary outcome (BMQ), the optimized leaflet contained four components; diagrams, benefits, concerns and quotes.Conclusions: Using a PEV approach and empirical information about component performance on multiple valued outcomes led to a different choice of optimized leaflet than was previously made using a CSA with a single primary outcome. This highlights the importance of specifying priority outcomes a priori, as well as points of contrast between a PEV approach and CSA.Effects of Chatbot Components to Facilitate Mental Health Services Use in Individuals withEating Disorders Following Online Screening: An Optimization Randomized Controlled Trial
AbstractFitzsimmons-Craft, E., Rackoff, G., Shah, J., Strayhorn, J., D’Adamo, L., Howe, C., DiPietro, B., Firebaugh, M.-L., Newman, M., Collins, L., Barr Taylor, C., & Wilfley, D. (n.d.).Publication year
2024Journal title
International Journal of Eating DisordersVolume
57Issue
11Page(s)
2204-2216Abstract~Explaining the Effects of Treatment Components in Internet-Based Cognitive-Behavioral Treatment for Social Anxiety Disorder: A Factorial Mediation Analysis
AbstractLopes, R., Sipka, D., Strayhorn, J., Fernandez-Alvarez, J., Krieger, T., Klein, J., & Berger, T. (n.d.).Publication year
2024Journal title
Behaviour Research and TherapyAbstract~Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology
AbstractCollins, L. M., Nahum-Shani, I., Guastaferro, K., Strayhorn, J., Vanness, D. J., & Murphy, S. A. (n.d.).Publication year
2024Journal title
Annual review of clinical psychologyAbstractTo build a coherent knowledge base about what psychological intervention strategies work, develop interventions that have positive societal impact, and maintain and increase this impact over time, it is necessary to replace the classical treatment package research paradigm. The multiphase optimization strategy (MOST) is an alternative paradigm that integrates ideas from behavioral science, engineering, implementation science, economics, and decision science. MOST enables optimization of interventions to strategically balance effectiveness, affordability, scalability, and efficiency. In this review we provide an overview of MOST, discuss several experimental designs that can be used in intervention optimization, consider how the investigator can use experimental results to select components for inclusion in the optimized intervention, discuss the application of MOST in implementation science, and list future issues in this rapidly evolving field. We highlight the feasibility of adopting this new research paradigm as well as its potential to hasten the progress of psychological intervention science. Expected final online publication date for the , Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.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 home visiting programs to improve reach: A case study in strategically balancing intervention effectiveness with provider time
AbstractGuastaferro, K., & Strayhorn, J. (n.d.).Publication year
2024Journal title
Child Protection and PracticeAbstract~Optimizing Interventions for Equitability: Some Initial Ideas
AbstractStrayhorn, J., Vanness, D. J., & Collins, L. M. (n.d.).Publication year
2024Journal title
Prevention science : the official journal of the Society for Prevention ResearchAbstractInterventions (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.Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy
AbstractStrayhorn, J., Cleland, C. M., Vanness, D. J., Wilton, L., Gwadz, M., & Collins, L. M. (n.d.).Publication year
2024Journal title
Health psychology : official journal of the Division of Health Psychology, American Psychological AssociationVolume
43Issue
2Page(s)
89-100AbstractOptimizing multicomponent behavioral and biobehavioral interventions presents a complex decision problem. To arrive at an intervention that is both effective and readily implementable, it may be necessary to weigh effectiveness against implementability when deciding which components to select for inclusion. Different components may have differential effectiveness on an array of outcome variables. Moreover, different decision-makers will approach this problem with different objectives and preferences. Recent advances in decision-making methodology in the multiphase optimization strategy (MOST) have opened new possibilities for intervention scientists to optimize interventions based on a wide variety of decision-maker preferences, including those that involve multiple outcome variables. In this study, we introduce decision analysis for intervention value efficiency (DAIVE), a decision-making framework for use in MOST that incorporates these new decision-making methods. We apply DAIVE to select optimized interventions based on empirical data from a factorial optimization trial.Multiphase optimization strategy: How to build more effective, affordable, scalable and efficient social and behavioural oral health interventions
AbstractGuastaferro, K., & Strayhorn, J. (n.d.).Publication year
2023Journal title
Community dentistry and oral epidemiologyVolume
51Issue
1Page(s)
103-107AbstractThis commentary introduces the field of social behavioural oral health interventions to the multiphase optimization strategy (MOST). MOST is a principled framework for the development, optimization and evaluation of multicomponent interventions. Drawing from the fields of engineering, behavioural science, economics, decision science and public health, intervention optimization requires a strategic balance of effectiveness with affordability, scalability and efficiency. We argue that interventions developed using MOST are more likely to maximize the public health impact of social behavioural oral health interventions.Operationalizing primary outcomes to achieve reach, effectiveness, and equity in multilevel interventions
AbstractGuastaferro, K., Sheldrick, R. C., Strayhorn, J., & Feinberg, E. (n.d.).Publication year
2023Journal title
Prevention ScienceAbstractWhen intervention scientists plan a clinical trial of an intervention, they select an outcome metric that operationalizes their definition of intervention success. The outcome metric that is selected has important implications for which interventions are eventually supported for implementation at scale and, therefore, what health benefits (including how much benefit and for whom) are experienced in a population. Particularly when an intervention is to be implemented in a population that experiences a health disparity, the outcome metric that is selected can also have implications for equity. Some outcome metrics risk exacerbating an existing health disparity, while others may decrease disparities for some but have less effect for the larger population. In this study, we use a computer to simulate implementation of a hypothetical multilevel, multicomponent intervention to highlight the tradeoffs that can occur between outcome metrics that reflect different operationalizations of intervention success. In particular, we highlight tradeoffs between overall mean population benefit and the distribution of health benefits in the population, which has direct implications for equity. We suggest that simulations like the one we present can be useful in the planning of a clinical trial for a multilevel and/or multicomponent intervention, since simulated implementation at scale can illustrate potential consequences of candidate operationalization of intervention success, such that unintended consequences for equity can be avoided.Optimizing Multicomponent Interventions to Accomplish a Strategic Balance of Effectiveness and Ready Implementability: Latest Advances in the Multiphase Optimization Strategy.
AbstractStrayhorn, J., Collins, L., & Vanness, D. (n.d.).Publication year
2023AbstractMany of the behavioral and biobehavioral interventions with the greatest potential for public health impact are multicomponent, containing different intervention components intended to work together to produce one or more preferred health outcomes. However, for the potential of multicomponent interventions to be realized, these interventions must be not only effective on preferred outcomes but also readily implementable (i.e., affordable, scalable, and otherwise efficient in their use of the available resources). Components incur costs (in money, time, and/or any other limited resource), and building a multicomponent intervention that strategically balances effectiveness and cost(s) poses a complex decision problem. In this symposium, we demonstrate the latest advances in how the multiphase optimization strategy (MOST) can be used to approach this decision problem empirically. In particular, we show how the optimization randomized control trial, which usually (but not always) uses a design from the factorial family of experiments, i) estimates individual and combined effects for candidate intervention components and ii) illuminates tradeoffs between effectiveness and cost. Using three presentations, we describe an intervention optimization perspective for deciding which components merit inclusion in multicomponent interventions; demonstrate the latest advances in decision-making methods in MOST, as applied to make decisions about candidate components in an empirical example from HIV care; and offer our perspective on the important contrast between optimizing for a strategic balance of effectiveness and cost and using cost-effectiveness analysis to make policy decisions about interventions. We reserve time at the end for questions and open discussion.