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
An Overview of Research and Evaluation Designs for Dissemination and Implementation
AbstractBrown, C. H., Curran, G., Palinkas, L. A., Aarons, G. A., Wells, K. B., Jones, L., Collins, L. M., Duan, N., Mittman, B. S., Wallace, A., Tabak, R. G., Ducharme, L., Chambers, D. A., Neta, G., Wiley, T., Landsverk, J., Cheung, K., & Cruden, G. (n.d.).Publication year
2017Journal title
Annual Review of Public HealthVolume
38Page(s)
1-22AbstractThe wide variety of dissemination and implementation designs now being used to evaluate and improve health systems and outcomes warrants review of the scope, features, and limitations of these designs. This article is one product of a design workgroup that was formed in 2013 by the National Institutes of Health to address dissemination and implementation research, and whose members represented diverse methodologic backgrounds, content focus areas, and health sectors. These experts integrated their collective knowledge on dissemination and implementation designs with searches of published evaluations strategies. This article emphasizes randomized and nonrandomized designs for the traditional translational research continuum or pipeline, which builds on existing efficacy and effectiveness trials to examine how one or more evidence-based clinicalprevention interventions are adopted, scaled up, and sustained in community or service delivery systems. We also mention other designs, including hybrid designs that combine effectiveness and implementation research, quality improvement designs for local knowledge, and designs that use simulation modeling.Implementing Clinical Research Using Factorial Designs: A Primer
AbstractBaker, T. B., Baker, T. B., Smith, S. S., Smith, S. S., Bolt, D. M., Loh, W. Y., Mermelstein, R., Fiore, M. C., Piper, M. E., Fiore, M. C., Piper, M. E., & Collins, L. M. (n.d.).Publication year
2017Journal title
Behavior TherapyVolume
48Issue
4Page(s)
567-580AbstractFactorial experiments have rarely been used in the development or evaluation of clinical interventions. However, factorial designs offer advantages over randomized controlled trial designs, the latter being much more frequently used in such research. Factorial designs are highly efficient (permitting evaluation of multiple intervention components with good statistical power) and present the opportunity to detect interactions amongst intervention components. Such advantages have led methodologists to advocate for the greater use of factorial designs in research on clinical interventions (Collins, Dziak, & Li, 2009). However, researchers considering the use of such designs in clinical research face a series of choices that have consequential implications for the interpretability and value of the experimental results. These choices include: whether to use a factorial design, selection of the number and type of factors to include, how to address the compatibility of the different factors included, whether and how to avoid confounds between the type and number of interventions a participant receives, and how to interpret interactions. The use of factorial designs in clinical intervention research poses choices that differ from those typically considered in randomized clinical trial designs. However, the great information yield of the former encourages clinical researchers’ increased and careful execution of such designs.The Positive Emotions after Acute Coronary Events behavioral health intervention: Design, rationale, and preliminary feasibility of a factorial design study
AbstractHuffman, J. C., Albanese, A. M., Campbell, K. A., Celano, C. M., Millstein, R. A., Mastromauro, C. A., Healy, B. C., Chung, W. J., Januzzi, J. L., Collins, L. M., & Park, E. R. (n.d.).Publication year
2017Journal title
Clinical TrialsVolume
14Issue
2Page(s)
128-139AbstractBackground: Positive psychological constructs, such as optimism, are associated with greater participation in cardiac health behaviors and improved cardiac outcomes. Positive psychology interventions, which target psychological well-being, may represent a promising approach to improving health behaviors in high-risk cardiac patients. However, no study has assessed whether a positive psychology intervention can promote physical activity following an acute coronary syndrome. Objective: In this article we will describe the methods of a novel factorial design study to aid the development of a positive psychology-based intervention for acute coronary syndrome patients and aim to provide preliminary feasibility data on study implementation. Methods: The Positive Emotions after Acute Coronary Events III study is an optimization study (planned N = 128), subsumed within a larger multiphase optimization strategy iterative treatment development project. The goal of Positive Emotions after Acute Coronary Events III is to identify the ideal components of a positive psychology-based intervention to improve post-acute coronary syndrome physical activity. Using a 2 × 2 × 2 factorial design, Positive Emotions after Acute Coronary Events III aims to: (1) evaluate the relative merits of using positive psychology exercises alone or combined with motivational interviewing, (2) assess whether weekly or daily positive psychology exercise completion is optimal, and (3) determine the utility of booster sessions. The study's primary outcome measure is moderate-to-vigorous physical activity at 16 weeks, measured via accelerometer. Secondary outcome measures include psychological, functional, and adherence-related behavioral outcomes, along with metrics of feasibility and acceptability. For the primary study outcome, we will use a mixed-effects model with a random intercept (to account for repeated measures) to assess the main effects of each component (inclusion of motivational interviewing in the exercises, duration of the intervention, and inclusion of booster sessions) from a full factorial model controlling for baseline activity. Similar analyses will be performed on self-report measures and objectively-measured medication adherence over 16 weeks. We hypothesize that the combined positive psychology and motivational interviewing intervention, weekly exercises, and booster sessions will be associated with superior physical activity. Results: Thus far, 78 participants have enrolled, with 72% of all possible exercises fully completed by participants. Conclusion: The Positive Emotions after Acute Coronary Events III study will help to determine the optimal content, intensity, and duration of a positive psychology intervention in post-acute coronary syndrome patients prior to testing in a randomized trial. This study is novel in its use of a factorial design within the multiphase optimization strategy framework to optimize a behavioral intervention and the use of a positive psychology intervention to promote physical activity in high-risk cardiac patients.Toward precision smoking cessation treatment I: Moderator results from a factorial experiment
AbstractPiper, M. E., Schlam, T. R., Cook, J. W., Smith, S. S., Bolt, D. M., Loh, W. Y., Mermelstein, R., Collins, L. M., Fiore, M. C., & Baker, T. B. (n.d.).Publication year
2017Journal title
Drug and alcohol dependenceVolume
171Page(s)
59-65AbstractBackground The development of tobacco use treatments that are effective for all smokers is critical to improving clinical and public health. The Multiphase Optimization Strategy (MOST) uses highly efficient factorial experiments to evaluate multiple intervention components for possible inclusion in an optimized tobacco use treatment. Factorial experiments permit analyses of the influence of patient characteristics on main and interaction effects of multiple, relatively discrete, intervention components. This study examined whether person-factor and smoking characteristics moderated the main or interactive effects of intervention components on 26-week self-reported abstinence rates. Methods This fractional factorial experiment evaluated six smoking cessation intervention components among primary care patients (N = 637): Prequit Nicotine Patch vs. None, Prequit Nicotine Gum vs. None, Preparation Counseling vs. None, Intensive Cessation In-Person Counseling vs. Minimal, Intensive Cessation Telephone Counseling vs. Minimal, and 16 vs. 8 Weeks of Combination Nicotine Replacement Therapy (NRT; nicotine patch + nicotine gum). Results Both psychiatric history and smoking heaviness moderated intervention component effects. In comparison with participants with no self-reported history of a psychiatric disorder, those with a positive history showed better response to 16- vs. 8-weeks of combination NRT, but a poorer response to counseling interventions. Also, in contrast to light smokers, heavier smokers showed a poorer response to counseling interventions. Conclusions Heavy smokers and those with psychiatric histories demonstrated a differential response to intervention components. This research illustrates the use of factorial designs to examine the interactions between person characteristics and relatively discrete intervention components. Future research is needed to replicate these findings.Toward precision smoking cessation treatment II: Proximal effects of smoking cessation intervention components on putative mechanisms of action
AbstractPiper, M. E., Cook, J. W., Schlam, T. R., Smith, S. S., Bolt, D. M., Collins, L. M., Mermelstein, R., Fiore, M. C., & Baker, T. B. (n.d.).Publication year
2017Journal title
Drug and alcohol dependenceVolume
171Page(s)
50-58AbstractBackground Understanding how smoking cessation treatments exert their effects can inform treatment development and use. Factorial designs allow researchers to examine whether multiple intervention components affect hypothesized change mechanisms, and whether the affected mechanisms are related to cessation. Methods This is a secondary data analysis of smokers recruited during primary care visits (N = 637, 55% women, 87% white) who were motivated to quit. Participants in this fractional factorial experiment were randomized to one level of each of six intervention factors: Prequit Nicotine Patch vs None, Prequit Nicotine Gum vs None, Preparation Counseling vs None, Intensive In-Person Counseling vs Minimal, Intensive Phone Counseling vs Minimal, and 16 vs 8 Weeks of Combination Nicotine Replacement (nicotine patch + nicotine gum). Data on putative mechanisms (e.g., medication use, withdrawal, self-efficacy) and smoking status were gathered using daily assessments and during follow-up assessment calls. Results Some intervention components influenced hypothesized mechanisms. Prequit Gum and Patch each reduced prequit smoking and enhanced prequit coping and self-efficacy. In-Person Counseling increased prequit motivation to quit, postquit self-efficacy, and postquit perceived intratreatment support. Withdrawal reduction and reduced prequit smoking produced the strongest effects on cessation. The significant effect of combining Prequit Gum and In-Person Counseling on 26-week abstinence was mediated by increased prequit self-efficacy. Conclusions This factorial experiment identified which putative treatment mechanisms were influenced by discrete intervention components and which mechanisms influenced cessation. Such information supports the combined use of prequit nicotine gum and intensive in-person counseling as cessation interventions that operate via increased prequit self-efficacy.Using the multiphase optimization strategy (MOST) to optimize an HIV care continuum intervention for vulnerable populations: A study protocol
AbstractGwadz, M. V., Collins, L. M., Cleland, C. M., Leonard, N. R., Wilton, L., Gandhi, M., Scott Braithwaite, R., Perlman, D. C., Kutnick, A., & Ritchie, A. S. (n.d.).Publication year
2017Journal title
BMC public healthVolume
17Issue
1AbstractBackground: More than half of persons living with HIV (PLWH) in the United States are insufficiently engaged in HIV primary care and not taking antiretroviral therapy (ART), mainly African Americans/Blacks and Hispanics. In the proposed project, a potent and innovative research methodology, the multiphase optimization strategy (MOST), will be employed to develop a highly efficacious, efficient, scalable, and cost-effective intervention to increase engagement along the HIV care continuum. Whereas randomized controlled trials are valuable for evaluating the efficacy of multi-component interventions as a package, they are not designed to evaluate which specific components contribute to efficacy. MOST, a pioneering, engineering-inspired framework, addresses this problem through highly efficient randomized experimentation to assess the performance of individual intervention components and their interactions. We propose to use MOST to engineer an intervention to increase engagement along the HIV care continuum for African American/Black and Hispanic PLWH not well engaged in care and not taking ART. Further, the intervention will be optimized for cost-effectiveness. A similar set of multi-level factors impede both HIV care and ART initiation for African American/Black and Hispanic PLWH, primary among them individual- (e.g., substance use, distrust, fear), social- (e.g., stigma), and structural-level barriers (e.g., difficulties accessing ancillary services). Guided by a multi-level social cognitive theory, and using the motivational interviewing approach, the study will evaluate five distinct culturally based intervention components (i.e., counseling sessions, pre-adherence preparation, support groups, peer mentorship, and patient navigation), each designed to address a specific barrier to HIV care and ART initiation. These components are well-grounded in the empirical literature and were found acceptable, feasible, and promising with respect to efficacy in a preliminary study. Methods/design: Study aims are: 1) using a highly efficient fractional factorial experimental design, identify which of five intervention components contribute meaningfully to improvement in HIV viral suppression, and secondary outcomes of ART adherence and engagement in HIV primary care; 2) identify mediators and moderators of intervention component efficacy; and 3) using a mathematical modeling approach, build the most cost-effective and efficient intervention package from the efficacious components. A heterogeneous sample of African American/Black and Hispanic PLWH (with respect to age, substance use, and sexual minority status) will be recruited with a proven hybrid sampling method using targeted sampling in community settings and peer recruitment (N = 512). Discussion: This is the first study to apply the MOST framework in the field of HIV prevention and treatment. This innovative study will produce a culturally based HIV care continuum intervention for the nation's most vulnerable PLWH, optimized for cost-effectiveness, and with exceptional levels of efficacy, efficiency, and scalability. Trial registration: ClinicalTrials.gov, NCT02801747, Registered June 8, 2016.Advancing Models and Theories for Digital Behavior Change Interventions
AbstractHekler, E. B., Michie, S., Pavel, M., Rivera, D. E., Collins, L. M., Jimison, H. B., Garnett, C., Parral, S., & Spruijt-Metz, D. (n.d.).Publication year
2016Journal title
American journal of preventive medicineVolume
51Issue
5Page(s)
825-832AbstractTo be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The “state” is that of the individual based on multiple variables that define the “space” when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions.Comparative effectiveness of intervention components for producing long-term abstinence from smoking: A factorial screening experiment
AbstractSchlam, T. R., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., Collins, L. M., Mermelstein, R., Piper, M. E., Cook, J. W., Jorenby, D. E., Loh, W. Y., & Baker, T. B. (n.d.).Publication year
2016Journal title
AddictionVolume
111Issue
1Page(s)
142-155AbstractAims: To identify promising intervention components that help smokers attain and maintain abstinence during a quit attempt. Design: A 2×2×2×2×2 randomized factorial experiment. Setting: Eleven primary care clinics in Wisconsin, USA. Participants: A total of 544 smokers (59% women, 86% white) recruited during primary care visits and motivated to quit. Interventions: Five intervention components designed to help smokers attain and maintain abstinence: (1) extended medication (26 versus 8 weeks of nicotine patch+nicotine gum); (2) maintenance (phone) counseling versus none (3) medication adherence counseling versus none (4) automated (medication) adherence calls versus none and (5) electronic medication monitoring with feedback and counseling versus electronic medication monitoring alone. Measurements: The primary outcome was 7-day self-reported point-prevalence abstinence 1 year after the target quit day. Findings: Only extended medication produced a main effect. Twenty-six versus 8 weeks of medication improved point-prevalence abstinence rates (43 versus 34% at 6 months; 34 versus 27% at 1 year; P =0.01 for both). There were four interaction effects at 1 year, showing that an intervention component's effectiveness depended upon the components with which it was combined. Conclusions: Twenty-six weeks of nicotine patch+nicotine gum (versus 8 weeks) and maintenance counseling provided by phone are promising intervention components for the cessation and maintenance phases of smoking treatment.Comparative effectiveness of motivation phase intervention components for use with smokers unwilling to quit: A factorial screening experiment
AbstractCook, J. W., Collins, L. M., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., Baker, T. B., Piper, M. E., Schlam, T. R., Jorenby, D., Loh, W. Y., & Mermelstein, R. (n.d.).Publication year
2016Journal title
AddictionVolume
111Issue
1Page(s)
117-128AbstractAims: To screen promising intervention components designed to reduce smoking and promote abstinence in smokers initially unwilling to quit. Design: A balanced, four-factor, randomized factorial experiment. Setting: Eleven primary care clinics in southern Wisconsin, USA. Participants: A total of 517 adult smokers (63.4% women, 91.1% white) recruited during primary care visits who were willing to reduce their smoking but not quit. Interventions: Four factors contrasted intervention components designed to reduce smoking and promote abstinence: (1) nicotine patch versus none (2) nicotine gum versus none (3) motivational interviewing (MI) versus none and (4) behavioral reduction counseling (BR) versus none. Participants could request cessation treatment at any point during the study. Measurements: The primary outcome was percentage change in cigarettes smoked per day at 26weeks post-study enrollment; the secondary outcomes were percentage change at 12 weeks and point-prevalence abstinence at 12 and 26 weeks post-study enrollment. Findings: There were few main effects, but a significant four-way interaction at 26weeks post-study enrollment (P=0.01, β=0.12) revealed relatively large smoking reductions by two component combinations: nicotine gum combined with BR and BR combined with MI. Further, BR improved 12-week abstinence rates (P=0.04), and nicotine gum, when used without MI, increased 26-week abstinence after a subsequent aided quit attempt (P=0.01). Conclusions: Motivation-phase nicotine gum and behavioral reduction counseling are promising intervention components for smokers who are initially unwilling to quit.Enhancing the effectiveness of smoking treatment research: Conceptual bases and progress
AbstractBaker, T. B., Collins, L. M., Mermelstein, R., Piper, M. E., Schlam, T. R., Cook, J. W., Bolt, D. M., Smith, S. S., Jorenby, D. E., Fraser, D., Loh, W. Y., Theobald, W. E., & Fiore, M. C. (n.d.).Publication year
2016Journal title
AddictionVolume
111Issue
1Page(s)
107-116AbstractBackground and aims: A chronic care strategy could potentially enhance the reach and effectiveness of smoking treatment by providing effective interventions for all smokers, including those who are initially unwilling to quit. This paper describes the conceptual bases of a National Cancer Institute-funded research program designed to develop an optimized, comprehensive, chronic care smoking treatment. Methods: This research is grounded in three methodological approaches: (1) the Phase-Based Model, which guides the selection of intervention components to be experimentally evaluated for the different phases of smoking treatment (motivation, preparation, cessation, and maintenance); (2) the Multiphase Optimization Strategy (MOST), which guides the screening of intervention components via efficient experimental designs and, ultimately, the assembly of promising components into an optimized treatment package and (3) pragmatic research methods, such as electronic health record recruitment, that facilitate the efficient translation of research findings into clinical practice. Using this foundation and working in primary care clinics, we conducted three factorial experiments (reported in three accompanying papers) to screen 15 motivation, preparation, cessation and maintenance phase intervention components for possible inclusion in a chronic care smoking treatment program. Results: This research identified intervention components with relatively strong evidence of effectiveness at particular phases of smoking treatment and it demonstrated the efficiency of the MOST approach in terms both of the number of intervention components tested and of the richness of the information yielded. Conclusions: A new, synthesized research approach efficiently evaluates multiple intervention components to identify promising components for every phase of smoking treatment. Many intervention components interact with one another, supporting the use of factorial experiments in smoking treatment development.Evaluating Digital Health Interventions: Key Questions and Approaches
AbstractMurray, E., Hekler, E. B., Andersson, G., Collins, L. M., Doherty, A., Hollis, C., Rivera, D. E., West, R., & Wyatt, J. C. (n.d.).Publication year
2016Journal title
American journal of preventive medicineVolume
51Issue
5Page(s)
843-851AbstractDigital health interventions have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety, and personalization. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of digital health interventions. However, evaluations of digital health interventions present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the research questions needed to appraise such interventions. As they are at the intersection of biomedical, behavioral, computing, and engineering research, methods drawn from all of these disciplines are required. Relevant research questions include defining the problem and the likely benefit of the digital health intervention, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components, and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost effectiveness, and harms. Although RCTs are important for evaluation of effectiveness and cost effectiveness, they are best undertaken only when: (1) the intervention and its delivery package are stable; (2) these can be implemented with high fidelity; and (3) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at lower cost). Broadening the portfolio of research questions and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice, and research.Identifying effective intervention components for smoking cessation: A factorial screening experiment
AbstractPiper, M. E., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., Collins, L. M., Mermelstein, R., Schlam, T. R., Cook, J. W., Jorenby, D. E., Loh, W. Y., & Baker, T. B. (n.d.).Publication year
2016Journal title
AddictionVolume
111Issue
1Page(s)
129-141AbstractAims: To identify promising intervention components intended to help smokers to attain and maintain abstinence in their quit smoking attempts. Design: A fully crossed, six-factor randomized fractional factorial experiment. Setting: Eleven primary care clinics in southern Wisconsin, USA. Participants: A total of 637 adult smokers (55% women, 88% white) motivated to quit smoking who visited primary care clinics. Interventions: Six intervention components designed to prepare smokers to quit, and achieve and maintain abstinence (i.e. for the preparation, cessation and maintenance phases of smoking treatment): (1) preparation nicotine patch versus none (2) preparation nicotine gum versus none (3) preparation counseling versus none (4) intensive cessation in-person counseling versus minimal; (5) intensive cessation telephone counseling versus minimal; and (6) 16 versus 8weeks of combination nicotine replacement therapy (nicotine patch + nicotine gum). Measurements: Seven-day self-reported point-prevalence abstinence at 16weeks. Findings: Preparation counseling significantly improved week 16 abstinence rates (P = .04), while both forms of preparation nicotine replacement therapy interacted synergistically with intensive cessation in-person counseling (P<0.05). Conversely, intensive cessation phone counseling and intensive cessation in-person counseling interacted antagonistically (P<0.05)-these components produced higher abstinence rates by themselves than in combination. Conclusions: Preparation counseling and the combination of intensive cessation in-person counseling with preparation nicotine gum or patch are promising intervention components for smoking and should be evaluated as an integrated treatment package.Implementing multifactorial psychotherapy research in online virtual environments (IMPROVE-2): Study protocol for a phase III trial of the MOST randomized component selection method for internet cognitive-behavioural therapy for depression
AbstractWatkins, E., Newbold, A., Tester-Jones, M., Javaid, M., Cadman, J., Collins, L. M., Graham, J., & Mostazir, M. (n.d.).Publication year
2016Journal title
BMC psychiatryVolume
16Issue
1AbstractBackground: Depression is a global health challenge. Although there are effective psychological and pharmaceutical interventions, our best treatments achieve remission rates less than 1/3 and limited sustained recovery. Underpinning this efficacy gap is limited understanding of how complex psychological interventions for depression work. Recent reviews have argued that the active ingredients of therapy need to be identified so that therapy can be made briefer, more potent, and to improve scalability. This in turn requires the use of rigorous study designs that test the presence or absence of individual therapeutic elements, rather than standard comparative randomised controlled trials. One such approach is the Multiphase Optimization Strategy, which uses efficient experimentation such as factorial designs to identify active factors in complex interventions. This approach has been successfully applied to behavioural health but not yet to mental health interventions. Methods/Design: A Phase III randomised, single-blind balanced fractional factorial trial, based in England and conducted on the internet, randomized at the level of the patient, will investigate the active ingredients of internet cognitive-behavioural therapy (CBT) for depression. Adults with depression (operationalized as PHQ-9 score ≥ 10), recruited directly from the internet and from an UK National Health Service Improving Access to Psychological Therapies service, will be randomized across seven experimental factors, each reflecting the presence versus absence of specific treatment components (activity scheduling, functional analysis, thought challenging, relaxation, concreteness training, absorption, self-compassion training) using a 32-condition balanced fractional factorial design (2IV 7-2). The primary outcome is symptoms of depression (PHQ-9) at 12 weeks. Secondary outcomes include symptoms of anxiety and process measures related to hypothesized mechanisms. Discussion: Better understanding of the active ingredients of efficacious therapies, such as CBT, is necessary in order to improve and further disseminate these interventions. This study is the first application of a component selection experiment to psychological interventions in depression and will enable us to determine the main effect of each treatment component and its relative efficacy, and cast light on underlying mechanisms, so that we can systematically enhance internet CBT. Trial registration: Current Controlled Trials ISRCTN24117387. Registered 26 August 2014.Mining health app data to find more and less successful weight loss subgroups
AbstractSerrano, K. J., Yu, M., Coa, K. I., Collins, L. M., & 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.Optimization of Multicomponent Behavioral and Biobehavioral Interventions for the Prevention and Treatment of HIV/AIDS
AbstractCollins, L. M., 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.Utilizing MOST frameworks and SMART designs for intervention research
Wilbur, J. E., Kolanowski, A. M., & Collins, L. M. (n.d.).Publication year
2016Journal title
Nursing outlookVolume
64Issue
4Page(s)
287-289A dynamical systems approach to understanding self-regulation in smoking cessation behavior change
AbstractTimms, K. P., Rivera, D. E., Collins, L. M., & Piper, M. E. (n.d.).Publication year
2014Journal title
Nicotine and Tobacco ResearchVolume
16Page(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.Continuous-time system identification of a smoking cessation intervention
AbstractTimms, K. P., Rivera, D. E., Collins, L. M., & 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
Faggiano, 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
10Evaluating individual intervention components: making decisions based on the results of a factorial screening experiment
AbstractCollins, L. M., 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. M., 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. M., 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. M. (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. M., 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. M., & 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.