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Advances in the multiphase optimization strategy (MOST) have suggested a new approach, decision analysis for intervention value efficiency (DAIVE), for selecting an optimized intervention based on the results of a factorial optimization trial. The new approach opens possibilities to select optimized interventions based on multiple valued outcomes. We applied DAIVE to identify an optimized information leaflet intended to support eventual adherence to adjuvant endocrine therapy for women with breast cancer. We used empirical performance data for five candidate leaflet components on three hypothesized antecedents of adherence: beliefs about the medication, objective knowledge about AET, and satisfaction with medication information. Using data from a 25 factorial trial (n = 1603), we applied the following steps: (i) We used Bayesian factorial analysis of variance to estimate main and interaction effects for the five factors on the three outcomes. (ii) We used posterior distributions for main and interaction effects to estimate expected outcomes for each leaflet version (32 total). (iii) We scaled and combined outcomes using a linear value function with predetermined weights indicating the relative importance of outcomes. (iv) We identified the leaflet that maximized the value function as the optimized leaflet, and we systematically varied outcome weights to explore robustness. The optimized leaflet included two candidate components, side-effects, and patient input, set to their higher levels. Selection was generally robust to weight variations consistent with the initial preferences for three outcomes. DAIVE enables selection of optimized interventions with the best-expected performance on multiple outcomes. [ABSTRACT FROM AUTHOR]

Background: Using the multiphase optimization strategy (MOST), we previously developed and optimized an online behavioral intervention, itMatters, aimed at reducing the risk of sexually transmitted infections (STI) among first-year college students by targeting the intersection of alcohol use and sexual behaviors.Purpose: We had two goals: (a) to evaluate the optimized itMatters intervention and (b) to determine whether the candidate sexual violence prevention (SVP) component (included at the request of participating universities) had a detectable effect and therefore should be added to create a new version of itMatters. We also describe the hybrid evaluation-optimization trial we conducted to accomplish these two goals in a single experiment.Methods: First year college students (N = 3,098) at four universities in the USA were individually randomized in a hybrid evaluation-optimization 2 × 2 factorial trial. Data were analyzed using regression models, with pre-test outcome variables included as covariates in the models. Analyses were conducted separately with (a) immediate post-test scores and (b) 60-day follow-up scores as outcome variables.Results: Experimental results indicated a significant effect of itMatters on targeted proximal outcomes (norms) and on one distal behavioral outcome (binge drinking). There were no significant effects on other behavioral outcomes, including the intersection of alcohol and sexual behaviors. In addition, there were mixed results (positive short-term effect; no effect at 60-day follow-up) of the SVP component on targeted proximal outcomes (students' self-efficacy to reduce/prevent sexual violence and perceived effectiveness of protective behavioral strategies).Conclusions: The hybrid evaluation-optimization trial enabled us to evaluate the individual and combined effectiveness of the optimized itMatters intervention and the SVP component in a single experiment, conserving resources and providing greatly improved efficiency.Trial Registration: NCT04095065. [ABSTRACT FROM AUTHOR]

As a new decade begins, we propose that the time is right to reexamine current methods and procedures and look for opportunities to accelerate progress in cancer prevention and control. In this article we offer our view of the next decade of research on behavioral and biobehavioral interventions for cancer prevention and control. We begin by discussing and questioning several implicit conventions. We then briefly introduce an alternative research framework: the multiphase optimization strategy (MOST). MOST, a principled framework for intervention development, optimization, and evaluation, stresses not only intervention effectiveness, but also intervention affordability, scalability, and efficiency. We review some current limitations of MOST along with future directions for methodological work in this area, and suggest some changes in the scientific environment we believe would permit wider adoption of intervention optimization. We propose that wider adoption of intervention optimization would have a positive impact on development and successful implementation of interventions for cancer prevention and control and on intervention science more broadly, including accumulation of a coherent base of knowledge about what works and what does not; establishment of an empirical basis for adaptation of interventions to different settings with different levels and types of resources; and, in the long run, acceleration of progress from Stage 0 to Stage V in the National Institutes of Health Model of Stages of Intervention Development. [ABSTRACT FROM AUTHOR]

Introduction: Most people who smoke cigarettes are not willing (ie, not ready) to make a quit attempt (QA) at any given time. Unfortunately, interventions intended to increase QAs and the success of QAs are only modestly effective. Identifying processes leading to QAs and quitting success could guide intervention development. Aims and Methods: This is a secondary analysis of a randomized factorial trial of 6 weeks of motivation-phase interventions among primary care patients (N = 517) who were initially unwilling to quit but were willing to reduce their smoking. Using logistic regression, we controlled for treatment condition and tested whether baseline or change in smoking-related constructs after 6 weeks of treatment predicted (1) making an at least 24 h QA between weeks 6 and 26 and (2) quitting success at week 26 (7-day point-prevalence abstinence among those who made a QA). Predictors included cigarettes/day, time to first cigarette, motivation to quit, quitting self-efficacy, anticipated urges to smoke if quit, positive affect, negative affect, and time spent around others who smoke. Results: In multivariable models that included all smoking-related constructs, changes in the following variables predicted initiating a QA above and beyond other variables: greater baseline time to first cigarette (odds ratio [OR] = 1.60), increases in time to first cigarette (OR = 1.27), and increases in quitting self-efficacy (OR = 1.14). Increased motivation to quit predicted conversion of a QA into quitting success at 26 weeks (OR = 1.36). Conclusion: Predictors of making a QA differed from predictors of quitting success. Predictors of QAs and success could each serve as important treatment targets of motivation-phase interventions. Implications: Motivation-phase interventions for people initially unwilling to quit smoking cigarettes may be improved by striving to increase their (1) time to first cigarette and quitting self-efficacy to promote QAs and (2) motivation to quit to promote quit success. Future experimental tests of such interventions are needed to identify causal determinants of QAs and quitting success. [ABSTRACT FROM AUTHOR]

Background: The Multiphase Optimization Strategy (MOST) is an approach to systematically and efficiently developing a behavioral intervention using a sequence of experiments to prepare and optimize the intervention.Purpose: Using a 6 year MOST-based behavioral intervention development project as an example, we outline the results-and resulting decision-making process-related to experiments at each step to display the practical challenges present at each stage.Methods: To develop a positive psychology (PP) based intervention to promote physical activity after an acute coronary syndrome (N = 255 across four phases), we utilized qualitative, proof-of-concept, factorial design, and randomized pilot experiments, with iterative modification of intervention content and delivery.Results: Through this multiphase approach, we ultimately developed a 12 week, phone-delivered, combined PP-motivational interviewing intervention to promote physical activity. Across stages, we learned several important lessons: (a) participant and interventionist feedback is important, even in later optimization stages; (b) a thoughtful and systematic approach using all information sources is required when conflicting results in experiments make next steps unclear; and (3) new approaches in the field over a multiyear project should be integrated into the development process.Conclusions: A MOST-based behavioral intervention development program can be efficient and effective in developing optimized new interventions, and it may require complex and nuanced decision-making at each phase. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of smoking cessation treatment is limited in real-world use, perhaps because we have not selected the components of such treatments optimally nor have treatments typically been developed for and evaluated in real-world clinical settings.Purpose: To validate an optimized smoking cessation treatment package that comprises intervention components identified as effective in factorial screening experiments conducted as per the Multiphase Optimization Strategy (MOST).Methods: Adult smokers motivated to quit were recruited from primary care clinics (N = 623). Participants were randomized to receive either recommended usual care (R-UC; 10 min of in-person counseling, 8 weeks of nicotine patch, and referral to quitline services) or abstinence-optimized treatment (A-OT; 3 weeks of prequit mini-lozenges, 26 weeks of nicotine patch + mini-lozenges, three in-person and eight phone counseling sessions, and 7-11 automated calls to prompt medication use). The key outcomes were self-reported and biochemically confirmed (carbon monoxide, CO <6 ppm) 7-day point-prevalence abstinence.Results: A-OT participants had significantly higher self-reported abstinence rates than R-UC participants at 4, 8, 16, and 26 weeks (ORs: 1.91-3.05; p <. 001). The biochemically confirmed 26-week abstinence rates were lower than the self-reported 26-week rates, but revealed a similar treatment effect size (OR = 2.94, p < .001). There was no moderation of treatment effects on 26-week abstinence by demographic, psychiatric, or nicotine dependence variables. A-OT had an incremental cost-effectiveness ratio for 26-week CO-confirmed abstinence of $7,800.Conclusions: A smoking cessation treatment that is optimized via MOST development meaningfully enhances cessation rates beyond R-UC smoking treatment in smokers seen in primary care.Clinical Trial Registration: NCT02301403. [ABSTRACT FROM AUTHOR]

Background: The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment.Purpose: Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap.Methods: Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration.Conclusions: As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention. [ABSTRACT FROM AUTHOR]

Given 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. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

Almost 35 million U.S. smokers visit primary care clinics annually, creating a need and opportunity to identify such smokers and engage them in evidence-based smoking treatment. The purpose of this study is to examine the feasibility and effectiveness of a chronic care model of treating tobacco dependence when it is integrated into primary care systems using electronic health records (EHRs). The EHR prompted primary care clinic staff to invite patients who smoked to participate in a tobacco treatment program. Patients who accepted and were eligible were offered smoking reduction or cessation treatment. More than 65 % of smokers were invited to participate, and 12.4 % of all smokers enrolled in treatment-30 % in smoking reduction and 70 % in cessation treatment. The chronic care model developed for treating tobacco dependence, integrated into the primary care system through the EHR, has the potential to engage up to 4.3 million smokers in treatment a year. [ABSTRACT FROM AUTHOR]

The multiphase optimization strategy (MOST) is a new methodological approach for building, optimizing, and evaluating multicomponent interventions. Conceptually rooted in engineering, MOST emphasizes efficiency and careful management of resources to move intervention science forward steadily and incrementally. MOST can be used to guide the evaluation of research evidence, develop an optimal intervention (the best set of intervention components), and enhance the translation of research findings, particularly type II translation. This article uses an ongoing study to illustrate the application of MOST in the evaluation of diverse intervention components derived from the phase-based framework reviewed in the companion article by Baker et al. (Ann Behav Med, in press, ). The article also discusses considerations, challenges, and potential benefits associated with using MOST and similar principled approaches to improving intervention efficacy, effectiveness, and cost-effectiveness. The applicability of this methodology may extend beyond smoking cessation to the development of behavioral interventions for other chronic health challenges. [ABSTRACT FROM AUTHOR]

Introduction: 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. [ABSTRACT FROM AUTHOR]

Background: Although the optimization of behavioral interventions offers the potential of both public health and research benefits, currently there is no widely agreed-upon principled procedure for accomplishing this.Purpose: This article suggests a multiphase optimization strategy (MOST) for achieving the dual goals of program optimization and program evaluation in the behavioral intervention field.Methods: MOST consists of the following three phases: (a) screening, in which randomized experimentation closely guided by theory is used to assess an array of program and/or delivery components and select the components that merit further investigation; (b) refining, in which interactions among the identified set of components and their interrelationships with covariates are investigated in detail, again via randomized experiments, and optimal dosage levels and combinations of components are identified; and (c) confirming, in which the resulting optimized intervention is evaluated by means of a standard randomized intervention trial. To make the best use of available resources, MOST relies on design and analysis tools that help maximize efficiency, such as fractional factorials.Results: A slightly modified version of an actual application of MOST to develop a smoking cessation intervention is used to develop and present the ideas.Conclusions: MOST has the potential to husband program development resources while increasing our understanding of the individual program and delivery components that make up interventions. Considerations, challenges, open questions, and other potential benefits are discussed. [ABSTRACT FROM AUTHOR]

Risk factors for invasive fungal infection in patients undergoing orthotopic liver transplantation were examined. Thirty-four of 168 transplants were complicated within 100 days after transplantation by documented invasive fungal infection (Candida species, 28 patients; mycelial fungi, 5; both Candida and Aspergillus species, 1). In the multivariate Cox proportional hazards model, three baseline and two posttransplant variables were independently significant risk factors for infection: level of creatinine (hazard ratio = 1.4), length of transplant operation (HR = 1.2), retransplantation (HR = 3.2), abdominal or intrathoracic reoperations (HR = 2.5), and cytomegalovirus infection (HR = 8.5). Four predictors (creatinine of >3.0 mg/dl., operative time of > 11 h, retransplantation, and early colonization) assessable at the time of transplantation or shortly thereafter were incorporated into a simple predictive model for risk stratification. The risk of invasive fungal infection ranged from 1% in patients with no predictors to 67% in patients with two or more predictors. Strategies to prevent invasive fungal infections after liver transplantation should be targeted to these high-risk groups. [ABSTRACT FROM PUBLISHER]

The purpose of this study was to investigate the relationship between simple and choice reaction time (RT) measures and neuropsychological test performance (as assessed by the Impairment Index of the Halstead-Reitan Neuropsychological Test Battery). Both median RT scores and intraindividual variability of RT scores were evaluated in three groups: impaired TBI, nonimpaired TBI, and normal controls. In all three groups, there was a statistically significant correlation between both reaction time measures and level of cognitive functioning. In addition, SRT, speed of information processing (as measured by CRT), and intraindividual variability of RT scores continued to be impaired in many brain-damaged individuals who, according to the Impairment Index, had fully recovered from their cognitive deficits. Median RT scores were better able to discriminate between impaired TBI patients and normal controls; but intraindividual variability of RT scores was better able to discriminate between nonimpaired TBI patients and normal controls. It was concluded that, by adding the two short RT tests to the neuropsychological test battery, more accurate predictions can be made about a patient's level of cognitive functioning and the nature of treatment needed for continued recovery. [ABSTRACT FROM PUBLISHER]

The article focuses on an evaluation of the effectiveness of classroom and mass media for the delivery of a psychosocial smoking prevention program and an accompanying smoking cessation program. It evaluates: the acceptance of the program by teachers, students and parents; program implementation level and quality; and program effectiveness. The program consisted of four distinct components: television prevention segments, a classroom curriculum, a home booklet and television cessation segments. The results of the article suggest that school-based and television social influences prevention programming are moderately acceptable to schools and very acceptable to students and parents.

Introduction: 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 self-regulation 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 self-regulation 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. [ABSTRACT FROM AUTHOR]

A correction to the article "New Methods for Tobacco Dependence Treatment Research" that was published on the April 2011 issue is presented.