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The National Institutes of Health (NIH) invests billions of dollars annually in basic research designed to increase scientific understanding of social, behavioral, biological, and biomedical factors that cause illness, afect recovery, and facilitate health. Because the ultimate goal of this basic research is to improve human health and well-being, translational science, the process by which basic research findings inform prevention and treatment practice, is critical. Two types of translational science are universally identified (although more complex translational frameworks exist1). One is translation of basic science discoveries into new approaches for prevention, diagnosis, or treatment. The other is translation of these new approaches into a form amenable to widespread adoption and implementation. Francis Collins, the current director of the Nil I, has called for a \"comprehensive, systematic, and creative approach to revolutionizing the science of translation. \"4(p1) Here we introduce the multiphase optimization strategy (MOST),5'6 an engineering-inspired framework for development, optimization, and evaluation of multicomponent behavioral, biobehavioral, and biomedical interventions, and show how it offers a novel approach to translational science.

To 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.

Background 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.

Interventions (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.

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.

Each year hundreds of thousands of children and families receive behavioral interventions designed to prevent child maltreatment; yet rates of maltreatment have not declined in over a decade. To reduce the prevalence and prevent the life-long negative consequences of child maltreatment, behavioral interventions must not only be effective, but also affordable, scalable, and efficient to meet the demand for these services. An innovative approach to intervention science is needed. The purpose of this article is to introduce the multiphase optimization strategy (MOST) to the field of child maltreatment prevention. MOST is an engineering-inspired framework for developing, optimizing, and evaluating multicomponent behavioral interventions. MOST enables intervention scientists to empirically examine the performance of each intervention component, independently and in combination. Using a hypothetical example of a home visiting intervention and artificial data, this article demonstrates how MOST may be used to optimize the content of a parent-focused in-home intervention and the engagement strategies of an intervention to increase completion rate to identify an intervention that is effective, efficient, economical, and scalable. We suggest that MOST will ultimately improve prevention science and hasten the progress of translational science to prevent child maltreatment. Highlights To reduce prevalence rates of child maltreatment an innovative approach to prevention is needed. Home visiting programs can be developed or refined to not only be more effective but also affordable, scalable, and efficient. Using the multiphase optimization strategy (MOST) can improve the public health impact.

The COVID-19 pandemic has great potential to disrupt the lives of persons living with HIV (PLWH). The present convergent parallel design mixed-methods study explored the early effects of COVID-19 on African American/Black or Latino (AABL) long-term survivors of HIV in a pandemic epicenter, New York City. A total of 96 AABL PLWH were recruited from a larger study of PLWH with non-suppressed HIV viral load. They engaged in structured assessments focused on knowledge, testing, trust in information sources, and potential emotional, social, and behavioral impacts. Twenty-six of these participants were randomly selected for in-depth semi-structured interviews. Participants were mostly men (64%), African American/Black (75%), and had lived with HIV for 17 years, on average (SD=9 years). Quantitative results revealed high levels of concern about and the adoption of recommended COVID-19 prevention recommendations. HIV care visits were commonly canceled but, overall, engagement in HIV care and antiretroviral therapy use were not seriously disrupted. Trust in local sources of information was higher than trust in various federal sources. Qualitative findings complemented and enriched quantitative results and provided a multifaceted description of both risk factors (e.g., phones/internet access were inadequate for some forms of telehealth) and resilience (e.g., “hustling” for food supplies). Participants drew a direct line between structural racism and the disproportional adverse effects of COVID-19 on communities of color, and their knowledge gleaned from the HIV pandemic was applied to COVID-19. Implications for future crisis preparedness are provided, including how the National HIV/AIDS Strategy can serve as a model to prevent COVID-19 from becoming another pandemic of the poor.

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. 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. 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. 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. 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.

Background 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.

Background The dense stroma of pancreatic ductal adenocarcinomas is a major barrier to drug delivery. To increase the local drug diffusion gradient, high doses of chemotherapeutic agent doxorubicin can be released from thermally-sensitive liposomes (ThermoDox®) using ultrasound-mediated hyperthermia at the tumour target. PanDox is designed as a Phase 1 single centre study to investigate enhancing drug delivery to adult patients with non-operable pancreatic ductal adenocarcinomas. The study compares a single cycle of either conventional doxorubicin alone or ThermoDox® with focused ultrasound-induced hyperthermia for targeted drug release. Methods Adults with non-resectable pancreatic ductal adenocarcinoma are allocated to receive a single cycle of either doxorubicin alone (Arm A) or ThermoDox® with focused ultrasound-induced hyperthermia (Arm B), based on patient- and tumour-specific safety conditions. Participants in Arm B will undergo a general anaesthetic and pre-heating of the tumour by extra-corporal focused ultrasound (FUS). Rather than employing invasive thermometry, ultrasound parameters are derived from a patient-specific treatment planning model to reach the 41 °C target temperature for drug release. ThermoDox® is then concurrently infused with further ultrasound exposure. Tumour biopsies at the targeted site from all patients are analysed post-treatment using high performance liquid chromatography to quantify doxorubicin delivered to the tumour. The primary endpoint is defined as a statistically significant enhancement in concentration of total intra-tumoural doxorubicin, comparing samples from patients receiving liposomal drug with FUS to free drug alone. Participants are followed for 21 days post-treatment to assess secondary endpoints, including radiological assessment to measure changes in tumour activity by Positron Emission Tomography Response Criteria in Solid Tumours  ( PERCIST) criteria, adverse events and patient-reported symptoms. Discussion This early phase study builds on previous work targeting tumours in the liver to investigate whether enhancement of chemotherapy delivery using ultrasound-mediated hyperthermia can be translated to the stroma-dense environment of pancreatic ductal adenocarcinoma. If successful, it could herald a new approach towards managing these difficult-to-treat tumours. Trial registration ClinicalTrials.gov Identifier: NCT04852367 . Registered 21 st April 2022. EudraCT number: 2019–003950-10 (Registered 2019) Iras Project ID: 272253 (Registered 2019) Ethics Number: 20/EE/0284.