A collection of online chapters that provide an introduction to selected behavioral and social science research approaches, including theory development and testing, survey methods, measurement, and study design. eSource was developed in 2010, and these chapters have not been updated to reflect advances in the past decade. However, they can still be used as supplementary teaching materials.
This archive provides a collection of webinars on methodology. The topics include HIV prevention, implementation methods, personalized medicine, complexity, and longitudinal data. In 2017, the Office of Disease Prevention (ODP) provided co-funding to the Center for Prevention Implementation Methodology to help create this archive.
In this Methods: Mind the Gap presentation, Dr. Valerie Earnshaw provides a cross-cutting conceptual overview of stigma, identifies targets for stigma measurement, recommends methodological approaches for stigma research, and reviews the intervention toolkit to address stigma. She draws on examples from her own and others’ research, with a focus on two highly stigmatized disease contexts: HIV and substance use. She advocates for theory-based cross-cutting research to improve understanding of stigma and the development of intersectional, multilevel, and longitudinal interventions to enhance efforts to address stigma.
The NIH-Duke Master's Program in Clinical Research, established in 1998, is one of the nation's first training programs in clinical research. This program allows participants to attend formal courses in research design, research management, medical genomics, and statistical analysis at the Clinical Center by means of video-conferencing from Duke or on-site by adjunct faculty.
The program leads to a Master of Health Sciences in Clinical Research, a professional degree awarded by the Duke University School of Medicine. There is also a non-degree option for qualified students who want to pursue specific areas of interest.
Applications will be accepted through August 1, 2020.
In this Methods: Mind the Gap presentation, Dr. Collins briefly describes MOST and contrasts it to the classical treatment package approach. She reviews examples of recent and current applications of MOST. Finally, she discusses where she sees the field of intervention optimization going, including future methodological directions.
In this Methods: Mind the Gap presentation, Dr. Rivera discusses the relevance of control engineering to mHealth using two interventions currently under development—Just Walk, an intervention to promote walking in sedentary adults, and Healthy Mom Zone, an intervention for managing gestational weight gain in overweight/obese pregnant women.
Arizona State University
In this Methods: Mind the Gap webinar, Dr. Linda Collins discusses why behavioral interventions are important in many areas of public health, for example, smoking cessation, drug abuse prevention, treatment of obesity, management of heart failure symptoms, and promotion of physical activity.
Behavioral interventions are typically developed and evaluated using a treatment package approach, in which the intervention is assembled a priori and evaluated by means of a randomized controlled trial (RCT). Dr. Collins reviews an alternative approach called the Multiphase Optimization Strategy (MOST), an engineering-inspired framework for developing, optimizing, and evaluating behavioral interventions. MOST includes the RCT, as well as other empirical steps aimed at intervention optimization.
Interventions to change behavior have great potential to improve health and well-being. In this Methods: Mind the Gap webinar, Dr. Michie describes a method for specifying the "active ingredients" of interventions. Such a method allows data pooling to identify effective component techniques. She also presents a systematic method for developing interventions, linking a "behavioral diagnosis" of the behavior needing to be changed with general strategies and specific techniques.
In this Methods: Mind the Gap webinar, Dr. Jacob Bor reviews the theory behind regression discontinuity designs and their implementation, with a focus on examples in public health research.
During this webinar, Drs. Riley and Willis focus on scale-up of effective interventions both conceptually and empirically. They have recently contributed a chapter on scale-up to an edited volume focusing on Advancing Implementation Science in Cancer Control. The session includes approximately 25 minutes of comments from the speakers and 35 minutes for engaged discussion and Q&A with the audience.
This course was developed by the National Institutes of Health (NIH) Office of Research on Women’s Health (ORWH), with funding support from the National Institute of General Medical Sciences.
The course consists of four independent, interactive modules that are designed to help the biomedical research community—including researchers, NIH grant applicants, and peer reviewers—account for and appropriately integrate Sex as a Biological Variable (SABV) across the full spectrum of biomedical sciences. The NIH SABV policy originated in a notice titled “Consideration of Sex as a Biological Variable in NIH-funded Research.” It summarizes NIH’s expectation that SABV will be factored into research designs, analyses, and reporting in vertebrate animal and human studies.
The objective of this course is to provide a thorough grounding in the conduct of randomized clinical trials to researchers and health professionals interested in developing competence in the planning, design, and execution of randomized clinical trials involving behavioral interventions.
The curriculum will enable participants to:
- Describe the principles underlying the conduct of unbiased clinical trials
- Identify the unique challenges posed by behavioral randomized clinical trials (RCTs)
- Evaluate RCT designs in terms of their appropriateness to scientific and clinical goals
- Select appropriate strategies for enrollment, randomization, and retention of participants
- Understand methods for monitoring, coordinating, and conducting RCTs
- Develop strategies for appropriate statistical analyses of RCT data
- Evaluate the quality of behavioral RCTs and interpret their results
- Design an RCT as part of a working group on a specific topic.
Dr. Sterman discusses systems approaches in public health, including the concepts of policy resistance, implementation feedbacks, and model boundaries and explores how these ideas can be applied to effect change in a complex system. He includes examples from healthcare and public health such as implementation of formulary drug lists and SARS epidemic modeling.
Dr. McLeroy discusses adoption of systems methodology, including multiple levels of analysis, utility for identifying points of change, testing models against reality, and applications to program evaluation and various research designs, including community-based participatory research and randomized clinical trials.
During this webinar, participants learn more about The Community Guide and activities underway to help communities use evidence-based recommendations and findings found in The Community Guide to take action and implement community health improvement activities in collaboration with health departments and other community partners.
In this Methods: Mind the Gap presentation, Dr. Hekler first reviews the need for optimization of adaptive interventions, building on MOST, followed by an overview of control systems engineering and attributes of problems that are well matched to control engineering. He then summarizes key steps in the development and optimization of an adaptive intervention using this approach, leading to a COT.
In this Methods: Mind the Gap presentation, Dr. Klasnja describes how micro-randomized trials can be used to make data-driven decisions about how exactly individual components of mHealth interventions should work to optimize their effectiveness. He argues that a key value of micro-randomized trials during intervention development is their ability to generate data for informing decisions about the many specifics—from the design of the interface to the adaptation algorithms—that must be determined to implement an mHealth intervention. Data from micro-randomized trials enable such decisions to be made in ways that maximize intervention effectiveness while minimizing user burden.