Website and content citations:

Please cite “www.RE-AIM.org” as well as Glasgow, Vogt, and Boles’ (1999) seminal paper.

The Basics
Applying RE-AIM
Support and Evidence of RE-AIM
Implementing RE-AIM


 The Basics


 Question: What is RE-AIM?

Answer: RE-AIM is an acronym that consists of five elements, or dimensions, that relate to health behavior interventions:

The goal of RE-AIM is to encourage program planners, evaluators, readers of journal articles, funders, and policy-makers to pay more attention to essential program elements including external validity that can improve the sustainable adoption and implementation of effective, generalizable, evidence-based interventions.

The five steps to translate research into action are:

  • Reach the target population
  • Effectiveness or efficacy
  • Adoption by target staff, settings, or institutions
  • Implementation consistency, costs and adaptions made during delivery
  • Maintenance of intervention effects in individuals and settings over time

 

 

Read about Applying the RE-AIM Framework

RE-AIM was originally developed as a framework for consistent reporting of research results and later used to organize reviews of the existing literature on health promotion and disease management in different settings. The acronym stands for Reach, Effectiveness, Adoption, Implementation, and Maintenance which together determine public health impact. Since the original paper in 1999, there have been approximately 100 publications on RE-AIM by a variety of authors in fields as diverse as aging, cancer screening, dietary change, physical activity, medication adherence, health policy, environmental change, chronic illness self-management, well-child care, eHealth, worksite health promotion, women’s health, smoking cessation, quality improvement, weight loss, diabetes prevention, and practice-based research.

More recently, RE-AIM has been used to translate research into practice and to help plan programs and improve their chances of working in “real-world” settings. The framework has also been used to understand the relative strengths and weaknesses of different approaches to health promotion and chronic disease self-management-such as in-person counseling, group education classes, telephone counseling, and internet resources. The overall goal of the RE-AIM framework is to encourage program planners, evaluators, readers of journal articles, funders, and policy-makers to pay more attention to essential program elements including external validity that can improve the sustainable adoption and implementation of effective, generalizable, evidence-based interventions.

See Applying the RE-AIM Framework for answers to some basic questions. You’ll also find ideas to improve your chances at having a positive impact on public health.

Acknowledgment

Many people have been involved in developing and advancing the RE-AIM model over the years. The list below acknowledges the primary contributors. Any oversights are unintentional and please bring these to our attention by contacting Paul Estabrooks.

Original Developers:
Russ Glasgow, Shawn Boles, Tom Vogt

RE-AIM Collaborators:

Russell Glasgow, PhD Visiting Professor, Family Medicine Associate Director, Colorado Health Outcomes Program University of Colorado School of Medicine. Dr. Glasgow is one of the original developers of RE-AIM, and has focused on its application in primary care settings and for chronic illness self-management.  He is also interested in applications of interactive technologies that have potential for reaching broad audiences and in programs to assist older adults, especially those with multiple chronic conditions. More information here.

Alice Ammerman, DrPH, Director, UNC Center for Health Promotion and Disease Prevention. Dr. Ammerman’s research activities include design and testing of innovative clinical and community-based nutrition and physical activity intervention approaches for chronic disease risk reduction in primarily low income and minority populations.

Borsika A. Rabin, PhD, MPH, PharmD is an Assistant Clinical Professor at the Department of Family Medicine and Colorado Health Outcomes Program at the School of Medicine, University of Colorado. Dr. Rabin’s research focuses on dissemination and implementation of evidence-based interventions, communication and coordination around cancer care with special interest in survivorship related issues, and the evaluation and development of interactive, web-based interventions and tools with a strong emphasis on cancer survival prediction tools and tools that can support planning for dissemination and implementation of interventions (i.e., designing for dissemination and implementation). She designed and developed a number of web-based resources including the Make Research Matter webtool and the Cancer Prognostic Resourceswebsite.

Bridget Gaglio, PhD, Program Officer, PCORI. Dr. Gaglio is a behavioral scientist who has a background in working with multiple healthcare settings and conducting health interventions in real-world. Her work is characterized by two main themes: (1) to understand the gaps in the quality of health status and health care that exists among diverse segments of the population and (2) to design interventions that can be implemented and disseminated in a variety of “real world” settings and will benefit their target audiences.

David Dzewaltowski, PhD, Kansas State University, Professor and Head of Kinesiology. Dr. Dzewaltowski is a social and behavioral scientist who has studied the human ecology of community-based interventions promoting physical activity and healthful eating in childcare settings, after-school programs, youth sport, Girl Scouts, middle schools and diverse community organizations.  His work has demonstrated that adults can engage children and youth and develop their capacity to influence the environments in which they live, learn and play to promote healthful behavior.

Deborah Toobert, PhD, Oregon Research Institute, Research Scientist. Dr. Toobert has conducted research and published numerous articles related to multiple-health-risk behavior self-management, including healthful eating and weight management, physical activity, smoking cessation, problem solving, stress management, and the challenges of assessing, changing, and maintaining improvements in multiple health-risk behaviors.

Diane King, PhD, Center for Behavioral Health Research & Services, University of Alaska Anchorage, Research Assistant Professor. King’s primary research interests include prevention and self-management of obesity and chronic disease through promotion of healthy eating, healthy communities, and active living. Dr. King’s research emphasizes practical “real world” interventions that have the potential for widespread dissemination and implementation at the individual, environment and policy levels.

Fabio Almeida, PhD, MSW, Virginia Tech, Department of Human Nutrition, Foods and Exercise Assistant Professor. Dr. Almeida’s work focuses on identifying the connections between systems and individuals which can create and sustain positive behavior changes associated with improved health outcomes. In 2014, Dr. Almeida led the team conducting the translation and adaptation of the RE-AIM framework to Portuguese and the Brazilian reality, and is now engaged in many activities to promote the dissemination of RE-AIM throughout Brazil.

Laura Linnan, ScD, University of North Carolina at Chapel Hill, Professor of Health Behavior, Director: Carolina Collaborative for Research on Work & Health. Dr. Linnan has research expertise in the design and evaluation of multi-level interventions to address chronic disease; with a focus on health disparities. Dr. Linnan has worked with a variety of settings including: worksites, schools and other community settings (beauty salons, public libraries, child care facilities).

Lisa Klesges, PhD, Dean, School of Public Health, Professor of Epidemiology & Social and Behavioral Sciences. Dr. Klesges has specialty training in Behavioral Epidemiology and her current work involves family‐based obesity prevention interventions in children, behavioral and environmental risk factor assessment related to childhood obesity, and in translational methods for behavioral interventions.

Marcia Ory, PhD, MPH, Texas A&M Health Science Center, Regents and Distinguished Professor. Dr. Ory’s research expertise is related to: healthy aging, chronic disease management; implementation and dissemination of evidence based programs; recruitment and retention of diverse populations. She has recently become involved in m-health approaches for assessing and changing lifestyle behaviors in clinical and community populations.

Paul A. Estabrooks, PhD, Professor of Human Nutrition, Foods, & Exercise, Virginia Tech, Professor of Family Medicine, Virginia Tech Carilion School of Medicine, Senior Director of Research, Carilion Clinic. Dr. Estabrooks conducts translational research as it relates to physical activity, nutrition, weight control, and population health management. He has designed a number of clinical and community studies using pragmatic approaches and RE-AIM as the basis for determining the potential public health impact of those interventions.

Renae Smith-Ray, PhD, University of Illinois Chicago, Research Scientist. Dr. Smith-Ray has expertise in public health and community- and clinic-based health behavior interventions for older adults. Specifically, Dr. Smith-Ray has extensive experience developing and testing cognitive and physical activity programs for older adults and examining the factors that influence the translation and dissemination of health promotion programs.

Rodger Kessler, PhD ABPP, Assistant Professor of Family Medicine, Fellow, Global Health Economics Unit, University of Vermont College of Medicine, Director, Collaborative Care Research Network. Dr. Kessler implements and evaluates behavioral interventions and models of behavioral care in primary care practices. Kessler focuses on both process engagement and translation of evidence based behavioral care in primary care settings. He works with the full range of primary care patients, with health care systems and insurers to create the opportunity to translate evidence-based interventions into their intended settings. His primary passion is creating alternatives to obstacles that have generated ineffective health care silos.

Samantha Harden, PhD, Assistant Professor of Human Nutrition, Foods, & Exercise, Virginia Tech and Exercise Extension Specialist, Virginia Cooperative Extension. Dr. Harden studies ‘physical activity’ as a mechanism for improved health outcomes and psychological well-being; especially from a group dynamics-based approach. Working with a variety of populations from prenatal women to older adults, Dr. Harden explores intrapersonal, interpersonal, and system-level factors that either speed or impede the rate of translating evidence-based interventions into their intended practice settings (e.g., clinic, community).

Website Development and Hosting Institutions:
Robert Wood Johnson Foundation (support for website development); Kansas State University (inception to 2007); Kaiser Permanente Colorado Institute for Health Research (2008- 2011); Division of Cancer Control and Population Sciences, National Cancer Institute (2011- 2013); Virginia Tech (August 2013-2016); University of Nebraska Medical Center (2016-present)

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 Question: How do you define each element?

Answer: Reach – The absolute number, proportion, and representativeness of individuals who participate in a given initiative, intervention or program.

Representativeness refers to whether participants have characteristics that reflect the target population’s characteristics. For example, if your intent is to increase physical activity in sedentary people between the ages of 35 and 70, you wouldn’t test your program on triathletes.

Effectiveness/Efficacy – The impact of an intervention on important outcomes. This includes potential negative effects, quality of life, and economic outcomes.

Adoption – The absolute number, proportion, and representativeness of settings and staff who are willing to initiate a program or approve a policy.

Implementation – At the setting level, implementation refers to how closely staff members follow the program that the developers provide. This includes consistency of delivery as intended and the time and cost of the program.

Maintenance – At the setting level, the extent to which a program or policy becomes part of the routine organizational practices and policies.

At the individual level, maintenance refers to the long-term effects of a program on outcomes after 6 or more months after the most recent intervention contact.

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 Question: How do the RE-AIM elements relate to planning?

Answer: As you design, plan, or evaluate an intervention, there are questions that you should ask yourself.

  • Reach your intended target population
  • Efficacy or efficacy
  • Adoption by target staff, settings, or institutions
  • Implementation consistency, costs and adaptations made during delivery
  • Maintenance of intervention effects in individuals and settings over time

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 Question: Which RE-AIM element is the most important? (Isn’t Reach really the bottom line in what you are trying to accomplish?)

Answer: Some have argued that Reach is the most important criteria, but we think that all five RE-AIM dimensions are equally important. An intervention with high Reach, but little or no Efficacy will have limited impact. Similarly, even if an intervention has high Reach and impressive Efficacy, if no organizations will Adopt the intervention, or if only a handful of experts can successfully Implement the program, it will have limited real-world impact.

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 Question: Why isn’t cost one of the RE-AIM dimensions – Isn’t it so important to adoption and other issues?

Answer: We agree that cost is often one of the key factors in determining how widely Adopted an intervention will be. However, we view cost, or cost-effectiveness and cost-benefit, as one of the factors that influences several RE-AIM dimensions in addition to Adoption; for example, cost is usually related to intensiveness of intervention which is often related (positively) to Effectiveness and (negatively) to Implementation.

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 Question: How is RE-AIM different from other evaluation approaches?

Answer: RE-AIM draws upon previous work in several areas including diffusion of innovations, multi-level models, and Precede-Proceed. The primary ways that it is different is that it a) is intended specifically to facilitate translation of research to practice, b) it places equal emphasis on internal and external validity issues and emphasizes representativeness, and c) it provides specific and standard ways of measuring key factors involved in evaluating potential for public health impact and widespread application.

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 Question: How is the RE-AIM definition of Implementation different from concepts such as intervention delivery, receipt of intervention, or implementation fidelity?

Answer: In the RE-AIM framework, Implementation is closely related to the above issues. However, it has a greater focus on the intervention setting level and on the staff delivering the program and what they do, rather than on what the individual participant who receives a program does. Both are important, but RE-AIM places emphasis on the potential implications for delivering intervention in applied settings, and on assessing Implementation for different components of the program and across diverse intervention staff. In addition, implementing RE-AIM is also concerned with cost and with adaptations that are made to the program or policy.

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 Question: Is RE-AIM used to design programs, or just to evaluate them?

Answer: Both. Although used more commonly at present to report results or compare interventions, it is also useful as a planning tool and as a method to review intervention studies.

These articles provide examples of reporting results:

Evaluating Initial Reach and Robustness of a Practical Randomized Trial of Smoking Reduction.
Glasgow RE, Estabrooks PA, Marcus AC, Smith TL, Gaglio B, Levinson AH, Tong S. (2008).
Health Psychol Nov 27(6):78-788.

Implementation, generalization, and long-term results of the “Choosing Well” diabetes self-management intervention.
Glasgow, R.E., Toobert, D.J., Hampson, S.E., & Strycker, L.A. (2002).
Pt Educ Couns, 48(2):115-122.

Tailored Behavioral Support for Smoking Reduction: Development and Pilot Results of an Innovative Intervention.
Levinson AH, Glasgow RE, Gaglio B, Smith TL, Cahoon J, Marcus AC. (2008).
Health Educ Res 2008 Apr;23(2):335-46.

The following articles discuss using RE-AIM for planning:

Beginning with the Application in Mind: Designing and Planning Health Behavior Change Interventions to Enhance Dissemination.
Klesges, L.M., Estabrooks, P.A., Glasgow, R.E., Dzewaltowski, D.A. (2005).
Ann Behav Med. 29:66S-75S.

RE-AIM for Program Planning and Evaluation: Overview and Recent Developments.
Glasgow, R.E., Toobert, D.J. (2007).
Center for Health Aging: Model Health Programs for Communities/National Council on Aging (NCOA).

These articles provide examples of using RE-AIM to evaluate evidence and review the literature:

Promoting smoking abstinence in pregnant and postpartum patients: A comparison of 2 approaches.
Lando, H.A., Valanis, B.G., Lichtenstein, E.L., et al. (2001).
American Journal of Managed Care, 7, 685-693.

Reporting of Validity from School Health Promotion Studies Published in 12 Leading Journals.
Estabrooks, P.A., Dzewaltowski, D.A., Glasgow, R.E., Klesges, L.M. (2003).
1996-2000. Journal of School Health, 73(1):21-28.

Review of External Validity Reporting in Childhood Obesity Prevention Research.
Klesges LM, Dzewaltowski DA, Glasgow RE. (2008).
Am J Prev Med; 34(3):216-223.

Smoking cessation interventions among hospitalized patients: What have we learned?
France, E.K., Glasgow, R.E., Marcus, A. (2001).
Preventive Medicine, 32(4):376-388.

Translating physical activity interventions for breast cancer survivors into practice: an evaluation of randomized controlled trials.
White SM, McAuley E, Estabrooks PA, Courneya KS. (2009).
Ann Behav Med. Feb;37(1):10-9. Epub 2009 Mar 3.

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 Applying RE-AIM

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 Question: We are only conducting efficacy studies; do the RE-AIM framework and evaluation criteria still apply to us?

Answer: Yes, they do, but the specific types of information that you collect may be different than in an effectiveness or dissemination study. Specifically, you may want to select your participant sample or setting(s) to be similar to the population to which you want to generalize. Also you may want to consider the practicality and intensiveness of your intervention, so that it has good potential for later implementation, but not actually collect measures of cost-effectiveness until later studies. Across all RE-AIM criteria, you may still want to have discussions with your intended target audiences of participants, of implementers and of potential settings even though you do not collect formal data. The effect of moderator variables is very important to assess in efficacy studies, although often the research team will end up purposefully selecting one or more levels of a plausible moderator variable (e.g., education level, experience of intervention agent) rather than attempting to ensure complete representativeness at this stage of research.

See also Table on Efficacy and Effectiveness Studies

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 Question: Can data on all RE-AIM dimensions be collected in a single study? Is it really possible to collect data on all or most of the RE-AIM dimensions in a single study?

Answer: Yes, it is possible, and there are relatively inexpensive ways of collecting data on most RE-AIM dimensions as you are making arrangements for your study. Example publications that have reported on all five or several RE-AIM factors are listed below. You can also refer readers to other documents or websites that report on RE-AIM issues such as representativeness in more detail than may be possible in a given study.

A brief smoking cessation intervention for women in low-income Planned Parenthood Clinics.
Glasgow, R.E., Whitlock, E.P., Eakin, E.G., Lichtenstein, E. (2000).
American Journal of Public Health, 90(5):786-789.

The D-Net Diabetes Self-Management Program: Long-term implementation, outcomes, and generalization results.
Glasgow, R.E., Boles, S.M., McKay, H.G., Feil, E.G., Barrera, M., Jr. (2003).
Preventive Medicine 36(4): 410-419.

Implementation, generalization, and long-term results of the “Choosing Well” diabetes self-management intervention.
Glasgow, R.E., Toobert, D.J., Hampson, S.E., & Strycker, L.A. (2002).
Patient Education and Counseling 48(2):115-122.

Long-term Results of Smoking Reduction Program.
Glasgow, R.E., Gaglio, B., Estabrooks, P.A., Marcus, A.C., Ritzwoller, D.P., Smith, T.L., Levinson, A.H., O’Donnell, C. (2009).
Med Care 47(1):115-120.

Promoting smoking abstinence in pregnant and postpartum patients: A comparison of 2 approaches.
Lando, H.A., Valanis, B.G., Lichtenstein, E.L., et al. (2001).
American Journal of Managed Care, 7, 685-693.

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 Question: Is there a simple way to get an overall RE-AIM score? The RE-AIM model seems very complicated-is there a simple way to get an overall score?

Answer: Some have suggested that a multiplicative model best fits the intent of the model that all five dimensions are equally important, and that if a program has a zero value on any dimension, that its overall public health impact will be zero. This builds upon the increasingly accepted notion of Reach X Efficacy = Impact to become Public Health Impact = R x E x A x I x M.

Others object to such formulas and feel that there is no one best way to combine the RE-AIM elements. One approach that allows the user to define their own criteria and to emphasize the issues that are most important to them is to visually display different programs on the RE-AIM dimensions so that the strengths and limitations of different programs can be quickly seen. Such visual displays appear in the articles listed below, and the following links display examples of such visual displays.

Sample Visual Displays

Interactive Technologies versus In-Person Counseling for Diabetes Self-Management: Comparison of RE-AIM Capabilities

1. This figure visually displays the relative strengths and limitations of interactive computer vs. in-person based behavior change counseling along the various RE-AIM dimensions (higher scores are better on the hypothetical scale).

Display of Two Different Intervention Programs on Various RE-AIM Dimensions

2. This figure visually displays the hypothetical performance of a group counseling program versus a policy approach to smoking behavior change on the various RE-AIM dimensions (higher scores are better on the hypothetical scale).

Articles containing visual displays of RE-AIM dimensions:

Making a difference with interactive technology: Considerations in using and evaluating computerized aids for diabetes self-management education.
Glasgow, R.E., & Bull, S. (2001).
Diabetes Spectrum, 14(2): 99-106

The RE-AIM framework for evaluating interventions: What can it tell us about approaches to chronic illness management?
Glasgow, R.E., McKay, H.G., Piette, J.D., Reynolds, K.D. (2001).
Patient Education and Counseling, 44:119-127.

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 Question: How specifically can RE-AIM be used to help translate research into practice?

Answer: By providing a set of standard criteria (Reach, Efficacy/Effectiveness, Adoption, Implementation, and Maintenance) it focuses attention on key factors important for application. By considering this set of RE-AIM issues in planning, conducting, evaluating and reporting on intervention programs or policies, one should be able to anticipate and prepare for most of the major challenges in translating research programs into real world applications.

Also, by comparing alternative interventions (See Lando et al, 2001.), program delivery modalities (see Glasgow, McKay et al, 2001.) or policies (See Jilcott et al, 2007.) on the RE-AIM criteria, decision makers in applied settings should be better able to judge the fit of a possible program with their needs and priorities.

Finally we refer you to these following two articles for more information on specific summary score formulas you may want to consider, especially the Efficiency Metric (Cost / (Reach x Effectiveness).

Evaluating the impact of health promotion programs: Using the RE-AIM Framework to form summary measures for Decision Making Involving Complex Issues.
Glasgow, R.E., Klesges, L.M., Dzewaltowski, D.A., Estabrooks, P.A., Vogt, T.M. (2006).
Health Educ Res 21(3):688-694

Using RE-AIM Metrics to Evaluate Diabetes Self-Management Support Interventions.
Glasgow, R.E., Nelson, C.C., Strycker, L.A., King, D.K. (2006).
Am J Prev Med 30(1):67-73.

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 Question: Do you have an example of RE-AIM being applied to a real program that promotes healthy behaviors?

Answer: In Australia, a program that helps doctors promote physical activity was evaluated using the RE-AIM framework.

The Victoria Council on Fitness and General Health Inc. (VICFIT) was established through the Ministers for Sport and Recreation and Health to provide advice to government and to coordinate the promotion of fitness in Victoria.

One of VICFIT’s initiatives, the Active Script Program (ASP), is designed to enable all general practitioners in Victoria to give consistent, effective and appropriate physical activity advice in their particular communities. Phase II of ASP was implemented from July 2000 to June 2001.

Visit the VICFIT website for more details and for the complete report.

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 Question: If sites say no when calculating adoption, then are their potential participants included in the reach calculations?

For example: 5 clinics are approached to participate, each having 5,000 patients. Two say no, thus 3 do the intervention. When calculating reach, is the target population for recruitment all 25,000 patients at all clinics approached or only 15,000 in the three clinics that said yes?

Answer: First step is Adoption. This defines the subset of potentially eligible participants. But for analyses of Reach, you only want to analyze participation among those who potentially could have participated – who had a chance, invited, etc.
Note: the overall impact of both reach and adoption would be indicated in the combine RE-AIM metrics and adoption X reach to get percent of population.

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 Support and Evidence of RE-AIM

 Question: Is it a theory of behavior change?

Answer: RE-AIM is not a theory, rather it is a framework and a set of criteria for planning and evaluating interventions that are intended to eventually be broadly implemented or widely adopted. As such, it is difficult to think of how one would “validate” RE-AIM or other approaches to evaluation such as the Precede-Proceed framework. The ultimate value of RE-AIM will be if both researchers and decision makers from potential adopting organizations feel that the framework is helpful to them in planning, conducting, reporting, and selecting interventions.

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 Question: Have any literature reviews been conducted using the RE-AIM framework? Have any reviews of the literature been conducted using the RE-AIM framework? If so, what have these reviews found?

Answer: Yes, several reviews have been conducted using the RE-AIM criteria. The general conclusion is that issues of representativeness (of both individuals and especially organizations and intervention agents) participating are the least often reported RE-AIM elements. Greater attention needs to be paid to the AIM factors.

If you are interested in the coding criteria used to score the various RE-AIM dimensions, view our Coding Sheet for Publications Reporting on RE-AIM Elements.

Behavior change intervention research in health care settings: A review of recent reports, with emphasis on external validity
Glasgow, R.E., Bull, S.S., Gillette, C., Klesges, L.M., & Dzewaltowski, D.A.
(2002). American Journal of Preventive Medicine. 23(1):62-69.

The future of health behavior change research: What is needed to improve translation of research into health promotion practice?
Glasgow, R.E., Klesges, L.M., Dzewaltowski, D.A., Bull, S.S., Estabrooks, P.
Ann Behav Med. 2004;27(1):3-12.

Reaching those most in need: A review of diabetes self-management interventions in disadvantaged populations.
Eakin, E.G., Bull, S.S., Glasgow, R.E., & Mason, M.
(2002) Diabetes Metab Res Rev., Jan.-Feb.(1):26-35.

Review of External Validity Reporting in Childhood Obesity Prevention Research.
Klesges LM, Dzewaltowski DA, Glasgow RE.
Am J Prev Med 2008;34(3):216-223.

Smoking cessation interventions among hospitalized patients: What have we learned?
France, E.K., Glasgow, R.E., Marcus, A. (2001)
Preventive Medicine, 32(4):376-388.

Translating physical activity interventions for breast cancer survivors into practice: an evaluation of randomized controlled trials.
White SM, McAuley E, Estabrooks PA, Courneya KS.
Ann Behav Med 2009 Feb;37(1):10-9. Epub 2009 Mar 3.

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 Implementing RE-AIM

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 Question: How do I calculate Reach when our “denominator” or target population is not known?

Answer: There are several ways of estimating Reach, and there are some websites listed on our Links page that may be of help. It is often possible to use either census data, data from national representative surveys such as NHANES or the CDC BRFSS, or information available from public agencies or marketing organizations that will allow you to estimate the number of eligible persons in a given geographic area. Two important guidelines to remember are to report the exclusion rate for your study (as well as exclusion criteria) and to state specifically if you drew your sample from some exhaustive list (such as all patients in a health plan, all students in the school); or if your initial list of potential participants were interested volunteers (such as those responding to an advertisement or self-selecting to participate)

See a table illustrating the impact of different ways to calculate Reach and our recommendations.

View a flow-diagram on “My Path” recruitment results.

See also Calculating and Reporting Reach

Read a list of suggestions for estimating target populations or “Reach denominators.”

See a table providing RE-AIM scoring examples for two built environment interventions.

See a table on RE-AIM Perspectives on Built Environment Strategies: Definitions, Challenges, and Metrics

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 Question: How do I calculate Adoption if the population of potentially eligible settings is not known?

Answer: There are several ways of estimating Adoption rates, and some websites listed below that may be of help. It is often possible to use either census data, data from national surveys, professional associations (e.g., of businesses, schools, churches, employers), chambers of commerce, state licensing bureaus, marketing organizations or even phone books that will allow you to estimate the number of eligible organizations or settings in a given geographic area. Two important guidelines to remember are to report the exclusion rate for your study (as well as exclusion criteria) and to state specifically if you drew your sample from some exhaustive list versus approached those settings that you judged to be best able to implement your protocol or most interested.

See also Adoption Calculator

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 Question: What types of variables should I use to determine the representativeness of the organizations involved in my program (and those who decline)?

Answer: This depends on what is known about organizational characteristics that are related to outcomes in the particular area you are studying. A fairly common list of issues to consider would include: size of organization; history and stability of the organization; number of full-time versus part time staff; if the organization is unionized; history of health promotion; relevant health policies; whether time off work is provided for participation in such activities; percent of members or employees by gender; race and ethnicity, level of education, and job title. Of course, not all of this information will be available in every case. Often information can be collected over the phone from a personnel or human resources representative, or from standard sources described above in the section on Adoption.

For community-wide applications, see a table giving examples of how to calculate adoption for built environment interventions

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 Question: What are the most important characteristics to assess the representativeness of participants when evaluating Reach?

Answer: This varies depending upon the science of what factors are most strongly related to the health behavior of interest (e.g., medication adherence vs. physical activity). In general, we recommend assessing factors demonstrated to be related to the outcome of interest and/or health disparities, such as race/ethnicity, age, education, health literacy and numeracy, quality of life, etc.

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 Question: Implementation is not well articulated in the model and thus the metric is a little ambiguous. As I see it there are two elements to Implementation: Fidelity and Dose/Exposure. You can “measure” the degree to which the staff/site implement the program as intended (# of lessons taught, # of minutes spent per session, # elements at each site) BUT you also want to know to what degree the participants were exposed to the program – meaning attendance, adherence to behavior change requirements, etc.(question and responses obtained via email discussion on 11 Feb 2010).

Answer: We have restricted our implementation results in published work to the percent of intervention delivered as intended, or if multiple components, then the percent to which (and quality with which if measures or applicable) program delivered. This would include consistency across delivery staff and settings. We have not used individual level indicators of participant receipt and enactment of intervention materials in our calculations, but typically provide descriptive statistics on attendance, participant reading of materials etc. Not sure how best to add this to a calculation, but in a paper one of my students we included participant reports of reading intervention materials and demonstrated that it moderated the outcome. It might be that due to the different modalities and structure of interventions that the ‘quality’ indices at the individual level may be more intervention specific while the quantity/proportion measures at the delivery level are more generic?

Answer: The “Dose” question raised comes up also in our community-level work, where we’ve been discussing whether impact on individual health/health behavior is greater for strategies where the exposure is continuous (i.e., an environmental change, such as opening a new grocery store) versus single or limited exposures (a cooking class or farmer’s market that’s only available on Saturday mornings during the summer). We are thinking about considering dose under “E” as an element of effectiveness (so would be at the individual level versus setting level).

Answer: I think every evaluation approach, including RE-AIM, addresses this somewhat, but none to my knowledge, comprehensively. There are several different constructs involved here…… and the interpretation of results is heavily dependent upon one’s perspective.

What others above are referring to as ‘dose/exposure’ is- from my perspective a mixture- and likely an interaction between several constructs…some of which are at the individual level- e.g. engagement; motivation, follow-through, participation, adherence….these behaviors are themselves the likely result of a combination of person, setting, and program variables.

Other aspects of this ‘dose/exposure’ are related to setting /program characteristics- some of which are measured by RE-AIM and other evaluation approaches- e.g., things like cost, consistency of delivery, and ESPECIALLY extensiveness (related to but distinct from intensiveness ) of intervention.

I think almost all evaluation approaches assess some, but none ALL of these features. The end result of what is commonly referred to as ‘dose/exposure’ is the interaction between these individual and program/ setting factors…and very likely -as most things are- also heavily influenced by societal, environmental and contextual variables such as income, class, competing demands, policy and economic factors.

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