The future of health behavior change research: What is needed to improve translation of research into health promotion practice?
Abstract
BACKGROUND: It is well documented that the results of most behavioral and health promotion studies have not been translated into practice. PURPOSE: In this article, reasons for this gap, focusing on study design characteristics as a central contributing barrier, are discussed. METHODS: Four reviews of recent controlled studies in work sites, health care, school, and community settings are briefly discussed and summarized. Their implications for future research and for closing the gap between research and practice are then discussed. RESULTS: These reviews come to consistent conclusions regarding key internal and external validity factors that have and have not been reported. It is very clear that moderating variables and generalization issues have not been included or reported in the majority of investigations, and that as a consequence little is known about the representatives or the robustness of results from current studies. CONCLUSIONS: To significantly improve the current state of affairs, substantial changes will be required on the part of researchers, funding agencies, and review and editorial boards. In conclusion, recommendations for each of these entities are provided.
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Contextual factors are key in implementation research.
“Typically viewed as ‘nuisance’ variance to be controlled in efficacy and effectiveness research, contextual factors are key in implementation research [65-67].”
From Writing implementation research grant proposals: ten key ingredients (2012) · cites this paper as [66]
Feasibility of system features can be assessed by gathering input from targeted participants, consumers, and providers during intervention and research program development.
“This feasibility can be assessed by gathering input from targeted participants, consumers, and providers during intervention and research program development (44).”
From The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions (2011) · cites this paper as [44]
Intervention effectiveness is an important driver of intervention adoption and support.
“Because intervention effectiveness is an important driver of intervention adoption and support, it is important that smoking cessation intervention optimization using MOST identifies intervention components and levels that deliver significant benefit in real-world healthcare settings with diverse real-world populations (10, 44).”
From The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions (2011) · cites this paper as [44]
In addition to effectiveness, the reach or proportion of the population that participates in or receives an intervention has a major effect on the potential population-level impact of that intervention.
“In addition to effectiveness, the reach or proportion of the population that participates in or receives an intervention has a major effect on the potential population-level impact of that intervention (Glasgow et al. 2004).”
From Intervention to Strengthen Emotional Self-Regulation in Children with Emerging Mental Health Problems: Proximal Impact on School Behavior (2010) · cites this paper as Glasgow et al. 2004
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- Authors
- Glasgow, R. E., Klesges, L. M., Dzewaltowski, D. A., Bull, S. S., & Estabrooks, P.
- Affiliations
- Kaiser Permanente (US)
- University of Tennessee Health Science Center (US)
- Kansas State University (US)
- Corresponding author
- Glasgow, Russell E.
- Venue
- Annals of Behavioral Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-02
- Volume
- 27
- Issue
- 1
- Pages
- 3-12
- ISSN
- 0742-5554 (ISSN-L)
- 0883-6612
- 1532-4796
- OpenAlex
- W2088228975
- Cited by
- 635
- References
- 47
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 10 · 2023: 19 · 2022: 15 · 2021: 30 · 2020: 74 · 2019: 24 · 2018: 27 · 2017: 18 · 2016: 26 · 2015: 43 · 2014: 35 · 2013: 28 · 2012: 40
- FWCI
- 6.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Behavioral Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- health behavior change, research-practice gap, clinical translation, health promotion, study design characteristics, internal validity, generalizability, moderators, healthcare settings, school environment, community settings, research funding, editorial standards
- MeSH terms
- Algorithms, Behavior Therapy, Diffusion of Innovation, Forecasting, Health Promotion, Health Services Needs and Demand, Humans, Research, Research Support as Topic, Health Behavior, Treatment Outcome, Controlled Clinical Trials as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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