Implementation Science in Nutrition: Concepts and Frameworks for an Emerging Field of Science and Practice.
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ABSTRACT:
SUBMITTER: Tumilowicz A
PROVIDER: S-EPMC6400593 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
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Project description:Adjuvants are vaccine components that enhance the magnitude, breadth and durability of the immune response. Following its introduction in the 1920s, alum remained the only adjuvant licensed for human use for the next 70 years. Since the 1990s, a further five adjuvants have been included in licensed vaccines, but the molecular mechanisms by which these adjuvants work remain only partially understood. However, a revolution in our understanding of the activation of the innate immune system through pattern recognition receptors (PRRs) is improving the mechanistic understanding of adjuvants, and recent conceptual advances highlight the notion that tissue damage, different forms of cell death, and metabolic and nutrient sensors can all modulate the innate immune system to activate adaptive immunity. Furthermore, recent advances in the use of systems biology to probe the molecular networks driving immune response to vaccines ('systems vaccinology') are revealing mechanistic insights and providing a new paradigm for the vaccine discovery and development process. Here, we review the 'known knowns' and 'known unknowns' of adjuvants, discuss these emerging concepts and highlight how our expanding knowledge about innate immunity and systems vaccinology are revitalizing the science and development of novel adjuvants for use in vaccines against COVID-19 and future pandemics.
| S-EPMC8023785 | biostudies-literature
Project description:Recent dietary guidance for heart health recommends a reduction (by ?50%) in saturated fatty acid (SFA) intake to reduce LDL cholesterol and to decrease risk of cardiovascular disease (CVD). The 2010 Dietary Guidelines for Americans recommends substituting unsaturated fat [both polyunsaturated and monounsaturated fatty acids (PUFAs and MUFAs, respectively)] for SFAs. There are many dietary options that can be implemented to replace SFAs, given the different sources of unsaturated fats in the food supply. Compelling evidence exists for the cardioprotective benefits of n-3 (?-3) PUFAs, both marine- and plant-derived. In addition, the evidence of cardioprotective benefits of n-6 (?-6) PUFAs is strong, whereas that for MUFAs is mixed, although there is emerging evidence of benefits. Quantitatively, lowering SFAs by 50% will require, in part, substituting food sources of n-6 and n-3 PUFAs and MUFAs for food sources of SFAs. The use of n-3 PUFAs as a replacement for SFAs will result in a shortfall in reaching the SFA goal because of the relatively low amounts that can be incorporated in the diet, even with very high n-3 PUFA substitution. SFAs also can be replaced with dietary carbohydrate and/or protein. Replacing SFAs with carbohydrate, specifically refined sources, however, has little impact on reducing CVD risk. There is evidence about the health benefits of dietary protein on CVD risk, which merits study. Dietary guidelines have advanced considerably with the "replacement of SFA with unsaturated fat message" instead of recommending decreasing SFAs alone. A key question that remains is what is the optimal mix of macronutrients to maximally reduce CVD risk.
| S-EPMC4424771 | biostudies-other
Project description:BackgroundRecent reviews of the use and application of implementation frameworks in implementation efforts highlight the limited use of frameworks, despite the value in doing so. As such, this article aims to provide recommendations to enhance the application of implementation frameworks, for implementation researchers, intermediaries, and practitioners.DiscussionIdeally, an implementation framework, or multiple frameworks should be used prior to and throughout an implementation effort. This includes both in implementation science research studies and in real-world implementation projects. To guide this application, outlined are ten recommendations for using implementation frameworks across the implementation process. The recommendations have been written in the rough chronological order of an implementation effort; however, we understand these may vary depending on the project or context: (1) select a suitable framework(s), (2) establish and maintain community stakeholder engagement and partnerships, (3) define issue and develop research or evaluation questions and hypotheses, (4) develop an implementation mechanistic process model or logic model, (5) select research and evaluation methods (6) determine implementation factors/determinants, (7) select and tailor, or develop, implementation strategy(s), (8) specify implementation outcomes and evaluate implementation, (9) use a framework(s) at micro level to conduct and tailor implementation, and (10) write the proposal and report. Ideally, a framework(s) would be applied to each of the recommendations. For this article, we begin by discussing each recommendation within the context of frameworks broadly, followed by specific examples using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework.SummaryThe use of conceptual and theoretical frameworks provides a foundation from which generalizable implementation knowledge can be advanced. On the contrary, superficial use of frameworks hinders being able to use, learn from, and work sequentially to progress the field. Following the provided ten recommendations, we hope to assist researchers, intermediaries, and practitioners to improve the use of implementation science frameworks.
| S-EPMC7427911 | biostudies-literature
Project description:BackgroundImplementation science has been growing as discipline in the past decades, producing an increasing number of models in the area. On the other hand, most frameworks are intended to guide the implementation of programs, focusing on identifying elements and stages that increase their success. This article aims to structure this discussion, proposing a simplified tool that synthesizes common elements of other frameworks, and highlight the usefulness to use implementation science not only in identifying successful implementation strategies but as a tool to assess gaps in global health initiatives.MethodsThe study was carried out through a combined methodology that included an initial search of implementation science frameworks, experts' opinions, and the use of references in frameworks to elaborate a list of articles to be reviewed. A total of 52 articles were analyzed, identifying their definitions of implementation science and the elements of different frameworks.ResultsThe analysis of articles allowed identifying the main goals and definitions of implementation science. In a second stage, frameworks were classified into "time-based", "component-based" and "mixed", and common elements of each type of model were used to propose a synthetic framework with six elements: Diagnosis, Intervention provider/ system, Intervention, Recipient, Environment, and Evaluation. Finally, this simplified framework was used to identify gaps in global health was using The Lancet Global Health Series. Potential areas of intervention arise for five different global health issues: malaria, non-communicable diseases, maternal and child health, HIV/AIDS, and tuberculosis. Prioritization strategies differ for the different health issues, and the proposed framework can help identify and classify all these different proposals.ConclusionsThere is a huge variety of definitions and models in implementation science. The analysis showed the usefulness of applying an implementation science approach to identify and prioritize gaps in implementation strategies in global health.
| S-EPMC6712702 | biostudies-literature
Project description:BackgroundIn several recent articles, authors have called for aligning the fields of implementation and improvement science. In this paper, we call for implementation science to also align with improvement practice. Multiple implementation scholars have highlighted the importance of designing implementation strategies to fit the existing culture, infrastructure, and practice of a healthcare system. Worldwide, healthcare systems are adopting improvement models as their primary approach to improving healthcare delivery and outcomes. The prevalence of improvement models raises the question of how implementation scientists might best align their efforts with healthcare systems' existing improvement infrastructure and practice.Main bodyWe describe three challenges and five benefits to aligning implementation science and improvement practice. Challenges include (1) use of different models, terminology, and methods, (2) a focus on generalizable versus local knowledge, and (3) limited evidence in support of the effectiveness of improvement tools and methods. We contend that implementation science needs to move beyond these challenges and work toward greater alignment with improvement practice. Aligning with improvement practice would benefit implementation science by (1) strengthening research/practice partnerships, (2) fostering local ownership of implementation, (3) generating practice-based evidence, (4) developing context-specific implementation strategies, and (5) building practice-level capacity to implement interventions and improve care. Each of these potential benefits is illustrated in a case study from the Centers for Disease Control and Prevention's Cancer Prevention and Control Research Network.ConclusionTo effectively integrate evidence-based interventions into routine practice, implementation scientists need to align their efforts with the improvement culture and practice that is driving change within healthcare systems worldwide. This paper provides concrete examples of how researchers have aligned implementation science with improvement practice across five implementation projects.
| S-EPMC8424169 | biostudies-literature
Project description:A single laboratory result actually represents a range of possible values, and a given laboratory result is impacted not just by the presence or absence of disease, but also by biological variation of the measurand in question and analytical variation of the equipment used to make the measurement. Biological variation refers to variability in measurand concentration or activity around a homeostatic set point. Knowledge of biological and analytical variation can be used to facilitate interpretation of patient clinicopathologic data and is particularly useful for interpreting serial patient data and data at or near reference limits or clinical decision thresholds. Understanding how biological and analytical variation impact laboratory results is of increasing importance, because veterinarians evaluate serial data from individual patients, interpret data from multiple testing sites, and use expert consensus guidelines that include decision thresholds for clinicopathologic data interpretation. The purpose of our report is to review current and emerging concepts in biological and analytical variation and discuss how biological and analytical variation data can be used to facilitate clinicopathologic data interpretation. Inclusion of veterinary clinical pathologists having expertise in laboratory quality management and biological variation on research teams and veterinary practice guideline development teams is recommended, to ensure that various considerations for clinicopathologic data interpretation are addressed.
| S-EPMC7694803 | biostudies-literature
Project description:BackgroundThe relevance of context in implementation science is reflected in the numerous theories, frameworks, models and taxonomies that have been proposed to analyse determinants of implementation (in this paper referred to as determinant frameworks). This scoping review aimed to investigate and map how determinant frameworks used in implementation science were developed, what terms are used for contextual determinants for implementation, how the context is conceptualized, and which context dimensions that can be discerned.MethodsA scoping review was conducted. MEDLINE and EMBASE were searched from inception to October 2017, and supplemented with implementation science text books and known published overviews. Publications in English that described a determinant framework (theory, model, taxonomy or checklist), of which context was one determinant, were eligible. Screening and inclusion were done in duplicate. Extracted data were analysed to address the study aims. A qualitative content analysis with an inductive approach was carried out concerning the development and core context dimensions of the frameworks. The review is reported according to the PRISMA guidelines.ResultsThe database searches yielded a total of 1113 publications, of which 67 were considered potentially relevant based on the predetermined eligibility criteria, and retrieved in full text. Seventeen unique determinant frameworks were identified and included. Most were developed based on the literature and/or the developers' implementation experiences. Six of the frameworks explicitly referred to "context", but only four frameworks provided a specific definition of the concept. Instead, context was defined indirectly by description of various categories and sub-categories that together made up the context. Twelve context dimensions were identified, pertaining to different aggregation levels. The most widely addressed context dimensions were organizational support, financial resources, social relations and support, and leadership.ConclusionsThe findings suggest variation with regard to how the frameworks were developed and considerable inconsistency in terms used for contextual determinants, how context is conceptualized, and which contextual determinants are accounted for in frameworks used in implementation science. Common context dimensions were identified, which can facilitate research that incorporates a theory of context, i.e. assumptions about how different dimensions may influence each other and affect implementation outcomes. A thoughtful application of the concept and a more consistent terminology would enhance transparency, simplify communication among researchers, and facilitate comparison across studies.
| S-EPMC6432749 | biostudies-literature
Project description:BackgroundThe uptake, adoption and integration of new medicines and treatment regimens within healthcare delivery can take a decade or more. Increasingly, implementation science (IS) research is being used to bridge this gap between the availability of new therapeutic evidence and its actual application in clinical practice. Little is known, however, about the quality of IS research in this area, including the degree to which theories, models and frameworks (TMFs) are being used. The objective of this study was to conduct a scoping review of the use of TMFs in implementation research involving medicinal products.MethodsA search was conducted for English language abstracts and manuscripts describing the application of TMFs in IS studies for medicinal products. Eligible publications were those published between 1 January 1974 and 12 December 2022. All records were screened at the title and abstract stage; included full-text papers were abstracted using data extraction tables designed for the study. Study quality was appraised using the Implementation Research Development Tool.ResultsThe initial scoping search identified 2697 publications, of which 9 were ultimately eligible for inclusion in the review. Most studies were published after 2020 and varied in their objectives, design and therapeutic area. Most studies had sample sizes of fewer than 50 participants, and all focused on the post-marketing phase of drug development. The TMF most frequently used was the Consolidated Framework for Implementation Research (CFIR). Although most studies applied all TMF domains, TMF use was limited to instrument development and/or qualitative analysis. Quality appraisals indicated the need for engaging patients and other stakeholders in the implementation research, reporting on the cost of implementation strategies, and evaluating the unintended consequences of implementation efforts.ConclusionsWe found that few IS studies involving medicinal products reported using TMFs. Those that did encompassed a wide variety of therapeutic indications and medicinal products; all were in the post-marketing phase and involved limited application of the TMFs. Researchers should consider conducting IS in earlier phases of drug development and integrating the TMFs throughout the research process. More consistent and in-depth use of TMFs may help advance research in this area.
| S-EPMC10823700 | biostudies-literature
Project description:BackgroundSignificant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.ObjectiveThis paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.MethodsA scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.ResultsIn total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.ConclusionsThis literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science.
| S-EPMC8832266 | biostudies-literature
Project description:Purpose of reviewGuidelines provide recommendations for clinicians based on the best available evidence and informed by clinical expertise. These recommendations often fail to be utilized by clinicians hindering the translation of evidence into practice. The purpose of this review is to describe novel ways in which implementation science has been used to improve translation of guidelines into clinical practice in the field of lipidology.Recent findingsWe searched PubMed for articles related to guideline implementation in lipidology published in 2021 and 2022. Identified articles were categorized into three domains: first, poor uptake of guideline recommendations in practice; second, implementation science as a solution to improve care; and third, examples of how implementation science can be incorporated into guidelines.SummaryThe field of lipidology has identified that many guideline recommendations fail to be translated into practice and has started to utilize methods from implementation science to assess ways to shrink this gap. Future work should focus on deploying tools from implementation science to address current gaps in guideline development. Such as, developing a systematic approach to restructure guideline recommendations so they are implementable in practice and aid in clinicians' ability to easily translate them into practice.
| S-EPMC9640274 | biostudies-literature