Limited research exists to evaluate nutrition guideline impact on clinical practice
and patient health outcomes. In this study we investigate (1) the impact of guideline
training on the implementation of the diabetes prevention Evidence-Based Nutrition
Practice Guideline (EBNPG), and (2) the relationship between EBNPG congruence and
resulting health outcomes in patients with prediabetes. We conducted an implementation
study in which registered dietitian nutritionists (RDNs) provided nutrition care with
3-month follow-up to 102 pre-diabetes patients before and after a professional training
on the implementation of the Diabetes Prevention EBNPG. Using the RDNs’ Nutrition
Care Process (NCP) documentation, we measured percent guideline congruence and health
outcomes (body weight, waist circumference, fasting glucose, glycosylated hemoglobin),
and modeled health outcomes. Guideline congruence improved after training by 4.3%
(P < 0.05). However, no significant associations were observed between guideline training,
or guideline congruence and health outcomes. Our model showed a reduction in waist
circumference (2.1 ± 0.92 cm; P = 0.023), and body weight (-1.78 ± 0.55 kg; P = 0.001) throughout the course of the study. Training of nutrition professionals
improved congruence to EBNPG for Diabetes Prevention. Nevertheless, improved guideline
congruence did not impact related health outcomes. Standard care including nutrition
intervention resulted in body weight and waist circumference reductions. Future research
needs to further address the impact of evidence-based guidelines on outcomes in all
areas of practice.
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Journal of the Academy of Nutrition and DieteticsAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Prediabetes diagnosis and treatment: a review.World J Diabetes. 2015; 6: 296-303
- National Diabetes Statistics Report, 2017: Estimates of Diabetes and Its Burden in the United States.U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta, GA2017
- Exercise or exercise and diet for preventing type 2 diabetes mellitus.Cochrane Database Syst Rev. 2008; : CD003054
- The effect of medical nutrition therapy by a registered dietitian nutritionist in patients with prediabetes participating in a randomized controlled clinical research trial.J Acad Nutr Diet. 2014; 114: 1739-1748
- Academy of Nutrition and Dietetics methodology for developing evidence-based nutrition practice guidelines.J Acad Nutr Diet. 2017; 117: 794-804
- Evidence Analysis Library. Evidence based nutrition practice guideline for the prevention of diabetes. 2014.(Accessed September 9th, 2014)
- Using a RE-AIM framework to identify promising practices in National Diabetes Prevention Program implementation.Implement Sci. 2019; 14: 81
- Diabetes prevention in the real world: effectiveness of pragmatic lifestyle interventions for the prevention of type 2 diabetes and of the impact of adherence to guideline recommendations: a systematic review and meta-analysis.Diabetes Care. 2014; 37: 922-933
- Sense of competence impedes uptake of new academy evidence-based practice guidelines: results of a survey.J Acad Nutr Diet. 2016; 116: 695-705
- Are there valid proxy measures of clinical behaviour? A systematic review.Implement Sci. 2009; 4: 37
- A new breed of evidence and the tools to generate it: introducing ANDHII.J Acad Nutr Diet. 2015; 115: 19-22
- Orientation to nutrition care process standards improves nutrition care documentation by nutrition practitioners.J Am Diet Assoc. 2005; 105: 1582-1589
- Nutrition care process chains: the "missing link" between research and evidence-based practice.J Acad Nutr Diet. 2015; 115: 1491-1498
- Development and pilot testing of a human subjects protection training course unique to registered dietitian nutritionists.J Acad Nutr Diet. 2014; 114: 2009-2016
- The diabetes risk score: a practical tool to predict type 2 diabetes risk.Diabetes Care. 2003; 26: 725-731
- Euroqol. EQ5D.https://euroqol.org/Date accessed: July 22, 2013
- Academy of Nutrition and Dietetics Health Informatics Infrastructure (ANDHII): a pilot study on the documentation of the nutrition care process and the usability of ANDHII by registered dietitian nutritionists.J Acad Nutr Diet. 2018; 118: 1966-1974
- R: A Language and Environment for Statistical Computing [computer program]. R Foundation for Statistical Computing, Vienna, Austria2017
- Psych: Procedures for Personality and Psychological Research.Northwestern University, Evanston, IL2015
- Open-source machine learning: R meets Weka.Computational Statistics. 2009; 24: 225-232
- Data Mining: Practical Machine Learning Tools and Techniques.2nd ed. Morgan Kaufmann, San Francisco2005
- tm: Text Mining Package. R package version 0.6-2. 2015.(Accessed January 26, 2018)
- Text Mining Infrastructure in R.Journal of Statistical Software. 2008; 25: 1-54
- NLP: Natural Language Processing Infrastructure. R package version 0.1-8.(Accessed January 26, 2018)
- koRpus: An R Package for Text Analysis (Version 0.05-6).(Accessed January 26, 2018)
- RODBC: ODBC Database Access. R package version 1.3-12.(Accessed January 26, 2018)
- Reshaping data with the reshape package.J Stat Soft. 2007; 21: 1-20
- magrittr: a forward-pipe operator for R. R package version 1.5.(Accessed January 26, 2018)
- dplyr: A grammar of data manipulation. R package version 0.7.4.(Accessed January 26, 2018)
- Easily tidy data with 'spread)'and 'gather)'functions. R package version 0.6.3.(Accessed January 26, 2018)
- ggplot2: Elegant Graphics for Data Analysis.Springer-Verlag, New York2009
- lmerTest Package: tests in linear mixed effects models.Journal of Statistical Software. 2017; 82
- Volume 16: How to Detect and Handle Outliers.1993
- A protocol for data exploration to avoid common statistical problems.Methods in Ecology and Evolution. 2010; 1: 3-14
- Moments: moments, cumulants, skewness, kurtosis and related tests. R package version 0.14.(Published 2015. Accessed January 26, 2018)
- The Unified Medical Language System (UMLS): integrating biomedical terminology.Nucl Acids Res. 2004; 32
- Controlling the false discovery rate: a practical and powerful approach to multiple testing.J R Stat Soc Series B (Methodological). 1995; 57: 289-300
- nlme: Linear and nonlinear mixed effects models. R package version 3.1-131.(Accessed January 26, 2018)
- Clinical Practice Guidelines We Can Trust.National Academies Press, Washington, DC2011
- A longitudinal study examining adherence to guidelines in diabetes care according to different definitions of adequacy and timeliness.PLoS One. 2011; 6e24278
- Why don't physicians follow clinical practice guidelines? A framework for improvement.JAMA. 1999; 282: 1458-1465
- 3. Prevention or delay of type 2 diabetes: standards of medical care in diabetes-2019.Diabetes Care. 2019; 42: S29-S33
- The effectiveness and cost of lifestyle interventions including nutrition education for diabetes prevention: a systematic review and meta-analysis.J Acad Nutr Diet. 2017; 117: 404-421
- Effect of dose of behavioral weight loss treatment on glycemic control in adults with prediabetes.BMJ Open Diabetes Res Care. 2019; 7: e000653
- Association of health plans' healthcare effectiveness data and information set (HEDIS) performance with outcomes of enrollees with diabetes.Med Care. 2010; 48: 217-223
- Treatment intensification and risk factor control: toward more clinically relevant quality measures.Med Care. 2009; 47: 395-402
- Treatment quality indicators predict short-term outcomes in patients with diabetes: a prospective cohort study using the GIANTT database.BMJ Qual Saf. 2013; 22: 339-347
- Managing complexity in evidence analysis: a worked example in pediatric weight management.J Acad Nutr Diet. 2018; 118: 1526-1542
- Normalisation process theory: a framework for developing, evaluating and implementing complex interventions.BMC Med. 2010; 8: 63
- Implementation science in nutrition: concepts and frameworks for an emerging field of science and practice.Curr Dev Nutr. 2019; 3: nzy080
Biography
W. J. Murphy is a data scientist, Academy of Nutrition and Dietetics, and a data scientist, Lumere Inc, a GHX Company, Chicago, IL.
Biography
R. K. Hand is an assistant professor, Department of Nutrition, Case Western Reserve University, Cleveland, OH. At the time of the study she was director, Dietetics Practice Based Research Network, Research International and Scientific Affairs with the Academy of Nutrition and Dietetics, Chicago, IL.
Biography
J. K. Abram is manager, Nutrition Research Network (former Dietetics Practice Based Research Network), Research International and Scientific Affairs with the Academy of Nutrition and Dietetics, Chicago, IL.
Biography
C Papoutsakis is senior director, Nutrition and Dietetics Data Science Center, Research International and Scientific Affairs with the Academy of Nutrition and Dietetics, Chicago, IL.
Article info
Publication history
Published online: December 03, 2020
Accepted:
November 2,
2020
Received in revised form:
October 29,
2020
Received:
August 27,
2020
Footnotes
Supplementary material: Supplemental Figure 1 is available at www.jandonline.org
STATEMENT OF POTENTIAL CONFLICT OF INTEREST J. K. Abram, and C. Papoutsakis are employees of the Academy of Nutrition and Dietetics which has a financial interest in the ANDHII platform and the NCPT described here.
FUNDING/SUPPORT Financial and material support for the development of ANDHII has been provided by the Academy of Nutrition and Dietetics, and the Commission on Dietetic Registration. Funding for this study was provided by the Diabetes Dietetic Practice Group (DDPG).
AUTHOR CONTRIBUTIONS W. J. Murphy, R. K. Hand, and C. Papoutsakis wrote different components of the initial draft of the article. W. J. Murphy analyzed the data. W. J. Murphy and C. Papoutsakis interpreted data and conducted critical revision of the paper. W. J. Murphy, R. K. Hand, and J. K. Abram contributed to collection of the data. All authors reviewed and commented on subsequent drafts of the manuscript. All authors read and approved the final manuscript.
Identification
Copyright
© 2021 by the Academy of Nutrition and Dietetics.
