"Waste the Waist": The development of an intervention to promote changes in diet and physical activity for people with high cardiovascular risk.

This is the accepted version of the article which has been published in final form in the British Journal of Health Psychology, which can be accessed via the DOI in this record.Objectives. To identify an evidence-based intervention to promote changes in diet and physical activity and adapt it for a UK primary care setting for people with high cardiovascular risk. Design. A three-stage mixed-methods design was used to facilitate a strategic approach to programme selection and adaptation. Method. Stage 1: Criteria for scientific quality and local appropriateness were developed for the selection/adaptation of an intervention to promote lifestyle change in people of high cardiovascular risk through (1) patient interviews, (2) a literature search to extract evidence-based criteria for behavioural interventions, and (3) stakeholder consultation. Stage 2: Potential interventions for adaptation were identified and ranked according to their performance against the criteria developed in Stage 1. Stage 3: Intervention mapping (IM) techniques were used to (1) specify the behavioural objectives that participants would need to reach in order to attain programme outcomes, and (2) adapt the selected intervention to ensure that evidence-based strategies to target all identified behavioural objectives were included. Results. Four of 23 potential interventions identified met the 11 essential criteria agreed by a multi-disciplinary stakeholder committee. Of these, the Greater Green Triangle programme (Laatikainen et al., 2007) was ranked highest and selected for adaptation. The IM process identified 13 additional behaviour change strategies that were used to adapt the intervention for the local context. Conclusions. IM provided a useful set of techniques for the systematic adaptation of an existing lifestyle intervention to a new population and context, and facilitated transparent working processes for a multi-disciplinary team.Department of Healt

"Waste the Waist": The development of an intervention to promote changes in diet and physical activity for people with high cardiovascular risk.

This is the accepted version of the article which has been published in final form in the British Journal of Health Psychology, which can be accessed via the DOI in this record.Objectives. To identify an evidence-based intervention to promote changes in diet and physical activity and adapt it for a UK primary care setting for people with high cardiovascular risk. Design. A three-stage mixed-methods design was used to facilitate a strategic approach to programme selection and adaptation. Method. Stage 1: Criteria for scientific quality and local appropriateness were developed for the selection/adaptation of an intervention to promote lifestyle change in people of high cardiovascular risk through (1) patient interviews, (2) a literature search to extract evidence-based criteria for behavioural interventions, and (3) stakeholder consultation. Stage 2: Potential interventions for adaptation were identified and ranked according to their performance against the criteria developed in Stage 1. Stage 3: Intervention mapping (IM) techniques were used to (1) specify the behavioural objectives that participants would need to reach in order to attain programme outcomes, and (2) adapt the selected intervention to ensure that evidence-based strategies to target all identified behavioural objectives were included. Results. Four of 23 potential interventions identified met the 11 essential criteria agreed by a multi-disciplinary stakeholder committee. Of these, the Greater Green Triangle programme (Laatikainen et al., 2007) was ranked highest and selected for adaptation. The IM process identified 13 additional behaviour change strategies that were used to adapt the intervention for the local context. Conclusions. IM provided a useful set of techniques for the systematic adaptation of an existing lifestyle intervention to a new population and context, and facilitated transparent working processes for a multi-disciplinary team.Department of Healt

sj-pdf-1-dst-10.1177_19322968221085273 – Supplemental material for A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

Supplemental material, sj-pdf-1-dst-10.1177_19322968221085273 for A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings by David C. Klonoff, Jing Wang, David Rodbard, Michael A. Kohn, Chengdong Li, Dorian Liepmann, David Kerr, David Ahn, Anne L. Peters, Guillermo E. Umpierrez, Jane Jeffrie Seley, Nicole Y. Xu, Kevin T. Nguyen, Gregg Simonson, Michael S. D. Agus, Mohammed E. Al-Sofiani, Gustavo Armaiz-Pena, Timothy S. Bailey, Ananda Basu, Tadej Battelino, Sewagegn Yeshiwas Bekele, Pierre-Yves Benhamou, B. Wayne Bequette, Thomas Blevins, Marc D. Breton, Jessica R. Castle, James Geoffrey Chase, Kong Y. Chen, Pratik Choudhary, Mark A. Clements, Kelly L. Close, Curtiss B. Cook, Thomas Danne, Francis J. Doyle, Angela Drincic, Kathleen M. Dungan, Steven V. Edelman, Niels Ejskjaer, Juan C. Espinoza, G. Alexander Fleming, Gregory P. Forlenza, Guido Freckmann, Rodolfo J. Galindo, Ana Maria Gomez, Hanna A. Gutow, Lutz Heinemann, Irl B. Hirsch, Thanh D. Hoang, Roman Hovorka, Johan H. Jendle, Linong Ji, Shashank R. Joshi, Michael Joubert, Suneil K. Koliwad, Rayhan A. Lal, M. Cecilia Lansang, Wei-An (Andy) Lee, Lalantha Leelarathna, Lawrence A. Leiter, Marcus Lind, Michelle L. Litchman, Julia K. Mader, Katherine M. Mahoney, Boris Mankovsky, Umesh Masharani, Nestoras N. Mathioudakis, Alexander Mayorov, Jordan Messler, Joshua D. Miller, Viswanathan Mohan, James H. Nichols, Kirsten Nørgaard, David N. O’Neal, Francisco J. Pasquel, Athena Philis-Tsimikas, Thomas Pieber, Moshe Phillip, William H. Polonsky, Rodica Pop-Busui, Gerry Rayman, Eun-Jung Rhee, Steven J. Russell, Viral N. Shah, Jennifer L. Sherr, Koji Sode, Elias K. Spanakis, Deborah J. Wake, Kayo Waki, Amisha Wallia, Melissa E. Weinberg, Howard Wolpert, Eugene E. Wright, Mihail Zilbermint and Boris Kovatchev in Journal of Diabetes Science and Technolog

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

Background:A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.Methods:We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.Results:The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.Conclusion:The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments