Literature DB >> 20144425

New approaches to display of self-monitoring of blood glucose data.

David Rodbard1.   

Abstract

BACKGROUND: There is a need for improved methods for display and analysis of self-monitoring of blood glucose (SMBG) data to facilitate identification of clinical problems, assist the clinician in the interpretation of daily patterns and longitudinal trends, serve as a guide to locating the most important segments of logbook data, and permit rapid analysis of the patient's pattern of glucose monitoring.
METHODS: We developed prototype software to display SMBG data in a two-dimensional color-coded array: Time of day is displayed on the horizontal axis; date or sequential day is displayed on the vertical axis. Each glucose value is shown by a color-coded symbol categorizing it as "very high," "high," "within target range," "low," or "very low." The number of categories and their ranges can be defined by the user, and different target ranges and limits for the categories can be used for different times of day. Placing the cursor over any observation activates a "pop-up box" showing the date, day of week, time of day, glucose value, and ancillary information. Several options and variations are available.
RESULTS: This new type of display is compact, serves as a guide to assist the physician in locating the most important segments of the logbook, and permits display of glucose data from 90 or more days in a chart as small as 4 by 4 inches. This analysis permits rapid identification of measurements that are above or below the target range and facilitates rapid evaluation of patterns observed on different days or days of the week.
CONCLUSION: These new approaches complement other popular graphical displays by conveying information efficiently and effectively to the physician, other health care providers, the patient, and family caregivers in a new and novel, concise, standardized yet flexible format. 2009 Diabetes Technology Society.

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Year:  2009        PMID: 20144425      PMCID: PMC2769916          DOI: 10.1177/193229680900300515

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  12 in total

1.  Longitudinal study of new and prevalent use of self-monitoring of blood glucose.

Authors:  Andrew J Karter; Melissa M Parker; Howard H Moffet; Michele M Spence; James Chan; Susan L Ettner; Joe V Selby
Journal:  Diabetes Care       Date:  2006-08       Impact factor: 19.112

2.  Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes.

Authors:  David Rodbard
Journal:  J Diabetes Sci Technol       Date:  2007-01

Review 3.  Potential role of computers in clinical investigation and management of diabetes mellitus.

Authors:  D Rodbard
Journal:  Diabetes Care       Date:  1988 Nov-Dec       Impact factor: 19.112

4.  Combining statistical, rule-based, and physiologic model-based methods to assist in the management of diabetes mellitus.

Authors:  M P Berger; R A Gelfand; P L Miller
Journal:  Comput Biomed Res       Date:  1990-08

Review 5.  Current evidence regarding the value of self-monitored blood glucose testing.

Authors:  Lawrence Blonde; Andrew J Karter
Journal:  Am J Med       Date:  2005-09       Impact factor: 4.965

Review 6.  Blood glucose monitoring technology: translating data into practice.

Authors:  Irl B Hirsch
Journal:  Endocr Pract       Date:  2004 Jan-Feb       Impact factor: 3.443

7.  A computer system for interpreting blood glucose data.

Authors:  T Deutsch; T Gergely; V Trunov
Journal:  Comput Methods Programs Biomed       Date:  2004-10       Impact factor: 5.428

8.  Ambulatory glucose profile: representation of verified self-monitored blood glucose data.

Authors:  R S Mazze; D Lucido; O Langer; K Hartmann; D Rodbard
Journal:  Diabetes Care       Date:  1987 Jan-Feb       Impact factor: 19.112

9.  Personal computer programs to assist with self-monitoring of blood glucose and self-adjustment of insulin dosage.

Authors:  N L Pernick; D Rodbard
Journal:  Diabetes Care       Date:  1986 Jan-Feb       Impact factor: 19.112

10.  Characterizing glucose exposure for individuals with normal glucose tolerance using continuous glucose monitoring and ambulatory glucose profile analysis.

Authors:  Roger S Mazze; Ellie Strock; David Wesley; Sarah Borgman; Blaine Morgan; Richard Bergenstal; Robert Cuddihy
Journal:  Diabetes Technol Ther       Date:  2008-06       Impact factor: 6.118

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  7 in total

1.  A semilogarithmic scale for glucose provides a balanced view of hyperglycemia and hypoglycemia.

Authors:  David Rodbard
Journal:  J Diabetes Sci Technol       Date:  2009-11-01

2.  Minding the gaps in continuous glucose monitoring: a method to repair gaps to achieve more accurate glucometrics.

Authors:  Stephanie J Fonda; Drew G Lewis; Robert A Vigersky
Journal:  J Diabetes Sci Technol       Date:  2013-01-01

3.  How Knowledge Emerges From Artificial Intelligence Algorithm and Data Visualization for Diabetes Management.

Authors:  Vincent Derozier; Sylvie Arnavielhe; Eric Renard; Gérard Dray; Sophie Martin
Journal:  J Diabetes Sci Technol       Date:  2019-05-21

4.  The minimum frequency of glucose measurements from which glycemic variation can be consistently assessed.

Authors:  Peter A Baghurst; David Rodbard; Fergus J Cameron
Journal:  J Diabetes Sci Technol       Date:  2010-11-01

5.  Display of glucose distributions by date, time of day, and day of week: new and improved methods.

Authors:  David Rodbard
Journal:  J Diabetes Sci Technol       Date:  2009-11-01

Review 6.  Approaches to display of multiple-point glucose profiles: A UK patient's perspective.

Authors:  Daniel Kay
Journal:  J Diabetes Sci Technol       Date:  2014-07-02

7.  Design of a decision support system to help clinicians manage glycemia in patients with type 2 diabetes mellitus.

Authors:  David Rodbard; Robert A Vigersky
Journal:  J Diabetes Sci Technol       Date:  2011-03-01
  7 in total

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