Literature DB >> 8261757

Application of physicians' predictions of meal and exercise effects on blood glucose control to a computer simulation.

T Hauser1, L V Campbell, E W Kraegen, D J Chisholm.   

Abstract

Our aim was to develop a computer simulator program that allows patients to practise insulin dose and dietary adjustment on a day of planned exercise, and shows the resulting blood glucose response in an average diabetic patient. The degree of blood glucose change predicted by the program was determined from changes predicted by five local specialists in seven hypothetical scenarios involving exercise +/- dietary or insulin dose adjustments. The program was then tested against 18 outside specialists' responses in 7 different scenarios. The program simulates the 24 h glycaemic response after 45 min mild or moderate exercise starting 2 h after meals, as well as changes to this response induced by alterations in dietary carbohydrate and/or insulin dose. Coefficients of variation of specialists' blood glucose predictions were greater for exercise (35% local, 31% outside specialists) than dietary change (7% local, 10% outside specialists; p = 0.002-0.04). The program's predicted change in blood glucose levels in the seven scenarios correlated well with the outside specialists' corresponding mean predictions (r = 0.97; p = 0.0001). We conclude that specialists are less consistent in predicting glycaemic change with exercise than with dietary alteration. Nevertheless it is possible to represent their predictions in a computerized simulator for diabetic patient education.

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Year:  1993        PMID: 8261757     DOI: 10.1111/j.1464-5491.1993.tb00158.x

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  2 in total

1.  Home blood glucose prediction: clinical feasibility and validation in islet cell transplantation candidates.

Authors:  A M Albisser; D Baidal; R Alejandro; C Ricordi
Journal:  Diabetologia       Date:  2005-06-03       Impact factor: 10.122

Review 2.  Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

Authors:  Nichole S Tyler; Peter G Jacobs
Journal:  Sensors (Basel)       Date:  2020-06-05       Impact factor: 3.576

  2 in total

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