Using a double blind controlled clinical trial to evaluate the function of a Diabetes Advisory System: a feasible approach?

Hejlesen, Ole K.; Andreassen, Steen; Frandsen, Niels Erik; Sørensen, Tina Brandt; Sandø, S. H.; Hovorka, Roman; Cavan, David

doi:10.1016/s0169-2607(98)00023-6

Cited by 18 publications

(20 citation statements)

References 19 publications

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“…Ten articles described day-by-day advisory systems, [7][8][9]11,[15][16][17][18][19]21 while three described visit-by-visit systems. 12,14,20 From the technological point of view, eight papers tested telemedicine systems, as selfmonitoring data were sent to the hospital from the patient's house, generally using a modem or the phone. [7][8][9][15][16][17]20,21 The outcome parameters reported in the 16 articles measured health status, social functioning, patient's satisfaction, patient's compliance with the system, and the system technical performances (especially in the telemedicine articles).…”

Section: Search Resultsmentioning

confidence: 99%

“…More precisely, the sign test calculation was extended to all the retrieved studies, since it was meant to be a completion of a previously reported work. 5 On the other hand, the method of effect sizes was first applied to 13 of the available papers (i.e., 14 trials), [7][8][9][10][11][12][14][15][16][17][18][19]22 as in the others some needed data were missing, from standard deviations, to HbA1c before and/or after the intervention, in the cases and/or in the controls. As a further step, the method was applied to three subsets of the 13 papers: the decision support systems set, 7-9,11,12,14-19 the even narrower dayby-day advisory systems set, [7][8][9]11,[15][16][17][18][19] and the telemedicine systems set.…”

Section: Statistical Analysis Resultsmentioning

confidence: 99%

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Meta-Analysis of the Effect of the Use of Computer-Based Systems on the Metabolic Control of Patients with Diabetes Mellitus

Montani

Bellazzi

d’Annunzio

2001

Diabetes Technology & Therapeutics

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The purpose of this study was to evaluate, through a meta-analysis study, whether the use of computer-based systems reported in the literature improves the metabolic control of diabetic patients. On the retrieved papers, a set of meta-analysis studies were performed: first the difference of HbA1c between cases and controls at follow-up was evaluated (sign test); then the difference between cases and controls in the total variation of HbA1c from the beginning to the end of the trial was considered (method of effect sizes). The latter methodology was reapplied also on three more homogeneous article subgroups. The sign test was performed on 16 papers: in two of them, the HbA1c level was higher in the intervention group than in the control group at follow-up: it is unlikely that this is a random occurrence (p < 0.01). The method of effect sizes was first applied to 13 papers, as in the others some needed data were missing: the results obtained showed a statistically significant amelioration of metabolic control in the intervention group in comparison to the control group (p < 0.01). A progressive reinforcement of this outcome was obtained on the trial subgroups. Our study supports the hypothesis that the use of computer-based systems can be an effective means of improving metabolic control. The differential benefit obtained in the amelioration of HbA1c does not justify, by itself, the applicability of such systems into clinical practice; additional investigations should be carried out to evaluate the enhancement of other clinical and organizational indicators.

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Section: Search Resultsmentioning

confidence: 99%

Section: Statistical Analysis Resultsmentioning

confidence: 99%

Meta-Analysis of the Effect of the Use of Computer-Based Systems on the Metabolic Control of Patients with Diabetes Mellitus

Montani

Bellazzi

d’Annunzio

2001

Diabetes Technology & Therapeutics

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“…2 Critical regions for G = 36. The trajectory depicts the values of I and X for a meal disturbance of 20 g problem therefore consists of (10), (16), (17) and (13)- (15). This asymmetric formulation allows positive and negative values of x to have different effects in the objective function.…”

Section: Asymmetric Objective Functionmentioning

confidence: 99%

“…These include controllers based upon integrated square error cost function [24], insulin infusion protocols for hyperglycemic patients [12] and internal model controllers for an approximation of the detailed physiological model [35] and nonlinear feedforward-feedback controller, synthesized using feedback linearization [7]. Insulin dosage advisory systems, which are based upon using mathematical models [20] to predict the blood glucose variations, have also been proposed in the open literature and compared with the advice given in clinical practice [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective blood glucose control for type 1 diabetes

Dua

Doyle

Pistikopoulos

2009

Med Biol Eng Comput

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For people with type 1 diabetes, automatic controllers aim to maintain the blood glucose concentration within the desired range of 60-120 mg/dL by infusing the appropriate amount of insulin in the presence of meal and exercise disturbances. Blood glucose concentration outside the desired range can be harmful to an individual's health but concentration below 60 mg/dL, a state known as hypoglycemia, is considered to be more harmful than the concentration above 120 mg/dL, a state known as hyperglycemia. In this paper, two techniques to address this issue within a multi-parametric model based control framework are presented. The first technique introduces asymmetry into the objective function to penalize the deviation towards hypoglycemia more than the deviation towards hyperglycemia. The second technique is based upon placing higher priority on satisfaction of constraints on hypoglycemia than on satisfaction of constraints on hyperglycemia. The performance of both the control techniques is analyzed and compared in the presence of disturbances.

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“…In a double blind randomized study in 12 poorly controlled subjects with type 1 diabetes admitted to hospital in Denmark to optimize insulin therapy, an average 1.9% reduction in HbA 1C was observed in the DIAS group (N=6) compared to 0.9% in the control group (N=6) [20]. The logistics and cost of running a double blind study contributed to the reduction of the studied population from 20 to 12 subjects.…”

Section: Off-line Adaptation}decision Support Systemsmentioning

confidence: 99%

Management of diabetes using adaptive control

Hovorka

2004

Adaptive Control & Signal

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The review focuses on adaptive systems for insulin treatment in type 1 diabetes. The review consists of two parts. First, adaptive approaches are described, which exploit infrequent glucose measurements (four to seven measurements per day). Second, adaptive approaches are described, which exploit frequent or continuous glucose measurements (every hour or more often). Each part is further divided into two subparts separating off-line and on-line adaptive techniques. The latter represents treatment strategies, which rely on continuous re-assessment of the glucoregulatory system. The former refers to treatment strategies, which are fixed for a day or longer and are revisited from time to time. It is concluded that the role of adaptive approaches will increase as new continuous glucose-sensing monitors reach the market.

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Using a double blind controlled clinical trial to evaluate the function of a Diabetes Advisory System: a feasible approach?

Cited by 18 publications

References 19 publications

Meta-Analysis of the Effect of the Use of Computer-Based Systems on the Metabolic Control of Patients with Diabetes Mellitus

Meta-Analysis of the Effect of the Use of Computer-Based Systems on the Metabolic Control of Patients with Diabetes Mellitus

Multi-objective blood glucose control for type 1 diabetes

Management of diabetes using adaptive control

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