Quest for the Artificial Pancreas: Combining Technology with Treatment

Harvey, Rebecca A.; Wang, Youqing; Grosman, Benyamin; Percival, Matthew W.; Bevier, Wendy C.; Finan, Daniel A.; Zisser, Howard; Seborg, Dale E.; Jovanovič, Lois; Doyle, Francis J.; Dassau, Eyal

doi:10.1109/memb.2009.935711

Cited by 87 publications

(37 citation statements)

References 39 publications

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“…1 Many preliminary AP studies were done at SDRI to lay the groundwork to develop a working AP/closed-loop system. [2][3][4][5][6][7][8] Our first closed-loop study at SDRI was done in April 2007; several studies followed this initial closed-loop design with different controller designs but with the common theme of a fully automated closed-loop system. 9,10 Additional AP studies are being conducted in many other settings, including across the United States, the United Kingdom, The Netherlands, Israel, France, and Italy.…”

mentioning

confidence: 99%

Artificial Pancreas (AP) Clinical Trial Participants' Acceptance of Future AP Technology

Bevier

Fuller

et al. 2014

Diabetes Technology & Therapeutics

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Background: Artificial pancreas (AP) systems are currently an active field of diabetes research. This pilot study examined the attitudes of AP clinical trial participants toward future acceptance of the technology, having gained firsthand experience. Subjects and Methods: After possible influencers of AP technology adoption were considered, a 34-question questionnaire was developed. The survey assessed current treatment satisfaction, dimensions of clinical trial participant motivation, and variables of the technology acceptance model (TAM). Forty-seven subjects were contacted to complete the survey. The reliability of the survey scales was tested using Cronbach's a. The relationship of the factors to the likelihood of AP technology adoption was explored using regression analysis. Results: Thirty-six subjects (76.6%) completed the survey. Of the respondents, 86.1% were either highly likely or likely to adopt the technology once available. Reliability analysis of the survey dimensions revealed good internal consistency, with scores of >0.7 for current treatment satisfaction, convenience (motivation), personal health benefit (motivation), perceived ease of use (TAM), and perceived usefulness (TAM). Linear modeling showed that future acceptance of the AP was significantly associated with TAM and the motivation variables of convenience plus the individual item benefit to others (R 2 = 0.26, P = 0.05). When insulin pump and continuous glucose monitor use were added, the model significance improved (R 2 = 0.37, P = 0.02). Conclusions: This pilot study demonstrated that individuals with direct AP technology experience expressed high likelihood of future acceptance. Results support the factors of personal benefit, convenience, perceived usefulness, and perceived ease of use as reliable scales that suggest system adoption in this highly motivated patient population.

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confidence: 99%

Artificial Pancreas (AP) Clinical Trial Participants' Acceptance of Future AP Technology

Bevier

Fuller

et al. 2014

Diabetes Technology & Therapeutics

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“…Applying R2R algorithm in insulin pump therapy optimization, a day can be treated as a run; u(k) is the manipulated variable, which corresponds to the basal and bolus insulin, i.e., V(k) = [V infusion rate V bolus ], where V infusion rate and V bolus in Eqs. (9) and (10). Therefore, on the k-th day, there should be a performance index according to the CGMS readings, which is used to adjust the insulin automatically for the (k + 1)-th day.…”

Section: Application In Basal-bolus Optimizationmentioning

confidence: 99%

Optimization of insulin pump therapy based on high order run-to-run control scheme

Tuo

Sun

Shen

et al. 2015

Computer Methods and Programs in Biomedicine

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“…In the last decades, there has been a growing interest in modeling, simulation, prediction and control of diabetes mellitus type 1 (DMT1), with the aim of improving blood glucose regulation so to avoid the complications resulting from hyperglycemia, Bequette (2012); Harvey et al (2010); Renard et al (2013). Control of diabetes is accomplished by planning and applying the most proper therapy strategy for a given patient.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear blind identification approach to modeling of diabetic patients

Novara

Pour

Vincent

et al. 2014

IFAC Proceedings Volumes

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Modeling, simulation and control have become effective tools for the treatment of type 1 diabetic patients in the last decades. The availability of reliable models able to predict and/or simulate the behavior of diabetic patients is thus fundamental in this context. Several models, based on first principles or black-box approaches, have been proposed to fulfill this need. However, a common problem to these approaches is that they are not able to recover or to systematically account for the various unmeasured signals which affect a diabetic patient (e.g. food, physical activity, emotions, etc.). In this paper, we propose a blind identification approach, which allows us to derive accurate models of type 1 diabetes patients and to efficiently recover the unmeasured input signals. A simulated example, regarding identification of the blood glucose concentration in type 1 diabetes patients, is presented to demonstrate the effectiveness of the proposed approach.

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Quest for the Artificial Pancreas: Combining Technology with Treatment

Cited by 87 publications

References 39 publications

Artificial Pancreas (AP) Clinical Trial Participants' Acceptance of Future AP Technology

Artificial Pancreas (AP) Clinical Trial Participants' Acceptance of Future AP Technology

Optimization of insulin pump therapy based on high order run-to-run control scheme

A nonlinear blind identification approach to modeling of diabetic patients

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