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2.
Artigo em Inglês | MEDLINE | ID: mdl-32319791

RESUMO

Background: Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Methods: Ten adults with type 1 diabetes completed two weeks of AID-based control and two weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal three times in random order). Surveys assessed satisfaction with AID use. Results: Postprandial differences in conventional therapy were 10,919.0 mg/dL x min (95% CI 3,190.5 to 18,648.0, p=0.009) for glucose area under the curve (AUC) and 40.9 mg/dL (95% CI 4.6 to 77.3, p=0.03) for peak CGM glucose, with rice showing greater increases than pasta. White rice resulted in a lower estimate over pasta by a factor of 0.22 (95% CI 0.08 to 0.63, p=0.004) for AUC under 70 mg/dL. These glycemic differences in both meal types were reduced under AID-based control and were not statistically significant, where 0h-2h insulin delivery decreased by 0.45 units for pasta (p=0.001) and by 0.27 units for white rice (p=0.01). Subjects reported high overall satisfaction with the iAPS. Conclusions: The AID system running on an unlocked smartphone improved postprandial glucose control over conventional therapy in the setting of challenging meals in the outpatient setting.

3.
J Diabetes ; 2020 Mar 03.
Artigo em Inglês | MEDLINE | ID: mdl-32125763

RESUMO

The significant risks associated with pregnancies complicated by type 1 diabetes (T1D) were first recognized in the medical literature in the mid-twentieth century. Stringent glycemic control with hemoglobin A1c (HbA1c) values ideally less than 6% has been shown to improve maternal and fetal outcomes. The management options for pregnant women with T1D in the modern era include a variety of technologies to support self-care. Although self-monitoring of blood glucose (SMBG) and multiple daily injections (MDI) are often the recommended management options during pregnancy, many people with T1D utilize a variety of different technologies, including continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (CSII), and CSII including automated delivery or suspension algorithms. These systems have yielded invaluable diagnostic and therapeutic capabilities and have the potential to benefit this understudied higher-risk group. A recent prospective, multicenter study evaluating pregnant patients with T1D revealed that CGM significantly improves maternal glycemic parameters, is associated with fewer adverse neonatal outcomes, and minimizes burden. Outcome data for CSII, which is approved for use in pregnancy and has been utilized for several decades, remain mixed. Current evidence, although limited, for commercially available and emerging technologies for the management of T1D in pregnancy holds promise for improving patient and fetal outcomes. HIGHLIGHTS: The management of T1D in pregnancy has been enhanced in large part due to development of more effective glucose monitoring and insulin delivery systems. CGM has been demonstrated to improve glycemic control and minimize excursions in pregnant mothers and leads to better neonatal outcomes. CSII has often led to better maternal glycemic control; however, outcome data for its use in pregnancy remain mixed. Further research evaluating newer and upcoming technologies in this understudied population is needed.

4.
Diabetes Technol Ther ; 22(S1): S47-S62, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-32069159
5.
Diabetes Care ; 43(3): 607-615, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31937608

RESUMO

OBJECTIVE: Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system. RESEARCH DESIGN AND METHODS: This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA1c <10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L. RESULTS: Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n = 65) versus SAP (n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference -1.7% [95% CI -2.4, -1.0]; P < 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference -3.0% [95% CI -6.1, 0.1]; P < 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device. CONCLUSIONS: In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.

6.
Anal Chem ; 92(2): 2291-2300, 2020 01 21.
Artigo em Inglês | MEDLINE | ID: mdl-31874029

RESUMO

Diabetic ketoacidosis (DKA), a severe complication of diabetes mellitus with potentially fatal consequences, is characterized by hyperglycemia and metabolic acidosis due to the accumulation of ketone bodies, which requires people with diabetes to monitor both glucose and ketone bodies. However, despite major advances in diabetes management mainly since the emergence of new-generation continuous glucose monitoring (CGM) devices capable of in vivo monitoring of glucose directly in the interstitial fluid (ISF), the continuous monitoring of ketone bodies is yet to be addressed. Here, we present the first use of a real-time continuous ketone bodies monitoring (CKM) microneedle platform. The system is based on the electrochemical monitoring of ß-hydroxybutyrate (HB) as the dominant biomarker of ketone formation. Such real-time HB detection has been realized using the ß-hydroxybutyrate dehydrogenase (HBD) enzymatic reaction and by addressing the major challenges associated with the stable confinement of the enzyme/cofactor couple (HBD/NAD+) and with a stable and selective low-potential fouling-free anodic detection of NADH. The resulting CKM microneedle device displays an attractive analytical performance, with high sensitivity (with low detection limit, 50 µM), high selectivity in the presence of potential interferences, along with good stability during prolonged operation in artificial ISF. The potential applicability of this microneedle sensor toward minimally invasive monitoring of ketone bodies has been demonstrated in a phantom gel skin-mimicking model. The ability to detect HB along with glucose and lactate on a single microneedle array has been demonstrated. These findings pave the way for CKM and for the simultaneous microneedle-based monitoring of multiple diabetes-related biomarkers toward a tight glycemic control.

7.
Artigo em Inglês | MEDLINE | ID: mdl-31714583

RESUMO

BACKGROUND: Post-bariatric hypoglycemia (PBH) can threaten safety and reduce quality of life. Current therapies are incompletely effective. METHODS: Patients with PBH were enrolled in a double-blind, placebo-controlled, crossover trial to evaluate a closed-loop glucose-responsive automated glucagon delivery system designed to reduce severe hypoglycemia. A hypoglycemia detection and mitigation algorithm was embedded in the Artificial Pancreas System connected to a continuous glucose monitor (CGM, Dexcom) driving a patch infusion pump (Insulet) filled with liquid investigational glucagon (Xeris) or placebo (vehicle). Sensor/plasma glucose responses to mixed meal were assessed during two study visits. The system delivered up to two doses of study drug (300/150 µg glucagon or equal-volume vehicle) if triggered by the algorithm. Rescue dextrose was given for plasma glucose <55 mg/dL or neuroglycopenia. FINDINGS: Twelve participants (11F/1M, age 52+2, 8+1 years post-surgery, mean+SEM) completed all visits. Predictive hypoglycemia alerts prompted automated drug delivery post-meal, when sensor glucose was 114+7 vs. 121+5 mg/dL (p=0.39). Seven participants required rescue glucose after vehicle but not glucagon (p=0.008). Five participants had severe hypoglycemia (<55 mg/dL) after vehicle but not glucagon (p=0.03). Nadir plasma glucose was higher with glucagon vs. vehicle (67±3 vs. 59±2 mg/dL, p=0.004). Plasma glucagon rose after glucagon delivery (1231±187 vs. 16±1 pg/mL at 30 minutes, p=0.001). No rebound hyperglycemia occurred. Transient infusion site discomfort was reported with both glucagon (n=11/12) and vehicle (n=10/12). No other adverse events were observed. INTERPRETATION: A CGM-guided closed-loop rescue system can detect imminent hypoglycemia and deliver glucagon, reducing severe hypoglycemia in PBH.

8.
N Engl J Med ; 381(18): 1707-1717, 2019 10 31.
Artigo em Inglês | MEDLINE | ID: mdl-31618560

RESUMO

BACKGROUND: Closed-loop systems that automate insulin delivery may improve glycemic outcomes in patients with type 1 diabetes. METHODS: In this 6-month randomized, multicenter trial, patients with type 1 diabetes were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group). The primary outcome was the percentage of time that the blood glucose level was within the target range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter), as measured by continuous glucose monitoring. RESULTS: A total of 168 patients underwent randomization; 112 were assigned to the closed-loop group, and 56 were assigned to the control group. The age range of the patients was 14 to 71 years, and the glycated hemoglobin level ranged from 5.4 to 10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% during the 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001). The results with regard to the main secondary outcomes (percentage of time that the glucose level was >180 mg per deciliter, mean glucose level, glycated hemoglobin level, and percentage of time that the glucose level was <70 mg per deciliter or <54 mg per deciliter [3.0 mmol per liter]) all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed loop minus control) in the percentage of time that the blood glucose level was lower than 70 mg per deciliter was -0.88 percentage points (95% CI, -1.19 to -0.57; P<0.001). The mean adjusted difference in glycated hemoglobin level after 6 months was -0.33 percentage points (95% CI, -0.53 to -0.13; P = 0.001). In the closed-loop group, the median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events occurred in either group; one episode of diabetic ketoacidosis occurred in the closed-loop group. CONCLUSIONS: In this 6-month trial involving patients with type 1 diabetes, the use of a closed-loop system was associated with a greater percentage of time spent in a target glycemic range than the use of a sensor-augmented insulin pump. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; iDCL ClinicalTrials.gov number, NCT03563313.).


Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina , Insulina/administração & dosagem , Pâncreas Artificial , Adolescente , Adulto , Idoso , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Desenho de Equipamento , Feminino , Hemoglobina A Glicada/análise , Humanos , Hipoglicemiantes/efeitos adversos , Insulina/efeitos adversos , Sistemas de Infusão de Insulina/efeitos adversos , Masculino , Pessoa de Meia-Idade , Pâncreas Artificial/efeitos adversos , Adulto Jovem
9.
J Process Control ; 76: 62-73, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31178632

RESUMO

Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems.

10.
Diabetes Care ; 42(8): 1593-1603, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31177185

RESUMO

Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.

11.
Diabetes Technol Ther ; 21(9): 485-492, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31225739

RESUMO

Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) "higher protein" pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (-32.6 mg/dL; 95% CI -48.4 to -17.2; P < 0.001) and regular pasta (-43.2 mg/dL, 95% CI -58.7 to -27.7; P < 0.001). The difference between the two types of pastas did not reach statistical significance (-11 mg/dL; 95% CI -24.1 to 3.4; P = 0.17). Total glucose area under the curve was also significantly higher for white rice compared with both pastas (P < 0.001 for both comparisons). Conclusions: This exploratory study concluded that different food types of similar macronutrient content (e.g., rice and pasta) generate significantly different postprandial glycemic responses in persons with T1D. These results provide useful insights into the impact of food choices on and optimization of glucose control. Clinical Trial Registry: clinicaltrials.gov NCT03362151.

12.
Angew Chem Int Ed Engl ; 58(19): 6376-6379, 2019 May 06.
Artigo em Inglês | MEDLINE | ID: mdl-30868724

RESUMO

Performing bioassay formats based on enzyme and antibody recognition reactions with a single detection chip remains an unmet challenge owing to the different requirements of such bioassays. Herein, we describe a dual-marker biosensor chip, integrating enzyme and antibody-based assays for simultaneous electrochemical measurements of insulin (I) and glucose (G). Simultaneous G/I sensing has been realized by addressing key fabrication and operational challenges associated with the different assay requirements and surface chemistry. The I immunosensor relies on a peroxidase-labeled sandwich immunoassay, while G is monitored through reaction with glucose oxidase. The dual diabetes biomarker chip offers selective and reproducible detection of picomolar I and millimolar G concentrations in a single microliter sample droplet within less than 30 min, including direct measurements in whole blood and saliva samples. The resulting integrated enzymatic-immunoassay biosensor chip opens a new realm in point-of-care multiplexed biomarker detection.

13.
Diabetes Technol Ther ; 21(5): 265-272, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30925077

RESUMO

Background: The objective of this study was to assess the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm with variable glucose setpoints and moderate intensity exercise using an investigational device in adults with type 1 diabetes (T1D). Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient standard treatment phase. Adults aged 18-65 years with T1D and HbA1c between 6.0% and 10.0% were eligible. Subjects completed two moderate intensity exercise sessions of >30 min duration on consecutive days: the first with the glucose set point increased from 130 to 150 mg/dL and the second with a temporary basal rate of 50%, both started 90 min pre-exercise. Primary endpoints were percentage time in hypoglycemia <70 mg/dL and hyperglycemia ≥250 mg/dL. Results: Twelve subjects participated in the study, with (mean ± standard deviation) age 36.5 ± 14.4 years, diabetes duration 21.7 ± 15.7 years, HbA1c 7.6% ± 1.1%, and total daily dose 0.60 ± 0.22 U/kg. Outcomes for the 54-h HCL period were mean glucose: 136 ± 14 mg/dL, percentage time <70 mg/dL: 1.4% ± 1.3%, 70-180 mg/dL: 85.1% ± 9.3%, and ≥250 mg/dL: 1.8% ± 2.4%. In the 12-h period after exercise start, percentage time <70 mg/dL was 1.4% ± 2.7% with the raised glucose set point and 1.6% ± 3.0% with reduced basal rate. The percentage time <70 mg/dL overnight was 0% ± 0% on both study nights. Conclusions: The Omnipod personalized MPC algorithm performed well and was safe during day and night use in response to variable glucose set points and with temporarily raised glucose set point or reduced basal rate 90 min in advance of moderate intensity exercise in adults with T1D.


Assuntos
Automonitorização da Glicemia/instrumentação , Glicemia/análise , Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/uso terapêutico , Sistemas de Infusão de Insulina , Insulina/uso terapêutico , Adolescente , Adulto , Idoso , Algoritmos , Diabetes Mellitus Tipo 1/sangue , Exercício Físico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Adulto Jovem
15.
Bioeng Transl Med ; 4(1): 61-74, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-30680319

RESUMO

The long-term use of the artificial pancreas (AP) requires an automated insulin delivery algorithm that can learn and adapt with the growth, development, and lifestyle changes of patients. In this work, we introduce a data-driven AP adaptation method for improved glucose management in a home environment. A two-phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate-ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111-adult cohort of the FDA-accepted UVA/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. For all the scenarios, the proposed method is able to robustly adapt AP parameters for improved glycemic regulation performance in terms of percent time in the euglycemic range [70, 180] mg/dl without causing risk of hypoglycemia in terms of percent time below 70 mg/dl.

16.
Diabetes Technol Ther ; 21(2): 73-80, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30649925

RESUMO

BACKGROUND: Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller. METHODS: Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use. RESULTS: The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70-180 mg/dL, and 1.7% [median] time <70 mg/dL). CONCLUSIONS: CGMs and insulin pumps can be interchangeable in the same Mobile AP system, as long as these devices achieve certain levels of reliability and wireless connection stability.


Assuntos
Glicemia/análise , Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina , Insulina/administração & dosagem , Pâncreas Artificial , Adolescente , Adulto , Idoso , Algoritmos , Automonitorização da Glicemia , Diabetes Mellitus Tipo 1/sangue , Feminino , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Smartphone , Resultado do Tratamento , Adulto Jovem
17.
Diabetes Technol Ther ; 21(1): 35-43, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30547670

RESUMO

BACKGROUND: There is an unmet need for a modular artificial pancreas (AP) system for clinical trials within the existing regulatory framework to further AP research projects from both academia and industry. We designed, developed, and tested the interoperable artificial pancreas system (iAPS) smartphone app that can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms while running on an unlocked smartphone. METHODS: After algorithm verification, hazard and mitigation analysis, and complete system verification of iAPS, six adults with type 1 diabetes completed 1 week of sensor-augmented pump (SAP) use followed by 48 h of AP use with the iAPS, a Dexcom G5 CGM, and either a Tandem or Insulet insulin pump in an investigational device exemption study. The AP system was challenged by participants performing extensive walking without exercise announcement to the controller, multiple large meals eaten out at restaurants, two overnight periods, and multiple intentional connectivity interruptions. RESULTS: Even with these intentional challenges, comparison of the SAP phase with the AP study showed a trend toward improved time in target glucose range 70-180 mg/dL (78.8% vs. 83.1%; P = 0.31), and a statistically significant reduction in time below 70 mg/dL (6.1% vs. 2.2%; P = 0.03). The iAPS system performed reliably and showed robust connectivity with the peripheral devices (99.8% time connected to CGM and 94.3% time in closed loop) while requiring limited user intervention. CONCLUSIONS: The iAPS system was safe and effective in regulating glucose levels under challenging conditions and is suitable for use in unconstrained environments.


Assuntos
Automonitorização da Glicemia/métodos , Diabetes Mellitus Tipo 1/terapia , Sistemas de Infusão de Insulina , Aplicativos Móveis , Pâncreas Artificial , Adulto , Algoritmos , Glicemia/efeitos dos fármacos , Diabetes Mellitus Tipo 1/sangue , Feminino , Humanos , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Masculino , Pesquisa , Smartphone , Resultado do Tratamento
18.
IEEE Trans Biomed Eng ; 66(4): 1045-1054, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30142748

RESUMO

OBJECTIVE: Zone model predictive control (MPC) has been proven to be an efficient approach to closed-loop insulin delivery in clinical studies. In this paper, we aim to safely reduce mean glucose levels by proposing control penalty adaptation in the cost function of zone MPC. METHODS: A zone MPC method with a dynamic cost function that updates its control penalty parameters in real time according to the predicted glucose and its rate of change is developed. The proposed method is evaluated on the entire 100-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the zone MPC tested in an extended outpatient study. RESULTS: For unannounced meals, the proposed method leads to statistically significant improvements in terms of mean glucose (153.8 mg/dL vs. 159.0 mg/dL; ) and percentage time in [70, 180] mg/dL ([Formula: see text] vs. [Formula: see text]; ) without increasing the risk of hypoglycemia. Performance for announced meals is similar to that obtained without adaptation. The proposed method also behaves properly and safely for scenarios of moderate meal-bolus and basal rate mismatches, as well as simulated unannounced exercise. Advisory-mode analysis based on clinical data indicates that the method can reduce glucose levels through suggesting additional safe amounts of insulin on top of those suggested by the zone MPC used in the study. CONCLUSION: The proposed method leads to improved glucose control without increasing hypoglycemia risks. SIGNIFICANCE: The results validate the feasibility of improving glucose regulation through glucose- and velocity-dependent control penalty adaptation in MPC design.


Assuntos
Glicemia , Sistemas de Infusão de Insulina , Modelos Estatísticos , Pâncreas Artificial , Adulto , Algoritmos , Glicemia/análise , Glicemia/efeitos dos fármacos , Simulação por Computador , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/tratamento farmacológico , Humanos , Insulina/administração & dosagem , Insulina/uso terapêutico , Refeições
19.
JMIR Mhealth Uhealth ; 6(12): e10338, 2018 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-30530451

RESUMO

BACKGROUND: Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. OBJECTIVE: The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. METHODS: We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. RESULTS: Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of -3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of -4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: -11.4% [SD 35.7]; Garmin: -14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: -1.7% [SD 11.5]; Garmin: -0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: -19.3% [SD 28.9], Garmin: -1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures. CONCLUSIONS: Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

20.
Diabetes Technol Ther ; 20(9): 585-595, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30070928

RESUMO

BACKGROUND: This study assessed the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm using an investigational device in adults with type 1 diabetes in response to overestimated and missed meal boluses and extended boluses for high-fat meals. MATERIALS AND METHODS: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient open-loop run-in phase. Adults aged 18-65 years with type 1 diabetes and HbA1c 6.0%-10.0% were eligible. Primary endpoints were percentage time in hypoglycemia <70 mg/dL and hyperglycemia ≥250 mg/dL. Glycemic responses for 4 h to a 130% overestimated bolus and a missed meal bolus were compared with a 100% bolus for identical meals, respectively. The 12-h postprandial responses to a high-fat meal were compared using either a standard or extended bolus. RESULTS: Twelve subjects participated in the study, with (mean ± standard deviation): age 35.4 ± 14.1 years, diabetes duration 16.5 ± 9.3 years, HbA1c 7.7 ± 0.9%, and total daily dose 0.58 ± 0.19 U/kg. Outcomes for the 54-h HCL period were mean glucose 153 ± 15 mg/dL, percentage time <70 mg/dL [median (interquartile range)]: 0.0% (0.0-1.2%), 70-180 mg/dL: 76.1% ± 8.0%, and ≥250 mg/dL: 4.5% ± 3.6%. After both the 100% and 130% boluses, postprandial percentage time <70 mg/dL was 0.0% (0.0-0.0%) (P = 0.50). After the 100% and missed boluses, postprandial percentage time ≥250 mg/dL was 0.2% ± 0.6% and 10.3% ± 16.5%, respectively (P = 0.06). Postprandial percentages time ≥250 mg/dL and <70 mg/dL were similar with standard or extended boluses for a high-fat meal. CONCLUSIONS: The Omnipod personalized MPC algorithm performed well and was safe during day and night use in response to overestimated, missed, and extended meal boluses in adults with type 1 diabetes.


Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Adulto , Algoritmos , Glicemia , Comportamento Alimentar , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pâncreas Artificial , Período Pós-Prandial , Adulto Jovem
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