المؤلفون: Nichole S. Tyler, Clara Mosquera-Lopez, Gavin M. Young, Joseph El Youssef, Jessica R. Castle, Peter G. Jacobs

المصدر: iScience, Vol 25, Iss 3, Pp 103888- (2022)

مصطلحات موضوعية: Physiology, Biocomputational method, Computational bioinformatics, Science

الوصف: Summary: Prevention of hypoglycemia (glucose

وصف الملف: electronic resource

العلاقة: http://www.sciencedirect.com/science/article/pii/S2589004222001584Test; https://doaj.org/toc/2589-0042Test

الوصول الحر: https://doaj.org/article/e5aad372884b4d23a848553ea1bb6249Test

المؤلفون: Florian H. Guillot, Peter G. Jacobs, Leah M. Wilson, Joseph El Youssef, Virginia B. Gabo, Deborah L. Branigan, Nichole S. Tyler, Katrina Ramsey, Michael C. Riddell, Jessica R. Castle

المصدر: Biosensors, Vol 10, Iss 10, p 138 (2020)

مصطلحات موضوعية: continuous glucose monitoring, type 1 diabetes, exercise, glucose sensor accuracy, high intensity interval training, aerobic exercise, Biotechnology, TP248.13-248.65

الوصف: The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6–9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: −6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: −8.3 to −2.9%, HIIT MARD: 12.1 to 16.8% and MRD: −14.3 to −9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/2079-6374/10/10/138Test; https://doaj.org/toc/2079-6374Test

الوصول الحر: https://doaj.org/article/24cfba3912074fc59674f38180acf2d5Test

المؤلفون: Nichole S. Tyler, Peter G. Jacobs

المصدر: Sensors, Vol 20, Iss 11, p 3214 (2020)

مصطلحات موضوعية: type 1 diabetes, decision support, Artificial Intelligence, insulin advisor, Chemical technology, TP1-1185

الوصف: Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/1424-8220/20/11/3214Test; https://doaj.org/toc/1424-8220Test

الوصول الحر: https://doaj.org/article/a56213ccd29544399ab801425b403f88Test

المؤلفون: Jessica R. Castle, Leah M. Wilson, Nichole S. Tyler, Alejandro Z. Espinoza, Clara M. Mosquera-Lopez, Taisa Kushner, Gavin M. Young, Joseph Pinsonault, Robert H. Dodier, Wade W. Hilts, Sos M. Oganessian, Deborah L. Branigan, Virginia B. Gabo, Jae H. Eom, Katrina Ramsey, Joseph El Youssef, Joseph A. Cafazzo, Kerri Winters-Stone, Peter G. Jacobs

المصدر: Diabetes technologytherapeutics. 24(12)

مصطلحات موضوعية: Adult, Blood Glucose, Glycated Hemoglobin, Medical Laboratory Technology, Endocrinology, Diabetes Mellitus, Type 1, Endocrinology, Diabetes and Metabolism, Blood Glucose Self-Monitoring, Humans, Insulin, Hypoglycemic Agents

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3aa574ae324768751367b5b76ae05447Test
https://pubmed.ncbi.nlm.nih.gov/35920839Test

المؤلفون: LEAH M. WILSON, PETER G. JACOBS, FLORIAN H. GUILLOT, NICHOLE S. TYLER, TAISA KUSHNER, ALEJANDRO Z. ESPINOZA, CLARA M. MOSQUERA-LOPEZ, JOSEPH PINSONAULT, ROBERT DODIER, SOS M. OGANESSIAN, DEBORAH BRANIGAN, VIRGINIA GABO, JAE H. EOM, JOSEPH EL YOUSSEF, KERRI WINTERS-STONE, JESSICA R. CASTLE

المصدر: Diabetes. 71

مصطلحات موضوعية: Endocrinology, Diabetes and Metabolism, Internal Medicine

الوصف: DailyDose is a smart-phone decision support system developed at Oregon Health & Science University that uses Dexcom G6 CGM and Medtonic’s InPen. The app calculates insulin doses using CGM value and trend, insulin-on-board, carbohydrate amount, and exercise information. Insulin dosing and carbohydrate intake recommendations before and after exercise are adjusted according to the 2017 consensus statement by Riddell et al. known as the PEAK Guidelines. Twenty-four adults with T1D, using multiple daily injections of insulin at baseline, completed a two-week run-in period then used the DailyDose intervention for 8 weeks. Participants completed 3 at-home exercise sessions per week, one aerobic exercise video and two other sessions of their choice. We examined the impact of the use of PEAK guidelines on glucose outcomes during exercise comparing 176 exercise sessions done during run-in and 471 sessions done during the intervention period. Glucose outcomes were assessed from start of exercise to 4 hours after the start or until either a meal was consumed, insulin dosed or new exercise session initiated. Mixed effects analysis was used to determine significance of the intervention on glucose outcomes. The nadir of the glucose was lower for the run-in compared with the intervention (120.6 vs. 130.9 mg/dL, P=.012) . Change in glucose from the start of exercise to the nadir was 58.0 for the run-in versus 46.9 for the intervention period (P=.016) . Time in hyperglycemia (>180mg/dL) during the exercise periods trended toward being lower during the intervention (43.8% run-in, 35.0% intervention, P=.059) , as did time in range of 70-180 mg/dL, (54.2% run-in, 62.2% intervention, P=.081) . There was no difference in time in hypoglycemia. Data suggest that use of PEAK within a decision support app can help prevent more severe glucose drops during and after exercise with a trend toward improving time in range. Disclosure L.M.Wilson: n/a. S.M.Oganessian: None. D.Branigan: None. V.Gabo: None. J.H.Eom: None. J.El youssef: None. K.Winters-stone: None. J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. F.H.Guillot: Stock/Shareholder; Pacific Diabetes Technologies. N.S.Tyler: None. T.Kushner: Consultant; Tandem Diabetes Care, Inc. A.Z.Espinoza: None. C.M.Mosquera-lopez: None. J.Pinsonault: None. R.Dodier: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (Grant 2018PG-T1D001) , OHSU Medical Research Foundation, Supplies provided by Dexcom

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::5579384b954ed6e37e114c6774f3a447Test
https://doi.org/10.2337/db22-291-orTest

المؤلفون: GAVIN YOUNG, PETER G. JACOBS, NICHOLE S. TYLER, THANH-TIN P. NGUYEN, JESSICA R. CASTLE, LEAH M. WILSON, DEBORAH BRANIGAN, VIRGINIA GABO, FLORIAN H. GUILLOT, MICHAEL RIDDELL, JOSEPH EL YOUSSEF

المصدر: Diabetes. 71

مصطلحات موضوعية: Endocrinology, Diabetes and Metabolism, Internal Medicine

الوصف: Glucose dynamics during and after resistance exercise in people with T1D is not well understood. We recruited 25 people with T1D to perform 3 separate sessions of in-clinic resistance exercise under low (basal) , medium (1.5x basal) , or high (3.0x basal) insulin infusion levels. Blood glucose was clamped by intravenous dextrose infusion 3 hours prior to, and 4 hours after a 45-minute session of strength training. Glucose tracer was infused to match the rate of endogenous glucose production (EGP) . Glucose and tracer data were used to fit a two-compartment model and quantify the rate of glucose disposal (Rd) and EGP. For 58 sessions across 25 participants, blood glucose levels remained constant during and after resistance exercise. The mean change in area under the curve (AUC) for Rd was not higher during resistance exercise compared to baseline (+1.2 mM, p=0.69) , but increased during previously published aerobic exercise (+12.5 mM, p Disclosure G.Young: None. M.Riddell: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Consultant; Eli Lilly and Company, Jaeb Center for Health Research, Speaker's Bureau; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. J.El youssef: None. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. N.S.Tyler: None. T.P.Nguyen: None. J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. L.M.Wilson: n/a. D.Branigan: None. V.Gabo: None. F.H.Guillot: Stock/Shareholder; Pacific Diabetes Technologies. Funding National Institutes of Health (5R01DK110175, 1F30DK128914)

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::d16a7f063b057eeb02d560e5a0d8e588Test
https://doi.org/10.2337/db22-240-orTest

المؤلفون: JESSICA R. CASTLE, ALEJANDRO Z. ESPINOZA, NICHOLE S. TYLER, LEAH M. WILSON, CLARA M. MOSQUERA-LOPEZ, JOSEPH PINSONAULT, ROBERT DODIER, SOS M. OGANESSIAN, DEBORAH BRANIGAN, VIRGINIA GABO, JAE H. EOM, JOSEPH EL YOUSSEF, KATRINA RAMSEY, TAISA KUSHNER, KERRI WINTERS-STONE, JOSEPH A. CAFAZZO, PETER G. JACOBS

المصدر: Diabetes. 71

مصطلحات موضوعية: Endocrinology, Diabetes and Metabolism, Internal Medicine

الوصف: DailyDose is a decision support system developed at Oregon Health & Science University designed for people with T1D on MDI to improve glycemic control. It connects with Dexcom G6 and Medtronic's InPen. DailyDose runs on a smartphone and calculates insulin doses using CGM value and trend, IOB, carbohydrate amount, and exercise information. The system analyzes CGM and insulin data and automatically provides weekly recommendations on insulin settings, such as basal insulin dose and carbohydrate ratios, based on a k-nearest neighbors algorithm. Twenty-four adults with T1D used DailyDose for 8 weeks. The primary outcome was change in % time in range (TIR, 70-180 mg/dL) on CGM comparing the two week run-in period before starting DailyDose vs. final two weeks of DailyDose use. A mixed effects model was used to determine the impact of the % of accepted recommendations on change in % TIR. Users who accepted and followed recommendations showed a mean week-to-week improvement in TIR of 2.0% (Figure) . The mixed effects model shows week-by-week TIR increased by 7.8% when recommendations were accepted compared with not accepted (CI, 3-12%, P=.001) . Overall, there were no significant differences between TIR or time in hypoglycemia comparing the run-in period and the final two weeks of use. Further work is needed to encourage people using decision support systems to follow recommendations. Disclosure J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. V.Gabo: None. J.H.Eom: None. J.El youssef: None. K.Ramsey: None. T.Kushner: Consultant; Tandem Diabetes Care, Inc. K.Winters-stone: None. J.A.Cafazzo: None. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. A.Z.Espinoza: None. N.S.Tyler: None. L.M.Wilson: n/a. C.M.Mosquera-lopez: None. J.Pinsonault: None. R.Dodier: None. S.M.Oganessian: None. D.Branigan: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (Grant 2018PG-T1D001) .

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::951c29326e3249a33aa497dc60dd1471Test
https://doi.org/10.2337/db22-771-pTest

المؤلفون: Virginia Gabo, Ravi Reddy, Peter G. Jacobs, Deborah Branigan, Brian Senf, Navid Resalat, Florian H. Guillot, Katrina Ramsey, Nichole S. Tyler, Jessica R. Castle, Joseph El Youssef, Joseph Leitschuh, Isabelle Isa Kristin Steineck, Leah M. Wilson

المصدر: Diabetes Care. 43:2721-2729

مصطلحات موضوعية: Adult, Blood Glucose, Male, Pancreas, Artificial, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, Urology, 030209 endocrinology & metabolism, Hypoglycemia, Artificial pancreas, Glucagon, Oregon, Young Adult, 03 medical and health sciences, Insulin Infusion Systems, 0302 clinical medicine, Diabetes mellitus, Outpatients, Internal Medicine, medicine, Humans, Hypoglycemic Agents, Insulin, 030212 general & internal medicine, Exercise physiology, Exercise, Advanced and Specialized Nursing, Type 1 diabetes, Cross-Over Studies, business.industry, Middle Aged, medicine.disease, Crossover study, Diabetes Mellitus, Type 1, Hyperglycemia, Feasibility Studies, Female, business

الوصف: OBJECTIVE To assess the efficacy and feasibility of a dual-hormone (DH) closed-loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed-loop system and a predictive low glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS In a 76-h, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: 1) dual-hormone (DH) closed-loop control, 2) insulin-only single-hormone (SH) closed-loop control, and 3) PLGS system. The primary end point was percentage time in hypoglycemia ( RESULTS DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [interquartile range 0.0–4.2], SH 8.3% [0.0–12.5], P = 0.025). There was an increased time in hyperglycemia (>180 mg/dL) during and after exercise for DH versus SH (20.8% DH vs. 6.3% SH, P = 0.038). Mean glucose during the entire study duration was DH, 159.2; SH, 151.6; and PLGS, 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70–180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, P = 0.044). For the entire study duration, DH had 28.2% time in hyperglycemia vs. 25.1% for SH (P = 0.044) and 34.7% for PLGS (P = 0.140). Four participants experienced nausea related to glucagon, leading three to withdraw from the study. CONCLUSIONS The glucagon formulation demonstrated feasibility in a closed-loop system. The DH system reduced hypoglycemia during and after exercise, with some increase in hyperglycemia.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::239d793cfe1b212548d7861dba424a0dTest
https://doi.org/10.2337/dc19-2267Test

المؤلفون: Peter G. Jacobs, Nichole S. Tyler, Joseph El Youssef, Navid Resalat, Jessica R. Castle, Wade Hilts

المصدر: J Diabetes Sci Technol

مصطلحات موضوعية: Blood Glucose, Pancreas, Artificial, medicine.medical_specialty, Adaptive control, Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, 0206 medical engineering, Biomedical Engineering, 030209 endocrinology & metabolism, Bioengineering, 02 engineering and technology, Models, Biological, Artificial pancreas, 03 medical and health sciences, 0302 clinical medicine, Heart Rate, Internal medicine, Accelerometry, Heart rate, Internal Medicine, medicine, Humans, Hypoglycemic Agents, Insulin, Computer Simulation, Exercise, Type 1 diabetes, business.industry, System identification, Postprandial Period, medicine.disease, 020601 biomedical engineering, Model predictive control, Diabetes Mellitus, Type 1, Postprandial, Cardiology, Special Section: Artificial Pancreas, business, Algorithms

الوصف: Background: People with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals’ different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions. Methods: A new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient’s insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control. Results: Incorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%. Conclusion: Adapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic outcomes.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ede8a9145943dd4828fdf0932b2052bTest
https://doi.org/10.1177/1932296819881467Test

المؤلفون: Ravi Reddy, Peter G. Jacobs, Nichole S. Tyler, Jessica R. Castle, Virginia Gabo, Brian Senf, Leah M. Wilson

المصدر: J Diabetes Sci Technol

مصطلحات موضوعية: Adult, Blood Glucose, Male, Decision support system, Time Factors, Computer science, Endocrinology, Diabetes and Metabolism, Biomedical Engineering, Monitoring, Ambulatory, 030209 endocrinology & metabolism, Bioengineering, Smartphone application, Decision Support Techniques, 03 medical and health sciences, 0302 clinical medicine, Human–computer interaction, Internal Medicine, medicine, Humans, Hypoglycemic Agents, Insulin, 030212 general & internal medicine, Glycemic, Type 1 diabetes, Attitude to Computers, Blood Glucose Self-Monitoring, Original Articles, Middle Aged, Patient Acceptance of Health Care, medicine.disease, Mobile Applications, Self Care, Diabetes Mellitus, Type 1, Treatment Outcome, Smartphone app, Female, Patient input, Smartphone, Diffusion of Innovation, Patient Participation, Glucose monitors, Biomarkers

الوصف: Background: Decision support smartphone applications integrated with continuous glucose monitors may improve glycemic control in type 1 diabetes (T1D). We conducted a survey to understand trends and needs of potential users to inform the design of decision support technology. Methods: A 70-question survey was distributed October 2017 through May 2018 to adults aged 18-80 with T1D from a specialty clinic and T1D Exchange online health community ( myglu.org ). The survey responses were used to evaluate potential features of a diabetes decision support tool by Likert scale and open responses. Results: There were 1542 responses (mean age 46.1 years [SD 15.2], mean duration of diabetes 26.5 years [SD 15.8]). The majority (84.2%) have never used an app to manage diabetes; however, a large majority (77.8%) expressed interest in using a decision support app. The ability to predict and avoid hypoglycemia was the most important feature identified by a majority of the respondents, with 91% of respondents indicating the highest level of interest in these features. The task that respondents find most difficult was management of glucose during exercise (only 47% of participants were confident in glucose management during exercise). The respondents also highly desired features that help manage glucose during exercise (85% of respondents were interested). The responses identified integration and interoperability with peripheral devices/apps and customization of alerts as important. Responses from participants were generally consistent across stratified categories. Conclusions: These results provide valuable insight into patient needs in decision support applications for management of T1D.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bd8d2b6006a320a1f9e6cc339336377Test
https://doi.org/10.1177/1932296819870231Test