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  • 1
    In: Diabetes, American Diabetes Association, Vol. 68, No. Supplement_1 ( 2019-06-01)
    Abstract: Introduction: Glycemic control during exercise is challenging for people with type 1 diabetes. Better quantification of insulin dynamics at different exercise intensities and across insulin loading conditions is necessary for accurate insulin dosing during exercise. Methods: Twenty seven participants with type 1 diabetes were evaluated during 3 separate 10-hour fasting aerobic exercise studies with low, medium, and high levels of insulin infusion (100%, 150%, and 300% of basal rate) across two exercise arms: moderate (40-45% of VO2 max), and intense (60-65% of VO2 max) exercise. Glucose levels were clamped during a 3-hour run-in period, followed by 45 minutes of exercise on a treadmill. 6,6-2H2-dideuterated glucose was infused to match endogenous glucose production during the entire trial. Glucose tracer data were fitted to a two-compartment ODE model, solved via Bayesian inference. We intend to quantify insulin and non-insulin mediated disposal of glucose by observing the rate of disposal (Rd) across different rates of insulin infusion in each exercise arm. Results: Area under the curve (AUC) of Rd during 45 minutes of moderate exercise increased by 0.084 mmol/L for every percent increase in baseline insulin infusion rate (95% CI=0.064-0.104, p & lt;0.001). AUC during intense exercise increased by 0.109 mmol/L (95% CI= 0.079-0.139, p & lt;0.001). Mixed-effects model analysis showed no difference between exercise arms. Conclusion: Results indicate Rd is substantial during exercise but that the difference in glucose uptake during aerobic exercise at 40-45% VO2 max may be comparable with 60-65% VO2 max. Disclosure T.P. Nguyen: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. L.M. Wilson: None. D. Branigan: None. V. Gabo: None. B. Senf: None. F.H. Guillot: None. J. El Youssef: None. Funding National Institutes of Health
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2019
    detail.hit.zdb_id: 1501252-9
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  • 2
    In: American Journal of Physiology-Endocrinology and Metabolism, American Physiological Society, Vol. 320, No. 3 ( 2021-03-01), p. E425-E437
    Abstract: Aerobic exercise in type 1 diabetes (T1D) causes rapid increase in glucose utilization due to muscle work during exercise, followed by increased insulin sensitivity after exercise. Better understanding of these changes is necessary for models of exercise in T1D. Twenty-six individuals with T1D underwent three sessions at three insulin rates (100%, 150%, 300% of basal). After 3-h run-in, participants performed 45 min aerobic exercise (moderate or intense). We determined area under the curve for endogenous glucose production (AUC EGP ) and rate of glucose disappearance (AUC Rd ) over 45 min from exercise start. A novel application of linear regression of R d across the three insulin sessions allowed separation of insulin-mediated from non-insulin-mediated glucose uptake before, during, and after exercise. AUC Rd increased 12.45 mmol/L (CI = 10.33–14.58, P 〈 0.001) and 13.13 mmol/L (CI = 11.01–15.26, P 〈 0.001) whereas AUC EGP increased 1.66 mmol/L (CI = 1.01–2.31, P 〈 0.001) and 3.46 mmol/L (CI = 2.81–4.11, P 〈 0.001) above baseline during moderate and intense exercise, respectively. AUC EGP increased during intense exercise by 2.14 mmol/L (CI = 0.91–3.37, P 〈 0.001) compared with moderate exercise. There was significant effect of insulin infusion rate on AUC Rd equal to 0.06 mmol/L per % above basal rate (CI = 0.05–0.07, P 〈 0.001). Insulin-mediated glucose uptake rose during exercise and persisted hours afterward, whereas non-insulin-mediated effect was limited to the exercise period. To our knowledge, this method of isolating dynamic insulin- and non-insulin-mediated uptake has not been previously employed during exercise. These results will be useful in informing glucoregulatory models of T1D. The study has been registered at www.clinicaltrials.gov as NCT03090451. NEW & NOTEWORTHY Separating insulin and non-insulin glucose uptake dynamically during exercise in type 1 diabetes has not been done before. We use a multistep process, including a previously described linear regression method, over three insulin infusion sessions, to perform this separation and can graph these components before, during, and after exercise for the first time.
    Type of Medium: Online Resource
    ISSN: 0193-1849 , 1522-1555
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2021
    detail.hit.zdb_id: 1477331-4
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  • 3
    In: American Journal of Physiology-Endocrinology and Metabolism, American Physiological Society, Vol. 325, No. 3 ( 2023-09-01), p. E192-E206
    Abstract: Exercise can cause dangerous fluctuations in blood glucose in people living with type 1 diabetes (T1D). Aerobic exercise, for example, can cause acute hypoglycemia secondary to increased insulin-mediated and noninsulin-mediated glucose utilization. Less is known about how resistance exercise (RE) impacts glucose dynamics. Twenty-five people with T1D underwent three sessions of either moderate or high-intensity RE at three insulin infusion rates during a glucose tracer clamp. We calculated time-varying rates of endogenous glucose production (EGP) and glucose disposal (R d ) across all sessions and used linear regression and extrapolation to estimate insulin- and noninsulin-mediated components of glucose utilization. Blood glucose did not change on average during exercise. The area under the curve (AUC) for EGP increased by 1.04 mM during RE (95% CI: 0.65–1.43, P 〈 0.001) and decreased proportionally to insulin infusion rate (0.003 mM per percent above basal rate, 95% CI: 0.001–0.006, P = 0.003). The AUC for R d rose by 1.26 mM during RE (95% CI: 0.41–2.10, P = 0.004) and increased proportionally with insulin infusion rate (0.04 mM per percent above basal rate, CI: 0.03–0.04, P 〈 0.001). No differences were observed between the moderate and high resistance groups. Noninsulin-mediated glucose utilization rose significantly during exercise before returning to baseline roughly 30-min postexercise. Insulin-mediated glucose utilization remained unchanged during exercise sessions. Circulating catecholamines and lactate rose during exercise despite relatively small changes observed in R d . Results provide an explanation of why RE may pose a lower overall risk for hypoglycemia. NEW & NOTEWORTHY Aerobic exercise is known to cause decreases in blood glucose secondary to increased glucose utilization in people living with type 1 diabetes (T1D). However, less is known about how resistance-type exercise impacts glucose dynamics. Twenty-five participants with T1D performed in-clinic weight-bearing exercises under a glucose clamp. Mathematical modeling of infused glucose tracer allowed for quantification of the rate of hepatic glucose production as well as rates of insulin-mediated and noninsulin-mediated glucose uptake experienced during resistance exercise.
    Type of Medium: Online Resource
    ISSN: 0193-1849 , 1522-1555
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2023
    detail.hit.zdb_id: 1477331-4
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  • 4
    In: Diabetes, American Diabetes Association, Vol. 71, No. Supplement_1 ( 2022-06-01)
    Abstract: 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) .
    Type of Medium: Online Resource
    ISSN: 0012-1797
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2022
    detail.hit.zdb_id: 1501252-9
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  • 5
    Online Resource
    Online Resource
    American Diabetes Association ; 2018
    In:  Diabetes Vol. 67, No. Supplement_1 ( 2018-07-01)
    In: Diabetes, American Diabetes Association, Vol. 67, No. Supplement_1 ( 2018-07-01)
    Abstract: The complexity of T1D results in inadequate glucose control for patients which can lead to acute and chronic complications. We are developing a decision support smartphone application (app) for patients using multiple daily injection (MDI) therapy and continuous glucose monitoring (CGM). This app will use glucose trends, insulin dosing, and physical activity to provide recommendations for titration of insulin dosing to improve glycemic control. We performed an online survey of patients age 18-80 with T1D from a diabetes specialty clinic, targeting MDI users, to determine current needs and preferences of candidates for decision support tools. Twenty-seven respondents are included in the analysis: mean age of 39.4 years, 54% female, 17.5 years mean duration of T1D, 89% use MDI. A total of 41% of respondents used CGM 50-100% of the time with all checking their CGM & gt;6 times per day. Over 90% use a smartphone, 88% have the phone with them 75-100% of the time. Only 19% used an app to calculate insulin doses, while 81% were interested in using an app to manage diabetes. Participants wanted the following features in a decision support app: hypoglycemia avoidance (89%), insulin dose calculation (78%), behavioral change suggestions (74%), predicted glucose trends (70%), and insulin-on-board (70%). Many expressed low confidence in their ability to manage glucose during exercise (51%). Most respondents were interested in viewing predicted glucose trends prior to exercise (85%). Preferences about alerts to improve glucose control were varied with 44% saying they would be comfortable with 1-3 alerts per day. Preferences about alerts for predicted hypoglycemia were also varied; 11% would want to be alerted 5 minutes prior, 19% 15 minutes, 44% 30 minutes, and 26% & gt;1 hour. These survey results confirm the need for and interest in a smartphone-based decision support app that will assist with management of T1D. The results will enable intelligent app design to better meet the needs of end users. Disclosure L.M. Wilson: None. V. Gabo: None. N.S. Tyler: None. R. Reddy: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Consultant; Self; Zealand Pharma A/S. Advisory Panel; Self; Novo Nordisk Inc..
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2018
    detail.hit.zdb_id: 1501252-9
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  • 6
    In: Diabetes, American Diabetes Association, Vol. 68, No. Supplement_1 ( 2019-06-01)
    Abstract: Dual-hormone systems show promise to reduce hypoglycemia, but require a stable liquid glucagon formulation. XeriSol™ glucagon is a shelf-stable glucagon product for this use. Nineteen subjects with T1D are completing a single-center three-day outpatient study comparing control modes of the Oregon Artificial Pancreas system: 1) dual hormone (DH) closed-loop with Novolog™ insulin and XeriSol™ glucagon, 2) insulin-only single hormone (SH) closed-loop, 3) insulin-only predictive low glucose suspend system (PLGS). In clinic aerobic exercise (45 minutes at 60% VO2max) and home exercise was completed with each arm. SH and DH used automated exercise detection to adaptively dose insulin/glucagon for predicted hypoglycemia and tailored mealtime dosing based on past meal responses with an adaptive algorithm. An interim analysis after 7 subjects was completed for safety. The primary outcome measures are % time 70-180 mg/dL and % time & lt;70 mg/dL for the 3-day study and the 4-hour period from start of exercise until the next meal. From start of exercise until the next meal, DH showed a clinically meaningful reduction of time in hypoglycemia compared with PLGS and SH. For the entire study, DH showed a clinically meaningful lower time & lt;70mg/dL. No serious events or unexpected side effects occurred. Disclosure L.M. Wilson: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. N. Resalat: None. R. Reddy: None. J. El Youssef: None. D. Branigan: None. J.A. Leitschuh: Other Relationship; Self; AgaMatrix. B. Senf: None. V. Gabo: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. Funding JDRF
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2019
    detail.hit.zdb_id: 1501252-9
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  • 7
    Online Resource
    Online Resource
    SAGE Publications ; 2020
    In:  Journal of Diabetes Science and Technology Vol. 14, No. 6 ( 2020-11), p. 1081-1087
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 14, No. 6 ( 2020-11), p. 1081-1087
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 2467312-2
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  • 8
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  • 9
    In: Diabetes, American Diabetes Association, Vol. 71, No. Supplement_1 ( 2022-06-01)
    Abstract: 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 ( & gt;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
    Type of Medium: Online Resource
    ISSN: 0012-1797
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2022
    detail.hit.zdb_id: 1501252-9
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  • 10
    In: npj Digital Medicine, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2023-03-13)
    Abstract: We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of 83.3%, false discovery rate of 16.6%, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose 〉 180 mg/dL) by 10.8% ( P  = 0.04) and trends toward increasing time in range (70–180 mg/dL) by 9.1% compared with MPC. Time below range (glucose 〈 70 mg/dL) is not significantly different between RAP and MPC.
    Type of Medium: Online Resource
    ISSN: 2398-6352
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2925182-5
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