Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

BACKGROUND: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. METHODS: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. RESULTS: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = –.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. CONCLUSIONS: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

BackgroundThe relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings.MethodsA total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses.ResultsWithin-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices.ConclusionsThese preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals

Randomized Controlled Trial of Mobile Closed-Loop Control.

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

Glycemic Outcomes of Use of CLC Versus PLGS in Type 1 Diabetes: A Randomized Controlled Trial.

OBJECTIVE: Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS: After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14-72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70-180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS: All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = -6.0%; 95% CI -8.4%, -3.7%; P < 0.001) while time <54 mg/dL was similar (0.04%; 95% CI -0.05%, 0.13%; P = 0.41). HbA1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference -0.34% [-3.7 mmol/mol]; 95% CI -0.57% [-6.2 mmol/mol], -0.11% [1.2 mmol/mol]; P = 0.0035). CONCLUSIONS: Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS

The International Diabetes Closed-Loop Study: Testing Artificial Pancreas Component Interoperability

International audienceBackground: 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