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Non-adjunctive continuous glucose monitoring for control of hypoglycaemia (COACH): Results of a post-approval observational study.
Beck, SE, Kelly, C, Price, DA, ,
Diabetic medicine : a journal of the British Diabetic Association. 2022;(2):e14739
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Abstract
OBJECTIVE Prior to the Continuous Monitoring and Control of Hypoglycaemia (COACH) study described herein, no study had been powered to evaluate the impact of non-adjunctive RT-CGM use on the rate of debilitating moderate or severe hypoglycaemic events. RESEARCH DESIGN AND METHODS In this 12-month observational study, adults with insulin-requiring diabetes who were new to RT-CGM participated in a 6-month control phase where insulin dosing decisions were based on self monitoring of blood glucose values, followed by a 6-month phase where decisions were based on RT-CGM data (i.e. non-adjunctive RT-CGM use); recommendations for RT-CGM use were made according to sites' usual care. The primary outcome was change in debilitating moderate (requiring second-party assistance) and severe (resulting in seizures or loss of consciousness) hypoglycaemic event frequency. Secondary outcomes included changes in HbA1c and diabetic ketoacidosis (DKA) frequency. RESULTS A total of 519 participants with mean (SD) age 50.3 (16.1) years and baseline HbA1c 8.0% (1.4%) completed the study, of whom 32.8% had impaired hypoglycaemia awareness and 33.5% had type 2 diabetes (T2D). The mean (SE) per-patient frequency of hypoglycaemic events decreased by 63% from 0.08 (0.016) during the SMBG phase to 0.03 (0.010) during the RT-CGM phase (p = 0.005). HbA1c decreased during the RT-CGM phase both for participants with type 1 diabetes (T1D) and T2D and there was a trend towards larger reductions among individuals with higher baseline HbA1c. CONCLUSIONS Among adults with insulin-requiring diabetes, non-adjunctive use of RT-CGM data is safe, resulting in significantly fewer debilitating hypoglycaemic events than management using SMBG.
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Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.
van Doorn, WPTM, Foreman, YD, Schaper, NC, Savelberg, HHCM, Koster, A, van der Kallen, CJH, Wesselius, A, Schram, MT, Henry, RMA, Dagnelie, PC, et al
PloS one. 2021;(6):e0253125
Abstract
BACKGROUND Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. METHODS We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). RESULTS Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). CONCLUSIONS Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.
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Impact of flash glucose monitoring on glycaemic control and quality of life in patients with type 1 diabetes: A 18-month follow-up in real life.
Rouhard, S, Buysschaert, M, Alexopoulou, O, Preumont, V
Diabetes & metabolic syndrome. 2020;(2):65-69
Abstract
We conducted a prospective observational study to evaluate the medium-term impact of the flash glucose monitoring system (FGM) in a type 1 diabetic population. We included 248 patients, switched from conventional blood glucose monitoring (BGM) to FGM. We evaluated glycaemic control at 2-4 (T1) and 5-11 (T2) months after initiation and at the last available visit (T3, 18 ± 4 months). We asked patients to fill in, at T0 and T2, two questionnaires based on the Diabetes Treatment Satisfaction Questionnaire; and on the Hypoglycaemia Fear Survey. Glycaemic control improved, from 8.1 ± 1.3% at T0 to 7.8 ± 1.2% at T1 (p < 0.001) and remained unchanged after. Average number of controls increased from 3.2 ± 1.2 BGM to 7.7 ± 3.9 at T1 (p < 0.001). We observed a modest decrease in daily insulin doses. We evidenced an increase in mild hypoglycaemic events, especially in well-controlled subjects, but no increase of severe events. Satisfaction score improved from 30.5 ± 7.7 points to 38.3 ± 5.1 points (p = 0.018), was correlated with the reduction in and was higher in less controlled patients at inclusion. "Behaviour" score regarding hypoglycaemias decrease from 5.7 ± 4.1 to 4.4 ± 3.6 points (p < 0.001). In conclusion, this 18-months study trial indicates that using the FGM technology in patients with T1D may improve glycaemic control, in real-life conditions, especially in less controlled patients. FGM was associated with an increase of patients' satisfaction regarding treatment. Hypoglycaemic events, however, were not reduced in frequency. Therefore, the need for an educational team and a structure program in the management of this new technology remains mandatory.
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The role of ambulatory 24-hour esophageal manometry in clinical practice.
Kamal, AN, Clarke, JO, Oors, JM, Bredenoord, AJ
Neurogastroenterology and motility. 2020;(10):e13861
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High-resolution manometry revolutionized the assessment of esophageal motility disorders and upgraded the classification through the Chicago Classification. A known disadvantage of standard HRM, however, is the inability to record esophageal motility function for an extended time interval; therefore, it represents only a more snapshot view of esophageal motor function. In contrast, ambulatory esophageal manometry measures esophageal motility over a prolonged period and detects motor activity during the entire circadian cycle. Furthermore, ambulatory manometry has the ability to measure temporal correlations between symptoms and motor events. This article aimed to review the clinical implications of ambulatory esophageal manometry for various symptoms, covering literature on the manometry catheter, interpretation of findings, and relevance in clinical practice specific to the evaluation of non-cardiac chest pain, chronic cough, and rumination syndrome.
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Stretchable, Patch-Type Calorie-Expenditure Measurement Device Based on Pop-Up Shaped Nanoscale Crack-Based Sensor.
Kwon, KY, Shin, YJ, Shin, JH, Jeong, C, Jung, YH, Park, B, Kim, TI
Advanced healthcare materials. 2019;(19):e1801593
Abstract
Demands for precise health information tracking techniques are increasing, especially for daily dietry requirements to prevent obesity, diabetes, etc. Many commercially available sensors that detect dynamic motions of the body lack accuracy, while novel strain sensors at the research level mostly lack the capability to analyze measurements in real life conditions. Here, a stretchable, patch-type calorie expenditure measurement system is demonstrated that integrates an ultrasensitive crack-based strain sensor and Bluetooth-enabled wireless communication circuit to offer both accurate measurements and practical diagnosis of motion. The crack-based strain gauge transformed into a pop-up-shaped structure provides reliable measurements and broad range of strain (≈100%). Combined with the stretchable analysis circuit, the skin attachable tool translates variation of the knee flexion angle into calorie expenditure amount, using relative resistance change (R/R0 ) data from the flexible sensor. As signals from the knee joint angular movement translates velocity and walking/running behavior, the total amount of calorie expenditure is accurately analyzed. Finally, theoretical, experimental, and simulation analysis of signal stability, dynamic noises, and calorie expenditure calculation obtained from the device during exercise are demonstrated. For further applications, the devices are expected to be used in broader range of dynamic motion of the body for diagnosis of abnormalities and for rehabilitation.
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Insulin Pump and Continuous Glucose Monitor Initiation in Hospitalized Patients with Type 2 Diabetes Mellitus.
Levitt, DL, Spanakis, EK, Ryan, KA, Silver, KD
Diabetes technology & therapeutics. 2018;(1):32-38
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BACKGROUND Insulin pumps and continuous glucose monitoring (CGM) are commonly used by patients with diabetes mellitus in the outpatient setting. The efficacy and safety of initiating inpatient insulin pumps and CGM in the nonintensive care unit setting is unknown. MATERIALS AND METHODS In a prospective pilot study, inpatients with type 2 diabetes were randomized to receive standard subcutaneous basal-bolus insulin and blinded CGM (group 1, n = 5), insulin pump and blinded CGM (group 2, n = 6), or insulin pump and nonblinded CGM (group 3, n = 5). Feasibility, glycemic control, and patient satisfaction were evaluated among groups. RESULTS Group 1 had lower mean capillary glucose levels, 144.5 ± 19.5 mg/dL, compared with groups 2 and 3, 191.5 ± 52.3 and 182.7 ± 59.9 mg/dL (P1 vs. 2+3 = 0.05). CGM detected 19 hypoglycemic episodes (glucose <70 mg/dL) among all treatment groups, compared with 12 episodes detected by capillary testing, although not statistically significant. No significant differences were found for the total daily dose of insulin or percentage of time spent below target glucose range (<90 mg/dL), in target glucose range (90-180 mg/dL), or above target glucose range (>180 mg/dL). On the Diabetes Treatment Satisfaction Questionnaire-Change, group 3 reported increased hyperglycemia and decreased hypoglycemia frequency compared with the other two groups, although the differences did not reach statistical significance. CONCLUSIONS Insulin pump and CGM initiation are feasible during hospitalization, although they are labor intensive. Although insulin pump initiation may not lead to improved glycemic control, there is a trend toward CGM detecting a greater number of hypoglycemic episodes. Larger studies are needed to determine whether use of this technology can lower inpatient morbidity and mortality.
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Continuous Glucose Monitoring in Older Adults With Type 1 and Type 2 Diabetes Using Multiple Daily Injections of Insulin: Results From the DIAMOND Trial.
Ruedy, KJ, Parkin, CG, Riddlesworth, TD, Graham, C, ,
Journal of diabetes science and technology. 2017;(6):1138-1146
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OBJECTIVE The objective was to determine the effectiveness of real-time continuous glucose monitoring (CGM) in adults ≥ 60 years of age with type 1 (T1D) or type 2 (T2D) diabetes using multiple daily insulin injections (MDI). METHODS A multicenter, randomized trial was conducted in the United States and Canada in which 116 individuals ≥60 years (mean 67 ± 5 years) with T1D (n = 34) or T2D (n = 82) using MDI therapy were randomly assigned to either CGM (Dexcom™ G4 Platinum CGM System® with software 505; n = 63) or continued management with self-monitoring blood glucose (SMBG; n = 53). Median diabetes duration was 21 (14, 30) years and mean baseline HbA1c was 8.5 ± 0.6%. The primary outcome, HbA1c at 24 weeks, was obtained for 114 (98%) participants. RESULTS HbA1c reduction from baseline to 24 weeks was greater in the CGM group than Control group (-0.9 ± 0.7% versus -0.5 ± 0.7%, adjusted difference in mean change was -0.4 ± 0.1%, P < .001). CGM-measured time >250 mg/dL ( P = .006) and glycemic variability ( P = .02) were lower in the CGM group. Among the 61 in the CGM group completing the trial, 97% used CGM ≥ 6 days/week in month 6. There were no severe hypoglycemic or diabetic ketoacidosis events in either group. CONCLUSION In adults ≥ 60 years of age with T1D and T2D using MDI, CGM use was high and associated with improved HbA1c and reduced glycemic variability. Therefore, CGM should be considered for older adults with diabetes using MDI.
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Reduction of energy intake using just-in-time feedback from a wearable sensor system.
Farooq, M, McCrory, MA, Sazonov, E
Obesity (Silver Spring, Md.). 2017;(4):676-681
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OBJECTIVE This work explored the potential use of a wearable sensor system for providing just-in-time (JIT) feedback on the progression of a meal and tested its ability to reduce the total food mass intake. METHODS Eighteen participants consumed three meals each in a lab while monitored by a wearable sensor system capable of accurately tracking chew counts. The baseline visit was used to establish the self-determined ingested mass and the associated chew counts. Real-time feedback on chew counts was provided in the next two visits, during which the target chew count was either the same as that at baseline or the baseline chew count reduced by 25% (in randomized order). The target was concealed from the participant and from the experimenter. Nonparametric repeated-measures ANOVAs were performed to compare mass of intake, meal duration, and ratings of hunger, appetite, and thirst across three meals. RESULTS JIT feedback targeting a 25% reduction in chew counts resulted in a reduction in mass and energy intake without affecting perceived hunger or fullness. CONCLUSIONS JIT feedback on chewing behavior may reduce intake within a meal. This system can be further used to help develop individualized strategies to provide JIT adaptive interventions for reducing energy intake.
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The effect of real-time electronic monitoring of patient-reported symptoms and clinical syndromes in outpatient workflow of medical oncologists: E-MOSAIC, a multicenter cluster-randomized phase III study (SAKK 95/06).
Strasser, F, Blum, D, von Moos, R, Cathomas, R, Ribi, K, Aebi, S, Betticher, D, Hayoz, S, Klingbiel, D, Brauchli, P, et al
Annals of oncology : official journal of the European Society for Medical Oncology. 2016;(2):324-32
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BACKGROUND Patients with advanced, incurable cancer receiving anticancer treatment often experience multidimensional symptoms. We hypothesize that real-time monitoring of both symptoms and clinical syndromes will improve symptom management by oncologists and patient outcomes. PATIENTS AND METHODS In this prospective multicenter cluster-randomized phase-III trial, patients with incurable, symptomatic, solid tumors, who received new outpatient chemotherapy with palliative intention, were eligible. Immediately before the weekly oncologists' visit, patients completed the palm-based E-MOSAIC assessment (Edmonton-Symptom-Assessment-Scale, ≤3 additional symptoms, estimated nutritional intake, body weight change, Karnofsky Performance Status, medications for pain, fatigue, nutrition). A cumulative, longitudinal monitoring sheet (LoMoS) was printed immediately. Eligible experienced oncologists were defined as one cluster each and randomized to receive the immediate print-out LoMoS (intervention) or not (control). Primary analysis limited to patients having uninterrupted (>4/6 visits with same oncologist) patient-oncologist sequences was a mixed model for the difference in patients global quality of life (G-QoL; items 29/30 of EORTC-QlQ-c30) between baseline (BL) and week 6. Intention-to-treat (ITT) analysis included all eligible patients. RESULTS In 8 centers, 82 oncologists treated 264 patients (median 66 years; overall survival intervention 6.3, control 5.4 months) with various tumors. The between-arm difference in G-QoL of 102 uninterrupted patients (intervention: 55; control: 47) was 6.8 (P = 0.11) in favor of the intervention; in a sensitivity analysis (oncologists treating ≥2 patients; 50, 39), it was 9.0 (P = 0.07). ITT analysis revealed improvement in symptoms (difference last study visit-BL: intervention -5.4 versus control 2.1, P = 0.003) and favored the intervention for communication and coping. More patients with high symptom load received immediate symptom management (chart review, nurse-patient interview) by oncologists getting the LoMoS. CONCLUSION Monitoring of patient symptoms, clinical syndromes and their management clearly reduced patients' symptoms, but not QoL. Our results encourage the implementation of real-time monitoring in the routine workflow of oncologist with a computer solution.
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Measuring free-living physical activity in COPD patients: Deriving methodology standards for clinical trials through a review of research studies.
Byrom, B, Rowe, DA
Contemporary clinical trials. 2016;:172-84
Abstract
This article presents a review of the research literature to identify the methodology used and outcome measures derived in the use of accelerometers to measure free-living activity in patients with COPD. Using this and existing empirical validity evidence we further identify standards for use, and recommended clinical outcome measures from continuous accelerometer data to describe pertinent measures of sedentary behaviour and physical activity in this and similar patient populations. We provide measures of the strength of evidence to support our recommendations and identify areas requiring continued research. Our findings support the use of accelerometry in clinical trials to understand and measure treatment-related changes in free-living physical activity and sedentary behaviour in patient populations with limited activity.