ArticlePDF Available

Increasing the Low-Glucose Alarm of a Continuous Glucose Monitoring System Prevents Exercise-Induced Hypoglycemia Without Triggering Any False Alarms

June 2011
Diabetes Care 34(6):e109

June 2011
34(6):e109

DOI:10.2337/dc10-2243

Source
PubMed

License
CC BY-NC-ND 3.0

Authors:

Katherine Iscoe

Raymond J Davey

Curtin University

Paul A Fournier

University of Western Australia

The use of continuous glucose monitoring systems (CGMSs) with low-glucose alarms is advocated as a means to decrease the risk of hypoglycemia in type 1 diabetes. Unfortunately, marked mismatches between CGMS readings and actual blood glucose (BG) concentrations limit the usefulness of CGMS in preventing hypoglycemia (1). Although we showed recently that raising the alarm level to compensate for this mismatch decreases the incidence and duration of hypoglycemic episodes, this results in an unacceptably high rate of false alarms (1), defined as an alarm triggered when BG levels are greater than the alarm threshold. This is an important issue because repeated exposure to false alarms can discourage individuals from using their CGMSs (2). Given that CGMSs overestimate BG levels when they rapidly decline (3,4), we propose that raising the CGMS alarm …

Content uploaded by Raymond J Davey

Content may be subject to copyright.

Available via license: CC BY-NC-ND 3.0

Content may be subject to copyright.

OBSERVATIONS

Increasing the

Low-Glucose Alarm

of a Continuous

Glucose Monitoring

System Prevents

Exercise-Induced

Hypoglycemia

Without Triggering

Any False Alarms

The use of continuous glucose mon-

itoring systems (CGMSs) with low-

glucose alarms is advocated as a

means to decrease the risk of hypoglyce-

mia in type 1 diabetes. Unfortunately,

marked mismatches between CGMS

readings and actual blood glucose (BG)

concentrations limit the usefulness of

CGMS in preventing hypoglycemia (1).

Although we showed recently that raising

the alarm level to compensate for this

mismatch decreases the incidence and

duration of hypoglycemic episodes, this

results in an unacceptably high rate of

false alarms (1), deﬁned as an alarm trig-

gered when BG levels are greater than the

alarm threshold. This is an important is-

sue because repeated exposure to false

alarms can discourage individuals from

using their CGMSs (2). Given that CGMSs

overestimate BG levels when they rapidly

decline (3,4), we propose that raising the

CGMS alarm in anticipation of a rapid fall

in glycemia could be one condition where

the incidence of hypoglycemia may be re-

duced without triggering any false alarms.

To test this hypothesis, four males

and four females with uncomplicated

type 1 diabetes and unimpaired aware-

ness of hypoglycemia (age 31.5 66.9

years; BMI 24.8 62.5 kg/m

;VO

2peak

43.2 64.9 mL /kg /min; diabetes duration

10.6 68.3 years; HbA

7.5 61.1%;

mean 6SD) wore a CGMS (abdomen;

Paradigm 722 Real-Time; Medtronic,

Northridge, CA) and attended the labora-

tory within 1.9 60.2 h of breakfast and

their usual insulin bolus (8.4 65.1 units).

When BG levels fell to between 8–10 mM,

participants exercised for 30 min (40%

2peak

) on a cycle ergometer to induce a

rapid fall in glycemia (5). During and for

2-h postexercise, the CGMS alarm was ei-

ther switched off or set to 4.0 or 5.5 mM,

with each treatment administered on con-

secutive mornings following a random-

ized counterbalanced design. Participants

were treated with carbohydrates when an

alarm was accompanied by a conﬁrmed

BG level #the alarm threshold or in

response to the verbal expression of hypo-

glycemic symptoms. One-way repeated-

measures ANOVA and Bonferroni post

hoc tests compared differences in BG and

CGMS levels. Hypoglycemic events were

compared using a Fisher exact squared

test.

In response to exercise, all partici-

pants in both the no alarm and 4.0 mM

alarm conditions experienced an episode

of hypoglycemia, deﬁned as a conﬁrmed

BG level ,3.8 mM (ABL 700 series; Radi-

ometer, Copenhagen, Denmark), with no

cases of false alarms in the 4.0 mM treat-

ment. In comparison, the 5.5 mM alarm

signiﬁcantly reduced by half the proportion

of hypoglycemic episodes (P50.048) with

no cases of false alarms. When the 5.5 mM

alarm was triggered, CGMS overestimated

BG values by 1.6 60.3 mM.

Our ﬁndings show for the ﬁrst time

that when glycemia is falling, the use of

CGMS alarms can provide an effective

means to reduce the risk of hypoglycemia

without the inconvenience of false alarms.

Although setting the CGMS alarm at 5.5

mM did not prevent all cases of hypogly-

cemia because of the large overestimation

of BG levels by the CGMS, this mismatch

had the beneﬁt of contributing to the

absence of false alarms. In conclusion,

future diabetes management guidelines

should highlight the beneﬁts of using

CGMS alarms for the prevention of hypo-

glycemia when a rapid fall in glycemia is

anticipated.

KATHERINE E. ISCOE,MSC

RAYMOND J. DAVEY,PHD

1,2

PAUL A. FOURNIER,PHD

From

The School of Sport Science, Exercise &

Health, The University of Western Australia,

Perth, Western Australia, Australia; and the

Telethon Institute for Child Health Research,

Centre for Child Health Research, The University

of Western Australia, Perth, Western Australia,

Australia.

Corresponding author: Katherine E. Iscoe, iscoek01@

student.uwa.edu.au.

DOI: 10.2337/dc10-2243

Readers may use this article as long as the work is

properly cited, the use is educational and not for

proﬁt, and the work is not altered. See http://

creativecommons.org/licenses/by-nc-nd/3.0/ for

details.

Acknowledgments—K.E.I. has received

speaking fees from Medtronic. No other

potential conﬂicts of interest relevant to this

article were reported.

K.E.I. collected the data and wrote and

edited the manuscript. R.J.D. contributed to

the study design and reviewed and edited the

manuscript. P.A.F. supervised the study, con-

tributed to the study design, and reviewed and

edited the manuscript.

Parts of this study were presented at the

70th Scientiﬁc Sessions of the American Di-

abetes Association, Orlando, Florida, 25–29

June 2010.

The authors acknowledge Medtronic

Australasia, which provided the CGMSs and

glucose sensors for the completion of this

study.

cccccccccccccccccccccccc

References

1. Davey RJ, Jones TW, Fournier PA. Effect of

short-term use of a continuous glucose

monitoring system with a real-time glucose

display and a low glucose alarm on inci-

dence and duration of hypoglycaemia in a

home setting in type 1 diabetes mellitus.

J Diabetes Sci Technol 2010;4:1457–1464

2. Buckingham B, Block J, Burdick J, et al.;

Diabetes Research in Children Network.

Response to nocturnal alarms using a real-

time glucose sensor. Diabetes Technol Ther

2005;7:440–447

3. Boyne MS, Silver DM, Kaplan J, Saudek

CD. Timing of changes in interstitial and

venous blood glucose measured with a

continuous subcutaneous glucose sensor.

Diabetes 2003;52:2790–2794

4. Davey RJ, Low CY, Fournier PA. The con-

tribution of an intrinsic lag of CGMS to

differences in measured and actual glucose

concentrations changing at variable rates in

vitro. J Diabetes Sci Technol 2010;4:1393–

1399

5. GuelﬁKJ, Jones TW, Fournier PA. The de-

cline in blood glucose levels is less with in-

termittent high-intensity compared with

moderate exercise in individuals with type 1

diabetes. Diabetes Care 2005;28:1289–

1294

care.diabetesjournals.org DIABETES CAR E,VOLUME 34, JUNE 2011 e109

ONLINE LETTERS

Raccomandazioni per la gestione dell'esercizio fisico nel diabete tipo 1 in età pediatrica

Book

Full-text available

May 2018

Exercise guidelines for children and adolescents with type 1 diabetes

Interstitial Glucose and Physical Exercise in Type 1 Diabetes: Integrative Physiology, Technology, and the Gap In-Between

Article

Full-text available

Jan 2018

Continuous and flash glucose monitoring systems measure interstitial fluid glucose concentrations within a body compartment that is dramatically altered by posture and is responsive to the physiological and metabolic changes that enable exercise performance in individuals with type 1 diabetes. Body fluid redistribution within the interstitial compartment, alterations in interstitial fluid volume, changes in rate and direction of fluid flow between the vasculature, interstitium and lymphatics, as well as alterations in the rate of glucose production and uptake by exercising tissues, make for caution when interpreting device read-outs in a rapidly changing internal environment during acute exercise. We present an understanding of the physiological and metabolic changes taking place with acute exercise and detail the blood and interstitial glucose responses with different forms of exercise, namely sustained endurance, high-intensity, and strength exercises in individuals with type 1 diabetes. Further, we detail novel technical information on currently available patient devices. As more health services and insurance companies advocate their use, understanding continuous and flash glucose monitoring for its strengths and limitations may offer more confidence for patients aiming to manage glycemia around exercise.

Effectiveness of a Predictive Algorithm in the Prevention of Exercise-Induced Hypoglycemia in Type 1 Diabetes

Article

Full-text available

Aug 2016

Background: Sensor-augmented pump therapy (SAPT) with a predictive algorithm to suspend insulin delivery has the potential to reduce hypoglycemia, a known obstacle in improving physical activity in patients with type 1 diabetes. The predictive low glucose management (PLGM) system employs a predictive algorithm that suspends basal insulin when hypoglycemia is predicted. The aim of this study was to determine the efficacy of this algorithm in the prevention of exercise-induced hypoglycemia under in-clinic conditions. Methods: This was a randomized, controlled cross-over study in which 25 participants performed 2 consecutive sessions of 30 min of moderate-intensity exercise while on basal continuous subcutaneous insulin infusion on 2 study days: a control day with SAPT alone and an intervention day with SAPT and PLGM. The predictive algorithm suspended basal insulin when sensor glucose was predicted to be below the preset hypoglycemic threshold in 30 min. We tested preset hypoglycemic thresholds of 70 and 80 mg/dL. The primary outcome was the requirement for hypoglycemia treatment (symptomatic hypoglycemia with plasma glucose <63 mg/dL or plasma glucose <50 mg/dL) and was compared in both control and intervention arms. Results: Results were analyzed in 19 participants. In the intervention arm with both thresholds, only 6 participants (32%) required treatment for hypoglycemia compared with 17 participants (89%) in the control arm (P = 0.003). In participants with a 2-h pump suspension on intervention days, the plasma glucose was 84 ± 12 and 99 ± 24 mg/dL at thresholds of 70 and 80 mg/dL, respectively. Conclusions: SAPT with PLGM reduced the need for hypoglycemia treatment after moderate-intensity exercise in an in-clinic setting.

Predicting Upcoming Glucose Levels in Patients with Type 1 Diabetes Using a Generalized Autoregressive Conditional Heteroscedasticity Modelling Approach

Article

Full-text available

May 2015

Continuous blood glucose monitoring systems (CGMS) capture interstitial glucose levels at frequent intervals over time, and are used by people with diabetes and their health care professionals to assess glycaemic variability. This information helps to adjust treatment to achieve optimum glycaemic control, as well as potentially providing early warning of imminent and dangerous hypoglycaemia. Although a number of studies has reported the possibilities of predicting hypoglycaemia in insulin dependent type 1 diabetes (T1DM) patients, the prediction paradigm is still unreliable, as glucose fluctuations in people with diabetes are highly volatile and depend on many factors. Studies have proposed the use of linear auto-regressive (AR) and state space time series models to analyse the glucose profiles for predicting upcoming glucose levels. However, these modelling approaches have not adequately addressed the inherent dependencies and volatility aspects in the glucose profiles. We have investigated the utility of generalized autoregressive conditional heteroscedasticity (GARCH) models to explore glucose time-series trends and volatility, and possibility of reliable short-term forecasting of glucose levels. GARCH models were explored using CGMS profiles of young children (4 to <10 years) with T1DM. The prediction performances of GARCH approach were compared with other contemporary modelling approaches such as lower and higher order AR, and the state space models. The GARCH approach appears to be successful in both realizing the volatility in glucose profiles and offering potentially more reliable forecasting of upcoming glucose levels.

Effects of Performing Morning Versus Afternoon Exercise on Glycemic Control and Hypoglycemia Frequency in Type 1 Diabetes Patients on Sensor-Augmented Insulin Pump Therapy

Article

Full-text available

Jan 2015

Although physical exercise (PE) is recommended for individuals with type 1 diabetes (DM1), participation in exercise is challenging because it increases the risk of severe hypoglycemia and the available therapeutic options to prevent it frequently result in hyperglycemia. There is no clear recommendation about the best timing for exercise. The aim of this study was to compare the risk of hypoglycemia after morning or afternoon exercise sessions up to 36 hours postworkout. This randomized crossover study enrolled subjects with DM1, older than 18 years of age, on sensor-augmented insulin pump (SAP) therapy. Participants underwent 2 moderate-intensity exercise sessions; 1 in the morning and 1 in the afternoon, separated by a 7 to 14 day wash-out period. Continuous glucose monitoring (CGM) data were collected 24 hours before, during and 36 hours after each session. Thirty-five subjects (mean age 30.31 ± 12.66 years) participated in the study. The rate of hypoglycemia was significantly lower following morning versus afternoon exercise sessions (5.6 vs 10.7 events per patient, incidence rate ratio, 0.52; 95% CI, 0.43-0.63; P < .0001). Most hypoglycemic events occurred 15-24 hours after the session. On days following morning exercise sessions, there were 20% more CGM readings in near-euglycemic range (70-200 mg/dL) than on days prior to morning exercise (P = .003). Morning exercise confers a lower risk of late-onset hypoglycemia than afternoon exercise and improves metabolic control on the subsequent day. © 2015 Diabetes Technology Society.

Type 1 diabetes

Chapter

Jan 2022

Exercise and physical activity are essential to longevity and health in individuals with type 1 diabetes (T1D). Due to the risk of hypoglycemia (low blood glucose levels), special considerations need to be made around the type, timing, and intensity of exercise, as these will all have different impacts on blood glucose levels both during and after the activity. To mitigate the risk of hypoglycemia, adjustments to insulin dosage can be made before, as well as after, either in place of or in addition to the consumption of extra carbohydrates. Additional special considerations will be necessary for the presence of diabetes complications, such as nephropathy, neuropathy, retinopathy, or cardiovascular disease. Otherwise, individuals with T1D should aim to obtain the same amount of physical activity (and avoid the same amount of sedentary time) as prescribed for those in their age group without diabetes since they will obtain the same health benefits from being active.

RT-CGM during endurance exercise - Seller 2012

Data

Full-text available

Dec 2017

Hypoglycemia associated with exercise in diabetes

Article

Oct 2011

Regular physical exercise is recommended for individuals with type 1 and type 2 diabetes. However, exogenous insulin or insulin secretagog use in these populations potentiates the risk of hypoglycemia at rest and particularly during and after exercise. A number of factors have been identified that augment exercise-related hypoglycemia risk, including excess delivery of exogenous insulin, inadequate carbohydrate intake, inability to detect or react to a fall in blood glucose (failure of symptom response and/or lack of blood glucose monitoring), increased duration of moderate-intensity exercise, and recent hypoglycemia or exercise. Implementation of behavioral strategies to modulate these risk factors substantially lowers the risk of hypoglycemia without compromising glycemic control.

The Role of Newer Technologies (CSII and CGM) and Novel Strategies in the Management of Type 1 Diabetes for Sport and Exercise

Chapter

Nov 2012

Alistair Lumb

Long-acting insulin analogues (insulin glargine (Lantus), sanofi-aventis; insulin detemir (Levemir), Novo Nordisk) are now used to provide basal insulin therapy for the majority of people using multiple daily injection (MDI) treatment regimens for type 1 diabetes. These insulins have provided significant benefit in terms of greater stability of circulating insulin levels [1, 2], which in turn has led to more stable blood glucose levels and a reduction in rates of hypoglycemia, particularly nocturnal hypoglycemia [3, 4]. However, as has been explained elsewhere in this volume, circulating insulin levels can vary considerably during sport and exercise in those without diabetes. An unfortunate consequence of stabilizing insulin levels in those with type 1 diabetes is, therefore, a significant risk of dysglycemia during exercise. For example, during endurance exercise in those without diabetes, such as prolonged running or cycling, insulin levels fall [5] to allow the mobilization of carbohydrate and lipid fuel sources [6], with insulin secretion falling to below fasting levels [7]. These fuel sources provide the energy required by exercising muscle and allow blood glucose levels to be maintained within a tight range. In people with diabetes using MDI therapy, insulin levels remain reasonably stable during exercise [5]. This limits the body’s ability to mobilize the required fuel sources and therefore results in a significant risk of hypoglycemia. The (somewhat inelegant) solution to this problem is usually the ingestion of carbohydrate, which can be problematic to maintain in some sports and also reduces the benefit of exercise if weight control is one of the intended outcomes.

Continuous Glucose Monitoring Algorithm: Accuracy Improvement and Evaluation in Type 1 Diabetes

Thesis

Full-text available

Aug 2014

Zeinab Mahmoudi

The biggest challenge in management of type 1 diabetes is lowering high blood glucose (BG) into normal glucose range by means of intensive insulin therapy, without increasing the risk of hypoglycemia. Therefore, it is of crucial importance that BG be monitored continuously for detection of hypoglycemia and subsequent treatment. The isolated self-monitoring of BG (SMBG) is unable to detect hypoglycemic episodes sufficiently. Continuous glucose monitoring (CGM) provides frequent and time-ordered information about BG through sampling of interstitial glucose (IG), which can facilitate detection of hypoglycemia. The main obstacle facing CGM systems is the large deviation of CGM readings from reference BG or plasma glucose (PG) levels that can cause substantial failure in detection of hypoglycemia, and consequently may result in incorrect recommendations for treatment adjustments. In order to have adequate accuracy, sophisticated signal processing algorithms-mainly consisting of filtering and calibration- must be applied toCGMdata. Although considerable advances have been achieved in the CGM technology, improving CGM algorithms is still a challenging issue in terms of reducing the filtering delay and increasing calibration sufficiency. In addition, it is of great importance to evaluate the enhanced CGM algorithms not only by measuring the proximity of the CGM reading to the concurrent BG values but also by investigating the effect of the improved accuracy on the quality of clinical decision making in diabetes. Hence, this PhD aimed at developing a sophisticated signal processing algorithm to enhance CGM accuracy, and explored novel methodologies to evaluate the algorithm. The first aim of the PhD project was to develop a CGM algorithm with an enhanced accuracy. This goal was achieved in study 1 and study 2. In study 1, a two-step CGM algorithm was presented. In the first step, a new method of filtering which is focused on minimizing the filtering delay was developed. In the second step, a calibration approach with several novel features targeting at reducing hypoglycemia inaccuracy was introduced. In study 2, the calibration part of the algorithm in study 1 was further enhanced by using a one-point xi xii Abstract calibration instead of a two-point calibration. The results of study 2 indicate further accuracy improvement compared to study 1. The second aim of the thesis was evaluating the accuracy of the new CGM algorithm from different aspects. The two main evaluation perspectives are assessing the numerical accuracy (study 3), and the clinical accuracy (study 4) of the algorithm. In study 3, the algorithm in study 1 was compared with the commercial algorithm used in the Guardian� REAL-Time (Medtronic Diabetes, Northridge, CA) CGM system, by using numerical performance metrics. The new CGM algorithm reduced PG-CGM deviation and increased hypoglycemia sensitivity, but it also slightly (not significantly) reduced the hypoglycemia specificity. In study 4, the impact of the improved numerical accuracy on the precision of the clinicians’ decision making was tested. Precision is an indicative of the CGM-based clinical decision making reliability. Precision was defined in terms of the inter-clinician agreement and intra-clinician reproducibility of the clinicians’ decisions to adjust the insulin dosage with the aim of avoiding glycemic excursions. The results indicate the potential of the new algorithm to increase the precision of the treatment adjustment. Based on the results of the studies performed in the thesis, a new signal processing algorithm for filtering and calibration of CGM data is presented. The numerical evaluation of the algorithm indicates that the algorithm is able to reduce the deviation of CGM readings from BG and PG levels, and has an improved performance over the compared state-of-the-art CGM algorithms. The clinical evaluation of the algorithm suggests that the higher numerical accuracy of CGM data achieved by the new algorithm can be translated into higher clinical reliability of the CGM-based decision making, which is the ultimate goal for the CGM accuracy improvement efforts.

Timing of Changes in Interstitial and Venous Blood Glucose Measured With a Continuous Subcutaneous Glucose Sensor

Article

Full-text available

Nov 2003
DIABETES

The objective of this study was to use a subcutaneous continuous glucose sensor to determine time differences in the dynamics of blood glucose and interstitial glucose. A total of 14 patients with type 1 diabetes each had two sensors (Medtronic/MiniMed CGMS) placed subcutaneously in the abdomen, acquiring data every 5 min. Blood glucose was sampled every 5 min for 8 h, and two liquid meals were given. A smoothing algorithm was applied to the blood glucose and interstitial glucose curves. The first derivatives of the glucose traces defined and quantified the timing of rises, peaks, falls, and nadirs. Altogether, 24 datasets were used for the analysis of time differences between interstitial and blood glucose and between sensors in each patient. Time differences between blood and interstitial glucose ranged from 4 to 10 min, with the interstitial glucose lagging behind blood glucose in 81% of cases (95% CIs 72.5 and 89.5%). The mean (+/-SD) difference between the two sensors in each patient was 6.7 +/- 5.1 min, representing random variation in sensor response. In conclusion, there is a time lag of interstitial glucose behind blood glucose, regardless of whether glycemia is rising or falling, but intersensor variability is considerable in this sensor system. Comparisons of interstitial and blood glucose kinetics must take statistical account of variability between sensors.

Effect of Short-Term Use of a Continuous Glucose Monitoring System with a Real-Time Glucose Display and a Low Glucose Alarm on Incidence and Duration of Hypoglycemia in a Home Setting in Type 1 Diabetes Mellitus

Article

Full-text available

Nov 2010

The objective of this study was to examine whether setting the low glucose alarm of a Guardian® REAL-Time continuous glucose monitoring system (CGMS) to 80 mg/dl for 3 days and providing instructions to users reduce the risk of hypoglycemia under free-living conditions in individuals with type 1 diabetes mellitus (T1DM). Fourteen participants with T1DM aged 26.1±6.0 years (mean±standard deviation) were fitted with a CGMS and assigned for 3 days to either an alarm [low and high blood glucose (BG) alarms set at 80 and 200 mg/dl, respectively] or no alarm condition, with each treatment administered to all participants following a counterbalanced design. All participants were given detailed instructions on how to respond appropriately to low glucose alarms. The CGMS with alarm reduced the incidence of hypoglycemia (CGMS readings≤65 mg/dl) by 44% as well as the time spent below this hypoglycemic threshold by 64% without increasing average BG levels. However, the CGMS with alarm had no effect on the incidence of symptomatic hypoglycemia. Short-term use of the CGMS with alarm, together with appropriate instructions for users, reduces the incidence and duration of hypoglycemia, but only to a limited extent, in part because it overestimates BG in the low glucose range.

Contribution of an Intrinsic Lag of Continuous Glucose Monitoring Systems to Differences in Measured and Actual Glucose Concentrations Changing at Variable Rates in Vitro

Article

Full-text available

Nov 2010

Current continuous glucose monitoring (CGM) systems measure glucose levels in the interstitial fluid to estimate blood glucose concentration. A lag time has been observed between CGM system glucose readings and blood glucose levels when glucose levels are changing. Although this lag has been attributed to the time it takes glucose to equilibrate between blood and interstitial fluid compartments, it is unclear to what extent these inaccuracies reflect an intrinsic delay of the device itself. Four Guardian® REAL-Time CGM systems (CGMSs) (Medtronic Diabetes, Minimed, CA) and eight glucose sensors were tested in glucose solutions prepared in Krebs bicarbonate buffers at 37 °C. Glucose readings obtained from CGMSs were compared with actual glucose concentrations during controlled changes in glucose concentration performed at four rates (30, 90, and 220 mg/dl/hr(-1) and an instantaneous change of 110 mg/dl) using a linear gradient maker. Irrespective of the rate and direction of changes in glucose concentration, the readings obtained from CGMSs were significantly different from actual glucose levels. The faster the rise or fall in actual glucose concentration, the more pronounced the mismatch with CGMS glucose readings. Furthermore, the intrinsic lag times (8.3 to 40.1 min) were high enough to account for the lags reported in previous in vivo studies. The lag intrinsic of the CGMS may make a significant contribution to the mismatch between CGM system readings and blood glucose concentrations.

Response to Nocturnal Alarms Using a Real-Time Glucose Sensor

Article

Full-text available

Jun 2005

The objective of this study was to determine how subjects responded to alarms for hypo- and hyperglycemia while they were sleeping. Twenty subjects with type 1 diabetes (4-17 years old) were admitted to a clinical research center for approximately 24 h. Each subject wore two GlucoWatch G2 Biographers (GW2B) (Cygnus, Inc., Redwood City, CA) and was videotaped using an infrared camera from 9 p.m. to 7 a.m. The videotapes were reviewed to determine if the GW2B alarms were audible on the tape and to document the subject's response to the alarms. Because many alarms can occur surrounding a change in blood glucose, GW2B alarm "events" are defined as a one or more alarms separated from previous alarms by more than 30 min. Downloaded data from the biographers identified 240 individual alarms, 75% of which occurred while the subject was sleeping. Of the 240 alarms 68% were audible on the videotape. Subjects awoke to 29% of individual alarms and to 66% of alarm events. Subjects 4-6 years old responded to 17% of alarms, 7-11 year olds responded to 20% of alarms, adolescents responded to 53% of alarms, and parents responded to 37% of alarms. Subjects awoke to 40% of the first alarm during the night, but to only 28% of subsequent alarms. There were 11 events when the glucose was confirmed to be < or = 70 mg/dL, and in each case the subject was awoken. Fifty-five percent of alarm events occurred when there was no hypo- or hyperglycemia confirmed by a reference glucose value. Subjects awoke to 29% of individual alarms and to 66% of alarm events. Subjects awoke during all alarm events when hypoglycemia was confirmed, but there was a high incidence of false alarms.

The Decline in Blood Glucose Levels Is Less With Intermittent High-Intensity Compared With Moderate Exercise in Individuals With Type 1 Diabetes

Article

Jul 2005
DIABETES CARE

To compare the response of blood glucose levels to intermittent high-intensity exercise (IHE) and moderate-intensity exercise (MOD) in individuals with type 1 diabetes. Seven healthy individuals with type 1 diabetes were tested on two separate occasions, during which either a 30-min MOD or IHE protocol was performed. MOD consisted of continuous exercise at 40% Vo(2peak), while the IHE protocol involved a combination of continuous exercise at 40% Vo(2peak) interspersed with 4-s sprints performed every 2 min to simulate the activity patterns of team sports. Both exercise protocols resulted in a decline in blood glucose levels. However, the decline was greater with MOD (-4.4 +/- 1.2 mmol/l) compared with IHE (-2.9 +/- 0.8 mmol/l; P < 0.05), despite the performance of a greater amount of total work with IHE (P < 0.05). During 60 min of recovery from exercise, glucose levels remained higher in IHE compared with MOD (P < 0.05). Furthermore, glucose levels remained stable during recovery from IHE, while they continued to decrease after MOD (P < 0.05). The stabilization of blood glucose levels with IHE was associated with elevated levels of lactate, catecholamines, and growth hormone during early recovery from exercise (P < 0.05). There were no differences in free insulin, glucagon, cortisol, or free fatty acids between MOD and IHE. The decline in blood glucose levels is less with IHE compared with MOD during both exercise and recovery in individuals with type 1 diabetes.

Diabetes Research in Children Network. Response to nocturnal alarms using a realtime glucose sensor

Jan 2005
440-447

B Buckingham
J Block
J Burdick

Buckingham B, Block J, Burdick J, et al.; Diabetes Research in Children Network. Response to nocturnal alarms using a realtime glucose sensor. Diabetes Technol Ther 2005;7:440–447

Increasing the Low-Glucose Alarm of a Continuous Glucose Monitoring System Prevents Exercise-Induced Hypoglycemia Without Triggering Any False Alarms

Abstract

Recommended publications

Extensive Assessment of Blood Glucose Monitoring During Postprandial Period and Its Impact on Closed...

Effect of Short-Term Use of a Continuous Glucose Monitoring System with a Real-Time Glucose Display...

The effect of short-term use of the Guardian RT continuous glucose monitoring system on fear of hypo...

Do Different Glucose Levels at Calibration Influence Accuracy of Continuous Glucose Monitoring Readi...

Is the Response of Continuous Glucose Monitors to Physiological Changes in Blood Glucose Levels Affe...