Accuracy of continuous glucose monitoring during differing exercise conditions

Effects of a 2-Week Kinect-Based Mixed-Reality Exercise Program on Prediabetes: A Pilot Trial during COVID-19

Background

Pre-diabetes can develop into type 2 diabetes mellitus, but can prevented by regular exercise. However, the outcomes when combining unsupervised Kinect-based mixed-reality (KMR) exercise with continuous glucose monitoring (CGM) remain unclear. Therefore, this single-arm pilot trial examined changes in blood glucose (BG) concentrations over 672 hours (4 weeks), including a 2-week period of KMR exercise and CGM in individuals with pre-diabetes.

Methods

This was a pre-and post-treatment case-control study with nine participants. General questionnaires were administered and body composition, fasting BG concentrations, and 2-hour oral glucose tolerance test (2-OGTT) results were measured pre-and post-treatment. Weekly average glucose concentrations, hyperglycemia rate, hypoglycemia rate, average glucose concentration over time, amount of physical activity, amount of food intake, and pre- and postprandial BG (immediately and 30, 60, 90, and 120 minutes after lunch) were measured over 4 weeks (pre-test, exercise, and post -test weeks). Glucose concentrations were measured before exercising, between sets, and 30 and 60 minutes after exercise during the 2 weeks of unsupervised exercise (3 days/week).

Results

In all participants, body mass index (27.16±2.92 kg/m2), fasting BG (108.00±7.19 mg/dL), 2-OGTT (162.56±18.12 mg/dL), hyperglycemia rate (P=0.040), and 90-minute postprandial BG (P=0.035) were significantly reduced during the 2 exercise weeks, and the 2-OGTT result (P=0.044) and diastolic blood pressure (DBP) (P=0.046) were significantly reduced at the post -test as compared with the pre-test.

Conclusion

This study found that 2 weeks of unsupervised KMR exercise reduced 2-OGTT, DBP, hyperglycemia rate, and 90-minute postprandial BG concentration. We believed this effect could be identified more clearly in studies involving a larger number of participants and longer durations of exercise.

Continuous Glucose Monitoring (CGM) in Sports-A Comparison between a CGM Device and Lab-Based Glucose Analyser under Resting and Exercising Conditions in Athletes

The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring (CGM) under different physical activity levels in a postprandial state in healthy athletes without diabetes. As a physiological shift occurs between glucose concentration from the CB into the ISF, the applicability of CGM in sports, especially during exercise, as well as the comparability of CB and ISF data necessitate an in-depth assessment. Ten subjects (26 ± 4 years, 67 ± 11 kg bodyweight (BW), 11 ± 3 h) were included in the study. Within 14 days, they underwent six tests consisting of (a) two tests resting fasted (HC_Rest/Fast and LC_Rest/Fast), (b) two tests resting with intake of 1 g glucose/kg BW (HC_Rest/Glc and LC_Rest/Glc), (c) running for 60 min at moderate (ModExerc/Glc), and (d) high intensity after intake of 1 g glucose/kg BW (IntExerc/Glc). Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to those determined from capillary blood samples collected at the time of sensor-based analyses and subjected to laboratory glucose measurements. Pearson's r correlation coefficient was highest for Rest/Glc (0.92, p < 0.001) compared to Rest/Fast (0.45, p < 0.001), ModExerc/Glc (0.60, p < 0.001) and IntExerc/Glc (0.70, p < 0.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (Rest/Fast: 8 ± 6%, Rest/Glc: 17 ± 12%, ModExerc/Glc: 22 ± 24%, IntExerc/Glc: 18 ± 17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. Based on the results of this study, the application of CGM in healthy athletes is not recommended without concomitant nutritional or medical advice.

Accuracy of CGM Systems During Continuous and Interval Exercise in Adults with Type 1 Diabetes

BACKGROUND

continuous glucose monitoring systems (CGMs) play an important role in the management of T1D, but their accuracy may reduce during rapid glucose excursions. The aim of study was to assess the accuracy of recent rt-CGMs available in Italy, in subjects with T1D during 2 sessions of physical activity: moderate continuous (CON) and interval exercise (IE).

METHOD

we recruited 22 patients with T1D, on CSII associated or integrated with a CGM, to which a second different sensor was applied. Data recorded by CGMs were compared with the corresponding plasma glucose (PG) values, measured every 5 minutes with the glucose analyzer. To assess the accuracy of the CGMs, we evaluated the Sensor Bias (SB), the Mean Absolute Relative Difference (MARD) and the Clarke error grid (CEG).

RESULTS

a total of 2355 plasma-sensor glucose paired points were collected. Both average plasma and interstitial glucose concentrations did not significantly differ during CON and IE. During CON: 1. PG change at the end of exercise was greater than during IE (P = .034); 2. all sensors overestimated PG more than during IE, as shown by SB (P < .001) and MARD (P < .001) comparisons. Classifying the performance according to the CEG, significant differences were found between the 2 sessions in distribution of points in A and B zones.

CONCLUSIONS

the exercise affects the accuracy of currently available CGMs, especially during CON, suggesting, in this circumstance, the need to maintain blood glucose in a "prudent" range, above that generally recommended. Further studies are needed to investigate additional types of activities.

Association of HbA1c with VO2max in Individuals with Type 1 Diabetes: A Systematic Review and Meta-Analysis

The aim of this systematic review and meta-analysis was to evaluate the association between glycemic control (HbA1c) and functional capacity (VO2max) in individuals with type 1 diabetes (T1DM). A systematic literature search was conducted in EMBASE, PubMed, Cochrane Central Register of Controlled Trials, and ISI Web of Knowledge for publications from January 1950 until July 2020. Randomized and observational controlled trials with a minimum number of three participants were included if cardio-pulmonary exercise tests to determine VO2max and HbA1c measurement has been performed. Pooled mean values were estimated for VO2max and HbA1c and weighted Pearson correlation and meta-regression were performed to assess the association between these parameters. We included 187 studies with a total of 3278 individuals with T1DM. The pooled mean HbA1c value was 8.1% (95%CI; 7.9−8.3%), and relative VO2max was 38.5 mL/min/kg (37.3−39.6). The pooled mean VO2max was significantly lower (36.9 vs. 40.7, p = 0.001) in studies reporting a mean HbA1c > 7.5% compared to studies with a mean HbA1c ≤ 7.5%. Weighted Pearson correlation coefficient was r = −0.19 (p < 0.001) between VO2max and HbA1c. Meta-regression adjusted for age and sex showed a significant decrease of −0.94 mL/min/kg in VO2max per HbA1c increase of 1% (p = 0.024). In conclusion, we were able to determine a statistically significant correlation between HbA1c and VO2max in individuals with T1DM. However, as the correlation was only weak, the association of HbA1c and VO2max might not be of clinical relevance in individuals with T1DM.

Considerations in the Care of Athletes With Type 1 Diabetes Mellitus

Type 1 diabetes mellitus is an autoimmune disease caused by affected individuals’ autoimmune response to their own pancreatic beta-cell. It affects millions of people worldwide. Exercise has numerous health and social benefits for patients with type 1 diabetes mellitus; however, careful management of blood glucose is crucial to minimize the risk of hypoglycemia and hyperglycemia. Anaerobic and aerobic exercises cause different glycemic responses during and after exercise, each of which will affect athletes’ ability to reach their target blood glucose ranges. The optimization of the patient’s macronutrient consumption, especially carbohydrates, the dosage of basal and short-acting insulin, and the frequent monitoring of blood glucose, will enable athletes to perform at peak levels while reducing their risk of dysglycemia. Despite best efforts, hypoglycemia can occur. Recognition of symptoms and rapid treatment with either fast-acting carbohydrates or glucagon is important. Continuous glucose monitoring devices have become more widely used in preventing hypoglycemia.

American Medical Society for Sports Medicine Position Statement on the Care of the Athlete and Athletic Person With Diabetes

ABSTRACT

The American Medical Society for Sports Medicine (AMSSM) developed this position statement to assist physicians and other health professionals in managing athletes and active people with diabetes. The AMSSM selected the author panel through an application process to identify members with clinical and academic expertise in the care of active patients with diabetes. This article reviews the current knowledge and gaps regarding the benefits and risks of various types of exercise and management issues for athletes and physically active people with diabetes, including nutrition and rehabilitation issues. Resistance exercises seem to be beneficial for patients with type 1 diabetes, and the new medications for patients with type 2 diabetes generally do not need adjustment with exercise. In preparing this statement, the authors conducted an evidence review and received open comment from the AMSSM Board of Directors before finalizing the recommendations.

Accuracy of Flash Glucose Monitoring During Postprandial Rest and Different Walking Conditions in Overweight or Obese Young Adults

Aims: To investigate the accuracy of FreeStyle Libre^TM flash glucose monitoring (FGM) relevant to plasma glucose (PG) measurements during postprandial rest and different walking conditions in overweight/obese young adults. Methods: Data of 40 overweight/obese participants from two randomized crossover studies were pooled into four trials: (1) sitting (SIT, n = 40); (2) walking continuously for 30 min initiated 20 min before individual postprandial glucose peak (PPGP) (20iP + CONT, n = 40); (3) walking continuously for 30 min initiated at PPGP (iP + CONT, n = 20); and (4) accumulated walking for 30 min initiated 20 min before PPGP (20iP + ACCU, n = 20). Paired FGM and PG were measured 4 h following breakfast. Results: The overall mean absolute relative difference (MARD) between PG and FGM readings was 16.4 ± 8.6% for SIT, 16.2 ± 4.7% for 20iP + CONT, 16.7 ± 12.2% for iP + CONT, and 19.1 ± 6.8% for 20iP + ACCU. The Bland-Altman analysis showed a bias of -1.03 mmol⋅L^-1 in SIT, -0.89 mmol⋅L^-1 in 20iP + CONT, -0.82 mmol⋅L^-1 in iP + CONT, and -1.23 mmol⋅L^-1 in 20iP + ACCU. The Clarke error grid analysis showed that 99.6-100% of the values in all trials fell within zones A and B. Conclusion: Although FGM readings underestimated PG, the FGM accuracy was overall clinically acceptable during postprandial rest and walking in overweight/obese young adults.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA)

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. Graphical abstract.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA)

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes

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.

The Athlete with Type 1 Diabetes: Transition from Case Reports to General Therapy Recommendations

Fear of hypoglycemia is a common barrier to exercise and physical activity for individuals with type 1 diabetes. While some of the earliest studies in this area involved only one or two participants, the link between exercise, exogenous insulin, and hypoglycemia was already clear, with the only suggested management strategies being to decrease insulin dosage and/or consume carbohydrates before and after exercise. Over the past 50 years, a great deal of knowledge has been developed around the impact of different types and intensities of exercise on blood glucose levels in this population. Recent decades have also seen the development of technologies such as continuous glucose monitors, faster-acting insulins and commercially available insulin pumps to allow for the real-time observation of interstitial glucose levels, and more precise adjustments to insulin dosage before, during and after activity. As such, there are now evidence-based exercise and physical activity guidelines for individuals with type 1 diabetes. While the risk of hypoglycemia has not been completely eliminated, therapy recommendations have evolved considerably. This review discusses the evolution of the knowledge and the technology related to type 1 diabetes and exercise that have allowed this evolution to take place.

Performance of the Freestyle Libre flash glucose monitoring (flash GM) system in individuals with type 1 diabetes: A secondary outcome analysis of a randomized crossover trial

AIMS

The efficacy of flash glucose monitoring (flash GM) systems has been demonstrated by improvements in glycaemia; however, during high rates of glucose flux, the performance of continuous glucose monitoring systems was impaired, as detailed in previous studies. This study aimed to determine the performance of the flash GM system during daily-life glycaemic challenges such as carbohydrate-rich meals, bolus insulin-induced glycaemic disturbances and acute physical exercise in individuals with type 1 diabetes.

MATERIALS AND METHODS

This study comprised four randomized trial visits with alternating pre- and post-exercise bolus insulin doses. Throughout the four 14-hour inpatient phases, 19 participants received three carbohydrate-rich meals and performed moderate-intensity exercise. Venous blood glucose and capillary blood glucose during exercise was compared to interstitial glucose concentrations. Flash GM accuracy was assessed by median absolute relative difference (MARD) (interquartile range [IQR]) using the Bland-Altman method and Clark error grid, as well as according to guidelines for integrated CGM approvals (Class II-510(K)).

RESULTS

The overall MARD (IQR) during inpatient phases was 14.3% (6.9%-22.8%), during hypoglycaemia (≤3.9 mmol/L) was 31.6% (16.2%-46.8%), during euglycaemia (4.0 mmol/L - 9.9 mmol/L) was 16.0% (8.5%-24.0%) and during hyperglycaemia (≥10 mmol/L) was 9.4% (5.1%-15.7%). Overall Bland-Altman analysis showed a bias (95% LoA) of 1.26 mmol/L (-1.67 to 4.19 mmol/L). The overall MARD during acute exercise was 29.8% (17.5%-39.8%), during hypoglycaemia was 45.1% (35.2%-51.1%), during euglycaemia was 30.7% (18.7%-39.2%) and during hyperglycaemia was 16.3% (10.0%-22.8%).

CONCLUSION

Flash GM interstitial glucose readings were not sufficiently accurate within the hypoglycaemic range and during acute exercise and require confirmatory blood glucose measurements.

Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives

Physical activity is important for patients living with type 1 diabetes (T1D) but limited by the challenges associated with physical activity induced glucose variability. Optimizing glycemic control without increasing the risk of hypoglycemia is still a hurdle despite many advances in insulin formulations, delivery methods, and continuous glucose monitoring systems. In this respect, the artificial pancreas (AP) system is a promising therapeutic option for a safer practice of physical activity in the context of T1D. It is important that healthcare professionals as well as patients acquire the necessary knowledge about how the AP system works, its limits, and how glucose control is regulated during physical activity. This review aims to examine the current state of knowledge on exercise-related glucose variations especially hypoglycemic risk in T1D and to discuss their effects on the use and development of AP systems. Though effective and highly promising, these systems warrant further research for an optimized use around exercise.

Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes

Background: Real-time continuous glucose monitoring (CGM) devices help detect glycemic excursions associated with exercise, meals, and insulin dosing in patients with type 1 diabetes (T1D). However, the delay between interstitial and blood glucose may result in CGM underestimating the true change in glycemia during activity. The purpose of this study was to examine CGM discrepancies during exercise and the meal postexercise versus self-monitoring of blood glucose (SMBG). Methods: Seventeen adults with T1D using insulin pump therapy and CGM completed 60 min of aerobic exercise on three occasions. A standardized meal was given 30 min postexercise. SMBG was measured during exercise and in recovery using OmniPod® Personal Diabetes Manager (PDM; Insulet, Billerica, MA) with built-in glucose meter (FreeStyle; Abbott Laboratories, Abbott Park, IL), while CGM was measured with Dexcom G4® with 505 algorithm (n = 4) or G5® (n = 13), which were calibrated with subjects' own PDM. Results: SMBG showed a large drop in glycemia during exercise, while CGM showed a lag of 12 ± 11 (mean ± standard deviation) minutes and bias of -7 ± 19 mg/dL/min during activity. Mean absolute relative difference (MARD) for CGM versus SMBG was 13 (6-22)% [median (interquartile range)] during exercise and 8 (5-14)% during mealtime. Clarke error grids showed CGM values were in zones A and B 94%-99% of the time for SMBG. Conclusion: In summary, the drop in CGM lags behind the drop in blood glucose during prolonged aerobic exercise by 12 ± 11 min, and MARD increases to 13 (6-22)% during exercise as well. Therefore, if hypoglycemia is suspected during exercise, individuals should confirm glucose levels with a capillary glucose measurement.

Changes in Accuracy of Continuous Glucose Monitoring Using Dexcom G4 Platinum Over the Course of Moderate Intensity Aerobic Exercise in Type 1 Diabetes

Continuous glucose monitoring (CGM) systems help diabetes management in patients with type 1 diabetes (T1D) but could have lower accuracy during exercise. We aim to evaluate the dynamics of CGM accuracy during exercise in patients with T1D. Secondary analysis of data was carried out on 22 patients with T1D (glycated hemoglobin [HbA_1c]: 7.3% ± 1.0%, diabetes duration: 23 ± 13 years), who did three exercise sessions (45 min at 60% VO_2max on an ergocycle, 3 h postmeal) with paired Dexcom G4 Platinum, and capillary glucose values that were collected every 5 min. Dexcom accuracy was evaluated using sensor bias (SB) and absolute relative difference (ARD). For dynamics of SB analysis, data pairs following hypoglycemia correction were excluded. The analyzed data included 792 pairs (594 during 66 exercise sessions, 198 at rest before exercise). Median ARD was 8.44 (5.35-12.13)% at rest and increased to 16.77 (10.75-26.72)% during exercise (P < 0.001). During exercise, mean SB values evolved from T0 minutes = 5.95 ± 16.04 mg/dL (exercise start); T5 = 9.55 ± 16.40; T10 = 13.51 ± 18.02; T15 = 15.32 ± 20.36; T20 = 17.30 ± 18.92; T25 = 19.46 ± 17.48; T30 = 21.08 ± 19.64; T35 = 19.10 ± 20.36; T40 = 19.82 ± 20.18; and T45 = 18.02 ± 20.90 (exercise end). CGM overestimated capillary at a mean SB of 14.23 ± 16.76 mg/dL over the whole exercise session. CGM accuracy decreased during moderate aerobic exercise as previously described. However, the trend to overestimate capillary glucose was maintained at relatively stable values within 15 min of exercise initiation, which could help patients in their clinical decisions. Similar analyses would be needed for other types of exercise.

Time Lag and Accuracy of Continuous Glucose Monitoring During High Intensity Interval Training in Adults with Type 1 Diabetes

Background: This study investigated the accuracy of real-time continuous glucose monitoring (rtCGM) during high intensity interval training (HIIT) in patients with type 1 diabetes (T1D). Methods: Seventeen participants with T1D, using multiple daily injections (MDI) with basal insulin glargine 300 U/mL (Gla-300), completed four fasted HIIT sessions over 4 weeks while wearing a Dexcom rtCGM G4 Platinum system. Each exercise consisted of high intensity interval cycling and multimodal training over 25 min. Reference venous plasma glucose (PG) was measured at 60- and 10-min before exercise (Stage 1), every 10 min during exercise and then every 15 min until 180 min after the end of exercise (Stage 2: during exercise and 45-min early recovery; Stage 3: 45 min to 3 h after the end of exercise); and at 6-, 10-, and 13-h postexercise (Stage 4). Results: In the 64 HIIT sessions that resulted in hyperglycemia, PG increased 90.0 ± 32.4 mg/dL (mean ± standard deviation), peaking at 68.0 ± 18.4 min from the start of HIIT. Mean absolute relative difference was highest during exercise and early recovery (Stage 2) at 17.8%, versus Stage 1 (10.4%), Stage 3 (10.6%), and Stage 4 (11.5%) (P < 0.001). During Stage 2, rtCGM showed a significant negative bias of 35.3 mg/dL (P < 0.001) compared to reference glucose. Lag time to reach the half-maximal glucose rise was 35 min in rtCGM versus PG. The Surveillance Error Grid found that in Stage 2, only 65.5% of paired values were in the no-risk zone and the %15/15 was 50%, significantly lower than the other stages (P < 0.001). Conclusions: During HIIT and early recovery, there is an increase in lag time and a related decline in accuracy of Dexcom rtCGM G4, compared to pre-exercise and later recovery, in patients with T1D using MDI.

Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals

The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an unscented Kalman filter. After detection, the EHRA triggers the following three actions: First, a carbohydrate suggestion, second, a reduction in basal insulin and the insulin-on-board maximum limit, and finally, a 30% reduction of the next insulin meal bolus. The EHRA was tested in silico using a 15-day scenario with 8 exercise sessions of 50 min at [Formula: see text] on alternating days. The EHRA was able to obtain improved results when compared to strategies with and without exercise announcement. The unannounced, announced, and EHRA strategies all obtained an overall percentage of time in range (70-180 mg/dl) of 94% and a percentage of time 70 mg/dl of 2%, 0%, and 0%, respectively. The EHRA was tested for robustness during exercise sessions of +25% and -25% intensity and results suggest that the EHRA is able to account for variability in exercise intensity, duration, and patient dynamics such as glucose uptake rate and insulin sensitivity.

Use of Continuous Glucose Monitoring Trends to Facilitate Exercise in Children with Type 1 Diabetes

Diabetes care during exercise frequently requires interruptions to activity and adds extra challenges particularly for young individuals with type 1 diabetes (T1D). This study investigated the use of a carbohydrate (CHO) intake algorithm based on continuous glucose monitoring (CGM) trends during physical activity. Children with T1D diagnosed for >1 year, ages 8-12 years, with a glycated hemoglobin of <10% were recruited into a randomized crossover study. They attended two similar mornings of fun-based physical activity and adhered to either a CHO intake algorithm based on CGM trends (intervention) or to standard exercise guidelines (consumption of 0.5 g CHO/kg/h when glucose <8 mmol/L) (control). Outcome measures included events such as exercise interruptions, CHO intake, and hypoglycemia events and percentage time spent in different sensor glucose ranges. Fourteen children completed the study. No episodes of significant hypoglycemia (sensor glucose level <3.0 mmol/L) occurred in either arm. Mean CHO intake was the same in both arms, 0.3 ± 0.2 g/kg/h. However, the intervention algorithm resulted in fewer CHO intake events per day: rate [95% confidence interval] 2.4 [1.6-2.3] versus 0.9 [0.4-1.5], P < 0.001, and exercise interruptions: 7.2 [5.9-8.8] versus 1.4 [0.8-2.1], P < 0.001, compared with control. There was no evidence of a difference in percentage time in range (3.9-10 mmol/L) and percentage time spent high between study arms. Both control and intervention protocols prevented significant hypoglycemia. Using a CHO intake algorithm based on CGM trends resulted in fewer CHO intake events and fewer interruptions to exercise. Use of this algorithm may reduce the burden of diabetes management with potential to facilitate activity in young people with T1D.

Exercise Management for Young People With Type 1 Diabetes: A Structured Approach to the Exercise Consultation

Regular physical activity during childhood is important for optimal physical and psychological development. For individuals with Type 1 Diabetes (T1D), physical activity offers many health benefits including improved glycemic control, cardiovascular function, blood lipid profiles, and psychological well-being. Despite these benefits, many young people with T1D do not meet physical activity recommendations. Barriers to engaging in a physically active lifestyle include fear of hypoglycemia, as well as insufficient knowledge in managing diabetes around exercise in both individuals and health care professionals. Diabetes and exercise management is complex, and many factors can influence an individual's glycemic response to exercise including exercise related factors (such as type, intensity and duration of the activity) and person specific factors (amount of insulin on board, person's stress/anxiety and fitness levels). International guidelines provide recommendations for clinical practice, however a gap remains in how to apply these guidelines to a pediatric exercise consultation. Consequently, it can be challenging for health care practitioners to advise young people with T1D how to approach exercise management in a busy clinic setting. This review provides a structured approach to the child/adolescent exercise consultation, based on a framework of questions, to assist the health care professional in formulating person-specific exercise management plans for young people with T1D.

Performance of the Flash Glucose Monitoring System during exercise in youth with Type 1 diabetes

AIM

Metabolic changes during exercise may affect the accuracy of glucose sensors impacting on Type 1 diabetes (T1D) management. The present study aimed at assessing the performance of the Flash Glucose Monitoring system (isCGM) during exercise and in free-living condition in youth with T1D.

METHODS

Seventeen youth (53% male), aged 13.7 ± 3.8 years, with T1D for 5.4 ± 3.8 years, HbA1c 7.4 ± 1.0% (57 ± 11 mmol/mol), were enrolled. Paired isCGM, plasma (PG) and capillary (CG) glucose values (total of 136) were collected during an interval exercise (45 min at 55% VO_2max load with 20 s sprints at 80% VO_2max every 10 min). Paired isCGM and CG (total of 832) were collected during free-living condition.

RESULTS

During exercise, isCGM absolute relative difference (ARDs) means/medians were 12.5/9.4% versus PG and 15.4/10.8% versus CG. During rest, ARDs means/medians were 16.6/12.0%. The Consensus Error Grid analysis showed 98.4% of readings during exercise and 97.24% during rest in zones A + B. Percentage of readings meeting the ISO criteria for CG levels <5.55 mmol/L was 62.5% during exercise, 53.4% during rest; for CG levels ≥5.55 mmol/L was 64.0% during exercise, 60.4% during rest.

CONCLUSIONS

isCGM demonstrated similar clinical safety and performance during exercise and in everyday life; further studies are needed to confirm its accuracy during exercise.

Insulin Management Strategies for Exercise in Diabetes

There is no question that regular exercise can be beneficial and lead to improvements in overall cardiovascular health. However, for patients with diabetes, exercise can also lead to challenges in maintaining blood glucose balance, particularly if patients are prescribed insulin or certain oral hypoglycemic agents. Hypoglycemia is the most common adverse event associated with exercise and insulin therapy, and the fear of hypoglycemia is also the greatest barrier to exercise for many patients. With the appropriate insulin dose adjustments and, in some cases, carbohydrate supplementation, blood glucose levels can be better managed during exercise and in recovery. In general, insulin strategies that help facilitate weight loss with regular exercise and recommendations around exercise adjustments to prevent hypoglycemia and hyperglycemia are often not discussed with patients because the recommendations can be complex and may differ from one individual to the next. This is a review of the current published literature on insulin dose adjustments and starting-point strategies for patients with diabetes in preparation for safe exercise.

Continuous Glucose Monitoring and Exercise in Type 1 Diabetes: Past, Present and Future

Prior to the widespread use of continuous glucose monitoring (CGM), knowledge of the effects of exercise in type 1 diabetes (T1D) was limited to the exercise period, with few studies having the budget or capacity to monitor participants overnight. Recently, CGM has become a staple of many exercise studies, allowing researchers to observe the otherwise elusive late post-exercise period. We performed a strategic search using PubMed and Academic Search Complete. Studies were included if they involved adults with T1D performing exercise or physical activity, had a sample size greater than 5, and involved the use of CGM. Upon completion of the search protocol, 26 articles were reviewed for inclusion. While outcomes have been variable, CGM use in exercise studies has allowed the assessment of post-exercise (especially nocturnal) trends for different exercise modalities in individuals with T1D. Sensor accuracy is currently considered adequate for exercise, which has been crucial to developing closed-loop and artificial pancreas systems. Until these systems are perfected, CGM continues to provide information about late post-exercise responses, to assist T1D patients in managing their glucose, and to be useful as a tool for teaching individuals with T1D about exercise.

The Role of Physical Exercise in Obesity and Diabetes

The prevalence of obesity is increasing world-wide. Obesity is associated with a plethora of metabolic and clinical constraints, which result in a higher risk for the development of cardiovascular complications and metabolic disease, particularly insulin resistance and type 2 diabetes. Obesity is an acknowledged determinant of glycemic control in patients with type 1 diabetes and accounts for the majority of premature death due to cardiovascular events. Physical exercise is generally recommended in patients with diabetes in order to prevent the development of or reduce existing obesity, as adopted by every international treatment guideline so far. Regular physical exercise has a beneficial impact on body composition, cardiovascular integrity, insulin sensitivity and quality of life. However, only a minority of patients participates in regular physical exercise, due to individual or disease-related barriers. In type 2 diabetes, there is robust evidence for beneficial effects of physical exercise on glycemic control, cardiovascular health and the development of diabetes-related long-term complications. In type 1 diabetes and patients treated with insulin, a higher risk for exercise-related hypoglycemia has to be considered, which requires certain prerequisites and adequate adaptions of insulin dosing. Current treatment guidelines do only incompletely address the development of exercise-related hypoglycemia. However, every patient with diabetes should participate in regular physical exercise in order to support and enable sufficient treatment and optimal glycemic control.

Accuracy of Continuous Glucose Monitoring before, during, and after Aerobic and Anaerobic Exercise in Patients with Type 1 Diabetes Mellitus

Continuous glucose monitoring (CGM) plays an important role in treatment decisions for patients with type 1 diabetes under conventional or closed-loop therapy. Physical activity represents a great challenge for diabetes management as well as for CGM systems. In this work, the accuracy of CGM in the context of exercise is addressed. Six adults performed aerobic and anaerobic exercise sessions and used two Medtronic Paradigm Enlite-2 sensors under closed-loop therapy. CGM readings were compared with plasma glucose during different periods: one hour before exercise, during exercise, and four hours after the end of exercise. In aerobic sessions, the median absolute relative difference (MARD) increased from 9.5% before the beginning of exercise to 16.5% during exercise (p < 0.001), and then decreased to 9.3% in the first hour after the end of exercise (p < 0.001). For the anaerobic sessions, the MARD before exercise was 15.5% and increased without statistical significance to 16.8% during exercise realisation (p = 0.993), and then decreased to 12.7% in the first hour after the cessation of anaerobic activities (p = 0.095). Results indicate that CGM might present lower accuracy during aerobic exercise, but return to regular operation a few hours after exercise cessation. No significant impact for anaerobic exercise was found.

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

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.

The Benefits and Limits of Technological Advances in Glucose Management Around Physical Activity in Patients Type 1 Diabetes

Physical activity is highly recommended for patients living with type 1 diabetes (T1D) due to its varied health benefits. Nevertheless, glucose management, during and in the hours following exercise, represents a great challenge for these patients who most often end up leading a sedentary life style. Important technological advances in insulin delivery devices and glucose monitoring are now available and continue to progress. These technologies could be used to alleviate glucose management related to physical activity in T1D. Continuous glucose monitoring (CGM) helps patients observe the trends of glycemic fluctuations when exercising and in the following night to deal pre-emptively with hypoglycemic risks and treat hypoglycemic episodes in a timely manner. Insulin pumps offer the flexibility of adjusting insulin basal rates and boluses according to patient's specific needs around exercise. The artificial pancreas links CGM to pump through an intelligent hormone dosing algorithm to close the loop of glucose control and has thus the potential to ease the burden of exercise in T1D. This review will examine and discuss the literature related to physical activity practice using each of these technologies. The aim is to discuss their benefits as well as their limitations and finally the additional research needed in the future to optimize their use in T1D.

Why should people with type 1 diabetes exercise regularly?

Plethoric evidence reminds of the protective effects of exercise against a number of health risks, across all ages, in the general population. The benefits of exercise for individuals with type 2 diabetes are indisputable. An in-depth understanding of energy metabolism has reasonably entailed exercise as a cornerstone in the lifestyle of almost all subjects with type 1 diabetes. Nevertheless, individuals with type 1 diabetes often fail in accomplishing exercise guidelines and they are less active than their peer without diabetes. Two major obstacles are feared by people with type 1 diabetes who wish to exercise regularly: management of blood glucose control and hypoglycemia. Nowadays, strategies, including glucose monitoring technology and insulin pump therapy, have significantly contributed to the participation in regular physical activity, and even in competitive sports, for people with type 1 diabetes. Novel modalities of training, like different intensity, interspersed exercise, are as well promising. The beneficial potential of exercise in type 1 diabetes is multi-faceted, and it has to be fully exploited because it goes beyond the insulin-mimetic action, possibly through immunomodulation.

The Effects of Basal Insulin Suspension at the Start of Exercise on Blood Glucose Levels During Continuous Versus Circuit-Based Exercise in Individuals with Type 1 Diabetes on Continuous Subcutaneous Insulin Infusion

BACKGROUND

Exercise causes glycemic disturbances in individuals with type 1 diabetes (T1D). Continuous moderate-intensity aerobic exercise (CON) generally lowers blood glucose (BG) levels and often leads to hypoglycemia. In comparison, circuit-based exercise (CIRC) may attenuate the drop in BG. The goal of this study is to contrast the effects of basal insulin suspension at the onset of two different forms of exercise (CON vs. CIRC).

METHODS

Twelve individuals (six men and six women) with T1D on insulin pump therapy were recruited for the study. All participants completed a maximal aerobic fitness test and two 40-min exercise sessions, consisting of either continuous treadmill walking or a circuit workout. Basal insulin infusion was stopped at the onset of exercise and resumed in recovery. After providing an initial reference value, volunteers were blinded to their [BG] and were asked to estimate their levels during exercise.

RESULTS

Oxygen consumption (47.5 ± 7.5 vs. 54.5 ± 13.5 mL·kg^-1·min^-1, P = 0.03) and heart rate (122 ± 20 vs. 144 ± 20 bpm, P = 0.003) were lower in CON vs. CIRC. Despite the lower workload, BG levels dropped more with CON vs. CIRC (delta BG = -3.8 ± 1.5 vs. -0.5 ± 3.0 mmol/L for CON vs. CIRC, respectively, P = 0.001). Participants were able to estimate their BG more accurately during CON (r = 0.83) vs. CIRC (r = 0.33) based on a regression analysis.

CONCLUSION

Despite a lower intensity of exercise, with full basal insulin suspension at the start of exercise, CON results in a larger drop in BG vs. CIRC. These findings have implications for single hormone-based artificial pancreas development for exercise. While this study does not negate the importance of frequent capillary BG monitoring during exercise, it does suggest that if persons are knowledgeable about their pre-exercise BG levels, they can accurately perceive the changes in BG during CON, but not during CIRC.

Exercise management in type 1 diabetes: a consensus statement

Type 1 diabetes is a challenging condition to manage for various physiological and behavioural reasons. Regular exercise is important, but management of different forms of physical activity is particularly difficult for both the individual with type 1 diabetes and the health-care provider. People with type 1 diabetes tend to be at least as inactive as the general population, with a large percentage of individuals not maintaining a healthy body mass nor achieving the minimum amount of moderate to vigorous aerobic activity per week. Regular exercise can improve health and wellbeing, and can help individuals to achieve their target lipid profile, body composition, and fitness and glycaemic goals. However, several additional barriers to exercise can exist for a person with diabetes, including fear of hypoglycaemia, loss of glycaemic control, and inadequate knowledge around exercise management. This Review provides an up-to-date consensus on exercise management for individuals with type 1 diabetes who exercise regularly, including glucose targets for safe and effective exercise, and nutritional and insulin dose adjustments to protect against exercise-related glucose excursions.

Update on Management of Type 1 Diabetes and Type 2 Diabetes in Athletes

Optimal blood glucose management still remains the biggest challenge in active individuals with diabetes, particularly in insulin users, but some newer strategies have been introduced to maintain blood glucose control. Recent studies emphasize the importance of exercise intensity on glycemic balance. In individuals with type 1 and type 2 diabetes, both resistance and high-intensity intermittent exercise have been shown to confer beneficial physiological adaptations in training studies, while also showing acute glycemic benefits from single sessions. At the same time, anyone training at higher intensities also should take into consideration potential impairments in thermoregulation in individuals with diabetes, which can increase the risk of heat stress during exercise in hot and/or humid conditions. Recent studies of medication effects on electrolyte balance and hydration give a more complete picture of potential exercise risks for athletes with diabetes. Use of the latest diabetes-related technologies also may benefit the athlete with diabetes.

Accuracy of Continuous Glucose Monitoring (CGM) during Continuous and High-Intensity Interval Exercise in Patients with Type 1 Diabetes Mellitus

Continuous exercise (CON) and high-intensity interval exercise (HIIE) can be safely performed with type 1 diabetes mellitus (T1DM). Additionally, continuous glucose monitoring (CGM) systems may serve as a tool to reduce the risk of exercise-induced hypoglycemia. It is unclear if CGM is accurate during CON and HIIE at different mean workloads. Seven T1DM patients performed CON and HIIE at 5% below (L) and above (M) the first lactate turn point (LTP₁), and 5% below the second lactate turn point (LTP₂) (H) on a cycle ergometer. Glucose was measured via CGM and in capillary blood (BG). Differences were found in comparison of CGM vs. BG in three out of the six tests (p < 0.05). In CON, bias and levels of agreement for L, M, and H were found at: 0.85 (−3.44, 5.15) mmol·L⁻¹, −0.45 (−3.95, 3.05) mmol·L⁻¹, −0.31 (−8.83, 8.20) mmol·L⁻¹ and at 1.17 (−2.06, 4.40) mmol·L⁻¹, 0.11 (−5.79, 6.01) mmol·L⁻¹, 1.48 (−2.60, 5.57) mmol·L⁻¹ in HIIE for the same intensities. Clinically-acceptable results (except for CON H) were found. CGM estimated BG to be clinically acceptable, except for CON H. Additionally, using CGM may increase avoidance of exercise-induced hypoglycemia, but usual BG control should be performed during intense exercise.