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Concurrent Validity of a Continuous Glucose-Monitoring System at Rest and During and Following a High-Intensity Interval Training Session


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Purpose: To assess the concurrent validity of a continuous blood-glucose-monitoring system (CGM) postbreakfast, preexercise, exercise, and postexercise, while assessing the impact of 2 different breakfasts on the observed level of validity. Methods: Eight nondiabetic recreational athletes (age = 30.8 [9.5] y; height = 173.6 [6.6] cm; body mass = 70.3 [8.1] kg) took part in the study. Blood glucose concentration was monitored every 10 minutes using both a CGM (FreeStyle Libre, Abbott, France) and finger-prick blood glucose measurements (FreeStyle Optimum) over 4 different periods (postbreakfast, preexercise, exercise, and postexercise). Two different breakfasts (carbohydrates [CHO] and protein oriented) over 2 days (2 × 2 d in total) were used. Statistical analyses included the Bland-Altman method, standardized mean bias (expressed in standardized units), median absolute relative difference, and the Clarke error grid analysis. Results: Overall, mean bias was trivial to small at postbreakfast (effect size ± 90% confidence limits: -0.12 ± 0.08), preexercise (-0.08 ± 0.08), and postexercise (0.25 ± 0.14), while moderate during exercise (0.66 ± 0.09). A higher median absolute relative difference was observed during exercise (13.6% vs 7%-9.5% for the other conditions). While there was no effect of the breakfast type on the median absolute relative difference results, error grid analysis revealed a higher value in zone D (ie, clinically unsafe zone) during exercise for CHO (10.5%) compared with protein (1.6%). Conclusion: The CGM device examined in this study can only be validly used at rest, after both a CHO and protein-rich breakfast. Using CGM to monitor blood glucose concentration during exercise is not recommended. Moreover, the accuracy decreased when CHO were consumed before exercise.
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Title: Concurrent validity of a continuous glucose monitoring system at rest, during and
following a high-intensity interval training session.
Submission type: Original research
Authors: Clavel P1,2, Tiollier E2, Leduc C1,3, Fabre M1,2, Lacome M1,2, Buchheit M2,4,5,6
1 Performance Department, Paris Saint-Germain FC, Saint-Germain-en-Laye, France
2 French National Institute of Sport (INSEP), Laboratory of Sport, Expertise and Performance
(EA 7370), Paris, France
3 Carnegie Applied Rugby Research (CARR) centre, Institute for Sport, Physical Activity and
Leisure, Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
4 HIITScience, Revelstoke, BC, Canada
5 Institute for Health and Sport, Victoria University, Melbourne, VIC, Australia
6 Kitman Labs, Performance Research Intelligence Initiative, Dublin, Ireland
Contact details:
Mathieu Lacome
Paris Saint-Germain FC, Saint-Germain-en-Laye, France
Mobile: +33 6 09 42 78 33
Running head: Glucose monitoring in athletes
Abstract count: 217
Text only word account: 1900
Numbers of tables: 3
Numbers of figures: 3
Conflicts of interest: The authors do not have any conflict of interest.
Purpose: To assess the concurrent validity of a continuous blood glucose monitoring system
(CGM) Post-Breakfast, Pre-exercise, Exercise and Post-exercise, while assessing the impact of
two different breakfasts on the observed level of validity. Methods: Eight non-diabetic
recreational athletes (age: 30.8±9.5 years; height: 173.6±6.6 cm; body mass: 70.3±8.1 kg) took
part in the study. Blood glucose concentration was monitored every 10 min using both a CGM
(FreeStyle Libre, Abbott, France) and finger-prick blood glucose measurements (FreeStyle
Optimum, Abbott, France) over 4 different periods (Post-Breakfast, Pre-Exercise, Exercise and
Post-Exercise). Two different breakfasts (carbohydrates- [CHO] and protein- [PROT] oriented)
over two days (2x2 days in total) were used. Statistical analyses included the Bland-Altman
method, standardized mean bias (expressed in standardized unit), median absolute relative
difference (MARD) and the Clarke Error Grid (EGA). Results: Overall, mean bias was trivial-
to-small at Post-Breakfast (effect size ± 90% confidence limits: -0.12±0.08), Pre-Exercise (-
0.08±0.08) and Post-Exercise (0.25±0.14), while moderate during Exercise (0.66±0.09). Higher
MARD was observed during Exercise (13.6% vs 7 to 9.5% for the other conditions). While
there was no effect of the breakfast type on the MARD results, EGA revealed higher value in
Zone D (i.e. clinically unsafe zone) during Exercise for CHO (10.5%) compared with PROT
(1.6%). Conclusion: The CGM device examined in this study can only be validly used at rest,
after both a CHO and PROT-rich breakfast. Using CGM to monitor blood glucose concentration
during exercise is not recommended. Moreover, the accuracy decreased when carbohydrates
are consumed before exercise.
Regulation of blood glucose has first been widely studied from a health perspective.
Hyperglycemia for example, is believed to be an independent risk factor for the development
of several diseases such as type II diabetes mellitus1 and cardiovascular disease.2 More recently,
the monitoring of blood glucose concentration has also elicited great interest in sport, as
hypoglycaemia influences both physical and cognitive performances.3
In particular, it is known that at the beginning of exercise or after half-time in team sports,
athletes experience transient hypoglycemia, which may affect physical and cognitive
performance.4 Moreover, it has then been shown that a large glycemic variability exists among
individuals in the general population.5 Additionally, similar results have been shown in sub-
elite athletes,6 suggesting that providing more individualized guidelines to regulate blood
glucose would be beneficial for both health and performance goals.
The emergence of new technologies such as continuous glucose monitoring (CGM) devices has
allowed blood glucose concentration dynamics to be captured more frequently and less
invasively than traditional measures such as finger pricks. Indeed, as CGM devices only need
to be placed once (usually on the back of the arm), it can be used for several days without
disturbing sport practices. So far, these devices have been mainly used by diabetic populations
but as the technology becomes more accurate, less invasive, and less expensive, their use has
increased in other populations and especially in healthy individuals. Therefore, the inclusion of
CGM in sport nutritionists’ monitoring tool box could help to optimize nutritional strategies
before and during exercise, and in turn, improve athletes’ performance by preventing
hypoglycemia. However, to date, the validity of these new systems at rest or during exercise
has been only assessed in diabetics patients and showed promising results.7 Evidence regarding
its relevance with an athletic population is still lacking. Moreover, the ability of such devices
to detect potential glucose fluctuations due to different nutritional intakes need to be confirmed.
Therefore, the aim of this study was to assess the concurrent validity of a new CGM device
during different periods, i.e. pre, during and after exercise, while assessing the potential impact
of different nutritional intakes in the observed level of validity.
Study Population
Eight non-diabetic recreational athletes (5 females, 3 males) (age: 30.8 ± 9.5 years; height:
173.6 ± 6.6 cm; body mass: 70.3 ± 8.1 kg) who regularly participate in running and resistance-
based training (8±2 hours per week) were included in the study. An a priori power analysis was
conducted using the package pwr from R software (Version 4.0.0) for t-tests for non-parametric
data with a significance level alpha of 0.05 a power of 0.8 and add a non-parametric correction
of 15%. Result showed a minimal sample of 310 paired observations for 8 participants were
necessary. Alcohol intake was prohibited during the study period. Regarding female
participants, we ensured they were all within the same menstrual phase during the study period.
Participants provided informed consent prior to starting the study. Ethics approval was granted
before any data collection wwas undertaken and the recommendations of the Declaration of
Helsinki were respected.
A concurrent validity design was employed to assess the validity of a CGM system against
finger prick measures which was considered as the reference method. Over 2 consecutive
weeks, participants took part in 4 nonconsecutive standardized days. Each standardized day was
broken-down into 4 distinct periods: 1) Post-Breakfast which corresponded to the first hour
after the end of the Breakfast 2) Pre-Exercise which corresponded to the first hour following
the Post-Breakfast, 3) Exercise, which started 2 hours after the end of the breakfast and lasted
from the beginning of the warm up to the end of the workout and 4) Post-exercise, which started
immediately at the end of the workout, and up to 30 min later. A detailed outline of the
standardized day structure is provided in Figure 1. Nutritional intake during breakfast was
manipulated in order to provide either a high carbohydrate (CHO) or protein (PROT) breakfast,
to induce different levels of resting ad pre-exercise glycemia. Each typical breakfast was
repeated twice. Over those standardized days, blood glucose was measured continuously with
a CGM, while finger prick measures were taken every 10 minutes and. Day 1 was used for each
participant to familiarize with the CGM and ensure calibration (as per manufacturer
recommendations) before the experimentation could start. Between day 2 and 13, participants
undertook at their convenience the 4 standardized days. They were also instructed to have at
least one full day of recovery between each experimental day.
**Insert Figure 1**
Continuous glucose monitoring. Each participant was provided with a CGM system (FreeStyle
Libre, Abbott, France) over the full duration of the study. Each participant inserted a sensor
(FreeStyle Libre, Abbott, France) in their non-dominant upper arm (i.e. back the triceps
brachialis) one day before the beginning of the study. Glucose concentration was recorded in
the interstitial fluid every minute.
Finger prick blood glucose. Finger prick (FreeStyle Optium, Abbott, France) measures were
collected following the procedure described by Gomez.8 Each sample was immediately
analysed using the FreeStyle Libre reader (FreeStyle Libre Reader, Abbott, France) (The
validity and reliability of this device has been previously confirmed.9
Breakfast. Two typical breakfasts were employed. The CHO breakfast contained a high
proportion of carbohydrates (CHO) with 1 g.Kg-1 of body mass with a ceiling set at 70g of
carbohydrates per breakfast (e.g. breakfast contained a mix of orange juice, bread and jam).The
macronutrients and energy were as follow: 65±7g of carbohydrates, 9±1g of proteins and 1±0g
of fat for a total of 311±31 Kcal. The protein (PROT) breakfast was isoenergetic compared with
CHO (e.g. breakfast contained a mix of eggs, ham and cheese). The macronutrients and energy
were as follow: 1±0g of carbohydrates, 30±0g of proteins and 23±0g of fat for a total of 311±31
Standardized exercise. Participants completed the 30-15 Intermittent Fitness Test (30-15IFT) as
described by Buchheit et al.10 prior the beginning of the study. The speed (km·hr-1) achieved
by each participant during the last successfully completed stage of the test was recorded (VIFT)
in order to prescribe exercise intensity. The standardized exercise started with a 10-min low-
intensity run (30 to 40% of VIFT) and was followed by a high-intensity intermittent training
exercise performed outdoor. The trials consisted of six reps of 3-min running intervals
interspersed with 2 min of passive recovery. Reps 1 and 2 were performed at 75% VIFT, reps 3
and 4 at 80% VIFT and reps 5 and 6 at 85% VIFT. The session was ended with a 10-min walk.
Data processing. Each time point within a specific period was averaged as described above to
perform the concurrent validity analysis for each method (CGM and finger prick) and per
specific period (Figure 1). Each standardized day was analyzed first without (overall) and then
as a function of breakfast type (CHO and PROT).
Statistical Analysis
Bland-Altmann method for repeated measures and standardized mean bias were first applied to
assess the agreement between CGM and finger prick measures at each specific period.11 The
following thresholds were applied to rate the magnitude of the bias as follow: >0.2 (small), >0.6
(moderate), >1.2 (large) and >2 (very large).12
Additionally, analysis of the median average relative difference (MARD)13 and the Clarke Error
Grid Analysis (EGA)14 were conducted. Regarding MARD, further comparisons between the
different periods were performed using Wilcoxon test and/or Kruskal-Wallis tests. Level of
statistical significance was set at P<0.05. Results were further analyzed while calculating
standardized differences, i.e. Wilcoxon effect sizes. The thresholds to rate the magnitude of
the effects were the same than those used for mean bias. Regarding EGA, results were divided
into 5 zones (A, B, C, D, E). Each zone denotes a degree of clinical implications of blood
glucose concentration measures. Zones A and B were considered clinically acceptable while
zone C, D and E (erroneous treatment) were deemed possibly unsafe.14
The Bland-Altman analysis for the 4 periods is presented in Figure 2 and reported as mean bias
(standard error). Irrespectively of the breakfast content, mean biases were trivial-to-small for
Post-Breakfast (-2.99 [17.75] mg/dL), Pre-Exercise (-1.67 [10.95] mg/dL), Post-Exercise (4.18
[17.88] mg/dL) and moderate during Exercise (12.25 [13.86] mg/dL). Regarding CHO
breakfast, mean biases were trivial-to-small for Post-Breakfast (-1.43 [25.98] mg/dL), Pre-
Exercise (-4.29 [11.66] mg/dL), Post-Exercise (3.32 [18.18] mg/dL) and moderate during
Exercise (14.06 [13.81] mg/dL). For PROT Breakfast, trivial mean bias was observed for Pre-
Exercise (0.91 [8.98] mg/dL), Post-Breakfast (-4.51 [8.31] mg/dL) and Post-Exercise (5.13
[15.98] mg/dL), while moderate mean biases were observed for Exercise (10.47 [13.19]
**Insert Figure 2**
**Insert Figure 3**
The results of the MARD analysis between the different periods are presented in Table 1 and
**Insert Table 1 and 2**
Results regarding EGA are presented in Table 3. Irrespectively of the breakfast content, Post-
Breakfast, Pre-Exercise, and Post-Exercise periods fell into Zone A (accurate) and B (benign
errors) (100%). However, during Exercise, 94% of the values fell into A (70.4%) and B
(23.6%), and 6% in Zone D (failure to treat errors). For CHO breakfast, 10.5% of data fell into
Zone D for Exercise, while the other periods fell into Zone A and B. Similarly, for PROT
breakfast, 1.6% fell into Zone D during the Exercise period.
**Insert Table 3**
The aim of this study was 1) to investigate the concurrent validity of a new CGM device in
recreational athletes at Post-Breakfast, Pre-exercise, Exercise and Post-exercise, and 2) to
assess the potential impact of either a CHO-rich or protein-rich breakfast on the observed level
of validity. The main results highlighted that, while the validity of CGM was acceptable at rest
(i.e. Post-Breakfast, Pre-Exercise and Post-Exercise), it was lower during Exercise and
especially after the CHO breakfast.
The first results demonstrated trivial-to-small mean bias during all the non-exercise periods,
irrespectively of nutritional intake. Moreover, all results from EGA fell into the “clinically safe
zone (A and B), albeit during Exercise. These results are similar to those shown previously in
non-athletic diabetic populations.15 Indeed, the present results suggest that assessing glucose
dynamics at rest is feasible with this CGM device. This could open the door to a better
individualization of nutritional strategies.5
Yet, we observed a higher bias during Exercise compared with the other periods, confirming
previous studies in a non-athletic diabetic population.16 Reasons that may contribute to the
reduced validity of the CGM device in this context include microcirculation perturbations as a
as a result of movements around or within the insertion area, increases in body temperature and
rapid fluxes in glucose levels during exercise.17 Regarding the likely physiological time lag of
glucose transport between blood and interstitial fluid compartments (see Figure 3, finger pricks
measures changed faster Post-Breakfast than that of the CGM device), it should be noted that
it might not have accounted for the observed difference in accuracy as the pattern is not only
delayed but it varies with time and conditions. Indeed, while a clear hypoglycemia was observed
with finger prick measures immediately at the start of exercise (which was the expected
physiological response), the CGM showed an increased blood glucose response (Figure 2).
Nonetheless, this discrepancy indicates that the CGM device was unable to detect a potential
hypoglycemia observed at the onset of exercise, and could therefore not be used to assess
strategies aiming at preventing this phenomenon in practice. It is worth mentioning that a trend
for a better agreement was observed toward the end of the exercise periods (Figure 2). If the
duration of the exercise also affects the accuracy of CGM, it means that while the device may
not be suitable for sport including short and intermittent exercise durations, its use could
perhaps be considered during longer event such as cycling, trail or triathlon. This potential
better accuracy toward longer exercise duration highlights the need to conduct further research
involving 1) longer exercise duration, 2) nutritional intake during long endurance race 3)
various exercise modalities and 4) different intensities.
To examine the potential effect of the absolute levels of glycemia on the validity of the CGM
device, different breakfasts were proposed (CHO and PRO). Similar MARD and EGA results
were observed, suggesting that the CGM validity was not affected by the breakfast content
during non-exercise periods (i.e. Post-Breakfast, Pre-Exercise, Post-Exercise). Specific pre-
competition nutritional strategies can have a positive influence on both the acute running
performance among rugby league players18 or endurance athletes,19 and the chronic training
adaptations to training.20 Consequently, the use of this CGM device could be considered by
practitioners willing to monitor glycemic responses before and after competition or training, to
ensure the efficacy of the nutritional strategies employed.
However, during the Exercise period, the CGM accuracy was modulated by breakfast content.
Indeed, a 10 times higher value in Zone D of the EGA (i.e. clinically unsafe) was observed post
CHO (10.5%) compared with post PROT (1.6%) breakfast. In our study, zone D corresponds
to the situation where finger prick measures indicate an hypoglycemic state whereas CGM
measures are within the normal range14 suggesting that CGM failed to detect the hypoglycemia
occurring during exercise after the CHO-rich breakfast. It is well known there is a rapid drop
of blood glucose concentration at the onset of exercise, due to an increased glucose uptake by
exercising muscles.21 This physiological mechanism could explain why the sensor lacks
sensitivity to rapid changes in glucose concentration, as observed in the present study. As it
stands, if practitioners want to monitor blood glucose during high-intensity intermittent
exercise, they need to consider other devices than CGM (e.g. finger prick).
Practical applications
- The present CGM system provided valid measures at rest. Therefore, the use of such a
system may allow for a better individualization of nutritional strategies before or after
- The level of validity was lower during high-intensity intermittent training and was in
addition influenced by the type of breakfast consumed (i.e. high carbohydrates or high
protein). Consequently, practitioners should avoid using this device during intermittent
Daily monitoring of blood glucose is of importance in athletes given the likely impact of
glycemia on performance and the individualized nutritional recommendations that can be made
with CGM. Our results highlighted that the CGM device examined in the present study
presented only trivial-to-small bias when compared with a traditional fingerpick device at rest,
suggesting that it could be used confidently during this specific period. The CGM device is not
valid enough to monitor glucose during intermittent exercise. Further analyses should however
evaluate the validity of this device over longer exercise duration.
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Table and figure caption
Figure 1. Schematic representation of the study design.
Figure 2. Bland-Altman analysis between the continuous glucose monitoring device (CGM)
and finger prick measures (FPBG). Dash lines represent the limits of agreements.
Figure 3. Continuous glucose monitoring (CGM) and finger prick measures during each
standardized condition, when ingesting a carbohydrate- (upper) and protein- (lower) oriented
breakfasts, with the 2 days of each breakfast condition pooled for each participant (n = 2 x 8
for each curve). Data are presented as mean (SE).
Table 1. Median Absolute Relative Difference between the continuous glucose monitoring
device (CGM) and finger prick measures. Data are median (interquartile range) and expressed
in percentage. *: significantly different from Post-Breakfast. #: significantly different from Pre-
Exercise. : significantly different from Exercise. Comparisons between period are presented
as effect size with 90% confidence interval.
Table 2. Comparisons between period are presented as effect size for Wilcoxon test with 90%
confidence interval.
Table 3. Clark Error Grid Analysis between the continuous glucose monitoring device (CGM)
and finger prick measures. Zone A represents a clinically accurate measure. Zone B stands for
benign errors. Zone C represents overcorrection errors. Zone D and E represent failure to treat
errors and erroneous treatment errors respectively. For more details see Clarke et al. (1987).
Table 1
(3.6-13.4) #
(5.0-17.3) #†
(3.9-13.2) *
(6.1-16.9) #†
Table 2
Post Exercise
(0.17 to 0.31)
(0.01 to 0.16)
(0.24 to 0.38)
(0.07 to 0.24)
(0.06 to 0.23)
(0.13 to 0.34)
(0.01 to 0.18)
(0.27 to 0.46)
(0.07 to 0.31)
(0.07 to 0.28)
(0.14 to 0.34)
(0.01 to 0.2)
(0.16 to 0.36)
(0.01 to 0.24)
(0.02 to 0.24)
Table 3
A (Accurate)
213 (93.4%)
176 (70.4%)
100 (76.3%)
B (Benign
14 (6.1%)
59 (23.6%)
31 (23.7%)
D (Failure to
treat errors)
1 (0.5%)
15 (6.0%)
A (Accurate)
104 (92.0%)
81 (65.3%)
52 (75.4%)
B (Benign
9 (8.0%)
30 (24.2%)
17 (24.7%)
D (Failure to
treat errors)
13 (10.5%)
A (Accurate)
109 (94.8%)
95 (75.4%)
48 (77.4%)
B (Benign
5 (4.3%)
29 (23.0%)
14 (22.6%)
D (Failure to
treat errors)
1 (0.9%)
2 (1.6%)
Figure 1
Figure 2
Figure 3
... To our knowledge, to date, there has been only one study published comparing ISF and CB data systematically in subjects without diabetes investigating different protocols, including the assessment of validity postprandially after different breakfasts, pre-, during and post-exercise [27]. In our study, systematic measurement difference shows a smaller mean bias under exercising compared to resting conditions (ModExerc/Glc: 4 mg/dL; IntExerc/Glc: 2 mg/dL vs. HC_Rest/Glc: 20 mg/dL; LC_Rest/Glc: 20 mg/dL). ...
... However, their 95% CI is higher under exercising conditions compared to resting conditions (DLOA: ModExerc/Glc: 125 mg/dL; IntExerc/Glc: 102 mg/dL vs. HC_Rest/Glc: 65 mg/dL; LC_Rest/Glc: 55 mg/dL). Clavel et al. (2022) compared the CGM system (Freestyle Libre, Abbott, France) with a finger prick system (FreeStyle Optimum, Abbott, France) [27]. The FreeStyle Optimum is a self-monitoring blood glucose device, which is different from our comparator lab-based device. ...
... Within 30 min post-exercise, the mean bias was 4.18 mg/dL (DLOA [58.8 mg/dL]). The findings by Clavel et al. (2022) were different from our findings, revealing the highest mean bias during exercising protocols and a similar DLAO between all conditions, concluding that accuracy is negatively influenced by physical activity [27]. However, we found a lower mean bias during exercise but a higher DLOA indicating a higher variability of ISF and CB glucose values. ...
Full-text available
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.
... There is a lack of information on the ability of FGM to detect glycaemic responses to feeding and exercise in healthy adolescents. To our knowledge, only a few studies have examined the validity of FreeStyle Libre in response to feeding and exercise in adults without diabetes [20][21][22][23] with only a single study validating the FreeStyle Libre Pro version in healthy children aged 7 to 12 years [9]. One study showed that FGM overestimated venous plasma glucose concentrations by 0.63 to 1.50 mmol·L −1 30 to 90 min after glucose loading in a small sample (n = 7) of healthy adults [23]. ...
... Two recent studies with healthy adults showed conflicting results of FGM performance during exercise. One study with healthy adults showed a reduced accuracy of FGM during high intensity intermittent exercise, as indicated by high values in the clinically unsafe zone (i.e., 10.5% in zone D) after consuming a carbohydrate-rich meal [22]. Another study showed FGM underestimated plasma glucose concentrations during different walking conditions, with 99.6 to 100% of glucose values within the clinically acceptable zones A and B [21]. ...
... The findings of our study showed a low (i.e., better accuracy) MARD result of 8.8 ± 6.4% compared to the [CPG] reference values after the maximal exercise test. In contrast, a recent study with healthy adults reported reduced sensor accuracy (median ARD of 16.2% with high values in the clinically unsafe zone) when FGM was compared with a glucometer during high intensity intermittent (not maximal) exercise performed 2 h after consuming a carbohydrate-rich breakfast [22]. That said, studies examining the accuracy of FreeStyle Libre during different exercise intensities remain rare. ...
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This study's aim was to assess FreeStyle Libre Flash glucose monitoring (FGM) performance during an oral glucose tolerance test (OGTT) and treadmill exercise in healthy adolescents. This should advance the feasibility and utility of user-friendly technologies for metabolic assessments in adolescents. Seventeen healthy adolescents (nine girls aged 12.8 ± 0.9 years) performed an OGTT and submaximal and maximal treadmill exercise tests in a laboratory setting. The scanned interstitial fluid glucose concentration ([ISFG]) obtained by FGM was compared against finger-prick capillary plasma glucose concentration ([CPG]) at 0 (pre-OGTT), -15, -30, -60, -120 min post-OGTT, pre-, mid-, post- submaximal exercise, and pre- and post- maximal exercise. Overall mean absolute relative difference (MARD) was 13.1 ± 8.5%, and 68% (n = 113) of the paired glucose data met the ISO 15197:2013 criteria. For clinical accuracy, 84% and 16% of FGM readings were within zones A and B in the Consensus Error Grid (CEG), respectively, which met the ISO 15197:2013 criteria of having at least 99% of results within these zones. Scanned [ISFG] were statistically lower than [CPG] at 15 (-1.16 mmol∙L-1, p < 0.001) and 30 min (-0.74 mmol∙L-1, p = 0.041) post-OGTT. Yet, post-OGTT glycaemic responses assessed by total and incremental areas under the curve (AUCs) were not significantly different, with trivial to small effect sizes (p ≥ 0.084, d = 0.14-0.45). Further, [ISFGs] were not different from [CPGs] during submaximal and maximal exercise tests (interaction p ≥ 0.614). FGM can be a feasible alternative to reflect postprandial glycaemia (AUCs) in healthy adolescents who may not endure repeated finger pricks.
... Glucose sampling of the ISF, as opposed to direct blood glucose measurement in arterial or capillary supply, introduces variability into the assessment of glucose during exercise (Clavel et al., 2022). While currently available CGMs are reported to have a typical mean absolute relative difference (MARD) of under 10% compared to blood glucose measures at rest, the variability of CGM data during exercise is increased, with MARD ranging from 8.7% to 29.8% during high-intensity intervals and/or endurance exercise (Fabra et al., 2021). ...
... Recent investigations have suggested that CGM readings during exercise may be insufficiently accurate to warrant their use in non-diabetic athlete populations (Clavel et al., 2022). However, as CGM-use has been approved for glycemic management during exercise in Type 1 diabetes (Moser et al., 2020), it is reasonable that CGMs may provide important information to nondiabetic athletes so long as the constraints of CGMs are understood. ...
... A principal issue with CGM-use during exercise is that there is a lag between the glucose concentration in the blood and what is being recorded from the ISF (Moser et al., 2020). This discrepancy in blood versus ISF glucose is made worse when large glucose boluses are ingested (Cengiz & Tamborlane, 2009;Clavel et al., 2022) due to the speed and magnitude of change in blood glucose that is not reflected as rapidly in other tissue (Cengiz & Tamborlane, 2009). As such, it can be expected that during the periods of rapid change in blood glucose that occur during exercise, CGM readings will have the greatest discrepancies when compared to more traditional blood glucose measures. ...
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Wearing a continuous glucose monitoring (CGM) sensor on the arm may better reflect capillary glucose concentrations compared to wearing a sensor on the inner thigh at rest.With passive or active leg-muscle contractions, site-specific differences compared to capillary samples are attenuated; therefore, wearing a CGM sensor on the active-muscle during exercise may provide greater information to non-diabetic athletes regarding glucose flux at the active muscle.Discrepancies in CGM sensors worn at different sites likely primarily reflects differences in blood flow, as passive skin heating caused the largest magnitude difference between arm and leg sensor readings compared to the other experimental conditions (control, electric muscle stimulation, and cycling exercise).
... To our knowledge, to date, there has been only one study published comparing ISF and CB data systematically in subjects without diabetes investigating different protocols including the assessment of validity postprandially after different breakfasts, pre-, during and post-exercise (Clavel et al., 2022). In our study, systematic measurement difference shows a smaller mean bias under exercising compared to resting conditions (65/Glc: 4 mg/dL; 85/Glc: 2 mg/dL vs. HC_R/Glc: 20 mg/dL; LC_R/Glc: 20 mg/dL)., ...
... Within 30 minutes post-exercise, mean bias was 4.18 mg/dL (∆LOA [58.8 mg/dL]). Findings by (Clavel et al., 2022) were different from our findings, revealing the highest mean bias during exercising protocols and a similar ∆LAO between all conditions concluding that accuracy is negatively influenced by physical activity. However, we found a lower mean bias during exercise but a higher ∆LOA indicating a higher variability of ISF and CB glucose values. ...
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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 under different physical activity levels in a postprandial state in healthy and active subjects without diabetes. Ten healthy, active 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) resting fasted (R/Fast), b) resting after intake of 1 g glucose/kg BW (R/Glc) and c) running for 60 minutes at moderate (65/Glc) and d) high (85/Glc) intensity after intake of 1 g glucose/kg BW. Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to simultaneous capillary blood glucose concentrations. Pearson’s r correlation coefficient was highest for R/Glc (.92, p<.001) compared to R/Fast (.45, p<.001), 65/Glc (.60, p<.001) and 85/Glc (.70, p<.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (R/Fast: 8±6%, R/Glc: 17±12%, 65/Glc: 22 ± 24%, 85/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. However, further data in healthy subjects need to be gathered considering physical activity and nutrition status.
... A recent study using CGM also demonstrated that individual variability of postprandial glucose responses to identical meals was as large as responses to different meals in two nondiabetic cohorts [66], indicating that additional factors in combination with food intake affect the glucose response. In addition, a lower precision of CGM measurements during exercise has been reported in type I diabetes subjects [67] and in subjects with normal glucose regulation [68]. Different sensors [69], and sites for sensor placement have also been shown to affect intestinal glucose after a glucose load, with sensors placed on the leg consistently reporting lower values than sensors placed on the upper arm during rest and when blood flow was elevated by heat exposure [70]. ...
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Blood glucose regulation has been studied for well over a century as it is intimately related to metabolic health. Research in glucose transport and uptake has also been substantial within the field of exercise physiology as glucose delivery to the working muscles affects exercise capacity and athletic achievements. However, although exceptions exist, less focus has been on blood glucose as a parameter to optimize training and competition outcomes in athletes with normal glucose control. During the last years, measuring glucose has gained popularity within the sports community and successful endurance athletes have been seen with skin-mounted sensors for continuous glucose monitoring (CGM). The technique offers real-time recording of glucose concentrations in the interstitium, which is assumed to be equivalent to concentrations in the blood. Although continuous measurements of a parameter that is intimately connected to metabolism and health can seem appealing, there is no current consensus on how to interpret measurements within this context. Well-defined approaches to use glucose monitoring to improve endurance athletes’ performance and health are lacking. In several studies, blood glucose regulation in endurance athletes has been shown to differ from that in healthy controls. Furthermore, endurance athletes regularly perform demanding training sessions and can be exposed to high or low energy and/or carbohydrate availability, which can affect blood glucose levels and regulation. In this current opinion, we aim to discuss blood glucose regulation in endurance athletes and highlight the existing research on glucose monitoring for performance and health in this population.
... While there is no doubt that CGM devices are useful in non-exercise contexts, their utility during exercise per se remains to be clearly established. Indeed, CGM devices appear to have limited validity during exercise [185,186], and this may be due to the complex nature of blood glucose regulation during varying types and intensities of exercise. Blood glucose concentrations are a result of glucose uptake by the tissue and glucose appearance (i.e., liver glucose output and carbohydrate ingestion). ...
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The importance of carbohydrate as a fuel source for exercise and athletic performance is well established. Equally well developed are dietary carbohydrate intake guidelines for endurance athletes seeking to optimize their performance. This narrative review provides a contemporary perspective on research into the role of, and application of, carbohydrate in the diet of endurance athletes. The review discusses how recommendations could become increasingly refined and what future research would further our understanding of how to optimize dietary carbohydrate intake to positively impact endurance performance. High carbohydrate availability for prolonged intense exercise and competition performance remains a priority. Recent advances have been made on the recommended type and quantity of carbohydrates to be ingested before, during and after intense exercise bouts. Whilst reducing carbohydrate availability around selected exercise bouts to augment metabolic adaptations to training is now widely recommended, a contemporary view of the so-called train-low approach based on the totality of the current evidence suggests limited utility for enhancing performance benefits from training. Nonetheless, such studies have focused importance on periodizing carbohydrate intake based on, among other factors, the goal and demand of training or competition. This calls for a much more personalized approach to carbohydrate recommendations that could be further supported through future research and technological innovation (e.g., continuous glucose monitoring). Despite more than a century of investigations into carbohydrate nutrition, exercise metabolism and endurance performance, there are numerous new important discoveries, both from an applied and mechanistic perspective, on the horizon.
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Fysisk prestasjonsevne, særlig ved vedvarende eller periodevis høy intensitet, påvirkes negativt av lav karbohydrattilgjengelighet fra muskulatur, lever eller begge deler samtidig. Ved lav karbohydrattilgjengelighet vil glykogenkonsentrasjonen og blodglukosen være lav relativt til substratbehovet for treningsøkten eller konkurransen. Ernæringsretningslinjer for utholdenhetsutøvere legger derfor ofte vekt på tilstrekkelig karbohydratinntak og karbohydrattilgjengelighet før og under trening/konkurranse. Monitorering av glukosekonsentrasjon i blodet er en kjent metode som har eksistert i mange år og som er relevant for flere formål. Herunder å tilpasse bruk av insulin for diabetikere, lære seg hvordan blodsukkeret responderer på ulike typer og mengder mat, fysisk aktivitet, stress og søvn. Kontinuerlig blodglukosemonitorering (KGM) har de siste par årene imidlertid blitt innført hos en ny målgruppe, nemlig eliteutøvere innen særlig utholdenhetsidretter. Stadig flere profilerte idrettsutøvere fronter KGM som et nyttig verktøy. Hvorvidt teknologien er nøyaktig nok og har praktisk bruksverdi for idrettsutøvere diskuteres.
This study aimed to investigate the association between scan frequency and intermittently scanned continuous glucose monitoring (isCGM) metrics and to clarify the factors affecting scan frequency in adults with type 1 diabetes mellitus (T1D). We enrolled adults with T1D who used FreeStyle® Libre. Scan and self-monitoring of blood glucose (SMBG) frequency and CGM metrics from the past 90-day glucose data were collected. The receiver operating characteristic curve was plotted to obtain the optimal cutoff values of scan frequency for the target values of time in range (TIR), time above range (TAR), and time below range (TBR). The study was conducted on 211 adults with T1D (mean age, 50.9 ± 15.2 years; male, 40.8%; diabetes duration, 16.4 ± 11.9 years; duration of CGM use, 2.1 ± 1.0 years; and mean HbA1c, 7.6 ± 0.9%). The average scan frequency was 10.5 ± 3.3 scan/day. Scan frequency was positively correlated with TIR and negatively correlated with TAR, although it was not significantly correlated with TBR. Scan frequency was positively correlated with the hypoglycemia fear survey-behavior score, while it was negatively correlated with some glycemic variability metrics. Adult patients with T1D and good exercise habits had a higher scan frequency than those without exercise habits. The AUC for > 70% of the TIR was 0.653, with an optimal cutoff of 11 scan/day. In real-world conditions, frequent scans were linked to improved CGM metrics, including increased TIR, reduced TAR, and some glycemic variability metrics. Exercise habits and hypoglycemia fear-related behavior might affect scan frequency. Our findings could help healthcare professionals use isCGM to support adults with T1D. Clinical Trial Registry No. UMIN000039376.
Zusammenfassung TherapeutInnen, PatientInnen und SportlerInnen werden in verschiedenen Therapie- und Trainingsmaßnahmen vermehrt mit mobilen und tragbaren Technologien, Smartphone Apps oder sogenannten „point-of-care“ (POCT) Geräten zur Individualisierung von Trainingsprozessen konfrontiert. Obwohl die technologische Entwicklung schnell voranschreitet, können nicht alle verfügbaren Parameter bei (hoch-)intensiver Beanspruchung valide erfasst werden. TherapeutInnen und PatientInnen sollten nur Geräte verwenden, die für den jeweiligen Anwendungsfall und Population valide Daten liefern. Tragbare und mobile Technologien werden beständig weiterentwickelt, und so können heutzutage beispielsweise (bei richtiger Geräteauswahl) die Herzfrequenz am Handgelenk auch bei hochintensiver Beanspruchung sowie die Herzratenvariabilität (bzw. die Pulsratenvariabilität) am Handgelenk unter Ruhebedingungen valide erfasst werden. Mittlerweile können tragbare Technologien wie „Smartwatches“ die maximale Sauerstoffaufnahme ohne Ausbelastungstest erfassen. Deren Validität ist jedoch abhängig von der jeweiligen Leistungsfähigkeit, und diese Technologien scheinen auf individueller Ebene noch verbesserungswürdig. Zur Überprüfung der mittel- und langfristigen Effektivität eines (hochintensiven) Trainingsprozesses ist diese tragbare Technologie zum aktuellen Zeitpunkt nur bedingt anwendbar.
Background Continuous glucose monitor (CGM) systems were originally intended only for people with diabetes. Recently, there has been interest in monitoring glucose concentrations in a variety of other situations. As data accumulate to support the use of CGM systems in additional states unrelated to diabetes, the use of CGM systems is likely to increase accordingly. Methods PubMed and Google Scholar were searched for articles about the use of CGM in individuals without diabetes. Relevant articles that included sufficient details were queried to identify what cohorts of individuals were adopting CGM use and to define trends of use. Results Four clinical user cases were identified: (1) metabolic diseases related to diabetes with a primary dysregulation of the insulin-glucose axis, (2) metabolic diseases without a primary pathophysiologic derangement of the insulin-glucose axis, (3) health and wellness, and (4) elite athletics. Seven trends in the use of CGM systems in people without diabetes were idenfitied which pertained to both FDA-cleared medical grade products as well as anticipated future products, which may be regulated differently based on intended populations and indications for use. Conclusions Wearing a CGM has been used not only for diabetes, but with a goal of improving glucose patterns to avoid diabetes, improving mental or physical performance, and promoting motivate healthy behavioral changes. We expect that clinicians will become increasingly aware of (1) glycemic patterns from CGM tracings that predict an increased risk of diabetes, (2) specific metabolic glucotypes from CGM tracings that predict an increased risk of diabetes, and (3) new genetic and genomic biomarkers in the future.
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The primary variables influencing the adaptive response to a bout of endurance training are exercise duration and exercise intensity. However, altering the availability of nutrients before and during exercise can also impact the training response by modulating the exercise stimulus and/or the physiological and molecular responses to the exercise-induced perturbations. The purpose of this review is to highlight the current knowledge of the influence of pre-exercise nutrition ingestion on the metabolic, physiological, and performance responses to endurance training and suggest directions for future research. Acutely, carbohydrate ingestion reduces fat oxidation, but there is little evidence showing enhanced fat burning capacity following long-term fasted-state training. Performance is improved following pre-exercise carbohydrate ingestion for longer but not shorter duration exercise, while training-induced performance improvements following nutrition strategies that modulate carbohydrate availability vary based on the type of nutrition protocol used. Contrasting findings related to the influence of acute carbohydrate ingestion on mitochondrial signaling may be related to the amount of carbohydrate consumed and the intensity of exercise. This review can help to guide athletes, coaches, and nutritionists in personalizing pre-exercise nutrition strategies, and for designing research studies to further elucidate the role of nutrition in endurance training adaptations.
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Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called “glucotypes” that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.
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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.
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It is becoming increasingly clear that adaptations, initiated by exercise, can be amplified or reduced by nutrition. Various methods have been discussed to optimize training adaptations and some of these methods have been subject to extensive study. To date, most methods have focused on skeletal muscle, but it is important to note that training effects also include adaptations in other tissues (e.g., brain, vasculature), improvements in the absorptive capacity of the intestine, increases in tolerance to dehydration, and other effects that have received less attention in the literature. The purpose of this review is to define the concept of periodized nutrition (also referred to as nutritional training) and summarize the wide variety of methods available to athletes. The reader is referred to several other recent review articles that have discussed aspects of periodized nutrition in much more detail with primarily a focus on adaptations in the muscle. The purpose of this review is not to discuss the literature in great detail but to clearly define the concept and to give a complete overview of the methods available, with an emphasis on adaptations that are not in the muscle. Whilst there is good evidence for some methods, other proposed methods are mere theories that remain to be tested. ‘Periodized nutrition’ refers to the strategic combined use of exercise training and nutrition, or nutrition only, with the overall aim to obtain adaptations that support exercise performance. The term nutritional training is sometimes used to describe the same methods and these terms can be used interchangeably. In this review, an overview is given of some of the most common methods of periodized nutrition including ‘training low’ and ‘training high’, and training with low- and high-carbohydrate availability, respectively. ‘Training low’ in particular has received considerable attention and several variations of ‘train low’ have been proposed. ‘Training-low’ studies have generally shown beneficial effects in terms of signaling and transcription, but to date, few studies have been able to show any effects on performance. In addition to ‘train low’ and ‘train high’, methods have been developed to ‘train the gut’, train hypohydrated (to reduce the negative effects of dehydration), and train with various supplements that may increase the training adaptations longer term. Which of these methods should be used depends on the specific goals of the individual and there is no method (or diet) that will address all needs of an individual in all situations. Therefore, appropriate practical application lies in the optimal combination of different nutritional training methods. Some of these methods have already found their way into training practices of athletes, even though evidence for their efficacy is sometimes scarce at best. Many pragmatic questions remain unanswered and another goal of this review is to identify some of the remaining questions that may have great practical relevance and should be the focus of future research.
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Muscle glycogen levels have a profound impact on an athlete’s sporting performance, thus measurement is vital. Carbohydrate manipulation is a fundamental component in an athlete’s lifestyle and is a critical part of elite performance, since it can provide necessary training adaptations. This paper provides a critical review of the current invasive and non-invasive methods for measuring skeletal muscle glycogen levels. These include the gold standard muscle biopsy, histochemical analysis, magnetic resonance spectroscopy, and musculoskeletal high frequency ultrasound, as well as pursuing future application of electromagnetic sensors in the pursuit of portable non-invasive quantification of muscle glycogen. This paper will be of interest to researchers who wish to understand the current and most appropriate techniques in measuring skeletal muscle glycogen. This will have applications both in the lab and in the field by improving the accuracy of research protocols and following the physiological adaptations to exercise.
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Background: Continuous glucose monitoring (CGM) devices, with their 1-5 min measurement interval, allow blood glucose (BG) concentration dynamics to be captured more frequently and less invasively than traditional BG measures. One cohort CGM could provide insight is athletes. This study investigates what impact their heightened energy expenditure and dietary intake may have on their ability to achieve optimal BG. Methods: Ten subelite athletes (resting HR<60 bpm, training>6 hrs per week) were recruited. Two Ipro2 CGM devices (Medtronic Minimed, Northridge, CA) were inserted into the abdomen and remained in place for ~6 days. Time in band was calculated as the percentage of CGM BG measurements with in the 4.0-6.0 mmol/L. Fasting glucose was calculated using CGM calibration BG measurements and postprandial glucose response was also calculated using the CGM values. Results: 4/10 athletes studied spent more than 70% of the total monitoring time above 6.0 mmol/L even with the 2-hour period after meals is excluded. Fasting BG was also in the ADA defined prediabetes range for 3/10 athletes. Only 1 participant spent substantial time below 4.0 mmol/L which was largely due to significantly lower energy intake compared to recommendations. Conclusions: Contrary to expectations high BG appears to be more of a concern for athletes then low BG even in those with the highest energy expenditure and consuming below the recommended carbohydrate intake. This study warrants further investigation on the recommended diets and the BG of athletes to better determine the causes and impact of this hyperglycemia on overall athlete health.
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Objectives: Although the physical demands of Rugby League (RL) match-play are well-known, the fuel sources supporting energy-production are poorly understood. We therefore assessed muscle glycogen utilisation and plasma metabolite responses to RL match-play after a relatively high (HCHO) or relatively low CHO (LCHO) diet. Design: Sixteen (mean±SD age; 18±1 years, body-mass; 88±12kg, height 180±8cm) professional players completed a RL match after 36-h consuming a non-isocaloric high carbohydrate (n=8; 6gkgday(-1)) or low carbohydrate (n=8; 3gkgday(-1)) diet. Methods: Muscle biopsies and blood samples were obtained pre- and post-match, alongside external and internal loads quantified using Global Positioning System technology and heart rate, respectively. Data were analysed using effects sizes ±90% CI and magnitude-based inferences. Results: Differences in pre-match muscle glycogen between high and low carbohydrate conditions (449±51 and 444±81mmolkg(-1)d.w.) were unclear. High (243±43mmolkg(-1)d.w.) and low carbohydrate groups (298±130mmolkg(-1)d.w.) were most and very likely reduced post-match, respectively. For both groups, differences in pre-match NEFA and glycerol were unclear, with a most likely increase in NEFA and glycerol post-match. NEFA was likely lower in the high compared with low carbohydrate group post-match (0.95±0.39mmoll(-1) and 1.45±0.51mmoll(-1), respectively), whereas differences between the 2 groups for glycerol were unclear (98.1±33.6mmoll(-1) and 123.1±39.6mmoll(-1)) in the high and low carbohydrate groups, respectively. Conclusions: Professional RL players can utilise ∼40% of their muscle glycogen during a competitive match regardless of their carbohydrate consumption in the preceding 36-h.
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Abstract Background: Continuous glucose monitoring (CGM) may improve the management of patients with type 2 diabetes hospitalized in the general ward by facilitating the detection of hyper- and hypoglycemic episodes. However, the lack of data on the accuracy and safety of CGM have limited its application. Methods: A prospective pilot study was conducted including 38 patients hospitalized in the general ward with a known diagnosis of type 2 diabetes mellitus (DM) and hyperglycemic individuals without a history of DM with a blood sugar of 140- 400 mg on admission treated with a basal bolus insulin regimen. Inpatient glycemic control and the incidence of hypoglycemic episodes were compared between detection by CGM of interstitial fluid for up to 6 days and point-of-care (POC) capillary blood glucose monitoring performed pre- and postprandially, before bedtime and at 3 am. Results: No differences in average daily glucose levels were observed between CGM and POC (176.2 ± 33.9 vs 176.6 ± 33.7 mg/dl, P = .828). However, CGM detected a higher number of hypoglycemic episodes than POC (55 vs 12, P < .01). Glucose measurements were clinically valid, with 91.9% of patients falling within the Clarke error grid A and B zones. Conclusions: Our preliminary results indicate that the use of CGM in type 2 patients hospitalized in the general Ward provides accurate estimation of blood sugar levels and is more effective than POC for the detection of hypoglycemic episodes and asymptomatic hypoglycemia
Background: There is a need to assess the accuracy of continuous glucose monitoring (CGM) systems for several uses. Mean absolute relative difference (MARD) is the measure of choice for this. Unfortunately, it is frequently overlooked that MARD values computed with data acquired during clinical studies do not reflect the accuracy of the CGM system only, but are strongly influenced by the design of the study. Thus, published MARD values must be understood not as precise values but as indications with some uncertainty. Data and methods: Data from a recent clinical trial, Monte Carlo simulations, and assumptions about the error distribution of the reference measurements have been used to determine the confidence region of MARD as a function of the number and the accuracy of the reference measurements. Results: The uncertainty of the computed MARD values can be quantified by a newly introduced MARD reliability index (MRI), which independently mirrors the reliability of the evaluation. Thus MARD conveys information on the accuracy of the CGM system, while MRI conveys information on the uncertainty of the computed MARD values. Conclusions: MARD values from clinical studies should not be used blindly but the reliability of the evaluation should be considered as well. Furthermore, it should not be ignored that MARD does not take into account the key feature of CGM sensors, the frequency of the measurements. Additional metrics, such as precision absolute relative difference (PARD) should be used as well to obtain a better evaluation of the CGM performance for specific uses, for example, for artificial pancreas.
The physiological and performance effects of carbohydrate-electrolyte gels consumed before the 30min extra-time period of prolonged soccer-specific exercise were investigated. Randomised, double-blind, crossover. Eight English Premier League academy soccer players performed 120min of soccer-specific exercise on two occasions while consuming fluid-electrolyte beverages before exercise, at half-time and 90min. Carbohydrate-electrolyte (0.7±0.1gkg(-1) BM) or energy-free placebo gels were consumed ∼5min before extra-time. Blood samples were taken before exercise, at half-time and every 15min during exercise. Physical (15-m and 30-m sprint speed, 30-m sprint maintenance and countermovement jump height) and technical (soccer dribbling) performance was assessed throughout each trial. Carbohydrate-electrolyte gels improved dribbling precision (+29±20%) and raised blood glucose concentrations by 0.7±0.8mmoll(-1) during extra-time (both p<0.01). Supplementation did not affect sprint velocities (15m and 30m), 30-m sprint maintenance or dribbling speed as reductions compared to 0-15min values occurred at 105-120min irrespective of trial (all p<0.05). Plasma osmolality and blood sodium concentrations increased post-exercise vs. the opening 15min (p<0.05) but no effect of supplementation existed. Selected markers of physical performance (jump height, 30-m sprint velocity and 30-m repeated sprint maintenance) also reduced by >3% during half-time (all p<0.05). Carbohydrate-electrolyte gel ingestion raised blood glucose concentrations and improved dribbling performance during the extra-time period of simulated soccer match-play. Supplementation did not attenuate reductions in physical performance and hydration status that occurred during extra-time. Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.