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Exercise and blood sugar monitoring

Exercise and blood sugar monitoring

There are steps you can take to avoid Caloric intake recommendation. What Exercise and blood sugar monitoring Monitoriing Diabetes? Exercisse from day-to-day routine, monitoding is also important to carefully manage situations that can complicate blood sugar control, such as sick days and vacations. Books ShopDiabetes. Font Size Small Normal Large. Blood pressure readings: Why higher at home?

Exercise and blood sugar monitoring -

So, the research on CGM device use among sports enthusiasts without type 1 diabetes is limited. Athletes with type 1 diabetes have used CGM technology for years to help plan training, nutrition and make more critical decisions like how much insulin they need.

An athlete without type 1 diabetes may not need to make decisions about insulin, but they can still use this device to help them gain important information about their training and fueling strategies.

You likely know of fasting blood glucose since this is a pretty standard blood test. Blood glucose, also known as blood sugar, is a type of sugar that your body uses for various processes.

Glucose is also an important fuel source for your heart, kidneys, nerve cells, and white blood cells. Glucose comes from carbohydrate foods such as grains, starchy foods, milk products, fruits, and sugary foods such as candies and soda.

Many carbohydrate foods contain many glucose or sugar molecules linked together, which enzymes in your gut then have to unlink. Glucose is then absorbed and goes into the bloodstream. When you eat carbohydrates throughout the day, your body uses glucose as fuel.

The rest is stored as liver and muscle glycogen glucose storage. However, if you exceed the capacity of the glycogen deposits , the glucose remains in the blood until it can be stored as fat, which leads to high blood glucose levels.

Most people use carbs as the primary source of energy before their training. Still, they also consume carbs after the exercise to restore glycogen deposits. The number of carbohydrates people consume are ususally based on estimations of energy and macronutrient needs and subjective measurements of how they feel during and after the training session.

But a glucose monitoring system can help here. It allows them to make better decisions regarding their carbohydrate intake around training to maximize performance without compromising their health.

Glucose control was once something only people who already had diabetes needed to monitor. But since monitoring your glucose data and glucose trends can be an effective preventive health measure, tools to monitor and track your blood glucose are becoming increasingly popular. One of these tools is the CGM device, traditionally used by people with diabetes to avoid health complications and monitor their glucose levels.

The body uses the hormone insulin to allow glucose to move into the cells. If your body cannot produce or use insulin properly, you can experience high glucose levels hyperglycemia.

So, people with diabetes can use the CGM to monitor their glucose levels and make decisions regarding food or medication that help minimize the risk of high blood sugar. What exactly is a CGM device and what are the benefits of using a CGM device? While sensor lifetimes differ, they typically can be used for two weeks continually and measure glucose levels every one to fifteen minutes depending on the device.

This continuous recording provides valuable information on glucose trends and fluctuations. The app connected to the device shows blood glucose levels in real-time. The CGM device allows everyone, including sports enthusiasts, to make diet and activity choices to improve glucose levels.

Constantly high blood glucose levels among those with diabetes can increase the risk of stroke, dementia, and kidney and nerve failure.

However, evidence shows that elevated blood glucose even in someone without diabetes can lead to a higher risk of cancer, cardiovascular disease, inflammation, blood vessel damage, and diabetes. Many runners and swimmers consume a higher carbohydrate diet to support their training.

But for some people, this can lead to higher blood sugar levels. In addition, the intensity or duration of their exercise can cause glucose to rise or fall during training.

Seeing the changes in glucose can help provide valuable information about what they should be eating pre, during, and post-exercise. For some people who experience a release of adrenaline during training or racing, it can cause a significant increase in glucose or hyperglycemia. Other considerations for a larger glucose spike during training could be due to the meal consumed prior to exercise or the release of glucose due to rapid glycogen breakdown during higher intensity training.

It is possible that several of these factors may be involved. This may be another good reason to monitor blood glucose levels. Many people in endurance sports follow a strict diet with high levels of carbohydrates that are meant to refill glycogen stores and improve energy levels and performance.

In some cases, this can positively impact their lifestyle. Unfortunately, there are cases in which the athlete can have a low tolerance for carbs, leading to significantly higher sugar levels in the blood.

High - intensity physical training can release hormones , including adrenaline epinephrine , causing a rise in blood sugar. However, if you have a history of high sugar levels, adrenaline can cause glucose levels to increase to very high levels, leading to an increased risk of cardiovascular disease.

Glucose spikes above can cause vascular damage so it is advised to keep glucose levels below during exercise to avoid any risk.

Keeping track of glucose levels can improve the quality of life for a fitness enthusiast. Having a positive impact on performance and energy levels is just one of the many benefits of glucose monitoring through a CGM. According to sports medicine researcher Dr. Andrew Jagim :. Endurance athletes may also be training for long periods, often hours at a time.

If they do not fuel appropriately before and during training, they can risk low blood sugar. They can even experience hypoglycemia , in which their glucose levels can become too low which may lead to bonking or feeling a noticeable drop in energy levels and decrease in performance.

Using the CGM, an athlete can monitor for dips in glucose or decreases in glucose during training and plan their fueling strategy accordingly.

CGMs can also help people with glycemic variability. Glycemic variability is a metric used to assess glucose fluctuations and, in most cases, will improve with exercise. However, if they're are engaging in a high-intensity workout in which their glucose is going very high or long-duration low-intensity exercise in which their glucose may be going low, they may see an increase in their glycemic variability.

A greater degree of glycemic variability is associated with an increased risk of oxidative stress and endothelial dysfunction cells that line our blood vessels. Glucose monitoring can help maximize athletic performance for various reasons. Before CGM technology, if an athlete wanted to measure blood glucose , they would have to use a finger-prick device glucometer.

It allows you to measure glucose at one point and involves pricking the finger and then measuring the glucose in a drop of blood. The CGM, on the other hand, allows for continuous glucose measurement, is less invasive, and allows them to measure glucose during training without having to stop and take measurements.

Some CGM devices are connected to Bluetooth, allowing the data to auto-populate. Others need to be scanned for the data to populate in the app.

The device itself usually holds about eight-hours worth of data. In addition, there is evidence that CGM devices are accurate during intense training.

Although there is limited research at this point, there is some evidence that the CGM can help increase performance and recovery.

Jagim continues, "I think it's really important to approach a lot of sports nutrition guidance at an individual level. It's making sure that a specific person, wherever they are on that spectrum of insulin sensitivity, is getting the nutritional advice that works for them based on their health history and unique response.

Like anything, a CGM can be a valuable tool for some athletes , but not for all. It enables them to make diet and training decisions to help improve their performance and minimize any health risks associated with high or low blood sugar.

With the help of a CGM device, a registered dietitian or nutritionist can plan a specific diet that meets all of the fitness enthusiast's energy needs. There are many situations in which a person will require the help of a licensed nutritionist, especially if it involves CGMs. Consider someone who uses a CGM and notices a considerable rise in blood glucose from low-intensity activities like biking, running, swimming, or yoga.

In this case, they may benefit from minimizing their carbohydrate intake before exercising. In the case of low-intensity exercise, blood sugar levels will not typically rise. In fact, someone who is training should notice the opposite in which blood sugar levels should decrease or remain steady as the body uses glucose and fat for energy.

During low-intensity or zone 2 cardio approximately 60 percent or less of VO2 Max, or maximal oxygen consumption most people will use more fat for fuel. As intensity levels rise there is a crossover in which the body starts to utilize more glucose and less fat. This is why the person may notice their glucose values remain steady or decline slowly during this zone 2 training depending on if they take in fuel during training.

The fitness enthusiast can use the information they get from the CGM to make changes to their fueling strategy with the goal to prevent bonking and improve their metabolic flexibility.

A slight initial rise in glucose at the start of low-intensity activity may be okay, but a significant increase in glucose may indicate that they're consuming too many carbohydrates before exercise. If a person sees a large spike during strength training or high-intensity interval training, it may indicate they're not getting enough fuel to support the movement.

So the body responds by increasing hormones that release stored energy from the liver. In this case, they may benefit from having a well-balanced meal two to three hours before the exercise.

Another example is if someone is experiencing extreme drops in glucose during a more extended training session like running or biking. It may be a sign they are not fueling properly before and during the workout. In this case, a typical recommendation would be having a balanced meal with carbohydrate, protein, and small amounts of fat macro amounts will differ depending on the person about two to three hours prior and more consistent fueling during the activity with a specific target for carbohydrates per hour of training.

If someone sees a large spike in glucose despite fueling properly before exercising, it might be a sign of dehydration or a need for additional electrolytes. In this case, they would need to work on adequate hydration and electrolyte intake before, during, and after training. If a low-carb fitness enthusiast is experiencing extreme drops in glucose accompanied by low energy or poor performance, they can work on a fat adaptation and targeted carbohydrate intake plan that works within their goals.

Sometimes, people may experience drops in blood sugar levels during moderate-intensity activities such as soccer, hockey, or lacrosse, combined with low energy levels. Studies were not eligible if data were combined for people with and without diabetes, or with people above and below 18 years of age.

Interventions that encouraged participants to become more active without providing structured prescriptions or monitoring e.

Since developing this criterion for our previous meta-analysis 5 several studies examined the effects of breaking up sedentary time with exercise. These studies were not included to facilitate comparisons with our previous meta-analysis and because they often involved restricting activities during the control condition e.

In such studies, it was unclear if differences between conditions were due to the activity itself or the impact of prolonged sitting in the control condition. Both randomized and non-randomized e.

post comparisons were eligible, as were trials that employed parallel or crossover designs. Studies comparing combined exercise and dietary interventions to a control condition not receiving the dietary intervention were not eligible.

Mean h glucose was considered the primary outcome of interest. Two reviewers extracted the following CGM outcomes in duplicate: mean h glucose, time in hyperglycemia, time in hypoglycemia, time in range, post-prandial glucose, fasting glucose, nocturnal glucose, and glucose variability.

Recent international consensus statements 6 suggest values of 3. Indicators of glucose variability included mean amplitude of glucose excursions MAGE , continuous overall net glycemic action CONGA , or standard deviation SD.

Participant characteristics and details of the interventions were extracted by a single reviewer and verified by a second reviewer. Participant characteristics included age, sex, body mass index BMI , duration of diabetes, menopausal status, the type of CGM, and type of glucose lowering medication they were treated with, and A1C.

Characteristics of the exercise intervention included the type of exercise, the frequency and duration of exercise sessions, as well as the intensity. We noted if meals were provided as a means of standardizing diet between the exercise and control conditions and categorized groups into: all meals provided, meals partially provided, or no meals provided.

The timing of exercise in relation to meals was categorized as fasting, after breakfast, afternoon i. Several data transformations were made before combining data from trials. Since CGM measures are provided in constant time intervals e.

The percent time in hyper- or hypoglycemia was transformed into minutes by multiplying the percentage by the total amount of time. Based on our previous meta-analysis 5 , we expected participants in the short-term studies to complete both the exercise and control conditions e.

The primary analyses for these studies were based on the within-person difference in glucose concentrations. In instances where the SD or standard error SE of the change was not reported, it was estimated from p -values as described in section 7.

In cases where information was displayed in a figure, mean difference and SD was estimated using plot digitizer software Plot Digitizer Version 2. In infrequent cases, we were unable to estimate the SE of the change from any of the above methods. In such cases, we used the correlation coefficient between exercise and control values that we calculated from other studies to estimate the SE of the change as described in section Two authors independently performed risk of bias assessment.

Risk of bias was assessed using a domain-based evaluation, in which seven specific domains were addressed: 1 sequence generation, 2 allocation concealment, 3 blinding of participants and personnel, 4 blinding of outcome assessment, 5 incomplete outcome data, 6 selective outcome reporting, and 7 other bias.

These criteria had been updated since our previous review 5. Statistical analyses were performed using Review Manager Software Revman 5. For all shorter-term studies, participants completed both conditions crossover trials or pre- and post-designs.

For these trials, the mean difference MD and the within participant SE of this difference were pooled using the generic inverse variance method to calculate a weighted mean difference WMD. For the longer-term trials that randomly assigned participants to either exercise vs. control conditions, the primary analyses considered mean differences between conditions which was pooled using a random effects model.

When a control condition was compared to multiple exercise conditions, the sample size of the control condition was divided by the number of comparisons.

Three of the five longer-term trials did not include a control condition. Therefore, secondary analysis compared pre- vs. post-exercise data from all longer-term trials using the generic inverse variance method.

Heterogeneity was examined through the chi-square test and also presented using the I 2 statistic, which describes the percentage of the variability that is due to heterogeneity rather than chance 7. As in previous meta-analyses 5 , 8 , subgroups were pre-defined according to type of exercise i.

high-intensity interval training, vs. As suggested in the study by Rees et al. Lastly factors such as the type of CGM real time vs. blinded vs. intermittently scanned and the type of glucose lowering medications taken by participants were added during the review process.

Meta-regression analyses included the proportion of participants who were female, A1C, and glucose concentrations from the control condition as predictors. For all the short-term studies, the same participants completed the control and exercise conditions.

The literature search retrieved records see PRISMA Trial Flow diagram in Figure 1. After duplicates were removed, records were reviewed. Fifty-four full text articles were screened and 26 were excluded for the following reasons:.

Studies that were not exclusively conducted in adults with T2D 10 — 14 were excluded. Of these, the study by Newton and White 12 was included in our first meta-analyses, but excluded this time because the age range was from 14 to 20 years old.

Studies were excluded when they had co-interventions, such as changes in medication or insulin 15 , 16 , which influenced the changes caused by exercise.

There was also one study in three records 17 — 19 examining the effect of Yoga, but it was excluded since we were unable to extract sufficient detail on the structure of exercise component, or control for any effect of the breathing exercise or meditation components of the intervention.

Two studies examined the effect of breaking up sedentary time 20 , 21 with several short bouts of activity. The control condition in these studies involved restricting movement by sitting from 8 to 14 h 20 , It therefore became difficult to know how much of the difference between the activity and control conditions was due to the physical activity itself or the prolonged sedentary behavior, which was likely greater than in free-living conditions.

The study by Blankenship et al. However, the control condition asked participants to maintain their habitual physical activity behavior and we therefore chose to include the control vs. walking comparison. Studies that did not include a non-exercise control condition were excluded 23 — Of these trials, the one by Bacchi et al.

We excluded it in the present analysis because the control condition started 24 h after the exercise condition and we could not rule out that the effect of exercise did not persist beyond 24 h.

In the study by Godkin et al. We only included the effects of the first session of exercise since this was more comparable to the other included studies.

Some studies did not have usable CGM data 31 , 32 or presented data which was available from the same population as another included study 33 — For example, Little et al. These articles were different in that Gillen et al.

examined participants after one bout of exercise while Little et al. examined participants after six bouts of exercise. Another difference was that Little et al. assessed glycemic control starting ~48 h after the last training bout; a period that was inconsistent with the rest of the short-term studies.

To favor homogeneity among studies only the results from Gillen et al. was included. The study from Savikj et al. Table 1 includes characteristics of the 23 eligible short-term studies. A total of participants were included. The majority of these participants were males males vs.

Many of the studies included multiple exercise groups for a total of 40 exercise groups. There were a variety of exercise prescriptions, with studies prescribing low, moderate, and high-intensity aerobic exercise, including different forms of high-intensity interval training HIIT.

The timing in relation to meals varied among studies but was reported in all but 2 studies. Eleven studies provided all of the meals to the participants throughout the h period, 6 studies provided some meals but not all, and 6 studies did not provide any meals.

In the studies that did not provide meals, or partially provided meals, participants were often asked to maintain similar dietary intakes across conditions.

Of the 23 short-term studies, one study used an intermittently scanned CGM Freestyle Libre, Abbott. Three studies used the Guardian or MiniMed Medtronic CGM which provided real-time data to participants.

Five studies used GlucoDay S A. Menarini Diagnostics CGM, which has the capability of showing real time glucose concentrations but was likely blinded.

An additional 12 studies used iPro Medtronic CGM technology, which are blinded to participants and researchers until the data is download after removal of the sensor. An additional three studies did not specify the type of Medtronic CGM but provided enough detail to suggest that the data were also examined retrospective and not available in real-time.

Twenty of the 23 short-term studies provided some information on the type of medication. Menopausal status was reported in 6 of the 15 short-term studies that included women. In these 6 studies, almost all participants were postmenopausal a total of only 3 women were not.

Table 2 describes the five eligible longer-term studies. Interventions ranged from 8 to 16 weeks in duration. A total of 99 participants 57 males and 42 females were included in 9 different exercise interventions, but only 15 participants in two separate control groups 60 , Francois et al.

Consequently, we only included the HIIT group that received the flavored water placebo from Francois et al. Of the five longer-term studies, two used the blinded iPro CGM, two used the Guardian CGM and one used a MiniMed system that also included a portable monitor all from Medtronic.

Among the 23 short-term studies, 22 reported h glucose concentrations. Several studies had multiple exercise conditions, which led to a total of 39 exercise groups included in the overall analyses.

Compared to control, exercise reduced h glucose concentrations by 0. Figure 2. CI, confidence interval; SE, standard error; 1RM, one repetition maximum; HIIT, high-intensity interval training; REHIT, reduced exertion high intensity interval training.

Due to the significant heterogeneity among studies, analysis was performed by dividing studies into subgroups according to the timing of exercise, type of exercise, dietary control, and type of CGM see Table 3. Table 3. Meta-regression was performed to predict changes in h glucose concentrations following exercise with other variables such as h glucose concentrations in the control condition, baseline A1C, age, BMI, or the percentage of female participants.

Note that the same participants completed both the control and exercise conditions i. Figure 3. Meta-regression to predict changes in mean h glucose concentrations following exercise according to: A mean h glucose concentrations in the control condition, and B percentage of females.

Change in secondary glycemic outcomes are summarized in Table 4. Time spent in hyperglycemia was analyzed from 16 studies, which included 30 exercise vs. control comparisons. The subgroup differences reflected the findings from the h glucose concentrations but are not presented as some of the subgroups were much smaller e.

Indices of glycemic variability were reported in 11 studies with a total of 18 subgroups. Many different measures e. MAGE was the most frequently reported index of glycemia variability and was available in all but two subgroups.

On the other hand, fasting glucose and time in hypoglycemia were not significantly affected by exercise. Four of the studies started post-training CGM measures 48—72 h after the last bout of exercise and described the post-intervention measurements within 1 week of the last bout of exercise.

However, only 4 exercise conditions were included in this exercise vs. control comparison with a total of 49 participants in the exercise groups and 15 in the control groups.

Figure 4. A Exercise vs. control pre-intervention, B exercise vs. control post-intervention. CI, confidence interval; SE, standard error; 1RM, one repetition maximum; HIIT, high-intensity interval training. Secondary analysis of the pre- and post-exercise comparisons resulted in the inclusion of 9 longer-term exercise conditions with a total of participants.

Subgroup analyses, regression analyses, and examination of other outcomes were not performed due to the low number of available comparisons. Figure 5. Summaries according to the Cochrane Collaboration Risk of Bias tool are provided in Supplementary Figures 1 , 2 for short and longer-term studies, respectively.

As expected in exercise trials, blinding of participants to the exercise intervention is not feasible. Funnel plots were also generated to examine the potential for publication bias.

For the primary outcome of mean h glucose concentrations, funnel plots are provided in Supplementary Figures 3 , 4 for short and longer-term studies, respectively. Visual inspection of the funnel plots did not reveal any asymmetries, with the exception of the outlier from Cruz et al.

However, this group also had average size SE, which would not be expected in a typical publication bias scenario where studies with the largest SE tend to show more beneficial effects. The present systematic review and meta-analyses confirms our previous findings that exercise reduces mean h glucose and time spent in hyperglycemia 5 , but also builds on this work in several ways:.

The number of eligible short-term studies reporting the effects of exercise on CGM outcomes in T2D has approximately tripled from 8 to 23 studies; or from to participants. The greater number of short-term studies allowed for hypothesis generating subgroup and meta-regression analyses, which helped explain the heterogeneous responses among trials e.

There were a sufficient number of trials to include outcomes that were not previously considered; including glycemic variability in short-term studies and mean h glucose in longer-term studies. The improvement in mean h glucose concentrations following short-term exercise was 0. The differences may be due to the higher variability among trials in our current review as reflected in the higher I 2 -value i.

It is possible that this would have reached statistical significance had fasting glucose been reported in more short-term studies. Nonetheless, it may be that exercise has a greater impact on postprandial glucose, which is more strongly linked to muscle insulin resistance, whereas fasting glucose is believed to be more strongly associated with hepatic insulin resistance 63 , Longer-term studies have shown reductions in fasting glucose with exercise 65 , but it is difficult to know to what extent this was due to weight loss.

To better understand the heterogeneity among short-term trials, we conducted a series of subgroup meta-regression analyses. It is important to note that since participants were not randomly assigned to the subgroups, we cannot determine if it was a causal relationship.

In addition, some variables in our subgroup and meta-regression analyses were not pre-specified. Consequently, results from our subgroup analyses should be interpreted with caution and confirmed by randomized trials. In our meta-regression analyses, the strongest predictor of greater improvements in glycemic control was the mean h glucose concentrations from the control condition, suggesting that participants with elevated glucose concentrations had greater reductions following exercise.

Although this may seem intuitive, it is potentially affected by a regression to the mean artifact [as previously reviewed by Sheppard 66 ]. Sex, but not age or BMI, was associated with changes in mean h glucose. Studies that had a higher proportion of males were associated with greater reductions in mean h glucose.

Our meta-analysis does not permit us to identify the reasons why males may have responded more favorably compared to females. However, a greater effect of exercise on insulin sensitivity 67 and post-exercise glucose metabolism 68 has been previously observed in males compared to females.

The reasons for these differences are not well-known, but may be related to differences in substrate oxidation during exercise and recovery Of note, only 3 women were not postmenopausal among the 6 studies that reported menopausal status. Consequently, it is possible that the results are not generalizable to women before menopause.

However, we cannot rule out that the association with sex was caused by other confounders and we noted very high heterogeneity among the studies that only included males see left side of Figure 3B. The association observed between the proportion of females and changes in mean h glucose following exercise was only observed after removing of a potential outlier.

Indeed, the study by Cruz et al. They compared a single bout of exercise performed at 80 vs. Resistance training was performed with a circuit in which each exercise was performed 3 times.

To put this in perspective, this reduction is more than 5 times as much as the mean reduction in our meta-analyses and nearly twice as much as the next largest reduction among the 39 exercise conditions. The authors suggest that the greater counterregulatory hormone responses with the greater resistance exercise intensity may have contributed to the differences between conditions.

It is also noteworthy that the participants in the Cruz et al. study were also the ones with the highest mean h glucose during the control condition and therefore had the potential for greater reductions without experiencing hypoglycemia. The timing of exercise was associated with some of the variance among short-term studies.

Again, in our subgroup analyses, most participants were not randomly assigned to different exercise timing and therefore causality cannot be inferred. However, five studies directly compared two similar amounts of exercise performed at different times of the day 39 , 43 , 54 , 56 , The results from Savikj et al.

However, this study involved HIIT training whereas most of the studies in our meta-analyses did not. They also offered a snack after morning exercise only. If changes in the timing of exercise can be found to consistently affect glycemic responses, this could be encouraging for people with T2D who could use such strategies to get more benefits from the same amount of exercise.

The decision to perform subgroup analyses based on exercise timing in relation to meals was a priori as a consequence of our findings in the study by Rees et al.

However, we were unsure of the exact subgroups that would be available e. and divided our subgroups in a way to have multiple studies in each subgroup. The reasons why fasting i. One potential explanation could be that, in the absence of exogenous fuels, fasting exercise must rely to a greater extent on endogenous fuels e.

The first two longer-term training studies comparing fasting exercise to postprandial exercise in T2D have been recently published 71 , These longer-terms studies did not support a more favorable effect of fasting exercise compared to postprandial exercise. However, the postprandial exercise was performed shortly after breakfast not in the afternoon in both of these studies 71 , It is currently difficult to understand to what extent the effects of fasting exercise are due to fasting itself or to the time of day i.

To further complicate matters, in people with T2D, many glucose lowering medications are taken with meals and we found an association with the use of sulfonylurea within a study in changes in hr glucose following exercise vs.

control, but not for other categories of medication. Interpretation of differences among subgroups is based on comparing results from different exercise conditions that did not benefit from randomization, therefore subgroup comparisons may be affected by several confounding variables and should be confirmed by randomized trials.

Several studies included in our meta-analysis did directly compare the effect of different exercise intensities. Some compared continuous exercise to different forms of higher intensity interval training 45 , 49 , 54 , 73 , one compared low vs.

moderate intensity continuous exercise 48 , and one compared different intensities of resistance exercise As in the subgroup analyses from our meta-analysis, no clear pattern emerged when examining these studies individually.

However, a previous meta-analysis of longer-term studies with head-to-head comparison of exercise of different intensities suggested that higher intensity exercise led to greater declines A1C 8. Another difference was that the trials in the earlier meta-analysis had similar or greater energy expenditures in the high intensity groups compared to the lower intensity groups from the same trial.

Likewise, the aerobic vs. resistance training comparison in the short-term trials may not reflect longer term adaptations. The mechanisms leading to improvements in glycemic control following continuous aerobic, HIIT and resistance training may be different, and are beyond the scope of our meta-analysis.

Methodological aspects unrelated to exercise, such as the type of CGM real-time vs. intermittently scanned as well as the level of dietary control i. However, the absence of significant subgroup differences may be due to the presence of other confounders as there was high heterogeneity within many different subgroups.

The type of CGM or the degree of dietary control may influence compensatory behaviors from participants e. Glycemic variability may be independently associated with cardiovascular disease When examining the change across all short-term studies, we observed a consistent and statistically significant reduction in MAGE.

There were several indices of glycemic variability. Although these indices differ in their calculations, they were highly related to each other. For example, correlation coefficients were all above 0. There were fewer longer-term studies identified and only two with randomization to a non-exercise control condition.

The pre- vs. post-analyses led to different conclusions than the randomized exercise vs. control comparison.

post-comparison had a smaller mean difference but reached statistical significance, in part due to the greater number of participants but also because of the increased statistical power within participant analyses.

Interestingly, the weighted mean difference in the pre- and post-analyses was similar to the weighted mean difference found in the acute studies i.

Based on conversions between A1C and estimated average glucose 76 , such a reductions could correspond to a 0. This is not surprising given that the post-training CGM measures typically started at least h after the last bout of exercise to minimize the acute effect from this last bout.

Therefore, we would expect the weekly average glucose to be lower in these participants who prescribed exercise three times per week or more. Weight loss in longer-term exercise trials may mediate some of the improvements in glycemic control.

Consequently, we believe that most of the changes were observed in the absence of meaningful weight loss. The main limitation of this meta-analysis is the high heterogeneity among the shorter-term studies and that we were only partially successful at explaining the heterogeneity.

Consequently, interpreting the overall effects should be done with caution. Based on our findings, it is unlikely that exercise increases blood glucose; it is more likely that the heterogeneity is in the degree of the positive to no effects.

The apparent heterogeneity may in fact be in part a result of the analytical approach that we chose. Indeed, the within participant mean change and SE used in the generic inverse method approach, leads to much narrower confidence internals than if we compared the mean glucose from the exercise vs.

control using the between participant standard deviation in each condition. When the latter approach is used, the weighted mean difference remained similar 0. The heterogeneity may also be caused my methodological issues. Several CGM devices require multiple calibrations per day and errors in calibration values can have a meaningful impact on h outcomes.

In addition, investigators often have to make difficult decisions on how to treat missing CGM values. Lastly, another limitation is the low number of longer-term studies and we would caution against inferring that chronic exercise training no more effective than shorter-term exercise due to the timing of the CGM measures in the longer-term studies.

In conclusion, both short-term and long-term exercise can reduce mean h glucose concentrations. Short-term exercise also reduces other CGM-derived outcomes such as glycemic variability, while additional longer-term studies are needed to examine such outcomes.

The glycemic response to short-term exercise can be variable, and exploratory analyses suggests that the heterogeneity among studies might in part be explained by the extent to which glycaemia is impaired on non-exercise days, or factors such as the timing of exercise and the sex of participants.

MM, CO, AM-C, and JR contributed to data extraction. MM, CO, and NB performed the statistical analysis and wrote sections of the manuscript.

All authors contributed to the conception and design of the study, manuscript revision, read, and approved the submitted version.

This research was performed without financial support. MM was supported by graduate student stipends from the Faculty of Kinesiology, Sport, and Recreation at the University of Alberta. AM-C was supported by the Fonds de recherche du Québec — Santé. NB has received continuous glucose monitors from Medtronic Canada for previous studies.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We would like to thank Linda Slater who helped us develop previous search strategies from which the current one was produced.

We also thank Dominic Tremblay who assisted AM-C with data extraction. Boulé NG, Haddad E, Kenny GP, Wells GA, Sigal RJ. Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: a meta-analysis of controlled clinical trials. doi: PubMed Abstract CrossRef Full Text Google Scholar.

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Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. Suh S, Kim JH. Glycemic variability: how do we measure it and why is it important? Diabet Metabol J. MacLeod SF, Terada T, Chahal BS, Boulé NG.

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Bblood R. ColbergRonald J. SigalJane E. YardleyMichael C. RiddellDavid W. Objective: To examine the usgar and chronic effects of structured exercise on glucose outcomes assessed by continuous glucose bkood in adults with type minitoring diabetes. Holistic approach PubMed, Mmonitoring, Exercise and blood sugar monitoring were searched Exercise and blood sugar monitoring to Monitorimg to identify studies prescribing structured exercise interventions with continuous glucose monitoring outcomes in adults with type 2 diabetes. Randomized controlled trials, crossover trials, and studies with pre- and post-designs were eligible. Results: A total of 28 studies were included. Of these, 23 studies were short-term exercise interventions. For all short-term studies, the same participants completed a control condition as well as at least one exercise condition. Exercise and blood sugar monitoring

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