Continuous glucose monitoring (CGM), a minimally invasive method of continuously measuring interstitial glucose levels to gain insight into their patterns and trends, has been increasingly adopted as a novel and feasible tool to improve glycemic control in individuals with diabetes and those with poor glucose control. Compared with conventional glucose monitoring, CGM provides much greater insight into glucose levels throughout the day (Klonoff, 2005). As it is now widely used in research, CGM allows for opportunities to continuously examine glycemic control and postprandial glucose (PPG), as well as glucose responses to exercise and meals (Macleod et al., 2013). While CGM has been widely evaluated as a reliable tool for use during resting conditions among individuals with diabetes, the CGM accuracy while exerting effort during common activities such as walking remains controversial (Baek et al., 2010; Biagi et al., 2018; Heden et al., 2018; Moser et al., 2019). Previous studies (Bally et al., 2016; Guillot et al., 2020) and a recent review (Houlder and Yardley, 2018) revealed good and comparable accuracy of the CGM system during different types of exercise, suggesting that current CGM sensors perform at an adequate level to reflect changes in plasma glucose (PG) during exercise in patients with type 1 diabetes mellitus (T1DM); however, PG is still underestimated in CGM readings. A more recent meta-analysis (Fabra et al., 2021) compared the mean absolute relative difference (MARD) during exercise and rest, showing that exercise negatively affects CGM accuracy. Some studies have reported poor performance during rapid glucose changes after a meal (Yan et al., 2020) or during exercise (Biagi et al., 2018; Heden et al., 2018; Moser et al., 2019). One possible reason for this underestimation is the time delay between blood and interstitial fluid compartments. Exercise significantly increases this time delay (Castle and Rodbard, 2019), likely, in part, by stimulating endogenous glucose production (Basu et al., 2013, 2014). Moreover, exercise impacts volume and fluid distribution within the interstitial compartment, contributing to the creation of gradients between the interstitial and blood glucose levels (Moser et al., 2018). However, as few studies have simultaneously reported on CGM accuracy for both exercise and rest periods, it was difficult to examine the influence of exercise on CGM accuracy (Fabra et al., 2021). Therefore, additional studies are warranted to characterize how well current CGM systems perform during exercise (Castle and Rodbard, 2019) and to provide further insight into their accuracy during exercise periods. Notably, glycemic responses to exercise vary according to multiple factors, including exercise timing, intensity, and duration (Riddell et al., 2017). Therefore, additional studies are needed to better explore the performance of CGM systems under different exercise conditions (Forlenza et al., 2019). A better understanding of CGM accuracy under exercise conditions may provide evidence to develop CGM algorithms that may be able to compensate for mitigating its impact (Laguna Sanz et al., 2019).

Continuous glucose monitoring has been widely used in many studies on individuals with diabetes; recently, it has also been increasingly used in studies on healthy volunteers (Basu et al., 2013; DiPietro et al., 2013; Wijsman et al., 2013; Parker et al., 2017; Shah et al., 2019; DuBose et al., 2021). For research purposes, CGM is portable, easy to use, and pain-free, allowing researchers to continuously monitor interstitial glucose levels in daily life. Evaluating CGM performance in healthy populations without diabetes is essential for the interpretation of CGM metrics, as well as for its wider use in research with this population (DuBose et al., 2021). However, limited information exists on CGM-measured glucose concentrations in individuals without diabetes (DuBose et al., 2021), and the validity of CGM-estimated glycemia has not been well-established in this population (Akintola et al., 2015). Although a recent multicenter study (Shah et al., 2019) reported on CGM profiles of healthy participants, a comparison between CGM and blood glucose readings is lacking. Additionally, overweight/obesity is associated with impaired glucose metabolism (Power and Thomas, 2011). A recent study reported that body mass index (BMI) may be associated with CGM accuracy, with CGM underestimating PG in overweight/obese youth without diabetes and overestimating it in those with a lower BMI (Ghane et al., 2019). However, little is known regarding the CGM accuracy during postprandial rest and under different exercise conditions in adults without diabetes who are overweight or obese.

Although a previous study evaluated the accuracy of CGM in healthy populations without diabetes, it did not explore CGM performance following a meal or under different exercise conditions (Akintola et al., 2015). The FreeStyle Libre^TM 14-day Flash Glucose Monitoring (FGM) system (FGM, Abbott, Chicago, IL, United States) is a variety of CGM in which the data are available on demand at any time for 14 days; it requires no calibration during its use. The purpose of this study was to evaluate the accuracy of FGM relevant to PG measurements during postmeal rest and different walking conditions in overweight or obese young adults. We hypothesized that FGM accuracy would be clinically acceptable relevant to PG measurements during postmeal rest and walking conditions in this population.

This work is a secondary analysis of two completed studies, both of which are registered at the Chinese Clinical Trial Registry [ChiCTR1900023175 (Zhang et al., 2021); ChiCTR2000035064] and approved by the Joint Chinese University of Hong Kong New Territories East Cluster Clinical Research Ethics Committee (CREC Ref. No.: 2019.285; 2020.434). Written informed consent was obtained from all participants prior to their participation in the study.

This work was the first comparison of techniques for FGM vs. PG monitoring under postprandial rest and different walking conditions among overweight or obese adults. Although FGM readings were significantly lower than those of PG during both postmeal sitting and walking in overweight or obese adults, a strong correlation was observed between FGM and PG, indicating that FGM could depict patterns of PG change. Furthermore, the MARD was higher when glucose rapidly increased after a meal or walking The FGM sensor performed well, showing stability when the glucose level was stable; timing and patterns of walking did not affect sensor performance. Moreover, the overall FGM performance was clinically acceptable, as shown through Clarke error grid analysis and the Bland–Altman method.

Continuous glucose monitoring readings have been reported to be lower than PG (Yardley et al., 2013; Yan et al., 2020). Consistently, FGM readings were 0.8–1.2 mmol⋅L^–1 lower than PG during postmeal sitting and walking conditions in the present study (Supplementary Table 1). A possible reason for this difference may be blood-to-interstitial glucose kinetics resulting in a time difference between venous blood and interstitial fluid (Lunn et al., 2011; Akintola et al., 2015). In the present study, PPGP showed a 15 min time delay using FGM in relation to using PG. This time lag may be overestimated, as glucose levels were measured at 15 min intervals. Previous studies have reported a 4–13 min time difference using different CGM systems in individuals with T1DM (Boyne et al., 2003; Wilson et al., 2007) and T2DM (Keenan et al., 2012).

However, as the FGM values were lower than the measured PG even when the glucose levels were steady in the fasted state, the underestimation of glucose by FGM could be a systematic sensor characteristic, rather than a physiological time delay between interstitial glucose and PG (Figure 1). Moreover, considerable inter-sensor variability was observed in a previous study using Medtronic/MiniMed CGM (Boyne et al., 2003), with the response characteristics of this glucose sensor system posing another reason for the observed PPGP time differences between FGM and PG. Interestingly, a recent study (Ghane et al., 2019) reported that BMI, total body fat mass, and fat percentage were similarly associated with CGM accuracy, suggesting that differences in subcutaneous fat may account for the changes in CGM accuracy in overweight/obese individuals. In contrast, Massa et al. (2018) and Ghane et al. (2019) reported that CGM overestimates glucose in individuals with a lower BMI. Furthermore, CGM overestimated blood glucose during a 30-min bout of high-intensity cycling performed 2 h postmeal in individuals with T1DM (Radermecker et al., 2013). Therefore, CGM accuracy may partly depend on participant characteristics, especially body composition.

The MARD in the present study was acceptable and comparable to that in previous studies. MARDs of 9.2% (Staal et al., 2018), 10.0% (Ji et al., 2017), and 11.4% (Bailey et al., 2015) during free-living conditions and of 13.2% (Bally et al., 2016) and 22.0% (Moser et al., 2019) during exercise were reported in populations with diabetes. Biagi et al. (2018) reported MARDs of 9.5, 16.5, and 9.3% before, during, and after aerobic exercise, respectively, in individuals with T1DM. Notably, the MARD is inflated in normal populations but not in populations with diabetes as its calculation is dependent on the glucose level, which is lower in populations without diabetes. In the present study, the MARD increased both after a meal and soon after walking (Figure 2), which may have been caused by a time delay. PG rapidly increased 15–30 min after starting a meal and after walking (Figure 1), while increases in interstitial glucose were delayed; interstitial glucose was lower than PG, especially in the fasting state (i.e., hypoglycemia for FGM readings) (Figure 1). However, the MARD decreased during walking in the present study (Figure 2). Similarly, Yardley et al. (2013) reported that CGM underestimated PG to the greatest extent during rest (−1.29–1.39 mmol⋅L^–1, P < 0.001), and least during aerobic walking (−0.11–1.71 mmol⋅L^–1, P = 0.416). The superior accuracy observed with aerobic walking may arise from augmented blood flow better equilibrating plasma and interstitial fluid or from a combination of systematic sensor underestimation and sensor lag time.

Overall, we observed no significant differences in the MARD between the postmeal sitting and walking conditions among overweight or obese young adults. In the present study, the %20/20 accuracy in the postprandial resting and walking conditions appeared lower than those reported by previous studies conducted in daily life among populations with diabetes (Shah et al., 2018; Wadwa et al., 2018; Zhou et al., 2018). Walking timing did not affect FGM accuracy. The MARD and %20/20 accuracy were slightly higher and lower, respectively, during accumulated walking, compared with those in the other three trials, potentially because it extended walking duration. As glucose variation increased, the MARD varied during the three bouts of walking.

In the present study, the Bland–Altman method and the Clarke error grid analysis showed that the FGM values were clinically acceptable (Figures 3, 4). In previous studies, almost 100% of the value pairs fell within zones A and B of the Clarke error grid analysis (Bally et al., 2016; Moser et al., 2016; Biagi et al., 2018; Yan et al., 2020). This excellent, clinically acceptable accuracy proved that the CGM accuracy during postmeal resting and walking was high and was further verified using the Bland–Altman method and Pearson’s correlation analysis.

As these findings confirm the clinical stability of FGM under postmeal sitting and walking conditions in overweight or obese young adults, it can be used as an efficient tool for collecting glucose measurements for clinical investigations. However, absolute values should be used with caution when controlling target blood glucose levels. The results of this study may provide a basis from which to develop models to estimate blood glucose levels using interstitial fluid glucose levels obtained via CGM systems (Castle and Rodbard, 2019), also establishing compensation schemes to correct the impact of exercise on CGM accuracy (Laguna Sanz et al., 2019). CGM accuracy should be validated during different exercise conditions in both populations with and without diabetes. As the FreeStyle Libre FGM system is indicated for the management of diabetes in persons aged 18 years and older, its performance requires validation in individuals without diabetes. As hypoglycemia is a common concern for individuals with diabetes during exercise, our findings provide some understanding of postmeal exercise and the combined effects of meal and exercise on CGM accuracy.

This study had several strengths. Both FGM and PG data were collected in two well-controlled randomized crossover studies. Several walking conditions with different timing and patterns were compared. Limited information exists regarding FGM accuracy in healthy individuals during different walking conditions. As the timing of walking initiation was individualized based on individualized PPGP, it allowed us to visualize the FGM accuracy during periods of large glucose level variation. With different postmeal walking timing and patterns, the combined effects of meal and walking on FGM accuracy could be observed. Although many previous studies failed to compare MARD values between exercise and rest periods (Fabra et al., 2021), the present study examined the CGM accuracy in a wide variety of conditions. In addition, the present study was conducted among healthy individuals who were overweight or obese, providing more information for this particular population. Venous blood, from which PG was measured as a reference, was collected; this offers more accurate data than those based on capillary glucose used in previous studies. Arterialized-venous samples were previously used as reference measurements, showing no significant impact on CGM accuracy (Kropff et al., 2017); therefore, this reference seems preferable because of its ease of operation.

A limitation of this study was its relatively small sample size (n = 40), but several rest and walking conditions were examined using a randomized crossover design. Furthermore, as we only recruited overweight or obese young adults and used a FreeStyle Libre FGM sensor, the generalizability of our findings may be limited. Further studies are needed to recruit diabetic participants with a large sample size.

Although absolute glucose readings derived using FGM underestimated PG, the overall accuracy of FGM was clinically acceptable during postprandial sitting and walking conditions in overweight or obese young adults.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.

XZ, FS, and SW designed the research. XZ and WW conducted the research. XZ analyzed the data and drafted the manuscript. FS, WW, WH, and SW made major contributions in revising the manuscript. SW had the primary responsibility for final content. All authors read and approved the final manuscript.

The 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.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.