The current study provides information regarding the type of data that can be obtained using CGM technology in young children with T1DM. In particular, the use of calculations for intra- and inter-daily variations, such as CONGA and MODD, can provide a more complete and accurate picture of participants' glucose levels than traditional measures of glycemic control. Participants in this study had greater glucose variability, as assessed by CONGAn and MODD, than adults without T1DM and older children and adolescents with T1DM, as would be expected given the challenges of maintaining euglycemia in young children with this condition. This finding supports the assertion that young children are more likely to experience extreme glucose variability. This is likely due to a number of factors including heightened insulin sensitivity, fluctuating activity levels, and erratic eating behaviors (7
) and this may place these young children at-increased risk for diabetes-related complications in the future (4
). It also suggests that glucose variability may be an important measure of glycemic control to study in this young population and target through intervention.
In the current study, we were also able to link measures of glycemic control and variability with diabetes-related variables. Our results suggest that children with a history of hypoglycemic seizures have increased glycemic variability and this finding echoes results found in older individuals with T1DM (4
). This may indicate these children have generally more fluctuating glucose levels, making seizures more likely. We also found that higher frequency of omitted meal boluses was related to lower mean CGM values. This finding may be explained by parents of children, with typically lower glucose levels, more frequently omitting the meal or snack insulin bolus due to hypoglycemia. Interestingly, our data shows a relation between HbA1c and mean CGM values but not with SMBG values. This finding supports the assertion that the use of SMBG data as a proxy for glycemic control may be ineffective. Given that SMBG data are highly dependent on the frequency and timing of BG checks (e.g., before meals, when they believe the child has a high or low glucose), it may not provide an accurate representation of overall glycemic control. Overall, our results suggest that utilizing CGM technology and the calculation of glycemic variability statistics can provide a richer, more accurate picture of young children's glucose levels. In contrast, SMBG data may not link with glycemic control in this population and the singular use of HbA1c values may mask important patterns in glucose variability which could impact overall glucose levels.
In addition to these benefits, there are a number of potentially exciting and novel uses of CGM data for behavioral researchers. For example, intra-daily measurement can provide useful data about how often or rapidly glucose levels are changing. In the future, researchers may be able to link daily variability to constructs such as neurocognitive functioning, behavior, mood, or even diabetes-specific factors such as fear of hypoglycemia. Inter-daily measurement of glucose variability provides researchers with the potential to document the impact of interventions targeting glucose control at a more refined level rather than only documenting mean change in glucose levels (i.e., HbA1c). For instance, researchers may be able to track how adherence to behavioral interventions impacts glucose variability on a day-to-day basis and if the interventions can lead to more stable improvements in glucose variability. Furthermore, CGM data can be used to test relations between psychological or behavioral constructs with specific time periods of interest, such as post-prandial glucose levels, overnight glucose levels, or the frequency of undetected hypoglycemic events. In older children, researchers have begun to examine the relations between child mood, behavior, and CGM data. McDonnell and colleagues (17
) utilized CGM technology in school-age children with T1DM. These researchers found that higher frequencies of externalizing behavior were related to greater length of time above the target glucose range. To our knowledge, this is the only study that has examined the link between behavior and CGM data in children, thus the potential for future work in this area is great.
The current study also demonstrates the feasibility of using CGM in young children with T1DM. Anecdotally, families in the current study were excited about participation and frequently cited the benefit of the retrospective CGM data in terms of improving their child's diabetes care. Two families in the study experienced insertion difficulties; however, they each opted to undergo a second insertion on the same day. While the manufacturer's instructions report that mild irritation at the insertion site is possible, participants in the current study did not experience this or other adverse events.
Despite the wealth of data provided by CGM, there are obstacles to widespread use in research. In the current study, some participants displayed anticipatory anxiety about the insertion which could hinder research participation. Additionally, CGM is vulnerable to malfunctions and misuse. Eight families experienced minor difficulties including two insertion problems, one sensor failure, one CGM monitor failure, and four parent mismanagement issues (e.g., parents not entering SMBG data required for calibration). Depending on the type of difficulty encountered some loss of data may occur. Based on our experience, we recommend planning for 5–10% more participants than needed in order to account for unexpected data loss. Finally, while CGM has been used successfully in the current study and two other research protocols (17
), it is not currently approved by the FDA for use in children under seven years old, which may create an obstacle when seeking institutional approval for research.
There are several limitations of the current study. While it is notable that associations were found despite the small sample size, additional participants could provide increased power to detect other associations of interest. The study only recruited children using an insulin pump. Thus, the ability to generalize the results to a conventionally-managed population may be limited. In addition, it would be useful for future studies to incorporate objective measures of diabetes management behaviors (e.g., insulin omission, carbohydrate intake) which could be linked with CGM data. Finally, in this study families did not provide standardized feedback about their experience with the CGM technology. Therefore, feasibility data presented are anecdotal. It would be useful for future work to incorporate standardized measures of acceptability and satisfaction to more objectively measure this construct.
To conclude, CGM provides detailed, objective data about glucose concentrations and trends and the use of this technology is feasible, even for young children with T1DM. Whereas more traditional measure of glycemic levels, such as SMBG provides data at discrete time points which may be predisposed to extreme values (e.g., pre-meal glucose or when hypo- or hyperglycemia is suspected), CGM data provide an unbiased sample of glucose values. Moreover, CGM is the only device available that can directly measure glycemic variability and the percent of time participants are at specific glycemic concentrations. These types of measurements can be particularly useful for behavioral researchers examining associations between patients' psychosocial functioning, self-care behaviors, and their glycemic control. These data may also become the best way to assess for glucose variability, which evidence suggests, should be a target for future behavioral interventions (4
). Overall, CGM provides a technologically advanced method of obtaining data that allows for a richer and more detailed examination of glucose trends and correlates in children with T1DM.