The challenge for any treatment designed to improve glycemic status in type 1 diabetes is to find a way to reduce the frequency of hyperglycemia (and thereby improve HbA1c
) while simultaneously avoiding an increase in the frequency of hypoglycemia (and thereby not worsening risk for SH). These findings demonstrate that an automated bio-behavioral feedback intervention, delivered in the field, via an HHC, may be able to achieve this goal. Notably, this type of automated intervention was most effective for those patients most at risk for poor glucose control and problematic SH, reducing HbA1c
in those with baseline HbA1c
>8.0, and reducing the incidence of SH among those with a history of SH. Although the improvement in HbA1c
was less than the 1% decrease typically accepted as clinically meaningful, it is worth noting that the reduction for patients in poor control was comparable to the degree of improvement achieved by contemporary studies using continuous glucose monitoring (CGM) (24
). The reduction in frequency of SH was clinically significant.
Studies have demonstrated the efficacy of behavioral interventions, such as BGAT, in improving glucose control, including reductions in HbA1c
and the frequency of SH episodes (9
). However, these interventions are labor- and resource-intensive, typically involving multiple face-to-face training sessions usually led by expert personnel who have to undergo specialty training in the delivery of the treatment program. Although BGAT has been operationalized as an Internet-based intervention (25
), issues of wide-scale dissemination and long-term sustainability remain a challenge. Web-based systems can offer comprehensive content, immersive experience, and great avenues for training and information delivery. However, a simple automated bio-behavioral feedback system, such as the one developed and tested in this study, may provide a more effective alternative, or complementary, method for delivering and disseminating this type of intervention. Such a system has low computational demand and can be built directly in contemporary SMBG devices, which have adequate computing power to handle the calculations needed for the estimation of HbA1c
, glucose variability, risks for hypoglycemia, and associated symptoms. The advantages of such a system would include the following: 1
) real-time feedback available immediately after each SMBG reading, without the need to transfer SMBG data elsewhere; 2
) real-time goal setting based on estimates of HbA1c
or risk for hypoglycemia and opportunities for immediate treatment adjustment if an increased risk for hypoglycemia is indicated; and 3
) educational experience allowing the person to relate self-treatment behaviors to changes in weekly or monthly markers of glycemic control.
This last point is particularly important because the current systems generally allow for relating a treatment action to its immediate consequences captured by an SMBG reading, but not to longer-term effects that are reflected by summary characteristics. The results of this study support the notion that knowledge of longer-term consequences of diabetes management behaviors is beneficial: Routine SMBG alone (level 1) was useful for reducing HbA1c in those patients with poor control, which suggests that simply increasing awareness of frequent hyperglycemia can sometimes be beneficial for this patient group. However, this level of feedback was not effective in reducing risk of SH, which did not occur until after both level 2 and 3 feedback was received. We can therefore speculate that higher-level data processing (and perhaps behavioral responses to data feedback) is needed to anticipate and prevent hypoglycemic episodes. In addition, by comparing level 3 with level 1 feedback, we found that subjects’ awareness of their BG levels decreased when real-time feedback about their symptoms was discontinued, which indicates that continuous engagement with feedback would be needed to maintain the intervention effect.
In addition to the objectively measured positive effect of feedback, the participants’ opinions about the perceived benefits of the feedback were positive. As would be expected, real-time estimation of HbA1c had the highest approval rating of all estimates included in the feedback, because HbA1c is the best known metric of glycemic control, with 89% of the participants wanting this feedback on a weekly basis. Feedback about glucose variability was also well accepted, with 75% of all patients (and 79% of those with high baseline HbA1c) wanting this feedback weekly. However, the positive ratings for variability were lower than those of HbA1c feedback, indicating that more in-depth training or patient education would be needed to clarify the meaning and the importance of glucose variability for maintaining glycemic control. The feedback about risk for hypoglycemia was rated highly by those with a history of SH, 81% of whom stated that they would look at this feature regularly. The rest of the patients, however, found little benefit in this type of feedback, indicating that such a feature should be offered only to those in need of risk assessment. Although these positive responses are promising, future population-based studies are needed to determine whether patients would use and benefit from this type of automated feedback in clinical settings.
More research is also obviously needed to test additional types of automated feedback that might further improve glucose control and reduce SH risk, as well as other ways to optimize the effectiveness of these interventions. Furthermore, this study did not clarify a relevant question: how participants used the feedback at each level. Subjects were given written instructions on the meaning of the presented parameters (e.g., estimated HbA1c
, risk for hypoglycemia), as well as general advice on measures that could be taken to improve HbA1c
or reduce risks for hypoglycemia, but were not asked to change their treatment on the basis of the presented parameters. Moreover, there were no specific recommendations about any changes in insulin dosing. Therefore, we can assume that providing glucose profile feedback on certain longer-term summary characteristics that go beyond the information contained in a single SMBG reading was useful. A similar effect was recently reported in a large trial of CGM; it was unclear how subjects interpreted CGM information, but the effect of receiving CGM feedback was significant (24
). It is therefore evident that future research needs to focus on how patients are using additional glucose feedback and what types of changes are occurring in diabetes management to improve outcome.