Use of Color Range Indicator Technology on Point-of-Care Meters May Ease Self-Management of Blood Glucose

Abstract

The study by Grady et al is timely and exciting striving toward better understanding of relevant human factors in the design of their glucose meter displays. Their investigation focused on meaningful device optimization objectives that could be important to improving diabetes self-management. Grady et al are to be complimented for their development of a PC-based computer simulator to further our understanding of how patients interpret their glucose meter readings. What is interesting is that important, and hopefully more appropriate decisions, can be made daily or hourly when a meter is capable of displaying the most relevant information in a manner that will prompt the user toward more favorable glucose management decisions.

After attending this year’s 4th Artificial Pancreas Workshop at the National Institutes of Health, ¹ I was inspired by one of the panelists’ comments on the tools available to people with diabetes. The individual, a person with type 1 diabetes who also happened to be an industrial design professional, criticized the manufacturers for deficiencies in the user interface of various technologies used by people with diabetes. This is not surprising to those of us who are innovators, clinicians or researchers in the field as we have all come across difficult to read screens (glucose meters or insulin pumps), difficult to activate buttons, poor choice of colors or features, and so on. An obvious question is: are manufacturers of those devices not sufficiently attentive to the users’ needs? It is indeed challenging for people with diabetes to remain vigilant about their glycemic control and take proper action, almost hourly, to maintain glucose levels within recommended ranges. It would however be too simplistic to suggest that technology alone could ensure good control although it is fair to say that careful consideration of human factors when designing these critical tools could help maximize the patient’s ability to self-manage their blood glucose (BG) and remain in control. ²

In that respect, the study by Grady et al ³ is timely and exciting striving toward better understanding of relevant human factors in the design of their glucose meter displays. Although the study was designed and performed by the device manufacturer, their investigation focused on meaningful device optimization objectives that could be important to improving diabetes self-management:

1. How do people with type 1 and 2 diabetes respond to a bright color range indicator (CRI, ColorSure™ technology) on their point-of-care meter, and is there a difference in this response between meters that display a CRI color range bar versus a CRI color dot with a simple text message to indicate out of range values?

2. Are the numeracy skills of people with diabetes related to their interpretation of self-monitoring of blood glucose (SMBG) data?

The fact that nearly half of people with type 2 diabetes are still having difficulty with the interpretation and use of their SMBG data has been a persistent problem, raising once again the nagging question of why are so many people with diabetes not able to achieve clinically recommended BG targets using available point-of-care technology. Polonsky et al showed from their large survey of people with type 2 diabetes that about 50% of insulin and non-insulin using patients took no action for low or high out-of-range BG values. ⁴ It is also intriguing that people with diabetes seem more tolerant of high BG levels than the recommended ranges. Furthermore, there are unfortunately no empirical data on patient-provider interaction regarding the use of collected glucose meter data. Emphasis on glycated hemoglobin as the reliable measure of glucose control by health care providers is also confusing to some patients who end up perceiving that self-monitoring may not be so important since the HbA1c value is used for treatment decisions. ⁵

Grady et al are to be complimented for their development of a PC-based computer simulator to further our understanding of how patients interpret their glucose meter readings. This strategy does provide a window into the interaction of a person with diabetes with their glucose meter and what actions they might take based on their interpretation of the data. What is interesting is that important and hopefully more appropriate decisions can be made daily or hourly when a meter is capable of displaying the most relevant information in a manner that will prompt the user toward more favorable glucose management decisions.

Although it may be argued that simulated learning and behavior are not equivalent to real life scenarios where patients must make decisions at any time rather than when placed in simulation room, Grady et al have adopted a user-centered design using simulation sessions that permit an iterative approach to improving the user interface of their meters. The software prompts the subject to interact with a particular glucose meter and record their actions and decisions specific to their BG management. The scenarios can also provide subject training on SBGM and give valuable information on some of the human factors related to the use of a particular meter and BG data interpretation. These types of studies are efficient in relation to prospective observational studies in people with diabetes that would take years to complete and prove to be costly when compared to the methodology used by Grady et al in this study. This was elegantly demonstrated by the 27% improvement from baseline in the subjects’ abilities to properly classify BG values into the appropriate ranges after exposure to the CRI tool. Of interest, no correlation was found between HbA1c level (5%-14.2%) and the patient’s ability to classify BG values into the appropriate ranges, nor did their performance in this task correlate to the duration of their diabetes. The same was true for the numeracy score noting that those results were for both type 1 and type 2 subjects (N = 179). The potential implications of increased learning about one’s glucose results, and hopefully behavior modification with continuous use of the individualized CRI technology (input of recommended BG targets), will facilitate examination of its long term effectiveness through HbA1c tracking by patients and their providers. Thanks to the work of Wei et al, ⁶ we now possess critical information on what the average BG premeal, postmeal, and at bedtime should be to achieve HbA1c individualized targets. The CRI technology of Grady et al allows input of the more realistic day-to-day BG targets required for patients to achieve HbA1c goals. Revised evidence-based guidelines will need to be developed with recommendations of target BG levels that will become more helpful to clinicians when discussing BG ranges with their patients. ⁷

Last, and of high importance, is the analysis of the patients’ responses to the 4 glucose meter screen features tested by Grady et al. Those clearly showed that the large majority of type 1 and type 2 patients (90%-98%) agreed that the CRI tool with a color bar indicator, or a color dot indicator with a short progress note, would make them feel reassured about their actions for controlling their BG. From a human factors perspective, these user interface features seem optimized for broad use because of their simplicity, that is, they do not require use of the Internet or computer skills, or even keeping a paper logbook. Hopefully a large number of patients across various age groups and technological abilities will improve SBGM and also feel more confident about their treatment decisions with long term adoption of carefully studied glucose meter features.