What Are the Quality of Life-Related Benefits and Losses Associated with Real-Time Continuous Glucose Monitoring? A Survey of Current Users

Abstract

Background:

How does real-time (RT) continuous glucose monitoring (CGM) affect quality of life (QOL)? We explored the types and frequencies of diabetes-specific QOL changes resulting from RT-CGM as reported by current users and investigated what patient-reported factors predict these changes.

Subjects and Methods:

Current RT-CGM users (n=877) completed an online questionnaire investigating perceived QOL benefits/losses since RT-CGM initiation and RT-CGM attitudes and behavior. Exploratory factor analysis (EFA) examined the 16 QOL benefit/loss items to identify underlying factors. Regression analyses examined associations between demographics and RT-CGM attitudes and behavior with the QOL factors emerging from the EFA.

Results:

Three major QOL factors emerged: Perceived Control over Diabetes, Hypoglycemic Safety, and Interpersonal Support. QOL improvement was common for Perceived Control over Diabetes and Hypoglycemic Safety (86% and 85% of respondents, respectively), although less common for Interpersonal Support (37%). Consistent independent predictors of perceived benefits were greater confidence in using RT-CGM data (P<0.001), satisfaction with device accuracy (P≤0.05) and usability (P<0.01), older age (P<0.01), more frequent receiver screen views (P<0.05), and use of multiple daily injections (Hypoglycemic Safety and Interpersonal Support, P≤0.05).

Conclusions:

Diabetes-specific QOL benefits resulting from RT-CGM were common. Major predictors of QOL benefits were satisfaction with device accuracy and usability and trust in one's ability to use RT-CGM data, suggesting that “perceived efficacy,” for both device and self, are key QOL determinants. Psychoeducational strategies to boost confidence in using RT-CGM data and provide reasonable device expectations might enhance QOL benefits.

Introduction

R eal-time (RT) continuous glucose monitoring (CGM) can contribute to significant improvement in long-term glycemic outcomes as well as reductions in severe hypoglycemic episodes and overall time spent in hypoglycemia,^1

–4 but what is the subjective experience of RT-CGM use, and how does it affect patients' quality of life (QOL)? By providing an ongoing stream of glucose data and audible alerts, it certainly seems possible that RT-CGM could enhance a patient's sense of confidence and control over diabetes, thus contributing to a positive impact on QOL. However, because of the overwhelming amount of data and the potential disruptiveness of the alarms, especially if the alarms are false, RT-CGM could also negatively influence QOL. Anecdotal reports are decidedly mixed: some patients reporting profound, life-changing benefits, whereas others find that RT-CGM has little or no value. Data from the recent Juvenile Diabetes Research Foundation CGM study^5,6 as well as several, small retrospective reports^7,8 indicate that favorable QOL responses to RT-CGM may be common. In contrast, a recent review of randomized controlled trials found no significant differences in QOL over time between patients assigned to RT-CGM versus those assigned to self-monitoring of blood glucose.⁹ A potentially important issue that has limited interpretation across studies is that few reports have attempted to distinguish among the various RT-CGM systems currently available, even though the systems are not homogeneous, with notable differences in usability, reliability, and performance.^10,11

Overall, the current body of data indicates that the subjective experience of RT-CGM is quite variable, although little is known concerning the specifics of such experiences, both positive or negative, or what factors might explain such variability. To address these gaps, we conducted a comprehensive survey of a large adult population of current users of one RT-CGM device, the Dexcom^® (San Diego, CA) SEVEN^® PLUS (DSP), in order to determine (1) how commonly patients perceive substantial, diabetes-specific, QOL-related benefits (or losses) from RT-CGM use and (2) what factors predict diabetes-specific, QOL-related benefits or losses associated with RT-CGM use. The key factors we hypothesized would be associated with QOL-related benefits or losses were patient-reported satisfaction with device features, confidence in their ability to make use of RT-CGM data, and physician involvement in RT-CGM data review.

Research Design and Methods

Subjects

Patients in the Dexcom company registry were identified who had purchased the DSP 6–12 months prior to study initiation and had agreed to receive further e-mail communications from Dexcom. E-mail invitations to complete the online survey were sent from the Behavioral Diabetes Institute (San Diego) to patients who met inclusion criteria: currently use insulin, ≥18 years old, and able to read and write English without assistance. Because of Food and Drug Administration regulations, Medicare, Medicaid, and Veterans Administration patients were excluded from the contact list. Towards the goal of collecting approximately 1,000 completed surveys (to ensure a large, diverse patient sample that could capture the full range of patient RT-CGM experiences), a first wave of 2,791 invitations was sent, which included patients whose records indicated that they remained current DSP users as well as those who had apparently quit using the device. For the current project, the investigation was limited to those invitations sent only to current users (n=2,400).

Procedures

In the e-mail invitation, it was explained that the survey was being conducted in collaboration with Dexcom, but it was emphasized that survey responses were anonymous, participation was voluntary, and a $25 gift certificate would be provided for survey completion. Furthermore, it was explained that the online questionnaire concerned their perception of QOL-related benefits and losses since RT-CGM initiation, current RT-CGM use, RT-CGM attitudes, and physician involvement. At survey conclusion, patients were asked to provide an e-mail address so the online gift certificate could be sent. It was stressed that that this address would not be linked to their questionnaire responses.

Collected data were entered into a central database using a HIPAA-protected server, with no linkages to personal health information or personal identifiers. The research protocol was reviewed by Chesapeake Research Review (Columbia, MD), a community-based institutional review board, and received an exemption.

Measures

The survey instrument consisted of four major sections:

1. Demographic data. This section included items about diabetes duration and treatments and personal characteristics.

2. QOL-related benefits and losses. Of the validated instruments available to assess the elements of diabetes-specific QOL, we found few that were developed to assess the patient's perception of QOL change retrospectively. In addition, although some instruments touched on QOL aspects directly related to RT-CGM (the CGM Satisfaction Scale⁵) and other instruments on QOL aspects that we suspected might be influenced by RT-CGM (e.g., perceived control over diabetes in the Diabetes Distress Scale,¹² worries about hypoglycemia in the Hypoglycemic Fear Survey¹³), we found none that assessed other aspects that we presumed might be crucial (e.g., confidence regarding one's ability to manage or avoid hypoglycemia, interpersonal issues associated with blood glucose variability and hypoglycemia). Therefore, it was decided that a new instrument would need to be developed. To do so, several dozen physicians, diabetes educators, and experienced RT-CGM patients from major diabetes treatment centers across the United States were asked about common emotional, behavioral, and interpersonal changes resulting from RT-CGM use. From this expert group, a pool of relevant items was developed and pilot-tested with a series of experienced RT-CGM patients (n=8). Feedback from these patients led to changes or deletion of items that were vague, difficult to comprehend, or duplicative, resulting in a final set of 16 items. For each item, respondents were asked to indicate how this issue had changed for them as a result of using the DSP on a 5-point scale: “things became much worse,” “things became slightly worse,” “no change,” “things became slightly better,” or “things became much better.”

3. RT-CGM patient and healthcare provider behavior. Patients reported their average number of receiver screen views/hour. They were also asked, “When you meet with your healthcare provider who helps you manage your diabetes, how often does he/she review and discuss your DSP readings with you?” Possible responses were “never,” “occasionally,” “at most visits,” or “at every visit.”

4. RT-CGM attitudes. On a 5-point scale (very dissatisfied, moderately dissatisfied, neutral, moderately satisfied, or very satisfied), respondents were asked three questions to indicate how satisfied or dissatisfied they were with device accuracy, ease of use, and cost. They were also asked to consider their confidence in using DSP data. On a 4-point scale (not confident at all, a little confident, moderately confident, or very confident), they responded to the question “How confident are you that you know what to do with Dexcom SEVEN PLUS numbers, trends and arrows, and other information that you see on the receiver?”

Statistical analysis

Statistical analysis included examination of each study measure to document ranges and distributions, and variables were transformed to improve skewness and kurtosis where needed. An exploratory factor analysis (EFA) was conducted on QOL-related benefit and loss items using principal component analysis with a Promax rotation. Interpretation of the factors was based upon eigenvalues (>1.0), scree plots, and the rotated factor item loadings (items loading >0.60 on a given factor and <0.40 on any other factor). Mean QOL-related benefit and loss scale scores were divided, only for descriptive purposes, into categories of “Broad Worsening” (mean scores of 1–2.49), “No change” (mean scores of 2.5–3.49), and “Broad Improvement” (mean scores of 3.5–5). The selected values for the three categories are based on the face validity of the response options (e.g., the “No change” category included mean responses equivalent to±0.5 mean score of “3,” which was the response option labeled as “no change”).

Individual regression analyses examined the univariate associations between patient demographics and RT-CGM-related attitudes and behaviors with QOL-related factors. Stepwise multiple regression analyses were then conducted for each of the three major QOL-related factors that emerged from the EFA. Patient demographics were entered on Step 1, followed by RT-CGM-related attitudes and behaviors on Step 2. Note that all univariate and multivariate regression models used continuous variables.

Results

After 11 days, it was estimated that a sufficient number of surveys had been received, and so the survey site was closed. In total, 877 completed surveys were received from patients currently using RT-CGM (overall response rate was 36.5%). As seen in Table 1, the majority of respondents were non-Hispanic white (89.7%), relatively well educated (64.6% had completed 4 years of college), and female (53.7%). Mean age was 41.7 (±13.0) years. Mean time since diagnosis was 20.5 (±12.6) years. Most patients had type 1 diabetes (93.3%). The majority used continuous subcutaneous insulin infusion (CSII) (72.3%); the rest used multiple daily insulin injections (MDI) (27.7%).

Table 1.

Sample Description: Demographics and Real-Time Continuous Glucose Monitoring Attitudes and Behaviors

	Total
Number of respondents	877
Age (years)	41.70 (13.0)
Gender (% male)	406 (46.3%)
Years since diagnosis	20.5 (12.6)
Ethnicity
Non-Hispanic white	787 (89.7%)
African American	12 (1.4%)
Hispanic	32 (3.6%)
Asian or Pacific Islander	19 (2.2%)
Native American	3 (0.3%)
Other	24 (2.7%)
Education
High school or less	62 (7.0%)
Some college	248 (28.3%)
College graduate	567 (64.6%)
Type 1 diabetes	818 (93.3%)
CSII	634 (72.3%)
RT-CGM attitudes
Satisfaction with accuracy^a	724 (82.5%)
Satisfaction with ease of use^a	829 (94.5%)
Satisfaction with cost^a	389 (44.4%)
Confidence in ability to use data^b	821 (93.6%)
RT-CGM behaviors
Physician involvement (≥at most visits)^c	517 (59.0%)
Receiver screen views (>2/h)	564 (64.3%)

Data are mean (SD) values or n (%).

Percentage of respondents who reported being moderately or very satisfied.

Percentage of respondents who reported being moderately or very confident.

Percentage of respondents who reported that their healthcare provider reviews and discusses their DSP results with them at most or every visit.

CSII, continuous subcutaneous insulin infusion; RT-CGM, real-time continuous glucose monitoring.

RT-CGM behavior and attitudes

As seen in Table 1, 82.5% reported that they were at least moderately satisfied with RT-CGM accuracy, and 94.5% were at least moderately satisfied with the device's ease of use. A much smaller percentage was satisfied with costs (44.4% reported at least moderate satisfaction). The vast majority reported being at least moderately confident in their ability to use RT-CGM data (93.6%).

Two-thirds of respondents (64.3%) reported checking the RT-CGM receiver screen more than twice per hour. Approximately half of the sample (59.0%) noted that their physician downloaded their CGM data at most visits or every visit.

The receiver screen view was transformed with a square root transformation, and satisfaction with ease of use was transformed with a log transformation to reduce skewness and kurtotsis. These transformed variables were used in all further analyses.

Perceived benefits and losses due to RT-CGM

Examination of the frequencies of the 16 individual items (Table 2) suggests that beneficial changes were common. For example, more than two-thirds of respondents reported that their sense of safety while exercising (80.2%), sleeping (79.9%), and driving (68.7%), their motivation to manage diabetes (81.5%), and their confidence that they can control diabetes (88.9%) and avoid serious hypoglycemia (86.0%) are all “slightly” or “much” better since starting RT-CGM. Of note is that reported worsening in QOL was relatively uncommon across the 16 items.

Table 2.

Quality of Life-Related Benefits and Losses

	Current RT-CGM users
	Much worse	Slightly worse	No change	Slightly better	Much better
Confident you can control your diabetes	0.2%	1.0%	9.8%	41.4%	47.5%
In control of your life and your diabetes	0.3%	1.3%	12.3%	44.0%	42.1%
Hopeful that you can avoid long-term complications	0.2%	1.4%	23.0%	41.5%	33.9%
Motivated to keep up your diabetes management	0%	0.9%	17.6%	35.8%	45.7%
Adjust your insulin doses correctly	0.1%	2.3%	19.2%	44.5%	34.0%
Blood glucoses become more or less a “rollercoaster”	0.6%	4.6%	16.8%	50.9%	27.3%
A1c improved or worsened	0.2%	3.5%	19.7%	44.7%	31.8%
Free to do the things in life you want to do	0.5%	3.4%	33.2%	31.8%	31.1%
Safe when exercising	0%	0.3%	19.5%	33.2%	47.0%
Safe about sleeping	0.6%	2.4%	17.2%	33.0%	46.9%
Fearful about hypoglycemia	0.3%	2.2%	18.8%	34.9%	43.8%
Confident you can avoid serious hypoglycemia	0.3%	0.9%	12.8%	37.5%	48.5%
Safe while driving	0.1%	0.5%	30.8%	27.5%	41.2%
Relationships with family and friends	0.1%	0.6%	75.7%	12.9%	10.7%
Partner worrying about sleeping at night	0.8%	4.8%	39.1%	27.8%	27.4%
Friends and family bothering you about your diabetes	0.3%	3.3%	70.5%	15.3%	10.6%

Principal components analyses of the 16 items for the total sample yielded three coherent factors that cumulatively explained 62.8% of the variance (Table 3). Factor loadings ranged from 0.67 to 0.87. Factor 1 (eigenvalue=7.21) included seven items and was labeled “Perceived Control over Diabetes” (Cronbach's α=0.88). Factor 2 (eigenvalue=1.52) included five items and was labeled “Hypoglycemia Safety” (Cronbach's α=0.84). Factor 3 (eigenvalue=1.32) included three items and was labeled as “Interpersonal Support” (Cronbach's α=0.75). One item did not load clearly on any factor and was not included in further analyses. Scale scores were calculated as the mean of the items: Perceived Control, 4.16 (±0.60); Hypoglycemic Safety, 4.23 (±0.64); and Interpersonal Support, 3.45 (±0.64). Intercorrelations among the scales ranged from r=0.45 to r=0.60.

Table 3.

Factor Loadings of Quality of Life-Related Benefits and Losses Items

	Perceived Control	Hypoglycemic Safety	Interpersonal Support
Confident you can control your diabetes	0.744	0.105	0.013
In control of your life and your diabetes	0.702	0.228	−0.044
Hopeful that you can avoid long-term complications	0.705	0.117	0.009
Motivated to keep up your diabetes management	0.789	0.012	−0.095
Adjust your insulin doses correctly	0.679	−0.023	0.144
Blood glucoses become more or less a “rollercoaster”	0.780	−0.052	0.063
A1c improved or worsened	0.869	−0.197	0.036
Free to do the things in life you want to do	0.423	0.380	−0.033
Safe when exercising	0.116	0.730	−0.191
Safe about sleeping	−0.153	0.868	0.068
Fearful about hypoglycemia	−0.006	0.823	0.043
Confident you can avoid serious hypoglycemia	0.034	0.800	0.031
Safe while driving	0.063	0.667	0.039
Relationships with family and friends	0.070	−0.079	0.843
Partner worrying about sleeping at night	−0.156	0.326	0.705
Friends and family bothering you about your diabetes	0.114	−0.115	0.842

Bolded items are those that load most highly on the individual factor.

The majority of respondents reported broad improvement in Perceived Control (86%) and Hypoglycemic Safety (85%), although improvement in Interpersonal Support was less common (37% of respondents). Broad worsening in any of the three subscales was rare: Perceived Control, 1.0% of respondents; Hypoglycemic Safety, 1.0%; and Interpersonal Support, 1.9%.

Predictors of perceived benefits and losses due to RT-CGM

Univariate analyses (Table 4) reveal that higher levels of Perceived Control, Hypoglycemic Safety, and Interpersonal Support were all associated with greater satisfaction with device accuracy, ease of use, and cost (in all cases, P<0.001), with greater confidence in using the resulting data (P<0.001), with more frequent physician review of RT-CGM data (P<0.05), and with MDI use (P<0.05). There were no consistent significant relationships between demographic variables and the three QOL subscales, except that greater Hypoglycemic Safety and Interpersonal Support was linked to older age (P<0.001).

Table 4.

Univariate and Multiple Regression Analyses for Quality of Life-Related Benefits and Losses

	Univariate						Multivariate
	Perceived Control		Hypoglycemic Safety		Interpersonal Support		Perceived Control		Hypoglycemic Safety		Interpersonal Support
	β	P	β	P	β	P	β	P	β	P	β	P
Age	0.06	0.07	0.13	<0.001	0.13	<0.001	0.11	0.002	0.21	<0.001	0.10	0.009
Gender (male=0, female=1)	−0.07	0.03	0.02	0.50	−0.03	0.31	−0.03	0.38	0.08	0.008	0.01	0.95
Education	−0.06	0.09	0.01	0.75	−0.10	0.002	−0.07	0.03	0.01	0.93	−0.11	0.001
Ethnicity (white=0, non-white=1)	0.02	0.65	0.02	0.50	−0.03	0.40	0.02	0.57	0.05	0.13	−0.02	0.62
Years since diagnosis	−0.04	0.22	−0.01	0.87	0.11	0.001	−0.10	0.003	−0.11	0.003	0.06	0.12
Diabetes (type 1=0, other=1)	−0.07	0.04	−0.02	0.64	−0.01	0.77	−0.05	0.15	0.03	0.41	0.01	0.75
Method of insulin use (MDI=0, CSII=1)	−0.07	0.03	−0.08	0.02	−0.11	0.001	−0.04	0.17	−0.06	0.05	−0.10	0.002
Satisfaction with accuracy	0.33	<0.001	0.28	<0.001	0.15	<0.001	0.19	<0.001	0.19	<0.001	0.07	0.05
Satisfaction with ease of use	0.29	<0.001	0.26	<0.001	0.19	<0.001	0.16	<0.001	0.14	<0.001	0.10	0.005
Satisfaction with cost	0.16	<0.001	0.13	<0.001	0.15	<0.001	0.08	0.008	0.06	0.05	0.11	0.001
Confidence using RT-CGM data	0.36	<0.001	0.30	<0.001	0.21	<0.001	0.26	<0.001	0.22	<0.001	0.19	<0.001
Physician download/review of RT-CGM	0.10	0.004	0.08	0.02	0.07	0.05	0.04	0.22	0.03	0.27	0.04	0.27
Receiver screen views	0.02	0.55	−0.01	0.83	0.01	0.67	0.08	0.008	0.06	0.05	0.11	0.001

Standardized regression β values are presented from Step 2 of the regression equation.

CSII, continuous subcutaneous insulin infusion; MDI, multiple daily insulin injections; RT-CGM, real-time continuous glucose monitoring.

Multiple regression analysis revealed (Table 4) that the most consistent independent predictors of perceived benefits across the QOL subscales were greater confidence in using the resulting data (P<0.001), greater satisfaction with device accuracy (P≤0.05) and ease of use (P<0.01), older age (P<0.01), and more frequent receiver screen views (P≤0.05). Method of insulin administration (CSII vs. MDI) was a significant independent predictor of Hypoglycemic Safety (P=0.05) and Interpersonal Support benefits (P<0.01), with greater perceived benefits seen in MDI than CSII. For Perceived Control and Hypoglycemic Safety only, one additional independent predictor was fewer years since diagnosis (P<0.005). Physician data review was not associated with greater perceived benefits in any of the three subscales.

Discussion

In this large sample of patients with recent RT-CGM experience, we found that substantial QOL-related benefits resulting from RT-CGM use are very common, with >80% noting that their broad sense of control over diabetes had improved as well as their confidence that problematic hypoglycemic events could be avoided or managed. In addition, approximately one-third reported improvement in their interpersonal relationships due to RT-CGM. Very few (<2%) noted broad worsening in any of the three QOL-related dimensions. Of note is that only DSP users were evaluated in this study, so whether these findings can be generalized to other RT-CGM systems, where there are differences in performance and usability, cannot be determined.

The key psychological predictors of QOL-related benefits across the subscales were satisfaction with device accuracy and ease of use as well as trust in one's own ability to make use of RT-CGM data. The overarching theme here is, we suspect, perceived efficacy—both device efficacy and personal efficacy. When patients felt confidence in the data that the device is delivering, comfort with how to use the device, and/or confidence in their ability to make use of these data, beneficial QOL results were most likely to occur. Although these data are cross-sectional and any causal argument must be tentative at best, the findings suggest that it may be worthwhile to develop psychoeducational strategies to enhance patients' confidence in using their own RT-CGM data and help them to have reasonable expectations when using a device that can be quite useful but where there are discrepancies between RT-CGM readings and blood glucose values.^14,15 In addition, we suspect that as RT-CGM technology advances, leading to greater device accuracy and ease of use, concomitant QOL benefits will continue to grow as well.

Three additional independent predictors were apparent: QOL-related benefits in Perceived Control, Hypoglycemic Safety, and Interpersonal Support were associated with older age, more frequent receiver screen views, and MDI use. This latter finding may seem surprising because RT-CGM is often associated with CSII users only.^1
–3 Recent evidence, however, suggests that RT-CGM provides equivalent glycemic benefits to MDI and CSII patients.^16,17 The current data indicate that MDI users are, at the least, at no disadvantage versus CSII users when it comes to QOL-related benefits. We hypothesize that MDI users report greater benefits than CSII patients because they do not enjoy the Perceived Control and Hypoglycemic Safety benefits that often result from CSII use¹⁸; in other words, there may be a ceiling effect for these types of QOL benefits in CSII, but not in MDI.

Finally, there are several negative findings worthy of mention. First, diabetes type is not associated with QOL-related benefits, suggesting that RT-CGM may be equally beneficial for patients with type 1 and type 2 diabetes. Recent evidence points to the glycemic benefits of RT-CGM in type 2 diabetes,¹⁹ and the current findings suggest that these benefits may extend to QOL outcomes. However, the percentage of type 2 diabetes patients in the current study is only 6.8%; therefore, the null finding here may be a result of low statistical power. Second, the degree of physician involvement (how frequently physicians downloaded and reviewed the patient's RT-CGM data) was not linked to QOL benefits in the multivariate analyses, although significant relationships were apparent in the univariate findings. It appears that physician involvement ceases to be influential when adjustments are made for the perceived efficacy variables, suggesting that active physician participation in RT-CGM discussions with their patients may contribute to QOL benefits, but only for patients lacking confidence in their abilities to use RT-CGM data and/or who are not satisfied with device performance.

There are noteworthy limitations of this study. First, although the sample is large, it may not be representative of the larger population of RT-CGM users. Only DSP users from the Dexcom registry were invited to participate, and the survey was limited to those who could access and complete the survey online. The relatively modest acceptance rate (36% of those contacted) may have added to the selection bias. Also, it is noteworthy that approximately two-thirds of the sample had at least 4 years of college, which may further limit generalizability of the findings. Although it was emphasized that survey responses were anonymous, respondents were aware that the study was conducted in collaboration with Dexcom, which may have biased responses and/or willingness to participate. In total, this suggests that the generally positive diabetes-specific, QOL-related benefits reported by these respondents may not accurately reflect the experience of most RT-CGM users (or even DSP users). Finally, these data are cross-sectional, and the critical QOL items were developed specifically for this study (i.e., were not part of a validated instrument) and were retrospective in nature. Therefore, without a formal pre–post testing design and further instrument validation, the actual impact of RT-CGM on these aspects of QOL cannot be determined.

Conclusions

RT-CGM is associated with a range of QOL-related benefits, with the majority of patients reporting a greater sense of hypoglycemic safety and feeling more control over their diabetes. Improvements in interpersonal relationships are also reported, although these benefits are less common. Relatively few patients reported that RT-CGM worsened QOL. The major independent predictors of QOL-related benefits are satisfaction with device accuracy and ease of use and trust in one's own ability to make use of the RT-CGM data, suggesting that “perceived efficacy,” both device efficacy and personal efficacy, are the key determinants of QOL benefits. It was somewhat surprising that MDI users reported greater QOL benefits than CSII users, and the role for frequent physician downloading and review of RT-CGM data may be less critical than is often assumed. To broaden and enhance QOL-related (and, perhaps, glycemic) benefits and to encourage RT-CGM users to continue using this new technology, we suggest the development of psychoeducational strategies to accompany the device initiation process that will boost the patients' confidence in using RT-CGM data and help them to develop reasonable expectations for a device that can be of great value but for which perfect accuracy has not yet been achieved.

Footnotes

Acknowledgments

We wish to thank Farah Bowman of Dexcom, Inc., for her active participation in the design and execution of this study and David Price, MD, and Claudia Graham, PhD, of Dexcom, Inc., for their editorial support during all phases of the study. Funding for the study was provided by Dexcom, San Diego, CA.

Author Disclosure Statement

W.H.P. has worked as a consultant for Dexcom. D.H. reports no competing interests. W.H.P. is responsible for the study conception/protocol. D.H. is responsible for statistical analysis. W.H.P. and D.H. are jointly responsible for manuscript development. Both authors read and approved the final manuscript.

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