Abstract
OBJECTIVE
To assess the effect of an Internet-based glucose monitoring system (IBGMS) on A1C levels in patients with type 2 diabetes treated with insulin.
RESEARCH DESIGN AND METHODS
This trial involved 50 patients randomly assigned to receive either conventional treatment alone or with additional follow-up through an IBGMS for 6 months. Patients randomized to the intervention group uploaded blood glucose readings every 2 weeks to a secure Web site for review and receipt of feedback from their endocrinologist. A1C and laboratory test results were collected at 0, 3, and 6 months.
RESULTS
The baseline parameters were not significantly different. Over a 6-month follow-up, A1C dropped from 8.8 to 7.6% (P < 0.001) in the intervention group compared with 8.5 to 8.4% (P = 0.51) in the control group.
CONCLUSIONS
The use of IBGMS significantly improved A1C levels in patients with type 2 diabetes treated with insulin.
In the management of diabetes, self-monitoring of blood glucose (SMBG) is performed as an adjunct to A1C measurements in order to assess and modify treatment (1–3); however, it often requires healthcare professionals to help interpret the results to refine treatment (4–6). The Internet provides a readily accessible platform for communication and remote health monitoring (7). In this study, we evaluated whether the use of an IBGMS would improve the outcome of treatment for patients with type 2 diabetes.
RESEARCH DESIGN AND METHODS
We enrolled 50 type 2 diabetic patients being treated with insulin alone or in combination with oral anti-hyperglycemic medications. Inclusion criteria were a recent A1C >7.0%, Internet access, and prior training in SMBG. Patients were randomly assigned to either of the groups using a computer random number generator. All patients were provided with a meter and test strips for testing at least three times daily and were asked to perform a laboratory blood test and visit their endocrinologist every 3 months. Patients randomized to the control group were asked to keep a diary of SMBG for every visit to their endocrinologist. Patients randomized to the intervention group were asked to upload their SMBG readings every 2 weeks to a secure Web site (ALR Technologies, Atlanta, GA). These data were presented in table and graph formats according to the time of day, and automatic calculations were done to show the average, standard deviation, and range for specific time periods. The system allowed the patient to input medications, set alarms, view a summary of readings, and send a message to their endocrinologist who then viewed the readings and sent feedback. The endocrinologist's recommendations included changes in insulin dosage, suggestions on testing frequency, and giving compliments.
Baseline demographical data were collected from patient charts. A1C values were measured using the ADVIA Centaur Immunoassay System (Tarrytown, NY). Data were analyzed using a computerized database (Excel, Microsoft) and the SAS statistical software (SAS Institute, Cary, NC). Paired sample and independent Student t tests were used to compare the within- and between-group changes respectively. Primary outcome was the change in A1C levels. The significance of the difference between the A1C levels was evaluated by performing ANCOVA, which tested between-group changes from the start to the end of study while adjusting for baseline values. For all analysis, statistical significance was established at P < 0.05.
RESULTS
Key demographic and baseline clinical characteristics are summarized in Table 1. Baseline demographic and clinical characteristics were similar. At the 3- and 6-month follow-up periods, the within-group change in laboratory measurements were significant only for A1C, total cholesterol, and triglycerides in the intervention group. A comparison of between-group changes was not significant for any of the measurements except A1C. Over the 3- and 6-month period, A1C levels in the IBGMS group dropped from 8.8 ± 1.3 to 8.2 ± 0.91% (P < 0.05) and further dropped to 7.6 ± 0.74% (P < 0.001), respectively. The control group, on the other hand, dropped from 8.5 ± 1.2 to 8.3 ± 1.1% (P = 0.42) after 3 months but rose to 8.4 ± 1.4% (P = 0.51) after 6 months. Furthermore, the difference between the two groups at 6 months postintervention was statistically significant even after adjusting for baseline A1C levels (P < 0.05).
Table 1.
Demographic and baseline clinical characteristics of the study population
| Usual care* | Intervention* | P | |
|---|---|---|---|
| n | 23 | 24 | |
| Age (years) | 62 ± 7.2 | 57 ± 10 | 0.081 |
| Male/female | 15/8 | 14/10 | |
| Duration of diabetes (years) | 18.8 ± 6.4 | 18.8 ± 9.2 | 0.983 |
| BMI (kg/m2) | 33.1 ± 6.0 | 33.7 ± 6.4 | 0.732 |
| A1C (%) | 8.5 ± 1.2 | 8.8 ± 1.3 | 0.425 |
| Blood pressure (mmHg) | 132.6 ± 14.4/71.3 ± 9.2 | 129.5 ± 20.1/74.2 ± 15.5 | 0.545/0.439 |
| Total cholesterol (mmol/l) | 4.3 ± 1.3 | 4.2 ± 1.0 | 0.789 |
| HDL cholesterol (mmol/l) | 1.0 ± 0.3 | 1.2 ± 0.4 | 0.171 |
| LDL cholesterol (mmol/l) | 2.2 ± 1.0 | 2.2 ± 0.8 | 0.947 |
| Triglyceride (mmol/l) | 2.2 ± 0.9 | 1.7 ± 0.7 | 0.058 |
| Total-to-HDL cholesterol ratio | 4.4 ± 1.3 | 3.7 ± 1.0 | 0.065 |
| Creatinine (μmol/l) | 98.2 ± 30.1 | 85.4 ± 39.0 | 0.225 |
| Daily insulin dosage (IU) | 57.9 ± 45.0 | 60.4 ± 36.0 | 0.829 |
Data are means ± SD.
*Two patients in the usual-care group and one patient in the intervention group did not follow protocol and were excluded.
CONCLUSIONS
Patients with diabetes treated with insulin are often concerned about the risk of hypoglycemia and/or hyperglycemia. A significant number of patients require communication with their physician regarding changes in insulin dosage to achieve stated glucose targets. We utilized and tested an IBGMS for communication between patients and their endocrinologists.
In our study, patients randomized to the intervention group had a significant A1C improvement after 3 months that was sustained over 6 months. Both study groups were provided the resources for testing their blood glucose levels. However, the intervention group was asked to upload their blood glucose levels and were reminded to test. In the intervention group, the ongoing communication allowed the endocrinologist to recommend changes in the insulin dosage and regimen and/or patterns of testing as needed to direct redistribution of the insulin regimen. Although not statistically significant, the intervention group had a higher increase in the average insulin dosages after 3 months (3.5 vs. 1.7 IU) and 6 months (5.7 vs. 5.0 IU) and more patients with changes in insulin regimen (3 vs. 0). Biweekly encounters allowed the endocrinologist to suggest small but more frequent changes in insulin dosage and to change insulin regimens when required. We believe the ongoing communication, patient motivation, and the ability to act upon the results from SMBG ultimately may have led to the significant improvement in the glucose levels.
Advantages of using an IBGMS include automatic uploading, thus removing the need for patients to keep a written diary. In addition, the uploaded data are analyzed and displayed in table and graph formats, giving an accurate sense of glucose trends and monitoring frequency. This can save time for the physician and increase the accuracy of data interpretation (8). Limitations of the system include patient's unwillingness or lack of desire to use the Internet and the absence of a payment model for reimbursing out-of-office consultations.
Previously published studies involving a remote blood glucose monitoring system have also shown improvements in the A1C levels of type 2 diabetic patients who used an IBGMS system compared with control subjects (9–11). However, these studies involved a system that included nurses, dietitians, or an electronic medical records system, whereas our study was limited to the patient's endocrinologist monitoring and making recommendations on an IBGMS. This was not a substitute for the patient-physician interaction in a clinical setting; however, it significantly improved the patients' A1C, and over time we observed good glycemic control and patient satisfaction. This method of follow-up can reduce the inconvenience of booking appointments solely for giving recommendations on changes in insulin dosage and may be a more cost-effective method of follow-up, especially for rural patients who have limited access to a diabetes specialist. In summary, the use of an IBGMS is an effective method of improving glucose control.
Acknowledgments
This work was supported by the Endocrine Research Society, Vancouver, British Columbia, which received funding from ALR Technologies.
Abbott Diabetes Care donated glucose meters and test strips used in the study. ALR Technologies and Abbott Diabetes Care had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.
No other potential conflicts of interest relevant to this article were reported.
H.T. designed the study, developed the protocol, collected and interpreted data, wrote the manuscript, and reviewed/edited the manuscript. A.M. contributed to protocol development, collected, analyzed, and interpreted data, wrote the manuscript, and reviewed/edited the manuscript. S.R. contributed to study design and protocol development, interpreted data, and reviewed/edited the manuscript. All authors read and approved the final manuscript.
The authors acknowledge Abbott Diabetes Care for their generous gifts of glucose meters and test strips used by the patients. The authors also thank Dr. Daniel Holmes, St. Paul's Hospital (Vancouver, BC), for his valuable comments and contribution to the research design and methods section.
Footnotes
Clinical trial reg. no. NCT00814190, clinicaltrials.gov.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
References
- 1. Dailey G. Assessing glycemic control with self-monitoring of blood glucose and hemoglobin A(1c) measurements. Mayo Clin Proc 2007;82:229–236 [DOI] [PubMed] [Google Scholar]
- 2. Austin MM, Haas L, Johnson T, Parkin CG, Parkin CL, Spollett G, Volpone MT. Self-monitoring of blood glucose: benefits and utilization. Diabetes Educ 2006;32:835–836, 844–847 [DOI] [PubMed] [Google Scholar]
- 3. Nielsen JK, Christiansen JS. Self-monitoring of blood glucose—epidemiological and practical aspects. Diabetes Technol Ther 2008;10:S35–S42 [Google Scholar]
- 4. American Diabetes Association. Standards of medical care in diabetes—2008 (Position Statement). Diabetes Care 2008;31:S12–S54 [DOI] [PubMed] [Google Scholar]
- 5. Klonoff DC, Bergenstal R, Blonde L, Boren SA, Church TS, Gaffaney J, Jovanovic L, Kendall DM, Kollman C, Kovatchev BP, Leippert C, Owens DR, Polonsky WH, Reach G, Renard E, Riddell MC, Rubin RR, Schnell O, Siminiero LM, Vigersky RA, Wilson DM, Wollitzer AO. Consensus report of the coalition for clinical research-self-monitoring of blood glucose. J Diabetes Sci Technol 2008;2:1030–1053 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Hirsch IB, Bode BW, Childs BP, Close KL, Fisher WA, Gavin JR, Ginsberg BH, Raine CH, Verderese CA. Self-Monitoring of Blood Glucose (SMBG) in insulin- and non-insulin-using adults with diabetes: consensus recommendations for improving SMBG accuracy, utilization, and research. Diabetes Technol Ther 2008;10:419–439 [DOI] [PubMed] [Google Scholar]
- 7. Meystre S. The current state of telemonitoring: a comment on the literature. Telemed J E Health 2005;11:63–69 [DOI] [PubMed] [Google Scholar]
- 8. Azar M, Gabbay R. Web-based management of diabetes through glucose uploads: has the time come for telemedicine? Diabetes Res Clin Pract 2009;83:9–17 [DOI] [PubMed] [Google Scholar]
- 9. Kwon HS, Cho JH, Kim HS, Song BR, Ko SH, Lee JM, Kim SR, Chang SA, Kim HS, Cha BY, Lee KW, Son HY, Lee JH, Lee WC, Yoon KH. Establishment of blood glucose monitoring system using the internet. Diabetes Care 2004;27:478–483 [DOI] [PubMed] [Google Scholar]
- 10. Cho JH, Chang SA, Kwon HS, Choi YH, Ko SH, Moon SD, Yoo SJ, Song KH, Son HS, Kim HS, Lee WC, Cha BY, Son HY, Yoon KH. Long-term effect of the Internet-based glucose monitoring system on HbA1c reduction and glucose stability: a 30-month follow-up study for diabetes management with a ubiquitous medical care system. Diabetes Care 2006;29:2625–2631 [DOI] [PubMed] [Google Scholar]
- 11. Ralston JD, Hirsch IB, Hoath J, Mullen M, Cheadle A, Goldberg HI. Web-based collaborative care for type 2 diabetes: a pilot randomized trial. Diabetes Care 2009;32:234–239 [DOI] [PMC free article] [PubMed] [Google Scholar]