HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kwon, H.-S. Articles by Yoon, K.-H. Search for Related Content PubMed PubMed Citation Articles by Kwon, H.-S. Articles by Yoon, K.-H. Social Bookmarking                What's this?

Establishment of Blood Glucose Monitoring System Using the Internet

¹ Department of Internal Medicine, The Catholic University of Korea, Seoul, Korea
² College of Nursing, The Catholic University of Korea, Seoul, Korea
³ Department of Preventive Medicine, The Catholic University of Korea, Seoul, Korea

Address correspondence and reprint requests to Kun-Ho Yoon, MD, Division of Endocrinology and Metabolism, Department of Internal Medicine, The Catholic University of Korea, Kangnam St. Mary’s Hospital, 505 Banpo-Dong, Seocho-Ku Seoul, Korea, 137-701. E-mail: yoonk{at}catholic.ac.kr

	ABSTRACT

TOP ABSTRACT INTRODUCTION RESERCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
OBJECTIVE—The Internet is used worldwide as a communication tool. To improve the quality of diabetes control, we investigated the effectiveness of an Internet-based blood glucose monitoring system (IBGMS) on controlling the changes in HbA_1c levels.

RESEARCH DESIGN AND METHODS—We conducted a randomized clinical trial involving 110 patients who visited the outpatient clinic at the Kangnam St. Mary’s Hospital for 3 months. The study subjects were treated with IBGMS for 12 weeks, and the control group received the usual outpatient management over the same period. HbA_1c and other laboratory tests were performed twice, once at the beginning of the study and again at the end of the study.

RESULTS—The test results from the beginning of the study established that there were no significant differences between the two groups with respect to age, sex, diabetes duration, BMI, blood pressure, HbA_1c, and other laboratory data. On follow-up examination 12 weeks later, HbA_1c levels were significantly decreased from 7.59 to 6.94% within the intervention group (P < 0.001). At the end of the study, HbA_1c levels in the intervention group were significantly lower than in the control group after adjusting the baseline HbA_1c (6.94 vs. 7.62%; P < 0.001, respectively). Among patients with baseline HbA_1c <7.0%, the patients in the intervention group had lower HbA_1c than those in the control group (6.38 vs. 6.99%; P < 0.05). Among the patients with a baseline HbA_1c 7.0%, the difference between the two groups appeared more obvious: HbA_1c levels at the end of the study were 8.12%.

CONCLUSIONS—This new IBGMS resulted in a significant reduction of HbA_1c during the study period. We propose that this IBGMS be used as a method for improving diabetes control.

Abbreviations: IBGMS, Internet-based blood glucose monitoring system

	INTRODUCTION

TOP ABSTRACT INTRODUCTION RESERCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
The importance of tight blood glucose control for the subsequent prevention of diabetes complication is well established (1,2). In addition, improving glucose control will also reduce the enormous economic burden associated with the disease (3,4). Therefore, various strategies for diabetes management have been designed to improve the quality and efficiency of care for patients with diabetes. To promote adherence for patients and providers to follow practical guidelines, studies have been carried out to establish educational programs for patients and health care workers. Other studies, including giving feedback to the providers as well as programs that remind providers and their patients of these guidelines, have been experimented with (5–8). Recently, a small number of computer-based or electronic management systems have been reported to improve diabetes care (9–11). However, many barriers are recognized in providing this care system into the community health care system (12,13). Diabetes care means managing vulnerable patients with chronic disease consistently in the outpatient setting. Chin et al. (13) reported that providers in health care centers indicated a need to enhance behavioral changes in diabetic patients to improve the health care delivery system. Reforms improving the affordability, accessibility, and efficiency of care are also required.

Therefore, a new patient communication system that can facilitate the patient’s behavioral change and allow accessible, efficient, and continued medical advice and prescriptions is needed. The Internet has established itself as a worldwide communication system that allows a person to contact other people anywhere and anytime and exchange information directly online. To develop a more efficient delivery system for diabetes care, we designed a new diabetes management model using the Internet. By applying this new method, a diabetic patient can contact physicians online, provide information to physicians, and receive suitable recommendations from the physicians unhindered by location or time.

In this study, we compared diabetic patients who used the Internet-based blood glucose monitoring system (IBGMS) for 12 weeks and patients who used the conventional outpatient management system over the same period.

	RESERCH DESIGN AND METHODS

TOP ABSTRACT INTRODUCTION RESERCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
Study population
Men and women diagnosed with type 2 diabetes for 1 year were recruited from the outpatient clinic of Kangnam St. Mary’s Hospital Diabetes Center between May and August 200l. All enrolled participants in this study had Internet access in their homes for this specialized web-based diabetes management system and were 30 years of age. Patients were excluded if they had any significant diseases that were likely to affect the outcome and compliance of this study. Such diseases or conditions included heart failure, hepatic dysfunction, renal insufficiency with a creatinine level >1.5 mg/dl, and use of insulin pumps. Patients who had any history of participating in other programs that provided any information or education for diabetes management from specific websites other than ours were also excluded. Among the 180 patients asked to participate in the study, 110 patients agreed and were randomly divided into the intervention and control groups, each with 55 patients. Fifty-one patients in the intervention group and 50 patients in the control group completed the protocol over a 12-week period. Written informed consent was obtained from each participant.

Randomization and study design
Upon enrollment, each participant was assigned to either the intervention or control group using adaptive randomization (14) after receiving the written informed consent. Ethics committee approval was obtained from our institution and the review board of Korea Institution for Social and Health Affairs. Patients in the intervention group visited Kangnam St. Mary’s Hospital Diabetes Center in September 2001. At the initial interview, weight, height, and blood pressures were checked. Laboratory tests including HbA_1c, which was measured by high-performance liquid chromatography (Variant II HbA_1c analyzer; Bio-Rad, Montreal, Quebec, Canada), fasting plasma glucose, serum cholesterol, triglyceride, HDL, blood urea nitrogen, and creatinine levels were performed. Patients in the intervention group were taught how to use the Internet-based system and used the IBGMS for 12 weeks without outpatient management visits. After 12 weeks, in December 2001, subjects revisited the diabetes center, and the same laboratory tests were carried out. Patients in the control group were unaware of any knowledge regarding the new diabetes management system. They visited the diabetes center monthly and received their usual outpatient treatment from their physicians during the study period.

The Internet-based system
Patients in the intervention group contacted our website (www.biodang.com) and logged on whenever it was convenient for them. Participants in our study could contact our website at any place where Internet access was possible, but most participants accessed the website in their own homes. The patients sent information about their self-monitored blood glucose levels before and after eating (fasting and postprandial) and drug information including the types and dosages of insulin and oral antidiabetic medication used for diabetes control. In addition, when necessary, changes in their blood pressure or weight and any questions or detailed information the patient may have (for example, diet, exercise, hypoglycemic event, or other factors that can cause changes in the glucose level) were also recorded.

Figure 1 shows the screen viewed by the patients and the physicians. Patients were able to see the recommendations from their physicians as well as the laboratory data. Physicians, meanwhile, were able to inspect the information for each of their patients, including the blood glucose levels, medication, and other details provided by the patients. In addition, physicians could also review basic personal data, including past history, family history, smoking habits, anthropometry, BMI, blood pressure, and baseline laboratory data. After integrating the above given facts, the physicians sent optimal recommendations back to each patient.

View larger version (54K): [in this window] [in a new window]   Figure 1— Screen viewed by patients and physicians on the website (www.biodang.com) for diabetes management. Basic profile, past history, family history, and laboratory data are shown on the top. Self-monitored blood glucose levels are shown (middle), which are recorded as fasting or postprandial (breakfast, lunch, and dinner). Drug information, notes from the patients, and recommendations from the physicians are shown at the bottom.

If the patient in the intervention group did not forward a blood glucose level for >1 week, a warning message was sent. If individuals continued to not record their glucose levels for >3 consecutive weeks despite such warning messages, the patients were withdrawn from the intervention group. The telephone was not used for follow-up, and our participants were only contacted via the Internet through their own individual electronic chart system.

Participants in the control group met the professor two or three times during the 12 weeks. As customary in our outpatient clinic for diabetic patients, when the patients in the control group visited our center, they were provided recommendations about medication, medication dosage, lifestyle modification, and so on from the endocrinology specialist (professor, not fellows). When the doctor chose to consult for special education or if the patient wished, the dietitian or nurse came to aid with a more individualized and detailed information for lifestyle modification.

Analysis and statistics
The sample size was sufficient to provide a power of 80% to detect a 10% (absolute value 0.75%, estimated SD 1.0) change in HbA_1c at the 5% level of significance, based on our previously unpublished data. We assumed a dropout rate of 10% from this study. Statistical analysis was performed using SAS (version 8.12). All results were expressed as means ± SD. To assess the significant changes of the variables observed within each cohort between the baseline and follow-up, we used the paired sample Student’s t test. Comparison between the patients who were treated with the new management system using the Internet and those who had routine outpatient diabetes care delivered by physicians in the diabetes clinic was based on the independent Student’s t test. HbA_1c was treated as the major outcome variable. The significance of difference between the intervention and control groups was evaluated by ANCOVA that tested changes between the two groups from the preintervention to postintervention period while controlling the different baseline values on the outcomes of interest. A P value <0.05 was considered to be statistically significant.

	RESULTS

TOP ABSTRACT INTRODUCTION RESERCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
In this study, we recruited participants who were able to access the Internet in their homes for this specialized web-based diabetes management system all from our outpatient clinic of Kangnam St. Mary’s hospital through the hospital bulletin board. Among 180 patients who expressed their intention to participate in our study, 70 patients were excluded due to several reasons. Seventeen patients were excluded due to significant diseases that could affect the study outcome, and 25 patients were excluded due to history of participation in other web-based management programs. Twenty-eight patients refused to participate in our study after learning that our program includes frequent access to our homepage and randomization into the intervention and control group.

Of 110 patients who participated in this study, 101 (91.8%) completed the final clinical examination. The dropout rates were very similar between the intervention and control group. Of the 55 patients randomly selected to belong to the intervention group, two patients were withdrawn for not following the study protocol. Two more patients were also withdrawn from the study because they did not revisit the diabetes center after 12 weeks. Five patients in the control group were excluded for not revisiting the diabetes center. Therefore, we report data from the 51 patients in the intervention group and 50 patients in the control group who remained to complete the study protocol.

The clinical characteristics and baseline laboratory data are shown in Table 1. There were no significant differences between the two groups with respect to age, BMI, diabetes duration, and glucose control methods or in terms of laboratory data, including baseline HbA_1c, fasting plasma glucose, total cholesterol, triglyceride, HDL, blood urea nitrogen, and creatinine. There were, respectively, 13 and 17 patients with a diagnosis of hypertension in the control group and intervention group.

The mean number of logon times per each patient during the study period was 42.3 ± 32.2/patient in the intervention group. Only participants in the intervention group mailed their questions to their health care providers and received a reply. The mean number of questions posted by patients was 13.5 ± 14.7/patient during the study period. A detailed list of questions posted by patients to their doctors is summarized in Table 2. According to the study protocol, participants in the intervention group sent their blood glucose monitoring data at least once a week. Among the participants in the intervention group, 23 patients got warning messages from their providers via e-mail. A final warning message notifying withdrawal from our program was sent out to two patients when 3 weeks passed by without any further blood glucose data feedback; their participation in the study was discontinued.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION RESERCH DESIGN AND METHODS RESULTS CONCLUSIONS References

Many reasons exist for the improved glucose control in the intervention group. First, the patients have more frequent contact with the physicians than those in the control group. In addition, the patients in the intervention group received medical advice according to their most recent data, confirming their current state. These factors may have stimulated and motivated the patients to control glucose levels more enthusiastically. There have been some suggestions that the Internet may be a useful method for encouraging patients before (19,20). We also confirmed that patients with a baseline HbA_1c <7.0% in the intervention group maintained good control, whereas those in the control group showed significant HbA_1c aggravation. Therefore, the IBGMS not only allowed for the patients to maintain steady levels of glucose but improved the degree of their glucose control as well.

Internet-based disease management for chronic diseases are becoming popular universally. Numerous online education, exercise, and nutrition programs are used by the diabetic patients and their family. Most of the Internet disease management programs for diabetes evolved from focusing on emotional support to providing more information to engage the patients and allow self-management and counseling (21–23). No study has been made to test the direct efficacy of the Internet systems on controlling glucose control. However, our study results show evidence that online services are as effective as face-to-face guidance and treatment in managing diabetes.

The IBGMS introduced here has further potential for development to more advanced networking systems, which would allow close communication between the network center (the physician) and the personal computer (the patients). For example, the mobile phones or camera phones of the patients could allow the physicians to send messages immediately after the test results come out. In addition, the system would allow the networked feedback of patient information, such as details of a hypoglycemic accident or images of a diabetic ulcer to the networks center.

Although we have demonstrated that this new Internet-based system can reduce HbA_1c levels remarkably during a short-term study period of 12 weeks, the long-term effectiveness remains to be determined. To further the development, more trials and studies are needed.

Finally, we would like to comment that the Internet technology makes it possible to transcend the traditional face-to-face treatment for diabetes management. We expect this IBGMS will become generalized around the world because of the ease with which both the physician and patient can use the system. Furthermore, the advantages of continued Internet penetration into the general population, as well as the development of more advanced information technology will help the distribution of this new system in daily clinical practice.

	Acknowledgments

 
This work was supported by the 2001 Korea Health Promotion Research Program and the Korea Health 21 R&D Project, Ministry of Health and Welfare of Republic of Korea Grant 02-PJ1-PG3-21906-0004.

We thank John D. Piette for valuable comments and advice.

	Footnotes

 
A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

Received for publication April 4, 2003. Accepted for publication September 25, 2003.

	References

TOP ABSTRACT INTRODUCTION RESERCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 

1. DCCT Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin dependent diabetes mellitus. N Engl J Med 329:977–986, 1989
   
2. UK Prospective Diabetes Study (UKPDS) Group: Intensive blood glucose control with sulfonylureas or insulin compared with conventional treatment and the risk of complication in patients with type 2 diabetes (UKPD33). Lancet 352:837–853, 1988
   
3. Caro JJ, Ward AJ, O’Brien JA: Lifetime costs of complications resulting from type 2 diabetes in the U.S. Diabetes Care 25:476–481, 2002[Abstract/Free Full Text]
   
4. American Diabetes Association: Economic consequences of diabetes mellitus in the U.S. in 1997. Diabetes Care 21:296–309, 1998[Abstract]
   
5. Deichmann RE, Castello E, Horswell R, Friday KE: Improvements in diabetic care as measured by HbA_1c after a physician education project. Diabetes Care 22:1612–1616, 1999[Abstract/Free Full Text]
   
6. Vaughan NJ, Shaw M, Boer F, Billett D, Martin C: Creation of a District Diabetes Register using the DIALOG system. Diabet Med 13:175–181, 1996[Medline]
   
7. Belcher DW: Implementing preventive services: success and failure in an outpatient trial. Arch Intern Med 150:2533–2541, 1990[Abstract]
   
8. Lobach DF, Hammond WE: Computerized decision support based on a clinical practice guideline improves compliance with care standards. Am J Med 102:89–98, 1997[Medline]
   
9. Smith SA, Murphy ME, Huschka TR, Dinneen SF, Gorman CA, Zimmerman BR, Rizza RA, Naessens JM: Impact of a diabetes electronic management system on the care of patients seen in a subspecialty diabetes clinic. Diabetes Care 21:972–976, 1998[Abstract]
   
10. Levetan CS, Dawn KR, Robbins DC, Ratner RE: Impact of computer-generated personalized goals on HbA_1c. Diabetes Care 25:2–8, 2002[Abstract/Free Full Text]
    
11. Meigs JB, Cagliero E, Dubey A, Murphy-Sheehy P, Gildesgame C, Chueh H, Barry MJ, Singer DE, Nathan DM: A controlled trial of web-based diabetes disease management: the MGH Diabetes Primary Care Improvement Project. Diabetes Care 26:750–757, 2003[Abstract/Free Full Text]
    
12. Zgibor JC, Songer TJ: External barriers to diabetes care: addressing personal and health systems issues. Diabetes Spectrum 14:23–28, 2001[Abstract/Free Full Text]
    
13. Chin MH, Cook S, Jin L, Drum ML, Harrison JF, Koppert J, Thiel F, Harrane AG, Schaefer CT, Takashima HT, Chiu SC: Barriers to providing diabetes care in community health centers. Diabetes Care 24:268–274, 2001[Abstract/Free Full Text]
    
14. Freidman LM, Furberg CD, DeMets DL (Eds): Fundamentals of Clinical Trials, 2nd ed. Littleton, MA, PSG Publishing, 1985, p. 59–62
    
15. Robertson KJ: Diabetes and the Internet. Horm Res 57(Suppl. 1):S110–S112, 2002
    
16. Barrera M Jr, Glasgow RE, McKay HG, Boles SM, Feil EG: Do Internet-based support interventions change perceptions of social support: an experimental trial of approached for supporting diabetes self-management. Am J Community Psychol 30:637–654, 2002[Medline]
    
17. McKay HG, Feil EG, Glasgow RE, Brown JE: Feasibility and use of an Internet support service for diabetes self-management. Diabetes Educ 24:174–179, 1998
    
18. Zrebiec JF, Jacobbson AM: What attracts patients with diabetes to an Internet support group: a 21-month longitudinal website study. Diabet Med 18:154–158, 2001[Medline]
    
19. Feil EG, Glasgow RE, Boles S, McKay HG: Who participates in Internet-based self-management programs: a study among novice computer users in a primary care setting. Diabetes Educ 26:806–811, 2000[Abstract/Free Full Text]
    
20. Coile RC: The digital transformation of health care. Physician Exec 26:8–14, 2000[Medline]
    
21. Mckay HG, King D, Eakin EG, Seeley JR, Glascow RE: The diabetes network Internet-based physical activity intervention. Diabetes Care 24:1328–1334, 2001[Abstract/Free Full Text]
    
22. Glasgow RE, McKay HG, Boles SM, Vogt TM: Interactive technology, behavioral science, and family practice. J Fam Pract 48:464–470, 1999[Medline]
    
23. Eng TR, Gustafson DH, Handerson J, Jimison H, Patrick K: Introduction of evaluation of interactive health communications. Am J Prev Med 16:10–15, 1999
    

CiteULike

Del.icio.us

Digg

Reddit

Technorati

This article has been cited by other articles:

				L. McAndrew, S. H. Schneider, E. Burns, and H. Leventhal Does Patient Blood Glucose Monitoring Improve Diabetes Control?: A Systematic Review of the Literature The Diabetes Educator, November 1, 2007; 33(6): 991 - 1010. [Abstract] [Full Text] [PDF]

				C. Kim, H. Kim, J. Nam, M. Cho, J. Park, E. Kang, C. Ahn, B. Cha, E. Lee, S. Lim, et al. Internet Diabetic Patient Management Using a Short Messaging Service Automatically Produced by a Knowledge Matrix System Diabetes Care, November 1, 2007; 30(11): 2857 - 2858. [Full Text] [PDF]

				J.-H. Cho, S.-A. Chang, H.-S. Kwon, Y.-H. Choi, S.-H. Ko, S.-D. Moon, S.-J. Yoo, K.-H. Song, H.-S. Son, H.-S. Kim, et al. 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, December 1, 2006; 29(12): 2625 - 2631. [Abstract] [Full Text] [PDF]

				K. G. Shojania, S. R. Ranji, K. M. McDonald, J. M. Grimshaw, V. Sundaram, R. J. Rushakoff, and D. K. Owens Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA, July 26, 2006; 296(4): 427 - 440. [Abstract] [Full Text] [PDF]

				C. D. Saudek, R. L. Derr, and R. R. Kalyani Assessing Glycemia in Diabetes Using Self-monitoring Blood Glucose and Hemoglobin A1c JAMA, April 12, 2006; 295(14): 1688 - 1697. [Abstract] [Full Text] [PDF]

				C.-T. Liu, Y.-T. Yeh, T.-I Lee, and Y.-C. Li Observations on Online Services for Diabetes Management Diabetes Care, November 1, 2005; 28(11): 2807a - 2808a. [Full Text] [PDF]

				J. Chodosh, S. C. Morton, W. Mojica, M. Maglione, M. J. Suttorp, L. Hilton, S. Rhodes, and P. Shekelle Meta-Analysis: Chronic Disease Self-Management Programs for Older Adults Ann Intern Med, September 20, 2005; 143(6): 427 - 438. [Abstract] [Full Text] [PDF]

				G. T. McMahon, H. E. Gomes, S. Hickson Hohne, T. M.-J. Hu, B. A. Levine, and P. R. Conlin Web-Based Care Management in Patients With Poorly Controlled Diabetes Diabetes Care, July 1, 2005; 28(7): 1624 - 1629. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kwon, H.-S. Articles by Yoon, K.-H. Search for Related Content PubMed PubMed Citation Articles by Kwon, H.-S. Articles by Yoon, K.-H. Social Bookmarking                What's this?