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Web-based management of diabetes through glucose uploads: Has the time come for telemedicine?
diabetes research and clinical practice 83 (2009) 9–17
Contents lists available at ScienceDirect
Diabetes Research and Clinical Practice journal homepage: www.elsevier.com/locate/diabres
Review
Web-based management of diabetes through glucose uploads: Has the time come for telemedicine? Madona Azar a, Robert Gabbay *,b a
Division of Endocrinology, Diabetes and Metabolism, Pennsylvania State College of Medicine, Hershey, PA, United States Penn State Hershey Institute for Diabetes and Obesity, Pennsylvania State University, College of Medicine, 500 University Drive, HO44, Hershey, PA 17033, United States
b
article info
abstract
Article history:
This review focuses on the burgeoning use of web-based systems allowing patient-initiated
Received 5 June 2008
glucometer uploads to facilitate provider treatment intensification. Studies in type 1
Received in revised form
diabetes tended to show equivalent HbA1c improvements in both intervention and control
28 September 2008
groups without statistically significant difference. In contrast, type 2 patients seemed to do
Accepted 30 September 2008
better than controls with significant differences in HbA1c. Patients were the beneficiaries of
Published on line 3 December 2008
web-based diabetes management both through savings in time and cost. Major obstacles to wider implementation are patient computer skills, adherence to the technology, architec-
Keywords:
tural and technical design, and the need to reimburse providers for their care. # 2008 Elsevier Ireland Ltd. All rights reserved.
Telemedicine Diabetes care Web-based Glucometer upload Telecare
Contents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General description of internet-based diabetes management Impact on glucose control in type 1 diabetes . . . . . . . . . . . . . Impact on glucose control in type 2 diabetes . . . . . . . . . . . . . Impact on glucose control in pregnancy . . . . . . . . . . . . . . . . . Time and cost savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Obstacles to dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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* Corresponding author. Tel.: +1 717 531 3592; fax: +1 717 531 5726. E-mail address: [email protected] (R. Gabbay). 0168-8227/$ – see front matter # 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2008.09.055
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10 1.
diabetes research and clinical practice 83 (2009) 9–17
Introduction
The prevalence of diabetes has been alarmingly increasing in conjunction with the obesity epidemic. There are currently 23.6 million individuals in the United States suffering from diabetes [1], and 246 million worldwide [2]. Each year, 7 million people are diagnosed with the disease, and every 10 s, a person dies from diabetes-related causes [2]. Unfortunately, this worsening burden has not been met with an increase in healthcare providers dedicated to diabetes care. Indeed, a significant fraction of the population with diabetes is undertreated or has no access to proper and timely care, particularly in underserved areas with rural or minority populations. These patients are the most likely to suffer from the paucity of care because of a high prevalence of the disease doubled with poor access to health care. Since control of diabetes has been shown to decrease mortality and prevent long-term complications, it is critical that healthcare systems develop innovative ways to improve diabetes management, and provide timely care to patients. Diabetes is a disease where there is a need for care between visits and where ‘velocity to goal’ may be slow, especially in primary care settings, where the term ‘‘clinical inertia’’ was applied to ‘‘failure of providers to intensify therapy when appropriate’’ [3,4]. The internet has proven itself to be a fast, efficient and reliable source of communication. Its widespread availability makes it an attractive communication tool among patients and providers. Indeed, it has been useful in multiple medical fields, ranging from video-conferencing, to patient support and education websites. Telemedicine has, in particular, caught the attention of patients and caregivers. For patients, it has the advantage of providing a quick, efficient way to communicate with their providers. The latter can, in turn, provide feedback and advice in a timely manner, therefore making care more efficient and responsive to patient needs. Indeed, close monitoring of blood glucose at home is a key component of diabetes management, but without timely provider feedback, it has somewhat of a lesser value. For those patients living in rural areas, it is potentially invaluable to have access to their caregiver from the comfort of their homes, thus sparing them the time and cost of traveling. Patient suffering from chronic diseases in general, and diabetes in particular, have benefited from online support systems and web-based education with improved clinical outcomes [5], although the magnitude of the impact of telemedical support on diabetes care remains debatable [6–9]. This review will focus on the burgeoning area of web-based systems allowing patients and providers to communicate and relate blood glucose levels via glucometer uploads to intensify therapy. Endpoints that will be looked at include glycosylated hemoglobin, time and cost savings, obstacles to implementation, and, finally, reimbursement issues will be discussed.
2.
Methods
Eligible studies were published randomized controlled trials or observational studies where the intervention consisted of web-
based upload of blood glucoses and other pertinent data (such as diet, anti-hyperglycemic regimen, level of physical activity, intercurrent illness) by patients followed by clinician feedback. The primary outcome was glycosylated hemoglobin. The electronic database PubMed was searched using the following keywords ‘‘diabetes and internet’’, ‘‘web-based’’, ‘‘diabetes management and Internet’’, ‘‘telemedicine’’, ‘‘meter upload’’, ‘‘telecare’’. English literature published in PubMed from January 1990 through October 2007 was selected. The goal of this review was not to perform a meta-analysis. Therefore, we elected to include non-randomized controlled trials in the literature review. Of the studies initially identified, eighteen met our eligibility criteria and were subsequently retained. We excluded studies that did not specifically focus on web-based glucose upload followed by clinician feedback.
3. General description of internet-based diabetes management Telemedicine interventions for diabetes management can range from very simple systems where patients and clinicians communicate by phone, email or short message service (SMS) to complex web interfaces. Patients will typically upload home meter data and may enter other pertinent data such as antihyperglycemic regimens, dietary habits, activity level, and medical history. Providers (physicians or nurses) then review this data and provide feedback regarding medication adjustments and lifestyle modification guidelines. Some available telemedicine systems include telephone assistance systems where patients will periodically receive phone calls from their clinician to help to adjust their regimen and/or other counseling [10]. Others, labeled ‘‘visit by visit systems’’, provide feedback at each clinical encounter rather than between visit care. Patients upload glucometer data from home, but will wait to get feedback at the visit with their provider. There are as well complete assistance systems. These systems provide day-by-day assistance to patients on therapeutic adjustments, diet and exercise. These systems include a built-in ‘‘patient unit’’ [10] as well as a ‘‘provider’’ unit. However, these systems are complex, costly and often require extensive user training. These are beyond the scope of this discussion, which will focus on web-based blood glucose upload followed by provider feedback, either by email and/or phone.
4.
Impact on glucose control in type 1 diabetes
Eight studies were identified that focused exclusively on type 1 diabetes patients, four of them involving pediatric populations [11–14], one pregnant type 1 diabetics [15]. The remaining three [16–18] enrolled adult type 1 patients. These studies are summarized in Table 1. The interventions consisted of modem transmission of blood glucoses with clinician feedback approximately every 2 weeks, whereas control patients received usual care. The clinician involved in feedback could be a physician [11,14,16,17] and/or a nurse under a physician’s supervision [11,18]. The majority of patients received feedback
Table 1 – Summary of studies for type 1 diabetes. Authors
Intervention group
Chase et al. [11]
70 adolescents type 1 DM 15–20 years
Clinic visit at 0, 3 and 6 months
8.9 1.1%
Marrero et al. [12]
106 type 1 pediatric patients multiple daily injections and poor control 28 teenagers with type 1 DM on multiple daily injections, poor control
Modem transmission BG q 2 weeks and clinician feedback clinic visit at 0 and 6 months with omission of a 3-month visit Modem transmission q 2 weeks clinician feedback
Conventional care
9.4
Glucometer data transmitted q 2 weeks via glucobeeb, web-based tools clinician feedback as 1 min voicemail the following week
Conventional care control introduced to glucobeeb after 6 months and showed a trend to decrease A1c at 3 and 6 months (9.4, 8.9, 8.7%) NA
Cross-over RCT
8.4%
Conventional care: clinic visit q 2 months
8.3 2.3%
Upload BG as well, but no clinician feedback; were free to contact RN for questions q 3 months clinic visit Conventional care
Cadario et al. [13]
Liesenfeld et al. [14]
61 pediatric type 1 DM patients in rural areas
Glucometer data transmitted through modem to diabetes center phone consultation with clinician
Gomez et al. [16]
10 type 1 DM with inadequate control 48 type 1 DM on intensified regimen
Modem transmission q 2 weeks clinician feedback within 24 h Modem transmission data q 2 weeks clinician feedback by phone q 2–4 weeks Modem transmission q 2 weeks at least BG 4 times/day clinician feedback within 24 h q 3 months clinic visit
Biermann et al. [17]
Montori et al. [18]
31 type 1 adults on intensive insulin regimens and inadequate control HbA1c > 7.8%
Wojcicki et al. [15]
30 pregnant type 1 DM
System automatically transfers data every night to clinical unit 3 years
Control group
Baseline A1c intervention
A1c end of study intervention
Baseline A1c controls
A1c end of study controls
9.0 1.2%
8.6 1.7%
NS
NS
9.6 (10% at 12 months)
9.9
9.7 (10.3% at 12 months)
NS
NS
NA
9.5
9.0 (3 months) 9.1 (6 months)
9.1
9.4 @ 3 months, 9.4 @ 6 months
NS
NS
NA
NA
A1c decreased by 0.4% reduced mean BG (167 to 158 mg/dL, p < 0.01) reduced standard deviation of BG 7.9%
NA
NA
8.1%
8.2%
NS
NS
6.9 1.3%
8.0 2.1%
7.0 1.0%
NS
NS
9.1 1.2%
7.8 1.2%
8.8 1.2%
8.2 1.3%
NA
0.03
7.96 1.1%
6.8 0.9%
8.1 1.7%
6.7 0.9%
NS
8.6 1.2%
P baseline
P end of study
Cost
Satisfaction
Complications DM (DKA, hypo/ hyperglycemia)
Similar p = 0.81
Similar
Similar
Similar
NA
NA
NA
NA
Reduction in hypoglycemia frequency 5.2–3.3 at the end of the study reduction in BG variability NA
Effective (travel and days off work expenses saved)
Effective travel expenses and time saved NA
85% telecare patients felt telecare better NA
Similar incidence hypoglycemia
diabetes research and clinical practice 83 (2009) 9–17
Patients
Similar
Similar
11
12
Table 2 – Summary of studies with type 2 diabetes or mixed. Authors
Patients
110 type 2 DM
Cho et al. [20]
80 type 2 DM
Kwon et al. [21]
185 16.2% type 1; 82.7% type 2; 1.1% secondary
Hee-Sung [22]
51 subjects 25 intervention 26 controls type 2 DM
McMahon et al. [23]
104 diabetic patients with HbA1c > 9%
Kim and Jeong [24]
60 patients. 30 in each group
Bergenstal et al. [25]
47 patients: 24 intervention 23 controls
Patient log BG, weight, diabetic regimen receive feedback Patients upload BG online and receive feedback q 2 weeks clinic q 3 months Patients upload online or send by SMS their BG, meds, diet, hypoglycemia, with clinician feedback Patients log their BG and medications; nurses send weekly feedback via internet and SMS DM education patients upload BG and BP, messages, questions; feedback within 1 day Patients log their BG and medications clinician feedback via weekly SMS Weekly modem transmission of BG
Control group
Baseline A1c intervention
A1c end of study intervention
Baseline A1c controls
A1c end of study controls
P baseline
P end of study
Cost
Satisfaction
Complications DM (DKA, hypo/ hyperglycemia)
Usual care
7.59 1.43%
6.94 0.13%
7.19 1.17%
7.62 0.13%
NS
<0.001
NA
NA
NA
Usual care
7.7 1.5%
6.9 0.9%
7.5 1.3%
7.5 1.5%
0.457
0.009
NA
NA
NA
NA
7.5 1.5%
7.0 1%
NA
NA
NA
0.003
NA
Good patient satisfaction
NA
Endocrinologist 1–2 times/12 weeks
HbA1c < 7% 6.92 0.35%, HbA1c > 7% 9.35 1.72%
HbA1c < 7% 6.71 0.61%, HbA1c > 7% 7.2 1.34%
HbA1c < 7% 6.71 0.38%, HbA1c > 7% 8.24 0.98%
HbA1c < 7% 7.14 0.52%, HbA1c > 7% 8.02 0.96%
DM education usual care
10 0.8%
1.6 1.4% the higher the number of uploads, the higher decline in HbA1c 6.94 1.04% @ 3 months, 7.04 1.39%
9.9 0.8%
8.09 1.72%
Phone communication with clinician
0.9 1.4%
7.59 1.09%
NA
NA
1.2 1.4%
NS
<0.005
NA
NA
7.66 0.91% @ 3 months, 7.70 0.90% @ 6 months
NS
<0.05
NA
NA
0.4 0.7%
NS 0.18
Similar
diabetes research and clinical practice 83 (2009) 9–17
Kwon et al. [19]
Intervention group
diabetes research and clinical practice 83 (2009) 9–17
by telephone call [11,14,17,18] or using text messages [16]. Most studies were small (<50 subjects) randomized controlled trials [11–15,17,18] with an intervention and control group. One study [16] was a cross-over randomized controlled trial within the same group. Most patients that were enrolled had inadequate control at baseline, with HbA1c > 8% [11–13,16–18] and occasionally > 9% [12,13]. In the studies that had control groups [11,13,15,17,18], both groups had a significant improvement in HbA1c at the end of the intervention, but there was no significant difference among intervention and control groups. For instance, in one study, mean baseline HbA1c for intervention and control patients were respectively 8.9 and 9.0%; at the end of the trial, mean HbA1c in intervention and control patients decreased to 8.6% [11]. Similarly, in another study, intervention and control patients started with an HbA1c of 8.3 and 8.0%, decreasing at 4 months to 6.9 and 7.0% respectively, without significant difference among groups [17]. This suggests a Hawthorne effect whereby the mere fact of being enrolled in a study improved outcomes, without any added impact of telemedicine. Interestingly, in the only small study that did show outcome differences among intervention and control patients [18], the control group had the advantage of being able to upload blood glucoses as well, therefore isolating the effect of clinician feedback on self-monitored blood glucose values. 29% of intervention patients reached a target HbA1c < 7% at 6 months (4/14) versus only 7% in the control group (1/15); there was a statistically significant difference in HbA1c at 6 months. Intervention patients transmitted their blood glucoses 6 times the first 2 months versus 5 times for controls, with some dropoff over the last 2 months. At 6 months, there was a 10.7% increase from baseline in the number of glucose testing in the intervention arm versus no increase in controls, demonstrating improved compliance with the intervention. Interestingly, the study focused on adult type 1 diabetics rather than pediatric patients, and the intervention arm had more documented dose changes (14 vs. 6). In the studies that looked into acute diabetes complication rates of severe hypoglycemia and diabetic ketoacidosis, the incidence was similar in both intervention and control groups [11,18]. Overall, these type 1 studies had several limitations which should be acknowledged in the interpretation. These include generally small patient groups (<50 patients), limited duration of follow-up (3–6 months in most studies, one up to 12 months [12]). Additionally, some studies reported significant noncompliance to the protocol and subsequent dropout. For instance, one group reported a 14.3% dropout rate (5/35 patients) in the intervention group [11], which was comparable to the control group, whereas another study [14] noted that 11.5% of the participants did not complete the protocol, more than half for having purposefully manipulated their data.
5.
13
diabetes patients, without serious co-morbidities. Results are summarized in Table 2. Feedback for medication adjustment was provided by physicians [19–21] or nurses [22] whereas nutritional and other lifestyle modification guidelines were provided by nurses and/ or dietitians [20–22]. Recommendations could be sent on the study website where patients could access them when they logged into their personal webpage [19–23]. Others received additional feedback by SMS [19,22,24] or telephone calls [25]. Dropout rates were noted to be comparable among intervention and control patients in the studies that reported them [20,21]. When compared to type 1 studies, the patients enrolled in type 2 trials seemed to have somewhat better control at baseline, with HbA1c ranging from 7%–8% in intervention and control groups [19–22], except in one study where patients included had to have an HbA1c > 9% [23]. In contrast to type 1 diabetes, type 2 patients seemed to do better than controls with telemedicine. Indeed, most of the studies showed significant difference among intervention and control groups at the end of the trial [19–24]. For instance, looking at Kwon et al. results [19], intervention patients started with an HbA1c of 7.59% and controls, 7.19%; at the end of the trial, their HbA1c was 6.94% versus 7.62% for controls ( p < 0.001). Dropout rates were similar among both groups. Cho et al. [20] showed comparable improvements. Their baseline HbA1c was 7.7% in intervention patients, versus 7.5% in controls; at the end of 30 weeks, the telemedicine group reached an HbA1c of 6.9% versus 7.5% for controls ( p = 0.009). Furthermore, they demonstrated a lower variability in blood glucoses and HbA1c in their intervention patients compared to controls. An HbA1c fluctuation index was significantly lower in the intervention group. Some studies not only looked into fasting blood glucose, but also evaluated the 2-h post-prandial blood glucose which is increasingly recognized as a major contributor of the cardiovascular risk related to diabetes [26]. For instance, in one study, there was an improvement in FBG and 2 h post-prandial blood glucose in the intervention group with HbA1c < 7% [22]. Patients who seemed to benefit most from the intervention were those with a baseline HbA1c > 7%, who had an average 2.15% decrease in HbA1c [22]. Another study demonstrated improved 2-h post-prandial blood glucoses in the intervention group (from 256.2 at baseline to 192.6 at 6 months) [27]. The degree of improvement in HbA1c in type 2 studies seemed to have been directly correlated to adherence to the intervention protocol. Indeed, patients who had more data uploads and visited the study website more often were the ones who did better [23]. ‘‘Persistent users’’ had an average decrease in HbA1c of 1.9% at 12 months, whereas ‘‘intermittent users’’ had a 1.2% decrease in HbA1c at 12 months, which was comparable to control patients. These authors also demonstrated a direct correlation between the decline of HbA1c and the number of data uploads. Patients in the higher tertile for data uploads had the greatest improvement in HbA1c ( 2.1%).
Impact on glucose control in type 2 diabetes 6.
Seven studies were identified that involved patients with type 2 diabetes or, in some, mixed populations [19] (including type 1, type 2 and secondary diabetes). These were adult, stable
Impact on glucose control in pregnancy
Pregnant patients with diabetes are a subgroup in whom quick velocity to goal is critical in order to avoid fetal and obstetrical
14
diabetes research and clinical practice 83 (2009) 9–17
complications. These patients might benefit significantly from telemedical support. Unfortunately, few researchers have addressed this particular group. Wojcicki et al. studied 32 pregnant patients with type 1 diabetes [15]. They assessed mean blood glucoses (MBG) and a measure of glycemic variation. Both groups had similar control at baseline and were< 16 weeks pregnant. Although there was a trend towards better MBG and glucose variability, there was no significant difference at the end of the study. Another small study involved 11 pregnant patients with type 1 diabetes using web-based glucose upload and compared it to 10 matched controls [28]. Telemedicine patients checked their blood glucoses 4–6 times/day and transmitted once a week. Feedback was provided by phone. HbA1c was improved in the telemedicine group (from 6.1 to 5.4%) over an average of 22 weeks, whereas the control patients’ HbA1c changed from 6.2 to 5.7%. Average and fasting blood glucoses improved as well as glucose variability. These were preliminary data and no comparison was made among groups. Kruger et al. [27] evaluated the impact of modem transmission of blood glucoses in gestational diabetes mellitus. No significant differences in telephone consultation time, clinic work-flow efficiency and accuracy were observed between groups. On the other hand, patient and staff satisfaction were improved in the intervention group.
7.
Time and cost savings
Telemedicine offers the opportunity for significant cost savings. From patients’ perspective, these include travel time and lost work time for appointments. From clinicians’ perspective, well-presented glucose upload data can facilitate analysis and treatment decisions while freeing up time to ultimately improve access to care. However, reimbursement for services will be a critical aspect to consider. Five studies addressed time and cost savings. Chase et al. showed significant cost savings by modem transmission of blood glucose and clinician feedback. There was an average saving of 142 US dollars per 6 months of care ( p < 0.001), mostly in saved patient travel expenses and days off work to go to the clinic visits [11]. Biermann also demonstrated time and cost savings for patients [17]. Data transmission patients spent at 4 months an average of 554 min compared to 656 min for control patients including travel, waiting room and consultation times. These differences narrowed at 8 months with 974 and 959 min for intervention and control patients respectively. Time was saved mostly in travel while time spent by physicians in the telemedicine group was higher (50 vs. 43 min/month for the 4 months period, reflecting more patient contact and time spent reviewing data). Cost saving consisted mostly of saved travel expenses and days off work. The estimated cost/year in the telemedicine group was 389 euros/year including modem fees, phone bills, data transfer, travel costs and costs for not working, versus 1037 euros/year for controls; the 648 euros saved per year were in travel costs and costs of not working. Ultimately, there was no significant difference in HbA1c among groups at the end of the trial, despite a significant improvement within groups [17]. Cho et al. [20] reported time
savings for patients in the web-based management group, mostly in travel time, and office consult and time waiting in the office. The impact of these interventions on provider time has generally not been beneficial. In Montori’s study, clinicians’ time spent in reviewing data and providing feedback to patients was higher [18] while Bergenstal showed no significant time saving for the healthcare provider by reviewing web-based patient data, in comparison to usual care in which data was provided by phone [25]. However, it was not quantified in the published article. In conclusion, it seems that patients are the most likely beneficiaries of web-based management of diabetes, both in time and cost saved. Employers might also find telemedicine attractive by saving employee time off, therefore increasing overall productivity. This might further encourage direct reimbursement for this technology.
8.
Obstacles to dissemination
One major issue seems to be developing computer skills in older computer naive patients. Potential barriers include motor dexterity (mouse and keyboard manipulation), faulty computer skills. Overall, patients with greater dexterity and literacy seem to far better [29]. Unfortunately, patients who did not have internet access or did not know how to use the internet were often excluded from the trials and the duration of training was not specified in most studies except for one which reported 2.3 h/patient (range 1.0–6.6) for training the intervention group [23] and, another, 30 min [22]. Enrolled patients had to be familiar with internet use at baseline to be eligible. Patient intelligence quotient (IQ) did not seem to have any impact on results in one study [15]. The 32 study patients were subdivided into ‘‘low intelligence: IQ < 100’’ and ‘‘high intelligence: IQ > 100’’. Within each IQ group, there was not difference in mean blood glucose or glucose variability indices among intervention and control patients, although the limited number of subjects likely underpowered the study to demonstrate any difference related to IQ. No comparison of ‘‘low intelligence’’ to ‘‘high intelligence’’ patients in terms of blood glucose control and impact of telemedicine were described. Other potential obstacles include architectural and technical issues such as security, privacy and confidentiality as well as ease of use. Data transfer was an issue in <5% of transfers in one study [17], and was generally solved by simply repeating the transfer. Problems that needed more than 5 min to solve occurred in< 1% of the 521 data transfers. Glucometer technology is also an important potential obstacle. Different glucometer brands have each their own downloading software programs, which are not compatible with each other. It becomes technically and economically challenging to provide different types of hardware and software to accommodate each and every patient’s glucometer although some vendors have begun to supply potential integrated solutions [30]. Patient adherence seems to be an issue according to some authors. Among other reasons, non-adherence with data
diabetes research and clinical practice 83 (2009) 9–17
uploads were reported as a cause for dropout, as well as not showing up to clinic appointments. One author reported that 7/61 (11.5%) participants did not complete the protocol, 4/7 for manipulating their data [14]. Interestingly, the patients who manipulated their data and/or dropped out were the ones with the highest HbA1c upon enrollment (9.7% for those who terminated early vs. 7.7% for those who completed the protocol). Therefore, motivation and straightforwardness are key elements for the success of telemedicine support. Reimbursement is a critical aspect of telemedicine dissemination. At this point, there is no mechanism to reimburse physicians for non-face-to-face services. Although new codes have been issued for phone and internet services, these are not covered under the current Medicare program. Another potential issue will be fee sharing among physicians and web administrators which will have to be defined specifically to avoid conflicts [30]. Ultimately, the potential cost savings to employers from employee absenteeism are likely to drive reimbursement opportunities. Some employers have begun to look at reimbursing these types of services. Medical licensure and liability coverage are potential concerns that also need to be addressed.
9.
Discussion
Telemedicine can be a useful tool to provide diabetes care and represents a potential solution for long distances and provider shortage. It cannot replace patient visit and direct interaction with providers, but it can supplement between-visit care and improve ‘velocity to goal’ —the speed of attainment of adequate metabolic control by the patient. Telemedicine can also potentially save time and travel expenses for patients [11,17,20]. A major advantage to electronic glucose upload is accuracy. When compared to paper data capture by patients, not only is electronic upload bound to be 100% accurate, but it also precludes back-filling and forward-filling, as well as data manipulation [31]. Practitioners therefore have an accurate sense of glucose levels and monitoring frequency. The impact of electronic glucose uploads and provider feedback on diabetes control is still controversial. Studies in patients with type 1 did not demonstrate any superiority of telemedicine over usual care — both intervention and control patients seemed to improve without any significant difference among groups. Most of these studies involved teenagers or young adults with very poor control at baseline, and groups were small. The studies may have not been powered to demonstrate any benefit in telemedicine support. Young patients with diabetes may also have psycho-social difficulties interfering with their diabetes self-care, and being enrolled in a study, regardless of which arm, may be enough to improve their diabetes control. Many patients with poor control at baseline may have adherence issues related to behavior and medications that may not be adequately addressed by telemedicine. Several previous reviews of telemedicine impact have confirmed a lack of statistically significant changes in HbA1c [18,32,33] although focus has primarily been on type 1 diabetes patients, and, unlike the current review, have generally not focused on blood glucose upload and clinician feedback. It
15
may be that for type 1 patients who are poorly controlled, factors other than clinical inertia and frequent provider insulin adjustment is needed to optimize care. Other approaches such as self-management skills, case management, behavior change approaches like motivational interviewing and psychosocial assistance may be more important. In contrast with type 1 patients, type 2 diabetics enrolled in the intervention arm of telemedicine studies seemed to fare better than controls. Surprisingly, older patients (averaging 50–60 years old) seem to be responding better. One might have assumed that young patients with diabetes would be more computer savvy and amenable to telemedicine support. It seems rather that type 2 patients seem to accept and manipulate the technology efficiently with the greatest benefit. Indeed, a recent study involving patients> 60 years of age demonstrated that these individuals responded very well to web-based interventions, even without prior internet experience [34]. Perhaps the potential target population for telemedicine would be patients who are closer to goal and more motivated to improve their care. One potential advantage of between visit care offered by telemedicine is an improvement in ‘velocity to goal’. This concept is defined as how fast the patient reaches good diabetes control, rather than remain exposed to a high glycemic burden for prolonged periods of time. Brown et al. [35] demonstrated that the average time before treatment intensification occurred may be as long as 27–35 months with a mean HbA1c of 8.8–9.1%. Telemedicine provides a significant opportunity to give providers updated clinical data for more frequent medication adjustment. Future directions could be providing clinicians with online algorithms that can assist clinical decision by interpreting the data from the glucometer downloads. Glucose variability may also be improved by telemedicine glucose meter upload and provider feedback. Glucose variability is increasingly recognized as an independent risk factor for complications of diabetes by increased free radical production and oxidative stress [36] which may contribute to the pathogenesis of diabetes complications. In pregnant patients with type 1 diabetes [15], indices of glycemic variability improved in the telecare group, without reaching statistical significance, which can be partially attributed to the small sample size. Other studies demonstrated similar results with type 2 diabetics who had decreased glucose variability [20] and/or decreased 2-h post-prandial blood glucose with the intervention [22,24]. Indeed, glycemic variability and velocity to goal should be outcome measures in future studies. Some of the weaknesses of these studies include, as aforementioned, small sample size, limited duration and for some, lack of control groups and/or suboptimal study design. There is also no evidence that the improvement in diabetes control observed in these studies persists beyond the short trial period. Finally, there is no data on the impact of telemedicine on long-term complications of diabetes.
10.
Summary and conclusions
Telemedicine based glucose meter upload and provider feedback continues to offer promise to improve diabetes outcomes. However, despite several randomized trials in type
16
diabetes research and clinical practice 83 (2009) 9–17
1 diabetes, results have been disappointing. Glycemic control in this patient population may require other interventions including face-to-face visits or adherence and behavior based approaches. In contrast, data supporting effectiveness in type 2 diabetes is more robust. Although the studies typically involve older individuals, technology acceptance has been good. Future studies are needed to better define patients who would most benefit from that approach, in comparing type 1 and type 2 patients in head-to-head randomized controlled trials. Attention to other outcomes measures such as velocity to goal and glycemic variability are needed as well. Reimbursement remains a barrier to widespread adoption of this technology. The greatest savings to date have been in patient time and loss of work. The hope is that improvements in employee productivity will drive employer based initiatives for reimbursement. Ultimately, if reimbursement is addressed, providers may find that more accurate glucose results provided in a more organized format could facilitate better diabetes care and outcomes. Medical legal issues related to timeliness of provider response and privacy further complicate future adoption. Ultimately, patient engagement in this technology over longer time periods will need to be addressed. Future studies should focus on indentifying target populations most likely to benefit, expansion of study outcomes to include glycemic variability and velocity to goal, as well as associated costeffectiveness. The addition of decision support algorithms may also help alleviate provider and patient burden.
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management: the T-IDDM project, Computer Methods and Programs in Biomedicine 69 (2) (2002) 147–161. H.P. Chase, J.A. Pearson, C. Wightman, M.D. Roberts, A.D. Oderberg, S.K. Garg, Modem transmission of glucose values reduces the costs and need for clinic visits, Diabetes Care 26 (5) (2003) 1475–1479. D.G. Marrero, J.L. Vandagriff, K. Kronz, N.S. Fineberg, M.P. Golden, D. Gray, et al., Using telecommunication technology to manage children with diabetes: the Computer-Linked Outpatient Clinic (CLOC) Study, The Diabetes Educator 21 (4) (1995) 313–319. F. Cadario, M. Binotti, M. Brustia, F. Mercandino, G. Moreno, S. Esposito, et al., Telecare for teenagers with type 1 diabetes: a trial, Minerva Pediatrica 59 (4) (2007) 299–305. B. Liesenfeld, R. Renner, M. Neese, K.D. Hepp, Telemedical care reduces hypoglycemias and improves glycemic control in children and adolescents with type 1 diabetes, Diabetes Technology & Therapeutics 2 (4) (2000) 561–567. J.M. Wojcicki, P. Ladyzynski, J. Krzymien, E. Jozwicka, J. Blachowicz, E. Janczewska, et al., What we can really expect from telemedicine in intensive diabetes treatment: results from 3-year study on type 1 pregnant diabetic women, Diabetes Technology & Therapeutics 3 (4) (2001) 581–589. E.J. Gomez, M.E. Hernando, A. Garcia, F. Del Pozo, J. Cermeno, R. Corcoy, et al., Telemedicine as a tool for intensive management of diabetes: the DIABTel experience, Computer Methods and Programs in Biomedicine 69 (2) (2002) 163–177. E. Biermann, W. Dietrich, E. Standl, Telecare of diabetic patients with intensified insulin therapy. A randomized clinical trial., Studies in Health Technology and Informatics 77 (2000) 327–332. V.M. Montori, P.K. Helgemoe, G.H. Guyatt, D.S. Dean, T.W. Leung, S.A. Smith, et al., Telecare for patients with type 1 diabetes and inadequate glycemic control: a randomized controlled trial and meta-analysis, Diabetes Care 27 (5) (2004) 1088–1094. H.S. Kwon, J.H. Cho, H.S. Kim, J.H. Lee, B.R. Song, J.A. Oh, et al., Development of web-based diabetic patient management system using short message service (SMS), Diabetes Research and Clinical Practice 66 (Suppl. 1) (2004) S133–S137. J.H. Cho, S.A. Chang, H.S. Kwon, Y.H. Choi, S.H. Ko, S.D. Moon, 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 29 (12) (2006) 2625–2631. H.S. Kwon, J.H. Cho, H.S. Kim, B.R. Song, S.H. Ko, J.M. Lee, et al., Establishment of blood glucose monitoring system using the internet, Diabetes Care 27 (2) (2004) 478–483. K. Hee-Sung, Impact of Web-based nurse’s education on glycosylated haemoglobin in type 2 diabetic patients, Journal of Clinical Nursing 16 (7) (2007) 1361–1366. G.T. McMahon, H.E. Gomes, S. Hickson Hohne, T.M. Hu, B.A. Levine, P.R. Conlin, Web-based care management in patients with poorly controlled diabetes, Diabetes Care 28 (7) (2005) 1624–1629. H.S. Kim, H.S. Jeong, A nurse short message service by cellular phone in type-2 diabetic patients for six months, Journal of Clinical Nursing 16 (6) (2007) 1082–1087. R.M. Bergenstal, R.L. Anderson, D.M. Bina, M.L. Johnson, J.L. Davidson, B. Solarz-Johnson, et al., Impact of modemtransferred blood glucose data on clinician work efficiency and patient glycemic control, Diabetes Technology & Therapeutics 7 (2) (2005) 241–247. A. Ceriello, Postprandial hyperglycemia and diabetes complications: is it time to treat? Diabetes 54 (1) (2005) 1–7.
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[27] D.F. Kruger, K. White, A. Galpern, et al., Effect of modem transmission of blood glucose data on telephone consultation time, clinic work flow, and patient satisfaction for patients with gestational diabetes mellitus, Journal of the American Academy of Nurse Practitioners 15 (8) (2003) 371–375. [28] D. Frost, W. Beischer, Telemedicine in the management of pregnancy in type 1 diabetic women, Diabetes Care 23 (6) (2000) 863–864. [29] J. Pevzner, D.R. Kaufmann, C. Hilliman, S. Shea, R.S. Weinstock, J. Starren, Developing computer skills and competencies in seniors, in: AMIA Annual Symposium Proceedings/AMIA Symposium, 2005, p. 1078. [30] J.M. Wojcicki, Analysis: transfer of the data collected by the patient to the health care providers: problems and solutions, Diabetes Technology & Therapeutics 7 (2) (2005) 248–252. [31] D.C. Klonoff, Diabetes and telemedicine: is the technology sound, effective, cost-effective, and practical? Diabetes Care 26 (5) (2003) 1626–1628.
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[32] A. Farmer, O.J. Gibson, L. Tarassenko, A. Neil, A systematic review of telemedicine interventions to support blood glucose self-monitoring in diabetes, Diabetes Medicine 22 (10) (2005) 1372–1378. [33] S. Montani, R. Bellazzi, S. Quaglini, G. d’Annunzio, Metaanalysis of the effect of the use of computer-based systems on the metabolic control of patients with diabetes mellitus, Diabetes Technology & Therapeutics 3 (3) (2001) 347–356. [34] G.E. Bond, R. Burr, F.M. Wolf, M. Price, S.M. McCurry, L. Teri, The effects of a web-based intervention on the physical outcomes associated with diabetes among adults age 60 and older: a randomized trial, Diabetes Technology & Therapeutics 9 (1) (2007) 52–59. [35] J.B. Brown, G.A. Nichols, A. Perry, The burden of treatment failure in type 2 diabetes, Diabetes Care 27 (7) (2004) 1535– 1540. [36] M. Brownlee, I.B. Hirsch, Glycemic variability: a hemoglobin A1c-independent risk factor for diabetic complications, JAMA 295 (14) (2006) 1707–1708.
Contents lists available at ScienceDirect
Diabetes Research and Clinical Practice journal homepage: www.elsevier.com/locate/diabres
Review
Web-based management of diabetes through glucose uploads: Has the time come for telemedicine? Madona Azar a, Robert Gabbay *,b a
Division of Endocrinology, Diabetes and Metabolism, Pennsylvania State College of Medicine, Hershey, PA, United States Penn State Hershey Institute for Diabetes and Obesity, Pennsylvania State University, College of Medicine, 500 University Drive, HO44, Hershey, PA 17033, United States
b
article info
abstract
Article history:
This review focuses on the burgeoning use of web-based systems allowing patient-initiated
Received 5 June 2008
glucometer uploads to facilitate provider treatment intensification. Studies in type 1
Received in revised form
diabetes tended to show equivalent HbA1c improvements in both intervention and control
28 September 2008
groups without statistically significant difference. In contrast, type 2 patients seemed to do
Accepted 30 September 2008
better than controls with significant differences in HbA1c. Patients were the beneficiaries of
Published on line 3 December 2008
web-based diabetes management both through savings in time and cost. Major obstacles to wider implementation are patient computer skills, adherence to the technology, architec-
Keywords:
tural and technical design, and the need to reimburse providers for their care. # 2008 Elsevier Ireland Ltd. All rights reserved.
Telemedicine Diabetes care Web-based Glucometer upload Telecare
Contents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General description of internet-based diabetes management Impact on glucose control in type 1 diabetes . . . . . . . . . . . . . Impact on glucose control in type 2 diabetes . . . . . . . . . . . . . Impact on glucose control in pregnancy . . . . . . . . . . . . . . . . . Time and cost savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Obstacles to dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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* Corresponding author. Tel.: +1 717 531 3592; fax: +1 717 531 5726. E-mail address: [email protected] (R. Gabbay). 0168-8227/$ – see front matter # 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2008.09.055
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10 1.
diabetes research and clinical practice 83 (2009) 9–17
Introduction
The prevalence of diabetes has been alarmingly increasing in conjunction with the obesity epidemic. There are currently 23.6 million individuals in the United States suffering from diabetes [1], and 246 million worldwide [2]. Each year, 7 million people are diagnosed with the disease, and every 10 s, a person dies from diabetes-related causes [2]. Unfortunately, this worsening burden has not been met with an increase in healthcare providers dedicated to diabetes care. Indeed, a significant fraction of the population with diabetes is undertreated or has no access to proper and timely care, particularly in underserved areas with rural or minority populations. These patients are the most likely to suffer from the paucity of care because of a high prevalence of the disease doubled with poor access to health care. Since control of diabetes has been shown to decrease mortality and prevent long-term complications, it is critical that healthcare systems develop innovative ways to improve diabetes management, and provide timely care to patients. Diabetes is a disease where there is a need for care between visits and where ‘velocity to goal’ may be slow, especially in primary care settings, where the term ‘‘clinical inertia’’ was applied to ‘‘failure of providers to intensify therapy when appropriate’’ [3,4]. The internet has proven itself to be a fast, efficient and reliable source of communication. Its widespread availability makes it an attractive communication tool among patients and providers. Indeed, it has been useful in multiple medical fields, ranging from video-conferencing, to patient support and education websites. Telemedicine has, in particular, caught the attention of patients and caregivers. For patients, it has the advantage of providing a quick, efficient way to communicate with their providers. The latter can, in turn, provide feedback and advice in a timely manner, therefore making care more efficient and responsive to patient needs. Indeed, close monitoring of blood glucose at home is a key component of diabetes management, but without timely provider feedback, it has somewhat of a lesser value. For those patients living in rural areas, it is potentially invaluable to have access to their caregiver from the comfort of their homes, thus sparing them the time and cost of traveling. Patient suffering from chronic diseases in general, and diabetes in particular, have benefited from online support systems and web-based education with improved clinical outcomes [5], although the magnitude of the impact of telemedical support on diabetes care remains debatable [6–9]. This review will focus on the burgeoning area of web-based systems allowing patients and providers to communicate and relate blood glucose levels via glucometer uploads to intensify therapy. Endpoints that will be looked at include glycosylated hemoglobin, time and cost savings, obstacles to implementation, and, finally, reimbursement issues will be discussed.
2.
Methods
Eligible studies were published randomized controlled trials or observational studies where the intervention consisted of web-
based upload of blood glucoses and other pertinent data (such as diet, anti-hyperglycemic regimen, level of physical activity, intercurrent illness) by patients followed by clinician feedback. The primary outcome was glycosylated hemoglobin. The electronic database PubMed was searched using the following keywords ‘‘diabetes and internet’’, ‘‘web-based’’, ‘‘diabetes management and Internet’’, ‘‘telemedicine’’, ‘‘meter upload’’, ‘‘telecare’’. English literature published in PubMed from January 1990 through October 2007 was selected. The goal of this review was not to perform a meta-analysis. Therefore, we elected to include non-randomized controlled trials in the literature review. Of the studies initially identified, eighteen met our eligibility criteria and were subsequently retained. We excluded studies that did not specifically focus on web-based glucose upload followed by clinician feedback.
3. General description of internet-based diabetes management Telemedicine interventions for diabetes management can range from very simple systems where patients and clinicians communicate by phone, email or short message service (SMS) to complex web interfaces. Patients will typically upload home meter data and may enter other pertinent data such as antihyperglycemic regimens, dietary habits, activity level, and medical history. Providers (physicians or nurses) then review this data and provide feedback regarding medication adjustments and lifestyle modification guidelines. Some available telemedicine systems include telephone assistance systems where patients will periodically receive phone calls from their clinician to help to adjust their regimen and/or other counseling [10]. Others, labeled ‘‘visit by visit systems’’, provide feedback at each clinical encounter rather than between visit care. Patients upload glucometer data from home, but will wait to get feedback at the visit with their provider. There are as well complete assistance systems. These systems provide day-by-day assistance to patients on therapeutic adjustments, diet and exercise. These systems include a built-in ‘‘patient unit’’ [10] as well as a ‘‘provider’’ unit. However, these systems are complex, costly and often require extensive user training. These are beyond the scope of this discussion, which will focus on web-based blood glucose upload followed by provider feedback, either by email and/or phone.
4.
Impact on glucose control in type 1 diabetes
Eight studies were identified that focused exclusively on type 1 diabetes patients, four of them involving pediatric populations [11–14], one pregnant type 1 diabetics [15]. The remaining three [16–18] enrolled adult type 1 patients. These studies are summarized in Table 1. The interventions consisted of modem transmission of blood glucoses with clinician feedback approximately every 2 weeks, whereas control patients received usual care. The clinician involved in feedback could be a physician [11,14,16,17] and/or a nurse under a physician’s supervision [11,18]. The majority of patients received feedback
Table 1 – Summary of studies for type 1 diabetes. Authors
Intervention group
Chase et al. [11]
70 adolescents type 1 DM 15–20 years
Clinic visit at 0, 3 and 6 months
8.9 1.1%
Marrero et al. [12]
106 type 1 pediatric patients multiple daily injections and poor control 28 teenagers with type 1 DM on multiple daily injections, poor control
Modem transmission BG q 2 weeks and clinician feedback clinic visit at 0 and 6 months with omission of a 3-month visit Modem transmission q 2 weeks clinician feedback
Conventional care
9.4
Glucometer data transmitted q 2 weeks via glucobeeb, web-based tools clinician feedback as 1 min voicemail the following week
Conventional care control introduced to glucobeeb after 6 months and showed a trend to decrease A1c at 3 and 6 months (9.4, 8.9, 8.7%) NA
Cross-over RCT
8.4%
Conventional care: clinic visit q 2 months
8.3 2.3%
Upload BG as well, but no clinician feedback; were free to contact RN for questions q 3 months clinic visit Conventional care
Cadario et al. [13]
Liesenfeld et al. [14]
61 pediatric type 1 DM patients in rural areas
Glucometer data transmitted through modem to diabetes center phone consultation with clinician
Gomez et al. [16]
10 type 1 DM with inadequate control 48 type 1 DM on intensified regimen
Modem transmission q 2 weeks clinician feedback within 24 h Modem transmission data q 2 weeks clinician feedback by phone q 2–4 weeks Modem transmission q 2 weeks at least BG 4 times/day clinician feedback within 24 h q 3 months clinic visit
Biermann et al. [17]
Montori et al. [18]
31 type 1 adults on intensive insulin regimens and inadequate control HbA1c > 7.8%
Wojcicki et al. [15]
30 pregnant type 1 DM
System automatically transfers data every night to clinical unit 3 years
Control group
Baseline A1c intervention
A1c end of study intervention
Baseline A1c controls
A1c end of study controls
9.0 1.2%
8.6 1.7%
NS
NS
9.6 (10% at 12 months)
9.9
9.7 (10.3% at 12 months)
NS
NS
NA
9.5
9.0 (3 months) 9.1 (6 months)
9.1
9.4 @ 3 months, 9.4 @ 6 months
NS
NS
NA
NA
A1c decreased by 0.4% reduced mean BG (167 to 158 mg/dL, p < 0.01) reduced standard deviation of BG 7.9%
NA
NA
8.1%
8.2%
NS
NS
6.9 1.3%
8.0 2.1%
7.0 1.0%
NS
NS
9.1 1.2%
7.8 1.2%
8.8 1.2%
8.2 1.3%
NA
0.03
7.96 1.1%
6.8 0.9%
8.1 1.7%
6.7 0.9%
NS
8.6 1.2%
P baseline
P end of study
Cost
Satisfaction
Complications DM (DKA, hypo/ hyperglycemia)
Similar p = 0.81
Similar
Similar
Similar
NA
NA
NA
NA
Reduction in hypoglycemia frequency 5.2–3.3 at the end of the study reduction in BG variability NA
Effective (travel and days off work expenses saved)
Effective travel expenses and time saved NA
85% telecare patients felt telecare better NA
Similar incidence hypoglycemia
diabetes research and clinical practice 83 (2009) 9–17
Patients
Similar
Similar
11
12
Table 2 – Summary of studies with type 2 diabetes or mixed. Authors
Patients
110 type 2 DM
Cho et al. [20]
80 type 2 DM
Kwon et al. [21]
185 16.2% type 1; 82.7% type 2; 1.1% secondary
Hee-Sung [22]
51 subjects 25 intervention 26 controls type 2 DM
McMahon et al. [23]
104 diabetic patients with HbA1c > 9%
Kim and Jeong [24]
60 patients. 30 in each group
Bergenstal et al. [25]
47 patients: 24 intervention 23 controls
Patient log BG, weight, diabetic regimen receive feedback Patients upload BG online and receive feedback q 2 weeks clinic q 3 months Patients upload online or send by SMS their BG, meds, diet, hypoglycemia, with clinician feedback Patients log their BG and medications; nurses send weekly feedback via internet and SMS DM education patients upload BG and BP, messages, questions; feedback within 1 day Patients log their BG and medications clinician feedback via weekly SMS Weekly modem transmission of BG
Control group
Baseline A1c intervention
A1c end of study intervention
Baseline A1c controls
A1c end of study controls
P baseline
P end of study
Cost
Satisfaction
Complications DM (DKA, hypo/ hyperglycemia)
Usual care
7.59 1.43%
6.94 0.13%
7.19 1.17%
7.62 0.13%
NS
<0.001
NA
NA
NA
Usual care
7.7 1.5%
6.9 0.9%
7.5 1.3%
7.5 1.5%
0.457
0.009
NA
NA
NA
NA
7.5 1.5%
7.0 1%
NA
NA
NA
0.003
NA
Good patient satisfaction
NA
Endocrinologist 1–2 times/12 weeks
HbA1c < 7% 6.92 0.35%, HbA1c > 7% 9.35 1.72%
HbA1c < 7% 6.71 0.61%, HbA1c > 7% 7.2 1.34%
HbA1c < 7% 6.71 0.38%, HbA1c > 7% 8.24 0.98%
HbA1c < 7% 7.14 0.52%, HbA1c > 7% 8.02 0.96%
DM education usual care
10 0.8%
1.6 1.4% the higher the number of uploads, the higher decline in HbA1c 6.94 1.04% @ 3 months, 7.04 1.39%
9.9 0.8%
8.09 1.72%
Phone communication with clinician
0.9 1.4%
7.59 1.09%
NA
NA
1.2 1.4%
NS
<0.005
NA
NA
7.66 0.91% @ 3 months, 7.70 0.90% @ 6 months
NS
<0.05
NA
NA
0.4 0.7%
NS 0.18
Similar
diabetes research and clinical practice 83 (2009) 9–17
Kwon et al. [19]
Intervention group
diabetes research and clinical practice 83 (2009) 9–17
by telephone call [11,14,17,18] or using text messages [16]. Most studies were small (<50 subjects) randomized controlled trials [11–15,17,18] with an intervention and control group. One study [16] was a cross-over randomized controlled trial within the same group. Most patients that were enrolled had inadequate control at baseline, with HbA1c > 8% [11–13,16–18] and occasionally > 9% [12,13]. In the studies that had control groups [11,13,15,17,18], both groups had a significant improvement in HbA1c at the end of the intervention, but there was no significant difference among intervention and control groups. For instance, in one study, mean baseline HbA1c for intervention and control patients were respectively 8.9 and 9.0%; at the end of the trial, mean HbA1c in intervention and control patients decreased to 8.6% [11]. Similarly, in another study, intervention and control patients started with an HbA1c of 8.3 and 8.0%, decreasing at 4 months to 6.9 and 7.0% respectively, without significant difference among groups [17]. This suggests a Hawthorne effect whereby the mere fact of being enrolled in a study improved outcomes, without any added impact of telemedicine. Interestingly, in the only small study that did show outcome differences among intervention and control patients [18], the control group had the advantage of being able to upload blood glucoses as well, therefore isolating the effect of clinician feedback on self-monitored blood glucose values. 29% of intervention patients reached a target HbA1c < 7% at 6 months (4/14) versus only 7% in the control group (1/15); there was a statistically significant difference in HbA1c at 6 months. Intervention patients transmitted their blood glucoses 6 times the first 2 months versus 5 times for controls, with some dropoff over the last 2 months. At 6 months, there was a 10.7% increase from baseline in the number of glucose testing in the intervention arm versus no increase in controls, demonstrating improved compliance with the intervention. Interestingly, the study focused on adult type 1 diabetics rather than pediatric patients, and the intervention arm had more documented dose changes (14 vs. 6). In the studies that looked into acute diabetes complication rates of severe hypoglycemia and diabetic ketoacidosis, the incidence was similar in both intervention and control groups [11,18]. Overall, these type 1 studies had several limitations which should be acknowledged in the interpretation. These include generally small patient groups (<50 patients), limited duration of follow-up (3–6 months in most studies, one up to 12 months [12]). Additionally, some studies reported significant noncompliance to the protocol and subsequent dropout. For instance, one group reported a 14.3% dropout rate (5/35 patients) in the intervention group [11], which was comparable to the control group, whereas another study [14] noted that 11.5% of the participants did not complete the protocol, more than half for having purposefully manipulated their data.
5.
13
diabetes patients, without serious co-morbidities. Results are summarized in Table 2. Feedback for medication adjustment was provided by physicians [19–21] or nurses [22] whereas nutritional and other lifestyle modification guidelines were provided by nurses and/ or dietitians [20–22]. Recommendations could be sent on the study website where patients could access them when they logged into their personal webpage [19–23]. Others received additional feedback by SMS [19,22,24] or telephone calls [25]. Dropout rates were noted to be comparable among intervention and control patients in the studies that reported them [20,21]. When compared to type 1 studies, the patients enrolled in type 2 trials seemed to have somewhat better control at baseline, with HbA1c ranging from 7%–8% in intervention and control groups [19–22], except in one study where patients included had to have an HbA1c > 9% [23]. In contrast to type 1 diabetes, type 2 patients seemed to do better than controls with telemedicine. Indeed, most of the studies showed significant difference among intervention and control groups at the end of the trial [19–24]. For instance, looking at Kwon et al. results [19], intervention patients started with an HbA1c of 7.59% and controls, 7.19%; at the end of the trial, their HbA1c was 6.94% versus 7.62% for controls ( p < 0.001). Dropout rates were similar among both groups. Cho et al. [20] showed comparable improvements. Their baseline HbA1c was 7.7% in intervention patients, versus 7.5% in controls; at the end of 30 weeks, the telemedicine group reached an HbA1c of 6.9% versus 7.5% for controls ( p = 0.009). Furthermore, they demonstrated a lower variability in blood glucoses and HbA1c in their intervention patients compared to controls. An HbA1c fluctuation index was significantly lower in the intervention group. Some studies not only looked into fasting blood glucose, but also evaluated the 2-h post-prandial blood glucose which is increasingly recognized as a major contributor of the cardiovascular risk related to diabetes [26]. For instance, in one study, there was an improvement in FBG and 2 h post-prandial blood glucose in the intervention group with HbA1c < 7% [22]. Patients who seemed to benefit most from the intervention were those with a baseline HbA1c > 7%, who had an average 2.15% decrease in HbA1c [22]. Another study demonstrated improved 2-h post-prandial blood glucoses in the intervention group (from 256.2 at baseline to 192.6 at 6 months) [27]. The degree of improvement in HbA1c in type 2 studies seemed to have been directly correlated to adherence to the intervention protocol. Indeed, patients who had more data uploads and visited the study website more often were the ones who did better [23]. ‘‘Persistent users’’ had an average decrease in HbA1c of 1.9% at 12 months, whereas ‘‘intermittent users’’ had a 1.2% decrease in HbA1c at 12 months, which was comparable to control patients. These authors also demonstrated a direct correlation between the decline of HbA1c and the number of data uploads. Patients in the higher tertile for data uploads had the greatest improvement in HbA1c ( 2.1%).
Impact on glucose control in type 2 diabetes 6.
Seven studies were identified that involved patients with type 2 diabetes or, in some, mixed populations [19] (including type 1, type 2 and secondary diabetes). These were adult, stable
Impact on glucose control in pregnancy
Pregnant patients with diabetes are a subgroup in whom quick velocity to goal is critical in order to avoid fetal and obstetrical
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diabetes research and clinical practice 83 (2009) 9–17
complications. These patients might benefit significantly from telemedical support. Unfortunately, few researchers have addressed this particular group. Wojcicki et al. studied 32 pregnant patients with type 1 diabetes [15]. They assessed mean blood glucoses (MBG) and a measure of glycemic variation. Both groups had similar control at baseline and were< 16 weeks pregnant. Although there was a trend towards better MBG and glucose variability, there was no significant difference at the end of the study. Another small study involved 11 pregnant patients with type 1 diabetes using web-based glucose upload and compared it to 10 matched controls [28]. Telemedicine patients checked their blood glucoses 4–6 times/day and transmitted once a week. Feedback was provided by phone. HbA1c was improved in the telemedicine group (from 6.1 to 5.4%) over an average of 22 weeks, whereas the control patients’ HbA1c changed from 6.2 to 5.7%. Average and fasting blood glucoses improved as well as glucose variability. These were preliminary data and no comparison was made among groups. Kruger et al. [27] evaluated the impact of modem transmission of blood glucoses in gestational diabetes mellitus. No significant differences in telephone consultation time, clinic work-flow efficiency and accuracy were observed between groups. On the other hand, patient and staff satisfaction were improved in the intervention group.
7.
Time and cost savings
Telemedicine offers the opportunity for significant cost savings. From patients’ perspective, these include travel time and lost work time for appointments. From clinicians’ perspective, well-presented glucose upload data can facilitate analysis and treatment decisions while freeing up time to ultimately improve access to care. However, reimbursement for services will be a critical aspect to consider. Five studies addressed time and cost savings. Chase et al. showed significant cost savings by modem transmission of blood glucose and clinician feedback. There was an average saving of 142 US dollars per 6 months of care ( p < 0.001), mostly in saved patient travel expenses and days off work to go to the clinic visits [11]. Biermann also demonstrated time and cost savings for patients [17]. Data transmission patients spent at 4 months an average of 554 min compared to 656 min for control patients including travel, waiting room and consultation times. These differences narrowed at 8 months with 974 and 959 min for intervention and control patients respectively. Time was saved mostly in travel while time spent by physicians in the telemedicine group was higher (50 vs. 43 min/month for the 4 months period, reflecting more patient contact and time spent reviewing data). Cost saving consisted mostly of saved travel expenses and days off work. The estimated cost/year in the telemedicine group was 389 euros/year including modem fees, phone bills, data transfer, travel costs and costs for not working, versus 1037 euros/year for controls; the 648 euros saved per year were in travel costs and costs of not working. Ultimately, there was no significant difference in HbA1c among groups at the end of the trial, despite a significant improvement within groups [17]. Cho et al. [20] reported time
savings for patients in the web-based management group, mostly in travel time, and office consult and time waiting in the office. The impact of these interventions on provider time has generally not been beneficial. In Montori’s study, clinicians’ time spent in reviewing data and providing feedback to patients was higher [18] while Bergenstal showed no significant time saving for the healthcare provider by reviewing web-based patient data, in comparison to usual care in which data was provided by phone [25]. However, it was not quantified in the published article. In conclusion, it seems that patients are the most likely beneficiaries of web-based management of diabetes, both in time and cost saved. Employers might also find telemedicine attractive by saving employee time off, therefore increasing overall productivity. This might further encourage direct reimbursement for this technology.
8.
Obstacles to dissemination
One major issue seems to be developing computer skills in older computer naive patients. Potential barriers include motor dexterity (mouse and keyboard manipulation), faulty computer skills. Overall, patients with greater dexterity and literacy seem to far better [29]. Unfortunately, patients who did not have internet access or did not know how to use the internet were often excluded from the trials and the duration of training was not specified in most studies except for one which reported 2.3 h/patient (range 1.0–6.6) for training the intervention group [23] and, another, 30 min [22]. Enrolled patients had to be familiar with internet use at baseline to be eligible. Patient intelligence quotient (IQ) did not seem to have any impact on results in one study [15]. The 32 study patients were subdivided into ‘‘low intelligence: IQ < 100’’ and ‘‘high intelligence: IQ > 100’’. Within each IQ group, there was not difference in mean blood glucose or glucose variability indices among intervention and control patients, although the limited number of subjects likely underpowered the study to demonstrate any difference related to IQ. No comparison of ‘‘low intelligence’’ to ‘‘high intelligence’’ patients in terms of blood glucose control and impact of telemedicine were described. Other potential obstacles include architectural and technical issues such as security, privacy and confidentiality as well as ease of use. Data transfer was an issue in <5% of transfers in one study [17], and was generally solved by simply repeating the transfer. Problems that needed more than 5 min to solve occurred in< 1% of the 521 data transfers. Glucometer technology is also an important potential obstacle. Different glucometer brands have each their own downloading software programs, which are not compatible with each other. It becomes technically and economically challenging to provide different types of hardware and software to accommodate each and every patient’s glucometer although some vendors have begun to supply potential integrated solutions [30]. Patient adherence seems to be an issue according to some authors. Among other reasons, non-adherence with data
diabetes research and clinical practice 83 (2009) 9–17
uploads were reported as a cause for dropout, as well as not showing up to clinic appointments. One author reported that 7/61 (11.5%) participants did not complete the protocol, 4/7 for manipulating their data [14]. Interestingly, the patients who manipulated their data and/or dropped out were the ones with the highest HbA1c upon enrollment (9.7% for those who terminated early vs. 7.7% for those who completed the protocol). Therefore, motivation and straightforwardness are key elements for the success of telemedicine support. Reimbursement is a critical aspect of telemedicine dissemination. At this point, there is no mechanism to reimburse physicians for non-face-to-face services. Although new codes have been issued for phone and internet services, these are not covered under the current Medicare program. Another potential issue will be fee sharing among physicians and web administrators which will have to be defined specifically to avoid conflicts [30]. Ultimately, the potential cost savings to employers from employee absenteeism are likely to drive reimbursement opportunities. Some employers have begun to look at reimbursing these types of services. Medical licensure and liability coverage are potential concerns that also need to be addressed.
9.
Discussion
Telemedicine can be a useful tool to provide diabetes care and represents a potential solution for long distances and provider shortage. It cannot replace patient visit and direct interaction with providers, but it can supplement between-visit care and improve ‘velocity to goal’ —the speed of attainment of adequate metabolic control by the patient. Telemedicine can also potentially save time and travel expenses for patients [11,17,20]. A major advantage to electronic glucose upload is accuracy. When compared to paper data capture by patients, not only is electronic upload bound to be 100% accurate, but it also precludes back-filling and forward-filling, as well as data manipulation [31]. Practitioners therefore have an accurate sense of glucose levels and monitoring frequency. The impact of electronic glucose uploads and provider feedback on diabetes control is still controversial. Studies in patients with type 1 did not demonstrate any superiority of telemedicine over usual care — both intervention and control patients seemed to improve without any significant difference among groups. Most of these studies involved teenagers or young adults with very poor control at baseline, and groups were small. The studies may have not been powered to demonstrate any benefit in telemedicine support. Young patients with diabetes may also have psycho-social difficulties interfering with their diabetes self-care, and being enrolled in a study, regardless of which arm, may be enough to improve their diabetes control. Many patients with poor control at baseline may have adherence issues related to behavior and medications that may not be adequately addressed by telemedicine. Several previous reviews of telemedicine impact have confirmed a lack of statistically significant changes in HbA1c [18,32,33] although focus has primarily been on type 1 diabetes patients, and, unlike the current review, have generally not focused on blood glucose upload and clinician feedback. It
15
may be that for type 1 patients who are poorly controlled, factors other than clinical inertia and frequent provider insulin adjustment is needed to optimize care. Other approaches such as self-management skills, case management, behavior change approaches like motivational interviewing and psychosocial assistance may be more important. In contrast with type 1 patients, type 2 diabetics enrolled in the intervention arm of telemedicine studies seemed to fare better than controls. Surprisingly, older patients (averaging 50–60 years old) seem to be responding better. One might have assumed that young patients with diabetes would be more computer savvy and amenable to telemedicine support. It seems rather that type 2 patients seem to accept and manipulate the technology efficiently with the greatest benefit. Indeed, a recent study involving patients> 60 years of age demonstrated that these individuals responded very well to web-based interventions, even without prior internet experience [34]. Perhaps the potential target population for telemedicine would be patients who are closer to goal and more motivated to improve their care. One potential advantage of between visit care offered by telemedicine is an improvement in ‘velocity to goal’. This concept is defined as how fast the patient reaches good diabetes control, rather than remain exposed to a high glycemic burden for prolonged periods of time. Brown et al. [35] demonstrated that the average time before treatment intensification occurred may be as long as 27–35 months with a mean HbA1c of 8.8–9.1%. Telemedicine provides a significant opportunity to give providers updated clinical data for more frequent medication adjustment. Future directions could be providing clinicians with online algorithms that can assist clinical decision by interpreting the data from the glucometer downloads. Glucose variability may also be improved by telemedicine glucose meter upload and provider feedback. Glucose variability is increasingly recognized as an independent risk factor for complications of diabetes by increased free radical production and oxidative stress [36] which may contribute to the pathogenesis of diabetes complications. In pregnant patients with type 1 diabetes [15], indices of glycemic variability improved in the telecare group, without reaching statistical significance, which can be partially attributed to the small sample size. Other studies demonstrated similar results with type 2 diabetics who had decreased glucose variability [20] and/or decreased 2-h post-prandial blood glucose with the intervention [22,24]. Indeed, glycemic variability and velocity to goal should be outcome measures in future studies. Some of the weaknesses of these studies include, as aforementioned, small sample size, limited duration and for some, lack of control groups and/or suboptimal study design. There is also no evidence that the improvement in diabetes control observed in these studies persists beyond the short trial period. Finally, there is no data on the impact of telemedicine on long-term complications of diabetes.
10.
Summary and conclusions
Telemedicine based glucose meter upload and provider feedback continues to offer promise to improve diabetes outcomes. However, despite several randomized trials in type
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1 diabetes, results have been disappointing. Glycemic control in this patient population may require other interventions including face-to-face visits or adherence and behavior based approaches. In contrast, data supporting effectiveness in type 2 diabetes is more robust. Although the studies typically involve older individuals, technology acceptance has been good. Future studies are needed to better define patients who would most benefit from that approach, in comparing type 1 and type 2 patients in head-to-head randomized controlled trials. Attention to other outcomes measures such as velocity to goal and glycemic variability are needed as well. Reimbursement remains a barrier to widespread adoption of this technology. The greatest savings to date have been in patient time and loss of work. The hope is that improvements in employee productivity will drive employer based initiatives for reimbursement. Ultimately, if reimbursement is addressed, providers may find that more accurate glucose results provided in a more organized format could facilitate better diabetes care and outcomes. Medical legal issues related to timeliness of provider response and privacy further complicate future adoption. Ultimately, patient engagement in this technology over longer time periods will need to be addressed. Future studies should focus on indentifying target populations most likely to benefit, expansion of study outcomes to include glycemic variability and velocity to goal, as well as associated costeffectiveness. The addition of decision support algorithms may also help alleviate provider and patient burden.
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