Web-Based Remote Monitoring Systems for Self-Managing Type 2 Diabetes: A Systematic Review

Abstract

This systematic review aims to evaluate evidence for viability and impact of Web-based telemonitoring for managing type 2 diabetes mellitus. A review protocol included searching Medline, EMBASE, CINAHL, AMED, the Cochrane Library, and PubMed using the following terms: telemonitoring, type 2 diabetes mellitus, self-management, and web-based Internet solutions. The technology used, trial design, quality of life measures, and the glycated hemoglobin (HbA1c) levels were extracted. This review identified 426 publications; of these, 19 met preset inclusion criteria. Ten quasi-experimental research designs were found, of which seven were pre–posttest studies, two were cohort studies, and one was an interrupted time-series study; in addition, there were nine randomized controlled trials. Web-based remote monitoring from home to hospital is a viable approach for healthcare delivery and enhances patients' quality of life. Six of these studies were conducted in South Korea, five in the United States, three in the United Kingdom, two in Taiwan, and one each in Spain, Poland, and India. The duration of the studies varied from 4 weeks to 18 months, and the participants were all adults. Fifteen studies showed positive improvement in HbA1c levels. One study showed high acceptance of the technology among participants. It remains challenging to identify clear evidence of effectiveness in the rapidly changing area of remote monitoring in diabetes care. Both the technology and its implementations are complex. The optimal design of a telemedicine system is still uncertain, and the value of the real-time blood glucose transmissions is still controversial.

Background

Chronic diseases are the leading cause of morbidity and mortality worldwide. There are many examples: cardiovascular diseases, stroke, diabetes, cancers, musculoskeletal conditions, and respiratory diseases.¹ Their prevalence is increasing worldwide: the Global Burden of Disease project estimated that by the year 2030, chronic diseases will cause 65% of deaths globally,² up from 55% in 1990.³

In 2008, chronic diseases caused nearly half a million deaths in the United Kingdom. It is estimated that 36% of the cases are cancer-related and that another 36% are caused by cardiovascular diseases and diabetes, with the remainder associated with a range of less common conditions. Diabetes mellitus is characterized by a shortage of the hormone insulin or a decreased ability to use insulin (or both).^4,5 There are three main types of diabetes: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), and gestational mellitus. T2DM is a globally common metabolic disorder, characterized by insulin resistance and hyperglycemia. It is often associated with obesity, physical inactivity, and stress.^5,6 T2DM is the predominant type of diabetes and accounts for 85–90% of all cases. It is commonly found in older adults, but the worrying trend starting to emerge is that it has recently also been found in young people and children. It has been linked to lifestyle factors.⁵

Diabetes is one of the leading threats to health globally; in 2010, 300 million people worldwide were living with diabetes, and 344 million others were at risk of developing T2DM. The epidemic is predicted to escalate to affect 438 million people by 2030.⁷ In the United Kingdom, over 2 million people suffer from T2DM,⁴ and over 210,000 adults 20 years of age and older on the island of Ireland have diabetes (T1DM and T2DM combined).⁸

Daily insulin therapy is used as an essential treatment for T1DM. Conversely, the more common T2DM is managed with less invasive treatment such as diet modification, lifestyle change, oral antihyperglycemic agents, and possibly insulin therapy,⁹ together with blood pressure and lipid control medication to reduce the risk of cardiovascular complications.^10
–12 Frequent support and reassurance are required to overcome barriers such as properly applying the complex rules for calculation of insulin doses or anxiety about needles and injections, concern about disease complications (e.g., retinopathy, neuropathy, cardiovascular and foot ulcers) or treatment side effects, and the difficulty of keeping a paper diary that informs the doctor of the patient's daily results.¹¹ Overcoming these barriers involves hospital visits or telephone calls to share blood glucose results and receive advice from the healthcare team on insulin dose adjustment (referred to as insulin titration). In situations like this the healthcare provider cannot rely on the information provided as a basis for any insulin adjustment advice.⁹ Moreover, patients with diabetes need to understand their disease and become involved in their own treatment and modify many lifestyle behaviors in order to self-manage their condition.^13,14 Remote monitoring (sometimes known as real-time monitoring) systems have been introduced to aid the process of self-managing T2DM.¹⁵ In addition, these monitoring systems move toward integrating commercially available technologies into healthcare systems. The Good Governance Institute recognizes the need for greater intervention in providing support for the population with long-term conditions, along with the need to offer home-based care to improve patients' quality of life.¹⁶

A meta-analysis by Swinnen and DeVries¹⁷ in 2009 revealed that a reduction in glycated hemoglobin (HbA1c)—a worldwide diabetes clinical marker—could be positively achieved with frequent glucose evaluation followed by possible insulin dose adjustment. Frequent assessment can optimize insulin intake, improve glucose control, and help the patient achieve self-management.^9,11,12 Healthcare systems could be improved and available resources optimally used by replacing traditional hospital visit consultations with shorter but more frequent remote consultations focusing on treatment adjustment.⁹ In 2011 it was suggested that technology is a more coordinated and cost-effective approach that has evidence of improved outcomes where the quality of the services is higher and that it also may be one solution making up part of an overall strategy for enhancing quality care services.¹⁸

Telemedicine is a broad term that can be defined in various ways.¹⁹ In 1995 Scannell et al.²⁰ offered the following definition: “Telemedicine is the use of telecommunications for medical diagnosis and patient care. It involves the use of telecommunications technology as a medium for the provision of medical services to sites that are at a distance from the provider. The concept encompasses everything from the use of standard telephone services through high speed, wide bandwidth transmission of digitized signals in conjunction with computers, fiber optics, satellites and other sophisticated peripheral equipment and software.” A more recent definition from World Health Organization²¹ in 2010 is “The delivery of health care services, where distance is a critical factor, by all health care professionals using information and communication technologies for exchange of valid information for diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of health care providers.”

Recently, telemedicine has been introduced as a powerful tool for healthcare delivery and chronic disease management, including diabetes, and there is an increased demand for this technology to support self-management. Healthcare providers and researchers have turned to technology to allow easier access to healthcare, improve monitoring, improve compliance in medication taking, and increase healthy eating and all the other elements needed to achieve diabetes control through self-management.^13,22 This technological approach offers many opportunities; for example, it can reduce geographic barriers, where long-range data transmission can provide automated feedback and facilitate patient–healthcare provider communication.²³ Collaboration among all those involved in a diabetes treatment plan—healthcare providers, clinicians, healthcare managers, and patients—will enable a more efficient delivery system for diabetes care.^13,24 Telemedicine and diabetes care have been growing and improving together for decades to bring patients to the desired level of self-management. Telemedicine is a combination of information management and communication technology that promotes, empowers, and facilitates the patient's well-being.²⁵ Telemedicine for diabetes has various forms that can be categorized into three groups: (1) real-time monitoring systems, (2) classical long-term monitoring systems, and (3) monitoring of diabetes complications.²⁴

Telemonitoring is defined as the use of information and communication technologies for the transmission of biometric data between the patients' homes and health professionals for data interpretation and decision-making²⁶ using the Internet for Web-based real-time systems. Typically, a hub is installed in patients' homes or an application installed on their personal computers, smartphones, and other digital devices²⁴ to facilitate regular monitoring of key physiological data. Such a system enables patients to monitor and transmit their biometric data from home, including blood glucose, blood pressure, body weight, dietary habits, and activity level, and transfer them remotely to a central data management system, where a healthcare provider may systematically monitors a patient's health status, following their care protocols and providing personalized feedback and recommendations regarding their medication adjustments and lifestyle modification via e-mail or phone.^27,28

This review will focus on remote monitoring systems for T2DM. It will exclude those telemonitoring solutions offered within the classification of “classic long-term” designed for scheduled videoconferencing and telephone-based consultations and of telematic solutions for diabetes complications (e.g., monitoring of late macro- and microvascular diabetes complications, which include mainly retinopathy and diabetic foot syndrome). These are very different in their synchronous services and their individual feedback provision services from the telemonitoring solution for managing T2DM. Remote monitoring systems were researched to satisfy the purpose of this review and to establish a better understanding of telemonitoring solutions for T2DM self-management as a starting point to conducting a study evaluating telemonitoring solutions used in Northern Ireland.

The purpose of this systematic review is to evaluate evidence for feasibility and impact of Web-based remote monitoring solutions on HbA1c levels and quality of life of T2DM patients.

Materials and Methods

Search strategy

The following databases were comprehensively searched: the Medical Literature Analysis and Retrieval System Online (Medline), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica Database (EMBASE), the Allied and Complementary Medicine Database (AMED), PubMed, and the Cochrane Library. In addition to that, a search was conducted through Google Scholar. Articles suggested by peers and related references used in searched articles were also reviewed.

Search terms

Using the Medical Subject Headings thesaurus combined the following terms were used: “type 2 diabetes,” “self-management,” “telemonitoring,” “telehealth,” “telemedicine,” “connected health technology,” and “blood glucose monitoring.”

Inclusion criteria

Primary research studies exploring technological interventions (Web-based systems with blood glucose transmission) for T2DM self-management and onward transmission to the healthcare providers, published in English and after 2000, were included. Research designs were categorized as follows: (1) quasi-experimental studies, such as interrupted time-series studies, cohort studies, and pre–post studies, or (2) randomized controlled trials (RCTs). The population had to include adults (18 years of age and above) diagnosed with T2DM and prescribed insulin treatment.

Exclusion criteria

Publications evaluating telemedicine and/or telemonitoring for other chronic diseases, reviews without primary clinical data, case reports, and personal opinion studies were excluded. Studies that evaluated the use of other forms of telemonitoring than Web-based telemonitoring—interventions such as videoconferencing, telephone-based consultations, and systems in which blood glucose results stored in a glucometer for the next clinical appointment—were excluded.

Data extraction

The first author reviewed titles, abstracts, and the methodology to determine if the full text should be included in this review. The articles were reviewed for eligibility by the second and last authors.

We recorded technology for blood glucose data transfer and self-management data. Outcomes were categorized according to which measurement was described and validated, for example, HbA1c.

Results

The above search strategy highlighted a total of 426 publications. As shown in Figure 1, only 19 articles met the inclusion/exclusion criteria and were included for analysis. Table 1 summarizes the key features of these articles. Six were conducted in South Korea (Kim et al.,³⁰ Cho et al.,^31,37 Kim and Jeong,³² Kim,³³ and Kim et al.³⁸), five studies in the United States (Stone et al.,²³ Stamp et al.,¹⁴ Durso et al.,²⁹ Katz et al.,⁴² and Tang et al.⁴³), three in the United Kingdom (Larsen et al.,¹¹ Turner et al.,³⁴ and Istepanian et al.³⁵), and two in Taiwan (Chen et al.⁴⁰ and Guo et al.⁴⁴). Single studies were conducted in Spain (Rodrigues-Idigoras et al.³⁶), Poland (Bujnowska-Fedak et al.³⁹), and India (Kesavadev et al.⁴¹).

FIG. 1.

Results of systematic search for Internet-based telemedicine interventions involving Web-based transmission of blood glucose measures. RCT, randomized controlled trial; T1D, type 1 diabetes.

Table 1.

Summary of the Studies Included in This Review, Listed in Chronological Order

Reference (year)	Country	Design	Sample ^a	Duration	Overview	Results
Durso et al.²⁹ (2003)	United States	Pre–post study	Age ≥60 years n=10/7	3 months	Study took place at Johns Hopkins Bayview. The participants used a Personal Diabetes Management System with a mobile phone to transmit data to a secured Web site with review by a clinical team.	• Four participants improved their HbA1c. • All participants were engaged in physical activities 1–2 times per week. Four participants improved their BMI. • Three participants increased their diabetes knowledge. • Participants' acceptance was highly improved.
Kim et al.³⁰ (2005)	South Korea	Pre–post study, random allocation	Age=18–65 years n=45/42	3 months	Study took place at a tertiary-care hospital. Participants used the Internet to manually input their data to a secured Web site using a PC or a mobile device and received individual recommendations from the research nurse via the mobile device or the PC.	• There was statistically significant improvement in FPG and 2HPPBS levels. • No significant difference was observed in TC, triglycerides, or HDL-C levels.
Cho et al.³¹ (2006)	South Korea	RCT	Age ≥30 years IG: n=40/45 CG: n=40/46	30 months	Study took place at Kangnam St. Mary's Hospital Diabetes Centre. Participants in the IG uploaded their data on a secured Web site and received individual recommendations from the clinical team biweekly. Participants in the CG visited the clinic every 3 months using a paper-based record to keep notes and record readings.	• Statistically significant improvement in HbA1c and triglyceride levels in the IG. • HbA1c level was irregular in the CG.
Kim and Jeong³² (2006)	South Korea	Pre–post study, random allocation	Age average=50 years IG: n=30/25 CG: n=30/26	6 months		• Participants in the IG had statistically significant improvement in HbA1c levels, whereas participants in the CG had no significant change.
Kim³³ (2007)	South Korea	Pre–post study, random allocation	Age average=50 yearsIG: n=30/25CG: n=30/26	6 months	Study took place at a tertiary-care hospital. Participants in the IG uploaded their data on a secured Web site and were able to view recommendations from the researcher via SMS or the Internet. The researcher provided continuous education throughout the study. Participants received warning messages for lack of transmission after 1 week. Participants in the CG made 1–2 visits to the clinic throughout the study.	• Participants in the CG with HbA1c<7% had significant improvement.• Participants in the IG with HbA1c≥7% had significant improvement.• Participants in the IG with HbA1c<7% had a decrease in the FPG and 2HPPBS levels.
Turner et al.³⁴ (2009)	United Kingdom	Cohort study	Age average=57.6 years n=23	3 months	Participants were given a mobile phone with a preloaded application, “t+Diabetes,” linked to a Bluetooth-enabled glucometer. This application provides real-time data transmission and feedback reception from the clinical nurse.	• HbA1c level was significantly improved by the end of the study• The system was perceived by the clinical staff to be a valuable support tool to assist in insulin adjustment and titration.
Istepanian et al.³⁵ (2009)	United Kingdom	RCT	Age ≥18 yearsIG: n=72/32CG: n=65/55	9 months	Study took place at Thomas Addison Diabetes Unit of St. George's Hospital. Participants transmitted their data wirelessly via Bluetooth to mobile phones and also used the phone to contact the researcher at any time. An alert was sent when a measurement was due. The clinical team viewed the data via a secured Web site and sent to the patients and their GP. Participants in the CG received standard care in the clinic and were free to contact the researcher at any time.	• There was no statistically significant difference between the IG and CG.• Subgroup analysis revealed that the IG had lower HbA1c levels than the CG.
Rodrigues-Idigoras et al.³⁶ (2009)	Spain	RCT	Age ≥30 yearsIG: n=161/146CG: n=167/151	12 months	Study took place in 35 family practices. Participants transmitted real-time data via a mobile phone with a preloaded application, “DIABECOM.” An alarm was sent to participants according to individual protocol. Participants were free to call the researcher. Clinical team viewed the participants' data on a secured Web site and were free to contact them via Web site and telephone.	• HbA1c levels decreased in both groups at 6 months.• HbA1c levels significantly improved only in the IG by the end of the study.• BG and lipid levels were improved in both groups.• BP and BMI decreased in the IG.
Cho et al.³⁷ (2009)	South Korea	RCT	Age average=48 yearsIG: n=37/34PG: n=38/35	3 months	This study took place at Kangnam St. Mary's Hospital Diabetes Center. Participants were assigned into two groups: an Internet-based group (IG), where participants input their readings manually to a secured Web site, and a PG, where participants got a phone with a glucometer and their readings were transmitted automatically to a secured server. The former group received recommendations via the Web site, and the latter group received recommendations by SMS. Participants in both groups received reminder messages when no reading was uploaded for a week and were withdrawn from the study if they did not upload any readings for 3 weeks in a row.	• HbA1c level was significantly reduced in both groups after 3 months.• No changes found in TC, HDL-C, triglyceride, or LDL-C levels before and after the interventions in both groups.• Level of satisfaction with the service was high in both groups.• More than 70% of the participants reported they accomplished the tasks recommended by their doctor.
Stone et al.²³ (2010)	United States	RCT	Age <80 yearsIG: n=73/64CG: n=77/73	18 months	Study took place at a primary care at the VA Pittsburgh Healthcare System. Participants received a diabetes support management support device, the “Viterion 100 Monitor.” The system allows continuous home messaging with reminders and ongoing monitoring of BG, BP, and weight. Data were transmitted daily to the research team. The clinical nurse reviewed the data daily and provided telephone follow-up and individual recommendations.	• HbA1c and triglyceride levels decreased significantly in the IG at 3 and 6 months.• HbA1c slightly improved in the CG.• BP, cholesterol, and LDL levels improved in both levels.
Larsen et al¹¹ (2010)	United Kingdom	Interrupted time-series	Age average=57.6 years n=23/22	6 months	Study took place in several practices in Oxfordshire. Participants were provided a mobile phone with a preloaded application linked to a Bluetooth-enabled glucometer for data transmission to a secured Web site. A clinical nurse reviewed the data every 2–3 days and provided individual recommendations.	• Significant reduction in HbA1c and FPG levels was observed (0.66%).• High level of BG monitoring compliance was observed (89%) and remained constant throughout the study.• Lower level of phone-use compliance was observed (50%).
Kim et al.³⁸ (2010)	South Korea	RCT	70>age≥18 yearsIG: n=50/47CG: n=50/45	3 months	This study took place at Hallym University Kangnam Sacred Heart Hospital, Seoul. The research team developed a system that produced automatic adjustments of insulin dose based on the mean fasting glucose level for 3 days. A Web site was also created to formulate appropriate messages about insulin dosage through an automated algorithm every day at 5 p.m. The patient's readings were automatically uploaded to his or her personalized profile after the glucometer was connected to the mobile phone. If the patient failed to upload his or her readings for more than 3 days, the system generated an encouraging reminder. Participants in the CG self-adjusted their basal insulin according to their glucose readings using a regular glucometer.	• There was a significant reduction in HbA1c in both groups.• The IG had a greater reduction in HbA1c than the CG: from 9.8±1.3% to 7.4±0.7% and 9.8±1.2% to 7.8±0.8%, respectively.• The reduction of postprandial BG occurred faster in the IG, but it became similar after 12 weeks.• No severe hypoglycemic event was reported during the trial in either group.
Bujnowska-Fedak et al.³⁹ (2011)	Poland	RCT	Age=18–75 yearsIG: n=50/47CG: n=50/48	6 months	Study took place in several GP practices. Participants in the IG received a wireless glucose monitor and infrared-enabled transmitter to send data at least once a week to a secured server. The system offered decision support tools and clinical protocols flagging for critical values where clinical parameters were established individually. At any abnormality in the values, the system sent an alarm to the clinic and/or a text to the clinician's mobile device to ensure participants' attention. Participants could also contact the physician by phone. Participants in the CG received the standard care offered by the GP.	• Reduction in HbA1c levels was observed in both groups, and the difference between IG and CG was not statistically significant.• Subgroup analysis of the non–insulin-treated participants showed a significant difference in the HbA1c level (P=0.02).• The IG scored higher that the CG on quality of life measures; however, there was no statistically significant difference between the groups.
Chen et al.⁴⁰ (2011)	Taiwan	RCT	Age average=53.8 yearsIG: n=32CG: n=32	12 months	This study took place at the Tri-Service General Hospital Diabetes Centre in Taipei. Participants received an educational session before being registered in the new telehealth system. The telehealth package included a glucometer, telehealth data analysis platform, telephone system, USB cable, and a login ID. Participants were asked to upload their readings before the scheduled date on which the DE would make the phone calls. These readings were accessed by the physician, who would adjust insulin doses if needed, and the DE. The DE was then scheduled to communicate this feedback and prompt education of the patient in every scheduled phone call.	• There was no significant improvement in the HbA1c levels, but there was significant increase in body weight in the CG.• Significant improvement was seen in HbA1c levels in the IG without body weight change.• There was significantly less hospitalization for any cause in the IG (none) than in the CG (six).
Stamp et al.¹⁴ (2012)	United States	Pre–post study	Age average=54 years n=330/161	16 months	Study took place at City Health and Hospitals Corporation. Participants transmitted data via a landline telemonitoring modem to a secured Web site. The study nurse reviewed the data and provided 10–20 min of telephone education and counseling weekly, based on the treatment plan.	• Reduction by 1.8% in HbA1c level was observed.• Subgroup analysis shows that participants completing the study or still using the technology had higher HbA1c level reduction (2.2%) compared with dropouts (1.4%).• Significant reduction in BP was observed.
Kesavadev et al.⁴¹ (2012)	India	Retrospective cohort study	Age=30–75 years n=1,000	6 months	Study took place in a diabetes center in Kerala, India. Participants manually inputted their readings via telephone, e-mail, or the Web site. Then the DTMS team entered these values in the patient's electronic medical record. The DTMS has facilities for treatment goals and follow-up reminders.	• HbA1c level was significantly reduced from baseline at 3 and 6 months by 2.2%.• Mean HbA1c was 8.5±1.4% and reduced to 6.3±0.6%.• There was no significant association of sex or age with HbA1c levels. • HbA1c reduction was seen in all subgroups irrespective of insulin use. • 84% of patients reported no hypoglycemia events.
Katz et al.⁴² (2012)	United States	Pre–post study	Age average=39 years n=32/16	12 months	Study took place at Chartered Health Family Clinic and Medicaid PPO. Participants were offered a Web-enabled handset and a low-cost prepaid phone connected to the WellDoc diabetes manager. A clinical team viewed the participants' electronic medical record as needed and sent real-time feedback to participants in case of medical concerns.	• Recruitment was limited by a high no-show rate.• 50% of participants dropped out of the study.• Reduction in hospitalization and emergency department visits was observed.• Improved BP, foot exams, and immunizations were observed.• Intervention effect on HbA1c was not assessed because blood testing was not required at end of study.
Tang et al.⁴³ (2013)	United States	RCT	Age ≥18 yearsIG: n=202/193CG: n=213/189	12 months	Study took place at Palo Alto Medical Foundation. Participants received a wireless glucometer uploading system that transmitted data to their electronic medical record. Participants were able to communicate with the clinical care team through the system and received texts and educational videos back electronically.	• At 6 months, the IG had significant difference in HbA1c level reduction compared with the CG (P<0.001).• At 12 months, however, there was no significant difference (P=0.133).• Participants in the IG had statistically significant better diabetes control than in the CG.
Guo et al.⁴⁴ (2013)	Taiwan	Pre–post study	Age ≥20 years n=30/27	6 weeks	Study took place in several outpatient departments of endocrinology and metabolism at three tertiary hospitals. This study aimed to assess a new diabetes support service development. Participants uploaded their data daily via a mobile application and used it to self-test and acquire self-learning about diabetes. Participants were able to call the clinical team if needed.	• This study aimed to assess participants' acceptance to the technology.• Most participants rated these functions of the services as useful/extremely useful.

Sample size shows the initial number of participants recruited and the final number of participants who completed the study.

2HPPBS, 2-h postprandial blood sugar; BG, blood glucose; BMI, body mass index; BP, blood pressure; CG, control group; DE, diabetes educator; DTMS, Diabetes Tele Management System; FPG, fasting plasma glucose; GP, general practitioner; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; IG, intervention group; LDL, low-density lipoprotein; LDL-C, low-density lipoprotein cholesterol; PC, personal computer; PG, phone group; PPO, preferred provider organization; RCT, randomized controlled trial; SMS, short message service; TC, total cholesterol; VA, Veterans Administration.

Quasi-experimental studies

Ten studies used quasi-experimental designs (Table 2). The sample size of these studies ranged from 10 to 1,000. All participants recruited were adults, and the duration of the experiments ranged from 6 to 70 weeks. Seven studies were pre–post design,^{14,29,30,32,33,42,44} out of which three divided the participants into an intervention group (IG) and a control group (CG). One was an interrupted time-series study,¹¹ and two were cohort studies,^34,41 one of which was a retrospective study.⁴¹ The complexity and design of the information technology intervention systems used in the studies differed slightly. All studies shared the process of blood glucose data transmission; the differences are found in the process and the technology used for data transmission.

Table 2.

Definition of Study Designs Included in This Review

Study design	Definition
Quasi-experimental study	An empirical study is used to determine the impact of an intervention on the study participants without randomization.^45,46
Pre–post study	All conditions are the same for both the intervention and the control groups, except that the intervention group is actually exposed to an intervention. Data are collected from participants in both groups before and after the intervention.^47,48
Interrupted time-series study	The participant serves as his or her own control with a collection of repeated observations made sequentially in time before and after an intervention to detect whether the intervention has a significant trend or not.^49,50
Cohort study	A group of participants is observed and followed over a period of time after being exposed to a treatment or an intervention compared with a control group. This type of study is normally used to look at the effect of suspected risk factors that cannot be controlled experimentally.^48,51
Randomized controlled trial	Participants are randomly assigned to an intervention group, where participants are introduced to an intervention being studied, compared with a control group where participants receive standard care, placebo, or no treatment at all.^48,51

All 10 studies evaluated a range of technological interventions, as follows: Five systems used a Bluetooth^® (Bluetooth SIG, Kirkland, WA)-enabled glucometer to transfer data to a mobile phone and display results using the mobile software.^{11,29,30,34,44} Another four systems had the participants or the clinical team enter their data manually into a Web site, whereas one system transferred data wirelessly from the glucometer to a secured Web site, where participants used a private online profile to display their results.^32,33,41,42 All the systems used a diabetes nurse to monitor the information transmitted to the other end. The healthcare team reviewed the data online and sent individually tailored recommendations to the participants receiving the technology.

Seven studies reported that remote telemonitoring systems were viable. HbA1c levels were improved significantly. However, one study reported that baseline HbA1c below 7% was improved in the CG, whereas baseline HbA1c at 7% and above was significantly improved in the IG. Two studies aimed to test patient acceptance rather than clinical outcomes and reported that participants were highly satisfied with the technology.

RCTs

Nine of the articles included used parallel RCT design (Table 1). The sample size ranged from 69 to 415 adult participants. These trials evaluated telemonitoring systems compared with the standard care provided in each setting. The nine studies varied in duration from 3 to 30 months. Four trials showed significant difference in HbA1c between IG and CG.^23,31,38,43 One trial showed significant improvement in HbA1c levels in the IG without significant body weight gain or hospitalization, in contrast to the CG, where there was no significant difference in HbA1c levels, there was significant body weight gain, and six patients were hospitalized.⁴⁰ Two trials showed no statistically significant difference in HbA1c levels, although one of these trials reported significant difference in a subgroup analysis, where the IG had a lower HbA1c than the CG: 7.76% and 8.40%, respectively (P=0.06).³⁵ The second trial reported no significant overall difference between IG and CG; however, a subgroup analysis of non–insulin-treated participants in both groups showed a significant reduction in HbA1c level, from 6.95±0.82% to 6.66±0.86% in the IG and from 7.21±2.02% to 7.2±1.86% in the CG. It also reported a higher overall score of the participants' quality of life and compliance.³⁹ One trial compared Internet-based with phone-based interventions, where HbA1c levels of both groups decreased significantly from 7.6% to 6.9% in the former group and from 8.3% to 7.1% in the latter group.³⁷ The remaining trial reported a significant decrease of HbA1c levels in both IG and CG after 6 months: from 7.62% to 7.21% (P<0.001) and from 7.44% to 7.30% (P=0.048), respectively. At the end of this study, the decreases in HbA1c, blood glucose, blood pressure, body mass index, cholesterol, and low-density lipoprotein levels were statistically significant only in the IG.³⁶

Discussion

Until T2DM can be prevented, it is important to continue with effective medical management, as the prevalence of the disease is rising rapidly.⁹ Self-management of diabetes involves effective patient education as an evidence-based component of treatment and care²² that aims to achieve optimal metabolic control, better compliance with medical treatments, prevention of complications, and enhanced quality of life.⁵² Eighty-five percent to 90% of diabetes patients have T2DM. Optimal care for this type of diabetes primarily relies on control of the patient's lifestyle and consistent monitoring from the clinical team. It is intended that telemonitoring support should enhance self-management of patients with T2DM. To address rapidly increasing demand, healthcare services are moving toward technological innovations. This review confirms that telemonitoring and the timely capturing of patient data related to medical conditions (e.g., clinical, physiological, and behavioral) has the potential to significantly enhance T2DM patients' clinical and behavioral factors, for the patient as well as the care provider.⁵³

The optimal design for a telemonitoring system is still uncertain,⁵⁴ and the impact of real-time blood glucose transmissions is still controversial. The system cannot replace direct interaction with a care provider.²⁷ Real-time management and remote monitoring of T2DM patients resulted in a significant decrease in HbA1c level, as reported in several studies; nevertheless, there were mixed results for clinical outcome measures such as HbA1c, body mass index, and cholesterol levels.⁵⁴ The studies included in this review showed that participants in both the intervention and the control groups had improved HbA1c after 6 months; however, the intervention group was consistent throughout the end of the studies, and care satisfaction scored higher among the IG.

The following systematic reviews were published in the past decade and reviewed evidence in the use of various types of technology to self-manage T2DM. Some of them even explored the same remote telemonitoring solutions we are exploring in our review but with a different point of view. The most recent review by Greenwood et al.,⁵⁵ published in 2014, shows improvement in HbA1c levels in general. Nonetheless, this review was conducted to identify key elements of telemonitoring solutions that are considered essentials for HbA1c improvement. The reviews of Cotter et al.⁵⁶ and van Vugt et al.⁵⁷ focused on studies using Web-based solutions to promote diabetes education and help modify the patient's lifestyle. van Vugt et al.⁵⁷ reported significant improvement in clinical outcomes measures in most of the studies reviewed. Another interesting review of the literature conducted by Chomutare et al.⁵⁸ targeted telemonitoring solutions studies and the mobile applications market, but they aimed to study the features of mobile applications for diabetes care rather than the efficacy of these solutions on the clinical outcome. Another review by Adaji et al.⁵⁴ in 2008 provided evidence on the use of information technology to enhance diabetes management in primary care, although this review focused on the feasibility of technological solutions designs rather than the clinical outcome of the solutions. Finally, Farmer et al.⁵⁹ and Jaana and Pare⁵³ (the latter article had final publication in 2007) published two similar reviews in 2005. They both reviewed studies concerning both T1DM and T2DM and/or insulin-treated patients. All of these reviews were thoroughly appraised by the researcher to avoid duplication in the literature.

Our article fills the 10-year gap with more focus on Web-based remote monitoring of insulin-treated T2DM and its impact on improving HbA1c and patients' quality of life. In our review we keep the evidence up to date in a rapidly changing domain, evaluating newer technologies used around the world and examining studies with recent developments in methods, to include more “natural” methodology such as cohort and interrupted time-series studies.

Conclusions

Telemedicine is one of the major concepts that were introduced to healthcare systems in the past decade as a new paradigm of pervasive healthcare. In this review we focused our search on trials and studies conducted to evaluate and assess remote telemonitoring solutions for T2DM patients. The methodology used to complete this review was efficient with inclusive evidence that strengthens the conclusion that the use of telemonitoring systems is a viable approach to the management of T2DM. This review adequately shows that telemonitoring solutions effectively improve HbA1c level reduction and the patient's quality of life.

In this review the search was narrowed to focus on one type of telemonitoring, “remote monitoring,” as it provides a baseline for further study evaluating different Web-based solutions. One limitation to this review is the considerable variability in methods used in the studies included. Quality of the RCTs was assessed using the Jadad Scale elements.⁶⁰ However, given the variation in the remaining study designs, they have not been inspected for methodological quality.

Telemonitoring may save time and travel expenses for patients as well as the care provider's time and resources; however, this remains to be clearly established. The understanding of how, why, and when technology can improve clinical care and quality of life of T2DM patients requires further intensive and comprehensive investigation. Barriers of implementation, impact of long-term sustainability of outcomes, and cost-effectiveness also remain subjects for further analysis.

Footnotes

Acknowledgments

The authors are thankful to Dr. Roy Harper for his valuable contribution and clinical input.

Author Disclosure Statement

No competing financial interests exist.

References

World Health Organization: Global Status Report on Noncommunicable Diseases. Geneva: World Health Organization, 2010.

Suhrcke

, Nugent

, Stuckler

, et al.: Chronic Disease: An Economic Perspective. London: Oxford Health Alliance, 2006.

O'Halloran

, Miller

, Britt

: Defining chronic conditions for primary care with ICPC-2. Fam Pract, 2004; 21:381–386.

Diabetes

. What Is Diabetes?. 2012. www.diabetes.org.uk/Guide-to-diabetes/Introduction-to-diabetes/What_is_diabetes/ (accessed November 9, 2012 ).

WHO Media Centre: Diabetes. August 2011. www.who.int/mediacentre/factsheets/fs312/en/index.html (accessed November 9, 2012 ).

myVMC Virtual Media Center: Diabetes Mellitus Type 2 (Non-Insulin Dependent, Mature Age Onset. January 2014. www.myvmc.com/diseases/diabetes-mellitus-type-2-non-insulin-dependent-mature-age-onset/ (accessed July 12, 2014 ).

Shaw

, Sicree

, Zimmet

: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract, 2010; 87:4–14.

Balanda

, Barron

, Fahy

: Making Chronic Conditions Count: Hypertension, Coronary Heart Disease, Stroke, Diabetes. A Systematic Approach to Estimating and Forecasting Population Prevalence on the Island of Ireland. Executive Summary. Dublin: Institute of Public Health in Ireland, 2010.

Franc

, Daoudi

, Mounier

, et al.: Telemedicine and diabetes: achievements and prospects. Diabetes Metab, 2011; 37:463–476.

10.

Tobe

, Wentworth

, Ironstand

, et al.: DreamTel; Diabetes Risk Evaluation and Management Tele-Monitoring study protocol. BMC Endocr Disord, 2009; 9:13.

11.

Larsen

, Turner

, Farmer

, et al.: Telemedicine-supported insulin optimisation in primary care. J Telemed Telecare, 2010; 16:433–440.

12.

Simon

, Holleman

, Gude

, et al.: Safety and usability evaluation of a Web-based insulin self-titration system for patients with type 2 diabetes mellitus. Artif Intell Med, 2013; 59:23–31.

13.

Fitzner

, Moss

: Telehealth—an effective delivery method for diabetes self-management education?. Popul Health Manage, 2013; 16:169–177.

14.

Stamp

, Allen

, Lehrer

, et al.: Telehealth program for Medicaid patients with type 2 diabetes lowers hemoglobin A1c. J Managed Care Med, 2012; 15:39–46.

15.

Bellazzi

: Telemedicine and diabetes management: current challenges and future research directions. J Diabetes Sci Technol, 2008; 2:98–104.

16.

Good Governance Institute: Board Assurance Prompt—Implementing Telehealth Services. London: Good Governance Institute, 2011.

17.

Swinnen

, DeVries

: Contact frequency determines outcome of basal insulin initiation trials in type 2 diabetes. Diabetologia, 2009; 52:2324–2327.

18.

Independent Living in Scotland: The Report of the Commission on Funding of Care and Support. Edinburgh, Scotland: Independent Living in Scotland, 2011.

19.

Currell

, Urquhart

, Wainwright

, et al.: Telemedicine versus face to face patient care: effects on professional practice and health care outcomes. Cochrane Database Syst Rev, 2000;(2):CD002098.

20.

Scannell

, Perednia

, Kissman

: Telemedicine: Past, Present, Future. Bethesda, MD: National Institutes of Health, 1995.

21.

World Health Organization: Telemedicine: Opportunities and Developments in Member States: Report on the Second Global Survey on eHealth. Geneva: World Health Organization, 2010.

22.

Nes

, van Dulmen

, Eide

, et al.: The development and feasibility of a Web-based intervention with diaries and situational feedback via smartphone to support self-management in patients with diabetes type 2. Diabetes Res Clin Pract, 2012; 97:385–393.

23.

Stone

, Rao

, Sevick

, et al.: Active care management supported by home telemonitoring in veterans with type 2 diabetes. Diabetes Care, 2010; 33:478–483.

24.

Wojcicki

, Ladyzynski

, Foltynski

: What we can really expect from telemedicine in intensive diabetes treatment: 10 years later. Diabetes Technol Ther, 2013; 15:260–268.

25.

Peate

: Technology, health and the home: eHealth and the community nurse. Br J Community Nurs, 2013; 18:222–227.

26.

Meystre

: The current state of telemonitoring: a comment on the literature. Telemed J E Health, 2005; 11:63–70.

27.

Azar

, Gabbay

: Web-based management of diabetes through glucose uploads: has the time come for telemedicine?. Diabetes Res Clin Pract, 2009; 83:9–17.

28.

Sicotte

, Pare

, Potvin

, et al.: Effects of home telemonitoring to support improved care for chronic obstructive pulmonary diseases. Telemed J E Health, 2011; 17:95–103.

29.

Durso

, Wendel

, Letzt

, et al.: Older adults using cellular telephones for diabetes management: a pilot study. Medsurg Nurs, 2003; 12:313–317.

30.

Kim

, Yoo

, Shim

: Effects of an Internet-based intervention on plasma glucose levels in patients with type 2 diabetes. J Nurs Care Qual, 2005; 20:335–340.

31.

Cho

, Chang

, Kwon

, 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, 2006; 29:2625–2631.

32.

Kim

H-S

, Jeong

H-S

: A nurse short message service by cellular phone in type-2 diabetic patients for six months. J Clin Nurs, 2006; 16:1082–1087.

33.

Kim

H-S

: Impact of Web-based nurse's education on glycosylated haemoglobin in type 2 diabetic patients. J Clin Nurs, 2007; 16:1361–1366.

34.

Turner

, Larsen

, Tarassenko

: Implementation of telehealth support for patients with type 2 diabetes using insulin treatment: an exploratory study. Inform Prim Care, 2009; 17:47–53.

35.

Istepanian

, Zitouni

, Harry

, et al.: Evaluation of a mobile phone telemonitoring system for glycaemic control in patients with diabetes. J Telemed Telecare, 2009; 15:125–128.

36.

Rodriguez-Idigoras

, Sepulveda-Munoz

, Sanchez-Garrido-Escudero

, et al.: Telemedicine influence on the follow-up of type 2 diabetes patients. Diabetes Technol Ther, 2009; 11:431–437.

37.

Cho

, Lee

, Lim

, et al.: Mobile communication using a mobile phone with a glucometer for glucose control in type 2 patients with diabetes: as effective as an Internet-based glucose monitoring system. J Telemed Telecare, 2009; 15:77–82.

38.

Kim

, Park

, Kang

, et al.: Insulin dose titration system in diabetes patients using a short messaging service automatically produced by a knowledge matrix. Diabetes Technol Ther, 2010; 12:663–669.

39.

Bujnowska-Fedak

, Puchala

, Steciwko

: The impact of telehome care on health status and quality of life among patients with diabetes in a primary care setting in Poland. Telemed J E Health, 2011; 17:153–160.

40.

Chen

, Chang

, Hsu

, et al.: One-year efficacy and safety of the telehealth system in poorly controlled type 2 diabetic receiving insulin therapy. Telemed J E Health, 2011; 17:683–687.

41.

Kesavadev

, Shankar

, Pillai

, et al.: Cost-effective use of telemedicine and self-monitoring of blood glucose via Diabetes Tele Management System (DTMS) to achieve target glycosylated hemoglobin values without serious symptomatic hypoglycemia in 1,000 subjects with type 2 diabetes mellitus—a retrospective study. Diabetes Technol Ther, 2012; 14:772–776.

42.

Katz

, Mesfin

, Barr

: Lessons from a community-based mHealth diabetes self-management program: “it's not just about the cell phone.”. J Health Commun, 2012; 17(Suppl 1):67–72.

43.

Tang

, Overhage

, Chan

, et al.: Online disease management of diabetes: Engaging and Motivating Patients Online With Enhanced Resources-Diabetes (EMPOWER-D), a randomized controlled trial. J Am Med Inform Assoc, 2013; 20:526–534.

44.

Guo

, Lin

, Chen

, et al.: Development and evaluation of theory-based diabetes support services. Comput Inform Nurs, 2013; 31:17–26.

45.

National Center for Technology Innovation: Quasi-Experimental Study. www.nationaltechcenter.org/index.php/products/at-research-matters/quasi-experimental-study/ (accessed July 16, 2014 ).

46.

Wikipedia: Quasi-experiment. http://en.wikipedia.org/wiki/Quasi-experiment (accessed July 16, 2014 ).

47.

Dimitrov

, Rumrill

Jr : Pretest-posttest designs and measurement of change. Speaking Res, 2003; 20:159–165.

48.

NHS Choices, Your Health, Your Choices: Health News Glossary. August 2009. www.nhs.uk/news/Pages/Newsglossary.aspx (accessed July 30, 2014 ).

49.

Ramsay

, Matowe

, Agenzia

, et al.: Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies. Int J Technol Assess Health Care, 2003; 19:613–623.

50.

Matowe

, Leister

, Crivera

, et al.: Interrupted time series analysis in clinical research. Ann Pharmacother, 2003; 37:1110–1116.

51.

Institute for Work & Health: Cohort Studies, Case Control Studies and Randomized Controlled Trials. 2005. https://www.iwh.on.ca/wrmb/cohort-studies-case-control-studies-and-rcts (accessed July 30, 2014 ).

52.

Carlisle

, Warren

, Scuffham

, et al.: Randomised controlled trial of an in-home monitoring intervention to improve health outcomes for type 2 diabetes: study protocol. Global Telehealth, 2012; 182:43–51.

53.

Jaana

, Pare

: Home telemonitoring of patients with diabetes: a systematic assessment of observed effects. J Eval Clin Pract, 2007; 13:242–253.

54.

Adaji

, Schattner

, Jones

: The use of information technology to enhance diabetes management in primary care: a literature review. Inform Prim Care, 2008; 16:229–237.

55.

Greenwood

, Young

, Quinn

: Telehealth remote monitoring systematic review: structured self-monitoring of blood glucose and impact on A1C. J Diabetes Sci Technol, 2014; 8:378–389.

56.

Cotter

, Durant

, Agne

, et al.: Internet interventions to support lifestyle modification for diabetes management: a systematic review of the evidence. J Diabetes Complications, 2014; 28:243–251.

57.

Van Vugt

, de Wit

, Cleijne

, et al.: Use of behavioral change techniques in Web-based self-management programs for type 2 diabetes patients: systematic review. J Med Internet Res, 2013; 15:e279.

58.

Chomutare

, Fernandez-Luque

, Arsand

, et al.: Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J Med Internet Res, 2011; 13:e65.

59.

Farmer

, Gibson

, Tarassenko

, et al.: A systematic review of telemedicine interventions to support blood glucose self-management in diabetes. Diabet Med, 2005; 22:1372–1378.

60.

Halpern

, Douglas

, eds.: Appendix: Jadad scale for reporting randomized controlled trials. Evid Based Obstet Anesth, 2005:237–238. doi: 10.1002/9780470988343.app1.