Remote Monitoring Systems for Self-Managing Type 2 Diabetes: A Systematic Review
Hayat Mushcab* 1, George Kernohan 1,2, PhD, Suzanne Martin 1,2, PhD
Abstract
Our systematic review aims to evaluate the impact web-based telemonitoring for managing
Type 2 Diabetes Mellitus. We used MEDLINE, EMBASE, CINAHL, AMED, Cochrane and
PubMed to conduct our search. The technology used, trial design, quality of life and Glycated
haemoglobin (HbA1c) outcome measures were extracted from systemic reviews, metaanalyses, randomised controlled trials and cohort studies. From 426 publications identified,
19 met our search criteria; 10 quasi-experiments out of which 7 are pre-post test studies, 2 are
cohort studies and 1 is interrupted time-series study, and 9 randomised controlled trials
(RCT). Electronic transfer of glucose results from home to hospital appears to be more
feasible for healthcare delivery. 15 studies showed positive improvement in HbA1c levels.
Finally, it remains challenging to identify evidence in the rapidly changing area of remote
monitoring, the optimal design of a telemonitoring system is still uncertain and the impact of
the remote glucose transmissions remains controversial.
Keywords: Type 2 diabetes; self-management; telemonitoring; telehealth; telemedicine;
connected health technology; blood glucose monitoring
* Email: hayat.mushcab@gmail.com, Mobile: +447460314350; corresponding author
1 University of Ulster, Jordanstown, Northern Ireland
2 Institute of Nursing and Health Research, Jordanstown, Northern Ireland
1.1 Background
According to the International Diabetes Federation, diabetes is one of the leading
threats to health globally. Whereas, in 2010, 300 million people worldwide suffered from
diabetes and 344 million others were at risk of developing T2DM; which overcomes 90% of
all patients with T2DM and it is anticipated by the Federation that if the disease was not
managed. The epidemic is predicted to escalate to affect 438 million people by 2030 (Shaw et
al. 2010). In the United Kingdom, over 2 million people suffer from T2DM (Diabetes UK
2012) and over 210,000 adults aged 20 years and older on the island of Ireland have diabetes
(types 1 and 2 combined) (Balanda et al. 2010).
These figures indicate that we are in the midst of an epidemic. Society’s ultimate aim
is to prevent T2DM through primary control; however, evidence shows that achieving this
goal will be most challenging as the incidence is rapidly rising (Tobe et al. 2009). Selfmanagement of diabetes involves effective patient education as a key element of care. Patient
education is an evidence-based component of treatment and care for patients with T2DM
(Nes et al. 2012). Such education aims to achieve optimal metabolic control, better
compliance with medical treatments, prevention of complications and enhanced quality of
life (Carlisle et al. 2012).
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
telemedicine to support self-management. Healthcare providers and researchers have turned
to technology for allowing easier access to healthcare, improving monitoring, better
compliance in medication taking, healthy eating and all the other elements needed to achieve
diabetes control through self-management (Nes et al. 2012, Fitzner, K. and Moss 2013). This
technological approach offers many opportunities; for example it can reduce geographic
barriers, where data transmission will provide automated feedback and facilitate patient-
healthcare provider communication (Stone et al 2010). 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 (Fitzner, K. and
Moss 2013, Wojcicki et al 2013).
Telemedicine is a broad term that can be defined in various ways (Currell et al 2010).
In 1995 Scannell 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 band width transmission of digitized
signals in conjunction with computers, fibre optics, satellites and other sophisticated
peripheral equipment and software” (Scannell et al 1995). A more recent definition from
World Health Organization in 2010 (WHO 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.” Telemedicine and diabetes 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 patient’s wellbeing (Peate 2013). Telemedicine for diabetes has
various forms that can be categorised in three groups: 1) real-time monitoring systems, 2)
classical long-term monitoring systems and 3) telemonitoring of diabetes complications
(Wojcicki et al 2013).
Telemonitoring is defined as the use of information and communication technologies
for the transmission of biologic or physiologic data between the patients’ homes and health
professionals for data interpretation and decision-making (Meystre 2005) using the Internet
for web-based real-time systems. Typically, a hub is installed in patients’ homes or an
application installed in their personal computers, smartphones and personal digital assistant
devices (PDAs) (Wojcicki et al 2013) so that they can be used regularly, feeding the system
data on their condition. This 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 it remotely to a central data management system, where a
healthcare provider on the receiving end of the system 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 email
or phone (Azar and Gabbay 2009, Sicotte et al 2011).
2.1 Method
2.1.1 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. A search
was also conducted through Google Scholar. Related references used in searched articles
were also reviewed.
2.1.2. Search terms
Using the Medical Subject Headings (MeSH) thesaurus combined: Type 2 diabetes; selfmanagement; telemonitoring; telehealth; telemedicine; connected health technology; blood
glucose monitoring.
2.1.3. Inclusion criteria
Primary research studies exploring technological interventions (web-based systems with
blood glucose transmission) for T2DM self-management and telecommunication with the
healthcare providers were included. Only articles published in English and after 2000 were
included. Research designs were categorised as: 1) Quasi-experimental studies, such as
interrupted time-series studies, cohort studies and pre-post studies, or 2) Randomised
controlled trials (RCTs). The study population in the studies had to be adults (18 years old
and above) diagnosed with type 2 diabetes mellitus and prescribed insulin treatment.
2.1.4. Exclusion criteria
Publications evaluating telemedicine for other chronic diseases, reviews without primary
clinical data, case reports and personal opinion studies were excluded. In addition, studies
were excluded which evaluated the use of other forms of telemedicine than web-based
telemonitoring: interventions such as video-conferencing, telephone-based consultations and
blood glucose results stored in a glucometer for the next clinical appointment.
2.1.5. Data extraction
The first author reviewed titles, abstracts and sometimes the methodology of the study to
determine if the full text should be included in this review. The articles were reviewed for
eligibility by the second and third authors.
We included technology for blood glucose data transfer and self-management data.
Outcomes were categorised according to which measurement was described and validated,
for example HbA1c.
Figure A.1: Results of systematic search for internet-based telemedicine interventions
involving transmission of blood glucose measures.
3.1. Results
The aforementioned search strategies surfaced a total of, 426 publications. As shown in Fig.
A.1, only 19 articles met the inclusion/exclusion criteria and were included for analysis. Six
were conducted in South Korea (Kim HS, Kim C. and Cho JH), five studies in the USA
(Durso S, Stone R, Stamp K, Tang P and Katz R), three in the UK (Turner J, Istepanian R
and Larsen M) and two in Taiwan (Guo S and Chen SY). Single studies were conducted in
Spain (Rodrigues-Idigoras M), Poland (Bujnowska-Fedak M) and India (Kesavadev).
3.1.1 Quasi-Experimental Studies
Ten studies used quasi-experimental designs. The sample size of these studies ranged from
10 to 1000. All participants recruited were adults and the duration of the experiments ranged
between six to 70 weeks. The complexity and design of the IT intervention systems used in
the studies slightly differed. All studies shared the process of blood glucose data
transmission. However, the process and the technology used for data transmission are where
the difference is found.
3.1.2 Randomised Controlled Trials
Nine studies of the articles included used parallel RCT design. The sample size ranged from
69 to 415 adult participants. These trials evaluated telemedicine systems compared to the
standard care provided in each setting. The six studies varied in duration from three to 30
months.
4.1 Discussion
Most people with diabetes (85–90%) have the type 2 variant. Optimal care for them relies on
the patient’s lifestyle and consistent monitoring from the clinical team. It has been widely
expected that telemedicine will enhance self-management of patients with T2DM. To address
rapidly increasing demand, healthcare services are moving towards such technological
innovations. This review confirms that telemedicine and the timely capturing of patient data
related to medical conditions (e.g. clinical, physiological and behavioural) has the potential to
significantly enhance type 2 diabetes patients’ clinical and behavioural factors, for the patient
as well as the care provider (Jaana and Pare 2007). However, the optimal design for a
telemedicine system is still uncertain (Adaji 2008) and the impact of real-time blood glucose
transmissions is controversial. The system cannot replace direct interaction with a care
provider (Azar and Gabbay 2009). Real-time management and remote monitoring of diabetic
patients resulted in significant decrease in HbA1c, as reported in several studies;
nevertheless, there were mixed results for biological outcome measures such as HbA1c, BMI
and cholesterol levels (Adaji 2008).
The studies included in this review showed that
participants in both the intervention and the control groups had improved HbA1c after six
months; however, the intervention group was consistent throughout the end of the studies and
care satisfaction scored higher among the intervention group.
In this review we focused our search on trials and studies conducted to evaluate and
assess remote telemonitoring solutions for type 2 diabetics. The methodology used to
complete this review was efficient with inclusive evidence that strengthens the conclusion
that the use of telemonitoring systems is feasible to manage T2DM. However, the scope of
remote telemonitoring solutions was limited to real-time interventions. Another limitation to
this review is the considerable variability in methods used in the studies included. Quality of
the RCTs was assessed using the Jaded Scale elements (Halpern and Douglas 2005).
However, given the variation in remaining study designs, they have not been inspected for
methodological quality.
Telemedicine may save time and travel expenses for patients as well as the care
provider’s time and resources; however, this is subject to future study. The understanding of
how, why and when technology can improve clinical care and quality of life of type 2
diabetics requires further intensive and comprehensive investigation. Barriers of
implementation and impact of long-term sustainability of outcomes also remain subjects for
further analysis.
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