Prevention of T2DM: eHealth

Evidence from landmark clinical trials in China, Finland, Sweden, the US and India has shown that lifestyle interventions that promote weight reduction (in overweight individuals), increased physical activity and a healthy diet can prevent, or at least delay, the onset of type 2 diabetes in people at high risk. Achieving long-time adherence to these lifestyle changes is one of the greatest challenges. With the increasing diabetes epidemic (estimates from the IDF suggest that 382 million people worlwide have diabetes, and that, by 2035, this number will skyrocket to 592 million) and with its severe and costly complications, new ways for delivering evidence-based lifestyle change programs to large numbers of people are needed. Advances in Information and Communication Technologies are emerging as promising tools to provide chronic disease management and to support behavioral changes. The aim of this article is to identify eHealth advances in T2DM prevention.

For an overview of diabetes prevention interventions, see the references provided at the end of this article[1][2][3][4][5]

The role of eHealth in the changing diabetes healthcare landscape

The St. Vincent Declaration[6], a set of recommendations established in 1989 by representatives of health departments, patient’s organizations and diabetes experts to drive progress on diabetes prevention and treatment, advocated for the use of ICT tools for quality assurance of diabetes care provision. Since the St.Vincent Declaration, treatment of diabetes has greatly changed driven by technological innovations (with breakthroughs such as the continuous glucose monitoring, the continuous subcutaneous insulin infusion and the prototypes of variants of artificial pancreas [7][8][9]).

Furthermore, the popularisation of the Internet has accelerated the pace of eHealth development and adoption. In light of this, the World Health Organization established in 2005 the Global Observatory for eHealth (GOe), to “provide Member States with strategic information and guidance on effective practices and standards in eHealth”.

The WHO defines eHealth broadly as the “use of ICT for health”[10] .

Examples of eHealth services or systems include:

  • Telemedicine: term coined in the 1970s which literally means “healing at a distance”. Telemedicine allows healthcare professionals to evaluate, diagnose and treat patients in remote locations by means of video consultations, digital transmission of medical imaging, etc. The broader term Telehealth includes also non-clinical uses (patient and professional health-related education, health data management and administration, etc.).

  • Electronic Health Records (HER)

  • Electronic prescribing

  • Patient Web Portals: secure online applications that give patients access to personal health information from anywhere (medication history, doctor visits, discharge summaries, etc.).

  • Mobile Health (mHealth): a subset of Telehealth, it’s defined by the Global Observatory for eHealth (GOe) as the “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices”[11]. It enables the real-time monitoring of patients, the electronic delivery of healthcare information and the delivery of SMS or voice messages with appointment or medication reminders and lifestyle and dietary advice.

    A growing number of health-related smartphone apps and wearable health devices are becoming available for the general public. Physical activity monitors and nutrition trackers make it easy to track exercise and food intake. They could have the potential to support health behavior change to prevent diabetes, or to support the self-management of the disease in diabetic patients in order to reduce the risk of developing diabetes-related complications and optimize their health and wellness. Nevertheless, more research is needed to assess the efficacy of mHealth. This will be commented below.

Besides developing lifestyle and wellbeing apps, tech giants Apple, Samsung and Google are researching wearable sensors to measure glucose levels in a non-invasive way.

  • Health-specific Social Media tools are Internet-based applications that allow the creation and exchange of user-generated content. Examples include online patient communities, social networking sites and support groups, message boards, wikis, blogs, etc. These tools encourage members to share information, personal stories and forge relationships.

EU policy for eHealth

In Europe, the European Commission has taken a number of policy initiatives to encourage the development of ICT in the healthcare sector (eHealth Action Plan 2004-2011 and 2012-2020). The EU currently funds several research projects related to diabetes[12] .

The unstoppable growth of mHealth

The rapid popularisation of smartphones and tablets is driving adoption of mHealth. The European Commission reported in April 2014[13] that there are nearly 100.000 mHealth apps available across multiple platforms, and that, by 2017, 3,4 billion people worldwide will own a smartphone and 50% will use health apps.

mHealth is a young field, and the evidence base to support mHealth diabetes prevention and interventions is increasingly growing. To help investigate and develop evidence-based mHealth solutions, Johns Hopkins University launched in 2013 a repository of published research findings on mHealth, the mHealth Evidence portal[14] .

mHealth in diabetes prevention and management

According to a report by the American nonprofit organisation eHealth Initiative [15], the number of smartphone applications for diabetes increased by almost 400% from 2010 to 2012. The usual parameters included in these apps are physical exercise, food intake, carb counting, blood sugar monitoring, medication alerts, weight tracking and recipe databases.

A meta-analysis, carried out by the Department of Evidence-Based Medicine and Division of Population Genetics at the Chinese Academy of Medical Sciences [16], of 22 clinical studies analyzed the use of mobile phones for diabetes self-management and its impact on A1C. The results showed that mobile phone interventions for diabetes self-management reduced HbA1c values by a mean of 0.5%. Greatest reductions occurred among Type 2 patients versus Type 1 patients. This is of course clinically not very impressive, but it might well be that these electronic support systems led to an improvement of therapy adherence, therapy satisfaction and overall a better quality of life.

In the United Kingdom, the NHS has made available an online library of reviewed diabetes apps[17] to ensure they are clinically safe. In the US, the American Association of Diabetes Educators (AADE) introduced in 2013 the AADE Diabetes Goal Tracker [18], an app to help people with diabetes to meet goals in seven areas of diabetes management: Healthy Eating, Being Active, Monitoring, Taking Medication, Problem Solving, Reducing Risks and Healthy Coping.

Most diabetes apps are used to support patient management. Nevertheless, apps promoting lifestyle changes can also play a role in diabetes prevention, in motivating people to make healthier choices and helping them monitor their physical activity and food intake. There are also apps that allow calculation of type 2 diabetes risk based on the Finnish Diabetes Risk Score (FINDRISC) questionnaire.

While mHealth is growing and is likely to significantly impact diabetes care and prevention in the future, more research is needed to better implement evidence-based clinical recommendations.

A note on the privacy and security challenges of mHealth

The accelerated development of mHealth technologies raises concerns about the processing of users’ data.

The 2013 study Technical Analysis of the Data Practices and Privacy Risks of 43 Popular Mobile Health and Fitness Applications [19] found that “only about 50 percent of free and paid apps posted a privacy policy and adhered to it (…) and of the sites that post a privacy policy, those privacy policies were less than 50% accurate in describing what was actually happening technically in the app”. The study also found that “both free and paid apps generally failed to use HTTPS (SSL-encrypted) connections when sending privacy-sensitive data over the network”. Additional privacy risks are that many apps send users’ data, without the user’s direct knowledge, to third parties to provide core functionality, for collection of user behavior, analytics and targeted advertising.

The Green Paper on mobile Health published in April 2014 by the European Commission[20] highlights the concerns about personal and health data protection. In fact, the legal vacuum around mHealth is considered to be one of the main barriers to mHealth deployment. To respond to the challenges posed by new technologies, the EU Personal Data Protection Directive is currently being revised.

There is also a need to ensure sufficient quality and safety safeguards of mHealth tools. This could be done through formal evaluation and certification mechanisms carried out by healthcare institutions. App certification programs are already emerging in some countries. As we mentioned before, the UK National Health Service runs an online Health Apps library. In Spain, the Andalusian Agency for Healthcare Quality created in 2013 the AppSaludable distinctive[21], that identifies applications that are safe and reliable to use. The evaluation is carried out by health professionals and experts in patient safety, usability and design. To unlock the full potential of mHealth so that it can have a safe and reliable role in the delivery of healthcare, policy makers and regulators must ensure a high level of protection of individuals through appropriate regulatory procedures.

A platform for the prevention and treatment of obesity, diabetes and cardiometabolic risk: PREDIRCAM

Figure 1: Functionalities of PREDIRCAM (Click to enlarge)
Figure 1: Functionalities of PREDIRCAM (Click to enlarge)
PREDIRCAM is a technological platform developed by a disciplinary team of endocrinologists, dietitians, engineers, nurses, cardiologists, psychiatrists, bioscientists and physical trainers, designed to improve the effectiveness of lifestyle modifications in the prevention and treatment of obesity, diabetes and cardiometabolic risk. The platform consists of a web-based application and a heart rate monitor (HRM) for physical activity monitoring. The web includes:

A front page with a login system

An electronic medical record Figure 2. PREDIRCAM (Click to enlarge)
Figure 2. PREDIRCAM (Click to enlarge)

A dietary module, with:

  • ­a database with more than 6.000 foods with their nutritional information and a picture of its associated mass or volume measure
  • U.S. Department of Agriculture food database (allows automatic importation of foods)
  • Dishes database (Mediterranean diet)
  • Food registration interface: » List of dishes » Dish creator to create new dishes if they’re not on the list » Food finder » Macronutrients graph » Personal assistant » Intake list with summaries of the daily and weekly caloric intake » Recipes list Figure 3. PREDIRCAM (Click to enlarge). Participants and collaborators
    Figure 3. PREDIRCAM (Click to enlarge). Participants and collaborators

An exercise module, with:

  • An infrared communication interface to upload the data registered by the heart rate monitor
  • A grap interface that shows the duration and caloric expenditure of the physical exercise executed and a calendar showing the days when exercise was carried out
  • Another graph interface that compares the cumulative weekly caloric expenditure with the prescribed goal
  • In case the user doesn’t exercise with the HRM, he can manually select the type of exercise from a list and introduce its duration

Notification module. Notifies of the following:

  • Too infrequent access to the platform, failure to upload enough HRM data, too infrequent entry of food intake data, exceeding the prescribed caloric intake, not reaching the prescribed caloric expenditure, successfully following the prescribed therapy

PREDIRCAM has been recently tested in a feasibility pilot study [22].


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  6. ^ WHO/IDF Saint Vincent Declaration Working Group. Diabetes Mellitus in Europe: a Problem at all Ages in all Countries. A Model for Prevention and Self Care. Acta Diabetol 1990;27:181-3.

  7. ^ Hanazaki K, Maeda H, Okabayashi T. Tight perioperative glycemic control using an artificial endocrine pancreas. Surg Today. 2010;40:1–7.

  8. ^ de Leiva A, Hernando ME; EDUAB-HSP; GBT-UPM, Rigla M, Capel I, Brugués E, Pons B, Erdozain L, Prados A, Corcoy R, Gómez EJ, García-Sáez G, Martínez-Sarriegui I, Rodríguez-Herrero A, Pérez-Gandía C, del Pozo F. Telemedical artificial pancreas: PARIS (Páncreas Artificial Telemédico Inteligente) research project. Diabetes Care. 2009;32(Suppl 2):S211–6.

  9. ^ Russell SJ, El-Khatib FH, Sinha M, Magyar KL, McKeon K, Goergen LG, Balliro C, Hillard MA, Nathan DM, Damiano ER. Outpatient glycemic control with a bionic pancreas in type 1 diabetes. N Engl J Med 2014; 371:313-325.

  10. ^ World Health Organisation. eHealth.

  11. ^ World Health Organisation. mHealth – New horizons for health through mobile technologies, Global Observatory for eHealth series. 2011;3:6.

  12. ^ AP@home (, which aims to build an artificial pancreas; Commodity12, a platform for continuous monitoring of diabetes; EMPOWER (, which aims to develops a modular and standard-based Patient Empowerment Framework to facilitate the self-management of diabetic patients; MISSION-T2D (, a patient-specific model for the simulation and prediction of metabolic and inflammatory processes in the onset and progress of type 2 diabetes. European Commission. eHealth projects. Research and innovation in the field of ICT for Health and Wellbeing: an overview. 2014.

  13. ^ European Commission. Healthcare in your pocket: unlocking the potential of mHealth. 2014. Available at:

  14. ^

  15. ^ eHealth initiative. A study and report on the use of eHealth tools for chronic disease care among socially disadvantaged populations. California healthcare foundation. 2012.

  16. ^ Liang X, Wang Q, Yang X, Cao J, Chen J, Mo X, Huang J, Wang L and Gu D. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabetic Medicine. 2011;28:455–463.

  17. ^ NHS. Health Apps Library – Diabetes.

  18. ^ American Association of Diabetes Educators. Diabetes Goal Tracker Mobile App.

  19. ^ Lie Njie, CM. Technical Analysis of the Data Practices and Privacy Risks of 43 Popular Mobile Health and Fitness Applications. 2013. Available at:

  20. ^ European Commission, Green Paper on mobile health. 2014. Available at:

  21. ^ Andalusian Agency for Healthcare Quality.

  22. ^ González C, Herrero P, Cubero JM, Iniesta JM, Hernando ME, García-Sáez G, Serrano AJ, Martinez-Sarriegui I, Perez-Gandia C, Gómez EJ, Rubinat E, Alcantara V, Brugués E, Chico A, Mato E, Bell O, Corcoy R, de Leiva A. PREDIRCAM eHealth Platform for Individualized Telemedical Assistance for Lifestyle Modification in the Treatment of Obesity and Diabetes and Cardiometabolic Risk Prevention: A Pilot Study (PREDIRCAM 1). J Diabetes Sci Technol 2013;7(4):888-897.


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