The role of Data & the Quantified Self in Diabetes Management

Can data drive behavioral change and help you improve your health and personal wellbeing?

Photo by Photo by Nick Jio

November is already behind us, and it is safe to say that one of the major trends during the designated Diabetes Awereness Month was the conversation surrounding wearables, personal data collection and the so called Quantified self. As a person with Insulin Resistance and analyst working on diabetes related projects, I decided to dive into how data can contribute to tangible change.

What is the Quantified Self?

Even without purchasing sophisticated new devices or software, any consumer who owns a smartphone can track and quantify vast amounts of versatile personal information: from calorie intake and daily exercise, through spending habits, productivity and biometric data.

Photo by Janita Sumeiko

Diabetes Monitoring & Self Measurement

“ Diabetes is somewhat unique among chronic conditions in that it’s very data-intensive” — Chuck Gammal, partner at Simon-Kucher

According to the NDSR report for 2017, a staggering 30.3 million people (9.4% of the US population) have diabetes. The World Health Organization (WHO) has estimated the figure to be over 422 million adults worldwide, with the potential to reach over half a billion by 2040. There is clearly a dire need to use both prevention and regular self-monitoring to avoid or at least mitigate the health hazards associated with the disease.

Monitoring glucose levels | BGM, CGM & Flash devices


BGM (blood glucose monitoring) devices self-administered lancet pricks to collect blood samples,wheareas new CGM (continuous glucose monitoring) solutions count on the insertion of temporary sensors for easy ongoing data collection. Flash devices use a sensor transmitting both instantaneous readings and 8-hout trend graphs when it is swiped close to a separate touchsreen reader (they do not provide hypo- or hyperglycemia alarms like CGMs). CGMs and Flash devices usually use patches with needles piercing below the skin of the abdomen or upper arm to collect samples from the Interstitial fluid (ISF) surrounding tissue cells).


Although research on CGM technology has been around since the late 1960s, thеse devices did not take off the ground until the late 1990s when MiniMed (now owned by Medtronic) came up with the first widely used CGM product.

A lot has happened on the market in the past decades as R&D investment, data collection capabilities and technological innovation pushed new solutions and device producers on the market: most notably the Flash device innovations by Abbott (FreeStyleLibre) and the CGMs by Dexcom.

Apps & Wearables for Data Collection

Recently, a growing number of people who do not have diabetes, are also using CGMs to improve their overall health and gain quantitative insight on the sleep patterns, eating habits and exercise which influence their mood, cognitive functions and physical well-being.

Photo by Brooke Lark

Patient Needs

*Exercise has been shown to improve insulin sensitivity, so it is a vital part of both prevention and treatment.

Do CGMs help?

Making a difference

  • Collect data from a properly placed and calibrated device. It often takes a while until new sensors start to provide proper measurements, so following product instructions for placement and calibration is vital
  • Monitor data for general trends and insights, not only at major peaks and lows. Identifying eating and behavioral patterns which lead to even subtle changes in readings can help you get even better long-term resultsin controlling and preventing hypo- and hyperlycemic episodes.

Innovation, Data, Machine Learning

Is there a link between the Quantified-Self (QS) and Behavioural Change?

However, for many QS activities, there is a gap between self-monitoring/data collection and taking the actions needed for long-term behavioral changes. Simply put, no matter how many data points and trends you can track, unless they prompt regular re-adjustment of habits and behavior, monitoring is not enough to produce a change on its own.

“Fritz et al. (2013)… found that numerical feedback motivated and reinforced participants’ activities, because it created a sense of achievement and helped them to reach their goals… However, despite these positive reactions and the rapid growth in the industry, new evidence suggests that the presence of data concerning one’s habits is often ineffective in instilling long-lasting behaviour change” — Zoe Adams, The Decision Lab

Of course,the findings above are relevant to the general population and TD2 patients, whereas TD1 patients are significantly more constrained when it comes to eating habits and need for timely intervention. For them, self-monitoring is not just helpful but vital so behavioral changes.

Flash Devices & CGM Use: Patient results

Clinical research data on the use of real-time CGM showed meaningful improvements in glycated hemoglobin (HbA1c) and a reduction of hypoglycemic events. This was also linked to a demonstrated to improve quality of life (reduced fear of hypoglycemia) and more cost-effective long-term projections”(Cappon et al., 2017).

More on the topic:

For more about the Quantified-Self and measurement not related to diabetes, read the excellent article “Does the Quantified-Self Lead to Behaviour Change?” by Zoe Adams, The Decision Lab.