Tracking Your Own Biometric Data – How far Are You Willing to Go?

2

October

2022

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We all know about the health applications on our phones that measure our step count during the day. Many of us, nowadays, also were smartwatches that measure our heart rate or our sleeping behavior. But what if you could continuously monitor your blood sugar levels?

Some of you might be familiar with an Abbott FreeStyle Libre continuous glucose monitor (CGM) (FreeStyle Libre, n.d.). A small biosensor with an enzyme-coated wire that is inserted into the skin that people with diabetes often wear on their arm to measure their glucose levels (Figure 1). This biosensor helps people with diabetes to manage their blood sugar levels and ensure that it remains in a healthy range (FreeStyle Libre, n.d.; Cumbers, 2021).

Figure 1: Abbott FreeStyle Libre

The company Levels has brought this technology, initially developed for people with diabetes, to mainstream customers, people such as you and me (O’Connor, 2021). Levels has introduced a wearable device, similar to the FreeStyle Libre CGM, that measures your body’s glucose levels throughout the day in real-time and transmits that data to your phone (Figure 2 and 3) (Levels, n.d.; O’Connor, 2021; Cumbers, 2021; Burns, 2020). This allows you to keep track of how your diet, sleep, exercise, and stress levels affect your glucose levels. The Levels application also studies your diet and activity choices and provides suggestions on how you can improve your health. The insights provided into your biometric data by Levels will provide you with a better understanding of how your way of living affects your health (Level, n.d.).

Figure 2: Levels CGM
Figure 3: Levels application

Levels smartly taps into the increasing demand for personalized nutrition (O’Connor, 2021; Cumbers, 2021; Burns, 2020). The mainstream population is now, for the first time, able to measure an internal biomarker. This is perhaps not only interesting for the fit customers that already spend quite some time on their health data, but also to make other mainstream customers more aware of how their lifestyle affects their glucose levels. It can potentially help with avoiding chronic illnesses, such as obesity, diabetes, heart disease, stroke, and even Alzheimer’s, which are all rooted in insulin resistance to some extent (Cumbers, 2021).

“Foods I had never suspected of being harmful turned out to be chock-full of sugar. It’s really opened my eyes to what I’m eating.”

– J. Cumbers (2021) on wearing the Levels CGM and using the Levels application.

I am not necessarily a big fan of continuously tracking my own health data, because I already spend quite some time thinking about how to best eat healthy and ensuring that I stay active. However, people close to me have diabetes and wear the FreeStyle Libre CGM and I indeed have learned about a few products from them that I thought were healthy but ended up containing a shockingly large amount of sugar. So, I think applications such as Levels can be important to create awareness among people that might need to change their lifestyle for the better. What do you think? Would you like to track internal biomarkers in real time? Do you think technologies such as these have a future? And is there a point where measuring your health data goes too far?

References

Burns, M. (2020, November 17). Levels raises $12M from a16z and others to bring its biowearable to market [Online]. TechCrunch. Available at: https://techcrunch.com/2020/11/17/levels-raises-12m-from-a16z-and-others-to-bring-its-biowearable-to-market/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cubGV2ZWxzaGVhbHRoLmNvbS8&guce_referrer_sig=AQAAAHp5fCIJYlrrUl8PIyfEwKJB4CxxwNrTxRpwR7T9mWy5VYUqYsRVViaChUAzsOQ9XKmSSIhuAwT-QVrwQon8uZqnlJAnJtr8vvr4w0AXFO–xwRURHFBe32yEvi4Y7_di1VMNC_pssjRC23rguaUvpKAiRIXxteMQhVJvVcfd5Kg (Accessed: 2 October 2022)

Cumbers, J. (2021, February 17). America, Your Diet Is Killing You: Why The Glucose Crisis Will Be Worse Than The Opioid Crisis [Online]. Forbes. Available at: https://www.forbes.com/sites/johncumbers/2021/02/17/america-your-diet-is-killing-you-why-the-glucose-crisis-will-be-worse-than-the-opioid-crisis/?sh=2361409a73d5 (Accessed: 2 October 2022)

Diabetesvereniging Nederland. (2020, February 13). Freestyle Libre readers vertraagd geleverd [Online]. Available at: https://www.dvn.nl/nieuws/nieuwsbericht/freestyle-libre-readers-vertraagd-geleverd (Accessed: 2 October 2022)

FreeStyle Libre. (n.d.). Home  [Online]. Available at: https://www.freestyle.abbott/nl-nl/home.html (Accessed: 2 October 2022)

Levels. (n.d.). Home [Online]. Available at: https://www.levelshealth.com/ (Accessed: 2 October 2022)

O’Connor, A. (2021, February 8). Can Technology Help Us Eat Better? [Online]. The New York Times. Available at: https://www.nytimes.com/2021/02/08/well/diet-glucose-monitor.html (Accessed: 2 October 2022)

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The Future of Healthcare – How Cognitive Computing Enhances Decision Making

13

September

2022

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Similar to many industries, the healthcare industry has been hit by digital waves bringing around a computing revolution (Behera, Bala and Dhir, 2019). Cognitive computing is one technology that has brought about the computing revolution. Cognitive computing makes it possible to process enormous amounts of data instantly to answer specific questions and provide customized intelligent recommendations enhancing decision-making in the healthcare sector.

Imagine a situation where a child with asthma starts to have trouble breathing at night (Voigt, 2017). An intelligent monitoring system, powered by cognitive intelligence, provides the child with proper medication via vents in the child’s room. The parents can sleep through the night and can analyze the episode via the system the next day. Cognitive computing has the potential of assisting medical professionals in faster and more informed decision-making, providing better treatment of diseases, improve patient outcomes and improve the overall quality of care (Behera, Bala and Dhir, 2019; Bhisey, 2021).

Cognitive computing can mimic the workings of the human brain by making use of neural network processes, deep learning algorithms, and self-learning algorithms that use data mining, pattern recognition, and natural language processes (Chen, Herrera and Hwang, 2018; Zhang et al., 2018). The human thought process is stimulated in a computerized model. By being able to mimic the human brain, cognitive computing can solve critical medical problems without constant human supervision similar to the previously mentioned example (Gupta et al., 2018; Sreedevi et al., 2022).

Figure 1: Cognitive computing allows for accessing multiple data sources to generate recommendations (Behera, Bala and Dhir, 2019).

Cognitive computing can be applied in several ways in healthcare. Cognitive computing can help identify the most critical attributes of a patient case and then generate easy-to-consume summaries of the best treatment options by analyzing unstructured data, best practice data, published clinical studies, and clinical trial data (IEEE PULSE, 2017; Voigt, 2017). This also allows for the generation of personalized treatment plans improving patient experiences. Cognitive computing can also allow researchers to discover new insights into the relationships among, for example, genes, proteins, and diseases (IEEE PULSE, 2017). Finally, it can be used in clinical trial matching to optimize patient selection and recruitment.

Many argue that cognitive computing has the potential of bridging the gap between humans and technology allowing for computers and the human brain to overlap improving human decision-making and transforming healthcare on a global scale (IEEE PULSE, 2017). I agree with this statement, as IBM (2016) mentions, since cognitive computing is able to access many data sources, medical expertise is scaled and medical knowledge is democratized. This allows healthcare professionals to better make use of the data available to make the best decision for their patients. Do you agree with this statement? How do you think cognitive computing will effect the healthcare sector?  

References

Behera, R. K., Bala, P. K. and Dhir, A. (2019) ‘The emerging role of cognitive computing in healthcare: A systematic literature review’, International Journal of Medical Informatics, 129(June), pp. 154–166. doi: 10.1016/j.ijmedinf.2019.04.024.

Bhisey, R. (2021) Improving Healthcare Using Cognitive Computing: An Application in Emergency Situation, Transparency Market Research. Available at: https://www.biospace.com/article/improving-healthcare-using-cognitive-computing-an-application-in-emergency-situation/ (Accessed: 13 September 2022).

Chen, M., Herrera, F. and Hwang, K. (2018) ‘Cognitive Computing: Architecture, Technologies and Intelligent Applications’, IEEE Access, 6, pp. 19774–19783. doi: 10.1109/ACCESS.2018.2791469.

Gupta, S. et al. (2018) ‘Big data with cognitive computing: A review for the future’, International Journal of Information Management, 42, pp. 78–89. doi: 10.1016/j.ijinfomgt.2018.06.005.

IBM (2016) The future of health is cognitive, IBM. Available at: https://www.ibm.com/downloads/cas/LQZ0O1WM (Accessed: 13 September 2022).

IEEE PULSE (2017) Cognitive Computing and the Future of Health Care, IEEE PULSE. Available at: https://www.embs.org/pulse/articles/cognitive-computing-and-the-future-of-health-care/ (Accessed: 13 September 2022).

Sreedevi, A. G. et al. (2022) ‘Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review’, Information Processing and Management, 59(2), p. 102888. doi: 10.1016/j.ipm.2022.102888.

Voigt, J. (2017) Cognitive Computing in Healthcare, Wharton Magazine. Available at: https://magazine.wharton.upenn.edu/digital/cognitive-computing-in-health-care/  (Accessed: 13 September 2022).

Zhang, Y. et al. (2018) ‘CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing’, Mobile Networks and Applications, 23(6), pp. 1610–1623. doi: 10.1007/s11036-018-1112-1.

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