Quantified self is a relatively new but exciting area of biohacking that is growing widely in popularity. In general, quantified self refers to the concept of tracking one's own physiological and mental states as well as the inputs or perturbations to himself or herself. The media likes to portray these efforts in association with high tech tracking devices such as step counters or fancy watches, but the methods for tracking are not as important as the consistency and frequency that the tracking is done.1,2,3
There are many potential benefits that someone can glean from engaging in quantified self activities. Rigorously tracking one's own biomarkers and physiological processes such as heart rate, sleep patterns, mood and cognitive capacity can allow oneself to make lifestyle decisions that helps him or herself function in a more optimal manner.
While there is no set way to perform quantified self, one should generally consider the body as a system which can be optimized with the aid of data analytics. At all times, a person would exhibit a state which is comprised of several features that can be measured with things like sensors, or the person's individual assessment. For example, this would comprise of objective physiological biomarkers (e.g., heart rate, blood ketone levels, glucose levels), psychometric tests (e.g. for attention span, short term memory) as well as subjective user-generated features (e.g., mood, fatigue levels).
When inputs to the body are made, the nature of the inputs are gathered as data and fed to the data analytic module. The data analytic module would train models based on an individual's state and inputs, and the models would be used to generate predictions or insight for the individual. These insights would lead to outputs, which the individual can assess and provide feedback back into the data analytic module in order to fine tune the predictive models and generate improved insight for the user.
A proposed framework for quantified self tracking that would empower an individual to leverage data science to make sound decisions based on past inputs and perturbations to his or her body.
Some physiological features to track, which would be helpful for future generation of actionable insights, include:
Cognitive features that are worth tracking include:
Genomic factors that are worth tracking include:
System inputs that are worth tracking include:
In general, after you have collected an adequate amount of data about yourself, you can start to make predictions with your past data. Predictions can be made on things such as how likely you are to focus on a task at a particular time of the day, or what time of day it would be most optimal for you to consume a nootropic supplement. This entire system runs with machine learning, which is the computerized act of observing enormous amounts of data, making generalizations about it, and using such generalizations to make predictions into the future and to guide decision-making on uncharted territory.
We should eventually aspire to the goal of running large scale clinical trials that leverage quantified self. Such studies which take into account a large amount of data collection, where the nature of the data is heterogenous and high-dimensional, will allow for state-of-the-art machine learning algorithms to be leveraged to their full potential.
Quantified self is a relatively new concept, and there is no right answer for how to approach it. In general, it is helpful to think of the body as a machine that has states (physiological, mental, etc.). These states can be perturbed by a number of things such as environment, diet and physiological factors. Unfortunately, often times it is hard to pinpoint a specific factor that may cause you to function better in a certain situation. Thus, it is heavily important to meticulously track inputs and states of your body, perhaps with the help of sensors. While this may seem cumbersome, better data will be more likely to lead to more accurate and actionable predictive models that can help you reach your full potential.
Swan, M. (2009). Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. International journal of environmental research and public health, 6(2), 492-525.
Nafus, D., & Sherman, J. (2014). Big data, big questions| this one does not go up to 11: the quantified self movement as an alternative big data practice. International journal of communication, 8, 11.
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