The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery

By Melanie Swan. Big Data, 2013

946 citations recorded by the source corpus.

Abstract A key contemporary trend emerging in big data science is the quantified self (QS)–individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity—barriers to widespread adoption and a critique regarding scientific soundness—but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/23a57b1e2beb4235d2020ed57f484c947e3d0816

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Citations in the corpus

Data Epistemologies, The Coloniality of Power, and Resistance (2019, methodology): “Our digital selves are quantified (Lupton 2015, 2016; Swan 2012, 2013), and our universe of objects and spaces has likewise been transformed into knowledge that fuels capital accumulation and power concentration.”
Concurrence of big data analytics and healthcare: A systematic review (2018, background): “On the other hand, Swan [41] categorized Big Data streams into (a) Traditional medical data obtained from EMRs, medication history and lab reports which assist in a better understanding of disease outcomes and optimizing healthcare delivery; (b) “Omics” data including genomics, microbiomics, proteom”
Our metrics, ourselves: A hundred years of self-tracking from the weight scale to the wrist wearable device (2015, background): “Swan (2013) defines a QS participant as Any individual engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information.”
Internet of Things in the 5G Era: Enablers, Architecture, and Business Models (2016, background): “paradigm [9] which is currently unfolding with the advent of fitness and health tracking systems, smart watches and sensor”
Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting (2017, background): “…upward spike of the R wave of the QRS complex which can best be determined through ECG. Finally, the emergence of the quantified self movement (Swan, 2013) – lay people willing to track and monitor their own psychophysiological data – is giving birth to many consumer devices aiming to measure…”
Review of Wearable Devices and Data Collection Considerations for Connected Health (2021, methodology): “Next-generation QS implementations may provide methods for setting baselines and variability in objective measurements, making QS data relevant in behaviour improvement, using novel pattern recognition techniques and aggregating several self-tracking data streams from biosensors, wearable electronic”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “Swan [49] suggested that data analysis should include a step to extract signal from noise directly following the steps of data collection and integration.”
Hop to it! The impact of organization type on innovation response time to the COVID-19 crisis (2020, background): “Then it was associated with innovations such as performance food or wearables enabling the “quantified self” (Swan, 2013), especially in developed and innovation-driven economies.”