Literature DB >> 27442063

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

Melanie Swan1.   

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.

Entities:  

Year:  2013        PMID: 27442063     DOI: 10.1089/big.2012.0002

Source DB:  PubMed          Journal:  Big Data        ISSN: 2167-6461            Impact factor:   2.128


  68 in total

Review 1.  A systems approach to clinical oncology uses deep phenotyping to deliver personalized care.

Authors:  James T Yurkovich; Qiang Tian; Nathan D Price; Leroy Hood
Journal:  Nat Rev Clin Oncol       Date:  2019-10-16       Impact factor: 66.675

Review 2.  Precision medicine in cardiology.

Authors:  Elliott M Antman; Joseph Loscalzo
Journal:  Nat Rev Cardiol       Date:  2016-06-30       Impact factor: 32.419

Review 3.  Teleneurology and mobile technologies: the future of neurological care.

Authors:  E Ray Dorsey; Alistair M Glidden; Melissa R Holloway; Gretchen L Birbeck; Lee H Schwamm
Journal:  Nat Rev Neurol       Date:  2018-04-06       Impact factor: 42.937

4.  Ethics and Epistemology in Big Data Research.

Authors:  Wendy Lipworth; Paul H Mason; Ian Kerridge; John P A Ioannidis
Journal:  J Bioeth Inq       Date:  2017-03-20       Impact factor: 1.352

5.  Crowdsourced Health Data: Comparability to a US National Survey, 2013-2015.

Authors:  Veronica Yank; Sanjhavi Agarwal; Pooja Loftus; Steven Asch; David Rehkopf
Journal:  Am J Public Health       Date:  2017-06-22       Impact factor: 9.308

Review 6.  Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.

Authors:  M M Hansen; T Miron-Shatz; A Y S Lau; C Paton
Journal:  Yearb Med Inform       Date:  2014-08-15

Review 7.  It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.

Authors:  Juan Xie; Anjun Ma; Anne Fennell; Qin Ma; Jing Zhao
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

8.  Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

Authors:  Ying Kuen Cheung; Pei-Yun Sabrina Hsueh; Min Qian; Sunmoo Yoon; Laura Meli; Keith M Diaz; Joseph E Schwartz; Ian M Kronish; Karina W Davidson
Journal:  Methods Inf Med       Date:  2018-02-10       Impact factor: 2.176

9.  Big Data for Nutrition Research in Pediatric Oncology: Current State and Framework for Advancement.

Authors:  Charles A Phillips; Brad H Pollock
Journal:  J Natl Cancer Inst Monogr       Date:  2019-09-01

10.  A Markov approach for increasing precision in the assessment of data-intensive behavioral interventions.

Authors:  Vincent Berardi; Ricardo Carretero-González; John Bellettiere; Marc A Adams; Suzanne Hughes; Melbourne Hovell
Journal:  J Biomed Inform       Date:  2018-07-31       Impact factor: 6.317

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.