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Erschienen in:
The Urgent Need for Advancing Urban Sustainability Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

7. Big Data for Urban Health and Well-Being

verfasst von : Stephen Jia Wang, Patrick Moriarty

Erschienen in: Big Data for Urban Sustainability

Verlag: Springer International Publishing

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Abstract

This chapter examines the potential for big data in improving urban health and well-being, in the face of the ageing of global society and the rise in real healthcare costs. It looks at how more use of big data could help solve these and other health challenges, then gives actual or planned examples of its use in healthcare. The Quantified Self movement, discussed next, could prove a forerunner of a more general move to greater patient involvement in monitoring their personal health. The data would come from various apps on their smart phones, wearable devices, or body sensors. The chapter stresses the connection with the transport and energy chapters, given the role of these two sectors in urban air pollution, UHI and global warming and for transport, traffic-related casualties. As a specific example, a case study of a design of an instrumented chair (‘Virtual Spine’) to improve spinal health and general well-being is included.

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Vorheriges Kapitel Big Data for Urban Energy Reductions
Nächstes Kapitel Big Data for a Future World
Fußnoten
1
This section is a slightly revised version of sections I–III of Wang and Yu (2013), Ref [92]. Used by permission of IEEE.
 
Literatur
1.
Agency for Health Care Policy & Research (AHCPR) (1992) Pressure ulcers in adults: prediction and prevention, clinical practice guideline. AHCPR Publication no. 92-0047
2.
3.
4.
Amankwah-Amoah J (2016) Emerging economies, emerging challenges: mobilising and capturing value from big data. Technol Forecast Soc Change 110:167–174CrossRef
5.
Anon (2016) Daunting data. Nature 539:467–468
6.
Anon (2017) AI takes on suicide. New Scientist 233:6
7.
Anon (2017) Big health data. New Scientist 234:4–5
8.
9.
Ausiello D, Lipnick S (2015) Real-time assessment of wellness and disease in daily life. Big Data 3(3):203–208CrossRef
10.
Azuma R et al (2001) Recent advances in augmented reality. IEEE Comput Graph Appl 21(6):34–47CrossRef
11.
Bauman A et al (2011) The descriptive epidemiology of sitting: a 20-country comparison using the international physical activity questionnaire (IPAQ). Am J Prev Med 41(2):228–235CrossRef
12.
Bietz MJ, Hayes GR, Morris ME, Patterson H, Stark L (2016) Creating meaning in a world of quantified selves. IEEE Pervasive Comput 15:82–85CrossRef
13.
Billinghurst M, Kato H (1999) Collaborative mixed reality. In: Proceedings of the international symposium on mixed reality (ISMR 99). Springer, Secaucus, NJ
14.
Boland P (2007) Managing chronic disease through mobile persuasion. In: Fogg BJ, Eckles D (eds) Mobile perusasion: 20 perspective on the future of behavior change. Stanford Captology Media, Stanford, CA, pp 39–45
15.
Brandeis GH et al (1994) A longitudinal study of risk factors associated with the formation of pressure ulcers in nursing homes. J Am Geriatr Soc 42(4):388–393CrossRef
16.
Brennan N, Oelschlaeger A, Cox C, Tavenner M (2014) Leveraging the big-data revolution: CMS is expanding capabilities to spur health system transformation. Health Aff 33(7):1195–1202CrossRef
17.
Brink Y, Louw QA (2013) A systematic review of the relationship between sitting and upper quadrant musculoskeletal pain in children and adolescents. Man Ther 18(4):281–288CrossRef
18.
Burnett A et al (2004) Spinal kinematics and trunk muscle activity in cyclists: a comparison between healthy controls and non-specific chronic low back pain subjects. Man Ther 9:211–219CrossRef
19.
Callaghan V et al (2004) Intelligent inhabited environments. BT Technol J 22(3):233–247CrossRef
20.
Campo E, Grangereau E (2008) Wireless fall sensor with GPS location for monitoring the elderly. 30th annual international IEEE EMBS conference, Vancouver, BC, Canada, August 20–24, pp 498–501
21.
Caneiro J et al (2010) The influence of different sitting postures on head/neck posture and muscle activity. Man Ther 50(1):54–60CrossRef
22.
Chawla NV, Davis DA (2015) Bringing big data to personalized healthcare: a patient-centered framework. J Gen Intern Med 28(Suppl 3):S660–S665
23.
24.
Claus AP et al (2009) Is ‘ideal’ sitting posture real?: measurement of spinal curves in four sitting postures. Man Ther 14(4):404–408CrossRef
25.
Cohen D (1998) An objective measure of seat comfort. Aviat Space Environ Med 69(4):410–414
26.
27.
Craven M, Page CD (2015) Big data in healthcare: opportunities and challenges. Big Data 3(4):209–210CrossRef
28.
Davies M, Callaghan V, Shen L (2007) Modelling pervasive environments using bespoke and commercial game-based simulators. Lect Notes Comput Sci 4689:67–77CrossRef
29.
30.
31.
32.
33.
Fogg BJ (2003) Persuasive technology: using computers to change what we think and do. Morgan Kaufmann, San Francisco, CA
34.
Fogg BJ, Eckles D (eds) (2007) Mobile persuasion: 20 perspectives on the future of behavior change. Stanford Captology Media, Stanford, CA
35.
Ford JD, Tilleard SE, Berrang-Ford L et al (2016) Big data has big potential for applications to climate change adaptation. Proc Natl Acad Sci U S A 113:10729–10732CrossRef
36.
Frank L, Engelke P, Schmid T (2003) Health and community design: the impact of the built environment on physical activity. Island Press, Washington, DC
37.
Fung IC-H, Tse ZTH, Fu K-W (2015) Converting big data into public health. Science 347:620CrossRef
38.
Greene JA, Choudhry NK, Kilabuk E et al (2010) Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook. J Gen Intern Med 26(3):287–292CrossRef
39.
40.
41.
Halender MG, Zhang L (1997) Field studies of comfort and discomfort in sitting. Ergonomics 40(9):895–915CrossRef
42.
Haller M et al (2011) Finding the right way for interrupting people improving their sitting posture. In INTERACT 2011, Part II, LNCS 6947, pp 1–17. © IFIP International Federation for Information Processing
43.
Hobbs WR, Burke M, Christakis NA (2016) Online social integration is associated with reduced mortality risk. Proc Natl Acad Sci U S A 113:12980–12984CrossRef
44.
Hodson H (2016) Google knows your ills. New Scientist 230:22–23
45.
Intille SS (2004) A new research challenge: persuasive technology to motivate healthy aging. IEEE Trans Inf Technol Biomed 8(3):235–237CrossRef
46.
Intille SS (2004) Ubiquitous computing technology for just-in-time motivation of behavior change. Stud Health Technol Inform 107(2):1434–1437
47.
48.
Kellermann AL, Jones SS (2013) What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Aff 32(1):63–68CrossRef
49.
Khoury MJ, Ioannidis JPA (2014) Big data meets public health. Science 346:1054–1055CrossRef
50.
Klein A (2017) Alert: you’re about to catch something. New Scientist 233:16CrossRef
51.
Kshetri N (2014) Big data’s impact on privacy, security and consumer welfare. Telecommun Policy 38:1134–1145CrossRef
52.
Kshetri N (2016) Big data’s big potential in developing economies: impact on agriculture, health and environmental security. CABI Press, BostonCrossRef
53.
Lazer D, Kennedy R, King G et al (2014) The parable of Google Flu: traps in big data analysis. Science 343:1203–1205CrossRef
54.
Lupton D (2013) Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit Public Health 23(4):393–403CrossRef
55.
Maheshwari M, Chatterjee S, Drew D (2008) Exploring the persuasiveness of “Just-in-time” motivational messages for obesity management. In: PERSUASIVE ‘08 proceedings of the 3rd international conference on persuasive technology. Springer, Berlin
56.
Mayer-Schӧnberger V, Cukier K (2014) Big data. Mariner Books, Boston
57.
McGrath MJ, Ni Scanaill C (2015) Sensor technologies: healthcare, wellness and environmental applications. Apress Open, New York
58.
Milinovich GJ, Soares Magalhães RJ, Hu W (2015) Role of big data in the early detection of Ebola and other emerging infectious diseases. Lancet Glob Health 3(1):e20–e21CrossRef
59.
Monette M, Weiss-Lambrou R, Dansereau J (1999) In search of a better understanding of wheelchair sitting comfort and discomfort. In RESNA annual conference
60.
Moriarty P, Honnery D (2015) Future cities in a warming world. Futures 66:45–53CrossRef
61.
Mutlu B et al (2007) Robust, low cost, nonintrusive sensing and recognition of seated postures. In: UIST ‘07 proceedings of the 20th annual ACM symposium on user interface software and technology. New York, ACM
62.
63.
Obermeyer Z, Emanuel EJ (2016) Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med 375(13):1216–1219CrossRef
64.
O’Sullivan K et al (2012) What do physiotherapists consider to be the best sitting spinal posture? Man Ther 17(5):432–437CrossRef
65.
Paoli P, Merllie D (2001) Third European survey on working conditions 2000. European Foundation for the Improvement of Living and Working Conditions, Luxembourg
66.
67.
Pope M, Goh K, Magnusson M (2002) Spine ergonomics. Annu Rev Biomed Eng 4(1):49–68CrossRef
68.
69.
70.
Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2(3):1–10
71.
Revell T (2017) AI turns phone into Parkinson’s test. New Scientist 233:14
72.
Reynolds M (2017) NHS to prescribe apps that keep tabs on health. New Scientist 233:11
73.
Reynolds M (2017) Smart meter knows if you need help. New Scientist 233:12
74.
Roffey D et al (2010) Causal assessment of occupational sitting and low back pain: results of a systematic review. Spine J 10(3):252–261CrossRef
75.
Roski J, Bo-Linn GW, Andrews TA (2014) Creating value in health care through big data: opportunities and policy implications. Health Aff 33(7):1115–1122CrossRef
76.
Rutkin A (2016) Medicine by machine. New Scientist 231:20–21
77.
78.
Shaw G (1992) Wheelchair seat comfort for the institutionalized elderly. Assist Technol 3(1):11–23CrossRef
79.
Sheth A, Anantharam P, Henson C (2013) Physical-cyber-social computing: an early 21st century approach. IEEE Intell Syst 28:78–82CrossRef
80.
Smith DM (1995) Pressure ulcers in the nursing home. Ann Intern Med 123(6):433–438CrossRef
81.
Solanas A, Patsakis C, Conti M, Vlachos IS et al (2014) Smart health: a context-aware health paradigm within smart cities. IEEE Commun Mag 52:74–81CrossRef
82.
Srivastava S, Agarwal N, Agarwal R (2013) Authenticating Indian E-Health system through “Aadhaar” a unique identification. Int J Sci Eng Res 4(6):2412–2416
83.
84.
Swan M (2013) The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1(2):85–98CrossRef
85.
Taleb N (2013) Beware the big errors of ‘big data’. Wired blog. http://​www.​wired.​com/​2013/​02/​big-data-means-big-errors-people/​.​ Accessed 15 Dec 2015
86.
Tatem AJ, Hay SI, Rogers DJ (2006) Global traffic and disease vector dispersal. Proc Natl Acad Sci U S A 103:6242–6247CrossRef
87.
88.
van Tulder M, Koes B, Bouter L (1995) A cost-of-illness study of back pain in The Netherlands. Pain 62(2):233–240CrossRef
89.
90.
Wang SJ (2014) Seated posture detection. Monash University, Australia. Provisional Patent, File No. 2014901381 (Patent Pending)
91.
Wang SJ (2014) System and method for monitoring user posture. Monash University, Australia. International Patent Application No PCT/AU2014/000488 (Patent Pending)
92.
93.
94.
95.
96.
97.
Wilbanks JT, Topol EJ (2016) Stop the privatization of health data. Nature 535:345–348CrossRef
98.
Wyber R, Vaillancourt S, Perry W et al (2015) Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ 93:203–208CrossRef
99.
Zimmet P, Alberti KGMM, Shaw J (2001) Global and societal implications of the diabetes epidemic. Nature 414:782–787CrossRef
100.
Boland P (2007) Managing chronic disease through mobile persuasion. In: Fogg BJ, Eckles D (eds) Mobile persuasion. 20 perspectives on the future of behavior change. Stanford Captology Media, Stanford, CA, pp 45–52
Metadaten
Titel
Big Data for Urban Health and Well-Being
verfasst von
Stephen Jia Wang
Patrick Moriarty
Copyright-Jahr
2018
Buch
Big Data for Urban Sustainability

Print ISBN: 978-3-319-73608-2

Electronic ISBN: 978-3-319-73610-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-73610-5_7