- Hamed Haddadi
- Ferda Ofli
- Yelena Mejova Ingmar Weber
- Jaideep Srivastava
Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals’ health, a perspective we call the 360° Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person’s ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.
1. C. Paton, M. M. Hansen, L. Fernandez-Luque and A. Y. S. Lau, “Self-Tracking Social Media and Personal Health Records for Patient Empowered Self-Care”, Tech. Rep. 17, 2012.
2. J. F. Pearson, C. A. Brownstein and J. S. Brownstein, “Potential for Electronic Health Records and Online Social Networking to Redefine Medical Research”, Clinical Chemistry, vol. 57, no. 2, pp. 196-204, Jan. 2011.
3. M. Swan, “Health 2050: The Realization of Personalized Medicine through Crowdsourcing the Quantified Self and the Participatory Biocitizen”, Journal of Personalized Medicine, vol. 2, no. 3, pp. 93-118, Dec. 2012.
4. K. Katevas, H. Haddadi and L. Tokarchuk, “Poster: Sensingkit-a multi-platform mobile sensing framework for large-scale experiments”, ACM MOBICOM, 2014.
5. W.-Y. S. Chou, Y. M. Hunt, E. B. Beckjord, R. P. Moser and B. W. Hesse, “Social media use in the united states: implications for health communication”, Journal of medical Internet research, vol. 11, no. 4, 2009.
6. C. Hawn, “Take two aspirin and tweet me in the morning: how twitter facebook and other social media are reshaping health care”, Health affairs, vol. 28, no. 2, pp. 361-368, 2009.
7. A. F. Dugas, Y.-H. Hsieh, S. R. Levin, J. M. Pines, D. P. Mareiniss, A. Mohareb, et al., “Google flu trends: correlation with emergency department influenza rates and crowding metrics”, Clinical infectious diseases, vol. 54, no. 4, pp. 463-469, 2012.
8. V. Lampos and N. Cristianini, Tracking the flu pandemic by monitoring the social web, CIP, pp. 411-416, 2010.
9. E. Aramaki, S. Maskawa and M. Morita, “Twitter catches the flu: detecting influenza epidemics using twitter”, EMNLP, pp. 1568-1576, 2011.
10. A. Lamb, M. J. Paul and M. Dredze, “Separating fact from fear: Tracking flu infections on twitter”, Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, pp. 789-795, 2013.
11. P. Kostkova, M. Szomszor and C. S. Luis, “#swineflu: The use of twitter as an early warning and risk communication tool in the 2009 swine flu pandemic”, ACM Trans. Management Inf. Syst., vol. 5, no. 2, pp. 8, 2014.
12. J. Gomide, A. Veloso, W. Meira, V. Almeida, F. Benevenuto, F. Ferraz, et al., “Dengue surveillance based on a computational model of spatio-temporal locality of twitter”, WebSci, pp. 3:1-3:8, 2011.
13. A. Culotta, “Estimating county health statistics with twitter”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1335-1344, 2014.
14. S. Abbar, Y. Mejova and I. Weber, “You tweet what you eat: Studying food consumption through twitter”, Conference on Human Factors in Computing Systems (CHI), pp. 3197-3206, 2015.
15. M. De Choudhury, S. Counts and E. Horvitz, “Social media as a measurement tool of depression in populations”, Proceedings of the 5th Annual ACM Web Science Conference, pp. 47-56, 2013.
16. P. Kostkova, “Public health” in Twitter: A Digital Socioscope, Cambridge University Press, pp. 111-130, 2015.
17. S. Balani and M. D. Choudhury, “Detecting and characterizing mental health related self-disclosure in social media”, CHI, pp. 1373-1378, 2015.
18. G. Coppersmith, C. Harman and M. Dredze, “Measuring post traumatic stress disorder in twitter”, Proceedings of the Eighth International Conference on Weblogs and Social Media ICWSM, 2014.
19. M. De Choudhury, S. Counts and E. Horvitz, “Predicting postpartum changes in emotion and behavior via social media”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3267-3276, 2013.
20. W. C. Willett, J. P. Koplan, R. Nugent, C. Dusenbury, P. Puska and T. A. Gaziano, “Prevention of chronic disease by means of diet and lifestyle changes” in Disease Control Priorities in Developing Countries, World Bank, 2006.
21. A. Sadilek and H. Kautz, “Modeling the impact of lifestyle on health at scale”, WSDM, pp. 637-646, 2013.
22. R. Chunara, L. Bouton, J. W. Ayers and J. S. Brownstein, “Assessing the online social environment for surveillance of obesity prevalence”, PLOS ONE, vol. 8, no. 4, pp. e61373, 2013.
23. T.-L. Bien and C. H. Lin, “Detection and recognition of indoor smoking events”, International Conference on Machine Vision, 2012.
24. P. Wu, J.-W. Hsieh, J.-C. Cheng, S.-C. Cheng and S.-Y. Tseng, “Human smoking event detection using visual interaction clues”, Pattern Recognition (ICPR) 2010 20th International Conference on, pp. 4344-4347, Aug 2010.
25. M. De Choudhury, “Anorexia on tumblr: A characterization study”, Proceedings of the 5th International Conference on Digital Health 2015, pp. 43-50, 2015.
26. E. Yom-Tov, L. Fernandez-Luque, I. Weber and S. Crain, “Pro-anorexia and pro-recovery photo sharing: A tale of two warring tribes”, Journal of Medical Internet Research, vol. 14, no. 6, pp. e151, 2012.
27. N. A. Christakis and J. H. Fowler, “The spread of obesity in a large social network over 32 years”, New England Journal of Medicine, vol. 357, pp. 370-379, 2007.
28. Y.-L. Zheng, X.-R. Ding, C. C. Y. Poon, B. P. L. Lo, H. Zhang, X.-L. Zhou, et al., “Unobtrusive Sensing and Wearable Devices for Health Informatics”, IEEE Transactions on Biomedical Engineering, vol. 61, no. 5, pp. 1538-1554, 2014.
29. A. Molina-Markham, R. Peterson, J. Skinner, T. Yun, B. Golla, K. Freeman, et al., “Amulet: a secure architecture for mHealth applications for low-power wearable devices”, Proceedings of the 1st Workshop on Mobile Medical Applications, pp. 16-21, 2014.
30. K. K. Rachuri, M. Musolesi, C. Mascolo, P. J. Rentfrow, C. Longworth and A. Aucinas, “Emotionsense: A mobile phones based adaptive platform for experimental social psychology research”, Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 281-290, 2010.
31. L. Clifton, D. A. Clifton, M. A. F. Pimentel, P. J. Watkinson and L. Tarassenko, “Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors”, IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 722-730, May 2014.
32. N. D. Lane, M. Lin, M. Mohammod, X. Yang, H. Lu, G. Cardone, et al., “BeWell: Sensing Sleep Physical Activities and Social Interactions to Promote Wellbeing”, Mobile Networks and Applications, vol. 19, no. 3, pp. 345-359, 2014.
33. R. Wang, F. Chen, Z. Chen, T. Li, G. Harari, S. Tignor, et al., “StudentLife: assessing mental health academic performance and behavioral trends of college students using smartphones”, ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3-14, Sep. 2014.
34. N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury and A. T. Campbell, “A survey of mobile phone sensing”, Communications Magazine IEEE, vol. 48, no. 9, pp. 140-150, 2010.
35. P. M. Gollwitzer, P. Sheeran, V. Michalski and A. E. Seifert, “When intentions go public does social reality widen the intention-behavior gap?”, Psychological science, vol. 20, no. 5, pp. 612-618, 2009.
36. R. Mortier, H. Haddadi, T. Henderson, D. McAuley and J. Crowcroft, “Human-data interaction: The human face of the data-driven society”, SSRN 2508051, 2014.
37. P. Desikan, R. Khare, J. Srivastava, R. Kaplan, J. Ghosh, L. Liu, et al., “Predictive modeling in healthcare: Challenges and opportunities”, Data Mining for Healthcare (DMH), 2013.
38. Y. Mejova, H. Haddadi, A. Noulas and I. Weber, “#FoodPorn: Obesity patterns in culi
39. J. Sametinger, J. Rozenblit, R. Lysecky and P. Ott, “Security challenges for medical devices”, Commun. ACM, vol. 58, no. 4, pp. 74-82, Mar. 2015.
40. I. Brown, A. A. Adams et al., “The ethical challenges of ubiquitous healthcare”, International Review of Information Ethics, vol. 8, no. 12, pp. 53-60, 2007.
41. I. Brown, “Social media surveillance”, The International Encyclopedia of Digital Communication and Society, 2015.
42. G. Loukides, A. Gkoulalas-Divanis and B. Malin, “Anonymization of electronic medical records for validating genome-wide association studies”, Proceedings of the National Academy of Sciences, vol. 107, no. 17, pp. 7898-7903, 2010.
43. H. Haddadi, H. Howard, A. Chaudhry, J. Crowcroft, A. Madhavapeddy and R. Mortier, “Personal data: Thinking inside the box”, The 5th decennial Aarhus conferences (Aarhus 2015), 2015.
44. A. Hannak, E. Anderson, L. F. Barrett, S. Lehmann, A. Mislove and M. Riedewald, “Tweetinin the rain: Exploring societal-scale effects of weather on mood”, ICWSM’12, 2012.
45. H. Haddadi, R. Mortier, S. Hand, I. Brown, E. Yoneki, D. McAuley, et al., “Privacy analytics”, SIGCOMM Comput. Commun. Rev., vol. 42, no. 2, pp. 94-98, Apr. 2012.
46. G. Alkhaldi, L. F. Hamilton, R. Lau, R. Webster, S. Michie and E. Murray, “The effectiveness of technology-based strategies to promote engagement with digital interventions: A systematic review protocol”, JMIR Res Protoc, vol. 4, no. 2, pp. e47, Apr 2015.
47. D. Estrin, “Small data where n = me”, Commun. ACM, vol. 57, no. 4, pp. 32-34, Apr. 2014.