Mobile Health pp 349-360 | Cite as

Exploratory Visual Analytics of Mobile Health Data: Sensemaking Challenges and Opportunities



With every advancement in mHealth sensing technology, we are presented with an abundance of data streams and models that enable us to make sense of health information we record. To distill this diverse and ever-growing data into meaningful information, we must first develop tools that can represent data intuitively and are flexible enough to handle the special characteristics of mHealth records. For example, whereas traditional health data such as electronic health records (EHR) often consist of discrete events that may be readily analyzed and visualized, mHealth entails sensor ensembles that generate continuous, multivariate data streams of high-resolution and often noisy measurements. Drawing from methodologies in machine learning and visualization, interactive visual analytics tools are an increasingly important aid to making sense of this complexity. Still, these computational and visual techniques must be employed attentively to represent this data not only intuitively, but also accurately, transparently, and in a way that is driven by user needs. Acknowledging these challenges, we review existing visual analytic tools to identify design solutions that are both useful for and adaptable to the demands of mHealth data analysis tasks. In doing so, we identify open problems for representing and understanding mHealth data, suggesting future research directions for developers in the field.


Mobile Health Data mHealth Data Multivariate Data Streams Interactive Visual Analysis Tool Insight Provenance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Obama proposes ‘precision medicine’ to end one-size-fits-all. Accessed: 2016-04-30
  2. 2.
    Aigner, W., Federico, P., Gschwandtner, T., Miksch, S., Rind, A.: Challenges of time-oriented data in visual analytics for healthcare. In: IEEE VisWeek Workshop on Visual Analytics in Healthcare, p. 4 (2012)Google Scholar
  3. 3.
    Angst, C.M., Agarwal, R.: Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS quarterly 33(2), 339–370 (2009)Google Scholar
  4. 4.
    Bade, R., Schlechtweg, S., Miksch, S.: Connecting time-oriented data and information to a coherent interactive visualization. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 105–112. ACM (2004)Google Scholar
  5. 5.
    Basole, R.C., Braunstein, M.L., Kumar, V., Park, H., Kahng, M., Chau, D.H.P., Tamersoy, A., Hirsh, D.A., Serban, N., Bost, J., et al.: Understanding variations in pediatric asthma care processes in the emergency department using visual analytics. Journal of the American Medical Informatics Association 22(2), 318–323 (2015)CrossRefGoogle Scholar
  6. 6.
    Basole, R.C., Park, H., Gupta, M., Braunstein, M.L., Chau, D.H., Thompson, M., Kumar, V., Pienta, R., Kahng, M.: A visual analytics approach to understanding care process variation and conformance. In: Proceedings of the 2015 Workshop on Visual Analytics in Healthcare. ACM (2015)Google Scholar
  7. 7.
    Bertini, E., Tatu, A., Keim, D.: Quality metrics in high-dimensional data visualization: An overview and systematization. IEEE Transactions on Visualization and Computer Graphics 17(12), 2203–2212 (2011)CrossRefGoogle Scholar
  8. 8.
    Bonneau, G.P., Hege, H.C., Johnson, C.R., Oliveira, M.M., Potter, K., Rheingans, P., Schultz, T.: Overview and state-of-the-art of uncertainty visualization. In: Scientific Visualization, pp. 3–27. Springer (2014)Google Scholar
  9. 9.
    Botsis, T., Hartvigsen, G., Chen, F., Weng, C.: Secondary use of ehr: data quality issues and informatics opportunities. AMIA Summits Transl Sci Proc 2010, 1–5 (2010)Google Scholar
  10. 10.
    Callahan, S.P., Freire, J., Santos, E., Scheidegger, C.E., Silva, C.T., Vo, H.T.: Vistrails: visualization meets data management. In: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp. 745–747. ACM (2006)Google Scholar
  11. 11.
    Cao, N., Gotz, D., Sun, J., Qu, H.: Dicon: Interactive visual analysis of multidimensional clusters. IEEE Transactions on Visualization and Computer Graphics 17(12), 2581–2590 (2011)CrossRefGoogle Scholar
  12. 12.
    Cavazza, M., Charles, F.: Towards interactive narrative medicine. In: MMVR, pp. 59–65 (2013)Google Scholar
  13. 13.
    Charon, R.: Narrative medicine: a model for empathy, reflection, profession, and trust. Jama 286(15), 1897–1902 (2001)CrossRefGoogle Scholar
  14. 14.
    Charon, R.: Narrative medicine: form, function, and ethics. Annals of internal medicine 134(1), 83–87 (2001)CrossRefGoogle Scholar
  15. 15.
    Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 493–498. ACM (2003)Google Scholar
  16. 16.
    Fails, J.A., Karlson, A., Shahamat, L., Shneiderman, B.: A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. In: Visual Analytics Science And Technology, 2006 IEEE Symposium On, pp. 167–174. IEEE (2006)Google Scholar
  17. 17.
    Gaber, M.M., Krishnaswamy, S., Gillick, B., Nicoloudis, N., Liono, J., AlTaiar, H., Zaslavsky, A.: Adaptive clutter-aware visualization for mobile data stream mining. In: Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on, vol. 2, pp. 304–311. IEEE (2010)Google Scholar
  18. 18.
    Goernitz, N., Braun, M., Kloft, M.: Hidden markov anomaly detection. In: Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp. 1833–1842 (2015)Google Scholar
  19. 19.
    Goetz Ducas, S.Z.F.F.A.R.E.D.L.S.R.M.N.O.L.: Visualizing health (2014)Google Scholar
  20. 20.
    Gotz, D., Sun, S., Cao, N.: Adaptive contextualization: Combating bias during high-dimensional visualization and data selection. In: Proceedings of the 21st International Conference on Intelligent User Interfaces, pp. 85–95. ACM (2016)Google Scholar
  21. 21.
    Gotz, D., Zhou, M.X.: Characterizing users’ visual analytic activity for insight provenance. Information Visualization 8(1), 42–55 (2009)CrossRefGoogle Scholar
  22. 22.
    Greenhalgh, T.: Narrative based medicine in an evidence based world. BMJ 318(7179), 323–325 (1999)CrossRefGoogle Scholar
  23. 23.
    Groth, D.P., Streefkerk, K.: Provenance and annotation for visual exploration systems. IEEE Transactions on Visualization and Computer Graphics 12(6), 1500–1510 (2006)CrossRefGoogle Scholar
  24. 24.
    Gschwandtner, T., Aigner, W., Kaiser, K., Miksch, S., Seyfang, A.: Carecruiser: Exploring and visualizing plans, events, and effects interactively. In: IEEE Pacific Visualization Symposium (PacificVis), pp. 43–50. IEEE (2011)Google Scholar
  25. 25.
    Haas, S., Wohlgemuth, S., Echizen, I., Sonehara, N., Müller, G.: Aspects of privacy for electronic health records. International journal of medical informatics 80(2), e26–e31 (2011)CrossRefGoogle Scholar
  26. 26.
    Hensley, Z., Sanyal, J., New, J.: Provenance in sensor data management. Queue 11(12), 50 (2013)Google Scholar
  27. 27.
    Hochheiser, H., Shneiderman, B.: Visual queries for finding patterns in time series data. University of Maryland, Computer Science Dept. Tech Report, CS-TR-4365 (2002)Google Scholar
  28. 28.
    Hong, T.P., Wang, C.Y., Tseng, S.S.: An incremental mining algorithm for maintaining sequential patterns using pre-large sequences. Expert Systems with Applications 38(6), 7051–7058 (2011)CrossRefGoogle Scholar
  29. 29.
    Huang, D., Tory, M., Aseniero, B.A., Bartram, L., Bateman, S., Carpendale, S., Tang, A., Woodbury, R.: Personal visualization and personal visual analytics. IEEE Transactions on Visualization and Computer Graphics 21(3), 420–433 (2015)CrossRefGoogle Scholar
  30. 30.
    Joshi, R., Szolovits, P.: Prognostic physiology: modeling patient severity in intensive care units using radial domain folding. In: AMIA Annual Symposium Proceedings, vol. 2012, p. 1276. American Medical Informatics Association (2012)Google Scholar
  31. 31.
    Kanarachos, S., Mathew, J., Chroneos, A., Fitzpatrick, M.: Anomaly detection in time series data using a combination of wavelets, neural networks and hilbert transform. In: 6th International Conference on Information, Intelligence, Systems and Applications, IISA 2015, Corfu, Greece, July 6–8, 2015, pp. 1–6 (2015)Google Scholar
  32. 32.
    Khovanskaya, V., Baumer, E.P., Cosley, D., Voida, S., Gay, G.: Everybody knows what you’re doing: a critical design approach to personal informatics. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3403–3412. ACM (2013)Google Scholar
  33. 33.
    Klimov, D., Shahar, Y., Taieb-Maimon, M.: Intelligent selection and retrieval of multiple time-oriented records. Journal of Intelligent Information Systems 35(2), 261–300 (2010)CrossRefGoogle Scholar
  34. 34.
    Krause, J., Perer, A., Stavropoulos, H.: Supporting iterative cohort construction with visual temporal queries. IEEE Transactions on Visualization and Computer Graphics 22(1), 91–100 (2016)CrossRefGoogle Scholar
  35. 35.
    Kreuseler, M., Nocke, T., Schumann, H.: A history mechanism for visual data mining. In: Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on, pp. 49–56. IEEE (2004)Google Scholar
  36. 36.
    Kumar, S., Nilsen, W.: State-of-the-science in mobile health for diagnostic, treatment, public health, and health research. AAAS Workshop on Exploring Legal Challenges to Fulfilling the Potential of mHealth in a Safe and Responsible Environment pp. 945–952 (2014)Google Scholar
  37. 37.
    Kumar, S., Nilsen, W., Pavel, M., Srivastava, M.: Mobile health: Revolutionizing healthcare through transdisciplinary research. IEEE Computer 46(1), 28–35 (2013)CrossRefGoogle Scholar
  38. 38.
    Li, Y., Lin, J., Oates, T.: Visualizing variable-length time series motifs. In: SDM, pp. 895–906. SIAM (2012)Google Scholar
  39. 39.
    Lin, J., Keogh, E., Fu, A., Van Herle, H.: Approximations to magic: Finding unusual medical time series. In: Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on, pp. 329–334. IEEE (2005)Google Scholar
  40. 40.
    Malik, S., Du, F., Monroe, M., Onukwugha, E., Plaisant, C., Shneiderman, B.: Cohort comparison of event sequences with balanced integration of visual analytics and statistics. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, pp. 38–49. ACM (2015)Google Scholar
  41. 41.
    Martin, C.M., Sturmberg, J.P.: Making sense: from complex systems theories, models, and analytics to adapting actions and practices in health and health care. In: Handbook of systems and complexity in health, pp. 797–813. Springer (2013)Google Scholar
  42. 42.
    Monroe, M., Lan, R., Lee, H., Plaisant, C., Shneiderman, B.: Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics 19(12), 2227–2236 (2013)CrossRefGoogle Scholar
  43. 43.
    Noirhomme-Fraiture, M., Randolet, F., Chittaro, L., Custinne, G.: Data visualizations on small and very small screens. In: Proceedings of ASMDA. Citeseer (2005)Google Scholar
  44. 44.
    Parascandola, M., Hawkins, J.S., Danis, M.: Patient autonomy and the challenge of clinical uncertainty. Kennedy Institute of Ethics Journal 12(3), 245–264 (2002)CrossRefGoogle Scholar
  45. 45.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: icccn, p. 0215. IEEE (2001)Google Scholar
  46. 46.
    Polack Jr, P.J., Chen, S.T., Kahng, M., Sharmin, M., Chau, D.H.: Timestitch: Interactive multi-focus cohort discovery and comparison. In: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 209–210. IEEE (2015)Google Scholar
  47. 47.
    Rind, A., Aigner, W., Miksch, S., Wiltner, S., Pohl, M., Drexler, F., Neubauer, B., Suchy, N.: Visually exploring multivariate trends in patient cohorts using animated scatter plots. In: Ergonomics and Health Aspects of Work with Computers, pp. 139–148. Springer (2011)Google Scholar
  48. 48.
    Sacha, D., Senaratne, H., Kwon, B.C., Keim, D.A.: Uncertainty propagation and trust building in visual analytics. In: IEEE VIS 2014 (2014)Google Scholar
  49. 49.
    Sharmin, M., Raij, A., Epstien, D., Nahum-Shani, I., Beck, J.G., Vhaduri, S., Preston, K., Kumar, S.: Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 505–516. ACM (2015)Google Scholar
  50. 50.
    Shneiderman, B., Plaisant, C., Hesse, B.W.: Improving health and healthcare with interactive visualization methods. Tech. rep., Citeseer (2013)Google Scholar
  51. 51.
    Sittig, D.F., Singh, H.: Defining health information technology–related errors: New developments since to err is human. Archives of internal medicine 171(14), 1281–1284 (2011)CrossRefGoogle Scholar
  52. 52.
    Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. Springer (1996)Google Scholar
  53. 53.
    Terry, N.P., Francis, L.P.: Ensuring the privacy and confidentiality of electronic health records. U. Ill. L. Rev. p. 681 (2007)Google Scholar
  54. 54.
    Vilalta, R., Ma, S.: Predicting rare events in temporal domains. In: Proceedings of the IEEE International Conference on Data Mining, pp. 474–481. IEEE (2002)Google Scholar
  55. 55.
    Walliser, M., Brantschen, S., Calisti, M., Schinkinger, S.: Whitestein series in software agent technologies and autonomic computing (2008)Google Scholar
  56. 56.
    Wang, T.D., Plaisant, C., Shneiderman, B., Spring, N., Roseman, D., Marchand, G., Mukherjee, V., Smith, M.: Temporal summaries: supporting temporal categorical searching, aggregation and comparison. IEEE Transactions on Visualization and Computer Graphics 15(6), 1049–1056 (2009)CrossRefGoogle Scholar
  57. 57.
    Wang, T.D., Wongsuphasawat, K., Plaisant, C., Shneiderman, B.: Extracting insights from electronic health records: case studies, a visual analytics process model, and design recommendations. Journal of medical systems 35(5), 1135–1152 (2011)CrossRefGoogle Scholar
  58. 58.
    West, V.L., Borland, D., Hammond, W.E.: Innovative information visualization of electronic health record data: a systematic review. Journal of the American Medical Informatics Association 22(2), 330–339 (2015)Google Scholar
  59. 59.
    Wilcox, L., Morris, D., Tan, D., Gatewood, J.: Designing patient-centric information displays for hospitals. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2123–2132. ACM (2010)Google Scholar
  60. 60.
    Wilton, R., Pennisi, A.J.: Evaluating the accuracy of transcribed clinical data. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 279. American Medical Informatics Association (1993)Google Scholar
  61. 61.
    Wongsuphasawat, K., Gotz, D.: Outflow: Visualizing patient flow by symptoms and outcome. In: IEEE VisWeek Workshop on Visual Analytics in Healthcare, Providence, Rhode Island, USA, pp. 25–28. American Medical Informatics Association (2011)Google Scholar
  62. 62.
    Wongsuphasawat, K., Guerra Gómez, J.A., Plaisant, C., Wang, T.D., Taieb-Maimon, M., Shneiderman, B.: Lifeflow: visualizing an overview of event sequences. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1747–1756. ACM (2011)Google Scholar
  63. 63.
    Yackel, T.R., Embi, P.J.: Unintended errors with ehr-based result management: a case series. Journal of the American Medical Informatics Association 17(1), 104–107 (2010)CrossRefGoogle Scholar
  64. 64.
    Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine learning 42(1–2), 31–60 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.Western Washington UniversityBellinghamUSA

Personalised recommendations