Biofeedback Revisited: Dynamic Displays to Improve Health Trajectories

  • Margaret Morris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3962)


This paper outlines an approach for prospective health technologies: systems that inspire changes in midlife to prevent onset and progression of disease. Motivational hooks related to wellness, appearance and relationship satisfaction are aligned with long term disease risks and supported through dynamic feedback displays. Wireless sensor networks, inferencing, ambient displays and mobile interfaces are explored to carry biofeedback into everyday life. Several examples of display concepts – created to facilitate self-regulation of social engagement, weight, physical exertion and stress reactivity – illustrate this approach. Future work will explore mind-body relationships and extend from informational displays to experiential feedback.


Wireless Sensor Network Social Engagement Allostatic Load Mobile Interface Dynamic Display 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pollack, M.E.: Intelligent technology for an aging population: The use of AI to assist elders with cognitive impairment. AI Magazine 26, 9–24 (2005)Google Scholar
  2. 2.
    Mihailidis, A., Fernie, G.: Context-aware assistive devices for older adults with dementia. Gerontechnology 2, 173–189 (2002)CrossRefGoogle Scholar
  3. 3.
    Shibata, T.: An overview of human interactive robots for psychological enrichment. Proceedings of the IEEE 92, 1749–1758 (2004)CrossRefGoogle Scholar
  4. 4.
    Snyderman, R.: Sanders Williams: Prospective Care: The next health care transformation. Academic Medicine 78, 1009–1084 (2003)CrossRefGoogle Scholar
  5. 5.
    McEwen, B.: The End of Stress as We Know It. National Academies Press, Washington (2002)Google Scholar
  6. 6.
  7. 7.
    Hebert, L.E., Scherr, P.A., Bienias, J.L., Bennett, D.A., Evans, D.A.: Alzheimer disease in the U.S. population: Prevalence estimates using the 2000 census. Archives of Neurology 60, 1119–1122 (2003)CrossRefGoogle Scholar
  8. 8.
    Ernst, R.L., Hay, J.W.: The U.S. economic and social Costs of Alzheimer’s Disease Revisited. American Journal of Public Health 84, 1261–1264 (1994)CrossRefGoogle Scholar
  9. 9.
    Kramer, P.: Against Depression. Viking Penguin (2005)Google Scholar
  10. 10.
    Fratiglioni, L., Wang, H., Ericsson, K., Maytan, M., Bengt, W.: Influence of social network on occurrence of dementia: A community-based longitudinal study. The Lancet 355, 1315–1319 (2000)CrossRefGoogle Scholar
  11. 11.
    Cole, G., et al.: Docosahexaenoic acid protects from dendritic pathology in an Alzheimer’s disease mouse model. Neuron 43, 633–645 (2004)CrossRefGoogle Scholar
  12. 12.
    Larson, E.B., Wang, L., Bowen, J.D., McCormick, W.C., Teri, L., Crane, P., Kukull, W.: Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Ann. Intern. Med. 144, 73–81 (2006)Google Scholar
  13. 13.
    Morris, M., Lundell, J., Dishman, E., Needham, B.: New perspectives on ubiquitous computing from ethnographic study of elders with cognitive decline. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 227–242. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Morris, M., Intille, S.S., Beaudin, J.S.: Embedded assessment: Overcoming barriers to early detection with pervasive computing. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 333–346. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Schwartz, G.: The brain as health care system. Health Psychology, 549–571 (1979)Google Scholar
  16. 16.
    Seligman, M.E., Seligman, M.E.P.: Explanatory style: Predicting depression, achievement, and health. In: Yapko, M.D. (ed.) Brief Therapy Approaches to Treating Anxiety and Depression, pp. 5–32. Brunner/Mazel (1989)Google Scholar
  17. 17.
    Langer, E.: Mindfulness. Perseus Books (1989)Google Scholar
  18. 18.
    Kumar, V.S., et al.: The DAILY trial: A wireless portable system to improve adherence and glycemic control in youth with diabetes. Diabetes Technology and Therapeutics 6, 445–453 (2004)CrossRefGoogle Scholar
  19. 19.
    Turkle, S.: Personal communicationGoogle Scholar
  20. 20.
    Morris, M.: Social networks as health feedback displays. IEEE Internet Computing 09(5), 29–37 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Margaret Morris
    • 1
  1. 1.Intel CorporationUSA

Personalised recommendations