User Modeling and User-Adapted Interaction

, Volume 24, Issue 5, pp 351–392 | Cite as

Tailoring real-time physical activity coaching systems: a literature survey and model

  • Harm op den Akker
  • Valerie M. Jones
  • Hermie J. Hermens


Technology mediated healthcare services designed to stimulate patients’ self-efficacy are widely regarded as a promising paradigm to reduce the burden on the healthcare system. The promotion of healthy, active living is a topic of growing interest in research and business. Recent advances in wireless sensor technology and the widespread availability of smartphones have made it possible to monitor and coach users continuously during daily life activities. Physical activity monitoring systems are frequently designed for use over long periods of time placing usability, acceptance and effectiveness in terms of compliance high on the list of design priorities to achieve sustainable behavioral change. Tailoring, or the process of adjusting the system’s behavior to individuals in a specific context, is an emerging topic of interest within the field. In this article we report a survey of tailoring techniques currently employed in state of the art real time physical activity coaching systems. We present a survey of state of the art activity coaching systems as well as a conceptual framework which identifies seven important tailoring concepts that are currently in use and how they relate to each other. A detailed analysis of current use of tailoring techniques in real time physical activity coaching applications is presented. According to the literature, tailoring is currently used only sparsely in this field. We underline the need to increase adoption of tailoring methods that are based on available theories, and call for innovative evaluation methods to demonstrate the effectiveness of individual tailoring approaches.


Tailoring Personalization Physical activity Real time coaching eHealth Telemedicine 


  1. Adams, J., White, M.: Why don’t stage-based activity promotion interventions work? Health Educ. Res. 20(2), 237–243 (2005)CrossRefGoogle Scholar
  2. Ahtinen, A., Isomursu, M., Mukhtar, M., Mäntyjärvi, J., Häkkilä, J., Blom, J.: Designing social features for mobile and ubiquitous wellness applications. In ‘Proceedings of the 8th International Conference on Mobile and Ubiquitous Multimedia’, Cambridge, United Kingdom (2009)Google Scholar
  3. Ajzen, I.: The theory of planned behavior. Org. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)CrossRefGoogle Scholar
  4. Arteaga, S. M., Kudeki, M., Woodworth, A., Kurniawan, S.: Mobile system to motivate teenagers’ physical activity. In ‘Proceedings of the 9th International Conference on Interaction Design and Children’, Barcelona, Spain (2010)Google Scholar
  5. Bailey, R.C., Olson, J., Pepper, S.L., Porszasz, J., Barstow, T.J., Cooper, D.M.: The level and tempo of children’s physical activities: an observational study. Med Sci Sports Exerc. 27(7), 1033–1041 (1995)CrossRefGoogle Scholar
  6. Bandura, A.: Social foundations of thought and action: a social cognitive theory. Prentice Hall, Englewood Cliffs (1986)Google Scholar
  7. Benyon, D., Hansen, P., Webb, N.: Evaluating human–computer conversation in companions. In ‘Proceedings of the Fourth International Workshop on Human–Computer Conversation’, Bellagio, Italy (2008)Google Scholar
  8. Bickmore, T.W., Caruso, L., Clough-Gorr, K., Heeren, T.: It’s just like you talk to a friend: relational agents for older adults. Interact. Comput. Spec. Issue HCI Older Popul 17(6), 711–735 (2005)Google Scholar
  9. Bickmore, T.W., Mauer, D., Brown, T.: Context awareness in a handheld exercise agent. Pervas. Mob. Comput. 5(3), 226–235 (2009)CrossRefGoogle Scholar
  10. Bickmore, T.W., Picard, R.W.: Establishing and maintaining long-term human–computer relationships. ACM Trans. Comput. Hum. Interact. 12(2), 293–327 (2005)CrossRefGoogle Scholar
  11. Bickmore, T.W., Schulman, D., Sidner, C.: Automated interventions for multiple health behaviors using conversational agents. Patient Educ. Couns. 92(2), 142–148 (2013)CrossRefGoogle Scholar
  12. Bielik, P., Tomlein, M., Krátky, P., Mitrík, v., Barla, M., Bieliková, M., : Move2Play: an innovative approach to encouraging people to be more physically active. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 61–70. Florida, USA, Miami (2012)Google Scholar
  13. Bouten, C.V., Koekkoek, K.T.M., Verduin, M., Kodde, R., Janssen, J.D.: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans. Biomed. Eng. 44(3), 136–147 (1997)CrossRefGoogle Scholar
  14. Bouvier, P., Sehaba, K., Lavoué, E.: A trace-based approach to identifying users’ engagement and qualifying their engaged-behaviours in interactive systems: application to a social game. User modeling and user-adapted interaction, special issue on personalization and behavior change (2014). doi: 10.1007/s11257-014-9150-2
  15. Burns, P., Lueg, C., Berkovsky, S.: ActivMON: A wearable ambient activity display. Proceedings of the 6th Conference on Mobile und Ubiquitäre Informationssysteme (MMS2011), pp. 11–24. Kaiserslautern, Germany (2011)Google Scholar
  16. Burns, P., Lueg, C., Berkovsky, S.: Activmon: encouraging physical activity through ambient social awareness. Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 2363–2368. Austin, Texas, USA (2012)Google Scholar
  17. Burns, P., Lueg, C., Berkovsky, S.: Colours that move you: persuasive ambient activity displays, in ‘Lecture Notes in Computer Science, Proceedings of the 8th International Conference on Persuasive Technology’, Vol. 7822 of Lecture Notes in Computer Science, pp. 27–32. Sydney, Australia (2013)Google Scholar
  18. Buttussi, F., Chittaro, L.: MOPET: a context-aware and user-adaptive wearable system for fitness training. Artif. Intell. Med. 42(2), 153–163 (2008)CrossRefGoogle Scholar
  19. Buttussi, F., Chittaro, L., Nadalutti, D.: Bringing mobile guides and fitness activities together: a solution based on an embodied virtual trainer. In: Proceedings of the 8th conference on human–computer interaction with mobile devices and services, pp. 29–36. Espoo, Finland (2006)Google Scholar
  20. Buttussi, F., Chittaro, L., Nadalutti, D.: Filtering fitness trail content generated by mobile users. In: Proceedings of the 17th International Conference on User Modelling Adaptation and Personalization (UMAP 2009), pp. 441–446. Trento, Italy (2009)Google Scholar
  21. Byrne, D., Nelson, D.: Attraction as a linear function of proportion of positive reinforcements. J. Personal. Soc. Psychol. 36(6), 659–663 (1965)CrossRefGoogle Scholar
  22. Campbell, T., Ngo, B., Fogarty, J.: Game design principles in everyday fitness applications. In Proceedings of the ACM 2008 Conference on Computer Supported Cooperative Work, p. 249. San Diego, California, USA (2008)Google Scholar
  23. Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bordello, G., Hemingway, B., Klasnja, P., Koscher, K., Landay, J.A., Lester, J., Wyatt, D., Haehnel, D.: The mobile sensing platform: an embedded activity recognition system. IEEE Pervas. Comput. 7(2), 32–41 (2008)CrossRefGoogle Scholar
  24. Colineau, N., Paris, C.: Motivating reflection about health within the family: the use of goal setting and tailored feedback. User Model. User Adapt. Interact. 21(4–5), 341–376 (2011)CrossRefGoogle Scholar
  25. Consolvo, S., Everitt, K., Smith, I., Landay, J.A.: Design requirements for technologies that encourage physical activity. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 457–466. Montréal, Québec, Canada (2006)Google Scholar
  26. Consolvo, S., Klasnja, P., McDonald, D.W., Landay, J.A.: Goal-setting considerations for persuasive technologies that encourage physical activity. In: Proceedings of the 4th international conference on persuasive technology. Claremont, California, USA (2009)Google Scholar
  27. Consolvo, S., McDonald, D.W., Landay, J.A.: Theory-driven design strategies for technologies that support behavior change in everyday life. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, pp. 405–414. Massachusetts, USA, Boston (2009)Google Scholar
  28. Consolvo, S., Mcdonald, D.W., Toscos, T., Chen, M.Y., Froehlich, J., Harrison, B., Klasnja, P., Lamarca, A., Legrand, L., Libby, R., Smith, I., Landay, J.A.: Activity sensing in the wild: a field trial of UbiFit garden. In: Proceedings of the 26th Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 1797–1806. Firenze, Italy (2008)Google Scholar
  29. Cortellese, F., Nalin, M., Morandi, A., Sanna, A., Grasso, F.: Static and dynamic population clustering in personality diagnosis for personalized eHealth services. In: Proceedings of the 3rd International Workshop on Personalisation for e-Health, AIME2009, pp. 1–9. Verona, Italy (2009)Google Scholar
  30. de Oliveira, R., Oliver, N.: TripleBeat: Enhancing Exercise Performance with Persuasion. In: Proceedings of the 10th International Conference on Human Computer Interaction with Mobile Devices and Services, pp. 255–264. Amsterdam, The Netherlands (2008)Google Scholar
  31. DeChant, H.K., Tohme, W.G., Mun, S.K., Hayes, W.S., Schulman, K.A.: Health systems evaluation of telemedicine: a staged approach. Telemed. J. 2(4), 303–312 (1996)CrossRefGoogle Scholar
  32. Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. In: Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing. Karlsruhe, Germany (1999)Google Scholar
  33. Di Tullio, E., Grasso, F.: A model for a motivational system grounded on value based abstract argumentation frameworks. Electronic Healthcare - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 91, pp. 43–50. Malaga, Spain (2012)Google Scholar
  34. Dijkstra, A.: The persuasive effects of personalization through: name mentioning in a smoking cessation message. User Modeling and User-Adapted Interaction, Special Issue on Personalization and Behavior Change (2014). doi: 10.1007/s11257-014-9147-x
  35. Ekeland, A.G., Bowes, A., Flottorp, S.: Effectiveness of telemedicine: a systematic review of reviews. Int. J. Med. Inf. 79(11), 736–771 (2010)CrossRefGoogle Scholar
  36. Enwald, H.P.K., Huotari, M.-L.A.: Preventing the obesity epidemic by second generation tailored health communication: an interdisciplinary review. J. Med. Internet Res. 12(2), e24 (2010)CrossRefGoogle Scholar
  37. Erriquez, E., Grasso, F.: Generation of personalised advisory messages: an ontology based approach. In: Proceedings of the 21st IEEE International International Symposium on Computer-Based Medical Systems (CMBS), pp. 437–442. Jyvaskyla, Finland (2008)Google Scholar
  38. Festinger, L.: A Theory of Cognitive Dissonance. Stanford University Press, Stanford (1957)Google Scholar
  39. Fischer, G.: User modeling in human–computer interaction. User Model. User Adapt. Interact. 11(1), 65–86 (2001)CrossRefzbMATHGoogle Scholar
  40. Fogg, B.: Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann, San Francisco (2003)Google Scholar
  41. Fry, J.P., Neff, R.A.: Periodic prompts and reminders in health promotion and health behavior interventions: systematic review. J. Med. Internet Res. 11(2), e16 (2009)CrossRefGoogle Scholar
  42. Fujiki, Y., Kazakos, K., Puri, C., Buddharaju, P., Pavlidis, I.: NEAT-o-Games: blending physical activity and fun in the daily routine. Comput. Entertain. (CIE) 6(2), 1–22 (2008)CrossRefGoogle Scholar
  43. Fujiki, Y., Kazakos, K., Puri, C., Pavlidis, I., Starren, J., Levine, J.: NEAT-o-Games: Ubiquitous Activity-Based Gaming. CHI ’07 Extended Abstracts on Human Factors in Computing Systems, pp. 2369–2374. California, USA, San Jose (2007)Google Scholar
  44. Garber, C.E., Blissmer, B., Deschenes, M.R., Franklin, B.A., Lamonte, M.J., Lee, I.-M., Nieman, D.C., Swain, D.P.: American college of sports medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med. Sci. Sports Exerc. 43(7), 1334–1359 (2011)CrossRefGoogle Scholar
  45. Gerber, S., Fry, M., Kay, J., Kummerfeld, B., Pink, G., Wasinger, R.: PersonisJ: mobile, client-side user modelling. In: Proceedings of the 18th International Conference on User Modelling Adaptation and Personalization (UMAP 2010), vol. 6075, pp. 111–122. Big Island of Hawaii, USA (2010)Google Scholar
  46. Goffmann, E.: The Presentation of Self in Everyday Life. Anchor Books, New York (1959)Google Scholar
  47. Hawkins, R.P., Kreuter, M.W., Resnicow, K., Fishbein, M., Dijkstra, A.: Understanding tailoring in communicating about health. Health Educ. Res. 23(3), 454–466 (2008)CrossRefGoogle Scholar
  48. Ijsselsteijn, W., De Kort, Y., Westerink, J., De Jager, M., Bonants, R.: Fun and Sports: Enhancing the Home Fitness Experience. In: Proceedings of the 3rd International Conference on Entertainment Computing (ICEC2004), Vol. 3166, pp. 46–56. Eindhoven, The Netherlands (2004)Google Scholar
  49. Kennedy, C.M., Powell, J., Payne, T.H., Ainsworth, J., Boyd, A., Buchan, I.: Active assistance technology for health-related behavior change: an interdisciplinary review. J. Med. Internet Res. 14(3), e80 (2012)CrossRefGoogle Scholar
  50. King, A.C., Hekler, E.B., Grieco, L.A., Winter, S.J., Sheats, J.L., Buman, M.P., Banerjee, B., Robinson, T.N., Cirimele, J.: Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PloS One 8(4), e62613 (2013)CrossRefGoogle Scholar
  51. Klasnja, P., Consolvo, S., McDonald, D.W., Landay, J.A., Pratt, W.: Using Mobile & Personal Sensing Technologies to Support Health Behavior Change in Everyday Life: Lessons Learned. AMIA Annual Symposium Proceedings, vol. 2009, pp. 338–342. San Francisco, California, USA (2009)Google Scholar
  52. Kohl, H.W., Fulton, J.E., Caspersen, C.J.: Assessment of physical activity among children and adolescents: a review and synthesis. Prev. Med. 31(2), 54–76 (2000)CrossRefGoogle Scholar
  53. Kreuter, M.W., Strecher, V.J., Glassman, B.: One size does not fit all: the case for tailoring print materials. An. Behav. Med. 21(4), 276–283 (1999)CrossRefGoogle Scholar
  54. Lane, N.D., Xu, Y., Lu, H., Hu, S., Choudhury, T., Campbell, A.T., Zhao, F.: Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In: Proceedings of the 13th international conference on Ubiquitous computing—UbiComp ’11, pp. 355–364. Beijing, China (2011)Google Scholar
  55. Lin, M., Lane, N.D., Mohammod, M., Yang, X., Lu, H., Cardone, G., Ali, S., Doryab, A., Berke, E., Campbell, A.T., Choudhury, T.: BeWell+: Multi-dimensional wellbeing monitoring with community-guided user feedback and energy optimization. Proceedings of the Conference on Wireless Health—WH ’12, pp. 1–8. San Diego, California, USA (2012)Google Scholar
  56. Locke, E.A., Latham, G.P.: Building a practically useful theory of goal setting and task motivation: a 35-year odyssey. Am. Psychol. 57(9), 705–717 (2002)CrossRefGoogle Scholar
  57. Lombard, D.N., Lombard, T.N., Winett, R.A.: Walking to meet health guidelines: the effect of prompting frequency and prompt structure, health psychology. Off. J. Div. Health Psychol. Am. Psychol. Assoc. 14(2), 164–170 (1995)Google Scholar
  58. Lustria, M.L.A., Cortese, J., Noar, S.M., Glueckauf, R.L.: Computer-tailored health interventions delivered over the web: review and analysis of key components. Patient Educ. Counsel. 74(2), 156–173 (2008)CrossRefGoogle Scholar
  59. Lutes, L.D., Steinbaugh, E.K.: Theoretical models for pedometer use in physical activity interventions. Phys. Ther. Rev. 15(3), 143–153 (2010)CrossRefGoogle Scholar
  60. Maitland, J., Sherwood, S., Barkhuus, L., Anderson, I., Hall, M., Brown, B., Chalmers, M., Muller, H.: Increasing the awareness of daily activity levels with pervasive computing. In: Proceedings of the 1st International Conference on Pervasive Computing Technologies for Healthcare, pp. 1–9. Innsbruck, Austria (2006)Google Scholar
  61. McKenzie, T.L.: The use of direct observation to assess physical activity. In: Welk, G. (ed.) Physical Activity Assessments for Health Related Research, pp. 179–195. Human Kinetics Publishers Inc, Champaign (2002)Google Scholar
  62. Mokka, S., Väätänen, A., Heinilä, J., Välkkynen, P.: Fitness computer game with a bodily user interface. In: Proceedings of the 2nd International Conference on Entertainment Computing, ICEC ’03, Pittsburgh, pp. 1–3. Pennsylvania, USA (2003)Google Scholar
  63. Morandi, A., Serafin, R.: A personalized motivation strategy for physical activity promotion in diabetic subjects. In: Proceedings of the 2nd Workshop on Personalisation for E-Health, pp. 51–55. Corfu, Greece (2007)Google Scholar
  64. Mulas, F., Carta, S., Pilloni, P., Manca, M.: Everywhere Run: a virtual personal trainer for supporting people in their running activity. In: Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology—ACE ’11’, Lisbon, Portugal (2011)Google Scholar
  65. Mulas, F., Pilloni, P., Carta, S.: Everywhere race!: a social mobile platform for sport engagement and motivation. Proceedings of the 2nd International Conference on Social Eco-Informatics, pp. 63–69. Venice, Italy (2012)Google Scholar
  66. Noar, S.M., Benac, C.N., Harris, M.S.: Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychol. Bull. 133(4), 673–693 (2007)CrossRefGoogle Scholar
  67. O’Brien, S., Mueller, F.F.: Jogging the distance. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems—CHI ’07, pp. 523–526. California, USA, San Jose (2007)Google Scholar
  68. Oinas-Kukkonen, H., Harjumaa, M.: Persuasive systems design: key issues, process model, and system features. Commun. Assoc. Inf. Syst. 24(1), 485–500 (2009)Google Scholar
  69. Oliver, N., Flores-Mangas, F.: MPTrain: a mobile, music and physiology-based personal trainer. Proceedings of the 8th Conference on Human Computer Interaction with Mobile Devices and Services, pp. 21–28. Espoo, Finland (2006)Google Scholar
  70. Oliver, N., Kreger-Stickles, L.: Enhancing Exercise Performance through real-time physiological monitoring and music: a user study. Pervasive Health Conference and Workshops, pp. 1–10. Innsbruck, Austria (2006a)Google Scholar
  71. Oliver, N., Kreger-stickles, L.: PAPA: physiology and purpose-aware automatic playlist generation. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR2006), pp. 250–253. Victoria, Canada (2006b)Google Scholar
  72. op den Akker, H., Jones, V.M., & Hermens, H.J.: Predicting feedback compliance in a teletreatment application. In: Proceedings of ISABEL 2010: the 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, Rome, Italy (2010)Google Scholar
  73. Orji, R., Vassileva, J., Mandryk, R.L.: Modeling the efficacy of persuasive strategies for different gamer types in serious games for health. User modeling and user-adapted interaction, special issue on personalization and behavior change (2014)Google Scholar
  74. Plasqui, G., Bonomi, A.G., Westerterp, K.R.: Daily physical activity assessment with accelerometers: new insights and validation studies. Obes. Rev. 14(6), 451–462 (2013)CrossRefGoogle Scholar
  75. Prochaska, J.O., DiClemente, C.: Toward a comprehensive model of change. Appl. Clin. Psychol. 13(1), 3–27 (1986)Google Scholar
  76. Prochaska, J.O., Velicer, W.: The transtheoretical model of health behavior change. Am. J. Health Promot. 12(1), 38–48 (1997)CrossRefGoogle Scholar
  77. Qian, H., Kuber, R., Sears, A.: Maintaining levels of activity using a haptic personal training application. CHI ’10 Extended Abstracts on Human Factors in Computing Systems, pp. 3217–3222. Atlanta, Georgia, USA (2010)Google Scholar
  78. Qian, H., Kuber, R., Sears, A.: Towards developing perceivable tactile feedback for mobile devices. Int. J. Hum. Comput. Stud. 69(11), 705–719 (2011)CrossRefGoogle Scholar
  79. Qian, H., Kuber, R., Sears, A., Murphy, E.: Maintaining and modifying pace through tactile and multimodal feedback. Interact. Comput. 23(3), 214–225 (2011)CrossRefGoogle Scholar
  80. Saini, P., Lacroix, J.: Self-setting of physical activity goals and effects on perceived difficulty, importance and competence. In: Proceedings of the 4th International Conference on Persuasive Technology - Persuasive ’09’, p. 1. Claremont, California, USA (2009)Google Scholar
  81. Sallis, J.F., Saelens, B.E.: Assessment of physical activity by self-report: status, limitations, and future directions. Res. Q. Exerc. Sport 71(4), S1–S14 (2000)Google Scholar
  82. Saris, W.H.M., Binkhorst, R.A.: The use of pedometer and actometer in studying daily physical activity in man. Part I: reliability of pedometer and actometer. Eur. J. Appl. Physiol. Occup. Physiol. 37(3), 219–228 (1977)CrossRefGoogle Scholar
  83. Short, C.E., James, E.L., Plotnikoff, R.C., Girgis, A.: Efficacy of tailored-print interventions to promote physical activity: a systematic review of randomised trials. Int. J. Behav. Nutr. Phys. Act. 8(1), 113 (2011)CrossRefGoogle Scholar
  84. Sohn, M., Lee, J.: UP health: ubiquitously persuasive health promotion with an instant messaging system. CHI ’07 Extended Abstracts on Human Factors in Computing Systems, pp. 2663–2668. California, USA, San Jose (2007)Google Scholar
  85. Speakman, J.R.: The history and theory of the doubly labeled water technique. Am. J. Clin. Nutr. 68(4), 932S–938S (1998)Google Scholar
  86. Ståhl, O., Gambäck, B., Hansen, P., Turunen, M., Hakulinen, J.: A mobile fitness companion. In: Proceedings of the Fourth International Workshop on Human–Computer Conversation, Bellagio, Italy (2008)Google Scholar
  87. Thaler, R.H., Sunstein, C.R.: Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press, New Haven (2008)Google Scholar
  88. Turunen, M., Hakulinen, J., Ståhl, O., Gambäck, B., Hansen, P., Rodríguez Gancedo, M.C., de la Cámara, R.S., Smith, C., Charlton, D., Cavazza, M.: Multimodal and mobile conversational health and fitness companions. Comput. Speech Lang. 25(2), 192–209 (2011)CrossRefGoogle Scholar
  89. Wang, Y., Burgener, D., Kuzmanovic, A., Macia-Fernandez, G.: Understanding the network and user-targeting properties of web advertising networks. In: Proceedings of the 31st International Conference on Distributed Computing Systems, pp. 613–622. Minneapolis, Minnesota, USA (2011)Google Scholar
  90. Yim, J., Graham, T.C.N.: Using games to increase exercise motivation. In: Proceedings of the 2007 Conference on Future Play, pp. 166–173. Canada, Toronto (2007)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Harm op den Akker
    • 1
  • Valerie M. Jones
    • 2
  • Hermie J. Hermens
    • 1
  1. 1.Telemedicine GroupRoessingh Research and DevelopmentEnschedeThe Netherlands
  2. 2.Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations