Skip to main content

Advertisement

SpringerLink
Log in

Feasibility Study of a Sensor-Based Autonomous Load Control Exercise Training System for COPD Patients

  • Mobile Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Decision support systems (DSSs) which are able to automatically supervise and control physical exercise training of patients affected by chronic obstructive pulmonary disease (COPD) are regarded as a novel method to promote rehabilitation. The objective of our research work for this paper was to evaluate the feasibility of a rule-based DSS for autonomous bicycle ergometer training of COPD patients. Load control is based on real-time analysis of sensor parameters oxygen saturation and heart rate. Ten COPD patients have participated in a study, performing altogether 18 training sessions. On average, 7.4 rules were fired in each training session. Four sessions had to be stopped for different reasons. The average ergometer training load ranged between 31 and 47 W. The average percentage of heart rate in or lower than the intended target zone was 45.9 and 41.6 %, respectively. The average patient-perceived Borg value was 12.6 ± 2.4. Patients reported a high satisfaction for the automatically controlled training. With the help of the DSS, patients may change their training place from a rehabilitation center to their own homes. More studies are needed to assess long-term clinical and motivational effects of the DSS in home environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Jemal, A., Ward, E., Hao, Y., and Thun, M., Trends in the leading causes of death in the United States, 1970–2002. JAMA 294(10):1255–1259, 2005. doi:10.1001/jama.294.10.1255.

    Article  Google Scholar 

  2. Sim, M. H., Kim, M. Y., Jeong, I. C., Park, S. B., Yong, S. J., Kim, W. K., and Yoon, H. R., Development and evaluation of an improved technique for pulmonary function testing using electrical impedance pneumography intended for the diagnosis of chronic obstructive pulmonary disease patients. Sensors 13(11):15846–15860, 2013. doi:10.3390/s131115846.

    Article  Google Scholar 

  3. Celli, B. R., MacNee, W., and Force, A. E. T., Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper. Eur. Respir. J. 23(6):932–946, 2004.

    Article  Google Scholar 

  4. National Heart, Lung and Blood Institute, Morbidity & Mortality: 2002 Chart Book on Cardiovascular, Lung, and Blood Diseases. National Heart, Blood and Lung Institute, Bethesda, 2002.

    Google Scholar 

  5. Ricci, C., Terzoni, S., Gaeta, M., Sorgente, A., Destrebecq, A., and Gigliotti, F., Physical training and noninvasive ventilation in COPD patients: a meta-analysis. Respir. Care 59(5):709–717, 2014. doi:10.4187/respcare.02626.

    Article  Google Scholar 

  6. Varga, J., Porszasz, J., Boda, K., Casaburi, R., and Somfay, A., Supervised high intensity continuous and interval training vs. self-paced training in COPD. Respir. Med. 101(11):2297–2304, 2007. doi:10.1016/j.rmed.2007.06.017.

    Article  Google Scholar 

  7. Song, B., Wolf, K. H., Gietzelt, M., Al Scharaa, O., Tegtbur, U., Haux, R., and Marschollek, M., Decision support for teletraining of COPD patients. Methods Inf. Med. 49(1):96–102, 2010.

    Google Scholar 

  8. Murray, J. A., Waterman, L. A., Ward, J., Baird, J. C., and Mahler, D. A., Perceptual and physiologic responses during treadmill and cycle exercise in patients with COPD. Chest 135(2):384–390, 2009. doi:10.1378/chest. 08-1258.

    Article  Google Scholar 

  9. Wu, M., Chen, C., Wen, C., and Hsu, J., Design of pervasive rehabilitation monitoring for chronic obstructive pulmonary disease. IEEE Sensors J. 13(11):4413–4422, 2013.

    Article  Google Scholar 

  10. Schulze, M., Song, B., Gietzelt, M., Wolf, K. H., Kayser, R., Tegtbur, U., and Marschollek, M., Supporting rehabilitation training of COPD patients through multivariate sensor-based monitoring and autonomous control using a Bayesian network: prototype and results of a feasibility study. Inform. Health Soc. Care 35(3–4):144–156, 2010.

    Article  Google Scholar 

  11. Gietzelt, M., Goltz, U., Grunwald, D., Lochau, M., Marschollek, M., Song, B., and Wolf, K. H., Arden2ByteCode: a one-pass Arden Syntax compiler for service-oriented decision support systems based on the OSGi platform. Comput. Methods Programs Biomed 106(2):114–125, 2011.

    Article  Google Scholar 

  12. Robergs, R. A., and Landwehr, R., The surprising history of the “HRmax = 220-age” equation. J. Exerc. Physiol. 5(2):1–10, 2002.

    Google Scholar 

  13. Borg, G., Perceived exertion as an indicator of somatic stress. Scand. J. Rehabil. Med. 2(2):92–98, 1970.

    Google Scholar 

  14. Spitalewsky, K., Rochon, J., Ganzinger, M., and Knaup, P., Potential and requirements of IT for ambient assisted living technologies. Results of a Delphi study. Methods Inf. Med. 52(3):231–238, 2013. doi:10.3414/ME12-01-0021. S231-233.

    Article  Google Scholar 

  15. Aquino-Santos, R., Martinez-Castro, D., Edwards-Block, A., and Murillo-Piedrahita, A. F., Wireless sensor networks for ambient assisted living. Sensors 13(12):16384–16405, 2013. doi:10.3390/s131216384.

    Article  Google Scholar 

  16. Haux, R., Howe, J., Marschollek, M., Plischke, M., and Wolf, K. H., Health-enabling technologies for pervasive health care: on services and ICT architecture paradigms. Inform. Health Soc. Care 33(2):77–89, 2008.

    Article  Google Scholar 

  17. Kuroda, T., Noma, H., Naito, C., Tada, M., Yamanaka, H., Takemura, T., Nin, K., and Yoshihara, H., Prototyping sensor network system for automatic vital signs collection. Evaluation of a location based automated assignment of measured vital signs to patients. Methods Inf. Med. 52(3):239–249, 2013. doi:10.3414/ME12-01-0096.

    Article  Google Scholar 

  18. Marschollek, M., and Becker, C., Health-enabling technologies in geriatric falls research: the path to evidence. Z. Gerontol. Geriatr. 46(8):704–705, 2013. doi:10.1007/s00391-013-0550-4.

    Article  Google Scholar 

  19. Kohlmann, M., Gietzelt, M., Jahne-Raden, N., Marschollek, M., Song, B., Wolf, K. H., and Haux, R., A collaboration tool based on SNOCAP-HET. J. Med. Syst. 38(1):9996, 2014. doi:10.1007/s10916-013-9996-6.

    Article  Google Scholar 

  20. Baig, M. M., and Gholamhosseini, H., Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2):9898, 2013. doi:10.1007/s10916-012-9898-z.

    Article  Google Scholar 

  21. Steventon, A., Bardsley, M., Billings, J., Dixon, J., Doll, H., Hirani, S., Cartwright, M., Rixon, L., Knapp, M., Henderson, C., Rogers, A., Fitzpatrick, R., Hendy, J., Newman, S., and Whole System Demonstrator Evaluation T, Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial. BMJ 344:e3874, 2012. doi:10.1136/bmj.e3874.

    Article  Google Scholar 

  22. Schwartze, J., Haarbrandt, B., Fortmeier, D., Haux, R., and Seidel, C., Authentication systems for securing clinical documentation workflows. A systematic literature review. Methods Inf. Med. 53(1):3–13, 2014. doi:10.3414/ME12-01-0078.

    Article  Google Scholar 

  23. Wu, F., and Xu, L., Security analysis and improvement of a privacy authentication scheme for telecare medical information systems. J. Med. Syst. 37(4):9958, 2013. doi:10.1007/s10916-013-9958-z.

    Article  Google Scholar 

  24. Chen, S. C., Liu, S. C., Li, S. H., and Yen, D. C., Understanding the mediating effects of relationship quality on technology acceptance: an empirical study of e-appointment system. J. Med. Syst. 37(6):9981, 2013. doi:10.1007/s10916-013-9981-0.

    Article  Google Scholar 

  25. Bates, D. W., Baysari, M. T., Dugas, M., Haefeli, W. E., Kushniruk, A. W., Lehmann, C. U., Liu, J., Mantas, J., Margolis, A., Miyo, K., Nohr, C., Peleg, M., de Quiros, F. G., Slight, S. P., Starmer, J., Takabayashi, K., and Westbrook, J. I., Discussion of “Attitude of physicians towards automatic alerting in computerized physician order entry systems”. Methods Inf. Med. 52(2):109–127, 2013.

    Google Scholar 

  26. Jung, M., Hoerbst, A., Hackl, W. O., Kirrane, F., Borbolla, D., Jaspers, M. W., Oertle, M., Koutkias, V., Ferret, L., Massari, P., Lawton, K., Riedmann, D., Darmoni, S., Maglaveras, N., Lovis, C., and Ammenwerth, E., Attitude of physicians towards automatic alerting in computerized physician order entry systems. A comparative international survey. Methods Inf. Med. 52(2):99–108, 2013. doi:10.3414/ME12-02-0007.

    Article  Google Scholar 

  27. Abdekhoda, M., Ahmadi, M., Dehnad, A., and Hosseini, A. F., Information technology acceptance in health information management. Methods Inf. Med. 53(1):14–20, 2014. doi:10.3414/ME13-01-0079.

    Article  Google Scholar 

  28. Song B, Huang B, Wolf KH, Gietzelt M, Haux R, Marschollek M A web 2.0 based HL7 Arden syntax Medical Logical Module platform for knowledge creating and sharing - the development of a rule repository for smart home care. In: 7th Asia Pacific Association for Medical Informatics conference (APAMI2012), Beijing, 2012.

  29. Samwald, M., Fehre, K., de Bruin, J., and Adlassnig, K. P., The Arden Syntax standard for clinical decision support: experiences and directions. J. Biomed. Inform. 45(4):711–718, 2012. doi:10.1016/j.jbi.2012.02.001.

    Article  Google Scholar 

  30. Busch, C., Baumbach, C., Willemsen, D., Nee, O., Gorath, T., Hein, A., and Scheffold, T., Supervised training with wireless monitoring of ECG, blood pressure and oxygen-saturation in cardiac patients. J. Telemed. Telecare 15(3):112–114, 2009. doi:10.1258/jtt.2009.003002.

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the Koronar Sportverein Braunschweig e.V. which provided access to its training facilities. In particular, we thank the exercise supervisors, Mr. and Mrs. Watling, for their invaluable support in recruiting the COPD patients. We thank the Braunschweiger Informatik- und Technologie-Zentrum (BITZ) gGmbH for supporting us in transportation of the equipment used in the study. Finally, we thank all participating COPD patients for their support and valuable feedback.

Conflict of interest

The authors declare no conflict of interest.

Ethical standards

The study in this paper complies with the current laws of Germany.

Author information

Authors and Affiliations

  1. Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany

    Bianying Song, Marcus Becker, Mareike Schulze & Michael Marschollek

  2. Institute for Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 305, 69120, Heidelberg, Germany

    Matthias Gietzelt

  3. Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Mühlenpfordtstraße 23, 38106, Braunschweig, Germany

    Reinhold Haux, Martin Kohlmann & Klaus-Hendrik Wolf

  4. Institute for Sports Medicine, Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany

    Uwe Tegtbur

Authors
  1. Bianying Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcus Becker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthias Gietzelt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Reinhold Haux
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Kohlmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mareike Schulze
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Uwe Tegtbur
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Klaus-Hendrik Wolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Michael Marschollek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Marschollek.

Additional information

This article is part of the Topical Collection on Mobile Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, B., Becker, M., Gietzelt, M. et al. Feasibility Study of a Sensor-Based Autonomous Load Control Exercise Training System for COPD Patients. J Med Syst 39, 150 (2015). https://doi.org/10.1007/s10916-014-0150-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-014-0150-x

Keywords

Search

Navigation