Methods Inf Med 2010; 49(01): 96-102
DOI: 10.3414/ME09-02-0005
Special Topic – Original Articles
Schattauer GmbH

Decision Support for Teletraining of COPD Patients

B. Song
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Braunschweig, Germany
,
K.-H. Wolf
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Braunschweig, Germany
,
M. Gietzelt
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Braunschweig, Germany
,
O. Al Scharaa
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Braunschweig, Germany
,
U. Tegtbur
2   Institute for Sports Medicine, Hannover Medical School, Hannover, Germany
,
R. Haux
1   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Braunschweig, Germany
,
M. Marschollek
3   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Hannover, Germany
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Weitere Informationen

Publikationsverlauf

received: 15. Juli 2009

accepted: 11. November 2009

Publikationsdatum:
17. Januar 2018 (online)

Summary

Background: Supervised physical training has been shown to promote rehabilitation of patients affected by chronic obstructive pulmonary disease (COPD). Currently, due to limited resources, not all COPD patients can be trained by an expert supervisor.

Objectives: The objective of our research is to construct a decision support system (DSS) which observes and controls physical ergometer training sessions of COPD patients.

Methods: A systematic literature review and expert interviews were carried out to build up the knowledge base for the DSS.

Results: Nine production rules were established and standardized by Drools and Arden Syntax. The developed software autonomously controls training sessions on a bicycle ergometer on the basis of vital signs data. Thus it offers a new way for the rehabilitation of COPD patients.

Conclusion: Evaluation with nine healthy subjects in a laboratory environment has confirmed its correct function, but the effects of its use for COPD patients’ rehabilitation and their quality of life have to be investigated in a further study.

 
  • References

  • 1 Celli BR, MacNee W. and committee members. Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper. Eur Respir J 2004; 23: 932-946.
  • 2 Fukuchi Y, Nishimura M, Ichinose M. COPD in Japan: the Nippon epidemiology study. Respirology 2004; 9: 458-465.
  • 3 Murray CJL, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Cambridge: Harvard University Press; 1996
  • 4 National Institutes of Health National Heart, Lung, and Blood Institute.. Morbidity & Mortality: 2002 Chart Book on Cardiovascular, Lung, and Blood Diseases. 2002
  • 5 Konietzko N, Fabel H. White paper lung. Stuttgart-New York: Thieme press; 2000
  • 6 Nishimura S, Zaher C. Cost impact of COPD in Japan: Opportunities and challenges?. Respirology 2004; 9: 466-473.
  • 7 Chavannes N, Vollenberg JJH, van Schayck CP, Wouters EFM. Effects of physical activity in mild to moderate COPD: a systematic review. Br J Gen Pract 2002; 52 (480) 532-534.
  • 8 Association of scientific medical societies.. Diagnosis and treatment of patients with COPD. http://www.uni-duesseldorf.de/AWMF/ll/020-006.htm. Accessed April 3, 2008
  • 9 Murray JA, Waterman LA, Ward J, Baird JC, Mahler DA. Perceptual and physiological responses during treadmill and cycle exercise in patients with COPD. Chest 2008; 135 (02) 384-390.
  • 10 Janos V, Janos P, Krisztina B, Richard C, Attila S. Supervised High intensity continuous and interval training vs. Self-paced training in COPD. Respiratory Medicine 2007; 101: 2297-2304.
  • 11 Worth H, Meyer A, Folgering H, Kirsten D, Lecheler J, Magnussen H, Pleyer K, Schmidt S, Schmitz M, Taube K, Wettengel R. Empfehlungen der Deutschen Atemwegsliga zum Sport und körperlichen Training bei Patienten mit obstruktiven Atemwegserkrankungen. [Recommendations of the German Airway League on sport and physical training in patients with obstructive airway diseases]. Pneumologie 2000; 54 (02) 61-67.
  • 12 Marschollek M, Mix S, Wolf KH, Effertz B, Haux R, Steinhagen-Thiessen E. ICT- based health information services for elderly people: past experiences, current trends, and future strategies. Med Inform Internet Med 2007; 32 (04) 251-261.
  • 13 Horwitz CM, Mueller M, Wiley D, Tentler A, Bocko M, Chen L, Leibovici A, Quinn J, Shar A, Pentland AP. Is home health technology adequate for proactive self-care?. Methods Inf Med 2008; 47 (01) 58-62.
  • 14 Volandes AE, Paasche-Orlow MK, Barry MJ, Gillick MR, Minaker KL, Chang Y, Cook EF, Abbo ED, ElJawahri A, Mitchell SL. Video decision support tool for advance care planning in dementia: randomised controlled trial. BMJ 2009; 338: b2159.
  • 15 Haux R, Howe J, Marschollek M, Plischke M, Wolf K-H. Health-enabling technologies for pervasive health care: on services and ICT architecture paradigms. Informatics for Health and Social Care 2008; 33 (02) 77-89.
  • 16 Hermie JH, Miriam MR, Vollenbroek H. Towards remote monitoring and remotely supervised training. Journal of Electromyography and Kinesiology 2008; 18: 908-919.
  • 17 Bardram JE. Pervasive healthcare as a scientific discipline. Methods Inf Med 2008; 47: 178-185.
  • 18 Triantafyllidis A, Koutkias V, Chouvarda I, Maglaveras N. An open and reconfigurable wireless sensor network for pervasive health monitoring. Methods Inf Med 2008; 47 (03) 229-234.
  • 19 Hein A, Nee O, Willemsen D, Scheffold T, Dogac A, Laleci GB. SAPHIRE – Intelligent Healthcare Monitoring based on Semantic Interoperability Platform – The Homecare Scenario. 1st European Conference on eHealth (ECEH’06), Fribourg, Switzerland: October 12-13, 2006
  • 20 Federal Institute for Sports Science.. http://www.bisp-datenbanken.de. Accessed April 6 2008
  • 21 Drools.. Documentation Library. http://www.jboss.org/Drools. Accessed July 7, 2008
  • 22 Forgy CL. Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence 1982; 19 (01) 17-37.
  • 23 ANSI/HL7 Arden V2.5–2005.. Health Level Seven Arden Syntax, Version 2.5 (revision of ANSI/HL7 Arden V2.1-2002). April 25, 2005
  • 24 Health Level Seven.. http://www.hl7.org. Accessed November 3, 2008
  • 25 Tegtbur U, Meyer H, Busse MW. Belastungsdiagnostische Kenngrößen und Katecholamine bei Koronarpatienten. [Stress diagnostic characteristics and catecholamines in coronary artery disease patients]. Zeitschrift für Kardiologie 2002; 91 (11) 927-936.
  • 26 Mediaportal.. http://www.team-mediaportal.de. Accessed July 25, 2008
  • 27 Winter A, Brigl B, Wendt T. Modeling hospital information systems. Part 1: The revised three-layer graph-based meta model 3LGM2. Methods Inf Med 2003; 42: 544-551.
  • 28 De Marees H. Sports physiology. Köln: Sport and Book Strauss Press; 2002
  • 29 Choi J, Lussier Y, Mendonca E. Adapting current Arden Syntax knowledge for an object oriented event monitor. AMIA Annu Symp Proc 2003 2003. p 814.
  • 30 Bott OJ, Marschollek M, Wolf K-H, Haux R. Towards new scopes: sensor-enhanced regional health information systems – part 1: architectural challenges. Methods Inf Med 2007; 46: 476-483.
  • 31 Haux R. Expert systems in medicine. Habilitation thesis. Aachen technical university: Aachen, Germany; 1987