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Methods Inf Med 2008; 47(03): 208-216
DOI: 10.3414/ME9112
Original Article
Schattauer GmbH

Activity and Heart Rate-based Measures for Outpatient Cardiac Rehabilitation

N. P. Bidargaddi
1   Australian E-Health Research Centre, ICT Centre, Commonwealth Scientific and Industrial Research Organization, Brisbane, Queensland, Australia
,
A. Sarela
1   Australian E-Health Research Centre, ICT Centre, Commonwealth Scientific and Industrial Research Organization, Brisbane, Queensland, Australia
› Author Affiliations
Further Information

Publication History

Publication Date:
18 January 2018 (online)

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Summary

Objectives: Derive activity and heart rate (HR) monitor-based clinically relevant measures for outpatient cardiac rehabilitation (CR).

Methods: We are currently collecting activity/ ECG data from patients undergoing cardiac rehabilitation over duration of six weeks. From these data sets, we a) derive various measures which can be used in assessing home-based CR patients remotely and b) investigate the usefulness of continuous ambulatory HR and heart rate variability (HRV) for various core components of CR.

Results: The information provided by these measures is interpreted according to the CR guidelines framework by American Association of Cardiovascular and Pul - monary Rehabilitation (AACVPR), thus showing how these tools can be used in assessing the progress of patients’ condition. The usefulness and significance of these measures from a health care professional perspective is also presented by evaluating them against the existing hospital-based measures through examples.

Conclusions: Hospital-based CR programs, despite their clinical benefits are severely under-utilized and resource-demanding. Ambulatory monitoring technologies, which provide a means for continuous physiological monitoring of patients at home compared to hospital-based tools, can enable home-based CR. The clinically relevant measures derived from these tools not only reflect patients´ condition in a similar way as conventional tools but also show the continuous status of functional capacity (FC).

Keywords

Cardiac rehabilitation - ambulatory monitoring - physiological monitoring - heart rate - physical activity
 

  • References

  • 1 Australian Institute of Health and Welfare 2002.. In: Australia’s health 2002. Canberra: AIHW; 2002 Available from: www.heartfoundation.org.au/document/NHF/World_trends.pdf.
    PubMedGoogle Scholar
  • 2 Balady GJ, Williams MA, Ades PA, Bittner V, Comoss P, Foody JM. et al. Core components of cardiac rehabilitation /secondary prevention programs: 2007 update: a scientific statement from the American Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee, the Council on Clinical Cardiology; the Councils on Cardiovascular Nursing, Epidemiology and Prevention, and Nutrition, Physical Activity, and Metabolism; and the American Association of Cardiovascular and Pulmonary Rehabilitation. Circulation 2007; 115 (20) 2675-2682.
    CrossrefPubMedGoogle Scholar
  • 3 Austin J, Williams R, Ross L, Moseley L, Hutchison S. Randomised controlled trial of cardiac rehabilitation in elderly patients with heart failure. Eur J Heart Fail 2005; 7 (03) 411-417. Available from: http://dx.doi.org/10.1016/j.ejheart.2004. 10.004.
    CrossrefPubMedGoogle Scholar
  • 4 Thomas RJ. Cardiac rehabilitation /secondary prevention programs: a raft for the rapids: why have we missed the boat?. Circulation 2007; 116 (15) 1644-1646. Available from: http://dx.doi. org/10.1161/CIRCULATIONAHA.107.728402.
    CrossrefPubMedGoogle Scholar
  • 5 Taylor RS, Watt A, Dalal HM, Evans PH, Campbell JL, Read KLQ. et al. Home-based cardiac rehabilitation versus hospital-based rehabilitation: a cost effectiveness analysis. Int J Cardiol 2007; 119 (02) 196-201. Available from: http://dx.doi.org/ 10.1016/j.ijcard.2006.07.218.
    CrossrefPubMedGoogle Scholar
  • 6 Sarela A, Bidargaddi NP, Karunanithi M. A Software Architecture and Data Model for Community- Based Healthcare Environments. In: Proc 2nd Intl Conf Pervasive Computing Technologies for Health Care (Pervasive Health 2008). Tampere, Finland: 2008. pp 1-6.
    Google Scholar
  • 7 Par G, Jaana M, Sicotte C. Systematic review of home telemonitoring for chronic diseases: the evidence base. J Am Med Inform Assoc 2007; 14 (03) 269-277. Available from: http://dx.doi.org/ 10.1197/jamia.M2270.
    CrossrefPubMedGoogle Scholar
  • 8 Najafi B, Aminian K, Loew F, Blanc Y, Robert PA. Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. Biomedical Engineering, IEEE Transactions on. 2002; 49 (08) 843-851.
    CrossrefPubMedGoogle Scholar
  • 9 Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG. Implementation of a realtime human movement classifier using a triaxial accelerometer for ambulatory monitoring. Information Technology in Biomedicine, IEEE Transactions on. 2006; 10 (01) 156-167.
    CrossrefPubMedGoogle Scholar
  • 10 Pinna GD, Maestri R, Torunski A, Danilowicz-Szymanowicz L, Szwoch M, Rovere MTL. et al. Heart rate variability measures: a fresh look at reliability. Clin Sci (Lond). 2007; 113 (03) 131-140. Available from: http://dx.doi.org/ 10.1042/CS20070055.
    CrossrefPubMedGoogle Scholar
  • 11 Zutz A, Ignaszewski A, Bates J, Lear SA. Utilization of the internet to deliver cardiac rehabilitation at a distance: a pilot study. Telemed J E Health 2007; 13 (03) 323-330. Available from: http:// dx.doi.org/10.1089/tmj.2006.0051.
    CrossrefPubMedGoogle Scholar
  • 12 Sandercock GRH, Grocott-Mason R, Brodie DA. Changes in short-term measures of heart rate variability after eight weeks of cardiac rehabilitation. Clin Auton Res 2007; 17 (01) 39-45. Available from: http://dx.doi.org/10.1007/ s10286-007–0392–5.
    CrossrefPubMedGoogle Scholar
  • 13 Langhammer B, Stanghelle JK. Co-variation of tests commonly used in stroke rehabilitation. Physiother Res Int 2006; 11 (04) 228-234.
    CrossrefPubMedGoogle Scholar
  • 14 Smith SC, Blair SN, Bonow RO, Brass LM, Cerqueira MD, Dracup K. et al. AHA/ACC Guidelines for Preventing Heart Attack and Death in Patients with Atherosclerotic Cardiovascular Disease: 2001 update. A statement for healthcare professionals from the American Heart Association and the American College of Cardiology. J Am Coll Cardiol 2001; 38 (05) 1581-1583.
    CrossrefPubMedGoogle Scholar
  • 15 Ayabe M, Brubaker PH, Dobrosielski D, Miller HS, Ishi K, Yahiro T. et al. The physical activity patterns of cardiac rehabilitation program participants. J Cardiopulm Rehabil 2004; 24 (02) 80-86.
    CrossrefPubMedGoogle Scholar
  • 16 Jordan K, Challis JH, Newell KM. Walking speed influences on gait cycle variability. Gait Posture 2007; 26 (01) 128-134. Available from: http:// dx.doi.org/10.1016/j.gaitpost.2006.08.010.
    CrossrefPubMedGoogle Scholar
  • 17 Akay M, Sekine M, Tamura T, Higashi Y, Fujimoto T. Fractal dynamics of body motion in poststroke hemiplegic patients during walking. J Neural Eng 2004; 1 (02) 111-116. Available from: http://dx.doi.org/10.1088/1741–2560/1/2/006.
    CrossrefPubMedGoogle Scholar
  • 18 Bidargaddi NP, Klingbeil L, Sarela A. Detecting walking activity in cardiac rehabilitation by using accelerometer. In: Proc Third Intl Conf Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007). Melbourne, Australia: 2007. pp 1-7.
    Google Scholar
  • 19 Hausdorff JM, Levy BR, Wei JY. The power of ageism on physical function of older persons: reversibility of age-related gait changes. J Am Geriatr Soc 1999; 47 (11) 1346-1349.
    CrossrefPubMedGoogle Scholar
  • 20 Cheng PT, Liaw MY, Wong MK, Tang FT, Lee MY, Lin PS. The sit-to-stand movement in stroke patients and its correlation with falling. Arch Phys Med Rehabil 1998; 79 (09) 1043-1046.
    CrossrefPubMedGoogle Scholar
  • 21 Era P, Sainio P, Koskinen S, Haavisto P, Vaara M, Aromaa A. Postural balance in a random sample of 7,979 subjects aged 30 years and over. Gerontology 2006; 52 (04) 204-213. Available from: http://dx.doi.org/10.1159/000093652.
    CrossrefPubMedGoogle Scholar
  • 22 Bidargaddi NP, Klingbeil L, Sarela A, Cheung V, Karunanithi M, Yelland C. et al. Wavelet based approach for posture transition estimation using a waist worn accelerometer. In: Proc 29th Annual Intl Conf IEEE Eng Med Biol Soc (EMBC 2007). Lyon, France: 2007. pp 1-4.
    Google Scholar
  • 23 Bourke AK, O’Brien JV, Lyons GM. Evaluation of a threshold based tri-axial accelerometer fall detection algorithm. Gait Posture 2007; 26 (02) 194-199. Available from: http://dx.doi.org/ 10.1016/j.gaitpost.2006.09.012.
    CrossrefPubMedGoogle Scholar
  • 24 Heart rate variability: standards of measurement, physiological interpretation and clinical use.. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996; 93 (05) 1043-1065.
    CrossrefPubMedGoogle Scholar
  • 25 Kamphuis MH, Geerlings MI, Dekker JM, Giampaoli S, Nissinen A, Grobbee DE. et al. Autonomic dysfunction: a link between depression and cardiovascular mortality? The FINE Study. Eur J Cardiovasc Prev Rehabil 2007; 14 (06) 796-802. Available from: http://dx.doi.org/10.1097/HJR. 0b013e32829c7d0c.
    CrossrefPubMedGoogle Scholar
  • 26 Dewey FE, Freeman JV, Engel G, Oviedo R, Abrol N, Ahmed N. et al. Novel predictor of prognosis from exercise stress testing: heart rate variability response to the exercise treadmill test. Am Heart J 2007; 153 (02) 281-288. Available from: http:// dx.doi.org/10.1016/j.ahj.2006.11.001.
    CrossrefPubMedGoogle Scholar