Predictive Modeling of Exercise Response in CVD Patients under Rehabilitation
Exercise-based rehabilitation plays a key role for patients with cardiovascular disease (CVD) in improving their well-being and reducing their symptoms. Monitoring and assessing the exercise response at an individual level is critical toward achieving better health outcomes. 15 exercise sessions performed by 5 CVD patients and 9 sessions from 3 regularly active individuals were monitored, and heart rate (HR) data were acquired. A model based on the HR dynamics during exercising at different intensities was built, and simulations were performed to assess performance in different scenarios of exercise selection. Our results show that the application of simple rules in exercise selection, which consider both the HR and the beneficial HR zones of individuals, can lead to beneficial execution of exercise programs (%time spent in beneficial HR zones: 60.6±27.5 for CVD patients). Personalized guidance during exercise has the potential to significantly contribute in the beneficial execution of exercise-based cardiac rehabilitation programs.
KeywordsCardiac rehabilitation Exercise Predictive modeling Cardiovascular disease Heart rate dynamics
Unable to display preview. Download preview PDF.
- 1. Heran BS, Chen JM, Ebrahim S et al. (2011) Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane database Syst Rev 7:CD001800Google Scholar
- 2. Gielen S, Laughlin MH, O’Conner C, Duncker DJ (2015) Exercise Training in Patients with Heart Disease: Review of Beneficial Effects and Clinical Recommendations. Prog Cardiovasc Dis 57(4):347–355Google Scholar
- 3. Cornelissen VA, Verheyden B, Aubert AE, Fagard RH (2010) Effects of aerobic training intensity on resting, exercise and post-exercise blood pressure, heart rate and heart-rate variability. J Hum Hypertens 24(3):175–182Google Scholar
- 4. Zwisler AD, Norton RJ, Dean SG et al. (2016) Home-based cardiac rehabilitation for people with heart failure: A systematic review and meta-analysis. Int J Cardiol 221: 963–969Google Scholar
- 5. Vanhees L, Geladas N, Hansen D et al. (2012) Importance of characteristics and modalities of physical activity and exercise in the management of cardiovascular health in individuals with cardiovascular risk factors: recommendations from the EACPR. Part II. Eur J Prev Cardiol 19(5):1005–33Google Scholar
- 6. Parak J, Member IS, Korhonen I, Member IS (2014) Evaluation of Wearable Consumer Heart Rate Monitors Based on Photopletysmography, Engineering in Medicine and Biology Society (EMBC), 36th Annual International Conference of the IEEE, pp. 3670–3673.Google Scholar
- 7. Stahl SE, An HS, Dinkel DM et al. (2016) How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough?. BMJ Open Sport Exerc Med2(1): e000106Google Scholar
- 8. Thompson PD, Arena R, Riebe D, Pescatello LS (2013) ACSM’s New Preparticipation Health Screening Recommendations from ACSM’s Guidelines for Exercise Testing and Prescription. Curr Sports Med Rep 12(4):215–217Google Scholar
- 9. Runtti H, Honka A, Chouvarda I et al. (2012) Biosignal processing methods to guide cardiac patients to perform safe and beneficial exercise for rehabilitation. Int J Bioelectromagn 15(1):20-25Google Scholar
- 10. Mooney, CZ (1997) Monte carlo simulation. Sage Publications.Google Scholar
- 11. Filos D, Triantafyllidis A, Chouvarda I et al. (2016) PATHway: Decision Support in Exercise Programmes for Cardiac Rehabilitation. Stud. Health Technol. Inform 224: 40–5Google Scholar