TY - JOUR
AU - Knackstedt, Christian
AB - Abstract Background Participation in phase-III cardiac rehabilitation (CR) remains low but adherence could potentially be improved with supervised home-based CR. New technological approaches are needed to provide sufficient supervision with respect to safety and performance of individual exercise programmes. Design The newly designed closed-loop tool, HeartCycle’s guided exercise (GEX) system, will support professionals and patients during exercise-based CR. Patients wear a dedicated shirt with incorporated wireless sensors, and ECG, heart rate (HR), breathing frequency (BF), and activity are monitored during exercise. This information is streamed live to a mobile device (PDA) that processes these parameters. Methods A phase-I study was performed to evaluate feasibility, function, and reliability of this GEX device and compare it to conventional cardiac exercise testing (CPX, spiroergometry) in 50 patients (seven women, mean ± SD age 69 ± 9 years, body mass index 26 ± 3 kg/m2, ejection fraction 58 ± 10%). ECG, HR, and BF were monitored using standard equipment and the GEX device simultaneously. Furthermore, HR recorded on the PDA was compared with CPX measurements. Results The fit of the shirt and the sensor was good. No technical problems were encountered. All occurring arrhythmia were reliably detected. There was an acceptable comparability between HR on the GEX device vs. CPX, a good comparability between HR on the PDA vs. CPX, and a moderate comparability between BF on the GEX device vs. CPX. Conclusions Comparability between CPX and the GEX device was acceptable for HR measurement and moderate for BF; arrhythmias were reliably detected. HR processing and display on the PDA was even better comparable. The whole system seems suitable for monitoring home-based CR. Further studies are now needed to implement training prescription to facilitate individual exercise. Cardiac rehabilitation, monitoring, training Introduction Cardiac rehabilitation (CR) aims to restore exercise performance in patients after a cardiac event such as myocardial infarction (MI). Traditionally, these programmes have tried to improve physical health and individual attitude through exercise-only CR or comprehensive CR (e.g. smoking cessation, dietary counselling as well as exercise).1 Meta-analyses on exercise-based CR show a significant reduction in cardiac mortality of about 27% compared to patients receiving conventional care.2 Still, there are relevant national differences in how CR programmes are organized,2 and despite efficacy and cost-effectiveness, CR is pursued by less than one-third of eligible patients.3 As the beneficial effects of CR largely depend on continuation of a lifetime exercise programme after a structured CR, patients should be encouraged to continue with an individual exercise programme to preserve an improvement in exercise capacity.1,3–10 In the Heartwatch-program, a secondary prevention program of CAD in Ireland, it was possible to increase participation to 17% with 63% of patients being within target range of exercise at 3.5 years.11 Reasons for non-participation include unavailability of hospital-based rehabilitation programmes, lack of motivation, or other reasons (e.g. excessive travel distance). Quality performance criteria, automated referral systems, and options for home-based CR services may increase adherence to therapies in some patients. In addition, innovative exercise training regimens may help enhancing beneficial effects of CR. Besides centre-based CR, home-based CR offers a potentially valuable alternative for many individuals and has been shown to introduce similar improvements compared to centre-based programmes across a range of measures12 at lower13 or comparable cost.14,15 Home- and centre-based CR appear to be equally effective in improving clinical and health-related quality of life outcomes.16 These findings would support an extension of home-based CR as an attempt to widen access and participation.17 Furthermore, the shortcomings of today’s situation have led to the design of technical solutions which could facilitate home-based CR and overcome patient safety concerns. But there is still a lack of the possibility to monitor exercise at home. Activity monitoring like the ActivPAL activity monitor is one possibility to monitor activity at home to report time in different positions.18 The GEX system, designed under those requirements, introduces a closed-loop disease management system and is intended for prescription and administration of CR. A feasibility study was performed applying this new system during inpatient phase-II CR and comparing the signals with standard cardiac exercise testing (CPX). Methods Patient characteristics Fifty patients (seven women, mean ± SD age 69 ± 9 years, body mass index 26 ± 3 kg/m2, ejection fraction 58 ± 10%) were included 36 ± 13 days after cardiac procedures (valve replacement, n = 9; MI, n = 29; coronary bypass surgery, n = 25; others, n = 2) under typical cardiac medication. The patients’ detailed characteristics are summarized in Tables 1 and 2. More specifically, atrial fibrillation was present in 12/50 patients, pacing in 2/50 (1×VVI pacing, 1×DDD pacing), and 1/50 had an implantable cardioverter defibrillator (Gem VR). Inclusion and device testing was performed at the Rosenquelle in Aachen, Germany. This is a dedicated CR clinic applying all measures in an inpatient situation; most of the patients were transferred from regional hospitals. A standard CR programme comprised 3 weeks of exercise training, psychological advice, and lifestyle recommendations. Patients who were in an unstable condition, still had an open thorax wound, or were unable to perform exercise testing were not included in the study; all other patients were eligible. Table 1. Patient characteristics Characteristic . Sample population (n = 50) . Male/female 43/7 Age (years) 70 ± 9 BMI (kg/m2) 26 ± 3 PCI 22 (44) CABG 26 (52) Time after intervention (days) 35 ± 13 Ejection fraction 58 ± 11 Peak exercise capacity (watt) 90 ± 22 VO2peak (ml/min/kg) 13.4 ± 4 Characteristic . Sample population (n = 50) . Male/female 43/7 Age (years) 70 ± 9 BMI (kg/m2) 26 ± 3 PCI 22 (44) CABG 26 (52) Time after intervention (days) 35 ± 13 Ejection fraction 58 ± 11 Peak exercise capacity (watt) 90 ± 22 VO2peak (ml/min/kg) 13.4 ± 4 Values are n/n, mean ± SD or n (%) BMI, body mass index; CABG, coronary arterial bypass graft; EF, ejection fraction; PCI, percutaneous coronary intervention. Open in new tab Table 1. Patient characteristics Characteristic . Sample population (n = 50) . Male/female 43/7 Age (years) 70 ± 9 BMI (kg/m2) 26 ± 3 PCI 22 (44) CABG 26 (52) Time after intervention (days) 35 ± 13 Ejection fraction 58 ± 11 Peak exercise capacity (watt) 90 ± 22 VO2peak (ml/min/kg) 13.4 ± 4 Characteristic . Sample population (n = 50) . Male/female 43/7 Age (years) 70 ± 9 BMI (kg/m2) 26 ± 3 PCI 22 (44) CABG 26 (52) Time after intervention (days) 35 ± 13 Ejection fraction 58 ± 11 Peak exercise capacity (watt) 90 ± 22 VO2peak (ml/min/kg) 13.4 ± 4 Values are n/n, mean ± SD or n (%) BMI, body mass index; CABG, coronary arterial bypass graft; EF, ejection fraction; PCI, percutaneous coronary intervention. Open in new tab Table 2. Patient medication Medication class . Sample population (n = 50) . Warfarin 10 (20) Aspirine 46 (92) Clopidogrel 23 (46) β-blockers 45 (90) Amiodaron 9 (18) ACE-inhibitors 26 (52) AT-II-blockers 9 (18) Calcium-channel blockers 22 (44) Aldactone 8 (16) Diuretics 30 (60) Medication class . Sample population (n = 50) . Warfarin 10 (20) Aspirine 46 (92) Clopidogrel 23 (46) β-blockers 45 (90) Amiodaron 9 (18) ACE-inhibitors 26 (52) AT-II-blockers 9 (18) Calcium-channel blockers 22 (44) Aldactone 8 (16) Diuretics 30 (60) Values are n (%) ACE, angiotensin-converting enzyme; AT, angiotensin. Open in new tab Table 2. Patient medication Medication class . Sample population (n = 50) . Warfarin 10 (20) Aspirine 46 (92) Clopidogrel 23 (46) β-blockers 45 (90) Amiodaron 9 (18) ACE-inhibitors 26 (52) AT-II-blockers 9 (18) Calcium-channel blockers 22 (44) Aldactone 8 (16) Diuretics 30 (60) Medication class . Sample population (n = 50) . Warfarin 10 (20) Aspirine 46 (92) Clopidogrel 23 (46) β-blockers 45 (90) Amiodaron 9 (18) ACE-inhibitors 26 (52) AT-II-blockers 9 (18) Calcium-channel blockers 22 (44) Aldactone 8 (16) Diuretics 30 (60) Values are n (%) ACE, angiotensin-converting enzyme; AT, angiotensin. Open in new tab GEX-system components The complete GEX system (Figure 1) is composed of: (a) a portable station, which is used by the patient during exercise; (b) a patient station, providing educational and motivational contents and transferring to the professional station; and (c) a professional system, which will be used by medical professionals to prescribe and adjust exercise-based CR therapies and follow up on patients’ exercise performance and compliance at a later stage. It is designed to record a 1-lead ECG, heart rate (HR), breathing frequency (BF), and exercise-related signals using an innovative sensor during exercise. Data are later uploaded to a central system using a tablet PC, where a training specialist can review the signals. The whole system was exclusively designed for the HeartCycle GEX trial, where all devices and information gathered so far will be implemented in a randomized CR study. Figure 1. Open in new tabDownload slide Components of the guided exercise system. The guided exercise system is a closed-loop disease management system for prescription and monitoring of CR in patients with coronary artery disease. Specific sensors (IMAGE sensor) are held in place by means of a dedicated shirt during exercise. It consist of three components: (a) a portable station used by the patient during exercise; (b) a patient station (tablet PC or laptop); and (c) a professional station that receives the data transferred from the patient station, which specialists can use to review exercise data (including ECG) and prescribe CR plans. The initial study described in this manuscript relates to the first component, the portable station that consists of two main components: a monitoring sensor (IMAGE sensor, Centre Suisse d’Electronique et de Microtechnique (CSEM), Switzerland)) and a mobile PDA device (Figure 2). The sensor (weight 75 g) records vital signs when positioned on the chest under the pectoral muscles using a specialized shirt. More specifically, further exercise-related information can be obtained (e.g. position, thus identifying different types of exercise) by use of an accelerometer and a position sensor included in the sensor. Figure 2. Open in new tabDownload slide Components of the portable station. Left, shirt with sensors; top right, PDA; bottom right, IMAGE sensor. ECG, heart rate, breathing frequency, and movement-related signals are transferred live via Bluetooth to the PDA, where they are processed. Figure 3. Open in new tabDownload slide Data acquired by the IMAGE sensor displayed on a laptop running visualization software. (1) ECG signal sampled at 256 Hz (AI-lead in EASI configuration); instant heart rate (HR) computed from the ECG signal (green value on the right); (2) accelerometer X-axis sampled at 25 Hz; (3) accelerometer Y-axis sampled at 25 Hz and activity classification based on the 3D accelerometer (text on the right); (4) accelerometer Z-axis sampled at 25 Hz; (5) breathing amplitude sampled at 25 Hz; (6) noise from an unused channel in the sensor and breathing frequency (number on right) measured from the breathing amplitude; and (7) movement classification and position correlated from data from the accelerometer. The PDA device runs a specific application used during exercise sessions. It also connects to the sensor using Bluetooth and processes data to provide real-time feedback to patients. On the PDA’s screen, the measured heart rate is displayed and directly compared to the prescribed heart rate (Figure 2). Exercise test and data acquisition All patients underwent two exercise tests wearing the new device. Each test consisted of a standard cardiopulmonary exercise bicycle test (2 minutes rest, 2 minutes warming up without work load, begin 25 watt, increment 20 watt/min). Peak oxygen consumption (VO2peak) and peak work load was calculated based on CPX data. The first test was used to compare data from the CPX system to raw data received from the sensor. ECG, BF, and HR were simultaneously monitored using standard equipment (Care Fusion Viasys Oxygon Metabolic Carts Master Screen CPX) and the IMAGE device. For evaluation and comparison of ECG, original data of the sensor was send via Bluetooth to a notebook where measured signals were plotted (Figure 3). During testing, HR and BF were measured each minute using both the GEX device and standard CPX. ECG tracings from both systems were visually compared in order to evaluate detection and identification of arrhythmias. These arrhythmias were manually classified as supraventricular extra systoles, ventricular extra systoles, atrial fibrillation, and pacing activation. Figure 4. Open in new tabDownload slide Evaluation of patient satisfaction with shirt (a) and sensor (b) in terms of comfort, appearance, usefulness, skin feeling, and handling. Scores 1–5: 1, extremely bad; 5, excellent. The second test was performed to compare HR based on CPX measurements and HR measured by the sensor that was computed and displayed on the PDA. Again, all patients had to undergo a standard CPX, and HR was recorded on the PDA and a 12-lead ECG simultaneously. HR information was not visible to patients and algorithms on exercise prescription were not tested during that phase of the study. Furthermore, a subset of patients (n = 32) was interviewed with respect to comfort and handling. More specifically, five items were evaluated (comfort, appearance, usefulness, skin feeling, handling) and scored from 1 (extremely bad) to 5 (excellent). This was performed for the shirt and sensor separately. Ethical consideration The Regional Ethical Review Board at the University of Aachen approved the study. Principles according to the Helsinki declaration (WMA 2008) were followed. Informed and written consent was obtained from all participants prior to inclusion in the study. Statistical analysis Data are presented as mean ± SD. Differences between groups were analysed using t-test after testing for standard distribution by univariate analysis. p < 0.05 were considered as significant. These statistical analyses were performed using SAS for Windows version 9.2 (SAS Institute, Cary, NC, USA). For the method comparison, a Bland–Altman analysis was performed using MedCalc version 12.4.0 (Belgium). Results All patients reported a perfect fit of the sensor and the shirt at rest and during exercise. A detailed analysis revealing good comfort, appearance, usefulness, skin feeling, and handling is provided in Figure 4. No technical problems were detected during use of the system, and sweating during the test did not influence skin electrode contact or signal quality. In all patients, adequate ECG signals were detectable and measurable using the sensor without technical problems. There were supraventricular extra systoles detected in 11 (22%) patients, ventricular extra systoles in 19 (38%), atrial fibrillation in six (12%) patients, and pacing activation in two (4%) patients during the tests. All occurring arrhythmia could reliably be detected on both the GEX and the CPX systems. Exercise performance was 90 ± 32 watts, with a VO2peak of 13.4 ± 4 ml/min/kg. Figure 5. Open in new tabDownload slide Comparison of heart rate measured by IMAGE sensor and 12-lead ECG at rest and during incremental exercise and recovery. Heart rate and breathing frequency compared between GEX sensor and standard CPX with 2-min rest followed by 2-min warming up and exercise starting with 25 watts and incremental 20 watts/min up to exhaustion, followed by recovery period. A direct comparison between CPX and IMAGE signals showed a comparability of ECG data and measured HR (Figure 5). There was an acceptable comparability of these two methods. A Bland–Altman analysis showed a mean difference of 0.0 beats for heart rate, with limits of agreement of 4.3 and −4.4 beats respectively. Still, some measurements were relevantly different revealing a difference of 5–10 beats per minute (Figure 6a). Additionally, the HR measured with the PDA exhibited a better comparability with HR measurement during exercise on standard 12-lead ECG. A Bland–Altman analysis showed a mean difference of 0.1 beats for heart rate, with limits of agreement of 1.9 and –2.0 beats respectively (Figure 6b). Furthermore, measurement of BF at rest and during exercise was also possible and changes during exercise (Figure 7) were adequately detected. Still, there was only a moderate comparability to standard measurement by CPX as Bland–Altman analysis showed a mean difference of 0.4 respiratory movements for breathing frequency, with limits of agreement of 5.2 and –5.9 movements respectively (Figure 6c). Figure 6. Open in new tabDownload slide Concordance of parameters measured by GEX/PDA and 12-lead ECG during CPX (Bland–Altman plots): (a) heart rate analysis on GEX and CPX; (b) heart rate on PDA and CPX; (c) breathing frequency on GEX and CPX. Solid lines represent the mean difference, whereas the dotted lines represent the upper and lower limits of agreement; BF, breathing frequency; CPX, cardiac exercise testing; GEX, HeartCycle’s guided exercise system; HR, heart rate; PDA, mobile device; SD, standard deviation. Figure 7. Open in new tabDownload slide Comparison of breathing frequency measured by IMAGE sensor and 12-lead ECG at rest and during incremental exercise and recovery. Discussion This portable station consisting of a sensor and mobile PDA was able to accurately report data on HR, BF, and ECG at rest and during exercise, and also arrhythmias were accurately detected. Furthermore, HR computed and displayed by the PDA was even better comparable. These findings imply that this system provides a safe technical solution for facilitating home-based CR. Up to now, ambulatory phase-III CR vary widely regarding mode of delivery, content provided, and supervision. Most importantly, lack of functional monitoring in many programmes may not only amplify safety concerns, it can also limit the ability to rapidly modify clinical management. This was emphasized when a recent UK study in cardiac patients noted a common belief amongst patients that exercise rehabilitation was potentially harmful.19 Furthermore, in 3877 hospital-based CR exercise sessions, a relevant number of patients showed an ‘untoward event’, which was in 56% detected by ECG. More than half of it occured in the first 2 weeks and led to change of patient’s medical management in 58%.19,20 Consequently, home-based CR should ideally permit such functional monitoring. There have been multiple technical attempts at home-based CR, e.g. transtelephonic transmission of baseline or exercise ECG.21–24 As that particular system was restricted to a landline, there was a definite need for more flexible, customized forms of CR that could be conducted at any suitable location. Worringham et al.25 evaluated a system running on a smart phone, enabling walking-based cardiac rehabilitation in 134 cardiac patients unable to undertake hospital-based rehabilitation. The patients’ single-lead ECG, HR, and GPS-based speed and location were transmitted to a secure server for real-time monitoring by a qualified exercise scientist. Using the new system described in this study, cardiovascular and respiratory parameters were readily and stably detected. Evaluation of the sensor compared to standard CPX showed good signal quality allowing monitoring patients at home during training. Thus, this GEX system could be useful for medical professionals involved in CR as they will be able to prescribe and monitor physical exercise training. Stable data for monitoring patients will allow individual prescription and administration of CR. The following steps will be implementation of training algorithms, feedback loops for patients, and monitoring tools for physicians. Such a closed-loop system will generate a safe and feasible way of training at home. This might possibly improve adherence to phase-III rehabilitation. By means of a guidance system, patients will be able to exercise according to training prescription and participate in exercise sessions under the system’s surveillance in a controlled way. Consequently, benefits from exercise sessions could potentially be optimized at the same time that patients’ safety is guaranteed. Simultaneously, the system will provide feedback to patients and delivers educational contents tailored to their specific condition and progress through the rehabilitation process. Users’ awareness on their condition will be measured with use of specific questionnaires in order to reinforce those areas where knowledge should be increased. Finally, transfer of data monitored during endurance exercise sessions to medical professional systems will close the loop. In this way, professionals will be able to follow patient’s sessions and implement any optimization to the rehabilitation plan necessary to improve its benefits. An example of how the professional station for training specialists might be used is the following scenario: HR is measured by the sensor prior to training and blood pressure is measured manually by the patient and typed into the PDA. During the home-training sessions of a CAD patient, the GEX device transfers data, including a scalable ECG wave which is also shown to detect arrhythmia during training, via the internet to the professional system. All data can be later reviewed. Study limitations This study was intended as a feasibility study with regard to the technical set up. Therefore, there is no data on the performance of the future system providing exercise prescriptions and surveillance of patients. Furthermore, as only a small number of patients were involved, this study is a pilot study, which will be followed by a trial evaluating the system during a CR training programme for CAD patients. Furthermore, there are some aspects of the system that need further elaboration, such as automatic arrhythmia detection. So far, arrhythmias can be reliably detected on the ECG derived from the GEX but the system does not automatically report on occurrence or kind. Technically, there is a difference with respect to obtaining BF between CPX and GEX. In case of using a conventional CPX device, BF is calculated by a respiratory sensor with direct measurement of gas exchange, whereas the sensor with GEX calculates it detecting changes in the thoracic circumference during breathing. Overall, the comparability was moderate of these two methods, with some measurements showing a difference of 5–10 breathing movements. Still, BF will not be used in the future algorithms for training prescriptions and, thus, this finding will not interfere with the performance of the system. However, there is some need of a robust detection of BF if this could also be implemented in an algorithm for training prescription. Thus, further technical development is warranted to develop such system. BF could then be used for detection of abnormalities such as arrhythmias: a trend of BR is computed over a certain time window, if BF is increasing abruptly (trend is bigger than a threshold), and BR is not increasing (trend is stable or decreasing), patients are advised to stop exercising as there is a likelihood of arrhythmia in contrast to exercise where BF and HR increase gradually and in parallel. There was no Bland–Altman repeatability analysis performed which would have allowed an even better analysis of the system. This was due to the fact that one test was performed prior to cardiac rehabilitation programme and the second one at the end. Therefore, participants had improved with respect to exercise capacity and the tests were not under the same circumstances. Furthermore, these two tests focused on two different aspects: during the first test comparability of CPX and GEX heart rate depiction was evaluated whereas the second test was carried out to analyse comparability between PDA and CPX measurement. Conclusion GEX’s sensor is able to accurately report data on HR, ECG, and BF during exercise and is able to monitor exercise in CAD patients. There was an acceptable comparability between standard CPX and the GEX measurements of HR, BF and good detection of arrhythmias. The algorithm used to compute and display HR on the PDA showed a good comparability with standard CPX. As this sensor in combination with a dedicated shirt is easy to use and wear, it seems suitable for monitoring home-based CR. Further studies are needed to evaluate the effectiveness of this device to home-based CR. Funding This work was supported by the European Community’s 7th FP project HeartCycle (grant agreement FP7-216695), coordinated by Philips. Conflict of interest Erik Skobel and Christian Knackstedt have received consultant honoraria from Philips, the coordinator of the Project HeartCycle. The other authors declare that there is no conflict of interest. Acknowledgements The data collection would not have been possible without the help of Mrs Sigrid Glöggler (Clinical Trial Center Aachen) and Mrs Cecilia Vera (Polytechnic University of Madrid). Mr Jean-Marc Koller (Centre Suisse d’Electronique et de Microtechnique), and Mr Harald Reiter (Philips technology) are thanked for their technical assistance and support. References 1 Harb BM , Wonisch M , Brandt D et al. . Long-term risk factor management after inpatient rehabilitation by means of a structured post-care programme . 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TI - Evaluation of a newly designed shirt-based ECG and breathing sensor for home-based training as part of cardiac rehabilitation for coronary artery disease
JF - European Journal of Preventive Cardiology
DO - 10.1177/2047487313493227
DA - 2014-11-01
UR - https://www.deepdyve.com/lp/oxford-university-press/evaluation-of-a-newly-designed-shirt-based-ecg-and-breathing-sensor-U7Oj02FUaV
SP - 1332
EP - 1340
VL - 21
IS - 11
DP - DeepDyve
ER -