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Remote telemonitoring holds great potential to augment management of patients with coronary heart disease (CHD) and atrial fibrillation (AF) by enabling regular physiological monitoring during physical activity. Remote physiological monitoring may improve home and community exercise-based cardiac rehabilitation (exCR) programs and could improve assessment of the impact and management of pharmacological interventions for heart rate control in individuals with AF.
Our aim was to evaluate the measurement validity and data transmission reliability of a remote telemonitoring system comprising a wireless multi-parameter physiological sensor, custom mobile app, and middleware platform, among individuals in sinus rhythm and AF.
Participants in sinus rhythm and with AF undertook simulated daily activities, low, moderate, and/or high intensity exercise. Remote monitoring system heart rate and respiratory rate were compared to reference measures (12-lead ECG and indirect calorimeter). Wireless data transmission loss was calculated between the sensor, mobile app, and remote Internet server.
Median heart rate (-0.30 to 1.10 b∙min-1) and respiratory rate (-1.25 to 0.39 br∙min-1) measurement biases were small, yet statistically significant (all
System validity was sufficient for remote monitoring of heart and respiratory rates across a range of exercise intensities. Remote exercise monitoring has potential to augment current exCR and heart rate control management approaches by enabling the provision of individually tailored care to individuals outside traditional clinical environments.
Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, accounting for around one third (approximately 17 million) of deaths globally, with the greatest proportion of deaths attributed to coronary heart disease (CHD) [
Home-based exCR has been introduced to broaden access and participation and confers similar improvements in mortality, cardiac events, and cardiac risk factors compared to center-based CR [
Physiological monitoring has recently been identified as a particularly important direction for the future development of home-based exCR [
A survey of wearable physiological monitoring devices identified several key requirements including measurement validity, data transmission integrity, real-time data processing, ease of use, and scalability [
Most wearable physiological sensors do not support long-range data transmission to remotely located monitoring stations. Therefore, remote monitoring requires physiological sensors to be combined with devices capable of collating and transmitting sensor data to remote monitoring stations for review and action by health care professionals. Smartphones are a preferable intermediary as, in combination with appropriate mobile or Web apps, they provide a ready-to-use mobile platform capable of logging and transmitting data via ubiquitous wireless data networks (eg, Bluetooth, Wi-Fi, 3G, and 4G). Portability, compatibility with several data networks, substantial computational capability, and routine integration of motion and location sensors further enhance the potential utility of smartphones for remote exercise monitoring. Moreover, continued rapid global smartphone market penetration growth [
We have developed a custom mobile app and middleware platform to provide real-time transmission of physiological and clinical data, via smartphones, to remotely located monitoring centers [
This study aimed to evaluate the sensor measurement validity and wireless data transmission reliability of a remote physiological monitoring system comprising the BioHarness, custom app, and middleware platform among individuals in sinus rhythm. Given that AF is the most common sustained cardiac arrhythmia and is a common comorbidity in CHD [
A dual-phase cross-sectional study was conducted to assess system validity among convenience samples of healthy recreationally active individuals in sinus rhythm (ie, systole initiated at the sinoatrial node and proliferated via normal cardiac conduction pathways; Phase One), and individuals with AF (Phase Two). Phase One participants were recruited via contacts and local sport clubs. Phase Two participants were recruited via outpatient cardiology clinics. This dual-phase approach enabled safe assessment of sensor measurement validity across a broad range of exercise intensities. Phase One participants completed constant, intermittent, and incremental intensity exercise at moderate to maximal levels of intensity. Phase Two participants completed constant intensity exercise and simulated daily activities at low to moderate levels of intensity. Phase One was approved by the University of Auckland Human Participants Ethics Committee (2011/7674). Phase Two was approved by the New Zealand Health and Disability Ethics Committee (CEN/11/11058), respectively. All volunteers provided written informed consent. Procedures common to Phases One and Two are outlined below, followed by phase-specific exercise procedures.
The remote physiological monitoring system comprised the BioHarness (version 3 with chest strap;
On arrival at the laboratory, participants underwent baseline measurement of stature and body mass, and familiarization with exercise ergometers. Participants were instrumented with a 12-lead ECG (AT-110, Schiller AG), BioHarness, and indirect calorimeter (Metalyzer, Cortex Biophysik GmBH). Adhesive electrodes were applied at standard ECG sites following recommended skin preparation procedures [
Activation of ECG, BioHarness, and calorimeter data logging followed a standardized procedure to ensure accurate data synchronization. Data were recorded during 180 seconds of seated rest prior to, and throughout exercise. A 60-second transition period was included prior to locomotive exercise to enable treadmill initiation.
Zephyr BioHarness.
Custom mobile app screenshot.
During Phase One, participants completed three discrete bouts of treadmill running during two laboratory-based trials. During Trial One, participants ran on a motorized treadmill (EX200, Powersport) at 0% incline to determine the velocity eliciting 50% heart rate reserve (V50%HRR). Following instrumentation, participants completed an incremental protocol to assess peak oxygen uptake (V̇O2peak), operationally defined as the highest measured V̇O2. Treadmill velocity (V50%HRR) remained constant, and the incline was increased by 1% every 60 seconds until volitional exhaustion. Mean V̇O2 during the final 30 seconds of each workload was plotted as a function of treadmill incline, and inclines eliciting 50%, 66%, 70% and 90% V̇O2peak were derived via linear interpolation. After 30 minutes of rest, participants completed a 30 minute constant intensity treadmill running protocol (C30) at an incline eliciting 66% V̇O2peak. During Trial Two, participants completed a 30-minute intermittent intensity treadmill protocol (I30) comprising three repetitions of a 10-minute exercise block. Each exercise block included five sequential 2-minute stages at inclines eliciting 50%, 70%, 90%, 70%, and 50% V̇O2peak, respectively. Mean levels of exercise intensity were equivalent in the C30 and I30 protocols.
During Phase Two, participants completed three bouts of exercise during a single laboratory-based trial. Participants self-selected light-to-moderate levels of exercise intensity during treadmill and cycle ergometer (Velotron, RacerMate Inc.) familiarization. Following instrumentation, participants undertook 10 minutes of treadmill walking, 10 minutes of cycling, and sequential 3-minute bouts of simulated daily activities (sweeping and vacuuming). Walk, cycle, and daily activity bouts were separated by 5 minutes of seated rest.
Reference heart rate measures were manually calculated from synchronized ECG waveforms as the average rate during the final 10 seconds of each minute. Reference respiratory rate was captured by the calorimeter at 0.10 Hz. BioHarness and calorimeter data were downloaded using the manufacturers’ software (BioHarness Log Downloader v1.0.24 and MetaSoft v3.9.3, respectively) and exported for manual analysis. BioHarness data were down-sampled to match reference measures. Data outside the manufacturers specified measurement ranges were excluded prior to analysis.
Phase One and Two data were analyzed separately following identical procedures using SPSS v20.0.0. Consistent with guidelines for assessing measurement validity in this field [
Spearman’s rank-order correlation coefficients (rho) and two-way random effects intraclass correlation coefficients (ICC) for absolute agreement were calculated to describe relative measurement reliability [
Wireless data transmission reliability was evaluated by determining data loss between the BioHarness, App, and remote monitoring server (Odin). Reference sample sizes were calculated as the product of exercise duration and sensor sampling frequency. These analyses utilized data logged at the BioHarness’ native summary frequency (1 Hz) as the aforementioned down-sampling procedures had potential to conceal intermittent data loss.
Participant characteristics are summarized in
Participant characteristics (Phase One: participants in sinus rhythm; Phase Two: participants with atrial fibrillation).
|
Phase One, mean (SD) | Phase Two, mean (SD) |
Sample size/male | 10/6 | 8/5 |
Age, years | 26.68 (3.26) | 69.68 (9.53) |
Body mass, kg | 71.10 (11.53) | 77.46 (18.81) |
Stature, m | 1.73 (0.06) | 1.69 (0.12) |
Peak oxygen consumption, ml∙kg-1∙min-1 | 50.82 (4.51) | Not assessed |
The BioHarness systematically underestimated heart rate (
The BioHarness systematically overestimated heart rate (
BioHarness measurement error was relatively consistent across the measurement ranges, although a degree of heteroscedasticity was apparent among Phase Two respiratory rate measures (
Biases between BioHarness and reference heart rate (Phase One: participants in sinus rhythm; Phase Two participants with atrial fibrillation)a.
|
Heart rate | ||||
Median b∙min-1 | Bias b∙min-1 | Bias % | |||
Phase One | Rest | REF | 72.00 (18.00) | 0.00 (4.45) | 0.02 (5.55) |
BH | 70.75 (23.38) | ||||
Transition | REF | 108.00 (30.00) | -0.80 (7.20) | -0.65 (7.38) | |
BH | 97.00 (31.18) | ||||
Run | REF | 162.00 (18.00) | -0.30 (4.60) | -0.20 (2.80) | |
BH | 163.50 (16.40) | ||||
Total | REF | 162.00 (24.00) | -0.30 (4.53)b | -0.20 (2.96) | |
BH | 160.70 (13.40) | ||||
Phase Two | Rest | REF | 84.00 (24.00) | 2.10 (4.55) | 2.06 (5.77) |
BH | 89.10 (29.45) | ||||
Transition | REF | 108.00 (6.00) | 1.10 (9.30) | 1.67 (8.78) | |
BH | 91.70 (21.30) | ||||
Walk | REF | 126.00 (40.50) | 0.65 (9.25) | 0.49 (7.82) | |
BH | 130.20 (46.58) | ||||
Cycle | REF | 120.00 (60.00) | 1.90 (11.50) | 1.79 (9.66) | |
BH | 121.80 (72.50) | ||||
Sweep | REF | 108.00 (27.00) | -3.60 (12.00) | -3.75 (9.70) | |
BH | 103.10 (20.05) | ||||
Vacuum | REF | 108.00 (30.00) | 3.20 (13.76) | 3.47 (13.46) | |
BH | 101.30 (33.34) | ||||
Total | REF | 108.00 (48.00) | 1.10 (9.75)c | 1.23 (8.61) | |
BH | 106.55 (51.68) |
aTable reports median (IQR) reference (REF) and BioHarness (BH) heart rates, absolute (b·min-1) and relative (%) biases.
b
c
Biases between BioHarness and reference respiratory rate (Phase One: participants in sinus rhythm; Phase Two participants with atrial fibrillation)a.
|
|
|
Median br∙min-1 | Bias br∙min-1 b | Bias % |
Phase One | Rest | REF | 17.00 (6.75) | -0.28 (4.00)tR | -1.56 (23.42) |
BH | 16.15 (5.44) | ||||
Transition | REF | 19.30 (7.33) | -2.20 (5.72)rR | -12.17 (29.52) | |
BH | 17.65 (6.32) | ||||
Run | REF | 41.70 (12.00) | -1.36 (4.58)rt | -3.30 (10.65) | |
BH | 40.80 (10.09) | ||||
Total | REF | 39.90 (15.30) | -1.25 (4.65)c | -3.33 (12.01) | |
BH | 39.02 (13.40) | ||||
Phase Two | Rest | REF | 18.50 (6.65) | -0.88 (4.30)twcsv | -4.89 (21.77) |
BH | 17.29 (4.80) | ||||
Transition | REF | 19.70 (8.30) | -5.73 (5.97)rwcsv | -28.02 (23.69) | |
BH | 14.34 (4.92) | ||||
Walk | REF | 22.30 (5.85) | 0.81 (6.34)rtsv | 3.12 (30.80) | |
BH | 25.16 (6.35) | ||||
Cycle | REF | 25.00 (7.28) | 0.28 (7.65)rtsv | 1.04 (28.84) | |
BH | 26.69 (6.94) | ||||
Sweep | REF | 22.00 (6.05) | 6.61 (16.35)rtwcv | 27.22 (77.73) | |
BH | 31.03 (11.87) | ||||
Vacuum | REF | 19.65 (9.33) | 9.42 (10.18)rtwcs | 43.89 (67.69) | |
BH | 31.55 (9.72) | ||||
Total | REF | 22.10 (7.18) | 0.39 (7.33)c | 1.56 (31.88) | |
BH | 24.26 (11.02) |
aTable reports median (IQR) reference (REF) and BioHarness (BH) respiratory rates, absolute (br·min-1) and relative (%) biases.
bThe letters r t R= rest, transition, run; statistically significantly different compared to Phase One rest, transition, Run (
cStatistically significantly different compared to reference measures (
Sensor measurement error as a function of mean measurement magnitude (solid reference lines = mean biases, dashed reference lines = 95% limits of agreement).
BioHarness and reference heart rate measures were strongly correlated during both phases (
Relative and absolute reliability of BioHarness heart rate and respiratory rate measuresa.
|
Relative | Absolute | ||||
|
rho | ICC | SEM, min-1 | LoA, min-1 | CV, % | |
Phase One | HR | .92b | .98b | 5.20 | (-21.87, 9.26) | 2.24 |
RR | .87b | .94b | 2.78 | (-13.73, 9.41) | 7.94 | |
Phase Two | HR | .97b | .98b | 4.77 | (-13.39, 23.79) | 4.05 |
RR | .43b | .55b | 4.60 | (-11.58, 18.91) | 16.61 |
aTable reports Spearman’s rank-order correlation coefficient (rho), two-way random effects intraclass correlation coefficient (ICC), standard error of measurement (SEM), non-parametric 95% limits of agreement (LoA), and coefficient of variation (CV) for heart rate (HR) and respiratory rate (RR).
bStatistically significant
Zero biases were observed between BioHarness, App, and Odin measurements, indicating sensor measurement validity was unaffected by wireless data transmission. Phase One BioHarness, App, and Odin data loss were 4.1%, 0.2%, and 21.3%, respectively. Failure to record data throughout two V̇O2peak bouts accounted for all BioHarness data loss. However, these errors did not compromise BioHarness-to-App data transmission. A terminal App crash during one exercise bout accounted for all Phase One App data loss. Outages of the data network hosting the Odin server precluded App-to-Odin data transmission throughout five exercise bouts, and this instability accounted for 15.5% Odin data loss. Unidentified intermittent data capture errors accounted for the remaining Odin data loss (5.9%).
Phase Two BioHarness, App, and Odin data loss were 0.0%, 0.6%, and 1.1%, respectively. Phase Two was unaffected by Odin network stability and data loss occurred as a result of intermittent errors similar to those observed during Phase One.
This study evaluated the sensor measurement and wireless data transmission validity of a remote physiological monitoring system among participants in sinus rhythm and AF. Heart and respiratory rates differed systematically from reference measures across a range of exercise intensities and activities, but the magnitudes of these biases were small. Measurement reliability was generally acceptable, and wireless data capture was excellent when all components of the monitoring system were operational. However, instability of the data network hosting the Odin remote monitoring server resulted in substantial data loss during some exercise bouts.
The small magnitudes of heart rate and respiratory rate measurement biases are unlikely to impair interpretation of physiological stress or workload during remote monitoring. As a caveat, larger biases during simulated sweeping and vacuuming may indicate reduced sensor stability and increased movement artefact during activities requiring substantial upper limb movement. Recent evidence suggests conductive fabric sensors embedded in a textile vest are subject to less movement artefact than traditional adhesive ECG electrodes [
Heart rate and respiratory rate measurement biases were comparable to some, but not all previous evaluations of similar sensors’ measurement validity. Biases were smaller than those reported for a previous model BioHarness during laboratory- and field-based locomotion [
Relative heart rate measurement reliability was excellent across a range of activities and workloads. Correlation coefficients compare favorably with evaluations of previous model BioHarness devices [
Respiratory rate measurement reliability was comparable with evaluations of previous BioHarness models [
Remote physiological monitoring is contingent on reliable data transmission to a remotely located monitoring station. Data capture was generally excellent throughout this experiment; however, several errors were identified. Unresolved data logging errors precluded data storage on the local BioHarness memory during two exercise bouts; however, remote data transmission was unaffected and all data were successfully transmitted to the remote monitoring server during these errors. While local BioHarness data capture was necessary to assess sensor measurement validity, the middleware platform responds to network instability by temporarily caching all data until a network connection is re-established. Thus local BioHarness data capture would not be required in a production-ready remote monitoring system. The institutional network that hosted the Odin server throughout this study was subject to inconsistent power supply and undisclosed maintenance events. Resulting Odin server outages affected five exercise bouts during three Phase One trials. Relocating Odin to a robust host network will resolve this issue and is an immediate priority for future iterations of the monitoring system. After accounting for host network instability, Odin captured 94.7% and 98.9% of data during Phases One and Two, respectively. Iterative development is required to resolve the remaining App and Odin data capture errors; however, data capture reliability was sufficient for real-time remote monitoring given that a stable App-to-Odin connection was confirmed before beginning exercise.
A potential limitation of this study was the small sample size. However, as the unit of analysis was the number of sensor observations, rather than the number of participants, the design had sufficient statistical power to detect clinically significant biases between BioHarness and reference measures of heart rate and respiratory rate.
As with all studies evaluating physiological sensor validity, these results may be confounded by factors influencing the quality of data from the BioHarness and reference sensors. Positional overlap between ECG (V1-V6) and BioHarness electrodes may have impaired BioHarness electrode skin contact, particularly among participants with small chest circumferences requiring ECG electrodes to be closely grouped. Interrupted skin contact could explain the occasional presence of large measurement errors apparent in the relatively wide heart rate LoA.
Similarly, the design of the BioHarness respiratory rate sensor dictated that it was typically located above ECG electrodes V5 and V6. Compression of the respiratory rate sensor against underlying ECG electrodes could impair measurement validity; however, this would be expected to affect both phases and is unlikely to explain the reduced measurement reliability observed during Phase Two.
Remote physiological monitoring has numerous potential applications in both clinical and non-clinical settings. Remote monitoring has been identified as an important future development in home-based exCR [
Remote physiological monitoring also has potential applications outside of exCR and could be used to monitor heart rate control in people with AF. Management of patients with AF involves consideration of either a rhythm control approach (attempt to maintain sinus rhythm) or one of rate control, which is often the preferred approach. Reduction of the rapid heart rate in AF increases the diastolic filling periods and left ventricular stroke volume [
The remote monitoring system evaluated in this experiment has sufficient measurement accuracy for quantifying heart rate and respiratory rate among individuals in sinus rhythm and with AF when gold standard clinical sensors are unavailable. Wireless data transmission reliability was generally excellent. Remote physiological monitoring has potential application as an alternate method for delivering exercise-based cardiac rehabilitation and enhancing the management of heart rate control for individuals with atrial fibrillation.
atrial fibrillation
constant intensity 30-minute exercise protocol
coronary heart disease
cardiac rehabilitation
coefficient of variation
electrocardiogram
exercise-based cardiac rehabilitation
global positioning system
intermittent intensity 30 minute exercise protocol
intraclass correlation coefficient
limits of agreement
standard error of measurement
peak oxygen consumption
This study was supported by a funding grant from the New Zealand Heart Foundation, a not-for-profit non-governmental organization. The Heart Foundation had no role in experimental design, data collection, or manuscript preparation.
All authors contributed to the design, data collection, analysis and interpretation, drafting of the article, and final approval of the manuscript.
None declared.