Pulmonary rehabilitation is widely recognized in the management of respiratory diseases. A key component to successful pulmonary rehabilitation is adherence to the recommended exercise training. The purpose of the present protocol is to describe how continuous data tracking technology can be used to precisely measure adherence to a prescribed aerobic training intensity.
Pulmonary rehabilitation (PR) is an important component in the management of respiratory diseases. The effectiveness of PR is dependent upon adherence to exercise training recommendations. The study of exercise adherence is thus a key step towards the optimization of PR programs. To date, mostly indirect measures, such as rates of participation, completion, and attendance, have been used to determine adherence to PR. The purpose of the present protocol is to describe how continuous data tracking technology can be used to measure adherence to a prescribed aerobic training intensity on a second-by-second basis.
In our investigations, adherence has been defined as the percent time spent within a specified target heart rate range. As such, using a combination of hardware and software, heart rate is measured, tracked, and recorded during cycling second-by-second for each participant, for each exercise session. Using statistical software, the data is subsequently extracted and analyzed. The same protocol can be applied to determine adherence to other measures of exercise intensity, such as time spent at a specified wattage, level, or speed on the cycle ergometer. Furthermore, the hardware and software is also available to measure adherence to other modes of training, such as the treadmill, elliptical, stepper, and arm ergometer. The present protocol, therefore, has a vast applicability to directly measure adherence to aerobic exercise.
Pulmonary rehabilitation (PR) combines exercise training, patient education and psychosocial support, and is widely recognized as a cornerstone in the management of pulmonary disease1-5. The goals of PR are to reduce symptoms, optimize functional status, improve health-related quality of life, and reduce health care costs4,5. In a meta-analysis of 31 randomized controlled trials in chronic obstructive pulmonary disease (COPD), PR was shown to significantly improve exercise capacity, reduce dyspnea and fatigue, improve emotional function and enhance patients’ sense of control over their condition6. Furthermore, evidence documents its effectiveness in reducing respiratory exacerbations7 and days spent in hospital8-13. Exercise training is considered the key to successful PR since it is responsible for much of the benefits associated with this intervention3-5. However, a major issue for several patients is adhering to the recommended amount or level of exercise. Nonadherence to recommended treatment may result in the failure of therapeutic interventions as well as inefficient use of health resources14.
According to the World Health Organization, the term ''adherence'' refers to the extent to which a person’s behavior coincides with recommendations given by a health care professional15. To date, adherence to exercise training in rehabilitation settings has been largely assessed as either the rate of participation (i.e registration to the program), the rate of completion (i.e. finishing the program), or the rate of attendance (i.e. number of exercise sessions attended)16-18. At present, no "gold standard" exists for measuring adherence15 and current methods do not allow for great precision. Furthermore, depending on the selected method, rates of adherence to PR have shown large variability16-19. For example, Hogg et al.16 measured adherence in COPD patients as the ratio between those who completed the program to those referred and found a low adherence of approximately 40%. However, other PR studies that have used attendance rates demonstrated, on average, a 90% adherence10,20,21. The lack of homogeneity in calculating adherence makes it difficult to compare results between studies. Another concern is the lack of precision with the existing calculation methods; attendance to an exercise training session does not guarantee adherence to the prescribed intensity. This gap in information led us to investigate how adherence could be calculated in a more precise way.
Recent advances in fitness equipment technology have allowed for continuous data tracking, which can be used to monitor adherence to a prescribed aerobic training intensity during individual exercise sessions in a PR context. More specifically, data tracking hardware and software permits for second-by-second recording of duration, speed, level, wattage, pace, heart rate, distance, calorie consumption, VO2, METS, and calories, and provides averages of all variables with the exception of level and VO2. The main advantage of this technology is the ability to record continuous detailed measures, which allows for the precise calculation of adherence to prescribed exercise versus previously reported general attendance or completion rates. This procedure can be of value for any study examining the impact of one or several aerobic exercise training programs. Using this technology, patient adherence to a prescribed intensity can be assessed by the percent time spent at a specified wattage, level, speed, or heart rate during the training phase of each session. For our investigations, adherence to an exercise training protocol has been defined as the percent time spent within a specified target heart rate range. Since heart rate response at a given submaximal workload decreases as cardiorespiratory fitness increases, this approach ensures that patients remain at the same relative (versus absolute) training intensity throughout the program. The present protocol describes in detail how continuous data tracking technology can be used to precisely measure adherence to a prescribed target heart rate range.
Once data is collected, a single file per subject per session of raw data is obtained. Using statistical software, all sessions per subject are combined into a single file. Subsequently, the target intensity must be calculated for each subject. The adherence rate to that target intensity can then be calculated per session per subject, for each session for all subjects combined, or per group.
1. Data Collection (carried out by personnel supervising the training session)
2. Data Extraction
CardioMemory software does not allow for the distinction of various exercise-training phases. As such, the data obtained must be exported to a statistical software in order to eliminate the phases that are not of interest (e.g. warm-up and cool-down), merge the data files, and compare achieved against target intensity.
3. Data Merging – Single Participant
4. Data Merging – Grouping Participants
5. Identification of Target Intensity (e.g. THR Range)
Old Value | New Value | Old–>New: | |
Range: -5 through 5 | 1 | Add | -5 thru 5 –> 1 |
Range, LOWEST through value: -5 | 0 | Lowest thru -5 –> 0 | |
Range, value through HIGHEST: 5 | 0 | 5 thru Highest –> 0 | |
System-missing | System-missing | SYSMIS–> SYSMIS |
→ Devam et → OK. See Figure 12.
6. Calculation of Percent Adherence
When the protocol is performed correctly, an adherence rate is obtained for each subject for each session (Figure 13), for each subject for all sessions (Figure 14), and for each session for all subjects combined. An estimate of the time required to complete the above protocol for a single session of one subject is approximately 5 min. Results for adherence can range from 0-100%. Using this information, additional analyses can be performed to determine differences between subjects (i.e. sex differences, disease severity, etc.),to identify changes over time, and to reveal patterns in adherence. Moreover, the comparison of adherence between groups can be performed; for example, different exercise training programs can be compared. Finally, through further investigation, causes of nonadherence can be identified at specific time points during PR.
Figure 1. Heart rate transmitter placement. Click here to view larger image.
Figure 2. Sample of data collected using data tracking software. Click here to view larger image.
Figure 3. Sample of data tracking software output. Click here to view larger image.
Figure 4. Sample database illustrating a sample of statistical software database. Click here to view larger image.
Figure 5. Sample database illustrating the eliminated warm-up phase. Click here to view larger image.
Figure 6. Sample database illustrating the eliminated cool-down phase. Click here to view larger image.
Figure 7. Sample database illustrating a column added for session number. Click here to view larger image.
Figure 8. Sample database illustrating the merged sessions for a single participant. Click here to view larger image.
Figure 9. Sample database illustrating a column added for subject identification number. Click here to view larger image.
Figure 10. Sample database illustrating a column added for target heart rate. Click here to view larger image.
Figure 11. Sample database illustrating the merged participants' files. Click here to view larger image.
Figure 12. Sample database illustrating the recoded heart rate variables. Click here to view larger image.
Figure 13. Sample database illustrating adherence as a percentage of time spent within the target heart rate range for each subject for each session (horizontal red line highlights the change in adherence between sessions for the same subject). Click here to view larger image.
Figure 14. Sample database illustrating adherence for the percentage of time spent within the target heart rate range for each subject for all sessions (horizontal red line highlights the difference between subjects). Click here to view larger image.
Continuous data tracking technology enables for a very precise measurement of exercise adherence. This procedure can be easily adapted to other definitions of adherence by replacing target heart rate range with target wattage, level, speed, or MET level. In the present example, the warm-up and cool-down phases were eliminated to isolate the exercise phase because of our specific research objective. Should the warm-up and cool-down phases be of interest to other researchers, step 2.3 ("Eliminate the nontraining phases") can be eliminated from the protocol. Furthermore, the hardware and software is also available to measure adherence to other modes of training, such as the treadmill, elliptical, stepper, and arm ergometer.
When following the above protocol, certain simple steps are critical. First, the CardioMemory software must be started before the exercise equipment (e.g. cycle ergometer) for exercise data to be tracked and subsequently recorded. Should data be lost at this initial step, the data extraction protocol will need to be adjusted accordingly. Secondly, sources of interference must be minimized to reduce the risk of crosstalk and/or lost data. The heart rate monitors communicate wirelessly with the equipment and software. Thus, interference is especially detrimental if using target heart rate to calculate adherence. Finally, it is imperative to select statistical software for the database that has the capacity to permit for large quantities of data. For example, in a study with 10 participants completing 36 sessions at 40 min each, 864,000 rows of data points will be generated. Excel 2007 and later versions have the capacity to contain 1,048,576 rows in a worksheet23, whereas SAS24 and SPSS25 have no limit for the number of rows. Depending on the total number of data points expected for a given study, the software needs to be selected accordingly.
Despite the notable advantages of this technology, two main limitations exist. The first is data loss, which can result from equipment and/or software failure. As mentioned above, data loss can be due to electrical interference with wireless devices (i.e. cell phones or Wi-Fi), and more specifically interference with the wireless data transmission of heart rate. However, at times, data loss can also be due to unidentifiable causes. A second limitation is that the software does not provide the option of marking or splitting the exercise protocol systematically in order to differentiate/identify different phases. If this option were available, the extraction of the exercise phase of interest could be performed directly in the software, which would limit steps in the adherence calculation protocol. As well, the option of placing markers would be practical for the study of adherence to interval or intermittent training protocols as it would allow for the differentiation of the different phases (e.g. low versus high intensity).
For future perspectives, the use of continuous data tracking technology to precisely quantify adherence will enable researchers to investigate patterns of exercise response to different interventions, identify determinants of adherence, and characterize good and poor adherers. Ultimately, a better understanding of exercise adherence will allow for the optimization of exercise rehabilitation programs.
The authors have nothing to disclose.
Canadian Lung Association – Canadian Respiratory Health Professionals; Fonds de recherche du Québec – Santé (FRQS)
Bike Excite Med 700 | Technogym – www.technogym.com | SCIFIT (http://scifit.com/) | |
CardioMemory software | Technogym – www.technogym.com | Version 1.0 | SCIFIT (http://scifit.com/) |
Polar heart rate monitor | Polar – www.polarca.com | T31 coded Transmitter | |
SPSS Statistical Software | SPSS Inc. – www.spss.com/ | Version 16.0 | SAS/STAT software (http://www.sas.com/) |