Physical activity is a new and challenging outcome in chronic diseases, including COPD. It is a direct measurement of an essential aspect of the patient's life: the quantity and intensity of the patient's engagement in daily life. Most patients report that the inability to carry out activities is one of the most disabling aspects of their disease (Citation[1]). Clearly physical activity is potentially a key aspect to target as an outcome measure for our interventions. Improving physical activity allows the patient to better participate in daily life and –perhaps more importantly- may spin-off long-term health benefits (Citation[2]).
THE CHALLENGES OF ACTIVITY MONITORING
Although seemingly simple, the methodology enabling physical activity monitoring and the interpretation of the results obtained is still in its infancy. Technology promises to capture physical activity of patients, but the accuracy of the results obtained needs to be challenged and confirmed in the COPD population. Good ‘activity’ monitors may actually fail to accurately capture the inactive life style of patients with COPD. A second challenge is the interpretation of the results obtained with activity monitors. Physical activity patterns vary from day to day due to the patient's health, or external factors such as demands or the weather (Citation[3]). This challenges the interpretation of change in physical activity. Many questions remain in this young field. Examples are: how many days should we measure patients, how many hours per day; what are meaningful and sensitive outcomes; what is the impact of artifacts –such as movement induced by driving a car- on the measurements.
In this issue of the Journal, Hecht and colleagues share their experience with a tri-axial accelerometer activity monitor that has also been used by others (Citation[4], Citation[5]). Papers like Hecht's should be welcomed as they may be of help to investigators currently designing studies. Hecht critically analyzed the signals obtained with their activity monitor and proposed an algorithm to fine-tune the analysis.
The authors found that several days of assessment are needed to have a more accurate estimate of physical activity. With six to seven days of assessment the physical activity level of a longer period was estimated with an error range averaging 10%. Others have also found that several days of assessment are needed (Citation[6], Citation[7]). This monitoring duration, however, only takes the short term variability into account. In long-term studies, another source of variability may be seasonal climate changes, hours of daylight and weather (Citation[3], Citation[8]). Investigators need to be aware of this when designing studies as more patients may be needed to adequately power studies. Alternatively, serial measurements over the whole study period can be made, necessitating more complex statistics such as time series analysis.
A second point made by Hecht et al., not frequently reported in studies, is the lack of compliance with wearing the monitor. Compliance with study procedures, including the wearing of a body sensor is crucial to the success of these trials. Rather than blaming patients for not wearing the monitor, investigators have to realize that some activity monitors may not be very user friendly and acceptable to patients (Citation[9]). This is an aspect that needs to be thoroughly considered by those developing these tools. In the study of Hecht, the average time that the monitor was worn was only 60–70% during the daytime hours. In order to have data that can be fully trusted, further research needs to focus on whether non-compliance is completely at random or whether patients prefer not to wear the monitors on days that are more or less active.
An important issue requiring attention is the difficulty Hecht and colleagues report in the interpretation of data obtained during driving a car. Correction of data obtained in four subjects who accurately reported their car driving habits revealed a significant reduction in the estimated activity level. Since driving a car has been reported as a very important aspect of a patient's activities (Citation[10]) and the present study clearly shows that patients are –on the average- very unreliable in reporting when they are driving, researchers should likely choose an activity monitor of which the outcomes are less influenced by car rides. The present study surely suggests that new activity monitors should be formally validated in this situation.
MOVING THE FIELD FORWARD
Although the study of Hecht et al. points at the difficulties of using activity monitoring as an outcome for clinical trials, we should be encouraged by its results. Monitoring of physical activity gives insight in a fascinating aspect of the patient's function. Physical activity is likely to become a key outcome measure in the future as it sits on the intersection of physiology and health related quality of life. It also predicts mortality and is associated with productivity. Whereas formal exercise tests may nowadays give a clear idea in the capacity of patients to perform, activity monitors may be promising to give insight in the level of engagement in daily life. When used for research purposes, the paper by Hecht correctly alerts us to be careful in the choice of the activity monitor, to ensure sufficient number of days of observation and to avoid using the time patients do not wear the device in the analysis.
Clearly, activity monitoring does not capture all aspects related to physical activity that are relevant to patients. For example, symptoms, perception of the intensity of activities and the distress experienced by not being able to carry out activities obviously need to be reported by patients and can not be captured with activity monitors. Comprehensive assessment of all dimensions of physical activity may ultimately require a combination of simultaneous activity monitoring and patient report. Such a combined end-point, subject to further study, may be more promising than each of its separate components.
Funding: FWO KAN 1.5.136.06 and FWO G.0386.06.05N
REFERENCES
- Miravitlles M, Anzueto A, Legnani D, et al. Patient's perception of exacerbations of COPD–the PERCEIVE study. Respir Med 2007; 101(3)453–460
- Garcia-Aymerich J, Lange P, Benet M, et al. Regular physical activity reduces hospital admission and mortality in chronic obstructive pulmonary disease: a population-based cohort study. Thorax 2006; 61(9)772–778
- Hamilton S L, Clemes S A, Griffiths P L. UK adults exhibit higher step counts in summer compared to winter months. Ann Hum Biol 2008; 35(2)154–169
- Nguyen H Q, Steele B, Benditt J O. Use of accelerometers to characterize physical activity patterns with COPD exacerbations. Int J Chron Obstruct Pulmon Dis 2006; 1(4)455–460
- Steele B G, Belza B, Cain K C, et al. A randomized clinical trial of an activity and exercise adherence intervention in chronic pulmonary disease. Arch Phys Med Rehabil 2008; 89(3)404–412
- Pitta F, Troosters T, Spruit M A, et al. Characteristics of Physical Activities in Daily Life in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 2005; 171(9)972–977
- Watz H, Waschki B, Meyer T, et al. Physical activity in patients with chronic obstructive pulmonary disease. Eur Respir J 2008, E-pub ahead of print Nov 2008
- Togo F, Watanabe E, Park H, et al. Meteorology and the physical activity of the elderly: the Nakanojo Study. Int J Biometeorol 2005; 50(2)83–89
- Fensli R, Pedersen P E, Gundersen T, et al. Sensor acceptance model - measuring patient acceptance of wearable sensors. Methods Inf Med 2008; 47(1)89–95
- Williams V, Bruton A, Ellis-Hill C, et al. What really matters to patients living with chronic obstructive pulmonary disease? An exploratory study. Chron Respir Dis 2007; 4(2)77–85