Functional exercise capacity is markedly impaired in patients with chronic obstructive pulmonary disease (COPD) and is an important predictor of mortality in this patient population (6,7,22). In many long-term diseases, the amount of daily physical activity is an equivalent measure of functional exercise capacity typically assessed using the 6-min walk test (7,11,23). The usefulness of activity monitors to measure daily activity levels is well documented in patients with COPD as it relates to respiratory disease severity (26,28,31,32). Recently, activity monitor–based physical activity levels were shown to be the strongest predictor of all-cause mortality in patients with COPD (30). In addition, low levels of exercise capacity due to pulmonary limitations on mild exertion severely affect the patient’s quality of life (QOL) (3,24,28). To date, however, no direct associations between physical activity levels assessed by an activity monitor and standardized measures of QOL have been made in patients with COPD.
Several standardized QOL questionnaires have been developed to measure health-related QOL in long-term diseases, in particular, for respiratory-specific illnesses such as COPD (5,14,15,20,33). Most of the questionnaires are divided into subscores or “domains” to cover the entire spectrum of disease-related limitations. These domains broadly consist of physical, emotional, social, and mental well-being. Questionnaires are implemented to obtain individualized information about the patient, which cannot be obtained by objective measurements alone. Nevertheless, health-related QOL questionnaires should also correlate with disease severity and objectively measured physiological parameters such as physical activity and ability to cope with everyday tasks (4).
Significant associations between functional exercise capacity assessed with the 6-min walk test and QOL are well established in patients with COPD (21,23,24,28). The purpose of this study was to evaluate the predictive strength of accelerometer-based physical activity on health-related QOL in patients with COPD assessed by two different questionnaires: the Short Form-36 (SF-36) and the Saint Georges Respiratory Questionnaire (SGRQ).
Setting and Study Population
This prospective cohort study assessed daily physical activity and QOL in patients with COPD recruited in the “Predicting Effects and Risk Factors in Exacerbations of Chronic Obstructive Pulmonary Disease” (PROMISE) study (27). In brief, one of the primary end points of the study was to explore predictors that might identify recurrence and poor outcome during and outside exacerbations. One hundred seven patients in the stable condition were consecutively included in this substudy analysis at the Clinic of Pulmonary Medicine of the University Hospital Basel, Switzerland, between November 2008 and March 2010.
To be eligible for the study, patients had to be diagnosed with COPD on the basis of clinical history and physical examination and to meet postbronchodilator spirometric criteria for COPD stages II–IV according to the GOLD guidelines at inclusion. Spirometry was performed by trained lung function technicians according to American Thoracic Guidelines (25). Once included, assessments included detailed medical history, such as smoking status, current medication, duration of disease, comorbidities, physical examination, QOL questionnaires (SF-36 (19) and SGRQ ), Modified Medical Research Council Dyspnea Scale, spirometry, and 8-d accelerometry. Patients were categorized into the according GOLD stages II–IV based on their level of airway obstruction with higher GOLD Stages indicating higher disease severity. Patients enrolled in the PROMISE study had an initial baseline examination, followed by a total of four scheduled visits every 6 months. For this subgroup analysis, QOL questionnaires and assessment of daily activity levels were introduced at the third scheduled visit. All other measures were repeated during each scheduled visit to document change over time. The study was carried out according to the principles of the Declaration of Helsinki and approved by our local ethics committee (Ethic Commission Beider Basel EKBB 295/07). Written informed consent was obtained from all participating patients.
Sample size calculation.
The PROMISE study defines several scientific goals instead of a single primary end point. This approach is believed to optimize information extraction, thereby unrestricting data analysis in the context of a noninterventional, longitudinal study. Power considerations were guided by the requirement to estimate recurrence rates of acute exacerbations with sufficient precision. The analysis of QOL and physical activity monitoring in a subgroup of the study population was a predefined secondary research question of the PROMISE study for which no separate power calculation was done.
This is a health-related QOL questionnaire, which has been used in a variety of chronic medical conditions including COPD. Its validity and reproducibility are well documented (18,19). The questionnaire covers a total of eight domains regarding QOL, including physical function, role limitation caused by physical impairment (role physical), bodily pain, general health, vitality, social functioning, role limitation caused by emotional impairment (role emotional), and mental health. Each domain is scored separately on a scale from 0 to 100. Higher scores indicate better QOL. From these eight domains, two summary subscale scores are computed; the standard physical component score (PCS) and the standard mental component score (MCS). Both scores provide a measure of the overall effect of physical and mental impairment on QOL (4). In this study, only six of the eight domains (physical function, role physical, general health, social functioning, role emotional, and mental health) were included in the overall statistical analysis in addition to the two summary subscale scores PCS and MCS. The domains vitality and bodily pain were omitted from the analysis because they did not add any valuable information regarding the investigated parameters.
This is a respiratory-specific instrument developed for patients with COPD. Here, too, its validity and reproducibility have been thoroughly documented in this patient population (14,33). The questionnaire covers three separate areas from which three scores are computed: symptoms score, which measures respiratory symptoms; activity score, which measures impairment of mobility or physical activity; and impact score, which measures the psychosocial impact of disease. In addition, a summary score is computed. All scores are on a 100-point scale, which is graded inversely to the scales of the SF-36 questionnaire. In the SGRQ, higher scores indicate worse health-related QOL.
Accelerometer activity monitoring.
Patients were handed the accelerometer (Aipermon GmbH, Germany) during their third scheduled visit and instructed to wear it while going about their daily business. The device was attached to the patient’s belt and positioned above the left hip. Patients were encouraged to wear the device continuously during normal waking hours for eight consecutive days. The accelerometer was to be attached on rising in the morning and only to be taken off for showering, bathing, and sleeping. The first and last days, where patients received or returned the device, respectively, were incomplete and thus excluded from the analysis. Therefore, only six consecutive days were included in the statistical analysis (a day started at 00:00 a.m. and ended at 11:59 p.m. the same day). All device settings were preprogrammed for each patient on receiving it, and the device was switched on throughout the measurement period to keep patient handling of the accelerometer to a minimum. The screen display was set to a figure picture to hide analysis. Patients therefore did not receive any direct feedback from the device regarding their daily activity time, energy expenditure, distance, or steps taken. On return, data were copied onto a PC, and their content was viewed via ActiCoach MPAT2Viewer, and Aipermon. Wearing time included minutes per day spent passively (i.e., sitting), actively (i.e., movement, but not walking), walking (0–5 km·h−1 or 0–80 m·min−1), and fast walking (>5 km·h−1 or 81–115 m·min−1). The device measured nonwearing time as “resting mode.” In addition to the activity modes, the device also measured steps, calories, and distance (m). The device used was a three-dimensional accelerometer measuring movement continuously in three axes (x, y, z). Data output is provided in 60-s intervals for each consecutive day (24 h) with the exact time and date for each epoch. Because patients with long-term diseases, in particular, patients with COPD, tend to show intermittent activity spurts instead of continuous movement, it is important to be able to allocate the periods when movement occurred. Activity modes and accelerometer detection accuracy were extensively validated, and detailed results are reported elsewhere (10). In summary, the device was able to accurately detect steps to 99% at walking speeds ranging as low as 20 m·min−1 onward. The thresholds used to classify walking speeds are set forth by the manufacturer based on walking behavior in older individuals with heart failure (10,12), a patient population for which it was originally designed for in a large multicenter study investigating the feasibility of remote telemedical patient monitoring (1,16,17).
Statistical analysis was done using SPSS software (version 17.0; SPSS, Inc.). Data were descriptively analyzed reporting mean ± SD for quantitative measurements and percentages for frequencies. Bivariate correlations of nonparametric variables were investigated using Spearman Rho correlation coefficient (R). Statistical comparisons of measurements across different GOLD stage categories were done using one-way ANOVA. P < 0.05 was considered statistically significant. All variables were standardized to their corresponding z-scores before fitting different multivariate linear regression models. In the first regression model, the SF-36 and SGRQ QOL domains were chosen as the dependent variables, and independent predictors of each domain were analyzed. In a second analysis, accelerometer-based walking intensity in terms of fast walk was considered as the dependent variable, and statistically significant independent predictors were evaluated.
A total of 107 patients (GOLD II, n = 31; GOLD III, n = 51; GOLD IV, n = 25) were included in the study. Demographics of the study population are depicted in Table 1. Mean age of patient population was 65.3 ± 10.8 yr and 70% were men. Most patients were taking a long-acting anticholinergic agent (79%) and a combination of long-acting β2 agonist/ICS (n = 82, 77%).
The overall mean wearing time of the accelerometer was 10.9 ± 2.8 h·d−1 (Table 2). Data were analyzed according to time spent (min·d−1) in each activity mode: passive, active, walk, and fast walk, as well as total distance covered (m·d−1), total steps per day, and daily energy expenditure (kcal·d−1). Total walk was computed from adding the parameters walk + fast walk together; however, it is, in itself, not a separate activity mode. There was no statistical difference in minutes per day spent passive or active; however, there was a statistically significant difference (P < 0.05) in time spent walking, fast walking, total walking time, steps per day, and kilocalories between GOLD II, III, and IV. From all accelerometer-derived activity indices, we focused on walking intensity (fast walk) in our further statistical analysis because this parameter showed the strongest associations with all other investigated health-related outcome measures (11,12).
SF-36 QOL and SGRQ.
Mean scores of each SF-36 and SGRQ were summarized as follows: there was a statistically significant difference in the SF-36 QOL domains across GOLD stages for physical function (GOLD II = 65.1 ± 27.9, GOLD III = 52.9 ± 22.3, GOLD IV = 41.8 ± 22.4, P = 0.003), role physical (GOLD II = 62.2 ± 42.2, GOLD II = 54.1 ± 45.5, GOLD III = 34.0 ± 38.2, P = 0.05), general health (GOLD II = 58.7 ± 23.2, GOLD III = 46.8 ± 25.7, GOLD IV = 42.2 ± 22.1, P = 0.01), and standard PCS (GOLD II = 43.9 ± 9.84, GOLD III = 41.4 ± 9.7, GOLD IV = 36.0 ± 8.1, P = 0.016). Likewise, there was a statistically significant difference in SGRQ domains across GOLD stages for activity score (GOLD II = 46.3 ± 24.2, GOLD III = 55.1 ± 18.7, GOLD IV = 69.2 ± 18.04, P = 0.0001) and total score (GOLD II = 22.1 ± 20.8, GOLD III = 38.8 ± 15.3, GOLD IV = 46.9 ± 16.5, P = 0.019). All significant correlations between physical activity measures and QOL parameters are listed in Table 3.
We ran a stepwise multivariate regression analysis with z-standardized SF-36 QOL domains as the dependent variables and age, sex, %predicted FEV1, depression, smoking status, and fast walk (min·d−1) as covariates. Fast walk was the only significant independent predictor of the domain “physical function” (P = 0.002) and “role physical” (P = 0.034). Age and depression were significant independent predictors of the domain “social functioning” (P = 0.035 and P = 0.002, respectively). Age and fast walk were significant independent predictors of the domain “mental health” (P = 0.006 and P = 0.017, respectively). Percent predicted FEV1 and fast walk were both significant independent predictors of the domains “general health” (P = 0.04 and P = 0.02, respectively) and PCS (P = 0.038 and P = 0.017, respectively). Age was the only significant independent predictor of the MCS (P = 0.04). None of the covariates were independently predictive of the domains “role emotional,” “vitality,” or “bodily pain.” The regression coefficients for the summary scores PCS and MCS are listed in Table 4.
Subsequently, multivariate regression models were computed for SGRQ domains as the dependent variables. Covariates were the same variables as those listed in the previous regression model for SF-36 QOL domains. None of the covariates were independently predictive of the SGRQ “symptom score.” Fast walk and %predicted FEV1 were significant independent predictors of the SGRQ “activity score” (P = 0.001 and P = 0.016, respectively). Fast walk was the only significant independent predictor of SGRQ domains “impact score” (P = 0.022) and “total score” (P = 0.01; Table 5).
To identify possible confounding factors of accelerometer-based daily walking intensity (fast walk) we ran an additional stepwise regression model with fast walk as the dependent variable. Breathlessness was the only significant independent predictor of accelerometer-based walking intensity both in a univariable regression model (P < 0.0001) and in a multivariable approach after adjusting for age, height, smoking status, and %predicted FEV1 (P = 0.02).
This study evaluates accelerometer-based walking intensity as a measure of functional exercise capacity and its association with health-related QOL in patients with COPD. We were able to demonstrate that accelerometer-based daily walking intensity shows significant predictive strength regarding health-related QOL measured using SF-36 and SGRQ. Patients with the lowest accelerometer-based daily walking intensities had the worst outcome in QOL measures and highest disease severity in terms of GOLD stage and Bode index. In this regard, our data add new clinical perspective regarding the use of daily activity monitoring by accelerometry to predict health-related QOL outcome in patients with COPD. This association has previously only been shown with the 6-min walk test (13,14). This study shows that accelerometers not only provide different means for estimating functional exercise capacity, and thus QOL, but also enable continuous measurement during longitudinal exercise training.
QOL in COPD is a major concern to patients and physicians alike because patients experience a spectrum of functional limitations owing to the progressive nature of the respiratory disease. Monitoring daily activity levels by accelerometry provides objective information about the patient’s coping abilities with everyday tasks and challenges, which indirectly reflects back on how they view their QOL. A multicenter study by Troosters et al. (28) showed that it is not only feasible to use activity monitors in patients with COPD, noting high compliance rates (90%), but also highlighted that walking intensity was indicative of disease severity and thus markedly reduced in patients with higher GOLD stages. Further, physical activity measures such as total time spent in daily activities inquired by questionnaire- or pedometer-based step count might overestimate habitual physical activity levels in COPD because patients walk at such slow intensities albeit reaching recommended physical activity levels of 30 min·d−1 on most days (8,28). Movement intensity, however, is crucial to maintain or improve health outcome (8). Pioneer work by Watz et al. (32) first described the reduction in movement intensity in patients with COPD by using an activity monitor. The predictive effect of walking intensity on multiple health-related QOL measures has not been studied thus far. Our study was able to show a strong association between QOL and functional exercise capacity, in particular movement intensity, measured with an activity monitor. In addition, our study participants also showed good compliance with wearing the device, and no problems were reported because patient handling with the device was kept to a minimum.
A recent study by Chang et al. (4) evaluated four different QOL questionnaires including the SF-36 and SGRQ and also found that functional exercise capacity in the form of the 6-min walk distance was significantly associated with health-related QOL outcome measures. Likewise, Curtis et al. (5) evaluated disease-specific associations of physiological parameters and health-related QOL in patients with COPD and found dyspnea, depression, anxiety, age, FEV1, forced vital capacity, educational level, socioeconomic status, and exercise tolerance to be the strongest contributors. A clinical review by Tsiligianni et al. (29) analyzed influencing factors of disease-specific QOL and health status in patients with COPD and concluded the strongest determinants to be dyspnea, depression, anxiety, and exercise tolerance. Our findings add to the findings presented in literature by demonstrating a significant association between functional exercise capacity measured with an activity monitor and health-related QOL. Moreover, it is the walking intensity that matters.
A study by Hernandes et al. (9) looked at the reproducibility of the 6-min walk test in patients with COPD and found significant improvement in distance walked between the first and second tests, emphasizing an average learning effect of 27 m. Therefore, the use of an activity monitor during a longer continuous period might counteract this initial “learning” effect. In previous work (12), we were able to demonstrate in a patient cohort with heart failure that 4 d of continuous activity monitoring is needed to overcome this bias and use walking intensity to accurately discriminate between disease severity.
In conclusion, we could show an independent and significant association between accelerometer-based functional exercise capacity and health-related QOL in patients with COPD. QOL is an important aspect to be integrated into long-tem disease management and the assessment of daily walking intensity using accelerometry can provide additional information about the patient’s functional status and well-being during a certain period. This might prove to be useful for therapeutic interventions and clinical prognosis. These findings need to be confirmed in a longitudinal study design in which changes in daily physical activity can be evaluated and directly associated to changes in QOL and disease outcome.
Schmidt-Trucksäss and Stolz share co-senior authorship. Daiana Stolz was supported by a grant from the Swiss National Foundation (PP00P3_128412/1). Additional funding was granted by the Clinic of Pulmonary Medicine and Respiratory Cell Research, University Hospital Basel, Department of Sports Medicine, Institute of Exercise and Health Sciences, University of Basel, and Swiss Tropical and Public Health Institute, Basel, Switzerland.
There are no conflicts of interest to declare.
The sponsors of this investigator-initiated project had no involvement in design and conduct of the study, collection, management, analysis, and interpretation of the data or in the preparation, review, and approval of the article or decision to submit the article.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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