Physical activity level and its clinical correlates in chronic obstructive pulmonary disease: a cross-sectional study

Chronic obstructive pulmonary disease (COPD) is characterized by non-reversible airflow limitation and systemic effects [1]. In addition to respiratory impairments reduced exercise capacity, systemic inflammation, loss of muscle mass and reduced physical activity are common [2‐6].

The impaired exercise capacity can be improved through pulmonary rehabilitation, but whether this increased functional reserve will lead to subjects utilizing their capacity in everyday life is still uncertain [7, 8]. Both decreased exercise capacity and decreased physical activity are associated with higher mortality from COPD [9‐11], indicating that further exploration of the factors involved in this complex interrelationship is warranted. If mediating factors are identified that contribute to variations in activity, they would be possible targets for interventions [12, 13].

We hypothesized that using a combination of objectively measured clinical characteristics would be useful for assessing physical activity levels in subjects with COPD. Therefore, the aim of this study was to explore the clinical characteristics of physical activity in patients with moderate and severe COPD, with special emphasis on variables that are amendable through rehabilitation efforts.

The analyses were performed using IBM SPSS statistics version 20. Descriptive statistics are presented as the means (standard deviations) for continuous and normally distributed variables or as the medians (interquartile ranges) for categorical variables (or non-normally distributed variables). Skewed variables were log-transformed prior to analysis. Using PAL as a grouping variable, the sample was split into tertiles to explore whether a linear trend would be evident in the independent variables. Pearson product–moment correlation coefficient, r and Spearman rank correlation, r _s were used to assess associations. The Kruskal-Wallis test was used to test for differences in PALs between GOLD grades with the Mann–Whitney U test used for post hoc pairwise comparisons and gender differences.

Hierarchical multiple linear regression analysis was used to investigate the explanatory values of age, gender, FEV₁% predicted, self-selected walk speed, quadriceps strength, FFMI and the log of CRP (independent variables) on PAL (dependent variable). In the case of missing values for independent variables, the multiple imputation technique was used to complete the data set. No variable had missing values in > 6% of cases. The significance level was set at p < .05. Due to the exploratory nature of the study no sample size calculation was performed.

Data from one subject’s activity monitoring were faulty and therefore excluded, leaving a total of 72 subjects (44 female) for analysis. The subjects’ mean age was 65 ± 7 years, and FEV₁% predicted was 43 ± 16 (Table 1). Airway obstruction according to GOLD was grade 1 (n = 1), grade 2 (n = 18), grade 3 (n = 37) or grade 4 (n = 16).

The mean PAL was 1.47 ± 0.19. There was a negative correlation between PALs and GOLD grades, r_S = -0.447 (p < 0.001), indicating less physical activity with worsening airway obstruction. The differences in PAL were significant between GOLD grades 2 and 3 (p < 0.001) but not between GOLD grades 3 and 4 (p = 0.358) (Figure 1). Sixty-six subjects (92%) were very inactive or sedentary, four were active or moderately active and two subjects were vigorously active. Subjects who were more physically active were characterized by better pulmonary function; higher Body Mass Index, FFMI, walking speed and muscle strength; and less dyspnea (Table 1). Forty-nine percent of the sample (27 female, 8 male) was classified as FFM-depleted. Forty-two percent (19 female, 11 male) did not reach normal walking speed. The maximal quadriceps strength was 31.2 ± 11.2 kg, corresponding to 82 ± 25% of predicted values.

The new information added from this study is that variations in physical activity can be explained only to a degree by lung function but by adding information from simple tests of walking speed and lower extremity strength, substantial improvements in the explanation of the variability in PAL is possible.

In this study, FEV₁ explained approximately 20% of the variability in PAL. Previous studies have shown moderate associations between higher GOLD grades and both lower PALs [23] and lower numbers of steps/day [24] and with not performing the recommended amount of physical activity [25]. Walker et al. [26] reported that changes in both overall activity and lower limb activity were related to FEV₁ and that there were higher levels of leg activity among subjects with better FEV₁. The mechanism behind this association could be dynamic hyperinflation [27]. The altered mechanical properties of the lungs and airways in COPD reduce expiratory flow and limit the time for expiration, especially during physical exertion, leading to increased end-expiratory lung volume [28]. If increased discomfort is apparent in physical activities, subjects are likely to limit their activity to avoid negative experiences of dyspnea, thereby contributing to a worsening of the condition from the disuse of muscles [29]. This model is supported by our data, given that both dyspnea and lung function worsened as PAL decreased. It is reported that walking distance from a 6-minute walk test (6MWT) is a stronger determinant of daily activity than FEV₁, self-efficacy and health-status [30].

A novel finding in the present study was that the explanatory value of self-selected walking speed towards PAL was of the same magnitude as the explanatory value of FEV₁. In this study, walking was measured as walking speed over 30 meters. The 30-meter walk test has been shown to correlate well with the 6MWT [19]. Pitta et al. showed that patients with COPD spent less time walking in daily life than did age-matched controls and also walked at lower walking speeds [31]. Furthermore, the authors concluded that the 6-minute walk distance (6MWD) was the best surrogate marker for inactivity in COPD. Our results are therefore in accordance with the results of previous studies confirming the value of walking performance with regard to daily activity [31] However, to our knowledge, the finding of walking speed as an independent predictor of daily physical activity has been reported in only one previous study [32]. DePew et al. measured walking performance using the 4-meter gait speed test and the 6MWT in a sample of chronic lung disease subjects. In agreement with our present results, the association between walking speed and PAL was modest (r 0.32), but DePew et al. did not present a multivariate prediction model for PAL, so further comparison is not possible. In the present study, 42% of the participants had a lower than normal walking speed (< 1.0 m/s). A slow walking phenotype has been described in COPD, characterized by both worse exercise capacity and health status [33], but the impact of slow walking on physical activity levels has not been reported previously. The mechanism behind the reduction in speed with COPD is likely quite complex, but a feasible explanation is that it reflects a global sign of the constraints imposed by the airway obstruction and dyspnea. Lower speed likely results in less oxygen consumption in the muscles and thereby less ventilatory stress and dyspnea. In this scenario, muscular dysfunction must also be considered because it could impact both walking performance and physical activity.

The proportion of variability of PAL explained by quadriceps strength in the present study was almost of the same magnitude as the variability explained by lung function and walking speed. An association between quadriceps strength and daily activity was also reported in the study by Pitta et al. [31]. However, the maximal muscle strength in our sample was well preserved at the group level (82% predicted) compared with the reference values [21], which is somewhat contradictory to the results of the Pitta study. However, it is important to consider that muscular performance might be affected despite preserved maximal strength [34, 35]. Coronell et al. found that the endurance of the quadriceps muscles was more impaired than was maximal strength and that impairment was present even in subjects with mild to moderate disease without sedentarism. Borst et al. [35] showed that reduced oxidative phenotype in COPD was related to reduced quadriceps endurance, but not with total activity or its intensity. Lower PALs were not associated with lower quadriceps force in newly diagnosed subjects, as shown by Van Reemortel et al. [36].

Nearly half of the participants were FFM depleted with the least physically active subjects being most affected. FFM depletion was more prevalent among women than men (61% vs. 29%), with a substantially larger gender discrepancy than was described in a Dutch outpatient sample [5]. In the present study, the explanatory value of FFMI towards PAL was relatively low. The reasons may be that we included quadriceps strength in our model and that our subjects had well-preserved quadriceps strength. In our study, there was a relatively strong association between quadriceps strength and FFMI (r = .52), which is in accordance with the findings of a previous study [5].

Systemic inflammation has previously been linked to muscle dysfunction in COPD [37] but was not confirmed in our study. Perhaps the large proportion of subjects displaying low FFMI contributed to this finding. Eagan et al. [38] reported that patients with low FFMI did not have increased levels of CRP or soluble TNF-receptor 1; instead, they found the opposite to be true.

We conclude that apart from lung function, walking speed and muscle strength are important correlates of physical activity. Further explorations of the longitudinal effects of the factors characterizing the most inactive subjects are warranted.

MA: contributed to study design, performed the statistical analysis, contributed to the interpretation of data and drafted the manuscript. FS contributed to study design, to the interpretation and acquisition of data and to the critical review of the manuscript. AMG contributed to the acquisition of data and to the critical review of the manuscript. US contributed to study design, to the acquisition of data and to the critical review of the manuscript. CJ participated in the statistical analysis, the interpretation of data and to the critical review of the manuscript. ME contributed to study design, to the interpretation of data and to the critical review of the manuscript. All authors have read and approved the final version of the manuscript.