https://orcid.org/0000-0001-9603-320X
Abstract
The relationship between lung function and performance in some functional tests, as the six-minute walk test (6MWT) and Glittre-ADL test (TGlittre) are still discrepant in patients with chronic obstructive pulmonary disease (COPD). This study aimed to verify which test better correlates and is better explained by the pulmonary function, and which test better discriminates patients regarding the severity of the disease. Seventy-four patients with moderate to very severe COPD (54 men; 66 ± 9 years; FEV1: 37.2 ± 14.3%pred) were included. Spirometry, 6MWT and TGlittre were performed. The results showed weak to moderate correlation between pulmonary function variables and 6MWT (0.36 ≤ r ≤ 0.45) and TGlittre (−0.44 ≤ r ≤ −0.53). In patients with performance of ≤400 m in the 6MWT, a strong correlation was observed between TGlittre with FEV1 (%pred) (r = −0.82; p < .001). The pulmonary function variable that better predict the functional tests performance was FEV1 (R2 = 0.17). Both functional tests were able to discriminate patients with COPD GOLD 4 from the other classifications. When compared to GOLD 2 patients, GOLD 4 patients presented higher time spent on TGlittre (p < .001). When compared to GOLD 3 patients, GOLD 4 patients had higher TGlittre (p = .001). No statistical differences were found in the 6MWT between GOLD 3 and 4, as well as between GOLD 2 and 3. In conclusion, the pulmonary function presents stronger correlations and better explain the variability of TGlittre than of the 6MWT, especially in patients with greater functional impairment. The TGlittre seems to better discriminate patients with COPD regarding the severity of lung function.
Introduction
Patients with chronic obstructive pulmonary disease (COPD) often present impaired functional status, which is associated with poorer quality of life [Citation1], higher rates of exacerbations [Citation2], hospital admissions and higher risk of death [Citation3]. Therefore, the assessment of functional status is relevant to establish more effective intervention strategies [Citation4]. Field tests, such as the six-minute walk test (6MWT) [Citation5] and the Glittre-ADL test (TGlittre) [Citation6] can be used for this purpose. Although they correlate (r= −0.58 to −0.90, p < .05) [Citation6–10], these tests have important differences: while 6MWT involves only walking activity, TGlittre encompasses a circuit of multiple tasks, which includes sitting and getting up from a chair, walking, going up and down steps and moving objects on the shelves [Citation6].
In the validation study of TGlittre, Skumlien et al. [Citation6], demonstrated that for those patients who walked larger distances in the 6MWT, the correlation between TGlittre and 6MWT seemed to be stronger. On the other hand, for those patients with distance covered in the 6MWT approximately <400 m, there was losing of linearity on the correlation between the two tests. The authors attributed the high variability in the time spent in the TGlittre observed in patients with a low exercise capacity to a possible coordination and balance deficits, in addition to the dynamic hyperinflation, as a consequence of the joint activation of the lower and upper limbs and trunk [Citation6]. A recent study by our group found that the tasks performed in front of the TGlittre shelves are the ones that generate the greatest ventilatory burden for patients with COPD [Citation11]. It is known that the activity of postural muscles is altered when there is an increase in ventilatory demand, which may be related to a competition between the muscle contribution to the functions of postural control and of ventilation [Citation12]. Thus, the execution of simultaneous squat tasks and use of unsupported upper limbs, which require balance and trunk activation, may cause greater dyspnea than the walk for patients with worse lung function and with low ventilatory reserve [Citation13]. So, it is possible that the performance of patients with COPD in TGlittre will be more impacted by the decline in lung function than the performance in single-task tests, such as the 6MWT.
The relationship between lung function and performance in 6MWT and TGlittre are still discrepant. Some studies have demonstrated weak to moderate correlations between 6MWT and forced expiratory volume in the first second (FEV1; r = 0.22 to 0.57; p < .05) [Citation6, Citation14–18], a moderate correlation between TGlittre and FEV1 (r = −0.61; p < .05) [Citation6], or did not find any correlation [Citation19]. Moreover, performance in 6MWT differs between the GOLD spirometric classification (GOLD 2–4) [Citation20,Citation21], which has not been demonstrated for TGlittre so far [Citation22,Citation23]. However, an association of the performance in these two tests with lung function in the same sample was only investigated in one study [Citation6], which demonstrated a stronger correlation of FEV1 with TGlittre (r = −0.66; p < .01) than with 6MWT (r = 0.44; p < .01). However, the study by Skumlien et al. [Citation6] was limited to demonstrate this and other correlations with lung function variables only for the TGlittre validation. The present study extensively explore these associations and the comparisons of functional test performance across the GOLD spirometric classification as primary aim.
Thus, the objectives were: to verify which test (6MWT or TGlittre): (1) better correlates and is better explained by the pulmonary function of patients with COPD and (2) better discriminates patients with COPD regarding the severity of the disease.
Methods
The study included patients with COPD referred to the Center for Assistance, Teaching and Research in Pulmonary Rehabilitation (NuReab) of the Santa Catarina State University (UDESC). This was a cross-sectional study, approved by the Human Research Ethics Committees of UDESC, Florianópolis (SC), Brazil (CAAE: 80831117.5.0000.0118). Patients with clinical and spirometric diagnosis of COPD [Citation24] were included; age between 40 and 80 years; and clinical stability in the last month before the beginning of the protocol. The study excluded patients who presented: associated disabling diseases; active smoking or smoking cessation for less than six months; participation in pulmonary rehabilitation programs for less than six months; hospitalization in the 12 weeks prior to the beginning of the protocol; exacerbation during the protocol; inability to carry out any assessment of the study; and the ones who did not use the medication according to the medical prescription. All participants signed the informed consent form.
The protocol consisted of three days: in the first, the measurement of lung function was performed and in the other two days, the functional tests (6MWT and TGlittre) were performed.
The lung function was assessed by spirometry (EasyOne – NDD Medical Technologies®, Switzerland), according to the recommendations of the American Thoracic Society e European Respiratory Society (ATS/ERS) [Citation25]. For the analyses, the COPD spirometric classification was used [Citation24], as well as the values of FEV1 and forced vital capacity (FVC) in liters and as a percentage of predicted (%pred) based on the equations proposed by Pereira et al. [Citation26].
Two 6MWT were performed following the recommendations of the ATS/ERS [Citation5], except for the length of the corridor, which was conducted in a 20-m corridor [Citation27], with a minimum interval of 30 min between them or until the return of signs and symptoms to baseline values. For the analyses, the distance of the best performance test was considered, in meters and in %pred, considering the second equation proposed by Britto et al. [Citation28].
TGlittre was performed twice as previously described by Skumlien et al. [Citation6], with an interval of 30 min between tests or until the return of signs and symptoms to baseline values. For the analyses, the time of the best performance test was considered, in minutes and in %pred, based on the equation proposed by Reis et al. [Citation8].
Statistical analysis
The data were analyzed using the IBM SPSS 20.0 software (SPSS, Chicago, Illinois) and presented as mean, standard deviation and 95% confidence interval (95% CI). The Shapiro–Wilk test was applied to analyze the data distribution. Pearson’s or Spearman’s correlation coefficients were applied to verify possible correlations between the performance in functional tests and pulmonary function variables. In a secondary analysis, the correlation between the tests and the pulmonary function variables was investigated in a subgroup of patients with performance of ≤400 m in the 6MWT. This cutoff point was chosen because it refers to the value in which there was a loss of linearity in the correlation between 6MWT and TGlittre in the study by Skumlien et al. [Citation6]. The strength of the correlations was classified as: weak (r ≥ 0.3), moderate (r ≥ 0.5), strong (r ≥ 0.7) and perfect (r = 1) [Citation29]. For the variables that showed a statistically significant correlation, simple and multiple linear regressions of the stepwise type were used to verify which test (6MWT or TGlittre) is best predicted by lung function. The performance in the tests was considered a dependent variable and the pulmonary function variables were independent. The performance comparison in the functional tests between the different GOLD spirometric classifications was performed by one-way ANOVA with Tukey’s post hoc. The level of significance adopted was 5%.
Sample size
The sample size was calculated considering the main objectives and data from a pilot study that included the first seven patients of each GOLD (2, 3 e 4). All calculations considered a bidirectional alpha of 0.05 and a power of 95%. Using the one-way ANOVA, the mean and standard deviation of the time spent in the TGlittre of the three groups, we found a necessary sample size of 60 patients in total. In order to achieve a minimum correlation coefficient of at least 0.4 between the performance in the functional tests and the pulmonary function variables, 71 patients were necessary. Considering an expected sample loss of 10%, 79 patients were estimated for this study.
Results
Seventy-nine patients with COPD were evaluated and, of these, five were excluded due to exacerbation of the disease during the protocol. Therefore, 74 patients (54 men) were included in the study. No patient had mild pulmonary severity (GOLD I). The anthropometric, pulmonary function and functional capacity data of the total sample and of the patients stratified by GOLD are described in .
Table 1. Anthropometric characteristics, lung function and functional capacity from total sample and GOLD classification.
Correlation between functional tests and pulmonary function
The pulmonary function variables FEV1/FVC, FEV1(L) and FEV1(%pred) correlated with TGlittre(min) (−0.40 ≤ r ≤ −0.47), TGlittre(%pred) (−0.44 ≤ r ≤ −0.53), 6MWT(m) (0.34 ≤ r ≤ 0.43) and 6MWT(%pred) (0.36 ≤ r ≤ 0.45). The FVC(L) correlated only with 6MWT(m), and the FVC(%pred) correlated with 6MWT(%pred), TGlittre(min) and TGlittre(%pred). The details of the correlations are described in .
Table 2. Correlation between lung function variables and functional tests performances.
In the subgroup of patients with performance of ≤400 m in the 6MWT (n = 27), a strong correlation was observed between TGlittre(min) and TGlittre(%pred) with FEV1(%pred) (r = −0.82; p < .001 for both) and with FVC(%pred) (r = −0.79 and −0.71, respectively; p < .001 for both). With the variables FEV1/FVC, FVC(L) e FEV1(L), TGlittre(min) and TGlittre(%pred) they presented a weak to moderate correlation (r = −0.38 to −0.61; p < .05). The correlations of 6MWT(m) and 6MWT(%pred) with the pulmonary function variables were all weak to moderate (r = 0.38 to 0.68; p < .05), except for the correlation with FEV1(%pred), which was strong (r = 0.72; p < .001).
In isolation, the variability of FEV1(L) and FEV1(%pred) was able to predict the variability of 6MWT(m), 6MWT(%pred), TGlittre(min) and TGlittre(%pred) (R2 = 0.10 to 0.24; p < .05 for all). The variability of FVC(L) and of FVC(%pred) explained the variability of TGlittre(min) and TGlittre(%pred) (R2 = 0.05 to 0.14; p < .05 for all). Regarding the variability of 6MWT, the FVC(L) explained only the 6MWT(m) (R2 = 0.07), while the FVC(%pred) explained only 6MWT(%pred) (R2 = 0.08). shows the details of the results from the simple and multiple linear regressions.
Table 3. Simple linear regression between lung function and functional tests, and predictor model for the 6MWT and Glittre-ADL test.
Comparison of performance in functional tests between different GOLD classifications
When compared to GOLD 2 patients, GOLD 4 patients presented lower 6MWT(m) and 6MWT(%pred) (p = .03; p = .001, respectively), as well as higher TGlittre(min) and TGlittre(%pred) (p < .001; p < .001, respectively). In addition, when compared to GOLD 3 patients, GOLD 4 patients had lower 6MWT(%pred) (p = .04) and higher TGlittre(min) and TGlittre(%pred) (p = .001; p < .001, respectively). No statistical differences were found in the TGlittre(min) and TGlittre(%pred) between GOLD 2 and 3, 6MWT(m) between GOLD 3 and 4, as well as between GOLD 2 and 3 (p > .05). The comparisons of performance in functional tests between GOLD ratings are described in .
Discussion
The main findings of this study were that both TGlittre and 6MWT showed a weak to moderate correlation with pulmonary function variables and were able to discriminate patients with COPD GOLD 4 from the other classifications. However, the magnitude of the difference in performance in TGlittre between patients GOLD 4 and patients GOLD 2 and 3 was greater than the magnitude of the difference in performance in the 6MWT. In addition, the pulmonary function variable that better predict the functional tests performance (TGlittre and 6MWT) was FEV1.
The TGlittre was developed and validated by Skumlien et al. [Citation6] to assess the functional capacity in a context of multiple tasks that are often problematic for patients with COPD. Since then, some studies have hypothesized that the multiple tasks of TGlittre, especially the ones developed facing the shelves, can be more strenuous than the walk for patients with worse lung function, since they can induce greater dynamic hyperinflation [Citation11, Citation30]. However, the correlation between FEV1 and 6MWT had already been demonstrated in several studies [Citation6, Citation14–18], but only Skumlien et al. [Citation6] found a moderate negative correlation between TGlittre and FEV1. In current study, we have also observed that the lower the FEV1, the longer the time spent on TGlittre, nevertheless, the correlation was weak. A possible justificative for this finding is that, in our study, only 36% of the sample presented a distance covered in the 6MWT ≤400 m.
When analyzing the subgroup of patients with COPD who had a performance of ≤400 m in the 6MWT, that is, with greater functional impairment, we observed that the pulmonary function variables correlated more strongly with TGlittre than with 6MWT. Perhaps the hypothesis that the dynamic hyperinflation and the deficit in coordination and balance caused by joint activation of the trunk, lower and upper limbs [Citation6] is the justification for this finding. Our previous study showed that oxygen uptake during TGlittre is around 7% higher than the amount consumed in the 6MWT [Citation10], which for more severe patients may cause a significant increase in the sensation of dyspnea. It is known that one of the factors that contribute to dyspnea on exertion in patients with COPD is the dynamic hyperinflation [Citation31], and that this tends to be greater during higher intensity exercises [Citation32]. Patients with worse pulmonary function have lower ventilatory capacity and, consequently, reach their inspiratory and expiratory reserve limits more quickly [Citation31]. Thus, it is possible that the TGlittre is able to better discriminate more severe patients than the 6MWT, as Skumlien et al. [Citation6] hypothesized in their study. Interestingly, that study showed higher correlation coefficient for TGlittre than for 6MWT with FEV1. Besides showing a similar correlation, the present study also found a slightly higher coefficient of determination containing FEV1 on the TGlittre’s predictor model than on predictor model of the 6MWT. Despite that, the pulmonary function variables evaluated in our study were weak predictors of performance in tests of functional capacity, explaining less than 25% of their variability. This confirms the findings of other studies, which have shown that pulmonary function is a low predictor of functional status in patients with COPD. Therefore, TGlittre as well as other functional tests, assess in large part something different from the impact of pulmonary function in disease impairment and should always be considered in the assessment of patients with COPD in clinical practice.
Another finding of present study was that GOLD 4 patients had a worse performance in the TGlittre compared to the GOLD 2 and 3 patients. The difference in the TGlittre between the groups was of 31% and 40%, respectively, while for 6MWT, of only 12% and 20%. Therefore, it is possible that patients with very severe pulmonary function and so with low ventilatory reserve, they have greater limitation to perform ADLs. Thus, a multiple task set involving activities of upper and lower limbs and trunk inclination may be even more problematic for these patients than only the walk. No differences were observed between GOLD 2 and 3 regarding the performance in functional tests. In previous studies, the time spent on TGlittre between the GOLD classifications did not differ [Citation22,Citation23], however, some comparisons between the sample of these studies and the current can be made. The sample by Karloh et al. [Citation22] was smaller and may not have had sufficient power for this analysis. Moreover, only ten patients were GOLD 4. In the study by Souza et al. [Citation23] GOLD 4 patients were not included, which was the only group that differed from the others in the present study.
This study has some limitations. The sample size for secondary subgroup correlation analyzes may have been small. However, there was sufficient power to find a correlation between pulmonary function variables and the functional tests. Also, it is not possible to generalize the results found for patients with GOLD 1 spirometric classification, since these were not included in this study. However, the different degrees of severity presented by the patients in this study (GOLD 2–4) are representative of those from other pulmonary rehabilitation centers.
As far as we know, this was the first study to demonstrate that GOLD 4 patients show significant reduction in TGlittre performance when compared to GOLD 2 and 3 patients. In addition, it was the first to identify that in the most functionally compromised patients, the TGlittre and FEV1 correlation is strong, supporting the hypothesis of previous studies [Citation6, Citation11] that for more severe patients, TGlittre can complement the assessment of functional capacity, for better discriminating these patients. In practice, the patients with worse performance on TGlittre and with significant impairment of lung function inserted in pulmonary rehabilitation programs probably will need more specific training of multiple daily tasks to reduce the time on the test and consequently on their ADLs. This hypothesis are also based from Gulart et al. [Citation33] study, that demonstrated a weak correlation between the change in TGlittre and 6MWT after the pulmonary rehabilitation, despite the strong correlation between these two tests before the program.
Conclusion
The pulmonary function presents stronger correlations and better explain the variability of TGlittre than of the 6MWT, especially in patients with greater functional impairment. However, the pulmonary function variables were weak predictors of functional capacity tests, explaining less than 25% of their variability. The magnitude of the difference in functional capacity assessed by TGlittre between GOLD’s spirometric stages is greater than that assessed by the 6MWT. Therefore, TGlittre seems to better discriminate patients with COPD in relation to the severity of lung function.
Declaration of interest
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
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Funding
References
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