Skip to main content
 

Main menu

  • Home
  • Content
    • Current Issue
    • Editor's Commentary
    • Archives
    • Most-Read Papers of 2022
  • Authors
    • Author Guidelines
    • Submit a Manuscript
  • Reviewers
    • Reviewer Information
    • Create Reviewer Account
    • Reviewer Guidelines: Original Research
    • Reviewer Guidelines: Reviews
    • Appreciation of Reviewers
  • CRCE
    • Through the Journal
    • JournalCasts
    • AARC University
    • PowerPoint Template
  • Open Forum
    • 2023 Open Forum
    • 2023 Abstracts
    • Previous Open Forums
  • Podcast
    • English
    • Español
    • Portugûes
    • 国语
  • Videos
    • Video Abstracts
    • Author Interviews
    • The Journal

User menu

  • Subscribe
  • My alerts
  • Log in
  • Log out

Search

  • Advanced search
American Association for Respiratory Care
  • Subscribe
  • My alerts
  • Log in
  • Log out
American Association for Respiratory Care

Advanced Search

  • Home
  • Content
    • Current Issue
    • Editor's Commentary
    • Archives
    • Most-Read Papers of 2022
  • Authors
    • Author Guidelines
    • Submit a Manuscript
  • Reviewers
    • Reviewer Information
    • Create Reviewer Account
    • Reviewer Guidelines: Original Research
    • Reviewer Guidelines: Reviews
    • Appreciation of Reviewers
  • CRCE
    • Through the Journal
    • JournalCasts
    • AARC University
    • PowerPoint Template
  • Open Forum
    • 2023 Open Forum
    • 2023 Abstracts
    • Previous Open Forums
  • Podcast
    • English
    • Español
    • Portugûes
    • 国语
  • Videos
    • Video Abstracts
    • Author Interviews
    • The Journal
  • Twitter
  • Facebook
  • YouTube
Research ArticleOriginal Research

Modified Medical Research Council Dyspnea Scale in GOLD Classification Better Reflects Physical Activities of Daily Living

Anelise B Munari, Aline A Gulart, Karoliny dos Santos, Raysa S Venâncio, Manuela Karloh and Anamaria F Mayer
Respiratory Care January 2018, 63 (1) 77-85; DOI: https://doi.org/10.4187/respcare.05636
Anelise B Munari
Núcleo de Assistência, Ensino e Pesquisa em Reabilitação Pulmonar, Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil and the Programa de Pós-Graduação em Fisioterapia, Centro de Ciências da Saúde e do Esporte (CEFID), Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil.
PT
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aline A Gulart
Núcleo de Assistência, Ensino e Pesquisa em Reabilitação Pulmonar, Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil and the Programa de Pós-Graduação em Fisioterapia, Centro de Ciências da Saúde e do Esporte (CEFID), Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil.
MSc
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Karoliny dos Santos
Núcleo de Assistência, Ensino e Pesquisa em Reabilitação Pulmonar, Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil and the Programa de Pós-Graduação em Fisioterapia, Centro de Ciências da Saúde e do Esporte (CEFID), Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil.
MSc
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Raysa S Venâncio
Núcleo de Assistência, Ensino e Pesquisa em Reabilitação Pulmonar, Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil and the Programa de Pós-Graduação em Fisioterapia, Centro de Ciências da Saúde e do Esporte (CEFID), Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil.
PT
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Manuela Karloh
Núcleo de Assistência, Ensino e Pesquisa em Reabilitação Pulmonar, Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil and the Programa de Pós-Graduação em Fisioterapia, Centro de Ciências da Saúde e do Esporte (CEFID), Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil.
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anamaria F Mayer
Núcleo de Assistência, Ensino e Pesquisa em Reabilitação Pulmonar, Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil and the Programa de Pós-Graduação em Fisioterapia, Centro de Ciências da Saúde e do Esporte (CEFID), Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Santa Catarina, Brazil.
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
  • Article
  • Figures & Data
  • Info & Metrics
  • References
  • PDF
Loading

Abstract

BACKGROUND: In multidimensional Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, the choice of the symptom assessment instrument (modified Medical Research Council dyspnea scale [mMRC] or COPD assessment test [CAT]) can lead to a different distribution of patients in each quadrant. Considering that physical activities of daily living (PADL) is an important functional outcome in COPD, the objective of this study was to determine which symptom assessment instrument is more strongly associated with and differentiates better the PADL of patients with COPD.

METHODS: The study included 115 subjects with COPD (GOLD 2–4), who were submitted to spirometry, the mMRC, the CAT, and monitoring of PADL (triaxial accelerometer). Subjects were divided into 2 groups using the cutoffs proposed by the multidimensional GOLD classification: mMRC < 2 and ≥ 2 and CAT < 10 and ≥ 10.

RESULTS: Both mMRC and CAT reflected the PADL of COPD subjects. Subjects with mMRC < 2 and CAT < 10 spent less time in physical activities < 1.5 metabolic equivalents of task (METs) (mean of the difference [95% CI] = −62.9 [−94.4 to −31.4], P < .001 vs −71.0 [−116 to −25.9], P = .002) and had a higher number of steps (3,076 [1,999–4,153], P < .001 vs 2,688 [1,042–4,333], P = .002) than subjects with mMRC > 2 and CAT > 10, respectively. Physical activities ≥ 3 METs differed only between mMRC < 2 and mMRC ≥ 2 (39.2 [18.8–59.6], P < .001). Furthermore, only the mMRC was able to predict the PADL alone (time active, r2 = 0.16; time sedentary, r2 = 0.12; time ≥ 3 METs, r2 = 0.12) and associated with lung function (number of steps, r2 = 0.35; walking time, r2 = 0.37; time < 1.5 METs, r2 = 0.25).

CONCLUSIONS: The mMRC should be adopted as the classification criterion for symptom assessment in the GOLD ABCD system when focusing on PADL.

  • activities of daily living
  • exercise
  • sedentary lifestyle
  • symptom assessment
  • dyspnea
  • chronic obstructive pulmonary disease
  • GOLD classification

Introduction

COPD is characterized by chronic and progressive air flow obstruction and several significant systemic manifestations that may result in reduced functional capacity and health status.1,2 Because of the diverse manifestations of this disease, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) proposed in 2011 a multidimensional assessment (GOLD ABCD) of patients based on the severity of air flow obstruction, in addition to the unidimensional classification (GOLD I/II/III/IV).3 The risk of future exacerbation, assessed by pulmonary function or history of exacerbations, and the symptoms, assessed by the COPD assessment test (CAT) questionnaire or the modified Medical Research Council dyspnea scale (mMRC), were used for classification. This classification system has been recently refined, and the recommendation is that the multidimensional assessment must take into account only the history of exacerbations and the evaluation of symptoms.4

The relationship between the multidimensional GOLD classification and physical activities in daily life (PADL) has been investigated in some studies. However, results are still controversial, probably because of the large number of framing possibilities in the former classification model. After the new recommendation, part of this difficulty seems to have been remedied because, from now on, the choice for the symptom assessment instrument (CAT or mMRC) represents the only aspect that may cause differences in the multidimensional classification.

Although GOLD states that it is not necessary to use more than one symptom assessment instrument to classify patients, the mMRC and CAT have been observed to have a moderate agreement.5,6 Zogg et al5 used the 2 symptom assessment instruments and found that the quadrants defined with the use of the mMRC correlated more strongly with the number of steps than did the quadrants established by CAT. Demeyer et al6 also suggested that the mMRC should be used along with risk assessment to better differentiate the PADL of patients with COPD. On the other hand, Moreira et al7 used the mMRC to establish the multidimensional GOLD classification and found that this classification was weakly associated with the PADL of patients with COPD.

PADL level is an important functional outcome in COPD because of its relation with the risk of exacerbations, hospitalizations, and mortality.8 However, because the symptom assessment instrument (mMRC or CAT) chosen can present different distribution of patients in the quadrants of the multidimensional classification, it is not clear whether the mMRC or CAT reflects their functional status in distinct ways. Therefore, the aim of the present study was to determine which symptom assessment instrument differentiates better the PADL of subjects with COPD and which is most strongly associated with this outcome.

QUICK LOOK

Current knowledge

In the multidimensional Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, 2 instruments can be used for symptom evaluation. The choice of instrument (modified Medical Research Council dyspnea scale [mMRC] or COPD assessment test [CAT]) can lead to a different categorization of patients in each quadrant.

What this paper contributes to our knowledge

The symptom assessment instrument used in the multidimensional GOLD classification can cause differences in the distribution of patients between the ABCD quadrants and also in the potential to reflect their physical activity of daily living. The mMRC must be used instead of the CAT when the goal is to better discriminate the physical activity of daily living, including the sedentary behavior.

Methods

Participants

Participants of the study were subjects with COPD referred to the Center of Assistance, Teaching, and Research in Pulmonary Rehabilitation (NuReab), and the recruitment occurred from March 2013 to August 2016. The inclusion criteria were: clinical diagnosis of COPD with spirometric classification II–IV9, age ≥ 40 y, and clinical stability in the last month preceding the beginning of the protocol. The study excluded active smokers, patients with COPD exacerbation during the study protocol, and patients with other respiratory, cardiovascular, neurological, musculoskeletal, and rheumatologic diseases that could influence the execution of the assessments proposed.

This study was approved by the Ethics Committee on Human Research of the University of the State of Santa Catarina - Florianópolis/SC, Brazil (CAAE: 38765814.7.0000.0118). All participants signed an informed consent form.

Sample Size

The sample size was calculated based on data from a pilot study with 20 subjects (14 men; 65 ± 6 y; 54.4 ± 35.6 pack-years; percent-of-predicted FEV1 = 37.5 ± 15.1%; body mass index = 26.2 ± 4.49 kg/m2), using the software G*Power 3.1.9.2. We use the mean of the difference and the highest SD of the number of steps and the walking time among subjects classified with mMRC < 2 and ≥ 2 (4,493 ± 3,328 steps and 53.8 ± 36.8 min) and CAT < 10 and ≥ 10 (1,996 ± 3,416 steps and 18.8 ± 40.8 min). Considering the estimation power of 80% and α of .05, a maximum sample size of 104 subjects was found. In addition, to obtain a reliable measure of the number of steps (0.80 < intraclass correlation coefficient > 0.85) on 2 days of monitoring of the ADL, a sample size of approximately 100 subjects is required.10

Protocol

This was a cross-sectional study with protocol carried out in 3 d. Subjects were submitted to lung function assessment, the mMRC, and the CAT questionnaire and to the monitoring of PADL.

Pulmonary Function

Pulmonary function was assessed with a portable EasyOne spirometer (ndd Medical Technologies, Zurich, Switzerland), whose calibration was checked before each assessment, following the methods and criteria recommended by the American Thoracic Society/European Respiratory Society.11 Spirometric measurements were obtained before inhalation of 400 μg of bronchodilator and 15 min after this. Equations proposed for the Brazilian population were used for calculation of predicted values.12

Symptoms

Subjects were divided into 2 groups for analysis using the cutoffs proposed by the multidimensional GOLD classification1: subjects with mMRC < 2 and mMRC ≥ 2 and those with CAT < 10 and CAT ≥ 10.

PADL

To evaluate the PADL, we used a triaxial accelerometer (DynaPort activity monitor, McRoberts BV, Hague, Netherlands).13 Monitoring took place on 2 consecutive weekdays, lasting 12 h from awakening. The mean of both days was considered for data analysis. In a previous study, 2 days of assessment were considered necessary to achieve a reliable measure (0.70 < intraclass reliability coefficient < 0.88).14 All participants received an explanatory manual and were instructed on how to use the equipment and register the exact time of placement and removal. Data processing and analysis were performed with the MiRA2 software (McRoberts BV, Hague, Netherlands). In cases of error of measurement after data processing and analysis, the subject used the equipment again. The following variables were considered: time spent standing, sitting, lying, and walking; movement intensity during walking; energy expenditure in PADL; and number of steps.

The sum of the time spent standing and walking corresponded to the active time, and the sum of the time spent sitting and lying represented the sedentary time. The time spent with sedentary behavior was also evaluated, considering physical activities with energy expenditure < 1.5 metabolic equivalents of task (METs).15 In this case, a time of ≥ 8.5 h corresponds to inactivity.16

The time spent in moderate and vigorous physical activity (≥ 3 METs), with a cutoff point of 80 min/d, was used to categorize subjects as to their level of physical (in)activity. Subjects were considered either active (physical activities ≥ 80 min/d) or inactive (physical activities < 80 min/d).17 The number of steps was used to categorize severe physical inactivity (< 4,580 steps).18

Statistical Analysis

Data were processed in the SPSS 20.0 (SPSS, Chicago, Illinois) and GraphPad Prism 5 (GraphPad Software, La Jolla, California) software. Data distribution was tested using the Kolmogorov-Smirnov test. The Chi-square test was used to check associations between the level of PADL and the mMRC groups < 2 or ≥ 2 and CAT < 10 or ≥ 10. The Cramer V coefficient demonstrated the strength of these associations. Simple and multiple linear regressions using the stepwise method were applied. The CAT, mMRC, and FEV1 (percent of predicted) were considered as dependent variables, and the PADL was considered an independent variable. Correlations between CAT, mMRC, and PADL were tested using the Pearson or Spearman correlation coefficient. The intraclass correlation coefficient between days 1 and 2 of the ADL monitoring was calculated. The level of significance adopted was P < .05.

Results

One hundred twenty-five subjects were recruited for the study, and 115 were potentially eligible. Five of these were excluded; 3 for not meeting the spirometric criteria for diagnosis of COPD and 2 for exacerbation of the disease during the protocol. Thus, 110 subjects (75 men; 68.2%) completed the study. Anthropometric data, pulmonary function, PADL, dyspnea, and health status are shown in Table 1. The intraclass correlation coefficient for the PADL variables was > 0.80.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 1.

Anthropometric Characteristics, Lung Function, Functional Status, Dyspnea, and Health Status

ADL Between the mMRC Cutoff 2 and CAT Cutoff 10

Fifty-one subjects presented mMRC < 2 (GOLD A and C), whereas 57 subjects had mMRC ≥ 2 (GOLD B and D). Subjects with mMRC < 2 spent less time sitting, sedentary, and in physical activities < 1.5 METs (mean of the difference [95% CI] = −50.7 min [−90.4 to −11.4 min] P = .01, −62.2 min [−99.8 to −24.5 min] P = .002, and −62.9 min [−94.4 to −31.4 min] P < .001, respectively) and had a higher number of steps and time standing, walking, active, and in physical activities ≥ 3 METs (mean of the difference [95% CI] = 3,076 [1,999–4,153] P < .001, 25.7 min [2.12–49.3 min] P = .033, 35.0 min [22.3–47.8 min] P < .001, 70.8 min [35.5–106 min] P < .001, and 39.2 min [18.8–59.6 min] P < .001, respectively). There were no significant differences between groups (mean of the difference [95% CI] = −11.5 min [−44.5 to 21.5 min], P = .300) with respect to the lying time.

Sixteen subjects presented CAT <10 (GOLD A and C), whereas 94 subjects presented CAT ≥ 10 (GOLD B and D). Subjects with CAT < 10 spent less time in physical activities < 1.5 METs (mean of the difference [95% CI] = −71.0 min [−116 to −25.9 min], P = .002) and had a higher number of steps and time walking and active (mean of the difference [95% CI] = 2,688 [1,042–4,333] P = .002, 33.0 min [13.8–52.2 min] P = .002, and 59.3 min [7.45–111 min] P = .036, respectively). Time sitting, lying, standing, and in physical activities ≥ 3 METs were similar between the 2 groups (mean of the difference [95% CI] = −50.3 min [−107 to 5.92 min] P = .08, −4.39 [−51.3 to 42.6 min] P = .83, 17.9 min [−15.5 to 51.4 min] P = .34, and 15.9 min [−14.3 to 46.0 min] P = .08, respectively). Figure 1 shows the main results of comparisons between mMRC < 2 and ≥ 2 (A) and between CAT < 10 and ≥ 10 (B).

Fig. 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 1.

Comparisons of time walking, active time, sedentary time, time in physical activity < 1.5 metabolic equivalents of task (METs), and time in physical activity ≥ 3 METs between Modified Medical Research Council dyspnea scale (mMRC) (A) and COPD assessment test (CAT) (B). Center lines represent the median; the top and bottom lines (box) represent interquartile range; and top and bottom whiskers represent quartile 3 + 1.5 (quartile 3 − quartile 1) and quartile 1 − 1.5 (quartile 3 − quartile 1), respectively.

Both classifications, the ones based on cutoff of 2 for mMRC and 10 for CAT, were associated with the classification based on the cutoff of 80 min in physical activities ≥ 3 METs, with the sedentarism classification based on the cutoff point of 8.5 h in physical activities < 1.5 METs and with the severe physical inactivity based on the cutoff of 4,580 steps/d. Details of results of the associations are listed in Table 2.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 2.

Distribution of Subjects' Physical Activity in Daily Life Outcomes and Association With the Modified Medical Research Council Dyspnea Scale Cutoff 2 and COPD Assessment Test Cutoff 10

Correlations Between Physical Activity in Daily Life and Dyspnea, Health Status, and Pulmonary Function

The mMRC generally showed stronger correlation with PADL than CAT. The results of the correlations between PADL variables and mMRC, CAT, and FEV1 (in liters and percent predicted) are described in Table 3.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 3.

Correlation Coefficient Between Physical Activity in Daily Life Variables and Dyspnea, Health Status, and Pulmonary Function

Simple Linear Regression and Predictive Models for ADL

The variability of FEV1 percent predicted, mMRC, and CAT were able to explain, in isolation, 23 (P < .001), 29 (P < .001), and 17% (P < .001) of the variability in the number of steps, respectively; 26 (P < .001), 28 (P < .001), and 17% (P < .001) of the variability of the time walking; 8 (P = .002), 16 (P < .001), and 8% (P = .003) of the variability of active time; 7 (P = .006), 12 (P < .001), and 7% (P = .007) of the variability of sedentary time; and 21 (P < .001), 17 (P < .001), and 11% (P < .001) of the variability of time in physical activities < 1.5 METs, respectively. The variability of the time in physical activities ≥ 3 METs was explained in 12% by mMRC (P < .001) and in 5% by CAT (P = .02), whereas percent-of-predicted FEV1 was not able to explain this variable (P = .055).

When tested in predictive models for variables of PADL, it was observed that CAT was not retained in any of them, whereas mMRC was in all models. The results of multiple regression are presented in Table 4.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 4.

Model Predictor for Time Walking, Time Active, Time Sedentary, Time in Physical Activities ≥ 3 Metabolic Equivalents of Task, and Time in Physical Activities < 1.5 Metabolic Equivalents of Task

Discussion

The present study aimed to determine which symptom assessment instrument better differentiates the PADL of subjects with COPD and is most strongly associated with this outcome. The main findings demonstrate that although the CAT and mMRC are able to reflect the level of ADL of COPD subjects, the mMRC has a stronger association. Furthermore, only the mMRC was able to predict the PADL alone, and this measure was also associated with lung function.

Since the publication of the new COPD classification model (GOLD ABCD) by GOLD in 2011, noted as an important advance because it incorporated multimodality assessment and symptom burden and highlighted the importance of exacerbation prevention in the management of COPD,1 a considerable number of studies have sought to analyze the equivalence of different classification criteria5,19–27 and their association with important outcomes, such as functional status,5–7,28–30 quality of life,29,31,32 and mortality.33–37 Recently, a systematic review4 found that there is an average classification disagreement of 13% in all quadrants, depending on the instrument used. The agreement between CAT and mMRC ranged from slight to moderate, and the meta-analysis showed a pooled kappa coefficient of 0.548 (95% CI 0.35–0.70, P < .001; I2 = 99.3; z = 4.84). These findings indicate that CAT ≥ 10 and mMRC ≥ 2 are not equivalent when assessing symptoms in patients with COPD.4

In the present study, both symptom assessment instruments were associated with categorizations of PADL (physical activity, sedentarism, and severe inactivity). However, the associations of PADL and symptoms with mMRC score were stronger than with CAT. Also, whereas all variables related to PADL (except for time lying) differed among subjects with mMRC < 2 and mMRC ≥ 2, the time lying, sitting, standing, and in physical activities ≥ 3 METs did not differ between subjects with CAT < 10 and CAT ≥ 10. These findings, added to the fact that CAT was not retained in any predictive model of PADL, suggest that the mMRC better reflects the performance of subjects in their activities than CAT, especially in high-energy expenditure activities (≥ 3 METs). A possible explanation is that although CAT encompasses the major symptoms of patients with COPD,38 some of its items may not substantially interfere with the realization of PADL, such as cough and expectoration. In contrast, the mMRC specifically rates dyspnea from minimum to maximum physical exertion, symptoms more strongly linked to functional limitations in patients with COPD.39

In a previous study,40 the FEV1 did not show a correlation with certain ADL variables, differing from the findings of the present study, which showed moderate correlations with steps and time walking. Furthermore, in isolation, mMRC and FEV1 were able to predict a large part of PADL variables and, when combined, explained more strongly the number of steps, the time walking, and the time in physical activities < 1.5 METs. Therefore, although FEV1 alone does not reflect ADL in patients with COPD as well, it may be possible to achieve a more complete analysis of this outcome when FEV1 is associated with a symptom scale, as was the case in the previous GOLD ABCD classification based on the mMRC.

To our knowledge, only 3 studies investigating the functional status in the multidimensional GOLD classification have objectively ascertained the differences in PADL between the ABCD quadrants.5–7 In a study developed by Zogg et al5 among the PADL variables (number of steps, active energy expenditure, level of physical activities, and time in physical activities > 3 METs), only the number of steps differed between quadrants, regardless of the use of CAT or mMRC. However, mMRC correlated more strongly with the number of steps than CAT (r = −0.51 vs r = −0.37, P < .001 in both cases, respectively). Moreira et al7 showed that both GOLD classifications (A–D and I–IV) are weakly associated with PADL variables (Cramer's V < 0.20 for all). In addition, no differences were found between active and inactive time (physical activities > 2 and 3 METs and physical activities < 2 and 3 METs) between quadrants (P = .09 to .39). More recently, Demeyer et al6 showed that the mMRC is preferable when used in combination with risk assessment to differentiate PADL of patients with COPD. Furthermore, regardless of risk assessment, the mMRC can be a good predictor of mortality,34,39 since the higher the score in mMRC, the fewer the number of steps.6

In contrast to previous studies, this study conducted a more detailed analysis of PADL, including sedentary behavior. Patients with COPD adopt sedentary behavior throughout most of the day, most frequently carrying out physical activities < 1.5 METs in seated or reclined positions.15,41 This pattern of behavior has also been observed even when patients are considered physically active (ie, when they perform ≥ 80 min of moderate to vigorous physical activities per day [≥ 3 METs]).17 It is known that sedentary behavior is associated with negative health effects in the general population, increasing the risk of cardiovascular and metabolic diseases and mortality.42 In patients with COPD, the risk of death is about 4 times higher in those who spend > 8.5 h in physical activities < 1.5 METs.16 Furthermore, for each hour of the day spent in sedentary physical activities, the risk of death increases by 42%.16 In the present study, only the score on the mMRC correlated with the time in physical activities < 1.5 METs, and the magnitude of the difference observed among subjects with mMRC < 2 and mMRC ≥ 2 was higher than among subjects with CAT < 10 and CAT ≥ 10. These results suggest that the mMRC reflects sedentary behavior better than CAT does.

Thus, the symptom assessment instrument used in the multidimensional GOLD classification can cause not only differences in the distribution of patients between the ABCD quadrants, but also in the potential to reflect their PADL. Therefore, standardizing the choice of the symptom assessment instrument can be a determining factor. This has been discussed in the literature in an analysis of 4 cohort studies.43 Although GOLD recommends the use of either one of the 2 instruments for the multidimensional classification,1 the results of the present study suggest that, supported by a previous study,6 the mMRC must be used instead of the CAT when the goal is to better discriminate the PADL, including the sedentary behavior. It is important to consider this outcome while evaluating patients with COPD, since sedentary behavior has a causal relationship with mortality in the general population44 and also in these patients.16

The heterogeneous distribution of subjects in the groups formed by CAT (16 subjects with CAT < 10; 94 subjects with CAT ≥ 10) could be considered a limitation of this study. This may have caused a type-2 error in some comparisons. However, the sample size in the present study exceeded the previous calculation. Furthermore, these same conditions are observed in most studies that have addressed GOLD classifications.5–7,24,28,29,31,34 The absence of GOLD I subjects in the sample of the present study prevents us from generalizing the results for these patients. However, the selection of patients in the disease's early stages is difficult because underdiagnosis is common, especially at this stage.1 In addition, GOLD I patients may be asymptomatic, and therefore the impact of the disease may be very low and clinically not significant. PADL analysis performed only in 2 consecutive days could also be considered a limitation of the present study, but both of the variables used to estimate sample size (number of steps and walking time) and sedentary behavior showed high intraclass correlation coefficient values (> 0.80).

To our knowledge, this was the first study to demonstrate that the symptom assessment instrument chosen for the multidimensional GOLD classification results in better differentiation of variables, reflecting physical inactivity and sedentary behavior. Furthermore, only the mMRC score, regardless of association with FEV1, was able to explain the variability of PADL in patients with COPD.

Conclusions

The multidimensional GOLD classification requires standardization regarding the criterion for symptom assessment. Although physical inactivity and sedentary lifestyles are striking features among patients in the D quadrant (mMRC ≥ 2 or CAT ≥ 10), we suggest that the mMRC should be adopted as the classification criterion in the GOLD ABCD system, especially when the focus is the level of PADL.

Footnotes

  • Correspondence: Anamaria Fleig Mayer PhD, Physiotherapy Department; Núcleo de Assistência, Ensino e Pesquisa em Reabilitação Pulmonar, Universidade do Estado de Santa Catarina (UDESC), Rua Pascoal Simone, 358, 88080-350, Florianópolis, Brazil. E-mail: anamaria.mayer{at}udesc.br.
  • The authors have disclosed no conflicts of interest.

  • Copyright © 2018 by Daedalus Enterprises

REFERENCES

  1. 1.↵
    Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. Updated 2017. http://goldcopd.org/gold-2017-global-strategy-diagnosis-management-prevention-copd/.
  2. 2.↵
    1. Barnes PJ,
    2. Celli BR
    . Systemic manifestations and comorbidities of COPD. Eur Respir J 2009;33(5):1165–1185.
    OpenUrlAbstract/FREE Full Text
  3. 3.↵
    From the Global Strategy for the Diagnosis, Management and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2011 Available from: http://www.goldcopd.org/.
  4. 4.↵
    1. Karloh M,
    2. Fleig Mayer A,
    3. Maurici R,
    4. Pizzichini MM,
    5. Jones PW,
    6. Pizzichini E
    . The COPD assessment test: what do we know so far?: a systematic review and meta-analysis about clinical outcomes prediction and classification of patients into GOLD stages. Chest 2016;149(2):413–425.
    OpenUrl
  5. 5.↵
    1. Zogg S,
    2. Dürr S,
    3. Miedinger D,
    4. Steveling EH,
    5. Maier S,
    6. Leuppi JD
    . Differences in classification of COPD patients into risk groups A–D: a cross-sectional study. BMC Res Notes 2014;7:562.
    OpenUrl
  6. 6.↵
    1. Demeyer H,
    2. Gimeno-Santos E,
    3. Rabinovich RA,
    4. Hornikx M,
    5. Louvaris Z,
    6. de Boer WI,
    7. et al
    . Physical activity characteristics across GOLD quadrants depend on the questionnaire used. PLoS One 2016;11(3):e0151255.
    OpenUrl
  7. 7.↵
    1. Moreira GL,
    2. Donaria L,
    3. Furlanetto KC,
    4. Paes T,
    5. Sant'Anna T,
    6. Hernandes NA,
    7. Pitta F
    . GOLD B-C-D groups or GOLD II-III-IV grades: which one better reflects the functionality of patients with chronic obstructive pulmonary disease? Chron Respir Dis 2015;12(2):102–110.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Gimeno-Santos E,
    2. Frei A,
    3. Steurer-Stey C,
    4. de Batlle J,
    5. Rabinovich RA,
    6. Raste Y,
    7. et al
    . Determinants and outcomes of physical activity in patients with COPD: a systematic review. Thorax 2014;69(8):731–739.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Vestbo J,
    2. Hurd SS,
    3. Agustí AG,
    4. Jones PW,
    5. Vogelmeier C,
    6. Anzueto A,
    7. et al
    . Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2013;187(4):347–365.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Demeyer H,
    2. Burtin C,
    3. Van Remoortel H,
    4. Hornikx M,
    5. Langer D,
    6. Decramer M,
    7. et al
    . Standardizing the analysis of physical activity in patients with COPD following a pulmonary rehabilitation program. Chest 2014;146(2):318–327.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Miller MR,
    2. Hankinson J,
    3. Brusasco V,
    4. Burgos F,
    5. Casaburi R,
    6. Coates A,
    7. et al
    . Standardisation of spirometry. Eur Respir J 2005;26(2):319–338.
    OpenUrlAbstract/FREE Full Text
  12. 12.↵
    1. Pereira CA,
    2. Sato T,
    3. Rodrigues SC
    . New reference values for forced spirometry in white adults in Brazil. J Bras Pneumol 2007;33(4):397–406.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Van Remoortel H,
    2. Raste Y,
    3. Louvaris Z,
    4. Giavedoni S,
    5. Burtin C,
    6. Langer D,
    7. et al
    . Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry. PLoS One 2012;7(6):e39198.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Pitta F,
    2. Troosters T,
    3. Spruit MA,
    4. Probst VS,
    5. Decramer M,
    6. Gosselink R
    . Characteristics of physical activities in daily life in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2005;171(9):972–977.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Hill K,
    2. Gardiner PA,
    3. Cavalheri V,
    4. Jenkins SC,
    5. Healy GN
    . Physical activity and sedentary behaviour: applying lessons to chronic obstructive pulmonary disease. Intern Med J 2015;45(5):474–482.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Furlanetto KC,
    2. Donária L,
    3. Schneider LP,
    4. Lopes JR,
    5. Ribeiro M,
    6. Hernandes KB,
    7. et al
    . Sedentary behavior is an independent predictor of mortality in subjects with COPD. Respir Care 2017;62(5):579–587.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. van Remoortel H,
    2. Camillo CA,
    3. Langer D,
    4. Hornikx M,
    5. Demeyer H,
    6. Burtin C,
    7. et al
    . Moderate intense physical activity depends on selected metabolic equivalent of task (MET) cut-off and type of data analysis. PLoS One 2013;8(12):e84365.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Depew ZS,
    2. Novotny PJ,
    3. Benzo RP
    . How many steps are enough to avoid severe physical inactivity in patients with chronic obstructive pulmonary disease? Respirology 2012;17(6):1026–1027.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Jones PW,
    2. Nadeau G,
    3. Small M,
    4. Adamek L
    . Characteristics of a COPD population categorised using the GOLD framework by health status and exacerbations. Respir Med 2014;108(1):129–135.
    OpenUrlCrossRefPubMed
  20. 20.
    1. Price DB,
    2. Baker CL,
    3. Zou KH,
    4. Higgins VS,
    5. Bailey JT,
    6. Pike JS
    . Real-world characterization and differentiation of the Global Initiative for Chronic Obstructive Lung Disease strategy classification. Int J Chron Obstruct Pulmon Dis 2014;9:551–561.
    OpenUrl
  21. 21.
    1. Han J,
    2. Dai L,
    3. Zhong N,
    4. Young D
    . Breathlessness or health status in chronic obstructive pulmonary disease: the impact of different definitions. COPD 2015;12(2):115–125.
    OpenUrlCrossRef
  22. 22.
    1. Jones PW,
    2. Adamek L,
    3. Nadeau G,
    4. Banik N
    . Comparisons of health status scores with MRC grades in a primary care COPD population: implications for the new GOLD 2011 classification. Eur Respir J 2013;42(3):647–654.
    OpenUrlAbstract/FREE Full Text
  23. 23.
    1. Kim S,
    2. Oh J,
    3. Kim YI,
    4. Ban HJ,
    5. Kwon YS,
    6. Oh IJ,
    7. et al
    . Differences in classification of COPD group using COPD assessment test (CAT) or modified Medical Research Council (mMRC) dyspnea scores: a cross-sectional analyses. BMC Pulm Med 2013(Can't get issue from record);13:35.
  24. 24.↵
    1. Rieger-Reyes C,
    2. García-Tirado FJ,
    3. Rubio-Galán FJ,
    4. Marín-Trigo JM
    . Classification of chronic obstructive pulmonary disease severity according to the new Global Initiative for Chronic Obstructive Lung Disease 2011 guidelines: COPD assessment test versus modified Medical Research Council scale. Arch Bronconeumol 2014;50(4):129–134.
    OpenUrl
  25. 25.
    1. Casanova C,
    2. Marin JM,
    3. Martinez-Gonzalez C,
    4. de Lucas-Ramos P,
    5. Mir-Viladrich I,
    6. Cosio B,
    7. et al
    . New GOLD classification: longitudinal data on group assignment. Respir Res 2014;15:3.
    OpenUrlCrossRefPubMed
  26. 26.
    1. Pillai AP,
    2. Turner AM,
    3. Stockley RA
    . Global Initiative for Chronic Obstructive Lung Disease 2011 symptom/risk assessment in α1-antitrypsin deficiency. Chest 2013;144(4):1152–1162.
    OpenUrl
  27. 27.↵
    1. Han MK,
    2. Muellerova H,
    3. Curran-Everett D,
    4. Dransfield MT,
    5. Washko GR,
    6. Regan EA,
    7. et al
    . GOLD 2011 disease severity classification in COPDGene: a prospective cohort study. Lancet Respir Med 2013;1(1):43–50.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. García-Rio F,
    2. Soriano JB,
    3. Miravitlles M,
    4. Muñoz L,
    5. Duran-Tauleria E,
    6. Sánchez G,
    7. et al
    . Frequency of multi-dimensional COPD indices and relation with disease activity markers. COPD 2013;10(4):436–443.
    OpenUrlCrossRef
  29. 29.↵
    1. Barusso MS,
    2. Gianjoppe-Santos J,
    3. Basso-Vanelli RP,
    4. Regueiro EM,
    5. Panin JC,
    6. Di Lorenzo VA
    . Limitation of activities of daily living and quality of life based on COPD combined classification. Respir Care 2015;60(3):388–398.
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    1. Durheim MT,
    2. Smith PJ,
    3. Babyak MA,
    4. Mabe SK,
    5. Martinu T,
    6. Welty-Wolf KE,
    7. et al
    . Six-minute-walk distance and accelerometry predict outcomes in chronic obstructive pulmonary disease independent of Global Initiative for Chronic Obstructive Lung Disease 2011 Group. Ann Am Thorac Soc 2015;12(3):349–356.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Miravitlles M,
    2. Huerta A,
    3. Fernández-Villar JA,
    4. Alcázar B,
    5. Villa G,
    6. Forné C,
    7. et al
    . Generic utilities in chronic obstructive pulmonary disease patients stratified according to different staging systems. Health Qual Life Outcomes 2014;12:120.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Boland MR,
    2. Tsiachristas A,
    3. Kruis AL,
    4. Chavannes NH,
    5. Rutten-van Mölken MP
    . Are GOLD ABCD groups better associated with health status and costs than GOLD 1234 grades? A cross-sectional study. Prim Care Respir J 2014;23(1):30–37.
    OpenUrlCrossRefPubMed
  33. 33.↵
    1. Leivseth L,
    2. Brumpton BM,
    3. Nilsen TI,
    4. Mai XM,
    5. Johnsen R,
    6. Langhammer A
    . GOLD classifications and mortality in chronic obstructive pulmonary disease: the HUNT Study, Norway. Thorax 2013;68(10):914–921.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    1. Casanova C,
    2. Marin JM,
    3. Martinez-Gonzalez C,
    4. de Lucas-Ramos P,
    5. Mir-Viladrich I,
    6. Cosio B,
    7. et al
    . Differential effect of modified medical research council dyspnea, COPD assessment test, and clinical COPD questionnaire for symptoms evaluation within the new GOLD staging and mortality in COPD. Chest 2015;148(1):159–168.
    OpenUrl
  35. 35.
    1. Soriano JB,
    2. Lamprecht B,
    3. Ramírez AS,
    4. Martinez-Camblor P,
    5. Kaiser B,
    6. Alfageme I,
    7. et al
    . Mortality prediction in chronic obstructive pulmonary disease comparing the GOLD 2007 and 2011 staging systems: a pooled analysis of individual patient data. Lancet Respir Med 2015;3(6):443–450.
    OpenUrl
  36. 36.
    1. Lange P,
    2. Marott JL,
    3. Vestbo J,
    4. Olsen KR,
    5. Ingebrigtsen TS,
    6. Dahl M,
    7. Nordestgaard BG
    . Prediction of the clinical course of chronic obstructive pulmonary disease, using the new GOLD classification: a study of the general population. Am J Respir Crit Care Med 2012;186(10):975–981.
    OpenUrlCrossRefPubMed
  37. 37.↵
    1. Agusti A,
    2. Edwards LD,
    3. Celli B,
    4. Macnee W,
    5. Calverley PM,
    6. Mullerova H,
    7. et al
    . Characteristics, stability and outcomes of the 2011 GOLD COPD groups in the ECLIPSE cohort. Eur Respir J 2013;42(3):636–646.
    OpenUrlAbstract/FREE Full Text
  38. 38.↵
    1. Jones PW,
    2. Harding G,
    3. Berry P,
    4. Wiklund I,
    5. Chen WH,
    6. Kline Leidy N
    . Development and first validation of the COPD assessment test. Eur Respir J 2009;34(3):648–654.
    OpenUrlAbstract/FREE Full Text
  39. 39.↵
    1. Nishimura K,
    2. Izumi T,
    3. Tsukino M,
    4. Oga T
    . Dyspnea is a better predictor of 5-year survival than airway obstruction in patients with COPD. Chest 2002;121(5):1434–1440.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. Pitta F,
    2. Takaki MY,
    3. Oliveira NH,
    4. Sant'anna TJ,
    5. Fontana AD,
    6. Kovelis D,
    7. et al
    . Relationship between pulmonary function and physical activity in daily life in patients with COPD. Respir Med 2008;102(8):1203–1207.
    OpenUrlCrossRefPubMed
  41. 41.↵
    1. Hunt T,
    2. Madigan S,
    3. Williams MT,
    4. Olds TS
    . Use of time in people with chronic obstructive pulmonary disease: a systematic review. Int J Chron Obstruct Pulmon Dis 2014;9:1377–1388.
    OpenUrl
  42. 42.↵
    1. Wilmot EG,
    2. Edwardson CL,
    3. Achana FA,
    4. Davies MJ,
    5. Gorely T,
    6. Gray LJ,
    7. et al
    . Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia 2012;55(11):2895–2905.
    OpenUrlCrossRefPubMed
  43. 43.↵
    1. Agusti A,
    2. Hurd S,
    3. Jones P,
    4. Fabbri LM,
    5. Martinez F,
    6. Vogelmeier C,
    7. et al
    . FAQs about the GOLD 2011 assessment proposal of COPD: a comparative analysis of four different cohorts. Eur Respir J 2013;42(5):1391–1401.
    OpenUrlAbstract/FREE Full Text
  44. 44.↵
    1. Biddle SJ,
    2. Bennie JA,
    3. Bauman AE,
    4. Chau JY,
    5. Dunstan D,
    6. Owen N,
    7. et al
    . Too much sitting and all-cause mortality: is there a causal link? BMC Public Health 2016;16:635.
    OpenUrlCrossRefPubMed
View Abstract
PreviousNext
Back to top

In this issue

Respiratory Care: 63 (1)
Respiratory Care
Vol. 63, Issue 1
1 Jan 2018
  • Table of Contents
  • Table of Contents (PDF)
  • Cover (PDF)
  • Index by author

 

Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on American Association for Respiratory Care.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Modified Medical Research Council Dyspnea Scale in GOLD Classification Better Reflects Physical Activities of Daily Living
(Your Name) has sent you a message from American Association for Respiratory Care
(Your Name) thought you would like to see the American Association for Respiratory Care web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Modified Medical Research Council Dyspnea Scale in GOLD Classification Better Reflects Physical Activities of Daily Living
Anelise B Munari, Aline A Gulart, Karoliny dos Santos, Raysa S Venâncio, Manuela Karloh, Anamaria F Mayer
Respiratory Care Jan 2018, 63 (1) 77-85; DOI: 10.4187/respcare.05636

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Share
Modified Medical Research Council Dyspnea Scale in GOLD Classification Better Reflects Physical Activities of Daily Living
Anelise B Munari, Aline A Gulart, Karoliny dos Santos, Raysa S Venâncio, Manuela Karloh, Anamaria F Mayer
Respiratory Care Jan 2018, 63 (1) 77-85; DOI: 10.4187/respcare.05636
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Methods
    • Results
    • Discussion
    • Conclusions
    • Footnotes
    • REFERENCES
  • Figures & Data
  • Info & Metrics
  • References
  • PDF

Related Articles

Cited By...

Keywords

  • activities of daily living
  • exercise
  • sedentary lifestyle
  • symptom assessment
  • dyspnea
  • chronic obstructive pulmonary disease
  • GOLD classification

Info For

  • Subscribers
  • Institutions
  • Advertisers

About Us

  • About the Journal
  • Editorial Board

AARC

  • Membership
  • Meetings
  • Clinical Practice Guidelines

More

  • Contact Us
  • RSS
American Association for Respiratory Care

Print ISSN: 0020-1324        Online ISSN: 1943-3654

© Daedalus Enterprises, Inc.

Powered by HighWire