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. 2022 Nov 30;25(3):143–149. doi: 10.1298/ptr.E10208

Prediction of Low-intensity Physical Activity in Stable Patients with Chronic Obstructive Pulmonary Disease

Atsuyoshi KAWAGOSHI 1, Masahiro IWAKURA 1, Yutaka FURUKAWA 1, Keiyu SUGAWARA 1, Hitomi TAKAHASHI 2, Takanobu SHIOYA 3
PMCID: PMC9910347  PMID: 36819916

Abstract

Objective: To develop an equation of the predicted amount of low-intensity physical activity (LPA) by analyzing clinical parameters in patients with chronic obstructive pulmonary disease (COPD). Methods: In this cross-sectional study, we analyzed the assessments of clinical parameters evaluated every 6 months from the start of pulmonary rehabilitation in 53 outpatients with stable COPD (age 77 ± 6 yrs; 46 men; body mass index 21.8 ± 4.1 kg/m2; forced expiratory volume in one second 63.0 ± 26.4% pred). An uniaxial accelerometer was used to measure the number of steps and the time spent in LPA of 1.8–2.3 metabolic equivalents during 14 consecutive days. We also evaluated body composition, respiratory function, skeletal muscle strength, inspiratory muscle strength, exercise capacity, and gait speed. Factors associated with the time spent in LPA were examined by multivariate regression analysis. Internal validity between the predicted amount of LPA obtained by the equation and the measured amount was examined by regression analysis. Results: Multivariate regression analysis revealed that gait speed (β = 0.369, p = 0.007) and maximum inspiratory mouth pressure (PImax) (β = 0.329, p = 0.016) were significant influence factors on LPA (R2 = 0.354, p <0.001). The stepwise regression analysis showed a moderate correlation between the measured amount and predicted amount of LPA calculated by the regression equation (r = 0.609, p <0.001; LPA = 31.909 × gait speed + 0.202 × PImax − 20.553). Conclusion: Gait speed and PImax were extracted as influence factors on LPA, suggesting that the regression equation could predict the amount of LPA.

Keywords: COPD, Low-intensity physical activity, Prediction equation

Physical inactivity is the strongest predictor of hospitalizations and mortality in patients with chronic obstructive pulmonary disease (COPD)1,2). Current COPD treatment guidelines state that it should be considered a high priority for future COPD therapies to ameliorate inactivity3–5). Being more physically active is the ultimate goal in patients with COPD undergoing pulmonary rehabilitation (PR). In recent years, many studies have verified and reviewed the effects of interventions including PR on physical activity (PA)6–9). A systematic review verified the effect of interventions using a pedometer as a feedback tool and confirmed a significant increase in PA8–9); however, the number of studies analyzed was not large. Burge et al.10) reported a Cochrane review of interventions for promoting PA in 2020, which stated that improvement in PA had not been systematically demonstrated following any particular intervention, and that the optimal timing, components, duration, and models of interventions were unclear. Therefore, it is necessary to develop a PR program with a more concrete approach for PA improvement.

Goal setting is an effective key component in a health coaching program for successful behavior change11). A pedometer is the most useful feedback tool as an intervention to improve PA9). A reference number of steps per day has been reported in various studies on goal setting12–14), and a simple equation to calculate a recommended number of daily steps in patients with COPD has been developed15). However, PA parameters that can be recommended other than daily steps remain unclear. Furthermore, daily steps using pedometers might be underestimated due to slow pace of walking in COPD patients16,17). Patients with severe COPD, especially those with limited walking ability, may need to set a target goal of low-intensity physical activity (LPA) with their allowable level instead of the number of steps. Even patients with mild to moderate COPD who have limited walking ability due to aging or are restricted by environmental factors like being admitted to hospitals or nursing homes also need to set this goal. Moreover, the predicted amount of LPA helps to suggest their individual recommended amount of PA in goal-setting interventions in PR. Since there is a report that the more severely ill the patients, the shorter their moderate-to-vigorous intensity physical activity (MVPA)18), we hypothesize that factors (airflow obstruction, dyspnea, exercise capacity, etc.) that define the stage and severity of COPD patients primarily determine LPA.

The objectives of this study therefore were to develop a prediction equation of LPA using clinical parameters of COPD patients.

Methods

Subjects and study design

This study was a cross-sectional study. Of the 77 outpatients with COPD who were undergoing home-based PR with low-intensity exercise at Akita City Hospital, 53 patients with mild to very severe COPD were enrolled in the study, and their physical activities were measured between June 2012 and March 2021. The patient’s flow diagram is shown in Figure 1. The prescriptions of medication for the patients included long-acting muscarinic antagonist or/and β2 agonist, inhaled corticosteroid, and short-acting β2 agonist as necessary. All patients were retired and met the following inclusion criteria: (1) diagnosis of COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD)19); (2) being in a stable condition with no infection or exacerbation of COPD in the prior 3 months; (3) being able to walk unassisted and operate the device to measure PA; and (4) having no severe and/or unstable cardiac disease, orthopedic disease, or mental disorder that could impair physical activities in daily life. The detailed comorbidities in the patients are as follows: 4 osteoporosis, 5 diabetes, 12 chronic heart failure, 15 hypertension, 3 anemia, 8 hyperlipidemia, 1 chronic renal failure, 5 arthritis, and 8 spinal canal stenosis.

Fig. 1.

Fig. 1.

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Patient’s flow diagram

Study protocol

The assessment of PA in daily life, body composition, pulmonary function, skeletal muscle strength, inspiratory muscle strength, submaximal exercise capacity (six-minute walk distance [6MWD]), and gait speed was performed every 6 months from the initiation of PR by well-trained physical therapists who were independent of this study. We analyzed 88 assessments of clinical parameters in 53 participants from the initiation of PR to 3 years after. This study was approved by the medical ethical committee of Akita City Hospital and Akita University School of Health Sciences (approval No. 658). Written consent was obtained after the objective and content of the study were orally explained to the participants. All participants were informed that their privacy would be sufficiently considered. All procedures were performed according to the research ethics guidelines of the Declaration of Helsinki20).

Outcome measures

(1) Assessment of LPA in daily living

PA in daily life was assessed using a uniaxial accelerometer, Lifecorder GS (Suzuken, Aichi, Japan)21), which is a small and lightweight activity monitor (625 × 465 × 260 mm3, 40 g). The validity and reliability of this device have been proven21,22). This device categorizes the magnitude of movement (MM), which represents exercise intensity, every 4 seconds, into nine levels (MM 1 [minimal] to MM 9 [maximal]), which are subsequently converted to energy expenditure (kcal). MM levels were converted to approximate metabolic equivalents (METs) of activities. MM 1 was equivalent to slow walking (approximately 1.3 METs), while MM 9 was equivalent to fast running (approximately 9.1 METs). The participants were instructed to wear the device on their waist belts for 12 hours after waking up every day for 2 to 4 weeks. PA data were then collected and analyzed using dedicated software. We excluded participants from the analysis if the number of valid days of activity measurement was less than four or/and if they wore the device for 10 hours or less per day. Mean MM per day was calculated for each participant23). Since PA decreases during winter, the measurement was performed during seasons other than winter24). The time spent in LPA (MM 1–2; approximately 1.8–2.3 METs) and MVPA (MM 3–9; approximately 2.9–9.1 METs) was evaluated (min/day)21).

(2) Other clinical measurements

Pulmonary function was assessed by forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1/FVC, and FEV1%pred25) using a spirometer (FUDAC-77; Fukuda Denshi, Tokyo, Japan). Mouth pressure was measured as respiratory muscle strength using a respiratory dynamometer (Autospiro AS-507; MINATO Medical Science, Osaka, Japan) following American Thoracic Society (ATS)/European Respiratory Society recommendations26). Body height was determined to the nearest 0.1 cm (HP-I; Fukui Doryoki, Kyoto, Japan) with subjects standing barefoot. Body weight was assessed with a beam scale to the nearest 0.1 kg (Omron, Kyoto, Japan) with subjects standing barefoot and in light clothing. Fat-free mass was estimated with single-frequency (50 kHz) bioelectrical impedance analysis (Omron). Resistance was measured with subjects in the supine position. Hand grip strength and quadriceps femoris muscle force (QF) were measured as skeletal muscle strength. Hand grip strength was measured twice using their preferred hand by using a Smedley-type hand dynamometer (Grip-D, T.K.K.5401; Takei Scientific Instruments, Niigata, Japan), and the higher value was recorded27). For QF, the maximum isometric extension and contraction were measured at 0°/sec 80° flexion using Hydro Musculator GT-160 (OG Giken, Okayama, Japan)28). For the measurements of exercise capacity, a 6-minute walk test (6MWT) was performed according to the ATS guidelines29). The patients were not encouraged during the 6MWT. Gait speed was measured as the time taken to walk 6 m at a normal pace without deceleration, according to the measurement recommendations by the Asian working group for sarcopenia 2019, and the average result of at least two trials was recorded for analysis27). Dyspnea was assessed using modified Medical Research Council (mMRC) dyspnea scale30). Disease-specific health-related quality of life was measured using the COPD Assessment Test31). The effect of comorbidities was assessed using Charlson comorbidity index (CCI)32).

Statistical analysis

The sample size was calculated using G*Power ver 3.1.9.2 (Heinrich-Heine-Universität, Düsseldorf, Germany)33). Based on the determination coefficient of multiple regression analysis between the time spent in walking per day and 6MWD reported in our previous study34), when we set the effect size to 0.35, alpha to 0.05 (two sided), beta to 0.05, and number of predictors to 11, the calculated total sample size was 83. Therefore, the minimum sample size was set at 83 participants. The data of the patients were entered and analyzed using IBM SPSS Statistics 21.0 (IBM, Armonk, NY, USA). Normality in data distribution was assessed using the Kolmogorov–Smirnov test with p values <0.05 considered significant. Univariate regression analysis was used to evaluate the association between the time spent in LPA and each clinical measurement. Multivariate regression analysis for the time spent in LPA was performed using each clinical measurements including age and FEV1%pred as confounding factors. Stepwise regression was used to create a multivariate linear regression equation for the time spent in LPA. Linear regression analysis and residual analysis by the Kolmogorov–Smirnov test were performed to determine the validity of this equation. Moreover, Bland–Altman plots were used for comparisons between the measured and calculated amount of LPA to detect fixed and proportional bias.

Results

The mean age of the 53 patients was 77 ± 6 years, and the mean FEV1%pred was 63.0 ± 26.4%. The patients’ characteristics are presented in Table 1. The mean number of steps/day was 3570 ± 3212, and the time spent in LPA and MVPA was 25.4 ± 16.7 min/day and 16.0 ± 21.7 min/day, respectively. In all, 23, 16, 19, 20, and 10 assessments were performed at the initiation of PR, 6 months, and 1, 2, and 3 years after PR, respectively. A total of 16, 17, 10, and 2 patients performed the assessment 1, 2, 3, and 4 times, respectively (Appendix 1). All assessments were done at being in a stable condition with no infection or exacerbation of COPD in the prior 3 months.

Table 1.

Patient’s characteristics (n = 53)

mean ± SD.
BMI, body mass index; FFM, fat-free mass; FFMI, fat-free mass index; mMRC, modified Medical Research Council; GOLD, Global Initiative for Chronic Obstructive Lung Disease; CCI, Charlson comorbidity index; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; QF, quadriceps femoris muscle force; PImax, maximum inspiratory mouth pressure; 6MWD, six-minute walk distance; CAT, chronic obstructive pulmonary disease assessment test; LPA, low-intensity physical activity; MVPA, moderate-to-vigorous intensity physical activity
Age, years 77 ± 6
Gender, M/F 46/7
BMI, kg/m2 21.8 ± 4.1
FFM, kg 45.3 ± 8.9
FFMI, kg/m2 16.2 ± 4.9
mMRC scale, 0/1/2/3/4 5/19/14/10/5
GOLD stage, I/II/III/IV 15/14/21/3
CCI 1.8 ± 2.0
FVC, L 2.9 ± 1.2
FEV1, L 1.6 ± 0.8
FEV1/FVC, % 63.0 ± 26.4
FEV1, %pred 57.2 ± 19.1
Hand grip strength, kg 31.5 ± 8.2
QF, kg 38.1 ± 14.2
PImax, cmH2O 66.3 ± 27.5
6MWD, m 360.1 ± 155.9
Gait speed, m/s 1.0 ± 0.3
CAT, score 13.7 ± 9.2
LPA, min/day 25.4 ± 16.7
MVPA, min/day 16.0 ± 21.7
Steps/day 3570 ± 3212
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Association between LPA and clinical measurements by regression analysis

The time spent in LPA was significantly correlated with age, mMRC scale, hand grip strength, QF, maximum inspiratory mouth pressure (PImax), 6MWD, and gait speed by univariate regression analysis (Table 2 and Fig. 2). In multivariate regression analysis using age and FEV1%pred as confounding factors for PA, mMRC scale, PImax, 6MWD, and gait speed were also correlated with the time spent in LPA (Table 2). When the stepwise regression was utilized with these factors, gait speed and PImax were extracted as independent variables to create a linear regression equation of the time spent in LPA:

Table 2.

Univariate and multivariate regression analysis of LPA

Variables Univariate analysis Multivariate analysis
B 95% CI p-value β p-value VIF
Standardized partial regression coefficient (β) was adjusted for age and FEV1%pred.
LPA, low-intensity physical activity; B, regression coefficient; CI, confidence interval; β, standardized partial regression coefficient; VIF, variance inflation factor; BMI, body mass index; FFMI, fat-free mass index; mMRC, modified Medical Research Council; CCI, Charlson comorbidity index; FEV1, forced expiratory volume in one second; QF, quadriceps femoris muscle force; PImax, maximum inspiratory mouth pressure; 6MWD, six-minute walk distance; CAT, chronic obstructive pulmonary disease assessment test
Age –1.339 –2.014, –0.665 <0.001 – – –
BMI 0.371 –0.817, 1.560 0.536 0.091 0.428 1.007
FFMI –0.374 –2.108, 1.361 0.669 0.021 0.864 1.028
mMRC –0.6532 –10.135, –2.928 0.001 –0.292 0.014 1.271
CCI –0.980 –2.825, 0.866 0.294 –0.066 0.541 1.027
FEV1%pred 0.064 –0.103, 0.231 0.447 – – –
Hand grip 0.757 0.277, 1.237 0.002 0.216 0.063 1.136
QF 0.397 0.088, 0.706 0.012 0.207 0.063 1.065
PImax 0.286 0.165, 0.407 <0.001 0.367 0.004 1.324
6MWD 0.055 0.027, 0.083 <0.001 0.359 0.006 1.404
Gait speed 41.351 24.939, 57.764 <0.001 0.392 0.001 1.286
CAT –0.457 –0.936, 0.022 0.061 –0.160 0.141 1.041
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Fig. 2.

Fig. 2.

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Association between LPA and associated variables

(A) PImax, (B) QF, (C) 6MWD, and (D) gait speed

LPA, low-intensity physical activity; PImax, maximum inspiratory mouth pressure; QF, quadriceps femoris muscle force; 6MWD, six-minute walk distance

The time spent in LPA (min/day) = gait speed (m/s) × 31.9 + PImax (cmH2O) × 0.2 - 20.553

Validity of calculated LPA

A significant correlation was observed between the measured and calculated time spent in LPA by linear regression analysis (R2 = 0.362, p <0.001; Appendix 2A), and the validity of this linear regression equation was examined by residual analysis (D = 0.081, p = 0.200; Appendix 3). There was no fixed bias (mean of difference 95% confidence interval: −3.013 to 3.381, limit of agreement: −27.1 to 27.1) but there was proportional bias (r = 0.479, p <0.001) by Bland–Altman plots (Appendix 2B).

Discussion

The presented data showed that the time spent in LPA was significantly correlated with age, mMRC scale, hand grip strength, QF, PImax, 6MWD, and gait speed by univariate and multivariate regression analyses. PImax and gait speed were utilized to create an equation for the time spent in LPA in patients with COPD using stepwise regression analysis (the time spent in LPA (min/day) = gait speed (m/s) × 31.9 + PImax (cmH2O) × 0.2 − 20.553). The validity of this equation was demonstrated by linear regression analysis and residual analysis. Using Bland–Altman plots, there was no fixed bias but there was proportional bias on the reliability of this equation.

A wide variety of factors are associated with or affect PA34–36). A systematic review by Gimeno-Santos et al.35) reported that PA is influenced by age, gender, social factors, lifestyle, environment, as well as clinical and functional factors, such as dyspnea, exercise capacity, and disease severity. As for the influence of medications and comorbidities, bronchodilators had been prescribed to all subjects, and CCI was not a statistically significant factor for LPA as presented in our multivariate analysis; thus, it is concluded that they were not major confounding factors in this study. In addition, gait speed is reported to be correlated with PA measured by daily steps in previous studies37,38). Exercise intensity referred to daily activities that require standing such as housework and gardening, which might be associated with gait speed that reflects lower limb function rather than QF. Moreover, the activity monitor used in the present study is a uniaxial accelerometer and can detect movement related to walking, so that gait speed was extracted as a factor related to walking in the analysis. Exercise capacity such as 6MWD has been found to be associated with PA34,35,39). It is considered that gait speed that prescribes 6MWD indirectly affects PA and that the parameters of lower limb function more strongly affect the daily activities that require standing than the factors of exercise ability.

The equation created using stepwise regression analysis did not include mMRC scale. This may be because the COPD patients had lower intensity of daily physical activities in order to not cause dyspnea. COPD patients tend to perform daily activities more slowly than healthy individuals, to prevent dyspnea16). Those who have mild dyspnea can conduct more PA with relatively higher intensity like MVPA rather than LPA, while on the other hand severe dyspnea patients are not able to achieve even LPA in daily life. The time spent in LPA as a dependent variable is thus unlikely to be related to dyspnea as mMRC scale. Likewise, LPA is considered not necessarily dependent on the degree of airflow obstruction like FEV1%pred, which was not a significant factor in the multivariate analysis. There have been reports that dynamic hyperinflation, which is a major factor causing dyspnea during exertion in patients with COPD, is associated with PA36,40). Inspiratory capacity (IC) that indicates the degree of dynamic hyperinflation has also been reported to affect daily steps36). Inspiratory muscle strength, which is measured as PImax, contributes to IC and is associated with time spent in walking and standing in daily life34). Since inspiratory muscle training may increase the amount of PA by improving dynamic pulmonary hyperinflation with the improvement of PI41)max, it is considered that PImax was also extracted in the stepwise regression analysis as a factor of respiratory function. As a result, it is suggested that a new prediction equation of the time spent in LPA as PA other than daily steps using PImax and gait speed may be generally utilized in various clinical practices.

The internal validity of the equation for the time spent in LPA in this study was confirmed by a significant correlation between the measured and calculated LPA by linear regression analysis and residual analysis. The multiple correlation coefficient of this equation (R2 = 0.349), however, was insufficient for the calculation of predicted amount of LPA. This may have been resulted from various confounding factors related to PA in the patients with COPD35). In our study, there was no fixed bias but there was proportional bias between the measured and calculated time spent in LPA. This proportional bias suggests that the measured time spent in LPA tends to be slightly higher than the calculated time. Based on this result, the calculated time spent in LPA is considered to be the predicted amount of LPA and can be used as a minimum amount of PA for goal setting in COPD patients, especially in severe and very severe cases.

The prevalence of sarcopenia, which is associated with a high morbidity rate in elderly COPD patients42), is reported to be higher in patients with severe COPD43). In patients with COPD and muscle wasting (fat-free mass index <16 kg/m2 for male and <15 kg/m2 for female), inflammatory markers increase with submaximal exercise44). Thus, it might be important to suggest not only the amount but also the intensity of PA in patients with sarcopenic COPD. Moreover, pedometers are known to have a reduced sensitivity when measuring steps during slow walking17), which is commonly observed in COPD patients16). As a target indicator of PA, low-intensity activity time is more useful than the number of steps, especially for severe COPD patients.

There are several limitations to be addressed in this study. First, the number of accumulated data for analysis was 88 assessments from 53 participants, which might be influenced by case bias of each characteristic and assessment data from the patients who have been multi-evaluated. In addition, the 88 assessments were influenced from confounding factors over time including treatment management and the type of exercise therapy and must also be considered. On the other hand, it is also a single-center study, and although the results are limited, it is possible that the relationship between the LPA and other indicators correlated with LPA might be shown as one trend. We calculated the sample size based on our previous study that investigated the correlation between the time spent in walking and 6MWD because there are no reports to analyze the relationship between LPA and other factors. Thus, we set the effect size to 0.35 and attained the result by power calculation for the sample size. On account of this, a different number of subjects other than 83 might be required when using another set of data. Second, this study did not investigate the correlation of LPA with other factors, including gender, comorbidities, mental status, and environmental and social factors; therefore, it had proportional bias between the measured and calculated LPA obtained from the equation. The predicted equation in this study uses PImax, which has not been easily obtainable in clinical practice so far, thus making the applicability limited. In addition, in a clinical setting, this predicted equation is limited to use for patients with COPD who have been assigned to a PR. Further study is required to create a more precise and suitable equation of LPA using other factors. Third, the predicted amount of LPA might be required in goal setting, especially for severe COPD patients, but we analyzed the data of the patients regardless of severity. The equation of the predicted amount of LPA should be created by analyzing data of COPD patients with all degrees of severity in a future study. Overall, this equation only indicates a reference value of LPA that might be associated with PImax and gait speed.

Conclusion

In summary, the equation for the predicted amount of LPA was created using gait speed and PImax in our present study. This equation could be useful for goal setting in patients with COPD. However, the precision and suitability of this equation create several limitations for application in all clinical settings. Further studies are needed to create a more precise equation in COPD patients with varying severity.

Acknowledgments

The authors are grateful to the members of the rehabilitation staff at Akita City Hospital for their assistance with data collection.

Conflict of Interest

The authors have no conflicts of interest to declare.

Supplementary Material (Appendix)

All supplementary files are available online.

Appendix 1.

Flow diagram of the number of assessments in 53 patients

Click here for additional data file. (49.8KB, pdf)
Appendix 2.

The validity between the measured and calculated LPA

Click here for additional data file. (90.8KB, pdf)
Appendix 3.

Residual analysis between the measured and calculated LPA by the Kolmogorov–Smirnov test (D = 0.081, p = 0.200)

Click here for additional data file. (44.7KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix 1.

Flow diagram of the number of assessments in 53 patients

Click here for additional data file. (49.8KB, pdf)
Appendix 2.

The validity between the measured and calculated LPA

Click here for additional data file. (90.8KB, pdf)
Appendix 3.

Residual analysis between the measured and calculated LPA by the Kolmogorov–Smirnov test (D = 0.081, p = 0.200)

Click here for additional data file. (44.7KB, pdf)

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