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

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Journal Phys Ther Res
Date 2023 Feb 23
PMID 36819916
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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/m; 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 (PI) (β = 0.329, p = 0.016) were significant influence factors on LPA (R = 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 × PI - 20.553).

Conclusion: Gait speed and PI were extracted as influence factors on LPA, suggesting that the regression equation could predict the amount of LPA.

Citing Articles

Neural Network Approach to Investigating the Importance of Test Items for Predicting Physical Activity in Chronic Obstructive Pulmonary Disease.

Nakahara Y, Mabu S, Hirano T, Murata Y, Doi K, Fukatsu-Chikumoto A J Clin Med. 2023; 12(13).

PMID: 37445332 PMC: 10342246. DOI: 10.3390/jcm12134297.

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