{"title":"Prediction of Low-intensity Physical Activity in Stable Patients with Chronic Obstructive Pulmonary Disease.","authors":"Atsuyoshi Kawagoshi, Masahiro Iwakura, Yutaka Furukawa, Keiyu Sugawara, Hitomi Takahashi, Takanobu Shioya","doi":"10.1298/ptr.E10208","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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).</p><p><strong>Methods: </strong>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<sup>2</sup>; 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.</p><p><strong>Results: </strong>Multivariate regression analysis revealed that gait speed (β = 0.369, p = 0.007) and maximum inspiratory mouth pressure (PI<sub>max</sub>) (β = 0.329, p = 0.016) were significant influence factors on LPA (R<sup>2</sup> = 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<sub>max</sub> - 20.553).</p><p><strong>Conclusion: </strong>Gait speed and PI<sub>max</sub> were extracted as influence factors on LPA, suggesting that the regression equation could predict the amount of LPA.</p>","PeriodicalId":74445,"journal":{"name":"Physical therapy research","volume":"25 3","pages":"143-149"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910347/pdf/ptr-25-143.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical therapy research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1298/ptr.E10208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
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.
目的:通过分析慢性阻塞性肺疾病(COPD)患者的临床参数,建立预测低强度体力活动(LPA)量的方程。方法:在这项横断面研究中,我们分析了53例稳定期COPD门诊患者(年龄77±6岁;46人。体质指数21.8±4.1 kg/m2;1秒用力呼气量(63.0±26.4%)。使用单轴加速度计测量连续14天1.8-2.3代谢当量的LPA步数和时间。我们还评估了身体组成、呼吸功能、骨骼肌力量、吸气肌力量、运动能力和步态速度。多变量回归分析与LPA时间相关的因素。用回归分析检验了方程预测的LPA量与实测量之间的内效度。结果:多因素回归分析显示,步态速度(β = 0.369, p = 0.007)和最大吸气口压(PImax) (β = 0.329, p = 0.016)是影响LPA的显著因素(R2 = 0.354, p max - 20.553)。结论:提取步态速度和PImax作为LPA的影响因素,表明回归方程可以预测LPA的量。