Lee L, Loudon R G, Jacobson B H, Stuebing R
Department of Communication Sciences and Disorders, University of Cincinnati, Ohio 45221.
Am Rev Respir Dis. 1993 May;147(5):1199-206. doi: 10.1164/ajrccm/147.5.1199.
Lung volumes and breathing patterns used during speech differ from those of quiet respiration and may be expected to vary with different types of lung disease. To test this possibility, 41 patients with asthma, emphysema, or sarcoidosis and 16 healthy subjects completed a speech protocol. Volumes, times, and flow rates were recorded during conversation and during a counting task. A total of 16 measured variables were derived for each breath and analyzed statistically. Alterations in speech breathing were disease and task specific. Discriminant function analysis applied to data from either speech task could correctly classify subjects with more than 50% accuracy, showing that different patterns were significantly disease specific. Compared with healthy subjects during conversation, all patients averaged a more rapid respiratory rate and increased the proportion of time spent on inspiration (Ti/Ttot). During counting, patient groups showed a variety of patterns, most commonly subordinating metabolic need to communication drive and sounding more breathless to observers. Regression analysis was used to determine how strongly changes in measured speech variables related to degree of physiologic impairment. The effect of severity of disease on speech production is distinguishable from the effect of the diagnostic category.
言语过程中所使用的肺容量和呼吸模式不同于安静呼吸时的情况，并且可能会因不同类型的肺部疾病而有所变化。为了验证这种可能性，41名患有哮喘、肺气肿或结节病的患者以及16名健康受试者完成了一项言语方案。在对话和计数任务期间记录了容量、时间和流速。每次呼吸共得出16个测量变量，并进行了统计分析。言语呼吸的改变具有疾病和任务特异性。应用于任一言语任务数据的判别函数分析能够以超过50%的准确率正确分类受试者，这表明不同模式具有显著的疾病特异性。与对话期间的健康受试者相比，所有患者的平均呼吸频率更快，吸气时间占总时间的比例（Ti/Ttot）增加。在计数期间，患者组呈现出多种模式，最常见的是将代谢需求置于交流驱动之下，并且在观察者听来呼吸更急促。回归分析用于确定所测量的言语变量的变化与生理损伤程度之间的关联强度。疾病严重程度对言语产生的影响与诊断类别所产生的影响是可区分的。
