Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 407, Taiwan.
Department of Medical Laboratory Science and Biotechnology, Central Taiwan University of Science and Technology, Taichung 407, Taiwan.
Biosensors (Basel). 2022 Oct 4;12(10):824. doi: 10.3390/bios12100824.
Self-monitoring for spirometry is beneficial to assess the progression of lung disease and the effect of pulmonary rehabilitation. However, home spirometry fails to meet both accuracy and repeatability criteria in a satisfactory manner. The study aimed to propose a pervasive spirometry estimation system with the six-minute walking test (6MWT), where the system with information management, communication protocol, predictive algorithms, and a wrist-worn device, was developed for pulmonary function. A total of 60 subjects suffering from respiratory diseases aged from 25 to 90 were enrolled in the study. Pulmonary function test, walking steps, and physical status were measured before and after performing the 6MWT. The significant variables were extracted to predict per step distance (PSD), forced vital capacity (FVC) and forced expiratory volume in one second (FEV). These predicted formulas were then implemented in a wrist-worn device of the proposed pervasive estimation system. The predicted models of PSD, and FVC, FEV with the 6MWT were created. The estimated difference for PSD was-0.7 ± 9.7 (cm). FVC and FEV before performing 6MWT were 0.2 ± 0.6 (L) and 0.1 ± 0.6 (L), respectively, and with a sensitivity (Sn) of 81.8%, a specificity (Sp) of 63.2% for obstructive lung diseases, while FVC and FEV after performing the 6MWT were 0.2 ± 0.7 (L) and 0.1 ± 0.6 (L), respectively, with an Sn of 90.9% and an Sp of 63.2% for obstructive lung diseases. Furthermore, the developed wristband prototype of the pulmonary function estimation system was demonstrated to provide effective self-estimation. The proposed system, consisting of hardware, application and algorithms was shown to provide pervasive assessment of the pulmonary function status with the 6MWT. This is a potential tool for self-estimation on FVC and FEV for those who cannot conduct home-based spirometry.
自我监测肺功能对于评估肺部疾病的进展和肺康复的效果是有益的。然而，家庭肺功能测试在准确性和可重复性方面都不能令人满意地满足标准。本研究旨在提出一种普及性的肺功能估计系统，结合六分钟步行测试（6MWT），该系统包括信息管理、通信协议、预测算法和腕戴设备，用于肺功能。共有 60 名年龄在 25 岁至 90 岁之间患有呼吸系统疾病的患者参加了这项研究。在进行 6MWT 前后，测量了肺功能测试、步行步数和身体状况。提取了显著变量来预测每步距离（PSD）、用力肺活量（FVC）和第一秒用力呼气量（FEV）。然后，将这些预测公式应用于所提出的普及性估计系统的腕戴设备中。创建了基于 6MWT 的 PSD、FVC 和 FEV 的预测模型。PSD 的估计差异为-0.7 ± 9.7（cm）。在进行 6MWT 之前，FVC 和 FEV 分别为 0.2 ± 0.6（L）和 0.1 ± 0.6（L），对于阻塞性肺部疾病的灵敏度（Sn）为 81.8%，特异性（Sp）为 63.2%，而在进行 6MWT 之后，FVC 和 FEV 分别为 0.2 ± 0.7（L）和 0.1 ± 0.6（L），对于阻塞性肺部疾病的 Sn 为 90.9%，Sp 为 63.2%。此外，还展示了用于肺功能估计系统的腕带原型，该原型被证明可以提供有效的自我估计。该系统由硬件、应用程序和算法组成，被证明可以通过 6MWT 对肺功能状态进行普及性评估。对于那些无法进行家庭肺功能测试的人来说，这是一种用于 FVC 和 FEV 自我估计的潜在工具。
