{"title":"Intelligent wearable devices with audio collection capabilities to assess chronic obstructive pulmonary disease severity.","authors":"Chunbo Zhang, Kunyao Yu, Zhe Jin, Yingcong Bao, Cheng Zhang, Jiping Liao, Guangfa Wang","doi":"10.1177/20552076251320730","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Intelligent wearable devices have potential for chronic obstructive pulmonary disease (COPD) monitoring, but the effectiveness of combining cough and blowing sounds for disease assessment is unclear.</p><p><strong>Objective: </strong>The objective was to assess COPD severity via physiological parameters and audio data collected by a smartwatch.</p><p><strong>Methods: </strong>COPD patients underwent lung function tests, electrocardiograms, blood gas analysis, and 6-min walk tests. The patients' peripheral arterial oxygen saturation (SpO<sub>2</sub>), heart rate variability (HRV), heart rate (HR), and respiratory rate (RR) were continuously monitored via a smartwatch for 7-14 days, and voluntary cough and forceful blowing sounds were recorded twice daily. The HR, SpO<sub>2</sub>, and RR were categorized into all-day, sleep, and wake periods and summarized using the mean, standard deviation, median, 25th percentile, 75th percentile and percent variation. The correlations among lung function, physiological parameters, and audio data were analyzed to develop a model for predicting COPD severity.</p><p><strong>Results: </strong>Twenty-nine stable patients, with a mean age of 67.0 ± 5.8 years, were enrolled, and 89.7% were male. HR, HRV, RR, cough sounds, and blowing sounds were significantly correlated with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, with cough sounds showing the highest correlation (r = 0.7617, <i>p </i>< .001). Cough sounds also had the strongest correlation with the mean 6-minute walking distance (r = 0.6847, <i>p </i>< .001), whereas blowing sounds had the strongest correlation with the Body mass index, airflow Obstruction, Dyspnea, and Exercise capacity index (r = -0.6749, <i>p </i>< .001). A logistic regression model using the RR and blowing sounds as key predictors achieved accuracies of 0.77-0.89 in determining the GOLD grade, with a Cohen's kappa coefficient of 0.6757.</p><p><strong>Conclusions: </strong>Audio data were more strongly correlated with lung function in COPD patients than were physiological parameters. A smartwatch with audio collection capabilities effectively assessed COPD severity.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT05551169.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251320730"},"PeriodicalIF":2.9000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11907614/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DIGITAL HEALTH","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/20552076251320730","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Intelligent wearable devices have potential for chronic obstructive pulmonary disease (COPD) monitoring, but the effectiveness of combining cough and blowing sounds for disease assessment is unclear.
Objective: The objective was to assess COPD severity via physiological parameters and audio data collected by a smartwatch.
Methods: COPD patients underwent lung function tests, electrocardiograms, blood gas analysis, and 6-min walk tests. The patients' peripheral arterial oxygen saturation (SpO2), heart rate variability (HRV), heart rate (HR), and respiratory rate (RR) were continuously monitored via a smartwatch for 7-14 days, and voluntary cough and forceful blowing sounds were recorded twice daily. The HR, SpO2, and RR were categorized into all-day, sleep, and wake periods and summarized using the mean, standard deviation, median, 25th percentile, 75th percentile and percent variation. The correlations among lung function, physiological parameters, and audio data were analyzed to develop a model for predicting COPD severity.
Results: Twenty-nine stable patients, with a mean age of 67.0 ± 5.8 years, were enrolled, and 89.7% were male. HR, HRV, RR, cough sounds, and blowing sounds were significantly correlated with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, with cough sounds showing the highest correlation (r = 0.7617, p < .001). Cough sounds also had the strongest correlation with the mean 6-minute walking distance (r = 0.6847, p < .001), whereas blowing sounds had the strongest correlation with the Body mass index, airflow Obstruction, Dyspnea, and Exercise capacity index (r = -0.6749, p < .001). A logistic regression model using the RR and blowing sounds as key predictors achieved accuracies of 0.77-0.89 in determining the GOLD grade, with a Cohen's kappa coefficient of 0.6757.
Conclusions: Audio data were more strongly correlated with lung function in COPD patients than were physiological parameters. A smartwatch with audio collection capabilities effectively assessed COPD severity.
背景：智能可穿戴设备具有监测慢性阻塞性肺疾病（COPD）的潜力，但结合咳嗽和吹气声进行疾病评估的有效性尚不清楚。目的：通过智能手表收集的生理参数和音频数据来评估COPD的严重程度。方法：对COPD患者进行肺功能检查、心电图、血气分析和6分钟步行试验。通过智能手表连续监测患者外周血动脉血氧饱和度（SpO2）、心率变异性（HRV）、心率（HR）和呼吸频率（RR），持续7 ~ 14天，每天记录2次自主咳嗽和用力吹气声。HR、SpO2和RR分为全天、睡眠和清醒时段，并使用平均值、标准差、中位数、第25百分位、第75百分位和变异百分比进行汇总。分析肺功能、生理参数和音频数据之间的相关性，建立预测COPD严重程度的模型。结果：入组29例稳定患者，平均年龄67.0±5.8岁，男性89.7%。HR、HRV、RR、咳嗽声和吹气声与全球慢性阻塞性肺疾病倡议（GOLD）分级显著相关，其中咳嗽声相关性最高（r = 0.7617, p p p p）。结论：音频数据与COPD患者肺功能的相关性强于生理参数。具有音频收集功能的智能手表可以有效评估COPD的严重程度。试验注册：ClinicalTrials.gov NCT05551169。
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