Digital health · 2025
Smartwatch-based ventilatory assessment for COPD screening: A diagnostic accuracy study.
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Chen Yibing, Cao Lu, Zhao Dahui, Meng Song, Li Dan, Li Jing, Cui Yuqi, Xie Lixin
Аннотация
BACKGROUND: COPD underdiagnosis persists in China due to limited spirometry access. Smart wearables enabling cough and physiological monitoring (SpO2, respiratory rate) offer a scalable screening solution.
METHODS: Participants were randomly allocated to training and validation cohorts. All underwent cough sound recordings, smartwatch monitoring (heart rate variability, respiratory rate, oxygen saturation), and pre-/post-bronchodilator spirometry. Machine learning algorithms extracted cough sound features to predict lung function (evaluated via MAE, Pearson correlation, and Bland-Altman analysis). These predictions were combined with physiological data in a multimodal COPD screening model, with diagnostic performance assessed against physician diagnosis.
RESULTS: The training cohort included 178 patients (112 males) with COPD or pulmonary dysfunctions, aged 54.42 ± 14.77 years, BMI 24.81 ± 3.73 kg/m², FVC 3.64 ± 1.09 L, and FEV1 2.42 ± 0.96 L, alongside 298 healthy volunteers (151 males) aged 35.3 ± 12.35 years, BMI 22.62 ± 3.12 kg/m², FVC 3.63 ± 0.89 L, and FEV1 3.14 ± 0.73 L. The validation cohort comprised 47 COPD patients (35 males) aged 65.53 ± 7.62 years, BMI 25.38 ± 4.38 kg/m², FVC 3.27 ± 0.59 L, and FEV1 1.91 ± 0.50 L, and 71 healthy controls (27 males) aged 45.51 ± 12.15 years, BMI 25.79 ± 4.00 kg/m², FVC 3.35 ± 0.80 L, and FEV1 2.72 ± 0.67 L. Using cough sounds, the model's mean absolute error for FEV1/FVC, FVC%, and FEV1% prediction was 7.4%, 10.6%, and 17.78% ( Table 3 - 5), respectively, compared to spirometry. Significant correlations were found between predicted and measured FVC (r = 0.798, P < 0.001), FEV1 (r = 0.752, P < 0.001), and FEV1/FVC (r = 0.784, < 0.001) ( Table 6). Combined with physiological parameters, our model's overall accuracy, sensitivity, and specificity for differentiating between COPD and normal controls were 87.82%, 86.96%, and 87.73% ( Table 9).
CONCLUSION: Our wearable-based algorithm effectively screens for ventilatory dysfunction and COPD, showing potential for large-scale population screening to reduce medical burdens.
TRIAL REGISTRATION: Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR2100050843; Registration Date: 2021-9-4 Clinical Trial Number: ChiCTR2100050843. https://www.chictr.org.cn/showproj.html?proj=126556.
METHODS: Participants were randomly allocated to training and validation cohorts. All underwent cough sound recordings, smartwatch monitoring (heart rate variability, respiratory rate, oxygen saturation), and pre-/post-bronchodilator spirometry. Machine learning algorithms extracted cough sound features to predict lung function (evaluated via MAE, Pearson correlation, and Bland-Altman analysis). These predictions were combined with physiological data in a multimodal COPD screening model, with diagnostic performance assessed against physician diagnosis.
RESULTS: The training cohort included 178 patients (112 males) with COPD or pulmonary dysfunctions, aged 54.42 ± 14.77 years, BMI 24.81 ± 3.73 kg/m², FVC 3.64 ± 1.09 L, and FEV1 2.42 ± 0.96 L, alongside 298 healthy volunteers (151 males) aged 35.3 ± 12.35 years, BMI 22.62 ± 3.12 kg/m², FVC 3.63 ± 0.89 L, and FEV1 3.14 ± 0.73 L. The validation cohort comprised 47 COPD patients (35 males) aged 65.53 ± 7.62 years, BMI 25.38 ± 4.38 kg/m², FVC 3.27 ± 0.59 L, and FEV1 1.91 ± 0.50 L, and 71 healthy controls (27 males) aged 45.51 ± 12.15 years, BMI 25.79 ± 4.00 kg/m², FVC 3.35 ± 0.80 L, and FEV1 2.72 ± 0.67 L. Using cough sounds, the model's mean absolute error for FEV1/FVC, FVC%, and FEV1% prediction was 7.4%, 10.6%, and 17.78% ( Table 3 - 5), respectively, compared to spirometry. Significant correlations were found between predicted and measured FVC (r = 0.798, P < 0.001), FEV1 (r = 0.752, P < 0.001), and FEV1/FVC (r = 0.784, < 0.001) ( Table 6). Combined with physiological parameters, our model's overall accuracy, sensitivity, and specificity for differentiating between COPD and normal controls were 87.82%, 86.96%, and 87.73% ( Table 9).
CONCLUSION: Our wearable-based algorithm effectively screens for ventilatory dysfunction and COPD, showing potential for large-scale population screening to reduce medical burdens.
TRIAL REGISTRATION: Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR2100050843; Registration Date: 2021-9-4 Clinical Trial Number: ChiCTR2100050843. https://www.chictr.org.cn/showproj.html?proj=126556.