Smartwatch-based ventilatory assessment for COPD screening: A diagnostic accuracy study.

Background: COPD underdiagnosis persists in China due to limited spirometry access. Smart wearables enabling cough and physiological monitoring (SpO 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 FEV 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.