Swaasa an AI-Based Novel Technique to Assess Lung Function in Yoga Practitioners in Comparison to Non-Yoga Practitioners: A Cross-Sectional study
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Background: Lung function is a key indicator of respiratory health and is known to be influenced by lifestyle practices such as yoga. Recent advances in artificial intelligence (AI) have enabled non-invasive, scalable, and real-time assessment of physiological parameters. Swaasa is an AI-based novel technique designed to assess lung function through cough analysis, offering a potential alternative to conventional spirometry. Objective: The present study aimed to evaluate lung function using Swaasa among yoga practitioners and compare the findings with age- and sex-matched non-yoga practitioners in a cross-sectional design. Methods: A cross-sectional comparative study was conducted on adult participants divided into two groups: regular yoga practitioners (≥1 year of practice) and non-yoga practitioners. Lung function parameters, including Forced Vital Capacity (FVC), Forced Expiratory Volume in one second (FEV₁), and cough pattern indices, were assessed using the Swaasa AI-based system. Results: Yoga practitioners demonstrated significantly higher AI-derived lung function indices, including FVC and FEV₁, compared to non-yoga practitioners (p < 0.05). Improved respiratory health and reduced respiratory variability were also observed in the yoga group, indicating enhanced respiratory efficiency. Conclusion: The findings suggest that regular yoga practice is associated with better lung function as assessed by Swaasa.
Source: medRxiv
Institutions: Government of India, Ministry of AYUSH
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225420539