Swaasa an AI-Based Novel Technique to Assess Lung Function in Yoga Practitioners in Comparison to Non-Yoga Practitioners: A Cross-Sectional study
- 1Salcit Technologies Private Limited
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 (FEV), 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 FEV, 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.
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BibTeX
@misc{rao2026swaasa,
title = {{Swaasa an AI-Based Novel Technique to Assess Lung Function in Yoga Practitioners in Comparison to Non-Yoga Practitioners: A Cross-Sectional study}},
author = {S. Rao and R. Pal and A. Rawat and P. Chaudhury and I. N. Acharya and S. V. Singh and K. Samagandi and S. Palreddy},
howpublished = {medRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.05.26359313},
url = {https://www.medrxiv.org/content/10.64898/2026.11.05.26359313v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Swaasa an AI-Based Novel Technique to Assess Lung Function in Yoga Practitioners in Comparison to Non-Yoga Practitioners: A Cross-Sectional study
AU - S. Rao
AU - R. Pal
AU - A. Rawat
AU - P. Chaudhury
AU - I. N. Acharya
AU - S. V. Singh
AU - K. Samagandi
AU - S. Palreddy
PY - 2026
DA - 2026/11/05
PB - medRxiv
DO - 10.64898/2026.11.05.26359313
UR - https://www.medrxiv.org/content/10.64898/2026.11.05.26359313v1
AB - 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 (FEV), 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 FEV, 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.
ER -