Evidence map›Paper›PMID 41022805›Full record

ArticleScientific reports2025

Association of daily lung condition in COPD patients with wearable speech and physiological data.

Sejal Bhalla, Deshang Kong, Salaar Liaqat, Daniyal Liaqat, Robert Wu, Andrea Gershon, Eyal de Lara, Alex Mariakakis

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sejal BhallaComputer Science, University of Toronto, Toronto, ON, Canada. sejal@cs.toronto.edu.ORCID http://orcid.org/0009-0006-4772-7815
Deshang KongComputer Science, University of Toronto, Toronto, ON, Canada.
Salaar LiaqatComputer Science, University of Toronto, Toronto, ON, Canada.
Daniyal LiaqatComputer Science, University of Toronto, Toronto, ON, Canada.
Robert WuUniversity Health Network, Toronto, ON, Canada.
Andrea GershonSunnybrook Health Sciences Centre, Toronto, ON, Canada.
Eyal de LaraComputer Science, University of Toronto, Toronto, ON, Canada.
Alex MariakakisComputer Science, University of Toronto, Toronto, ON, Canada.

Funding

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology) RGPIN-2017-06618Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology) RGPIN-2021-03457Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre (Skin Research Training Centre) 155458
6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) is a prevalent condition that imposes significant burden on patients and healthcare systems, with COPD exacerbations being a leading cause of hospitalizations and mortality worldwide. Continuous monitoring of lung function is crucial for effective management, yet traditional methods are often cumbersome and require clinic visits. Wearable technology offers a non-invasive alternative, enabling the monitoring of physiological parameters in real-world settings. In this study, we analyzed free-living speech data collected via smartwatches from 18 COPD patients over an average period of 198.9 ± 122.6 days. Utilizing linear mixed-effects models, we discovered that phonation features are negatively associated with adverse COPD outcomes, while prosodic speech features show a positive correlation with increased exacerbation risk. Further analysis revealed that these associations are significantly moderated by physiological covariates such as heart rate variability and physical activity levels. These findings highlight the complex interplay between respiratory function, autonomic regulation, and vocal production, suggesting that the integration of speech analysis with physiological monitoring through wearables can lead to the development of composite digital biomarkers of impaired lung function.

Indexed as

LungPulmonary Disease, Chronic ObstructiveSpeechWearable Electronic DevicesAgedFemaleHeart RateHumansMaleMiddle AgedMonitoring, PhysiologicRespiratory Function TestsContinuous health sensingCOPDPhysiological data

Identifiers

PMID41022805
PMCPMC12480672

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.