What question did this study set out to answer?

The study aims to assess if quantifiable breathing pattern parameters can differentiate asthma patients with dysfunctional breathing from those without it.

April 18, 2026Open Access

Characterising dysfunctional breathing in patients with asthma by quantifying breathing pattern parameters

Key Points

The study aims to assess if quantifiable breathing pattern parameters can differentiate asthma patients with dysfunctional breathing from those without it.
Observational cross-sectional design with 122 adults diagnosed with asthma
Participants completed the Nijmegen Questionnaire to classify as DB or non-DB
Resting breathing was recorded for 5 minutes using structured light plethysmography to measure breathing parameters
Breathing parameters included respiratory rate, inspiration time, and more
Statistical analysis included Mann-Whitney U tests and binary logistic regression models.
38 patients were identified with dysfunctional breathing.
The DB group had significantly higher respiratory rates and lower inspiration and expiration times.
Within-subject variability was significantly greater in the DB group across all measured parameters.
The model using variability demonstrated improved predictive power, indicating significant predictors for DB.

Abstract

Aim: Dysfunctional breathing (DB) is a common comorbidity in asthma, yet its objective characterisation remains challenging due to reliance on subjective questionnaires. This study aimed to determine whether quantifiable breathing pattern parameters can be used to characterise asthma patients with perceived DB from those without DB. Methods: This observational cross-sectional study involved 122 adults with physician-diagnosed asthma (GINA Steps 2–5). Participants completed the Nijmegen Questionnaire (NQ) to determine DB (NQ > 23) or non-DB (NDB). Resting breathing was recorded for 5 minutes using structured light plethysmography (SLP), a contactless optical motion-analysis system that quantifies thoracoabdominal displacement. Extracted parameters included respiratory rate (RR), inspiration time (Ti), expiration time (Te), Ti/total breath cycle duration (Ttot), and the ratio of ribcage to abdominal displacement during the inspiration phase (RCampinsp/ABampinsp). Both absolute values and within-subject variability coefficient of variation expressed in percentage (CoV%) were estimated. Between-group comparisons used Mann-Whitney U tests. Two binary logistic regression models evaluated the predictive value of mean parameters and their variability for characterising DB. Results: Of the 122 participants, 38 asthmatic patients were determined with DB. The DB group showed significantly higher RR and lower Ti and Te, but no differences in Ti/Ttot or RCampinsp/ABampinsp. In contrast, within-subject variability across all parameters was significantly greater in the DB group. The regression model using absolute values showed limited predictive power (R2 = 0.251). The model incorporating variability demonstrated substantially improved predictive power (R2 = 0.540), with CoV% RR, Te, Ti/Ttot, and RCampinsp/ABampinsp emerging as significant predictors. Conclusions: Asthma patients with DB exhibit breathing-pattern alterations, most notably increased within-subject variability of timing and TA movement parameters. Breathing pattern variability may be a promising surrogate marker to characterise DB, highlighting the value of dynamic objective physiological assessment of breathing beyond conventional symptom-based assessments of DB.

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