Relationship between measures of dysfunctional breathing in a population with concerns about their breathing

Relationship between measures of dysfunctional breathing in a population with concerns about their breathing

Courtney, R., Greenwood, K.M., Cohen M., (2011) “Relationship between measures of dysfunctional breathing in a population with concerns about their breathing” Journal of Bodywork and Movement Therapies 15(1) 24-34. doi:10.1016/j.jbmt.2010.06.004

Abstract

 

Background: Dysfunctional breathing (DB) is implicated in physical and psychological
health, however evaluation is hampered by lack of rigorous definition and clearly defined measures. Screening tools for DB include biochemical measures such as end-tidal CO2, biomechanical measures such assessments of breathing pattern, breathing symptom questionnaires
and tests of breathing function such as breath holding time.
Aim: This study investigates whether screening tools for dysfunctional breathing measure distinct or associated aspects of breathing functionality.
Method: 84 self-referred or practitioner-referred individuals with concerns about their breathing were assessed using screening tools proposed to identify DB. Correlations between these measures were determined.
Results: Significant correlations where found within categories of measures however ccorrelations between variables in different categories were generally not significant. No measures were found to correlate with carbon dioxide levels.
Conclusion: DB cannot be simply defined. For practical purposes DB is probably best characterised as a multi-dimensional construct with at least 3 dimensions, biochemical, biomechanical and breathing related symptoms. Comprehensive evaluation of breathing dysfunction
should include measures of breathing symptoms, breathing pattern, resting CO2 and also include functional measures such a breath holding time and response of breathing to physical and psychological challenges including stress testing with CO2 monitoring.

Relationship between dysfunctional breathing patterns and ability to achieve target heart rate variability with features of “coherence” during biofeedback.

Relationship between dysfunctional breathing patterns and ability to achieve target heart rate variability with features of “coherence” during biofeedback.

Courtney, R., Cohen, M., van Dixhoorn J., Relationship between dysfunctional breathing patterns and ability to achieve target heart rate variability with features of “coherence” during biofeedback. Altern Ther Health Med. 2011 May-Jun;17(3):38-44.

Abstract

 

BACKGROUND:
Heart rate variability (HRV) biofeedback is a self-regulation strategy used to improve conditions including asthma, stress, hypertension, and chronic obstructive pulmonary disease. Respiratory muscle function affects hemodynamic influences on respiratory sinus arrhythmia (RSA), and HRV and HRV-biofeedback protocols often include slow abdominal breathing to achieve physiologically optimal patterns of HRV with power spectral distribution concentrated around the 0.1-Hz frequency and large amplitude. It is likely that optimal balanced breathing patterns and ability to entrain heart rhythms to breathing reflect physiological efficiency and resilience and that individuals with dysfunctional breathing patterns may have difficulty voluntarily modulating HRV and RSA. The relationship between breathing movement patterns and HRV, however, has not been investigated. This study examines how individuals’ habitual breathing patterns correspond with their ability to optimize HRV and RSA.
METHOD:
Breathing pattern was assessed using the Manual Assessment of Respiratory Motion (MARM) and the Hi Lo manual palpation techniques in 83 people with possible dysfunctional breathing before they attempted HRV biofeedback. Mean respiratory rate was also assessed. Subsequently, participants applied a brief 5-minute biofeedback protocol, involving breathing and positive emotional focus, to achieve HRV patterns proposed to reflect physiological “coherence” and entrainment of heart rhythm oscillations to other oscillating body systems.
RESULTS:
Thoracic-dominant breathing was associated with decreased coherence of HRV (r = -.463, P = .0001). Individuals with paradoxical breathing had the lowest HRV coherence (t(8) = 10.7, P = .001), and the negative relationship between coherence of HRV and extent of thoracic breathing was strongest in this group (r = -.768, P = .03).
CONCLUSION:
Dysfunctional breathing patterns are associated with decreased ability to achieve HRV patterns that reflect cardiorespiratory efficiency and autonomic nervous system balance. This suggests that dysfunctional breathing patterns are not only biomechanically inefficient but also reflect decreased physiological resilience. Breathing assessment using simple manual techniques such as the MARM and Hi Lo may be useful in HRV biofeedback to identify if poor responders require more emphasis on correction of dysfunctional breathing.