Background A new digital peak flow meter, known as Smart Peak Flow (SPF), has been developed to monitor asthma patients’ peak expiratory flow (PEF) at home. It is connected wirelessly to any type of smartphone and it is used by asthma patients to self-monitor progress of their clinical condition. Thus evaluation of the SPF’s ability to provide accurate PEF values is essential. The aim of this pilot work was to provide preliminary in-vivo data about the measurement agreement between the SPF and a lab spirometer for a first time. Methods PEF measurements were obtained by 9 healthy adults as this pilot work was terminated earlier than it was expected due to COVID-19 restrictions. PEF readings (n=27) were recorded by the comparable devices at the same time during three different expiratory maneuvers performed by each participant. The Bland and Altman plot was used to assess the agreement. Results Good agreement between the SPF and the lab spirometer was found with the mean bias being estimated 0.29 L/min. The lower and upper limits of agreement (LOA) were estimated 30.03 L/min and −30.61 L/min respectively. Conclusion Due to a small sample size, no firm conclusions can be drawn regarding the SPF’s accuracy. However the current promising results encourage further testing in the future.
Background Breathing pattern disorders are frequently reported in uncontrolled asthma. At present, this is primarily assessed by questionnaires, which are subjective. Objective measures of breathing pattern components may provide additional useful information about asthma control. This study examined whether respiratory timing parameters and thoracoabdominal (TA) motion measures could predict and classify levels of asthma control. Methods One hundred twenty-two asthma patients at STEP 2- STEP 5 GINA asthma medication were enrolled. Asthma control was determined by the Asthma Control Questionnaire (ACQ7-item) and patients divided into ‘well controlled’ or ‘uncontrolled’ groups. Breathing pattern components (respiratory rate (RR), ratio of inspiration duration to expiration duration (Ti/Te), ratio of ribcage amplitude over abdominal amplitude during expiration phase (RCampe/ABampe), were measured using Structured Light Plethysmography (SLP) in a sitting position for 5-min. Breath-by-breath analysis was performed to extract mean values and within-subject variability (measured by the Coefficient of Variance (CoV%). Binary multiple logistic regression was used to test whether breathing pattern components are predictive of asthma control. A post-hoc analysis determined the discriminant accuracy of any statistically significant predictive model. Results Fifty-nine out of 122 asthma patients had an ACQ7-item < 0.75 (well-controlled asthma) with the rest being uncontrolled (n = 63). The absolute mean values of breathing pattern components did not predict asthma control (R2 = 0.09) with only mean RR being a significant predictor (p < 0.01). The CoV% of the examined breathing components did predict asthma control (R2 = 0.45) with all predictors having significant odds ratios (p < 0.01). The ROC curve showed that cut-off points > 7.40% for the COV% of the RR, > 21.66% for the CoV% of Ti/Te and > 18.78% for the CoV% of RCampe/ABampe indicated uncontrolled asthma. Conclusion The within-subject variability of timing parameters and TA motion can be used to predict asthma control. Higher breathing pattern variability was associated with uncontrolled asthma suggesting that irregular resting breathing can be an indicator of poor asthma control.
Background: Breathing pattern disorders are frequently reported in uncontrolled asthma. At present, this is primarily assessed by questionnaires, which are subjective. Objective measures of breathing pattern components can provide additional useful information about asthma control. This study examined whether respiratory timing parameters and thoracoabdominal (TA) motion measures could predict and classify levels of asthma control. Methods: 122 asthma patients at STEP 2- STEP 5 GINA asthma medication were enrolled. Asthma control was determined by the Asthma Control Questionnaire (ACQ7-item) and patients divided into ‘well controlled’ or ‘uncontrolled’ groups. Breathing pattern components (respiratory rate (RR), ratio of inspiration duration to expiration duration (Ti/Te), ratio of ribcage amplitude over abdominal amplitude during expiration phase (RCampe/ABampe), were measured using Structured Light Plethysmography (SLP) in a sitting position for 5-minutes. Breath-by-breath analysis was performed to extract mean values and within-subject variability (measured by the Coefficient of Variance (CoV%). Binary multiple logistic regression was used to test whether breathing pattern components are predictive of asthma control. A post-hoc analysis determined the discriminant accuracy of any statistically significant predictive model. Results: Fifty-nine out of 122 asthma patients had an ACQ7-item < 0.75 (well-controlled asthma) with the rest being uncontrolled (n= 63). The absolute mean values of breathing pattern components did not predict asthma control (R2 = 0.09) with only mean RR being a significant predictor (p < 0.01). The CoV% of the examined breathing components did predict asthma control (R2 = 0.45) with all predictors having significant odds ratios (p < 0.01). The ROC curve showed that cut-off points > 7.40% for the COV% of the RR, > 21.66% for the CoV% of Ti/Te and > 18.78% for the CoV% of RCampe/ABampe indicated uncontrolled asthma. Conclusion: The within-subject variability of timing parameters and TA motion can be used to predict asthma control. Higher breathing pattern variability was associated with uncontrolled asthma suggesting that irregular resting breathing is an indicator of poor asthma control.
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