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A method to assess adherence in inhaler use through analysis of acoustic recordings of inhaler events.

D'Arcy S, MacHale E, Seheult J, Holmes MS, Hughes C, Sulaiman I, Hyland D, O'Reilly C, Glynn S, Al-Zaabi T, McCourt J, Taylor T, Keane F, Killane I, Reilly RB, Costello RW - PLoS ONE (2014)

Bottom Line: The correlation between clinical outcomes and adherence, as determined by this device, was compared for temporal adherence alone and combined temporal and technique adherence.Repeated training reduced this to 7% of participants (p = 0.03).EudraCT 2011-004149-42.

View Article: PubMed Central - PubMed

Affiliation: Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland.

ABSTRACT

Rationale: Poor adherence to inhaler use can be due to poor temporal and/or technique adherence. Up until now there has been no way of reliably tracking both these factors in everyday inhaler use.

Objectives: This paper introduces a device developed to create time stamped acoustic recordings of an individual's inhaler use, in which empirical evidence of temporal and technique adherence in inhaler use can be monitored over time. The correlation between clinical outcomes and adherence, as determined by this device, was compared for temporal adherence alone and combined temporal and technique adherence.

Findings: The technology was validated by showing that the doses taken matched the number of audio recordings (r2 = 0.94, p<0.01). To demonstrate that audio analysis of inhaler use gives objective information, in vitro studies were performed. These showed that acoustic profiles of inhalations correlated with the peak inspiratory flow rate (r2 = 0.97, p<0.01), and that the acoustic energy of exhalations into the inhaler was related to the amount of drug removed. Despite training, 16% of participants exhaled into the mouthpiece after priming, in >20% of their inhaler events. Repeated training reduced this to 7% of participants (p = 0.03). When time of use was considered, there was no evidence of a relationship between adherence and changes in AQLQ (r2 = 0.2) or PEFR (r2 = 0.2). Combining time and technique the rate of adherence was related to changes in AQLQ (r2 = 0.53, p = 0.01) and PEFR (r2 = 0.29, p = 0.01).

Conclusions: This study presents a novel method to objectively assess how errors in both time and technique of inhaler use impact on clinical outcomes.

Trial registration: EudraCT 2011-004149-42.

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A Bland Altman plot showing the relationship of the doses taken, recorded by the dose counter on the Diskus and the number of audio files logged on the metadata of the INCA device is shown in (A).In (B) the same data is displayed as a correlation of the doses taken to the number of audio recordings.
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pone-0098701-g004: A Bland Altman plot showing the relationship of the doses taken, recorded by the dose counter on the Diskus and the number of audio files logged on the metadata of the INCA device is shown in (A).In (B) the same data is displayed as a correlation of the doses taken to the number of audio recordings.

Mentions: The number of INCA audio recordings, which contained acoustic evidence that the drug was dispensed, was compared to the number of doses taken by the user, quantified by the mechanical counter on the inhaler. The level of agreement between these two methods of dose counting was investigated using Pearson's correlation coefficient and a Bland Altman plot, figure 4A. A Bland Altman [18] plot is a difference plot used to demonstrate the agreement between two types of measurements. In order to determine the rate of adherence the cumulative number of correctly taken doses was plotted against the cumulative number of prescribed doses (2 per day) in 7 days. A regression line was fitted to the plot and the slope of this line was calculated. This slope is compared to that of the perfect adherence regression line, i.e. slope = 2, to assess deviation from perfect adherence. The slope of the line is calculated from , where r is the correlation between X and Y, sx is the standard deviation of the X and sy is the standard deviation of the Y, see figures 5D & E. Slopes of regression lines, plotting adherence rates over time, were compared using ANCOVA to determine if the slopes of the lines are statistically different.


A method to assess adherence in inhaler use through analysis of acoustic recordings of inhaler events.

D'Arcy S, MacHale E, Seheult J, Holmes MS, Hughes C, Sulaiman I, Hyland D, O'Reilly C, Glynn S, Al-Zaabi T, McCourt J, Taylor T, Keane F, Killane I, Reilly RB, Costello RW - PLoS ONE (2014)

A Bland Altman plot showing the relationship of the doses taken, recorded by the dose counter on the Diskus and the number of audio files logged on the metadata of the INCA device is shown in (A).In (B) the same data is displayed as a correlation of the doses taken to the number of audio recordings.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4048229&req=5

pone-0098701-g004: A Bland Altman plot showing the relationship of the doses taken, recorded by the dose counter on the Diskus and the number of audio files logged on the metadata of the INCA device is shown in (A).In (B) the same data is displayed as a correlation of the doses taken to the number of audio recordings.
Mentions: The number of INCA audio recordings, which contained acoustic evidence that the drug was dispensed, was compared to the number of doses taken by the user, quantified by the mechanical counter on the inhaler. The level of agreement between these two methods of dose counting was investigated using Pearson's correlation coefficient and a Bland Altman plot, figure 4A. A Bland Altman [18] plot is a difference plot used to demonstrate the agreement between two types of measurements. In order to determine the rate of adherence the cumulative number of correctly taken doses was plotted against the cumulative number of prescribed doses (2 per day) in 7 days. A regression line was fitted to the plot and the slope of this line was calculated. This slope is compared to that of the perfect adherence regression line, i.e. slope = 2, to assess deviation from perfect adherence. The slope of the line is calculated from , where r is the correlation between X and Y, sx is the standard deviation of the X and sy is the standard deviation of the Y, see figures 5D & E. Slopes of regression lines, plotting adherence rates over time, were compared using ANCOVA to determine if the slopes of the lines are statistically different.

Bottom Line: The correlation between clinical outcomes and adherence, as determined by this device, was compared for temporal adherence alone and combined temporal and technique adherence.Repeated training reduced this to 7% of participants (p = 0.03).EudraCT 2011-004149-42.

View Article: PubMed Central - PubMed

Affiliation: Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland.

ABSTRACT

Rationale: Poor adherence to inhaler use can be due to poor temporal and/or technique adherence. Up until now there has been no way of reliably tracking both these factors in everyday inhaler use.

Objectives: This paper introduces a device developed to create time stamped acoustic recordings of an individual's inhaler use, in which empirical evidence of temporal and technique adherence in inhaler use can be monitored over time. The correlation between clinical outcomes and adherence, as determined by this device, was compared for temporal adherence alone and combined temporal and technique adherence.

Findings: The technology was validated by showing that the doses taken matched the number of audio recordings (r2 = 0.94, p<0.01). To demonstrate that audio analysis of inhaler use gives objective information, in vitro studies were performed. These showed that acoustic profiles of inhalations correlated with the peak inspiratory flow rate (r2 = 0.97, p<0.01), and that the acoustic energy of exhalations into the inhaler was related to the amount of drug removed. Despite training, 16% of participants exhaled into the mouthpiece after priming, in >20% of their inhaler events. Repeated training reduced this to 7% of participants (p = 0.03). When time of use was considered, there was no evidence of a relationship between adherence and changes in AQLQ (r2 = 0.2) or PEFR (r2 = 0.2). Combining time and technique the rate of adherence was related to changes in AQLQ (r2 = 0.53, p = 0.01) and PEFR (r2 = 0.29, p = 0.01).

Conclusions: This study presents a novel method to objectively assess how errors in both time and technique of inhaler use impact on clinical outcomes.

Trial registration: EudraCT 2011-004149-42.

Show MeSH