Predicting asthma exacerbations employing remotely monitored adherence.

This Letter investigated the efficacy of a decision-support system, designed for respiratory medicine, at predicting asthma exacerbations in a multi-site longitudinal randomised control trial. Adherence to inhaler medication was acquired over 3 months from patients with asthma employing a dose counter and a remote monitoring adherence device which recorded participant's inhaler use: n = 184 (23,656 audio files), 61% women, age (mean ± sd) 49.3 ± 16.4. Data on occurrence of exacerbations was collected at three clinical visits, 1 month apart. The relative risk of an asthma exacerbation for those with good and poor adherence was examined employing a univariate and multivariate modified Poisson regression approach; adjusting for age, gender and body mass index. For all months dose counter adherence was significantly (p < 0.01) higher than remote monitoring adherence. Overall, those with poor adherence had a 1.38 ± 0.34 and 1.42 ± 0.39 (remotely monitored) and 1.25 ± 0.32 and 1.18 ± 0.31 (dose counter) higher relative risk of an exacerbation in model 1 and model 2, respectively. However, this was not found to be statistically significantly different. Remotely monitored adherence holds important clinical information and future research should focus on refining adherence and exacerbation measures. Decision-support systems based on remote monitoring may enhance patient-physician communication, possibly reducing preventable adverse events.

Monitoring Inhaler Inhalations Using an Acoustic Sensor Proximal to Inhaler Devices.

BACKGROUND: The efficacy of drug delivery from inhalers is very much dependent on the user's peak inspiratory flow rate (PIFR). Current methods to measure PIFR in inhalers are based on subjective checklists. There is a lack of methods currently available to objectively remotely monitor PIFR in pressurized metered dose inhalers (pMDIs) and dry powder inhalers (DPIs). In this study, for the first time, non-contact acoustic methods were employed to estimate PIFR through three commonly used inhalers (Diskus™ DPI, Turbuhaler™ DPI, and Evohaler™ pMDI) with the aim of applying these methods to remotely monitor inhaler inhalation technique in future clinical applications. METHODS: Each inhaler was placed inside an airtight container connected to a spirometer to measure PIFR. A high quality microphone was placed 5 cm from the mouthpiece of the inhalers to record inhalation sounds. Over 2000 inhaler inhalation sounds were recorded from 11 healthy participants. A range of temporal and spectral acoustic features from the inhalation sounds were correlated with PIFR. The variation of acoustic features and the repeatability of the inhalation acoustic spectral profile were investigated to further characterize inhaler inhalation sounds and to determine the reliability of acoustics to estimate PIFR. RESULTS: All acoustic features were significantly correlated with PIFR (p < 0.001). The mean power of the inhalation sound generated the most consistent correlation across all inhalers [R2 = 0.77 (Diskus™), R2 = 0.7 (Turbuhaler™), R2 = 0.75 (Evohaler™)]. Acoustic features generated low variation and the spectral profile of inhalation sounds was repeatable regardless of flow rate, suggesting that acoustic methods are a reliable method of estimating PIFR. CONCLUSIONS: The methods presented in this study may be employed in a wearable monitoring device in future applications to measure inhaler PIFR. Objective monitoring of PIFR in inhalers may help patients improve their inhaler inhalation technique and therefore may be of significant clinical benefit to both patients and clinicians.

Influences of gender and anthropometric features on inspiratory inhaler acoustics and peak inspiratory flow rate.

Inhalers are hand-held devices used to treat chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Medication is delivered from an inhaler to the user through an inhalation maneuver. It is unclear whether gender and anthropometric features such as age, height, weight and body mass index (BMI) influence the acoustic properties of inspiratory inhaler sounds and peak inspiratory flow rate (PIFR) in inhalers. In this study, healthy male (n=9) and female (n=7) participants were asked to inhale at an inspiratory flow rate (IFR) of 60 L/min in four commonly used inhalers (Turbuhaler(™), Diskus(™), Ellipta(™) and Evohaler(™)). Ambient inspiratory sounds were recorded from the mouthpiece of each inhaler and over the trachea of each participant. Each participant's PIFR was also recorded for each of the four inhalers. Results showed that gender and anthropometric features have the potential to influence the spectral properties of ambient and tracheal inspiratory inhaler sounds. It was also observed that males achieved statistically significantly higher PIFRs in each inhaler in comparison to females (p<;0.05). Acoustic features were found to be significantly different across inhalers suggesting that acoustic features are modulated by the inhaler design and its internal resistance to airflow.

An Acoustic-Based Method to Detect and Quantify the Effect of Exhalation into a Dry Powder Inhaler.

BACKGROUND: Dry powder inhaler (DPI) users frequently exhale into their inhaler mouthpiece before the inhalation step. This error in technique compromises the integrity of the drug and results in poor bronchodilation. This study investigated the effect of four exhalation factors (exhalation flow rate, distance from mouth to inhaler, exhalation duration, and relative air humidity) on dry powder dose delivery. Given that acoustic energy can be related to the factors associated with exhalation sounds, we then aimed to develop a method of identifying and quantifying this critical inhaler technique error using acoustic based methods. METHODS: An in vitro test rig was developed to simulate this critical error. The effect of the four factors on subsequent drug delivery were investigated using multivariate regression models. In a further study we then used an acoustic monitoring device to unobtrusively record the sounds 22 asthmatic patients made whilst using a Diskus(™) DPI. Acoustic energy was employed to automatically detect and analyze exhalation events in the audio files. RESULTS: All exhalation factors had a statistically significant effect on drug delivery (p<0.05); distance from the inhaler mouthpiece had the largest effect size. Humid air exhalations were found to reduce the fine particle fraction (FPF) compared to dry air. In a dataset of 110 audio files from 22 asthmatic patients, the acoustic method detected exhalations with an accuracy of 89.1%. We were able to classify exhalations occurring 5 cm or less in the direction of the inhaler mouthpiece or recording device with a sensitivity of 72.2% and specificity of 85.7%. CONCLUSIONS: Exhaling into a DPI has a significant detrimental effect. Acoustic based methods can be employed to objectively detect and analyze exhalations during inhaler use, thus providing a method of remotely monitoring inhaler technique and providing personalized inhaler technique feedback.

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

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.

The acoustic features of inhalation can be used to quantify aerosol delivery from a Diskus™ dry powder inhaler.

PURPOSE: Some patients are unable to generate the peak inspiratory flow rate (PIFR) necessary to de-agglomerate drug particles from dry powder inhalers (DPIs). In this study we tested the hypothesis that the acoustic parameters of an inhalation are related to the PIFR and hence reflect drug delivery. METHODS: A sensitivity analysis of the relationship of the acoustics of inhalation to simultaneously recorded airflow, in a cohort of volunteers (n = 92) was performed. The Next Generation Impactor (NGI) was used to assess in vitro drug delivery from salmeterol/fluticasone and salbutamol Diskus™ DPIs. Fine particle fraction, FPF, (<5 µm) was measured at 30-90 l/min for 2-6 s and correlated with acoustically determined flow rate (IFRc). In pharmacokinetic studies using a salbutamol (200 µg) Diskus™, volunteers inhaled either at maximal or minimal effort on separate days. RESULTS: PIFRc was correlated with spirometrically determined values (R (2) = 0.88). In in vitro studies, FPF increased as both flow rate and inhalation duration increased for the salmeterol/fluticasone Diskus™ (Adjusted R (2) = 0.95) and was proportional to flow rate only for the salbutamol Diskus™ (Adjusted R (2) = 0.71). In pharmacokinetic studies, blood salbutamol levels measured at 20 min were significantly lower when PIFRc was less than 60 l/min, p < 0.0001. CONCLUSION: Acoustically-determined PIFR is a suitable method for estimating drug delivery and for monitoring inhalation technique over time.

Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence.

Inhalers are devices which deliver medication to the airways in the treatment of chronic respiratory diseases. When used correctly inhalers relieve and improve patients' symptoms. However, adherence to inhaler medication has been demonstrated to be poor, leading to reduced clinical outcomes, wasted medication, and higher healthcare costs. There is a clinical need for a system that can accurately monitor inhaler adherence as currently no method exists to evaluate how patients use their inhalers between clinic visits. This paper presents a method of automatically evaluating inhaler adherence through acoustic analysis of inhaler sounds. An acoustic monitoring device was employed to record the sounds patients produce while using a Diskus dry powder inhaler, in addition to the time and date patients use the inhaler. An algorithm was designed and developed to automatically detect inhaler events from the audio signals and provide feedback regarding patient adherence. The algorithm was evaluated on 407 audio files obtained from 12 community dwelling asthmatic patients. Results of the automatic classification were compared against two expert human raters. For patient data for whom the human raters Cohen's kappa agreement score was [Formula: see text], results indicated that the algorithm's accuracy was 83% in determining the correct inhaler technique score compared with the raters. This paper has several clinical implications as it demonstrates the feasibility of using acoustics to objectively monitor patient inhaler adherence and provide real-time personalized medical care for a chronic respiratory illness.

An acoustic method to automatically detect pressurized metered dose inhaler actuations.

Chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) affect over 400 million people and are incurable. The pressurized metered dose inhaler (pMDI) has been the most popular inhaler device in inhaled therapy in recent times. However the pMDIs require good coordination between inhaling and actuating the inhaler to deliver the aerosolized drug most effectively. Poor coordination can greatly reduce the amount of drug delivered to a patient and therefore reducing the control of respiratory disease symptoms. Acoustic methods have been recently employed to monitor inhaler technique quite effectively. This study employs a noninvasive acoustic method to detect actuation sounds in a portable monitoring device. A total of 158 actuation sounds were obtained from a group of healthy subjects (n=5) and subjects suffering from respiratory diseases (n=15). The developed algorithm generated an overall accuracy of 99.7% demonstrating that this method may have clinical potential to monitor pMDI actuation coordination. The informative feedback from this method may also be employed in clinical training to highlight patient actuation technique.

An acoustic method of automatically evaluating patient inhaler technique.

Chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) affect millions of people worldwide. Inhalers are devices utilized to deliver medication in small doses directly to the airways in the treatment of asthma and COPD. Despite the proven effectiveness of inhaler medication in controlling symptoms, many patients suffer from technique errors leading to decreased levels of medication efficacy. This study employs a recording device attached to a commonly used dry powder inhaler (DPI) to obtain the acoustic signals of patients taking their inhaler medication. The audio files provide information on how a patient uses their inhaler over a period of one month. Manually listening to such a large quantity of audio files would be a time consuming and monotonous process and therefore an algorithm that could automatically carry out this task would be of great benefit. An algorithm was thus designed and developed to detect inhalation, exhalation and blister events in the audio signals, analyze the quantity of each event, the order in which the events took place and finally provide a score on the overall performance. The algorithm was tested on a dataset of 185 audio files obtained from five community dwelling asthmatic patients in real world environments. Evaluation of the algorithm on this dataset revealed that it had an accuracy of 92.8% in deciding the correct technique score compared to manual detection methods.

Using acoustics to estimate inspiratory flow rate and drug removed from a dry powder inhaler.

Morbidity and mortality rates of chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) are rising. There is a strong requirement for more effective management of these chronic diseases. Dry powder inhalers (DPIs) are one kind of devices currently employed to deliver medication aimed at controlling asthma and COPD symptoms. Despite their proven effectiveness when used correctly, some patients are unable to reach the inspiratory flow rate required to remove medication from the breath actuated devices and as a result, the medication does not reach the airways. This study employs an acoustic recording device, attached to a common DPI to record the audio signals of simulated inhalations. A rotameter was used to measure the flow rate through the inhaler while a milligram weighing scale was used to measure the amount of drug removed from each simulated inhalation. It was found that a strong correlation existed (R(2)>0.96) when average power, median amplitude, root mean square and mean absolute deviation were used to predict peak inspiratory flow rate. At a flow of 30 L/Min (mean absolute deviation=0.0049), it was found that 77% of the total emitted dose was removed from the inhaler. Results indicate that acoustic measurements may be used in the prediction of inspiratory flow rate and quantity of medication removed from an inhaler.

A method of estimating inspiratory flow rate and volume from an inhaler using acoustic measurements.

Inhalers are devices employed to deliver medication to the airways in the treatment of respiratory diseases such as asthma and chronic obstructive pulmonary disease. A dry powder inhaler (DPI) is a breath actuated inhaler that delivers medication in dry powder form. When used correctly, DPIs improve patients' clinical outcomes. However, some patients are unable to reach the peak inspiratory flow rate (PIFR) necessary to fully extract the medication. Presently clinicians have no reliable method of objectively measuring PIFR in inhalers. In this study, we propose a novel method of estimating PIFR and also the inspiratory capacity (IC) of patients' inhalations from a commonly used DPI, using acoustic measurements. With a recording device, the acoustic signal of 15 healthy subjects using a DPI over a range of varying PIFR and IC values was obtained. Temporal and spectral signal analysis revealed that the inhalation signal contains sufficient information that can be employed to estimate PIFR and IC. It was found that the average power (Pave) in the frequency band 300-600 Hz had the strongest correlation with PIFR (R(2) = 0.9079), while the power in the same frequency band was also highly correlated with IC (R(2) = 0.9245). This study has several clinical implications as it demonstrates the feasibility of using acoustics to objectively monitor inhaler use.

Automatic identification and accurate temporal detection of inhalations in asthma inhaler recordings.

Asthma is chronic airways disease characterized by recurrent attacks of breathlessness and wheezing. Adherence to medication regimes is a common failing for asthmatic patients and there exists a requirement to monitor such patients' adherence. The detection of inhalations from recordings of inhaler use can provide empirical evidence about patients' adherence to their asthma medication regime. Manually listening to recordings of inhaler use is a tedious and time consuming process and thus an algorithm which can automatically and accurately carry out this task would be of great value. This study employs a recording device attached to a commonly used dry powder inhaler to record the acoustic signals of patients taking their prescribed medication. An algorithm was developed to automatically detect and accurately demarcate inhalations from the acoustic signals. This algorithm was tested on a dataset of 255 separate recordings of inhaler use in real world environments. The dataset was obtained from 12 asthma outpatients who attended a respiratory clinic over a three month period. Evaluation of the algorithm on this dataset achieved sensitivity of 95%, specificity of 94% and an accuracy of 89% in detecting inhalations compared to manual inhalation detection.