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Inhalation adherence monitoring using smart electonic add-on device: Accuracy evalutaion using simulated real-life test program
Abstract
We present an evaluation of the accuracy of an adherence monitoring add-on device (SmartTurbo v2.0, Nexus6 Limited, Auckland, New Zealand) designed to fit a commercial inhalation device (Turbuhaler®, dry powder inhaler, AstraZeneca). The evaluation has been based on simulated reallife placebo usage by 11 patients and carried out during a 12 day period. The simulated usage covered low and high inhalation patterns. Of the simulated total 2089 inhalation events 2073 were correctly detected and recorded on the devices' memory. The above indicated an overall accuracy of detection of 99.2%, including possible human errors from the testers. The results confirm that the tested add-on device could successfully be utilized in clinical trials as a reliable replacement of a patient diary report.
... • The SmartTrack is an innovative adherence monitoring device, for pressurized metered-dose inhalers, that consists of an LCD screen and four push buttons that allow the navigation in the device menu [43], [44]. • The SmartTurbo (Adherium (NZ) Ltd, Auckland, New Zealand) is an electronic monitoring device that combines its use with a Turbuhaler device (AstraZeneca, UK) and consists of electromechanical sensors to identify the state on the mouthpiece of the inhaler [45]- [47]. • The Asthmapolis system relies on technology that monitors the location of blister actuations, allowing the user to gain information about the disease, such as date and time of the usage [48] and to collect timely and geographically specific information about asthma management, with a clear picture of health status [49], [50]. ...
Asthma is a common, usually long-term respiratory disease with negative impact on global society and economy. Treatment involves using medical devices (inhalers) that distribute medication to the airways and its efficiency depends on the precision of the inhalation technique. There is a clinical need for objective methods to assess the inhalation technique, during clinical consultation. Integrated health monitoring systems, equipped with sensors, enable the recognition of drug actuation, embedded with sound signal detection, analysis and identification from intelligent structures, that could provide powerful tools for reliable content management. Health monitoring systems equipped with sensors, embedded with sound signal detection, enable the recognition of drug actuation and could be used for effective audio content analysis. This paper revisits sound pattern recognition with machine learning techniques for asthma medication adherence assessment and presents the
Respiratory and Drug Actuation (RDA)
Suite
a
for benchmarking and further research. The RDA Suite includes a set of tools for audio processing, feature extraction and classification procedures and is provided along with a dataset, consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep networks’ architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses on challenges and future tendencies.
Asthma is an inflammatory condition often punctuated by episodic symptomatic worsening, and accordingly, patients with asthma might have waxing and waning adherence to controller therapy.
We sought to measure changes in inhaled corticosteroid (ICS) adherence over time and to estimate the effect of this changing pattern of use on asthma exacerbations.
ICS adherence was estimated from electronic prescription and fill information for 298 participants in the Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity. For each patient, we calculated a moving average of ICS adherence for each day of follow-up. Asthma exacerbations were defined as the need for oral corticosteroids, an asthma-related emergency department visit, or an asthma-related hospitalization. Proportional hazard models were used to assess the relationship between ICS medication adherence and asthma exacerbations.
Adherence to ICS medications began to increase before the first asthma exacerbation and continued afterward. Adherence was associated with a reduction in exacerbations but was only statistically significant among patients whose adherence was greater than 75% of the prescribed dose (hazard ratio, 0.61; 95% CI, 0.41-0.90) when compared with patients whose adherence was 25% or less. This pattern was largely confined to patients whose asthma was not well controlled initially. An estimated 24% of asthma exacerbations were attributable to ICS medication nonadherence.
ICS adherence varies in the time period leading up to and after an asthma exacerbation, and nonadherence likely contributes to a large number of these exacerbations. High levels of adherence are likely required to prevent these events.