Peer Reviewed

1

Document Type

Article

Publication Date

1-2-2018

Keywords

Administration, Inhalation, Adolescent, Adult, Aged, Aged, 80 and over, Asthma, Bronchodilator Agents, Female, Humans, Male, Middle Aged, Monitoring, Physiologic, Nebulizers and Vaporizers, Pulmonary Disease, Chronic Obstructive, Sound, Young Adult

Funder/Sponsor

Enterprise Ireland Innovation Partnership (2015/0412/B). Irish Research Council (IRC) Enterprise Partnership Scheme (EPS), Vitalograph Ireland (Ltd.) Health Research Board (HRB) Clinician Scientist Award (CSA/59)

Comments

The original article is available at www.nature.com

Abstract

Many patients make critical user technique errors when using pressurised metered dose inhalers (pMDIs) which reduce the clinical efficacy of respiratory medication. Such critical errors include poor actuation coordination (poor timing of medication release during inhalation) and inhaling too fast (peak inspiratory flow rate over 90 L/min). Here, we present a novel audio-based method that objectively assesses patient pMDI user technique. The Inhaler Compliance Assessment device was employed to record inhaler audio signals from 62 respiratory patients as they used a pMDI with an In-Check Flo-Tone device attached to the inhaler mouthpiece. Using a quadratic discriminant analysis approach, the audio-based method generated a total frame-by-frame accuracy of 88.2% in classifying sound events (actuation, inhalation and exhalation). The audio-based method estimated the peak inspiratory flow rate and volume of inhalations with an accuracy of 88.2% and 83.94% respectively. It was detected that 89% of patients made at least one critical user technique error even after tuition from an expert clinical reviewer. This method provides a more clinically accurate assessment of patient inhaler user technique than standard checklist methods.

Disciplines

Medicine and Health Sciences

Citation

Taylor TE, Zigel Y, Egan C, Hughes F, Costello RW, Reilly RB. Objective Assessment of Patient Inhaler User Technique Using an Audio-Based Classification Approach. Scientific Reports. 2018;8(1):2164.

PubMed ID

29391489

DOI Link

10.1038/s41598-018-20523-w

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

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