Electronic Monitoring of Adherence to Inhaled Medication in Asthma

Howard, Sam; Lang, Alexandra; Patel, Mitesh; Sharples, Sarah; Shaw, Dominick

doi:10.2174/1573398x10666140429005007

Cited by 29 publications

(25 citation statements)

References 66 publications

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“…For example, D’Arcy et al 30 developed a Bluetooth inhaler device that used acoustic recordings of inhaler usage to monitor temporal and technique adherence and assess the correlation between clinical outcomes and adherence. Furthermore, studies using the US Food and Drug Administration (FDA)-approved Propellor Health (Madison, WI, USA) digital platform and inhalers demonstrated the feasibility of tracking inhaler usage and triggers 31–34 . Moreover, individuals randomized to the Propeller Health arm had more significant reductions in inhaler usage than those under routine care 35 .…”

Section: Discussionmentioning

confidence: 99%

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

et al. 2017

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The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.

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Section: Discussionmentioning

confidence: 99%

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

et al. 2017

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“…Early methodologies encompass electronic or mechanical meters integrated into the device, activated with the drug delivery button. Howard et al [16] reported the existence of several such devices, able to record the time of each drug actuation, or the total number of them. The use of audio analysis came up later as a method, which can characterize the quality of inhaler usage, while, also, monitoring the timings of each audio event.…”

Section: Related Workmentioning

confidence: 99%

Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification

Ntalianis

Fakotakis

Nousias

et al. 2020

Sensors

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Effective management of chronic constrictive pulmonary conditions lies in proper and timely administration of medication. As a series of studies indicates, medication adherence can effectively be monitored by successfully identifying actions performed by patients during inhaler usage. This study focuses on the recognition of inhaler audio events during usage of pressurized metered dose inhalers (pMDI). Aiming at real-time performance, we investigate deep sparse coding techniques including convolutional filter pruning, scalar pruning and vector quantization, for different convolutional neural network (CNN) architectures. The recognition performance has been assessed on three healthy subjects following both within and across subjects modeling strategies. The selected CNN architecture classified drug actuation, inhalation and exhalation events, with 100%, 92.6% and 97.9% accuracy, respectively, when assessed in a leave-one-subject-out cross-validation setting. Moreover, sparse coding of the same architecture with an increasing compression rate from 1 to 7 resulted in only a small decrease in classification accuracy (from 95.7% to 94.5%), obtained by random (subject-agnostic) cross-validation. A more thorough assessment on a larger dataset, including recordings of subjects with multiple respiratory disease manifestations, is still required in order to better evaluate the method's generalization ability and robustness.

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“…10 12 13 The stakeholders who should have access to the data produced by EMDs have not been determined. However, if data indicating a patient's inhaler use was putting their health at risk were available to a healthcare professional, but was not acted on, they could be open to criticism for not intervening.…”

Section: Key Messagesmentioning

confidence: 99%

“…However, if data indicating a patient's inhaler use was putting their health at risk were available to a healthcare professional, but was not acted on, they could be open to criticism for not intervening. 10 This potentially major issue is highlighted by a recent clinical trial using EMDs where researchers reported 'extreme overuse' of β-agonists in 26% of their 152 participants, with these patients actuating 32 or more doses of reliever inhaler a day. 14 To understand the potential benefits and issues that EMDs could create for the asthma care system, it is important to involve asthma stakeholders in the introduction of these devices, to help ensure they are ultimately safe and effective.…”

Section: Key Messagesmentioning

confidence: 99%

“…8 9 However, a recent review of currently available EMDs highlighted a lack of consideration for patient attitudes and stakeholder involvement as key issues facing these devices going forward. 10 Initial evidence now suggests that adolescents feel positively towards the monitoring and reminding capabilities of EMDs for helping them to demonstrate their adherence and for ensuring they remember to take their inhaler on time, with their main concerns surrounding the bulkiness and unusual appearance of the devices. 11 However, in order to improve asthma care at a population level, EMDs would also require interaction from a variety of healthcare professionals; all of whom have clinical…”

Section: Introductionmentioning

confidence: 99%

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What are the pros and cons of electronically monitoring inhaler use in asthma? A multistakeholder perspective

Howard,

Lang,

Sharples

et al. 2016

BMJ Open Resp Res

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IntroductionElectronic monitoring devices (EMDs) are the optimal method for collecting objective data on inhaler use in asthma. Recent research has investigated the attitudes of patients with asthma towards these devices. However, no research to date has formally considered the opinions of stakeholders and decision-makers in asthma care. These individuals have important clinical requirements that need to be taken into account if EMDs are to be successfully provisioned, making collecting their opinions on the key barriers facing these devices a valuable process.MethodsThree rounds of surveys in a Delphi format were used to assess the most important pros and cons of EMDs for asthma care in a sample of 31 stakeholders which included healthcare professionals and members of clinical commissioning groups.ResultsThe respondents identified 29 pros and 32 cons. Pros that were rated as most important included new visual evidence to aid clinical discussions with a patient and an increase in patient involvement and motivation. The cons that were rated as most important included a need for more clinical evidence of the effectiveness of EMDs, as well as better clarity over who has responsibilities in managing, interpreting and discussing data with a patient.ConclusionsThe research provides a guide for EMD developers by highlighting where these devices may provide the most benefit as well as prioritising the key issues that need addressing if they are to be used effectively in everyday asthma care.

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Electronic Monitoring of Adherence to Inhaled Medication in Asthma

Cited by 29 publications

References 66 publications

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification

What are the pros and cons of electronically monitoring inhaler use in asthma? A multistakeholder perspective

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