Optimizing asthma adherence with EMM

31 Mar 2022

In the 2022 American Academy of Allergy, Asthma & Immunology (AAAAI) Annual Meeting regarding asthma, Dr. William Anderson, Associate Professor of Pediatrics at the Children’s Hospital Colorado and the University of Colorado, the United States, discussed electronic medication monitoring (EMM) as part of the session entitled “Improving Adherence Outcomes for the Difficult-to-Treat Asthma Patient”.1

Dr. Anderson’s presentation aimed to identify additional data provided by utilizing the EMM platforms, examine new strategies that can be incorporated using the platforms to personalize patient care, and use the EMM data to provide predictive patient care models.1

In general, EMM includes a sensor component that is embedded or attached to an inhaler for the recording of device actuation.1 This information is then transferred to the patient’s smartphone via Bluetooth, followed by its storage on a cloud-based system, and will be made available for review by the patient and healthcare providers.1

A Cochrane database meta-analysis of EMM utilization showed a 19% increase in adherence to its use compared with the controls.1 Similarly, a systematic review based on 7 studies from 6 different populations studying the effects of consumer-directed EMM health applications integrating an inhaler-based sensor demonstrated an improvement in adherence to controller medications.1 Among these studies, 4 of them utilized the Propeller Health platforms and 2 of them utilized the Cohero/BreatheSmart platforms.1

In addition to providing medication reminders, the data collected from EMM can record time-stamped use of controller/quick-relief medications, location of inhaler use the via global positioning system (GPS) as well as symptoms and triggers, while they can also provide peak expiratory flow (PEF), inspiratory flow, spirometry measurements and asthma control test assessments.1 These personalized data allow the adjustment of patients’ medications based on their needs and exacerbations, and predict their worsening symptoms and triggers.1 The data also enable early identification of high-risk populations at the risk of adverse outcomes in order to provide them with timely interventions, transforming disease management from a reactive to a proactive approach.1

Furthermore, EMM provides better insights into a patient’s daily home routine.1 In a study of adult patients with uncontrolled asthma prescribed  inhaled corticosteroids (ICS) and short-acting beta 2-agonists (SABAs), controller use was compared using EMM data and self-reported paper diaries.1 Results showed a 25% of overestimation in controller use in patients’ diaries compared with the objective EMM data.1 Another study examined controller medication use in patients at 4 years of age or older, using the Propeller Health database and dividing patients based on their adherence into 4 groups, namely optimal, moderate, sub-optimal, and poor.1 Adherence was shown to decrease over the study period.1 However, patients in the optimal group were still adherent, while those in the poor group continued to have a low adherence.1

Controller medication use is not necessarily static over time, as patients may have variable medication use patterns.1 In patients with unintentional non-adherence due to poor inhaler technique, insufficient resources, or non-conforming patients, who miss doses or do not take medication as prescribed, the EMM platforms may provide interventions to stabilize these patterns.1

An evidence-based test of adherence to inhaler (TAI) toolkit has been developed to address barriers to adherence based on the patient’s response to inhaler adherence or TAI questionnaire that clinically classifies the challenges in inhaler use in asthma and chronic obstructive pulmonary disease (COPD).1 In this model, sporadic non-adherence will be managed with reminders, unconscious non-adherence with inhalation education or a comprehensive medication plan, and deliberate non-adherence with counseling and education.1 Hence, the incorporation of objective data from EMM may provide more accurate answers leading to more robust interventions as the TAI relies on patients recalling their medication use.1

Machine learning is the incorporation of diverse data from a multitude of sources to provide increased predictive accuracy.1 A study explored the use of telemonitoring data of daily asthma symptoms, self-reported medication administration, asthma trigger exposure, and PEF to predict exacerbations.1 Results showed that machine learning allowed the prediction of asthma exacerbations on day 8, following a 7-day window with good sensitivity and specificity.1

In conclusion, EMM can provide multiple benefits in asthma management, particularly with regard to controller medication adherence.1 It allows the utilization of an individual’s controller data to provide personalized patient care plan and management, while interventions may be needed, if necessary.1

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