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Background: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management, and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation., Objective: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma., Methods: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the Assessment of the Validation Status of Health-Economic (AdViSHE) tool. The INCA (Inhaler Compliance Assessment) Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics])., Results: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (ie, regular inhalers) of €7,546 ($7,946) and €10,752 ($11,322), respectively, reflecting cost savings of €3,207 ($3,377) for digital inhalers. Using a 10-year intervention duration and time horizon resulted in cost savings of €26,309 ($27,703) for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 ($2,408) (introduction biosimilars) to €4,581 ($4,824) (increased control, decreased exacerbations)., Conclusions: In patients with difficult-to-treat asthma, digital inhaler-based interventions can be cost-saving in the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Goal-orientated health care accounts for patient preferences and values, not just physician treatment aims. The Global Initiative for Asthma (GINA) management strategy states that clinicians should elicit patients' own treatment goals as a central part of care. Despite this recommendation, data on patients' treatment goals are sparse among patients with severe asthma., Objective: The objective of this study is to investigate the relationship between rates of treatment adherence and goal achievement, and patient-selected goals., Methods: Thematic analysis was used to characterize patient-selected goals. Previously undescribed goal categories in asthma were identified, quantified, and related to clinical characteristics. Goal achievement was aligned with objectively measured treatment adherence., Results: Three categories of patients-selected goals were identified from 2 randomized control trials: disease-specific (n = 98 [51%] and n = 92 [54%], respectively), function-related (n = 90 [48%] and n = 61 [36%]), and knowledge (n = 1 [1%] and n = 17 [10%]). Only 53% of goals aligned with clinician treatment goals. Patients who chose disease-specific goals were more likely to achieve both control and their specified goal (n = 98 [45%], odds ratio: 1.789, confidence interval: 1.066-3.001). Male participants are more likely to focus on disease-specific goals. Patients who achieved their goals were more likely to be T2-high, have an elevated fractional exhaled nitric oxide (FeNO) at their first visit, and have a lower FeNO value at their final visit. Interestingly, adherence rates decline significantly for those who achieve their goals., Conclusion: Almost half of patient-selected goals do not align with GINA clinical asthma management goals. Participants who chose goals that do align with clinicians were more likely to achieve them., (Copyright © 2021 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Adherence to inhaled maintenance therapy in severe asthma is rarely adequately assessed, and its influence on trial outcomes is unknown. We systematically determined how adherence to maintenance therapy is assessed in clinical trials of "add-on" therapy for severe asthma. We model the improvement in trial power that could be achieved by accurately assessing adherence.A systematic search of six major databases identified randomised trials of add-on therapy for severe asthma. The relationship between measuring adherence and study outcomes was assessed. An estimate of potential improvements in statistical power and sample size was derived using digitally recorded adherence trial data.87 randomised controlled trials enrolling 22 173 participants were included. Adherence assessment was not reported in 67 trials (n=13 931, 63%). Studies that reported adherence used a range of self-report and subjective methods. None of the studies employed an objective assessment of adherence. Studies that reported adherence had a significantly reduced pooled variance in forced expiratory volume in 1 s (FEV 1 ) compared to those that did not assess adherence: s 2 =0.144 L 2 versus s 2 =0.168 L 2 , p<0.0001. Power to detect clinically relevant changes in FEV 1 was significantly higher in trials that reported adherence assessment (mean power achieved 59% versus 49%). Modelling suggests that up to 50% of variance in FEV 1 outcomes is attributable to undetected variations in adherence. Controlling for such variations could potentially halve the required sample size.Few trials of add-on therapy monitor adherence to maintenance inhaled therapy, resulting in a greater variance in trial outcomes and inadequate power for determining efficacy., Competing Interests: Conflict of interest: M.C. Mokoka has nothing to disclose. Conflict of interest: M.J. McDonnell has nothing to disclose. Conflict of interest: E. MacHale has nothing to disclose. Conflict of interest: B. Cushen has nothing to disclose. Conflict of interest: F. Boland has nothing to disclose. Conflict of interest: S. Cormican has nothing to disclose. Conflict of interest: C. Doherty has nothing to disclose. Conflict of interest: F. Doyle has nothing to disclose. Conflict of interest: R.W. Costello has nothing to disclose. Conflict of interest: G. Greene has nothing to disclose., (Copyright ©ERS 2019.)

Introduction: Exhaled nitric oxide is believed be a useful surrogate for airways inflammation while non-adherence with therapy is known to be associated with worsening of asthma control., Case: We present the case of a 49-year-old female with steroid-dependent asthma and an exacerbation rate of >20/year. She was enrolled in a 3-month-long prospective study using a validated diagnostic inhaler device that provided objective evidence of inhaler compliance. Fractional exhaled nitric oxide (FeNO), peak expiratory flow rates, asthma control questionnaires were measured throughout the study period. Peripheral eosinophil count was obtained prior to the study, during the study, and immediately afterwards., Results: Improvement in compliance at the end of the study led to significant improvements in lung function peak expiratory flow rate (PEFR), and objective scores of asthma. There was an observed improvement in PEFR after 4 weeks, with an associated decrease in FeNO from 92 to 9 ppb that plateaued over the remainder of the study. Her eosinophil count was 0.79 × 10 9 /litre prior to starting in the study, 0.37 × 10 9 /litre after 2 months, and 0.1 × 10 9 /litre at the end of the study., Conclusion: We believe that this is the first case study to objectively prove that improvements in compliance can lead to dramatic reductions in the overall inflammatory airway response and in particular that improvements in patient compliance are mirrored by marked reduction in FeNO levels. These changes occurred in tandem with an observed clinical improvement in our patient.

In severe asthma, poor control could reflect issues of medication adherence or inhaler technique, or that the condition is refractory. This study aimed to determine if an intervention with (bio)feedback on the features of inhaler use would identify refractory asthma and enhance inhaler technique and adherence.Patients with severe uncontrolled asthma were subjected to a stratified-by-site random block design. The intensive education group received repeated training in inhaler use, adherence and disease management. The intervention group received the same intervention, enhanced by (bio)feedback-guided training. The primary outcome was rate of actual inhaler adherence. Secondary outcomes included a pre-defined assessment of clinical outcome. Outcome assessors were blinded to group allocation. Data were analysed on an intention-to-treat and per-protocol basis.The mean rate of adherence during the third month in the (bio)feedback group (n=111) was higher than that in the enhanced education group (intention-to-treat, n=107; 73% versus 63%; 95% CI 2.8%-17.6%; p=0.02). By the end of the study, asthma was either stable or improved in 54 patients (38%); uncontrolled, but poorly adherent in 52 (35%); and uncontrolled, but adherent in 40 (27%).Repeated feedback significantly improved inhaler adherence. After a programme of adherence and inhaler technique assessment, only 40 patients (27%) were refractory and adherent, and might therefore need add-on therapy., Competing Interests: Conflict of interest: Disclosures can be found alongside this article at erj.ersjournals.com, (Copyright ©ERS 2018.)

Rationale: Currently, studies on adherence to inhaled medications report average adherence over time. This measure does not account for variations in the interval between doses, nor for errors in inhaler use., Objectives: To investigate whether adherence calculated as a single area under the (concentration-time) curve (AUC) measure, incorporating the interval between doses and inhaler technique, was more reflective of patient outcomes than were current methods of assessing adherence., Methods: We attached a digital audio device (INhaler Compliance Assessment) to a dry powder inhaler. This recorded when the inhaler was used, and analysis of the audio data indicated if the inhaler had been used correctly. These aspects of inhaler use were combined to calculate adherence over time, as an AUC measure. Over a 3-month period, a cohort of patients with asthma was studied. Adherence to a twice-daily inhaler preventer therapy using this device and clinical measures were assessed., Measurements and Main Results: Recordings from 239 patients with severe asthma were analyzed. Average adherence that was based on the dose counter was 84.4%, whereas the ratio of expected to observed accumulated AUC, actual adherence, was 61.8% (P < 0.01). Of all the adherence measures, only adherence calculated as AUC reflected changes in asthma quality of life, β-agonist reliever use, and peak expiratory flow over the 3 months (P < 0.05 compared with other measures of adherence)., Conclusions: Adherence that incorporates the interval between doses and inhaler technique, and calculated as AUC, is more reflective of changes in quality of life and lung function than are the currently used measures of adherence. Clinical trial registered with www.clinicaltrials.gov (NCT 01529697).

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.