Monitoring Adherence to Asthma Inhalers Using the InspirerMundi App: Analysis of Real-World, Medium-Term Feasibility Studies

Background: Poor medication adherence is a major challenge in asthma and objective assessment of inhaler adherence is needed. InspirerMundi app aims to monitor inhaler adherence while turning it into a positive experience through gamification and social support.Objective: We assessed the medium-term feasibility of the InspirerMundi app to monitor inhaler adherence in real-world patients with persistent asthma (treated with daily inhaled medication). In addition, we attempted to identify the characteristics of the patients related to higher app use.Methods: Two real-world multicenter observational studies, with one initial face-to-face visit and a 4-month telephone interview, were conducted in 29 secondary care centers from Portugal. During an initial face-to-face visit, patients were invited to use the app daily to register their asthma medication intakes. A scheduled intake was considered taken when patients took a photo of the medication (inhaler, blister, or others) using the image-based medication detection tool. Medication adherence was calculated as the number of doses taken as a percentage of the number scheduled. Interacting with the app ≥30 days was used as the cut-off for higher app use.Results: A total of 114 patients {median 20 [percentile 25 to percentile 75 (P25–P75) 16–36] years, 62% adults} were invited, 107 (94%) installed the app and 83 (73%) completed the 4-month interview. Patients interacted with the app for a median of 18 [3–45] days, translated on a median use rate of 15 [3–38]%. Median inhaler adherence assessed through the app was 34 [4–73]% when considering all scheduled inhalations for the study period. Inhaler adherence assessed was not significantly correlated with self-reported estimates. Median adherence for oral and other medication was 41 [6–83]% and 43 [3–73]%, respectively. Patients with higher app use were slightly older (p = 0.012), more frequently taking medication for other health conditions (p = 0.040), and more frequently prescribed long-acting muscarinic antagonists (LAMA, p = 0.024). After 4 months, Control of Allergic Rhinitis and Asthma Test (CARAT) scores improved (p < 0.001), but no differences between patients interacting with the app for 30 days or less were seen.Conclusions: The InspirerMundi app was feasible to monitor inhaler adherence in patients with persistent asthma. The persistent use of this mHealth technology varies widely. A better understanding of characteristics related to higher app use is still needed before effectiveness studies are undertaken.

Critical Inhaler Administration Errors of Patients on Pressurized Meter Dose Inhaler (pMDI): A Hospital-Based Cross-Sectional Study in Malaysia

Background: Inhaler technique errors can affect the disease outcome through reduced deposition of medication in the lungs with certain errors were more likely to deter the optimum drug from reaching the lung. The objective of this study was to evaluate the pressurized metered-dose inhaler (pMDI) critical technique errors among asthma and COPD patients, and to investigate the predictors of those errors. Methods: A cross-sectional, observational, and multi-centered study conducted in 3 major hospitals in the state of Penang, Malaysia. Asthma and COPD patients aged 18 years or older consented to the study were consecutively recruited if they were at least on 1 pMDI without mouthpiece spacer or facemask space. Direct observation of the patient’s technique was recorded using a standard checklist and certain steps in the checklist were pre-determined as critical steps. The number of patients missed or incorrectly performed the steps including the critical steps were documented. Using logistic regression, the factors associated with the critical errors were determined, and the correlation was evaluated between age and pMDI knowledge score with critical inhalation steps score. Results: Of 209 patients observed, only less than half (40.7%) could perform all the critical pMDI steps correctly. Low education level was the only factor associated with critical inhaler errors. The increased knowledge of their inhaled medication was found to positively correlate with the correct critical inhalation steps. The critical errors also increase with the increased age of patients. Conclusion: The majority of the study population make inhalation error particularly in critical steps. Training and education intervention programs may help patients with lower education level, as well as those of advanced age and lack of knowledge of their inhaled medication because those populations are at a greater risk of committing critical errors in their use of pMDI.

A Clinical Perspective on the Role of Electronic Devices in Monitoring and Promoting Adherence in Airways Disease

Poor adherence to treatment is a common reason why patients with chronic disease have worse outcomes than might be expected. Poor treatment adherence is of particular concern among people with airways disease because, apart from not taking treatment as prescribed, inhaled medication can also be administered incorrectly. Recently, a number of technological advances that accurately document when an inhaled treatment has been used and, in certain instances, how it was used have been developed. There is good evidence from a number of research groups that these devices, either by patient reminders or physician feedback, promote adherence to inhaled treatments. What is less certain is how, in a real-world setting, these devices change outcomes. In this perspective article, the role of electronic devices in quantifying treatment use and addressing poor treatment adherence and their potential role in clinical practice outside of clinical validation trials are described.

Feasibility and acceptability of an asthma app to monitor medication adherence: a mixed methods study (Preprint)

BACKGROUND Poor medication adherence is a major challenge in asthma and objective assessment of inhaler adherence is needed. InspirerMundi app aims to monitor adherence while turning into a positive experience through gamification and social support. OBJECTIVE This study aimed to evaluate the feasibility and acceptability of the InspirerMundi app to monitor medication adherence in adolescents and adults with persistent asthma (treated with daily inhaled medication). METHODS A 1-month mixed method multicenter observational study was conducted in 26 secondary care centers from Portugal and Spain. During an initial face-to-face visit, physicians reported patients’ asthma therapeutic plan in a structured questionnaire. During the visits, patients were invited to use the app daily to register their asthma medication intakes. A scheduled intake was considered taken when patients registered the intake (inhaler, blister, or other recipient) by using the image-based medication detection tool. At 1-month, patients were interviewed by phone and app satisfaction was assessed on a 1(low)-5(high) scale. Patients were also asked to point out the most and least preferred app features and to make suggestions for future app improvements. RESULTS A total of 107 patients (median 27[P25-P75 14-40] years) were invited, 99 (93%) installed the app and 79 (74%) completed the 1-month interview. Patients interacted with the app a median of 9[1-24] days. At least one medication was registered in the app by 77 patients (72%). Fifty-two (53%) participants registered all prescribed inhalers and 34 (34%) registered the complete asthma therapeutic plan. Median medication adherence was 75[25-90]% for inhalers and 82[50-94]% for other drug formulations. Patients were globally satisfied with the app, with 75% scoring ≥4, with the adherence monitoring, symptom monitoring and gamification features being the most highly scored components and the medication detection tool among the lowest scored (53% ≥4). 53% of the patients stated that the app had motivated them to improve adherence to inhaled medication and 77% would recommend the app to other patients. Patients’ feedback was reflected in four major themes: Medication-related features (n=67, 85%), Gamification and social network (n=33, 42%), Symptom monitoring and physician communication (n=21, 27, and Other aspects (n=16, 20%). CONCLUSIONS The InspirerMundi app was feasible and acceptable to monitor medication adherence in patients with asthma. Based on patients’ feedback and to increase the registering of medications, the therapeutic plan registration and the medication detection tool were redesigned. Our results highlight the importance of patient participation to produce a patient-centered and engaging mHealth asthma app.

Identification of clusters of asthma control: A preliminary analysis of the Inspirers studies

Aims: To identify distinct asthma control clusters based on Control of Allergic Rhinitis and Asthma Test (CARAT) and to compare patients’ characteristics among these clusters. Methods: Adults and adolescents (≥13 years) with persistent asthma were recruited at 29 Portuguese hospital outpatient clinics, in the context of two observational studies of the INSPIRERS project. Demographic and clinical characteristics, adherence to inhaled medication, beliefs about inhaled medication, anxiety and depression, quality of life, and asthma control (CARAT, >24 good control) were collected. Hierarchical cluster analysis was performed using CARAT total score (CARAT -T). Results: 410 patients (68% adults), with a median (percentile 25–percentile 75) age of 28 (16­‑46) years, were analysed. Three clusters were identified [mean CARAT -T (min -max)]: cluster 1 [27(24 -30)], cluster 2 [19(14 -23)] and cluster 3 [10(2 -13)]. Patients in cluster 1 (34%) were characterised by better asthma control, better quality of life, higher inhaler adherence and use of a single inhaler. Patients in clusters 2 (50%) and 3 (16%) had uncontrolled asthma, lower inhaler adherence, more symptoms of anxiety and depression and more than half had at least one exacerbation in the previous year. Furthermore, patients in cluster 3 were predominantly female, had more unscheduled medical visits and more anxiety symptoms, perceived a higher necessity of their prescribed inhalers but also higher levels of concern about taking these inhalers. There were no differences in age, body mass index, lung function, smoking status, hospital admissions or specialist physician follow -up time among the three clusters. Conclusion: An unsupervised method based on CARAT- -T, identified 3 clusters of patients with distinct, clinically meaningful characteristics. The cluster with better asthma control had a cut -off similar to the established in the validation study of CARAT and an additional cut -off seems to distinguish more severe disease. Further research is necessary to validate the asthma control clusters identified.

Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts

Background Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. Methods In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates – age, pack-years, inhaled medication use, and specimen collection timing. Results In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37–53%) and a sensitivity of 91% (95%CI 81–97%), resulting in a negative predictive value of 95% (95% CI 89–98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44–82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78–97%). Conclusions The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.