Predicting asthma exacerbations employing remotely monitored adherence

Abstract

This Letter investigated the efficacy of a decision-support system, designed for respiratory medicine, at predicting asthma exacerbations in a multi-site longitudinal randomised control trial. Adherence to inhaler medication was acquired over 3 months from patients with asthma employing a dose counter and a remote monitoring adherence device which recorded participant's inhaler use: n = 184 (23,656 audio files), 61% women, age (mean ± sd) 49.3 ± 16.4. Data on occurrence of exacerbations was collected at three clinical visits, 1 month apart. The relative risk of an asthma exacerbation for those with good and poor adherence was examined employing a univariate and multivariate modified Poisson regression approach; adjusting for age, gender and body mass index. For all months dose counter adherence was significantly (p < 0.01) higher than remote monitoring adherence. Overall, those with poor adherence had a 1.38 ± 0.34 and 1.42 ± 0.39 (remotely monitored) and 1.25 ± 0.32 and 1.18 ± 0.31 (dose counter) higher relative risk of an exacerbation in model 1 and model 2, respectively. However, this was not found to be statistically significantly different. Remotely monitored adherence holds important clinical information and future research should focus on refining adherence and exacerbation measures. Decision-support systems based on remote monitoring may enhance patient-physician communication, possibly reducing preventable adverse events.

Digital Inhalers for Asthma or Chronic Obstructive Pulmonary Disease: A Scientific Perspective

“…However, self-report measures are prone to overestimation of adherence due to social desirability bias [ 68 ]. Dose-counters on inhalers are also used to measure adherence in trials but have been found to overestimate actual use compared with the use of a digital inhaler in practice [ 69 ]. While run-in periods and study design can lessen the influence of inhaler nonadherence on outcomes, the actual impact is unknown without the use of these digital devices throughout the study.…”

Realising the potential of mHealth to improve asthma and allergy care: how to shape the future

“…My Asthma Portal enhances asthma quality of life but not asthma control [31]. Another study was able to predict asthma exacerbations using remotely monitored adherence [32].…”

Digital technologies and adherence in respiratory diseases: the road ahead

“…Longitudinal data on patients' adherence collected via digital applications, particularly when supported by digitally collected information on symptoms and physiological and environmental parameters, can provide a detailed and precise basis for understanding an individual's disease [44, 45]. For example, in patients with asthma, digitally collected real-time data on adherence and symptoms can help HCPs differentiate between symptoms or changes in lung function due to low adherence versus those indicating refractory disease or disease progression [46].…”

“…A major strategy for improving health outcomes in respiratory patients is through decreasing the likelihood and magnitude of clinical deterioration, exacerbations and disease complications. Digitally collected patient data on physiology, patterns of medication adherence, trends in reliever usage and exposure to environmental pollution may identify “digital biomarkers” that help predict future exacerbations [41, 45, 57]. The use of newer data mining and analysis techniques leveraging growing mobile computing power holds particular promise to extract useful information from large datasets, as does the use of existing large and comprehensive databases as “training sets” for the development of prediction tools [58, 59].…”

“…The use of newer data mining and analysis techniques leveraging growing mobile computing power holds particular promise to extract useful information from large datasets, as does the use of existing large and comprehensive databases as “training sets” for the development of prediction tools [58, 59]. Thus, as technology advances, data analysis and risk stratification tools could be incorporated into smart inhalers that suggest measures to prevent severe exacerbations before they occur [44, 45]. A recent study monitoring lung function in patients with idiopathic pulmonary fibrosis showed the value of serial measures of lung function as a way to identify different disease trajectories [60].…”