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. 2016 Mar 23;3(1):51-5.
doi: 10.1049/htl.2015.0058. eCollection 2016 Mar.

Predicting asthma exacerbations employing remotely monitored adherence

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Predicting asthma exacerbations employing remotely monitored adherence

Isabelle Killane et al. Healthc Technol Lett. .

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.

Keywords: Poisson distribution; age; asthma exacerbations; body mass index; decision support systems; decision-support system; diseases; dose counter adherence; gender; inhaler medication; multisite longitudinal randomised control trial; multivariate modified Poisson regression; patient monitoring; pneumodynamics; regression analysis; remote monitoring adherence device; respiratory medicine; univariate modified Poisson regression.

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Figures

Fig. 1
Fig. 1
INCA device attached to a DiskusTM DPI (top) [16]. Functional components of the inhaler (bottom) [14]
Fig. 2
Fig. 2
Acoustic signals recorded from the INCA device showing correct inhaler technique [14]
Fig. 3
Fig. 3
Odds of participants with poor adherence having experienced an exacerbation when compared with those with good adherence, from left to right, over the total 3-month duration of the clinical trial (overall), at month 1, month 2 and month 3, respectively. Dose counter adherence is in blue. Remotely monitored adherence is in red

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