A data-driven typology of asthma medication adherence using cluster analysis

Tibble, Holly; Chan, Amy Hai Yan; Mitchell, Edwin A.; Horne, Elsie; Doudesis, Dimitrios; Horne, Rob; Mizani, Mehrdad A.; Sheikh, Aziz; Tsanas, Athanasios

doi:10.1038/s41598-020-72060-0

Cited by 17 publications

(23 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[34][35][36][37][38][39][40][41][42] Patient clustering refers to studies which subtype the asthma population using unsupervised learning algorithms. 43,44 See Table 2 for a summary of the papers.…”

Section: Search Resultsmentioning

confidence: 99%

“…Many methods and devices for monitoring different aspects of a person have been studied individually and in combination. Machine learning can be applied to breath monitoring, 37,41 sleep monitoring, 23,[34][35][36]38,39,42 cough and wheeze, 24,26,27,[29][30][31]36 lung function monitoring, 23,25,[33][34][35]38,40 adherence monitoring, 32,35,38,43 and environment monitoring. 39,40,44 However, studies had different outcome measures; hence, it is difficult to conduct a direct comparison between studies.…”

Section: Search Resultsmentioning

confidence: 99%

“…Two studies 43,44 used unsupervised learning to form data-driven clusters using data collected via mHealth. One study was investigated clusters in children with asthma, 43 the other had focused on data collected by adults with asthma. 44…”

Section: Patient Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

View full text Add to dashboard Cite

Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on longterm management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

show abstract

Section: Search Resultsmentioning

confidence: 99%

Section: Search Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

View full text Add to dashboard Cite

show abstract

“…Digital inhalers can also be used to evaluate patterns of controller medication use, similarly to informing about SABA use patterns [ 64 ]. Previous studies using digital inhalers have identified different patterns of adherence, ranging from regular adherence and irregular adherence, to regular nonadherence and irregular nonadherence [ 64 , 65 ]. Clinicians will likely encounter patients who are non-adherent but controlled, though little has been published in this area.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

Impressive advances in inhalation therapy for patients with asthma and chronic obstructive pulmonary disease (COPD) have occurred in recent years. However, important gaps in care remain, particularly relating to poor adherence to inhaled therapies. Digital inhaler health platforms which incorporate digital inhalers to monitor time and date of dosing are an effective disease and medication management tool, promoting collaborative care between clinicians and patients, and providing more in-depth understanding of actual inhaler use. With advances in technology, nearly all inhalers can be digitalized with add-on or embedded sensors to record and transmit data quantitating inhaler actuations, and some have additional capabilities to evaluate inhaler technique. In addition to providing an objective and readily available measure of adherence, they allow patients to interact with the device directly or through their self-management smartphone application such as via alerts and recording of health status. Clinicians can access these data remotely and

show abstract

“…For patients receiving r-hGH treatment (somatropin; Saizen ® , Merck Healthcare KGaA, Darmstadt, Germany), the easypod™ auto-injector device, in combination with easypod™ connect, allows automatic recording and real-time data transmission of the date, time, and dose injected [8] enabling healthcare professionals to monitor patient adherence and growth outcomes. Data from connected devices has also contributed to the development of machinelearning algorithms to predict adherence behavior in multiple therapy areas [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Intervention Framework for Improving Adherence to Growth Hormone Therapy Based on Clustering Analysis and Traffic Light Alerting Systems

Araújo

Dommelen

Srivastava

et al. 2021

Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era

View full text Add to dashboard Cite

Recombinant human growth hormone (r-hGH) is an established therapy for growth hormone deficiency (GHD); yet, some patients fail to achieve their full height potential, with poor adherence and persistence with the prescribed regimen often a contributing factor. A data-driven clinical decision support system based on “traffic light” visualizations for adherence risk management of patients receiving r-hGH treatment was developed. This research was feasible thanks to data-sharing agreements that allowed the creation of these models using real-world data of r-hGH adherence from easypod™ connect; data was retrieved for 11,015 children receiving r-hGH therapy for ≥180 days. Patients’ adherence to therapy was represented using four values (mean and standard deviation [SD] of daily adherence and hours to next injection). Cluster analysis was used to categorize adherence patterns using a Gaussian mixture model. Following a traffic lights-inspired visualization approach, the algorithm was set to generate three clusters: green, yellow, or red status, corresponding to high, medium, and low adherence, respectively. The area under the receiver operating characteristic curve (AUC-ROC) was used to find optimum thresholds for independent traffic lights according to each metric. The most appropriate traffic light used the SD of the hours to the next injection, with an AUC-ROC value of 0.85 when compared to the complex clustering algorithm. For the daily adherence-based traffic lights, optimum thresholds were >0.82 (SD, <0.37), 0.53–0.82 (SD, 0.37–0.61), and <0.53 (SD, >0.61) for high, medium, and low adherence, respectively. For hours to next injection, the corresponding optimum thresholds were <27.18 (SD, <10.06), 27.18–34.01 (SD, 10.06–29.63), and >34.01 (SD, >29.63). Our research indicates that implementation of a practical data-driven alert system based on recognised traffic-light coding would enable healthcare practitioners to monitor sub-optimally-adherent patients to r-hGH treatment for early intervention to improve treatment outcomes.

show abstract

A data-driven typology of asthma medication adherence using cluster analysis

Cited by 17 publications

References 68 publications

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

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

A Data-Driven Intervention Framework for Improving Adherence to Growth Hormone Therapy Based on Clustering Analysis and Traffic Light Alerting Systems

Contact Info

Product

Resources

About