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An empirical method to cluster objective nebulizer adherence data among adults with cystic fibrosis

Overview of attention for article published in Patient preference and adherence, March 2017
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14 Mendeley
Title
An empirical method to cluster objective nebulizer adherence data among adults with cystic fibrosis
Published in
Patient preference and adherence, March 2017
DOI 10.2147/ppa.s131497
Pubmed ID
Authors

Zhe Hoo, Michael Campbell, Rachael Curley, Martin Wildman

Abstract

The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions. This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster ("cluster 1" = very low adherence; "cluster 2" = low adherence; "cluster 3" = moderate adherence; "cluster 4" = high adherence), and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data. We present three cases to illustrate the use of the proposed clustering method. This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months' worth of data are unavailable) and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested classifications provide a structure to investigate parameters in future prospective datasets in which there are accurate measurements of nebulizer adherence and health outcomes.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 7 50%
Researcher 4 29%
Student > Doctoral Student 1 7%
Student > Ph. D. Student 1 7%
Student > Master 1 7%
Other 0 0%
Readers by discipline Count As %
Unspecified 11 79%
Medicine and Dentistry 2 14%
Psychology 1 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 April 2017.
All research outputs
#7,070,490
of 9,278,926 outputs
Outputs from Patient preference and adherence
#708
of 879 outputs
Outputs of similar age
#188,504
of 260,898 outputs
Outputs of similar age from Patient preference and adherence
#32
of 40 outputs
Altmetric has tracked 9,278,926 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 879 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 260,898 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.