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Designing effective visualizations of habits data to aid clinical decision making

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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4 X users

Citations

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15 Dimensions

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74 Mendeley
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Title
Designing effective visualizations of habits data to aid clinical decision making
Published in
BMC Medical Informatics and Decision Making, November 2014
DOI 10.1186/s12911-014-0102-x
Pubmed ID
Authors

Joost de Folter, Hulya Gokalp, Joanna Fursse, Urvashi Sharma, Malcolm Clarke

Abstract

BackgroundChanges in daily habits can provide important information regarding the overall health status of an individual. This research aimed to determine how meaningful information may be extracted from limited sensor data and transformed to provide clear visualization for the clinicians who must use and interact with the data and make judgments on the condition of patients. We ascertained that a number of insightful features related to habits and physical condition could be determined from usage and motion sensor data.MethodsOur approach to the design of the visualization follows User Centered Design, specifically, defining requirements, designing corresponding visualizations and finally evaluating results. This cycle was iterated three times.ResultsThe User Centered Design method was successfully employed to converge to a design that met the main objective of this study. The resulting visualizations of relevant features that were extracted from the sensor data were considered highly effective and intuitive to the clinicians and were considered suitable for monitoring the behavior patterns of patients.ConclusionsWe observed important differences in the approach and attitude of the researchers and clinicians. Whereas the researchers would prefer to have as many features and information as possible in each visualization, the clinicians would prefer clarity and simplicity, often each visualization having only a single feature, with several visualizations per page. In addition, concepts considered intuitive to the researchers were not always to the clinicians.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Austria 1 1%
Switzerland 1 1%
Unknown 72 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 18%
Student > Master 13 18%
Researcher 10 14%
Student > Doctoral Student 6 8%
Student > Bachelor 6 8%
Other 11 15%
Unknown 15 20%
Readers by discipline Count As %
Medicine and Dentistry 12 16%
Nursing and Health Professions 6 8%
Computer Science 6 8%
Agricultural and Biological Sciences 5 7%
Business, Management and Accounting 5 7%
Other 21 28%
Unknown 19 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 December 2014.
All research outputs
#12,614,930
of 22,772,779 outputs
Outputs from BMC Medical Informatics and Decision Making
#831
of 1,984 outputs
Outputs of similar age
#165,100
of 361,296 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#14
of 36 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,984 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 57% of its peers.
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 361,296 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.