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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

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
4 tweeters

Citations

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14 Dimensions

Readers on

mendeley
64 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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Austria 1 2%
Switzerland 1 2%
Unknown 62 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 20%
Student > Ph. D. Student 12 19%
Researcher 9 14%
Student > Doctoral Student 6 9%
Student > Bachelor 5 8%
Other 9 14%
Unknown 10 16%
Readers by discipline Count As %
Medicine and Dentistry 12 19%
Nursing and Health Professions 6 9%
Computer Science 5 8%
Business, Management and Accounting 5 8%
Agricultural and Biological Sciences 4 6%
Other 19 30%
Unknown 13 20%

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
#6,294,561
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#484
of 1,122 outputs
Outputs of similar age
#92,161
of 278,479 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#47
of 120 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,122 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 55% 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 278,479 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 66% of its contemporaries.
We're also able to compare this research output to 120 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 60% of its contemporaries.