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Designing effective visualizations of habits data to aid clinical decision making.

de Folter J, Gokalp H, Fursse J, Sharma U, Clarke M - BMC Med Inform Decis Mak (2014)

Bottom Line: The 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.In addition, concepts considered intuitive to the researchers were not always to the clinicians.

View Article: PubMed Central - PubMed

Affiliation: Brunel University, Uxbridge, UB8 3PH, UK. joost.defolter@brunel.ac.uk.

ABSTRACT

Background: Changes 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.

Methods: Our 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.

Results: The 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.

Conclusions: We 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.

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Related in: MedlinePlus

Photo of motion sensor.
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Fig1: Photo of motion sensor.

Mentions: Two types of sensor were used for habits monitoring. One or more motion sensors recorded movement within the field of vision. The sensor used in this study (Figure 1) had a dead time of 2 minutes, during which time further movement was ignored. This would reduce both the number of repetitions of movement, and thus the amount of data that had to be transmitted and battery power. The location for this sensor was determined to be the place where most activity was observed during the day, typically the living room. We determined that the bed should not be in the field of vision of the sensor, so any motion in bed would be excluded.Figure 1


Designing effective visualizations of habits data to aid clinical decision making.

de Folter J, Gokalp H, Fursse J, Sharma U, Clarke M - BMC Med Inform Decis Mak (2014)

Photo of motion sensor.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4265320&req=5

Fig1: Photo of motion sensor.
Mentions: Two types of sensor were used for habits monitoring. One or more motion sensors recorded movement within the field of vision. The sensor used in this study (Figure 1) had a dead time of 2 minutes, during which time further movement was ignored. This would reduce both the number of repetitions of movement, and thus the amount of data that had to be transmitted and battery power. The location for this sensor was determined to be the place where most activity was observed during the day, typically the living room. We determined that the bed should not be in the field of vision of the sensor, so any motion in bed would be excluded.Figure 1

Bottom Line: The 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.In addition, concepts considered intuitive to the researchers were not always to the clinicians.

View Article: PubMed Central - PubMed

Affiliation: Brunel University, Uxbridge, UB8 3PH, UK. joost.defolter@brunel.ac.uk.

ABSTRACT

Background: Changes 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.

Methods: Our 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.

Results: The 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.

Conclusions: We 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.

Show MeSH
Related in: MedlinePlus