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Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Blood Pressure Data
Victoria Anne Shaffer;
Pete Wegier;
KD Valentine;
Jeffery L Belden;
Shannon M Canfield;
Sonal J Patil;
Mihail Popescu;
Linsey M Steege;
Akshay Jain;
Richelle J Koopman
ABSTRACT
Background:
Uncontrolled hypertension is a significant health problem in the United States, even though multiple drugs exist to effectively treat this chronic disease.
Objective:
As part of a larger project developing data visualizations to support shared decision making about hypertension treatment, we conducted a series of studies to understand how perceptions of hypertension control were impacted by data variations inherent in the visualization of blood pressure (BP) data.
Methods:
In 3 Web studies, participants (internet sample of patients with hypertension) reviewed a series of vignettes depicting patients with hypertension; each vignette included a graph of a patient’s BP. We examined how data visualizations that varied by BP mean and SD (Study 1), the pattern of change over time (Study 2), and the presence of extreme values (Study 3) affected patients’ judgments about hypertension control and the need for a medication change.
Results:
Participants’ judgments about hypertension control were significantly influenced by BP mean and SD (Study 1), data trends (whether BP was increasing or decreasing over time—Study 2), and extreme values (ie, outliers—Study 3).
Conclusions:
Patients’ judgment about hypertension control is influenced both by factors that are important predictors of hypertension related-health outcomes (eg, BP mean) and factors that are not (eg, variability and outliers). This study highlights the importance of developing data visualizations that direct attention toward clinically meaningful information.
Citation
Please cite as:
Shaffer VA, Wegier P, Valentine K, Belden JL, Canfield SM, Patil SJ, Popescu M, Steege LM, Jain A, Koopman RJ
Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Visualized Blood Pressure Data