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Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Visualized Blood Pressure Data
Author(s) -
Victoria A. Shaffer,
Pete Wegier,
KD Valentine,
Jeffery L. Belden,
Shan M. Canfield,
Sonal J. Patil,
Mihail Popescu,
Linsey M. Steege,
Akshay Jain,
Richelle J. Koopman
Publication year - 2018
Publication title -
journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/11366
Subject(s) - outlier , blood pressure , control (management) , medicine , computer science , data mining , statistics , data science , artificial intelligence , mathematics
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.

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