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Understanding Women's Cardiovascular Health Using MyStrengths+MyHealth: A Patient-Generated Data Visualization Study
Robin R Austin1, Michelle A Mathiason2, Ruth A Lindquist3
1Assistant Professor, University of Minnesota, School of Nursing, Minneapolis, MN, USA.
Insights
Patient-generated health data (PGHD) visualization revealed distinct patterns in women's strengths, challenges, and needs. Women without circulation symptoms reported more strengths and fewer challenges than those with symptoms, highlighting the value of PGHD in research.
Area of Science:
- Health Informatics
- Data Visualization
- Patient-Reported Outcomes
Background:
- Patient-generated health data (PGHD) offers valuable insights into individual health experiences.
- Understanding the whole-person health of women, including strengths, challenges, and needs, is crucial for effective care.
- Previous research has not fully leveraged data visualization techniques to analyze complex PGHD patterns.
Purpose of the Study:
- To identify patterns in PGHD for women with and without circulation signs or symptoms using data visualization.
- To visualize and interpret relationships among strengths, challenges, and needs in these groups.
- To generate and test hypotheses based on observed PGHD patterns.
Main Methods:
- Retrospective, observational, case-controlled, and exploratory design.
- Utilized de-identified PGHD from the MyStrengths+MyHealth mobile application (N=383).
- Matched women with (n=80) and without circulation symptoms; analyzed data using visualization, descriptive, and inferential statistics.
Main Results:
- Generated nine hypotheses, with four supported by the data.
- Women without circulation symptoms reported more strengths, challenges, and needs compared to those with symptoms.
- Specific differences included strengths in connecting, challenges in emotions/vision/healthcare, and needs for information/guidance.
Conclusions:
- Data visualization of whole-person health data effectively identified and tested new health patterns.
- Patient perspectives captured through PGHD are essential and should be valued in research and clinical practice.
- Standardization of PGHD can facilitate clinically relevant research and improve clinical interactions and health policies.
Purpose:
The purpose of this data visualization study was to identify patterns in patient-generated health data (PGHD) of women with and without Circulation signs or symptoms. Specific aims were to (a) visualize and interpret relationships among strengths, challenges, and needs of women with and without Circulation signs or symptoms; (b) generate hypotheses based on these patterns; and (c) test hypotheses generated in Aim 2.
Design:
The design of this visualization study was retrospective, observational, case controlled, and exploratory.
Methods:
We used existing de-identified PGHD from a mobile health application, MyStrengths+MyHealth (N = 383). From the data, women identified with Circulation signs or symptoms (n = 80) were matched to an equal number of women without Circulation signs or symptoms. Data were analyzed using data visualization techniques and descriptive and inferential statistics.
Findings:
Based on the patterns, we generated nine hypotheses, of which four were supported. Visualization and interpretation of relationships revealed that women without Circulation signs or symptoms compared to women with Circulation signs or symptoms had more strengths, challenges, and needs-specifically, strengths in connecting; challenges in emotions, vision, and health care; and needs related to info and guidance.
Conclusions:
This study suggests that visualization of whole-person health including strengths, challenges, and needs enabled detection and testing of new health patterns. Some findings were unexpected, and perspectives of the patient would not have been detected without PGHD, which should be valued and sought. Such data may support improved clinical interactions as well as policies for standardization of PGHD as sharable and comparable data across clinical and community settings.
Clinical Relevance:
Standardization of patient-generated whole-person health data enabled clinically relevant research that included the patients' perspective.
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