Robin R Austin1, Michelle A Mathiason2, Ruth A Lindquist3, Siobhan K McMahon4, David S Pieczkiewicz5, Karen A Monsen6. 1. Assistant Professor, University of Minnesota, School of Nursing, Minneapolis, MN, USA. 2. Statistician, University of Minnesota, School of Nursing, Minneapolis, MN, USA. 3. Professor Emeriti, University of Minnesota, School of Nursing, Minneapolis, MN, USA. 4. Associate Professor, University of Minnesota, School of Nursing, Minneapolis, MN, USA. 5. Clinical Associate Professor, University of Minnesota, Institute for Health Informatics, Minneapolis, MN, USA. 6. Professor, University of Minnesota, School of Nursing and Institute for Health Informatics, Minneapolis, MN, USA.
Abstract
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.
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.