Understanding Women’s Cardiovascular Health Using MyStrengths+MyHealth: A Patient‐Generated Data Visualization Study of Strengths, Challenges, and Needs Differences

Austin, Robin; Mathiason, Michelle A.; Lindquist, Ruth; McMahon, Siobhan; Pieczkiewicz, David S.; Monsen, Karen A.

doi:10.1111/jnu.12674

Cited by 7 publications

(14 citation statements)

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“…The last two patterns identified were no relationship between the number of strengths and challenges by domain and more needs among respondents with challenges in all four domains. The pattern related to an inverse correlation between strengths and challenges by concept was similar to patterns noted in previous studies (Austin, Mathiason, et al, 2021; Monsen et al, 2014; Monsen, Peters, et al, 2015; Radhakrishnan et al, 2016). The exception was the unexpected relationship between strengths and needs, for the subgroup with 21–30 of 42 maximum total strengths.…”

Section: Discussionsupporting

confidence: 88%

“…The standardized assessment within MSMH is a simplified translation of a rigorous, multidisciplinary health terminology, the Omaha System. The Omaha System has been used for holistic assessments and documentation of care since the 1990s (Austin, Mathiason, et al, 2021; Martin, 2005). The Simplified Omaha System Terms (SOST) has been validated at the 5th grade reading level (Austin, Martin, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Using data visualization to detect patterns in whole‐person health data

Austin

Mathiason

Monsen

2022

Research in Nursing & Health

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Data visualization techniques are useful for examining large multidimensional data sets. In this exploratory data analysis (EDA) study, we applied a visualization pattern detection and testing process to deidentified data to discover patterns in wholeperson health for adults 65 and older. Whole-person health examines a person's environmental, psychosocial, and physical health, as well as their health-related behaviors; and assesses their strengths, challenges, and needs. Strengths are defined as assets and capabilities in the face of short-or long-term stressors. We collected data using a mobile application that delivers a comprehensive whole-person assessment using a simplified version of a standardized instrument, the Omaha System. The visualization pattern detection process is iterative, includes various techniques, and requires visualization literacy. The data visualization techniques applied in this analysis included bubble charts, parallel coordinates line graphs, box plots, and alluvial flow diagrams. We discovered six patterns within the visualizations. We formulated and tested six hypotheses based on these six patterns, and all six hypotheses were supported. Adults 65 and older had more strengths than challenges and more challenges than needs (p < 0.001). Strengths and challenges were negatively correlated (p < 0.001). Unexpectedly, a subset of adults 65 and older who had many, but not all, strengths had significantly more needs (p = 0.04). The use of standardized terminology with its inherent data interrelationships was key to discovering patterns in whole-person health. This methodology may be used in future EDA research using new data sets.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Using data visualization to detect patterns in whole‐person health data

Austin

Mathiason

Monsen

2022

Research in Nursing & Health

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“…Prior research has led to the development of a consumer-facing app MyStrengths + MyHealth (MSMH) [ 16 ] to standardize SCN data capture from a whole-person perspective using SOST [ 17 - 19 ]. Within MSMH and SOST, the integrity and rigor of the structure and concepts of the Omaha System are retained.…”

Section: Introductionmentioning

confidence: 99%

“…The MSMH is freely available to researchers, educators, and clinicians, and is licensed through the university [ 16 ]. MSMH data are housed in a secure computing network from which license holders may download their complete data.…”

Section: Introductionmentioning

confidence: 99%

“…MSMH data are housed in a secure computing network from which license holders may download their complete data. Studies based on the MSMH app have detailed the development and pilot testing [ 18 ], analyzed women’s cardiovascular health using the app [ 20 ], described whole-person health of older adults [ 21 ], and examined local data on whole-person health and opioids in the community [ 22 ]. Recent research has analyzed MSMH data for resilience at the community level [ 7 ] and examined the feasibility of using MSMH-aided consumer-generated data for knowledge discovery [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Understanding Whole-Person Health and Resilience During the COVID-19 Pandemic and Beyond: A Cross-sectional and Descriptive Correlation Study

Rajamani¹,

Austin²,

Geiger-Simpson³

et al. 2022

JMIR Nursing

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Background The COVID-19 pandemic has prompted an interest in whole-person health and emotional well-being. Informatics solutions through user-friendly tools such as mobile health apps offer immense value. Prior research developed a consumer-facing app MyStrengths + MyHealth using Simplified Omaha System Terms (SOST) to assess whole-person health. The MyStrengths + MyHealth app assesses strengths, challenges, and needs (SCN) for 42 concepts across four domains (My Living, My Mind and Networks, My Body, My Self-care; eg, Income, Emotions, Pain, and Nutrition, respectively). Given that emotional well-being was a predominant concern during the COVID-19 pandemic, we sought to understand whole-person health for participants with/without Emotions challenges. Objective This study aims to use visualization techniques and data from attendees at a Midwest state fair to examine SCN overall and by groups with/without Emotions challenges, and to explore the resilience of participants. Methods This cross-sectional and descriptive correlational study surveyed adult attendees at a 2021 Midwest state fair. Data were visualized using Excel and analyzed using descriptive and inferential statistics using SPSS. Results The study participants (N=182) were primarily female (n=123, 67.6%), aged ≥45 years (n=112, 61.5%), White (n=154, 84.6%), and non-Hispanic (n=177, 97.3%). Compared to those without Emotions challenges, those with Emotions challenges were aged 18-44 (P<.001) years, more often female (P=.02), and not married (P=.01). Overall, participants had more strengths (mean 28.6, SD 10.5) than challenges (mean 12, SD 7.5) and needs (mean 4.2, SD 7.5). The most frequent needs were in Emotions, Nutrition, Income, Sleeping, and Exercising. Compared to those without Emotions challenges, those with Emotions challenges had fewer strengths (P<.001), more challenges (P<.001), and more needs (P<.001), along with fewer strengths for Emotions (P<.001) and for the cluster of health-related behaviors domain concepts, Sleeping (P=.002), Nutrition (P<.001), and Exercising (P<.001). Resilience was operationalized as correlations among strengths for SOST concepts and visualized for participants with/without an Emotions challenge. Those without Emotions challenges had more positive strengths correlations across multiple concepts/domains. Conclusions This survey study explored a large community-generated data set to understand whole-person health and showed between-group differences in SCN and resilience for participants with/without Emotions challenges. It contributes to the literature regarding an app-aided and data-driven approach to whole-person health and resilience. This research demonstrates the power of health informatics and provides researchers with a data-driven methodology for additional studies to build evidence on whole-person health and resilience.

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Examining standardized consumer-generated social determinants of health and resilience data supported by Omaha System terminology

Austin

Rajamani

Jantraporn

et al. 2023

Journal of the American Medical Informatics Association

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Nursing terminologies like the Omaha System are foundational in realizing the vision of formal representation of social determinants of health (SDOH) data and whole-person health across biological, behavioral, social, and environmental domains. This study objective was to examine standardized consumer-generated SDOH data and resilience (strengths) using the MyStrengths+MyHealth (MSMH) app built using Omaha System. Overall, 19 SDOH concepts were analyzed including 19 Strengths, 175 Challenges, and 76 Needs with additional analysis around Income Challenges. Data from 919 participants presented an average of 11(SD = 6.1) Strengths, 21(SD = 15.8) Challenges, and 15(SD = 14.9) Needs. Participants with at least one Income Challenge (n = 573) had significantly (P < .001) less Strengths [9.4(6.4)], more Challenges [27.4(15.5)], and more Needs [15.1(14.9)] compared to without an Income Challenge (n = 337) Strengths [13.4(4.5)], Challenges [10.5(8.9)], and Needs [5.1(10.0)]. This standards-based approach to examining consumer-generated SDOH and resilience data presents a great opportunity in understanding 360-degree whole-person health as a step towards addressing health inequities.

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Understanding Women’s Cardiovascular Health Using MyStrengths+MyHealth: A Patient‐Generated Data Visualization Study of Strengths, Challenges, and Needs Differences

Cited by 7 publications

References 50 publications

Using data visualization to detect patterns in whole‐person health data

Using data visualization to detect patterns in whole‐person health data

Understanding Whole-Person Health and Resilience During the COVID-19 Pandemic and Beyond: A Cross-sectional and Descriptive Correlation Study

Examining standardized consumer-generated social determinants of health and resilience data supported by Omaha System terminology

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