The cross-sectional visualization survey study received approval from the university’s institutional review board (approval number: #STUDY00009465). Researchers collected deidentified data using the SOST and MSMH app.

Data collection occurred at a popular Midwest state fair event that is held over 12 days and attracts attendance of more than 2 million annually. This study was conducted in August and September 2021. Participants self-selected to participate in the study. Participation was restricted to adults (18 years or older) who could complete the MSMH app in English. An informed consent was displayed in the app; participants agreed to reuse their anonymous data for research prior to completing the assessment. They completed the MSMH assessment on university-owned iPads. Participants were given a university-branded drawstring backpack (US $1.79 value) as an incentive for participation. At the end of the assessment, a unique code was displayed that participants could use to download a summary of their responses. The link to download the summary along with the participants’ unique code was provided to each participant in a business card format. Appropriate COVID-19 protocols were followed: masks were mandatory, iPads were sanitized after every use, and hand-sanitizing lotions were available in multiple places.

As displayed in Figure 1, the MSMH app presents SCN for each concept as previously described using the SOST [21,23]. If all concepts are rated, there would be a total of 42 strengths, 335 challenges, and 168 needs. Of these, 37 concepts were chosen for analysis based on the study objectives and the setting/sample, excluding 5 concepts that did not apply to the majority of participants (Pregnancy, Postpartum, Family Planning, Consciousness, and Growth and Development). The total time to complete the assessment was approximately 15 minutes per user.

Data were stored in a secure computing environment hosted by the university. Data were analyzed using visualization techniques in Excel (Microsoft Corporation), and descriptive and inferential statistics were analyzed using SPSS (IBM Corp).

For aim 1, the overall SCN were examined for all participants using descriptive statistics and parallel coordinates visualization techniques [7,26]. Two cohorts were then formed for participants with one or more Emotions challenges and those without Emotions challenges. SCN were compared using independent sample t tests and parallel coordinates visualization techniques. In examining whole-person health and the Emotions concept using data visualization techniques, a novel cluster of health-related behaviors domain concepts (Sleeping, Nutrition, and Exercising) were uncovered and were examined in detail. In addition, bubble charts were created to visualize relationships among SCN across the four domains (My Living, My Mind and Network, My Body, and My Self-care).

For aim 2, to examine resilience, a correlational analysis was conducted on the strengths of participants with and without Emotions challenge. Co-occurrences of various strengths across the 37 study concepts were analyzed using the Pearson correlation coefficient. The resulting correlation matrix was conditionally formatted in Excel with blue (most correlated), white (midrange), and red (least correlated).

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%). Almost half of the respondents indicated their marital status as married (n=84, 46.2%). The demographics of participants by age, gender, race, ethnicity, and marital status with and without Emotions challenges are presented in Table 1. Compared to those without Emotions challenges, those with Emotions challenges were aged 18-44 years (P<.001), more often female (P=.04), and not married (P=.02). The Emotions challenges identified by participants are highlighted in Table 2, with tired (n=69) and hard to manage my stress (n=46) identified as the top two challenges.

Figure 2 presents the overall SCN of participants.

The correlational analysis to identify associations in strengths for participants with and without Emotions challenges are presented in Figures 8 and 9, respectively. A higher correlation indicates co-occurrence of strengths [7,27]. A total of 666 boxes are displayed for strength correlations with Emotions challenges and likewise for without Emotions challenges. A red box is an indicator of less correlation and a blue box is an indicator of high correlation, with the white box being in between. For those with Emotions challenges, 22.5% (n=150) are blue boxes out of the total of 666 boxes, and for those without Emotions challenges, 51.7% (n=344) are blue boxes. As shown in Figures 8 and 9, the correlations among concepts are less strong for those with Emotions challenges (more red boxes) and stronger for those without Emotions challenges (more blue boxes indicating more and greater positive correlations). The correlation matrix for the group without an Emotions challenge depicts strengths that extend across concepts/domains as indicated by the blue boxes that span horizontally and vertically.

In this descriptive correlational study, SCN data from participants in a Midwest state fair over August-September 2021 were examined using visualization techniques. Overall, participants had more strengths than challenges and more challenges than needs. The data visualization techniques used to examine whole-person health and the Emotions concept revealed a novel cluster of health-related behaviors (My Self-care) domain concepts (Sleeping, Nutrition, and Exercising) with fewer strengths and more challenges and needs. This aligns with the association between sleep, physical activity, and diet during COVID-19 [28-30]. Furthermore, differences in SCN for Sleeping in groups with/without an Emotions challenge aligns with prior research on the impact of sleeping on overall mental health [31,32]. It is notable that more than half of the participants had challenges in the Emotions concept. That those with Emotions challenges had fewer strengths and more challenges and needs across all concepts underscores the importance of a whole-person health perspective. Further research is needed to understand nuances related to SCN across and among these concepts in respect to whole-person health.

The finding that more than half of the participants had challenges in the Emotions concept underscores the impact and emotional toll of the pandemic. This aligns with the evidence that emotional issues such as depression, anxiety, and suicidal thoughts have increased since 2020 [2]; for example, the number of US adults who reported symptoms of anxiety or depression in January 2021 increased 4 times compared to June 2019 [2]. The How Right Now communications campaign by the Centers for Disease Control and Prevention [2] aims to promote and strengthen the emotional well-being and resilience of people affected by stress, grief, and loss during the COVID-19 pandemic. Data on whole-person health such as those offered by this study has the potential to provide a quantitative data perspective to add to the qualitative lived experiences/narratives being shared as part of this initiative.

The finding that those with Emotions challenges also had many strengths is promising; and it is important to identify these as a tool to help understand and potentially bolster resilience. These findings align with prior strength-oriented studies [14,15,33-35]. Strengths can be used as tools to counter challenges, as an individual who has strength in Socializing or Spirituality or Faith is more likely to use social support systems to mitigate challenges related to concepts such as Emotions, Relationships, or Grief or loss and use these as their mechanisms to preserve and heal and, hence, build resilience [36,37].

The finding that those with Emotions challenges have fewer strengths and more challenges and needs across all four domains compared to those without Emotions challenges aligns with the literature regarding the impact of mental health on all of health [38,39]. Results align with prior studies that health and disease are a dynamic interconnected state with a ripple effect on other aspects of health [3,40]. Tools such as SOST and MSMH can provide a whole-person assessment and identify areas of strengths to leverage and help boost resilience. The SOST terminology within the MSMH app enabled comparisons across communities and clinical data [7,22], and has been used to analyze community-level resilience [7]. Given the potential for assessment at the individual and community level, powerful tools such as SOST and the MSMH app should be adopted within EHRs and personal health records to generate meaningful data for population health management.

This aligns with the current National Institutes of Health Bridge2AI Initiative that aims to build and leverage robust multidimensional data sets [41]. By tapping into increasing computing power along with machine learning, artificial intelligence, and transformative analytic techniques, these data sets can be used to draw insights on factors that facilitate whole-person health and resilience. Apps such as MSMH leverage the power of terminology standards and health informatics, and provide options for addressing the recommendations of the National Academies of Sciences, Engineering, and Medicine on design and use of health information technology for whole-person health [42].

This research demonstrates the power of health informatics, standardized data, and technology to assess, visualize, and test individual- and community-level data. It demonstrates a data-driven methodology for additional studies to build evidence on whole-person health and resilience. Furthermore, such research offers a starting point for initiating conversations about whole-person health with individuals and communities regarding their strengths, challenges, and needs, beginning a shift from a deficit model of health toward whole-person health.

Some limitations were noted. Recruitment challenges due to COVID-19 were considerable, given limited attendance at the state fair and the fact that participants needed to participate indoors and wear a mask inside the research building. The sample may have been biased by these pandemic conditions, as fair attendees in 2021 may be unique in some way that is not known. Lastly, the survey took 15 to 20 minutes to complete, which was a deterrent to some potential participants.

This study examined standardized whole-person data using an app-aided and data-driven approach, quantifying SCN of individuals across all of health (environmental, psychosocial, physiological, and health-related behaviors domains). Examining SCN data for groups with and without Emotions challenges revealed patterns in overall health and for important health-related behaviors concepts. This study lays a foundation for numerous research opportunities, such as metric development to measure resilience and the use of SOST and MSMH in clinical care settings to reframe health care encounters in a whole-person perspective.