Illustrating Resource Needs through Data Visualization: Creation of Life-Space Maps for Rural Veterans with Dementia and their Caregivers

Abstract

Rural-dwelling individuals with dementia and their caregivers face unique challenges compared to urban-dwelling peers. Barriers to accessing services and supports are common, and individual resources and informal networks available to support rural families can be difficult to track for providers and healthcare systems outside of the local community. This study uses qualitative data from rural-dwelling dyads, individuals with dementia (n = 12) and informal caregivers (n = 18), to demonstrate how rural patients’ daily life needs can be summarized through life-space map visualizations. Thirty semi-structured qualitative interviews were analyzed using a two-step process. First, rapid qualitative analysis was completed to generate daily-life needs of the participants’ home and community context. Next, life-space maps were developed to synthesize and visualize dyads’ met and unmet needs. Results suggest life-space mapping may offer a pathway for improved needs-based information integration for busy care providers and time-sensitive quality improvement efforts by learning healthcare systems.

Keywords

dementia rural caregiving veterans mixed methods data visualization

What this paper adds

• This paper is the first to create visual life-space maps of met and unmet needs to contextualize gaps in care for rural individuals with dementia and their caregivers.

• Results include maps that depict the complexities of daily life supports and vulnerabilities (e.g., financial instability and distance to groceries) for individuals with dementia and their care partners in rural communities. Visualization rural home contexts through mapping can be used to prioritize clinical assessment and interventions from learning, quality improvement, and systems perspectives.

Applications of study findings

• Life-space maps may aid busy providers to identify and prioritize care needs and applicable interventions during appointments, reducing blind spots in care continuity over time.

• Implementing visual life-space maps into traditional models of health care utilization offers a new avenue to improve health record documentation, visual tracking of individual changes, and prediction of service use and access gaps at the systems level.

• As demand for telehealth-services grows, providers may be increasingly unfamiliar with the communities they serve. Life-space maps may provide essential insights into the care context to more effectively support individualized goals at a distance.

Introduction

Dementia prevalence among U.S. veterans over the age of 65 is estimated to be between 9% and 10%, a rate that is higher than that of the general populace but, like the general populace, also demonstrates a steadily increasing rate of growth as the baby boomer cohort enters later life (Alzheimer’s Association, 20182018; Krishnan et al., 2005; Williamson et al., 2018; VHA ADUSH/PP, 2013). Fiscal and logistical challenges face the state and federal agencies tasked with the care of this growing population (Hurd et al., 2013; National Alliance on Caregiving, 2014; Shih et al., 2014). The Veteran Health Administration (VHA) Office of Rural Health’s (ORH) most recent strategic plan encourages researchers and providers to pursue targeted research solutions to improve, personalize, and proactively develop rural patient-driven health care (Office of Rural Health, 2015). Inclusion of the informal dementia caregivers (CG) (e.g., partners, children, close friends and, relatives)—83% of whom are unpaid and 33% of whom are older adults themselves—is a necessary aspect of supporting this expanding veteran population at home (Alzheimer’s Association, 20182018; Spillman et al., 2014).

Established models of healthcare utilization and provision such as the Anderson Healthcare Utilization Model underscore the predictive nature of need factors, among others, for healthcare service usage (Andersen, 1969; 1995). Geriatric mental health research on needs-based assessment of older adults, including persons with dementia (PWD) and CG, has found that attending to daily met and unmet needs can impact care provision and late-life well-being (Hoe et al., 2006; Orrell et al., 2008). Thus far, several qualitative and mixed-methods studies have found rural-dwelling dyads have significantly less access to formal healthcare resources such as respite services, daycare, and hospital appointments, compared to their urban counterparts (McCutchen, 2004; Tommis et al., 2007). Rural-dwelling informal CGs also report difficulties managing their own and PWD’s activities of daily living, accessing reliable transportation, and maintaining social connections compared to urban counterparts (Hansen et al., 2005; Innes et al., 2005; Li, 2006; Li et al., 2012; McCabe et al., 1995).

Unmet needs have been found to magnify rural dyads’ stress symptoms and “caregiver burden” ratings, both of which align with the reduced physical and emotional capacity to seek out solutions and resources (Li, 2006; Li et al., 2012). Research has found that not attending to the needs of a PWD and/or their CG can result in reduced health-related quality of life, increased distress behavior, and higher rates of institutionalization (Algase et al., 1996; Livingston et al., 2014; Scholzel-Dorenbos et al., 2010). These results underscore learning healthcare systems’, like the VHA, emphasis on supporting older patients’ independence and ensuring less reliance on costly residential and long-term care resources provided by the healthcare system.

However, a recent systematic review found a shortage of reliable, expandable, and valid measurement tools that identify and address the needs of rural caregivers and dementia dyads (Bangerter, Griffin, Zarit, & Rachel Havyer, 2019). These findings align with the VHA’s recent strategic plan calling for innovative ways to “incorporate the voice of the rural veterans to inform policy, future research, and communications” and enable directed change in rural-dwelling veteran caregiving dyads’ lives (Office of Rural Health, 2015, p. 2; VHA ADUSH/PP, 2013; Office of Rural Health, 2015). Novel methods that identify and efficiently demonstrate the needs of rural-dwelling patients could help demystify the facets of rural dementia dyads’ home and community contexts and add to the VHA’s growing body of quality improvement (QI) methods for patient-centered care provision (O'Hanlon et al., 2017; Hamilton & Finley, 2019). For example, efficient user-friendly tools built into the health care visit or electronic health record may increase providers’ and affiliated agencies’ awareness of rural dementia dyads’ unmet needs and offer an avenue to address resource barriers. As such, this proof-of-concept study offers a novel approach to measuring and responding to unmet needs through the use of rapid data analysis and data visualization for busy care providers and healthcare systems.

Life-space Assessment and Visually Mapping Rural Patients’ Needs

Much like the rest of the nation, the majority of residents living in southeastern states access their healthcare resources in urban areas while living in areas determined as rural (USDA, 2019). Relevant to the Veteran PWDs and CGs of the current study, the highest rates of dementia among veterans over 65 years were found in southeastern states such as Alabama—a state that has 81.2% of its land area designated as rural (Krishnan et al., 2005; USDA, 2019). Aiming to better understand the nature of rural community living in late life, researchers at the University of Alabama at Birmingham developed a quantitative mobility instrument entitled the Life-Space Assessment (LSA) to notate how one’s life-space mobility, that is, context of one’s home and community, interacts with other factors such as service access, transportation, well-being, and other sociodemographic factors of older adults (Baker et al., 2003; Parker et al., 2002; Peel et al., 2005).

LSA was unique when first developed as it offers a quantitative measure of life-space mobility associated with the distance and/or the length of time needed for rural-dwelling older adults to travel to access resources unavailable in their area. However, while LSA’s numeric measurement takes into consideration independent living difficulties (IADLs and ADLs), financial resources, and physical fitness of rural older adults, it does not offer a dynamic qualitative view of the specific resources accessed and needed (Innes et al., 2011; Peel et al., 2005). In comparison, mapping visualizations of rural older PWDs and CGs home and community life-space may offer a more descriptive, yet still efficient, method of presenting needs associated with the home context of rural older patients. Mapping visualizations further improve upon LSA by visually synthesizing complex hierarchies and relationships between need themes deduced from participants’ qualitative responses (Jackson & Trochim, 2002; Roth, 2021; Slone, 2009; Wheeldon & Faubert, 2009).

Although mapping has often been used in research to condense big data, mapping information of individual patients’ and dyad’s needs has been infrequently applied to clinical care interactions due to limited time available to review and synthesize traditionally presented data (e.g., tables and themes presented in research manuscripts) and general lack of research on the user-friendliness of mapping methods for providers and care settings. However, if paired with the increased efficiency of rapid qualitative analysis, compared to more laborious traditional qualitative methods, visual data mapping in clinical settings may offer an avenue for efficient information synthesizing for providers and QI initiatives alike (Beebe, 1995; Brooks & King, 2012; Hamilton & Finley, 2019).

In response, this study offers a proof of concept of a novel measurement approach for rural dementia dyads’ met and unmet needs using rapid qualitative data analysis to develop visual life-space maps. By combining visual mapping techniques with a rapid qualitative needs assessment, this approach accurately and efficiently offers the rich information found in qualitative data to all invested parties in learning healthcare systems, regardless of time, position, and training (e.g., rural dyads, clinicians, administrations, and implementation/QI scientists). The authors additionally present example maps that demonstrate how life-space mapping can create provider-friendly images that quickly communicate the complex needs of rural older adults and families. To our knowledge, this is the first study to demonstrate the potential benefits and impact of life-space mapping for rural healthcare provision using the efficiency of rapid qualitative data analysis.

Method

Rapid qualitative analysis was performed on secondary interview data to create life-space map visualizations that efficiently illustrate the context of daily life needs of rural-dwelling Veteran dementia dyads. Data came from existing secondary qualitative interviews with informal, Veteran dementia caregiving dyads (i.e., a person with dementia, PWD, and their informal caregivers, CG), living in rural areas of Alabama and eastern Mississippi. Authors synthesized rapid analysis results into life-space maps for each dyad. To target the met and unmet needs of Veteran dyads from a rural-dwelling, dementia caregiving perspective, rapid qualitative analysis was guided by the following question: What needs are present in and integral to care for rural-dwelling dyads’ daily experiences of the dementia caregiving process?

Design Overview

Secondary, rapid qualitative analysis and visualization methods were used to assess the contextual needs (including daily activities and resources) of rural-dwelling informal caregiving dementia dyads affiliated with Tuscaloosa VA Medical Center (TVAMC). Secondary data were sourced from a VHA Rehabilitation Research and Development supported qualitative study, Outpatient Needs Assessment (ONA) (TVAMC IRB #00170). The ONA study aimed to better understand perceived healthcare needs and barriers to healthcare resources. Rural residence of the dyads was defined based on the VA’s urban–rural–highly rural (URH) PSSG designation, created according to ZIP and census block information adapted from US Census data under what is called Rural Urban Commuting Area Codes (RUCA) (Phibbs & Fan, 2015; USDA ERS, 2016) (Table 1).

Table 1.

Dyad RUCA Codes, County of Dyads’ Home Residence, and Roundtrip Miles Traveled to the TVAMC.

Dyad	RUCA Code	RUCA Descriptor	County (AL and MS)	Roundtrip Miles to TVAMC
1	1	Metro area, core	Jefferson	70 miles
2	4.1	Micropolitan area, core	Dallas	150 miles
3	1	Metro area, core	Tuscaloosa	30 miles
4	7.2	Small town, core	Lowndes (MS)	128.36 miles
5	6	Micropolitan area, low commuter	Pickens	103 miles
6	7.1	Small town, core	Marshall	237.58 miles
7	2	Metro area, high commuter	Bibb	72 miles
8	4	Micropolitan area, core	Lowndes (MS)	128.36 miles
9	2	Metro area, high commuter	Tuscaloosa	28.04 miles
10	2	Metro area, high commuter	Bibb	75.68 miles

Note. RUCA = Rural Urban Commuting Area Codes. TVAMC = Tuscaloosa VA Medical Center.

To focus on qualitative perspectives of rural daily life, authors narrowed the present study’s data sources to (1) audio-recorded semi-structured interviews and (2) individual demographics, including age, gender, race/ethnicity, education, relationship to PWD, and zip code. This study was approved by the TVAMC and University of Alabama IRBs as an unfunded, secondary analysis, exempt study.

Participants

Data include N = 30 participant interviews (12 veteran PWDs; 18 informal family CGs). Dyads were defined as “complete” pairs of PWDs and CGs that participated in the parent study. There were 10 dyads that participated together. A separate set of eight CGs participated without a PWD, and two separate PWDs participated without a CG. The eight CGs participated alone when their loved one, the PWD, did not want to participate or was unable to consent due to cognitive impairment. The remaining two PWDs participated without a CG because they did not live with or have an informal CG at the time of participation. PWDs’ capacity to consent individually and within a dyad was determined through a VA medical chart review by ONA’s PI and research team, and then again before conducting the interview.

Participants were required to be living in the community (i.e., not a nursing home or assisted living community), could be living independently (e.g., separate from their dyadic partner), and be older than 19 years of age to ensure an adult sample according to the local age of consent. CGs were required to self-identify as assisting and caring for a PWD, whereas PWDs were required to have a diagnosis of dementia or dementia-related disorder by their VA medical doctor. Participants could not be currently incarcerated, pregnant, or have severe cognitive impairment that would impair a participant’s ability to communicate during the interview.

All veteran PWDs were men at an average of 76.5 years of age (age range 63–83) and were either non-Hispanic White (92%) or Black/African American (8%). All PWDs Montreal Cognitive Assessment (MOCA) scores were collected at the time of the interview and reflected mild to moderate levels of impairment (range = 9–28; M = 16.1, SD = 5.6). Informal CGs were all women and were an average of 70.9 years of age (age range 57–82). Some CGs were daughters of the veteran (11%) but a majority were wives (89%). The CGs were 83% non-Hispanic White and 17% Black/African American.

Measures

Semi-structured Interview. The audio-recorded semi-structured interviews for both CG and PWD included questions regarding psychosocial needs and daily resources relevant to the caregiving process (e.g., “:Are there things that you could use help with now?”). CGs were asked additional questions regarding specific health and resource needs for both themselves and the PWD (e.g., “Does the PWD have a problem keeping occupied; Lack structure in his/her day?”) (see Supplemental File: Interview Guide).

All interviews lasted between 60 and 90 minutes and were completed in the participants’ homes by two health science specialists from the TVAMC. The interviewers were trained to (1) administer consent processes for both the PWD and CG, (2) complete any necessary cognitive screening assessments for PWDs (data not utilized in the proposed study), and (3) follow a semi-structured qualitative interview schedule. Informed consent and consent to audio record both interviews were obtained from CGs and PWDs.

When possible, semi-structured interviews for dyads were carried out in separate rooms to allow participants to share any difficulties or concerns related to dyadic roles. If separation was not desired or possible due to medical risk, the interviews became joint interviews, and protocols were followed for both members of the dyad.

Data Analysis

A rapid qualitative analysis approach was used to audio code the secondary interview data according to the rural-dwelling caregiving dyads’ daily needs (Beebe, 1995; Brooks & King, 2012). Using a multi-coder interrater design, three team members (Authors Loup, Snow, and Hilgeman) began coding using a simple Microsoft Excel template that involved audio transcribing (verbatim) participant responses that referenced needs and labeling if the needs therein were “met” or “unmet.”

After the first three interviews were coded, the coding authors conducted interrater reliability meetings and updated the Excel templates to (1) label the specific and geographically relevant met and unmet needs within each interview response (e.g., “Transportation”; “Local Church”; “Shop at Walmart instead of Piggly Wiggly”) and (2) indicate when “needs assessment” statements from the participants conflicted with the three coders’ clinically informed analysis perspective (note: Authors Loup, Snow, and Hilgeman are trained in the field of clinical geroscience). A conflicting response from a CG may include stating she does not need driving assistance even though she is the dyad’s primary driver and has macular degeneration; the resulting met or unmet need designation would therefore differ from the CG’s perspective as “met” and would be listed as ‘unmet’ during coding and analysis.

After an additional three interviews (total of six) were coded and interrater agreement was found to be consistent, a final template model was used to code the remaining 24 audio interviews (e.g., process used in Hartmann et al., 2018). Rater memos and an audit trail were among the methods used to ensure rigor during design and analysis stages.

Rapid template data were then evaluated using both thematic and content analysis to iteratively create a code book categorizing participants’ daily life activity and resource needs (Braun & Clarke, 2006; Charmaz, 2006; Hartmann et al., 2018; Timmermans & Tavory, 2012; Vaismoradi et al., 2013). Repeated recoding of templates occurred over five rapid stages to narrow down and combine code labels until saturated cross-coding was achieved. The final codebook included 53 needs-based codes representative of daily life and resource needs across the entire sample (N = 30) (see Supplementary File: Codebook). Summative content analysis was then employed to numerically tally each interview’s identified need codes (Hsieh & Shannon, 2005; Krippendorff, 1980, 2004). The resulting code counts provided ordered sets of needs for each dyad and individual relevant to the entire sample and other participants (i.e., how many and what type of met and unmet needs per dyad and individual) (see Supplementary File: Code Counts).

The most recurrent met and unmet needs related to daily life activities and resources within each individual dyad were identified and transferred visually to life-space maps using color to designate priority and severity. No locational data (i.e., distance in miles from home to a resource) were included due to the secondary nature and confidentiality of the interview data. Moreover, while interview data from all participants (N = 30) were used to develop a thematic needs-based codebook, life-space maps were only created for the dyadically participating individuals (n = 20, 10 dyads in total) to demonstrate life-space mapping’s ability to visualize the combined context of a PWD and their CG in the home. Future studies will expand upon independently living and participating PWDs and CGs.

Results

Rapid template qualitative analyses yielded need-based insights from a sample of rural-dwelling Veteran dementia dyads, which were then synthesized into life-space maps to visualize the daily life needs (met and unmet) of the participating dyads. In total, ten life-space maps were created to communicate the coded daily met and unmet needs in terms of priority for each dyad. The maps use a shared visual language (structure, key, icons, and colors) to consistently demonstrate the severity and priority of resources and needs across dyads. Complicated codes were visually broken down into their various components, for example, family support was broken down into supportive members and missing links—the CG’s son and neighbor often visit, but CG’s daughter neither calls nor visits.

Herein, we review three dyadic maps of the total ten to offer a snapshot of the range of life contexts and resource needs discovered within this rural dementia caregiving sample. Dyad #2’s life-space map illustrates five core met and five unmet needs within their daily caregiving context (Figure 1). Using green as safe/met access, the map demonstrates the dyad (1) lives in a socially supportive neighborhood, (2) has a strong church community support, (3) has access to exercise opportunities for the CG, (4) receives frequent calls from a distant living son, and (5) has access to light housework support. In contrast, unmet needs are labeled in red or yellow for unmet or at risk/unsafe. These included (1) difficulties driving due to troubles getting PWD in the car, (2) needing to travel to a distant physical therapist two times each week, (3) increased fall risk in the home, (4) no longer pursuing hobbies and exercise, and (5) not having strong family support despite living close by (Figure 1).

Figure 1.

Dyad #2 Life-Space Map. Note. Figure best viewed in color. Please visit online version of the article to view in color.

Dyad #5’s life-space map is an example of how maps can concisely communicate complex, contextually dependent information (Figure 2). This map visually identifies a primary need to increase PWD’s mobility beyond his house to get him to care appointments at the VA Medical Center. The dyad’s map visually outlines several avenues to address this need, one being increasing driving access by harnessing his large social/family support network. Another avenue would be to get the dyad connected to the Internet and Wi-Fi to allow for access to VA home health care. Dyad #5’s map is therefore a rich visual data source for interested parties, such as a clinician, to observe what barriers or assistance may be present when getting in touch with or treating the PWD during their visit.

Figure 2.

Dyad #5 Life-space map. Note. Figure best viewed in color. Please visit online version of the article to view in color.

Moreover, dyad #4’s life-space map demonstrates how maps can convey meaning around contradictory needs unique to a dyad (Figure 3). During their interview, this couple noted their food access needs are met; however upon qualitatively analyzing and visually mapping their data in context, it appears that due to driving distance and the physical demands, it takes the dyad an entire day to complete a grocery shopping run. This dyad in particular also struggles with driving because the CG “hates” and fears driving, and the PWD is unable to assist due to physical and cognitive limitations. Building upon LSA, life-space maps like this one are able to emphasize a dyad’s current mobility and community interaction capability as it relates to their met and unmet needs, as well as what may be getting in the way of extending their life-space mobility.

Figure 3.

Dyad #4 Life-space map. Note. Figure best viewed in color. Please visit online version of the article to view in color.

Discussion

This study demonstrates the potential utility of combining rapid qualitative analysis and visual life-space mapping for busy providers and learning healthcare systems like the VHA. Considering the significant predicted 22% increase of older veteran VHA patients diagnosed with dementia by 2033 across the nation, the clinical utility of life-space maps lies in their potential to more efficiently communicate the PWD’s and CG’s needs, the reasoning behind said needs, and options to address said needs through implementation science methods and QI efforts (U.S. Department of Veterans Affairs VHA, 2013; O’Hanlon et al., 2017). In contrast, traditional methods of rich, qualitative, needs-based data presentation, including tables and thematic summaries, can be difficult to access and integrate into fast-paced clinics where providers of all levels may not have the time to apply generalized findings to individual patient care plans (Bangerter et al., 2019). In response, life-space maps offer a meaningful, readily understood visual tool for improved awareness of rural Veteran dementia dyad’s care context and met and unmet needs therein.

Moreover, life-space maps offer numerous benefits to healthcare systems’ QI efforts and can support providers wanting to synthesize rural patients’ home context and needs into care visits (West et al., 2015; O'Hanlon et al., 2017). First, life-space maps uniquely offer care systems a visual determination of what services are currently being used by rural at-risk patients (e.g., dementia dyads), and what resources rural populations may be more likely to seek out in the future. For example, life-space maps may help urban providers and care systems efficiently identify and prioritize applicable interventions for dementia dyads, thus reducing the risk of skipped or lower quality collaborative clinical action planning during patients’ infrequent visits to urban centers (Bangerter et al., 2019; Innes et al., 2011). Secondly, future versions of life-space maps could be designed to indicate care outcomes over time, indicative of the efficiency and efficacy of a care system’s given model of provision (Andersen, 1995; Aday et al., 1993; Hamilton & Finley, 2019). Third, following the COVID-19 pandemic and increase of modern care provision models that rely upon “encounterless” telehealth appointments, rapid analysis of rural patients’ needs combined with life-space mapping may help clinical settings adjust to telehealth and gain a better understanding of the need-based context in which rural dementia dyads live and now attend remote appointments (Fortney et al., 2011, p. 639–640).

To encourage life-space maps’ potential impact on providers and learning healthcare systems like the VHA, authors have identified several next steps to improve the applicability and user-friendliness of the presented life-space map prototypes for clinical settings. Authors first envision a survey study that aims to understand busy providers’ efforts to synthesize and retain traditional academic methods of rural needs assessment data and extant data visualization for care provision. Survey results could inform on life-space maps’ location and role in the electronic medical record, as well as the frequency and content upon which maps should be updated (e.g., annual screener of unmet needs entered into the mapping tool) (West et al., 2015). Additionally, the authors would like to engage providers of all levels within learning healthcare systems (e.g., social work, medical, nursing, and administration) in participatory design methods to further develop the design and application of the maps, as well as learn how maps could most benefit care continuity over time (e.g., Mold & Peterson, 2005). Subsequent design updates may include adding distance indicators (e.g., line length refers to the mileage between a PWD’s home and local health care center) or working with software developers to create more dynamic electronic maps that have the ability to shift and grow according to patient outcomes over time. If achieved, ever-evolving life-space maps could more effectively communicate what needs and resources for rural dementia dyads continue to demand attention, and what daily life factors are going well and represent a strength for the dyad (Bangerter et al., 2019; Bodenheimer, 2005; Orsulic-Jeras, Whitlatch, Powers, & Johnson, 2020; Wilson & Childs, 2002).

This proof-of-concept study was limited in several ways. First, future studies could conduct similar life-space map development methods with larger, more diverse, rural dyadic samples beyond southeastern, rural Veteran dementia dyads. Moreover, this study was limited by the parent study’s design and interview structure which focused on subjects beyond met and unmet needs. The parent study additionally included novice interviewers who produced less structured, more variable interview data (e.g., inconsistent question order and different degrees of follow-up questions).

In sum, expanding life-space maps application, user-friendliness, and use for rural care continuity for dementia and, potentially, other chronic and life-limiting illness patient populations aligns with the most recent Department of Veterans Affairs Strategic plan to guarantee “Veterans receive highly integrated and coordinated benefits, care, and support services that…are tailored to meet their economic and health needs” (U.S. Department of Veterans Affairs VHA, 2019; p. 17). With this study and mapping results, authors propose that a more contextualized understanding of rural dementia caregiving can only be possible when disciplines are combined and data are no longer viewed via static, traditional methods but rather can become the basis for visual, iterative intervention and care-continuity methods.

Supplemental Material

Supplemental Material - Illustrating Resource Needs through Data Visualization: Creation of Life-Space Maps for Rural Veterans with Dementia and their Caregivers

Supplemental Material for Illustrating Resource Needs through Data Visualization: Creation of Life-Space Maps for Rural Veterans with Dementia and their Caregivers by Julia Loup, Kate Smith, A. Lynn Snow, and Michelle M. Hilgeman in Journal of Applied Gerontology.

Supplemental Material

Supplemental Material - Illustrating Resource Needs through Data Visualization: Creation of Life-Space Maps for Rural Veterans with Dementia and their Caregivers

Supplemental Material

Supplemental Material - Illustrating Resource Needs through Data Visualization: Creation of Life-Space Maps for Rural Veterans with Dementia and their Caregivers

Footnotes

Acknowledgments

We would like to acknowledge the contributions of Ms Whitney Gay and Ms Dedria Smith who completed the original qualitative interviews used for the current analyses, as well as Ms Kimberly Alexander who provided research coordinator support at the Tuscaloosa Veterans Affairs Medical Center. Views expressed represent those of the authors and not the Department of Veterans Affairs or the US Government.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Department of Veterans Affairs Rehabilitation Research & Development Service (CDA1 Award #1 IK1 RX000791-01A1, Hilgeman PI).

Data Sharing

Data can be made available by contacting the senior author, Dr. Michelle Hilgeman, Michelle.Hilgeman@va.gov.

IRB protocol/ Human Subjects Numbers

University of Alabama IRB Protocol ID: 19-06-2423, exempt; VA IRB Protocol ID: 00265, exempt.

ORCID iD

Julia Loup

Supplemental Material

Supplemental material for this article is available online.

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