Abstract
Background and Objectives
American Indian and Alaska Native peoples have disproportionately low rates of advance care planning (ACP). To address this problem, we culturally tailored and evaluated an intervention for ACP to the needs of a specific American Indian Tribe. The goal of our study was to examine the culturally tailored Make Your Wishes About You (MY WAY) ACP intervention with respect to 5 ACP outcomes: barriers, facilitators, readiness, self-efficacy, and notarized advance care plan completion.
Research Design and Methods
We engaged Tribal community members in a quasi-experimental, waitlist-controlled trial design to test the effects of the program. Our sample included 2, self-selected groups totaling 113 participants. Data were collected via interviewer-administered surveys with participants on 3 occasions. The intervention group completed an intervention baseline survey, postprogram survey, and a follow-up survey 6 months after the intervention, and the waitlist comparison group completed a control baseline survey, intervention baseline survey, and postprogram survey.
Results
Our results showed that, on average, ACP barriers decreased, and facilitators, readiness, self-efficacy, and notarized advance care plan completion increased postintervention for participants who completed the ACP program. These changes were significantly greater for the intervention group than the waitlist comparison group and were sustained at the 6-month follow-up for the intervention group. In total, 76 more individuals completed their notarized advance care plans by the end of program participation than at intervention baseline, a 79.1% increase.
Discussion and Implications
The culturally tailored program was found to increase readiness and self-efficacy for ACP and increased the likelihood of a participant having a notarized advance care plan postprogram. Our study affects clinical and public health practice by testing a program that is responsive to cultural values, beliefs, and practices and shown to increase ACP among American Indian peoples.
Clinical Trial Registration
Keywords: Advance directive, Barriers and facilitators, End-of-life, Readiness, Self-efficacy
Transnational Significance:
To address the issue of low levels of advance care planning (ACP) among American Indian and Alaska Native peoples (AIANs), we first adapted and then tested the efficacy of an intervention known to be effective at increasing planning for end-of-life care for the general population. Our study shows the successful impact of the adapted program on increasing ACP readiness and self-efficacy as well as completion of advance care plans. These results imply that AIAN rates of ACP can be increased when interventions are tailored to be culturally responsive, which can lead to better end-of-life care for aging AIAN populations.
Advance care planning (ACP) is a process that can improve quality of care received and other end-of-life outcomes as it enables a person to discuss, decide on, and document their preferences for care in an advance care plan to serve as instructions if they are unable to speak for themselves, as is often the case near the end-of-life. An advance care plan or directive is a legal form that a person completes outlining their wishes identified during ACP that can be used as guidance for caregivers and medical professionals. ACP offers an opportunity to improve receipt of quality of end-of-life care as it has been shown to be associated with care that reflects patients’ goals, improves patient–provider communication, and reduces caregiver and patient distress (Carney et al., 2021; McMahan et al., 2021; Sellars et al., 2019).
American Indian and Alaska Native peoples (AIANs), usually described together for the purposes of population-level descriptions, unfortunately experience inequities with respect to quality end-of-life care. AIANs are more likely than Whites to be hospitalized and receive futile treatment at the end-of-life and experience lower rates of hospice and palliative care (Hospice Facts and Figures, 2020), do not resuscitate orders, and advance care plans (Isaacson, 2017, 2018; Kwak et al., 2019). These disparities indicate that AIANs experience lower quality end-of-life care which includes hospice and palliative care referrals, pain and symptom management, coordinated care, and attention to social, emotional, and spiritual needs of patient and family (Carney et al., 2021; Institute of Medicine, 2015).
The disproportionately low ACP rates among AIANs indicate a critical need to develop and provide appropriate ACP programs. A study with 270 AIANs found that none had heard of palliative care, and few (7%) had ever heard of a living will (Isaacson, 2017, 2018). Further, AIANs have been found to be half as likely as Whites to have a living will and/or healthcare power of attorney (Kwak et al., 2019). Though informing patients about advance directives is required by the 1990 Patient Self-Determination Act, the most recent and available medical chart review of an Indian Health Service facility was over 20 years ago and found that this information was in only 20% of the charts (Kitzes & Berger, 2004). Low ACP rates among AIANs could reflect the belief that many do not wish to discuss death. Yet, prior research has shown AIANs are willing to engage in culturally sensitive conversations about death (Anderson et al., 2022; Dennis & Washington, 2018), but face barriers in the form of stereotypes held by healthcare professionals and limited access to end-of-life planning opportunities (Findling et al., 2019; Isaacson, 2017; Isaacson & Lynch, 2018; Isaacson et al., 2022; Shahid et al., 2018; Soltoff et al., 2022). Further, historical and modern challenges in receiving care from healthcare professionals, including systemic racism in clinical settings, have contributed to many AIANs being less trustful of healthcare providers (Anderson et al., 2022; Isaacson & Lynch, 2018).
ACP approaches are needed specifically for AIANs that are developed with guidance and input from the intended community (Hordyk et al., 2017; Isaacson & Lynch, 2018) and incorporate culturally relevant values, beliefs, and practices (Lillie et al., 2020). Further, ACP programs must consider goodness of fit for intended populations creating a need to assess the effectiveness of cultural tailoring (Jones et al., 2021). A one size fits all approach with ACP does not consider the diverse social and cultural factors surrounding death and dying among AIANs. Despite such recommendations, there has been minimal work to increase opportunities for AIANs to express their end-of-life care wishes. Little is known with respect to which culturally tailoring efforts have been successful among Indigenous communities (Walters et al., 2020). As of 2021, there were no randomized controlled trials (RCTs) demonstrating the efficacy of ACP with AIANs (Jones et al., 2021) and there have been limited recent studies on ACP initiatives with AIANs (Dennis & Washington, 2018; Isaacson, 2017; Isaacson & Lynch, 2018; Kwak et al., 2019; Pataria, 2022).
Successfully tailored interventions are patient-centered, inclusive of family and community, and provide participants with valuable health information (Joo & Liu, 2021). There are several published studies that have examined the efficacy of a tailored ACP intervention for other populations. For example, using a pretest/posttest study design, a church-based culturally tailored ACP intervention program for Asian Americans was found to increase participant’s ACP knowledge, intentions, and advance care plan completion (Sun et al., 2017). With a RCT design, both the FAmily CEntered (FACE) ACP program for patients with HIV (Lyon et al., 2019) and the PREPARE program for Spanish-speaking patients in a primary care setting also increased advance care plan completion rates (Sudore et al., 2018).
A recently developed ACP program for older adults with chronic kidney disease, Make Your Wishes About You (MY WAY), was evaluated using an RCT and found significant increases in ACP readiness, self-efficacy, and advance care plan completion rates (Anderson et al., 2018; Lupu et al., 2022). Based on experience with the original MY WAY program, we engaged in a cultural tailoring process to adapt this program to better meet the needs of a specific federally recognized American Indian Tribe (described in Goins et al., 2024). While the participating Tribe has a legal aid office and a healthcare system, there has not been a concerted effort to increase ACP among Tribal citizens (anonymized in-person communication February 20, 2020). We hypothesized that experiencing this culturally specific ACP intervention would decrease the number of reported barriers and increase the number of reported facilitators to ACP as well as increase readiness and self-efficacy for ACP. We also predicted that these changes would lead to more reports of advance care plan completion among those participating in the intervention.
Research Design and Methods
For the present study, we attempted to determine if the adapted program would lead to improvement in ACP outcomes using a quasi-experimental waitlist control design. We examined the extent to which the culturally tailored program would reduce ACP barriers and improve ACP facilitators, readiness, self-efficacy, and advance care plan completion for participants.
Participants and Recruitment
During the cultural tailoring process, the study’s Community Advisory Board expressed that inclusivity was important to the community. To meet this request, study eligibility criteria were broad and included being a Tribal citizen, spouse of a Tribal member, first descendant, or a citizen of another AIAN Tribe, aged ≥ 18 years, and residing within the Tribal service area. We recruited potential participants using convenience sampling techniques. We made in-person study recruitment announcements at 13 Tribal venues and left flyers and hung posters at 26 locations on or near Tribal lands. We also ran an ad in the Tribal newspaper and on three billboards. Tribal programs distributed MY WAY information on our behalf via their social media and/or email listserv. We also developed a project website and ran a short video on the local Tribal television station. Interested people were directed to call the Project Coordinator by telephone and/or speak with staff immediately before the intervention and then screened for study eligibility and enrolled, if eligible.
Setting
The participating Tribe is in the Southeastern United States with approximately 16,000 enrolled citizens (anonymized, personal communication June 9, 2022). Within Tribal Lands there are three distinct Tribal communities separated by natural geography: one large community and two smaller ones. All study activities were conducted on Tribal lands at either Tribal Community Clubs, senior centers, the Tribe’s extension Office, public libraries, participants’ homes, restaurants, or the Tribe’s American Legion Auxiliary. The culturally tailored MY WAY waitlist control trial began in March 2022 and concluded in January 2023.
Intervention
The intervention consisted of two steps: attendance at a Community Information Session (CIS) and attendance at an individual Sharing Session (SS). For our study’s purposes, participants who attended both CIS and SS were considered as having completed the intervention. The CIS was a group-based event held at different community-based venues such as senior centers and Community Clubs. Free meals were provided, and a trained project staff member presented an overview of the project and described what a healthcare power of attorney is, how to discuss future healthcare wishes, write an advance directive, share healthcare wishes with loved ones and providers, and complete the physician or medical order for life-sustaining treatment form. The average length of a CIS was 45 min.
Attending an SS was the second step of the intervention, set to occur about 2 weeks after CIS attendance for participants who completed evaluations after the CIS. SSs were hosted by a trained staff member who relied on a curriculum guide to help each participant think through their end-of-life wishes and complete the necessary ACP forms. Staff used the same ACP forms used by the Tribal legal aid office, which included a Part A and Part B, which collectively serve as the participant’s advance care plan. Part A allowed the participant to designate a healthcare power of attorney, and Part B allowed the participant to express a living will, stating whether they would like to receive life-prolonging measures. SSs were held in the location of each participant’s choice. Many participants were accompanied by family members at their SS and project staff were able to notarize the participant’s advance care plan at the end of the SS. The average length of an SS was 41 min.
Participants completed the intervention in one of two groups: the intervention and the waitlist comparison groups. Data were collected via surveys from the intervention group immediately before the CIS (“intervention baseline survey”), after the CIS at posttest (“postprogram survey”), and 6-month follow-up (“follow-up survey”). Once recruitment was complete for the intervention group, the waitlist comparison group was recruited, enrolled, and completed a baseline survey (“control baseline survey”), but then waited to complete the CIS, SS, and other data collection steps of the intervention. Participants in the intervention group finished the intervention before participants in the waitlist comparison group started their CIS. The waitlist comparison group participants then completed another survey immediately before CIS attendance (“intervention baseline survey”), and 2 months after the CIS (“postprogram survey”). The waitlist comparison group participants completed no study activities during the waitlist period. Combined, both intervention components took less than 2 hr to complete. All study participants who completed the full intervention, and the interviewer-administered surveys received $85 in compensation distributed throughout the intervention (i.e., in increments after CIS, surveys, and SS).
Objectives
The goal of our study was to examine preliminary efficacy of the culturally tailored ACP intervention. We planned to show that any changes in ACP outcomes were due to the intervention itself by including a group of participants with the same eligibility in a waitlist comparison group. We hypothesized that the intervention would help the participants to complete their advance care plans by increasing readiness and self-efficacy for ACP as well as decreasing barriers and increasing facilitators to ACP and that these favorable changes would occur after the intervention for both groups and not in the interim for the waitlist comparison group.
Outcomes
Primary outcomes of ACP readiness and self-efficacy were captured with the nine-item ACP Engagement Survey Process Measures identified from Behavioral Change Theory. The six-item readiness and the three-item self-efficacy subscales have five-point, Likert responses of “not at all, a little, somewhat, fairly, extremely.” In a field test of these subscales, they demonstrated good reliability and discriminant validity (Sudore et al., 2013, 2017).
Secondary outcomes of ACP barriers and facilitators were assessed with 16 yes/no questions (eight barriers and eight facilitators) derived from qualitative data regarding ACP and developed during the original MY WAY clinical trial but not published.
For the final secondary outcome of advance care plan completion, participants were asked if they had a legal advance care plan. To confirm self-reports postprogram, project staff documented whether the participant’s advance care plan was notarized after SS and compared this to notary records. All sources of the participant’s advance care plan completion measure were combined into a single dichotomous variable (yes/no). In the case of discrepancies where participant reports could not be confirmed, advanced care plan completion was recorded as “no.”
Interview-administered intervention and control baseline surveys also included self-reported demographic characteristics of age (in years), gender (male or female), marital status (married or not married), Tribal affiliation (citizen, first descendent, family of a citizen, citizen of another Tribe), education (on a scale of 1 = less than high school education to 5 = graduate degree), self-rated health (on a scale of 1 = very poor to 6 = excellent), and self-reported diagnosis of any chronic conditions. Chronic conditions included chronic obstructive pulmonary disease, history of stroke, history of heart attack, cancer, chronic kidney disease, type 2 diabetes, heart disease, high cholesterol, and chronic pain. All study participants provided informed consent.
Sample Size and Assignment
Based on the risk ratio of completing an advance care plan indicated in the previously implemented RCT for the untailored intervention (1.79, 95% CI: 1.18–2.72) and using 90% power as a threshold, we used G*Power (Axel Buchner) with 3 predictors and the secondary outcome of advance care plan completion to estimate that we would need at least 60 participants to find effects. As effect sizes for the primary outcomes of ACP self-efficacy and readiness were smaller in the original intervention, our goal was to enroll a total of 70 participants with 35 in each group.
To help minimize the spread of information across communities while assuring that all willing participants were able to complete the intervention, participants were assigned to intervention and waitlist comparison groups based on geographic region. Using a coin flip, we randomly decided that the intervention group would be recruited from the single, larger, community while the waitlist comparison group would be recruited from the combined, smaller communities. Participants were then recruited via purposive sampling based on the area of the Tribe in which they resided and were informed of the purpose of the study.
Study personnel first recruited the intervention group in the larger community and then began recruitment in the smaller communities for the waitlist comparison group. Assignment was captured by documenting the number of potential participants who were screened, deemed eligible, enrolled, and engaged in the full intervention, and the retention rates (number who completed the entire intervention, number who completed postprogram surveys from both groups, number in the intervention group who completed the follow-up survey, and the number who dropped out from the control baseline to the intervention baseline in the waitlist comparison group). The Community Advisory Board engaged in the cultural tailoring process advised against ending recruitment at a specific number of participants, so we instead continued recruiting in both regions until the day of the last scheduled CIS.
Analyses
We used a quasi-experimental waitlist-controlled study design to test our hypotheses. All analyses were conducted with IBM SPSS Statistical Software (27) or Microsoft Excel. Differences such as age, overall health, geographical region, number of chronic health conditions, and group assignment, between the intervention and waitlist comparison groups could potentially confound our results. Further, those recruited to the study could be those who are most interested in ACP in the first place. We examined participant demographic characteristics using descriptive statistics and stratified analyses and compared the groups along these confounding variables before analyzing change over time. To determine potential covariates, we conducted crosstabulation analyses with Bonferroni correction applied (see Supplementary Material) and dichotomized relevant variables for inclusion in the overall analysis.
Before combining the individual readiness and self-efficacy items, we ran selectivity and reliability analyses. As barriers and facilitators were asked as simple “yes/no” questions, the number of barriers and facilitators listed by participants was calculated before and after the intervention for comparison. To check for differences in the sample due to attrition, we computed Little’s Missing Completely At Random (MCAR) test.
To analyze change over time in ACP readiness and self-efficacy, we conducted a 2 (readiness and self-efficacy) × 2 (pretest to 8-week posttest) way repeated measures multivariate analysis of covariance (MANCOVA) with three levels of group as the between subject’s factor. Intervention group baseline to postprogram scores on ACP measures were compared to waitlist comparison group control baseline to intervention baseline scores and waitlist comparison group intervention baseline to postprogram results using pairwise comparisons. This analysis compares the intervention group to the waitlist comparison group (scores from control baseline to intervention baseline) and a second implementation of the intervention (waitlist comparison scores from intervention baseline to postprogram scores). As ACP completion is dichotomous and was only measured twice for participants regardless of intervention or waitlist comparison, we used a McNemar test to examine pre- and postintervention change. Finally, for 6-month follow-up surveys with the intervention group, we conducted a separate, repeated measure ANOVA (intervention baseline, postprogram, follow-up) to examine how changes in ACP readiness and self-efficacy were sustained. This study received approval from Western Carolina University institutional review board, Tribal institutional review board, Tribal Health Board, and Tribal Council.
Results
In total, 166 participants completed an enrollment survey to determine eligibility and interest. Figure 1 outlines the flow of the study including each measurement and exclusion stage. Recruitment was more successful in the larger Tribal community than in the two smaller regions such that the intervention group (n = 114) ended up with many more participants than the waitlist comparison group. To reach the required enrollment of at least 35 participants, recruitment for the waitlist comparison group was extended back into the larger Tribal community (n = 41). In total, 155 participants enrolled and participated in at least one survey. Nearly three-quarters of people who enrolled in the study attended both intervention activities (n = 113, 73.5%). Most participants heard about the project through in-person presentations and recruitment events (n = 54) or through word of mouth (n = 45). Data were collected via surveys in March and April 2022 from the intervention group immediately before the CIS (intervention baseline survey), 8–9 weeks after the CIS (postprogram survey), and 6-month follow-up (follow-up survey). The waitlist comparison group participated in baseline data collection from April to June 2022 (control baseline survey) and then waited approximately 4 months and completed surveys again, immediately before CIS attendance (intervention baseline survey), and an average of 12 weeks after the CIS (postprogram survey).
Figure 1.
Consort flow diagram.
Table 1 describes the total participant sample and each group. Most participants were female (69.7%), and the majority were not married (54.2%). On average, participants had at least some college (M = 2.74 ± 1.01, n = 1 missing), were just over 65 years old, and ranged from 19 to 89 years old. Participants also rated their health as fair or good (M = 2.67 ± 1.05). Each chronic condition asked about in the survey was reported by least 5% of participants, but the most often reported conditions were “other” which included chronic knee conditions, arthritis, asthma, and thyroid conditions. When asked about ACP completion at control or intervention baseline, few participants indicated that they had a notarized advance care plan (n = 17, 11%). These overall sample trends were similar in both groups. SS occurred, on average, 17 days after CIS attendance for participants who completed evaluations after the CIS and SS (intervention group n = 88, M = 17.8 ± 27.4 days; waitlist comparison group, n = 33, M = 14.3 ± 18.1 days).
Table 1.
Culturally Tailored MY WAY Participant Characteristics
| Variable | Baseline total sample (N = 155) |
Intervention (n = 114) |
Comparison (n = 41) |
||||||
|---|---|---|---|---|---|---|---|---|---|
| n | (%) | M ± SD | n | (%) | M ± SD | n | (%) | M ± SD | |
| Gender | |||||||||
| Male | 47 | 30.3 | 34 | 29.8 | 13 | 31.7 | |||
| Female | 108 | 69.7 | 80 | 70.2 | 28 | 68.3 | |||
| Marital status | |||||||||
| Married | 71 | 45.8 | 49 | 43.0 | 22 | 53.7 | |||
| Not married | 84 | 54.2 | 65 | 57.0 | 19 | 46.3 | |||
| Education | |||||||||
| Less than high school | 16 | 10.3 | 14 | 12.3 | 2 | 4.9 | |||
| High school | 45 | 29.0 | 29 | 25.4 | 16 | 39.0 | |||
| Some college | 69 | 44.5 | 50 | 43.9 | 19 | 46.3 | |||
| College degree | 15 | 9.7 | 13 | 11.4 | 2 | 4.9 | |||
| Graduate school | 9 | 5.8 | 8 | 7.0 | 1 | 2.4 | |||
| Refused to answer | 1 | 0.6 | 0 | – | 1 | 2.4 | |||
| Overall self-rated health | |||||||||
| Excellent | 18 | 11.6 | 15 | 13.2 | 3 | 7.3 | |||
| Very good | 55 | 35.5 | 41 | 36.0 | 14 | 34.1 | |||
| Good | 51 | 32.9 | 35 | 30.7 | 16 | 39.0 | |||
| Fair | 23 | 14.8 | 18 | 15.8 | 5 | 12.2 | |||
| Poor | 7 | 4.5 | 4 | 3.5 | 3 | 7.3 | |||
| Very poor | 1 | 0.6 | 1 | 0.9 | 0 | – | |||
| Self-reported chronic conditions | |||||||||
| Heart disease | 22 | 14.2 | 16 | 14.0 | 6 | 14.0 | |||
| Type 2 diabetes | 59 | 38.1 | 42 | 36.8 | 17 | 41.5 | |||
| High blood pressure | 77 | 49.7 | 55 | 48.2 | 22 | 53.7 | |||
| Stroke | 13 | 8.4 | 9 | 7.9 | 4 | 9.8 | |||
| Heart attack | 10 | 6.5 | 7 | 6.1 | 3 | 7.3 | |||
| Kidney disease | 8 | 5.2 | 5 | 4.4 | 3 | 7.3 | |||
| Cholesterol | 40 | 25.8 | 30 | 26.3 | 10 | 24.4 | |||
| COPD | 8 | 5.2 | 6 | 5.3 | 2 | 4.9 | |||
| Cancer | 12 | 7.7 | 10 | 8.8 | 2 | 4.9 | |||
| Pain | 29 | 18.7 | 21 | 18.4 | 8 | 19.5 | |||
| None | 28 | 18.1 | 25 | 21.9 | 3 | 7.3 | |||
| Other | 84 | 54.2 | 64 | 56.1 | 20 | 48.8 | |||
| Age (in years) | 65.3 ± 15.4 | 64.2 ± 15.9 | 68.3 ± 13.8 | ||||||
| Barriers selected at baseline | 2.06 ± 2.03 | 1.84 ± 1.87 | 2.56 ± 2.23 | ||||||
| Facilitators selected at baseline | 5.79 ± 1.42 | 5.69 ± 1.51 | 6.50 ± 1.19 | ||||||
| Do you have a notarized advance care plan? | |||||||||
| Yes | 15 | 9.6 | 10 | 8.7 | 5 | 12.1 | |||
| No | 138 | 89.0 | 104 | 91.2 | 34 | 82.9 | |||
| Attended CIS and SS | |||||||||
| Yes | 113 | 72.9 | 81 | 71.0 | 32 | 78.0 | |||
Notes: CIS = Community information Session; COPD = chronic obstructive pulmonary disease; SD = standard deviation; SS = sharing session.
aLess than high school degree (1–11 years), high school degree/GED (12 years), some college (associate degree, technical degree), college degree (16 years), more than college/graduate school.
Cronbach’s α scores indicated that there was good reliability for readiness and self-efficacy (α > 0.7) therefore, the items measuring readiness were combined into a single score and the items measuring self-efficacy were combined into a single score. Little’s MCAR test was not significant (χ2(126) = 115.60, p = .74), so, we did not run a selectivity model or include corrections for attrition or missing data. Supplementary Table 1 shows a significant association between gender and having completed an advance care plan at intervention baseline. Additionally, education was significantly associated with self-efficacy and advance care plan completion at intervention baseline. Thus, gender and education were included in the MANCOVA as covariates with intervention versus waitlist comparison group as the between-subjects factors.
Barriers and Facilitators
Figure 2 shows changes in barriers and facilitators from intervention baseline to 8-week postprogram survey for the entire sample of participants. On average, people reported encountering fewer barriers to making ACP decisions after the program than they did before the program. The average percentage of participants agreeing with the barrier statements decreased by 5.2% from intervention baseline to postprogram survey. Similarly, the average percentage of participants agreeing with facilitators increased by 6.3% intervention baseline to postprogram survey, indicating that more participants believed in facilitating statements after the program than they did before the program. Unlike the overall trend, the barrier, “I do not wish to make decisions about the healthcare I would want if I could not speak for myself because I prefer to leave the choices to my doctors” increased slightly from 7.5% of participants agreeing with this statement at pretest to 10.2% agreeing at posttest. Similarly, the facilitators, “I want to avoid conflict among family members by making my wishes clear” and “I think making decisions about the healthcare that I would want will make it more likely I will get the care I want if I am not able to speak for myself” both decreased slightly in the percentage of participants agreeing with these statements from pretest to posttest (see Figure 2).
Figure 2.
Change in ACP barriers and facilitators. ACP = advance care planning.
Readiness, Self-Efficacy, and Completion
Participating in the intervention increased ACP readiness, self-efficacy, and completion. The repeated measures MANCOVA showed a significant main effect of time (F(2, 159) = 6.49, p = .002) that differed by intervention and waitlist comparison groups (F(4, 320) = 3.15, p = .015) for measures of readiness (F(2, 160) = 6.24, p = .002) and self-efficacy (F(2, 160) = 3.35, p = .038). Table 2 shows the full factorial results including pairwise comparisons with Bonferroni correction which revealed that while baseline levels of readiness and self-efficacy were not significantly different between the two intervention groups or the waitlist comparison group, these measures did differ significantly between all three groups at the second measurement time (all p > .05; see Table 2). Additionally, the McNear’s exact test revealed that significantly more participants completed an advance care plan after the intervention than had at baseline (p < .001), reflecting a 79.1% increase.
Table 2.
Impact of Culturally Tailored MY WAY on ACP Outcomes: Results of Repeated Measures MANCOVA
| Variable | df | F | p Value | Partial eta squared |
|---|---|---|---|---|
| Multivariant effects | ||||
| Time (baseline to postprogram survey)* | 2, 159 | 6.49 | .002 | 0.075 |
| Gender | 2, 159 | 1.02 | .365 | 0.013 |
| Education* | 2, 159 | 6.47 | .002 | 0.075 |
| Group | 2, 159 | 2.20 | .069 | 0.027 |
| Time × Gender* | 2, 159 | 4.47 | .013 | 0.053 |
| Time × Education | 2, 159 | 0.30 | .745 | 0.004 |
| Time × Group* | 4, 320 | 3.15 | .015 | 0.038 |
| Univariant effect | ||||
| Time × Readiness* | 1, 160 | 9.31 | .003 | 0.055 |
| Time × Self-efficacy* | 1, 160 | 11.10 | .001 | 0.065 |
| Time × Gender | ||||
| Readiness* | 1, 160 | 6.51 | .012 | 0.039 |
| Self-efficacy* | 1, 160 | 7.58 | .007 | 0.045 |
| Time × Group | ||||
| Readiness* | 2, 160 | 6.24 | .002 | 0.072 |
| Self-efficacy* | 2, 160 | 3.35 | .038 | 0.040 |
| Pairwise comparisons (Group × Time) | ||||
| Readiness (time 1 vs. 2) | ||||
| Intervention | < .001 | |||
| Waitlist intervention | <.001 | |||
| Waitlist comparison | .066 | |||
| Self-efficacy (time 1 vs. 2) | <.001 | |||
| Intervention | <.001 | |||
| Waitlist intervention | .001 | |||
| Waitlist comparison | ||||
| Pairwise comparisons (Time × Group) | ||||
| First measure of readiness | ||||
| Group 1 versus 2 intervention | n.s. | |||
| Intervention 1 versus comparison | n.s. | |||
| Intervention 2 versus comparison | n.s. | |||
| Second measure of readiness* | ||||
| Group 1 versus 2 intervention | .004 | |||
| Intervention 1 versus comparison | <.001 | |||
| Intervention 2 versus comparison | .028 | |||
| First measure of self-efficacy | ||||
| Group 1 versus 2 intervention | n.s. | |||
| Intervention 1 versus comparison | n.s. | |||
| Intervention 2 versus comparison | n.s. | |||
| Second measure of self-efficacy* | ||||
| Group 1 versus 2 intervention | .115 | |||
| Intervention 1 versus comparison | <.001 | |||
| Intervention 2 versus comparison | .043 |
Notes: MANCOVA = multivariate analysis of covariance; n.s. = not significant (p = 1).
*Denotes significant relationships at α = .05.
Although the main effect of gender was not significant (p = .37), there was a significant interaction between time and gender (F(2, 159) = 4.47, p = .01) that extended to both readiness (F(1, 160) = 6.51, p = .01) and self-efficacy (F(1, 160) = 7.58, p = .01). Figure 3 shows the intervention baseline and postprogram survey results for all ACP outcomes and that male participants started with lower levels of readiness and self-efficacy but increased to postprogram levels that were like those of females. The effect of time overall and time on readiness and self-efficacy were medium-sized effects (partial η2 ≥ 0.05). In the model, participants that attended the intervention scored on average 0.9 points higher on readiness and 0.1 points higher on self-efficacy after the intervention whereas waitlist comparison group participants showed much smaller changes (control baseline to intervention baseline readiness increased 0.03 units and self-efficacy increased by 0.57 units). As Table 2 shows, there was also a significant main effect of education (F(2, 159) = 6.47, p = .002) such that participants with lower levels of education started with lower levels of ACP readiness and self-efficacy, but the interaction between time and education was not significant (F(2, 159) = 0.3, p = .75).
Figure 3.
Change over time in ACP outcomes for intervention and waitlist comparison sample and intervention group follow-up. ACP = advance care planning.
A one-way ANOVA showed that, for intervention group participants, both readiness (F(2, 274) = 34.36, p < .001) and self-efficacy (F(2, 276) = 30.58, p < .001) statistically significantly increased from intervention baseline to postprogram survey to 6-month follow-up (Figure 3). Tukey HSD post hoc tests for multiple comparisons showed that readiness significantly increased from intervention baseline to postprogram survey (p < .001) and from intervention baseline to follow-up (p < .001), but not from postprogram survey to follow-up (p = .91). Similarly, self-efficacy showed a significant increase from intervention baseline to postprogram survey (p < .001) and from intervention baseline to follow-up (p < .001), but not from postprogram survey to follow-up (p = 1). These results are also captured in Figure 2.
Discussion
As hypothesized, the culturally tailored MY WAY ACP intervention was shown to decrease the average number of barriers and increase the average number of facilitators toward ACP as well as increase ACP readiness, self-efficacy, and completion among study participants. Participants showed increases in positive ACP outcomes with more than twice as many participants having a notarized advance care plan by the end of the program than at intervention baseline. We are unaware of any other culturally tailored ACP intervention that has been delivered and tested for an American Indian population (Jones et al., 2021). For ACP interventions tailored to Asian Americans, Spanish-speaking patients, and patients with HIV, the increase in ACP completion rates was lower than the increase we report here (Lyon et al., 2019; Sudore et al., 2018; Sun et al., 2017). In contrast to the original program, which was delivered in chronic kidney disease clinics, our intervention took place in the community and not a medical setting. Compared to the outcomes from the original program, our participants had higher ACP completion rates at intervention baseline (4% vs. 18%, respectively) and at study completion (33% vs. 70%, respectively). Readiness and self-efficacy scores are not comparable between the original program and our study as the response scale for each measure differed (Lupu et al., 2022).
Overall, our participants reported fewer barriers and more ACP facilitators after the program than at intervention baseline. Our study addressed many ACP barriers for American Indian peoples as identified in prior research, including lack of availability of culturally relevant and appropriate information, lack of trustworthy persons to talk with, and dealing with providers who do not understand American Indian culture. Prior research also identified facilitators for American Indian peoples, which included having friends and family members willing to participate and being able to decrease burden and conflict at the end-of-life (Lillie et al., 2020). The culturally tailored program encouraged participants to engage with their loved ones in the ACP process and included them in the SS. To address the concern of not wanting to burden loved ones with any end-of-life care decisions CIS covered alleviating burden as a key reason to engage in ACP. The single barrier which increased and the two facilitators that decreased from intervention baseline to postprogram survey were complicated questions that participants had difficulty answering. It is possible that the complex nature of this question led to results that were opposite to the general trend (i.e., increases instead of decreases for the barrier question and vice versa for the facilitators).
The intervention was likely effective for several reasons. First, we spent approximately 3 years engaging the community to obtain guidance as to how best undergo cultural tailoring to maximize receptivity and use. It is well-documented that AIANs experience racism and discrimination in the healthcare system causing barriers to care (Anderson et al., 2022; Findling et al., 2019; Soltoff et al., 2022). During the tailoring period, it was determined that the program would be delivered in the community and outside of the Tribal healthcare system. The community input also led us to offer the program in a two-step manner with a group-based general overview of ACP followed by private one-on-sessions with project staff where the participant had privacy to pose questions and express concerns regarding their end-of-life care decisions. Further, since the state in which this intervention occurred requires that the advance care plan be notarized, the project staff became notaries to facilitate this step for participants. Because personal and cultural differences can affect decisions about the end-of-life (Hordyk et al., 2017; Isaacson & Lynch, 2018; Isaacson et al., 2022; Shahid et al., 2018), the tailored program was responsive to the range of values and beliefs present in the participating Tribal community. A greater description of the cultural tailoring of the MY WAY ACP program for this project can be found elsewhere (Goins et al., 2024).
Although females indicated greater ACP readiness scores at intervention baseline, we found that males indicated a greater change in ACP readiness after the intervention. This unexpected finding suggests that our intervention was especially helpful at increasing ACP readiness for men. Less is known about the role of gender in ACP completion. A prior ACP study reported that men are more likely to avoid conversations that engage in end-of-life discussions than women (Seifart et al., 2019) and have found that divorced men are much less likely than married men to engage in end-of-life care conversations (Cooney et al., 2019) indicating a need for development of ACP interventions in that area.
The waitlist comparison group showed smaller increases in readiness and self-efficacy than the intervention groups. For ACP readiness, the increase in ACP readiness over time was not significant for participants in the waitlist comparison group while it was significant after the intervention. However, self-efficacy to engage in ACP did increase significantly for the waitlist comparison group from control baseline to intervention baseline. This increase in self-efficacy was smaller for the waitlist comparison group than the increase from intervention baseline to postprogram for the intervention group which could indicate that the change itself was smaller. It is also possible that participating in the control baseline itself led to increases in self-efficacy. That is, the increase overtime with no intervention could be related to practice effects of taking the same surveys multiple times. The waitlist comparison group also showed increases in both readiness and self-efficacy from intervention baseline to postprogram survey, indicating that the intervention did also serve to increase readiness and self-efficacy.
Limitations
Our study has several limitations. First, the sample of this study only reflects one Tribal-specific population, yet there are 574 federally recognized tribes, each of which has their own mix of end-of-life beliefs, values, and practices (Bureau of Indian Affairs, 2023) which would warrant additional tailoring and testing prior to using with a different Tribal community. Second, the size of the study intervention and waitlist groups differed significantly, and the measurements were taken at different time points between groups. We originally divided the Tribe into communities for recruitment to keep information about the intervention from spreading across the intervention and waitlist comparison groups (contamination). However, recruitment in the smaller Tribal communities was so difficult that the waitlist comparison group ended up including participants from the geographic region of the intervention group as well, nullifying our attempt at limiting contamination effects. It is unclear why recruitment was so difficult in the smaller Tribal communities in comparison to the larger one, although we might hypothesize that having to wait 4 months to receive the intervention may have deterred participants from enrolling in the waitlist comparison group. Although we did not see significant differences in demographic characteristics at intervention baseline between our two groups, the size difference does limit the comparison analysis of our results. For intervention group participants about 9 weeks passed between the intervention baseline and postprogram surveys whereas the waitlist comparison group waited closer to 13 weeks between the control baseline and intervention baseline surveys. Another potential reason, then, that the waitlist comparison group showed change from control to intervention baseline in self-efficacy could be because the time in between was about 40% longer.
A third study limitation similarly relates to the challenges in adhering to a strict timeline of an RCT in a community-based participatory research approach and the ethical implications of not providing a potentially beneficial intervention to the waitlist comparison group. As such, we used a waitlist-controlled design, which is inherently less rigorous than an RCT and research suggests that behavioral health interventions using this approach may inflate intervention effect estimates (Cunningham et al., 2013). Our results showed that from control baseline to intervention baseline, the waitlist comparison group showed increased self-efficacy. This increase could be related to exposure to ACP concepts through the study’s recruitment efforts and interaction with the intervention group. As the intervention and waitlist groups were in the same geographical location and from the same Tribe, it is possible that information about ACP spread throughout the community. However, self-efficacy also increased from control baseline to postprogram and from intervention baseline to postprogram for the waitlist comparison group, indicating that experiencing the SS still positively affected self-efficacy. Future research should investigate the generalizability of cultural tailoring to broader groups in addition to attempting more balanced methods.
Conclusions
AIANs experience inequities related to end-of-life care and ACP (Isaacson, 2017; Kwak et al., 2019; Hospice Facts and Figures, 2023). The Institute of Medicine (2015) has identified patient and provider ACP education as an important goal. Given that AIANs have life expectancy that is 11.2 years less than Whites (Arias et al., 2022), are twice as likely to die from type 2 diabetes, and four times as likely to die from chronic liver disease than Whites (Heron, 2021), it is especially critical that AIANs have the opportunity to express their wishes at the end-of-life. We have shown that engaging a community in an intervention specifically tailored to their desires for end-of-life planning can increase ACP outcomes. It is hoped the success of this project demonstrates that American Indian peoples are willing to engage in ACP and will encourage additional efforts led by Tribes and their relevant partners to facilitate upstream ACP.
Supplementary Material
Acknowledgments
The authors wish to thank the community partners and Tribal citizens who made this research possible.
Contributor Information
April Schweinhart, Pacific Institute for Research and Evaluation, Louisville, Kentucky, USA.
R Turner Goins, Department of Social Work, College of Health and Human Sciences at Western Carolina University, Cullowhee, North Carolina, USA.
Elizabeth Anderson, Guidance Through Grief Counseling Center, PLLC, Candler, North Carolina, USA.
Funding
This work was supported by the National Institutes of Health, National Institute for Nursing Research (NINR 1R21NR019910-01 to R. T. Goins and E. Anderson).
Conflict of Interest
None.
Data Availability
This study was preregistered with National Institutes of Health ClinicalTrials.gov Identifier: NCT05304117. Study design, results, and materials can be found of materials can be found at https://classic.clinicaltrials.gov/ct2/show/NCT05304117. Data are not shared at the case level to protect the identity of participants.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
This study was preregistered with National Institutes of Health ClinicalTrials.gov Identifier: NCT05304117. Study design, results, and materials can be found of materials can be found at https://classic.clinicaltrials.gov/ct2/show/NCT05304117. Data are not shared at the case level to protect the identity of participants.