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Abstract

Background

American Indian and Alaska Native (AI/AN) communities in the United States experience significant health disparities, compounded by socioeconomic challenges, systemic racism, limited health information access, and mistrust of health research, mainstream healthcare, and federal health organizations. Misinformation on digital media and variable digital health literacy further hinder informed decision-making.

Objective

Explore digital health literacy, digital technology use for health information-seeking, and the perceived influence of misinformation on health decision-making among AI/AN populations.

Methods

In 2022, 540 AI/AN individuals aged 18 and older were surveyed at two large cultural events. Cross-sectional data were collected on health decision-making, influence of misinformation on decisions, digital health literacy, and perceptions of misinformation on specific health topics. Paired t-tests examined differences in perceived effects of misinformation on health decisions.

Results

Although 62.7% of respondents felt confident evaluating online health information, only 35.5% used online information for health decisions. The perceived impact of misinformation on one's own health decisions was significantly lower for oneself (16.2%) than for friends and family (26.9%, p < 0.001) or other AI/AN individuals (25.9%, p < 0.001). Participants also believed AI/AN peoples were less vulnerable to misinformation than others (25.9 vs. 28.7%, p < 0.001).

Conclusions

Addressing misinformation and digital barriers in AI/AN communities requires targeted public health initiatives. Strategies should focus on improving access to culturally relevant health information, integrating digital literacy education, and strengthening digital infrastructure with supportive policies. Collaborating with trusted community leaders can enhance culturally aligned health communication and information dissemination.

Keywords

Misinformation digital health literacy health communication indigenous science communication

Introduction

Health disparities among American Indian and Alaska Native (AI/AN) peoples in the United States (U.S.) remain significant and are driven by socioeconomic challenges, limited health literacy, and inadequate access to culturally competent healthcare services.^1–3 Centuries of colonization, systemic racism, and ongoing discrimination have fostered deep-rooted mistrust in health research, mainstream healthcare systems, and federal health information.^4,5 This mistrust, compounded by historical and ongoing injustices, profoundly shapes how AI/AN individuals perceive and engage with health information, particularly in digital spaces.⁶ Understanding these dynamics is critical for mitigating persisting health disparities—including cardiovascular disease, obesity, diabetes, and metabolic syndrome⁷—and for developing strategies to rebuild trust to improve health outcomes within AI/AN communities.

Health information is available from a variety of sources, including healthcare professionals, family and friends, print media, radio, and television. In recent years, the internet has emerged as a prominent source due to its accessibility and immediacy.⁸ Digital tools present opportunities to empower AI/AN individuals by enhancing their digital health literacy and providing them access to reliable health information online. These tools enable patients to actively participate in their care through access to resources, tracking tools, and remote monitoring, effectively promoting health literacy skills that are essential for evaluating online information and identifying reputable sources. Empowering AI/AN individuals through such means transforms their role from passive recipients to active, informed participants in health decision-making, which is crucial for navigating health information effectively, making autonomous health decisions, and combating the spread of misinformation.⁹ While this shift offers new opportunities for health education and empowerment, it has also widened the digital divide within AI/AN communities.^6,10 Many AI/AN individuals continue to rely on trusted sources such as Indian Health Service (IHS) providers, pharmacists, and tribal clinical providers, which are recognized as the most useful and reliable sources of health information, reflecting the history of medical distrust within AI/AN communities.⁹ Despite regular internet usage, AI/AN individuals report lower rates of using modern wireless devices to access health information compared to the general U.S. population.¹⁰ This disparity is, in part, due to the rural and underserved nature of many AI/AN communities with limited technical infrastructure,¹¹ and the longstanding underfunding and related operational challenges faced by the IHS.¹²

Beyond barriers to accessing technology and reliable health information, AI/AN communities, such as other historically disadvantaged populations, are more likely to encounter, consume, and share misinformation on digital platforms than the general population.^13,14 This increased exposure to and perpetuation of misinformation may be linked to barriers to accessing reliable information sources, shaping perceptions of the trustworthiness of health information on social media. The rise of digital media has fundamentally changed how people seek and disseminate health information, with the internet now serving as a primary resource for understanding symptoms, researching treatments, and making healthcare decisions. However, its largely unregulated nature has turned it into a breeding ground for misinformation. A significant portion of health-related content online—estimated to range between 0.2% and 28.8%—is categorized as misinformation.¹⁵ This misinformation has serious consequences, including vaccine hesitancy, care delay, reliance on ineffective treatments, deepened mistrust in healthcare systems, worsened mental health outcomes, and misallocation of health resources.^15,16 Compounding the issue, attempts to verify misinformation through online searches can inadvertently lend credibility to falsehoods, making it more difficult to discern what is accurate and reliable.¹⁷ These dynamics highlight the harmful impact of misinformation on health decision-making.

Perceptions of how others are affected by misinformation also play an important role in health decision-making. People often perceive others to be more susceptible to digital misinformation than themselves, underestimating the negative effects of media on their own beliefs.^18,19 The phenomenon, known as “third-person effects,” highlights the perceptual discrepancy of undesired media influence on self and others. This perception can shape responses and lead to behavior change. Empowering AI/AN individuals to critically evaluate online information and make informed health decisions is therefore critical. This strategic focus not only mitigates the impact of misinformation but also strengthens community health resilience by improving access to accurate health information and supporting proactive health behaviors. These issues are particularly pressing for AI/AN populations, where historical mistrust in healthcare and limited access to reliable information already create significant barriers to care.^20–22 The spread of misinformation exacerbates these existing barriers, contributing to poorer health outcomes, greater vulnerability to preventable diseases, and a widening of the health equity gap.

Despite the increasing role of digital platforms in health information access, little is known about how AI/AN populations utilize digital technology to seek health information or about their digital health literacy levels. Furthermore, there is limited research on how AI/AN communities perceive the influence of health misinformation on their decision-making.²³ This study addresses these gaps by reporting results from two surveys collected at AI/AN cultural events, examining how AI/AN individuals use digital media to access health information and how their perceptions of misinformation influence health decision-making. By analyzing internet usage patterns and the perceived impact of misinformation, this research aims to shed light on the challenges AI/AN communities face in accessing reliable health information and making informed health decisions in a digital age.

Methods

Setting and participants

Washington State University staff attended AI/AN cultural events in Denver, Colorado, and Seattle, Washington, for two rounds of data collection in this cross-sectional study. The powwow in Denver referred to as the Denver March Powwow, is one of the largest powwows in the U.S. It attracts AI/AN peoples from more than 100 tribes from 38 states.²⁴ The powwow in Seattle referred to as the Seafair Powwow,²⁵ is the largest powwow in the Pacific Northwest and attracts more than 15,000 people during the 3-day event. Washington State University staff and faculty set up a booth at both events to engage attendees. The booth had educational materials to disseminate results of other ongoing research projects, snacks, and water bottles, and offered attendees the opportunity to participate in a survey. The survey, which took 20–30 min to complete, was disseminated as a pencil-and-paper survey and covered topics such as attitudes around clinical trials, health history, and demographics. Participation required event attendees to be at least 18 years of age and to self-identify as AI/AN. Each participant received a $25 gift card for their time.

Measures

The final surveys used at both events were generated by the authors with input from other investigators at our institution; these other investigators also frequently conduct research in this population and planned to utilize other data elements for additional analyses. Questions included in the survey came from a variety of validated instruments (specified below).

We sought to assess the perceived influence of misinformation on people's health decision-making by asking four questions. The first question asked, “to what extent do you think your decisions about health are affected by misinformation in the media?” Participants were asked to respond on a five-point scale ranging from “not at all” to “a lot.” Subsequent questions asked participants to reflect on the influence of misinformation on decisions made by friends and relatives, AI/AN peoples, and other people. These measures were adapted from existing scales designed to assess perceptions of the differential impact of misinformation.^18,19,26

Participants were asked how much they agreed with the statement: “there is a lot of inaccurate information on the internet about [disease].” We asked about their perceptions around misinformation on Alzheimer's disease, cancer, heart disease, and COVID-19. This method of measuring the perceived prevalence of misinformation has been previously used elsewhere.²⁷

Finally, we used a five-item scale about digital health literacy. Participants were then asked how much they agreed with five statements about the prevalence of misinformation and their ability to assess the accuracy of health information in the media and online. Each item garnered a response on a scale of 1 to 5. The overall score was the sum of all items and could range from 5 to 25, with higher scores indicating higher digital health literacy. This scale was a modified version of an existing metric of digital health literacy. All questions are available in Appendix 1.

Statistical analysis

For questions where participants were asked how much they agreed with a statement, we combined “agree” and “strongly agree” responses to be affirmative and “neutral,” “disagree,” and “strongly disagree” were considered negative. Similarly, we grouped responses of “moderately” and “a lot” to be affirmative, and “not at all,” “somewhat,” and “neutral” to be negative. Due to generational differences in the ubiquity of technology and the role that this may have in digital health literacy specifically,²⁸ we stratified our findings by age.

We used paired t-tests to assess differences in the perceived effects of health misinformation. This included assessing: (a) whether respondents perceived themselves as less likely to be influenced by misinformation than their friends and relatives; (b) whether respondents thought they were as likely to be influenced by misinformation as were other AI/AN peoples; and (c) perceived influence of misinformation on in-groups (AI/AN) as compared to out-groups (others). We also calculated Cronbach's alpha to assess agreement within the five-item digital literacy scale. Finally, we performed linear regression to assess the relationship between demographic characteristics (gender, educational attainment, and age) and Digital Literacy Score. We calculated mean differences in score and 95% confidence intervals. All analyses were conducted in R.²⁹

Ethics review

As all data were collected anonymously and participants provided no identifying information, the study was deemed exempt by the Washington State University Institutional Review Board. Our attendance and survey administration at the powwow events were approved by the Denver March Powwow and United Indians of All Tribes Foundation.

Results

Respondent demographics

During the two events, there were 540 survey responses (Table 1). The sample was predominantly female (64.3%), and most participants had completed some post-secondary education, with 42.7% having completed some college or a two-year degree and 27.5% having at least a four-year degree. Less than one-quarter of respondents rated their health as “fair” or “poor,” with lower health status reports most common in the eldest age category.

Table 1.

Demographic characteristics of American Indian and Alaska Native respondents to a survey on misinformation and digital health literacy

Variables	Total	n by age group distribution (%)
Variables	Total	18–24	25–34	35–44	45–54	55–64	65+
	540*	64	88	101	92	97	78
Gender
Male	171 (31.7)	14 (21.9)	30 (34.1)	37 (36.6)	27 (29.3)	31 (32.0)	25 (32.1)
Female	347 (64.3)	45 (70.3)	54 (61.4)	61 (60.4)	63 (68.5)	65 (67.0)	52 (66.7)
Nonbinary, transgender, or other	22 (4.1)	5 (7.8)	4 (4.5)	3 (3.0)	2 (2.2)	1 (1.0)	1 (1.3)
Education
Less than high school	36 (6.8)	8 (12.7)	4 (4.6)	6 (5.9)	2 (2.2)	5 (5.2)	9 (11.5)
High school	122 (23.0)	16 (25.4)	19 (21.8)	19 (18.8)	23 (25.3)	28 (29.2)	11 (14.1)
Some college and/or 2-year degree	227 (42.7)	25 (39.7)	31 (35.6)	50 (49.5)	43 (47.3)	39 (40.6)	33 (42.3)
4-year degree or graduate/ professional degree	146 (27.5)	14 (22.2)	33 (37.9)	26 (25.7)	23 (25.3)	24 (25.0)	25 (32.1)
General health
Excellent	40 (7.6)	7 (11.1)	11 (12.8)	5 (5.0)	6 (6.7)	6 (6.3)	4 (5.3)
Very good	153 (28.9)	20 (31.7)	25 (29.1)	24 (23.8)	21 (23.3)	26 (27.4)	31 (41.3)
Good	211 (39.9)	23 (36.5)	39 (45.3)	42 (41.6)	40 (44.4)	41 (43.2)	18 (24.0)
Fair/poor	125 (23.6)	13 (20.6)	11 (12.8)	30 (29.7)	23 (25.6)	22 (23.2)	22 (29.3)

*Does not sum to total because not all participants provided their age (n = 20)

People and health conditions most impacted by misinformation

Participants tended to believe that they were the least susceptible to health misinformation, with only 16.2% reporting that they thought online misinformation impacted their own health decisions either “moderately” or “a lot” (Table 2). Respondents reported that friends and relatives were influenced by misinformation as often as reporting AI/AN people's decisions were influenced by misinformation (26.9% and 25.9%, respectively). They were most likely to indicate that other people were influenced by misinformation (28.7%).

Table 2.

American Indian and Alaska Native adults’ perception of health misinformation

Variables	n by age group distribution (%)
Variables	Total	18–24	25–34	35–44	45–54	55–64	65+
Misinformation impacts…*
My own health decisions	86 (16.2)	10 (16.4)	20 (23.0)	13 (13.0)	18 (19.8)	12 (12.4)	9 (11.8)
Friends’ & relatives’ decisions	142 (26.9)	22 (36.1)	27 (31.0)	29 (29.0)	25 (27.8)	18 (18.8)	16 (21.3)
AI/AN people's decisions	137 (25.9)	15 (24.6)	29 (33.0)	28 (28.3)	26 (28.9)	18 (18.6)	15 (19.7)
Other people's decisions	152 (28.7)	19 (31.1)	26 (30.2)	31 (31.0)	29 (31.9)	18 (18.8)	22 (28.9)
There is a lot of inaccurate information about…**
Alzheimer's disease	153 (28.9)	17 (27.0)	23 (26.4)	27 (27.3)	24 (26.1)	33 (34.4)	26 (34.7)
Cancer	198 (37.2)	28 (44.4)	29 (33.0)	40 (40.4)	31 (33.7)	35 (36.1)	30 (40.0)
Heart disease	188 (35.5)	21 (33.3)	31 (35.6)	35 (35.7)	33 (35.9)	35 (36.1)	26 (35.1)
COVID-19	316 (59.5)	44 (69.8)	58 (65.9)	58 (59.2)	49 (53.3)	55 (56.7)	41 (54.7)

*Moderately or a lot.

**Agree or strongly agree.

Using the five-point Likert scale, we found a significant difference in the perceived impact of misinformation on one's own health decisions compared to the perceived impact on friends and family (mean scores of 2.5 vs. 3.1, p < 0.001). Respondents also thought that misinformation impacted them less than it impacted other AI/AN people (mean scores of 2.5 vs. 3.4, p < 0.001). Participants also indicated that AI/AN people were less susceptible to misinformation than other people; however, this difference was not as striking (mean scores of 3.4 vs. 3.5, p < 0.001).

When asked which health conditions were most often misrepresented online, results were consistent across age groups (Table 2). Roughly one-third of people indicated that they thought there was a lot of inaccurate information about Alzheimer's disease, cancer, and heart disease. A notably larger portion of respondents (59.5%) indicated that there was a lot of inaccurate information online about COVID-19.

Digital health literacy

Finally, we considered digital health literacy. About half of respondents indicated that they had seen fake or misleading news stories about health issues in the past 14 days (Table 3). A similar proportion reported that they use many media sources to follow news to ensure that they are well-informed. Nearly two-thirds of people agreed that they knew how to use the internet to answer questions about health and 62.7% said they had the skills they needed to evaluate the health resources they find online. Only 35.5% of respondents indicated that they used online information to make decisions about their health.

Table 3.

Five-item digital health literacy scale among American Indian and Alaska Native adults.

Variables	n by age group distribution (%)
Variables	Total	18–24	25–34	35–44	45–54	55–64	65+
Seen fake news*	284 (53.4)	38 (60.3)	44 (50.0)	53 (53.5)	49 (53.3)	53 (54.6)	39 (52.0)
Well-informed*	255 (48.4)	31 (48.4)	36 (41.4)	39 (39.8)	48 (52.7)	56 (58.3)	37 (50.7)
Internet savvy*	360 (68.1)	44 (68.8)	61 (70.1)	70 (70.7)	67 (73.6)	70 (72.2)	37 (50.7)
Skills to evaluate*	333 (62.7)	37 (57.8)	60 (68.2)	70 (70.7)	60 (65.9)	65 (67.0)	29 (39.2)
Decisions*	189 (35.5)	28 (43.8)	33 (37.5)	33 (33.3)	38 (41.3)	34 (35.1)	14 (18.9)

*Agree or strongly agree.

There was reasonable agreement between five items on the digital literacy scale (Cronbach's α = 0.687; 95% CI: 0.630–0.737). When we measured the overall digital literacy score in relation to gender, education, and age we found that gender was not significantly associated with digital literacy (Table 4). However, educational attainment and age were independently associated with digital literacy. Individuals who had completed a four-year degree scored, on average, 2.77 points higher than individuals who had not completed high school (95% CI: 1.25–4.29). Conversely, we found that individuals aged 65 or older scored an average of 3.25 points lower than respondents aged 18–24 (95% CI: −4.60 to −1.90).

Table 4.

Demographic characteristics and associations with digital health literacy score.

	Unadjusted		Adjusted*
	Difference	95% CI	Difference	95% CI
Gender
Female	0.00	Reference	0.00	Reference
Male	−0.66	−1.40 to 0.09	−0.41	−1.12 to 0.31
Nonbinary, transgender, or other	−0.48	−2.29 to 1.34	−1.24	−3.18 to 0.71
Education
Less than high school	0.00	Reference	0.00	Reference
High school	−0.01	−1.58 to 1.56	0.15	−1.41 to 1.71
Some college and/or 2-year degree	0.46	−1.03 to 1.95	0.66	−0.82 to 2.15
4-year degree or graduate/professional degree	2.47	0.94 to 4.00	2.77	1.25 to 4.29
Age
18–24	0.00	Reference	0.00	Reference
25–34	−0.34	−1.61 to 0.93	−0.85	−2.07 to 0.38
35–44	−0.9	−2.14 to 0.33	−1.15	−2.35 to 0.04
45–54	−0.49	−1.77 to 0.78	−0.83	−2.06 to 0.40
55–64	−0.27	−1.52 to 0.98	−0.54	−1.75 to 0.66
65+	−2.8	−4.20 to −1.40	−3.25	−4.60 to −1.90

95% CI: 95% confidence interval.

*Adjusted for present categories.

Digital health literacy is assessed on a scale from 5 to 25.

Discussion

This study explored digital health literacy, digital technology use for health information-seeking, and the perceived influence of misinformation on health decision-making among AI/AN communities. The findings offer insights into the perceived impact of misinformation on health decisions among AI/AN individuals attending powwow events in Denver, Colorado, and Seattle, Washington. These results align with broader research on digital health literacy and misinformation while shedding light on unique cultural contexts within AI/AN communities.

A key finding is that participants perceived themselves as less susceptible to the influence of health misinformation than others, perceiving greater influence on their friends, relatives, other AI/AN peoples, and the general population. This aligns with the “third-person effect,” where individuals believe others are more susceptible to misinformation.³⁰ This perspective may stem from how AI/AN individuals use the internet, the topics they explore, and the trust placed in information sources. Previous research, including work by Geana et al.,¹⁰ suggests that while AI/AN individuals frequently use the internet for general health information, it is not typically considered a primary resource for maintaining or improving health, despite high trust in the information accessed. If AI/AN individuals primarily use the internet for general health information, which could be common knowledge, this might explain the perceived lower susceptibility to the influence of health misinformation. Additionally, given that AI/AN individuals often rely on trusted sources such as the IHS and tribal clinic providers for health information,⁹ the internet content may be seen more as confirmation of professional advice than a primary source.

Despite the low perceived personal susceptibility to misinformation (16.2%), participants were much more likely to indicate that misinformation affected their friends and relatives (26.9%), other AI/AN individuals (25.9%), and the general population (28.7%). These differences in perceived susceptibility between self and others suggest that although individuals may view themselves as more discerning, they recognize misinformation's potential to impact decision-making in their communities. Their view on the perceptual discrepancy between self and others of misinformation impacts can affect how AI/AN individual takes actions of misinformation correction. That is, this recognition of misinformation's influence on others is important, as it reflects a protective factor within AI/AN populations, fostering communal awareness and potentially motivating efforts to combat misinformation through things such as misinformation correction and fact-checking. This aligns with literature showing that group solidarity in AI/AN populations can lead to higher engagement in health initiatives and shared learning around health issues.³¹

Our findings also underscore the prominence of certain health topics in misinformation narratives. While participants expressed concerns about misinformation on diseases such as Alzheimer's disease and related dementias, cancer, and heart disease, COVID-19 emerged as the most salient issue. This finding is consistent with other studies that highlight the disproportionate burden of COVID-19 misinformation on minority populations, including AI/AN communities, exacerbated by historical mistrust in healthcare systems and inadequate access to accurate health information.^32,33 The digital divide in many AI/AN communities further compounds the issue, enabling misinformation to spread unchecked through social media and other digital platforms, undermining public health efforts.^10,15 Additionally, the critical role of parental support in fostering health literacy among children highlights the importance of interventions aimed at strengthening family and community resilience against misinformation.³⁴ These interventions are especially crucial in AI/AN communities, where strong familial ties can greatly enhance community well-being by enabling individuals to effectively manage health information and distinguish accurate from misleading content.

Digital health literacy emerged as a relative strength, with 62.7% of participants reporting that they had the skills to assess the credibility of online health information. However, only 35.5% of participants used online information for health decisions, suggesting barriers either related to trust in online sources and the general institutional distrust or the applicability of available information to their specific health contexts. Other studies have shown similar trends, where AI/AN populations favor trusted community sources over internet-based information due to past experiences with cultural dissonance in mainstream health messaging.^10,32

Generational differences were also evident, with older participants reporting lower digital literacy and internet use compared to younger counterparts, emphasizing the need for age-specific interventions. Older participants were more likely to report fair or poor health and may have experienced greater challenges in accessing reliable health information online due to being the least internet savvy, having the least skills to evaluate online health resources, and using online resources the least when making health decisions, as per their own report. This is consistent with previous research showing that older adults, particularly in AI/AN communities, face greater barriers to digital literacy and health information-seeking behavior.³⁵ However, younger participants seemed more adept at using the internet for health information, suggesting that interventions aimed at improving health literacy and combating misinformation should be tailored to these generational differences. These tailored interventions should recognize that older AI/AN adults may require more support in navigating digital health resources, as they may face greater barriers to digital literacy, such as limited digital skills, skepticism toward online information, and preferences for traditional sources of health information.³⁶

Addressing misinformation in AI/AN communities requires more than simply correcting false information. Public health initiatives must focus on building trust by addressing the root causes of mistrust and fostering “discriminant trust”—the ability to discern credible information from misinformation.³⁷ Improving digital health literacy is essential for AI/AN communities, empowering individuals to critically evaluate online health information and expanding access to reliable digital resources. These efforts can help bridge the gap between identifying misinformation and acting on accurate health information to maintain or improve health.³⁸ Currently, however, the internet is not a primary health resource for many AI/AN individuals.¹⁰

A comprehensive approach is necessary to address these challenges. This includes enhancing digital health literacy, increasing access to culturally relevant health information on digital platforms, and involving trusted community figures—such as tribal leaders and community health representatives—in online and offline health education.^32,39,40 Research highlights the effectiveness of culturally tailored strategies, including storytelling, community-engaged practices, and partnerships with Tribal Epidemiology Centers (entities that offer epidemiologic and public health support to AI/AN communities),⁴¹ in delivering health information.^42,43 To further empower AI/AN communities in navigating digital health, integrating metaliteracy⁴⁴ into literacy programs is essential. Metaliteracy expands traditional literacy by incorporating cognitive and metacognitive components, equipping individuals with the skills to critically evaluate and effectively use information across digital platforms. By helping individuals manage information overload and discern credible sources from misinformation, metaliteracy can strengthen health education efforts and promote informed decision-making. Adapting these culturally relevant strategies and incorporating metaliteracy principles into digital platforms can provide AI/AN communities with accessible, trustworthy, and contextually relevant health information. By aligning public health initiatives with the cultural and historical experiences of AI/AN populations, efforts can rebuild trust and better counter the spread of misinformation online.⁴⁵ These tailored strategies can empower AI/AN individuals to navigate the digital health landscape with greater confidence, improving both health literacy and outcomes.

Limitations and future research

While this study provides important insights into digital health literacy among AI/ANs, there are limitations to consider. The survey was conducted at cultural events that may have attracted individuals more engaged in community activities, potentially limiting the generalizability of findings. Nonetheless, the diversity of participants at two large events that attracted AI/ANs from across the nation makes the findings more broadly applicable to AI/AN populations compared to studies focused on specific tribal communities. It is also possible that an individual attended both events and completed a survey at both events.

Future research should examine the types of health information AI/AN people seek online, their trust levels in internet sources for healthcare information, and how they accurately identify health misinformation. Longitudinal studies could provide insights into how digital literacy and misinformation susceptibility evolve over time, particularly in the context of shifting public health challenges. Finally, qualitative research could explore how AI/AN individuals discern credible information from misinformation and the role of cultural knowledge systems in these processes. The results from these studies could provide important insight into AI/AN individuals’ use of and preferences for using the internet for health information-seeking and, ultimately, in the development of communication strategies to assist in health decision-making.

Conclusions

AI/AN communities face distinct challenges in navigating the digital health landscape, compounded by systemic inequities, historical mistrust, and exposure to misinformation. While digital health literacy is relatively high, the reliance on trusted sources over online information highlights the need for culturally informed public health strategies.^9,45,46 Interventions should focus on enhancing access to culturally relevant resources and reliable health information, fostering trust in digital platforms, and addressing misinformation's pervasive influence on health decision-making.

Our findings underscore the necessity for targeted educational programs aimed at enhancing digital literacy within AI/AN communities. Schools, community centers, and online platforms are ideally positioned to integrate a curriculum that focuses on evaluating health information, understanding the impact of misinformation, and utilizing digital tools for health management. Such educational initiatives are critical for empowering community members to make informed health decisions and mitigate the spread of health misinformation.

In terms of policymaking, the research underlines the critical role of policy in supporting the expansion of digital infrastructure in rural and underserved AI/AN areas. Policymakers are encouraged to provide incentives for telehealth services and mobile health applications that are accessible and culturally tailored. There is also a significant need for increased funding for public health programs dedicated to combating misinformation and enhancing health literacy at the community level.

For healthcare administrators and tribal health managers, this study suggests the implementation of robust digital communication strategies to effectively engage with community members. Employing feedback mechanisms through digital platforms can assist in tailoring health messages and interventions that align with community values and needs. Additionally, collaboration with trusted local figures, such as tribal leaders and community health representatives, is essential to disseminate accurate health information and support community-led health education initiatives.

By addressing these barriers, public health initiatives can empower AI/AN communities to navigate the complexities of the digital health landscape, make informed decisions, and improve health outcomes in the digital age. These efforts are crucial for building sustainable health environments that not only improve individual health outcomes but also enhance the overall resilience of AI/AN communities against misinformation and health inequities.

Footnotes

Acknowledgements

We would like to acknowledge Lucas Gillespie and Angie Suh for their contributions to the study. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the American Heart Association or the National Institutes of Health.

ORCID iDs

Marija Bogic

Juliana K Garcia

Erin Morgan

Ying-Chia Louise-Hsu

Amanda Boyd

Ethical considerations

This study was deemed exempt by the Washington State University Institutional Review Board (approval no. 19570-005) on 12 July 2022 and received an approved waiver of consent. All participant information was de-identified and participant data were not and will not be shared with third parties.

Informed Consent

Informed consent for information published in this article was not obtained as this study was deemed as minimal risk and institutional review board exempt by Washington State University. A cover letter was provided with the survey questionnaire which explained the purpose, anticipated duration, rights to decline/skip any question(s), confidentiality and potential risks, whom to contact for questions about the research, and institutional information. Study staff were also available to answer any questions that participants had about the survey throughout the completion process as desired.

Author contributions

MB and JG were responsible for the investigation, writing—original draft, reviewing, and editing. EM was responsible for conceptualization, formal analysis, methodology, and writing—original draft, reviewing, and editing. Y-CH was responsible for conceptualization, methodology, and writing—reviewing and editing. AB was responsible for conceptualization, funding acquisition, methodology, project administration, supervision, and writing—reviewing and editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the American Heart Association (grant number 960047) and the National Institutes of Health (grant number K01AG066063).

Declaration of conflicting interests

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

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to maintained relationships with tribal partners and due to tribal sovereignty and data laws.

Appendix 1

The questions in this section ask about where you look for health information.

How often do you use the Internet (go online)? Please check one answer.

□ Never

□ Less than once a month

□ Once a month

□ Once a week

□ Once a day

□ Several times a day

How often do you look for health information on the Internet? Please check one answer.

□ Never

□ Less than once a month

□ Once a month

□ Once a week

□ Once a day

□ Several times a day

Which device do you use the most? Please check one answer.

□ Laptop or desktop computer (personal or public)

□ Smartphone

□ Tablet

□ None

□ Other (please specify):

Which social media platforms do you use? Please check one answer for each line.

	Yes	No
Facebook	□	□
LinkedIn	□	□
Instagram	□	□
TikTok	□	□
Twitter	□	□
YouTube	□	□
Reddit	□	□
None	□	□
Other (please specify):

Please check one answer for each question below. Make your best guess if you are not sure of the answer.

	Not at all	Somewhat	Neutral	Moderately	A lot
To what extent do you think your decisions about health are affected by misinformation in the media?	□	□	□	□	□
To what extent do you think your friends’ and relatives’ health decision-making is affected by misinformation in the media?	□	□	□	□	□
To what extent do you think American Indian and Alaska Native people’s health decision-making is affected by misinformation in the media?	□	□	□	□	□
To what extent do you think other people’s health decision-making is affected by misinformation in the media?	□	☒	□	□	□

How strongly do you agree or disagree with the following statements? Please check one answer for each line.

	Strongly disagree	Disagree	Neutral	Agree	Strongly agree	Don’t know
I have seen fake or misleading news stories about health issues in the past 14 days.	□	□	□	□	□	□
I follow the news using many media sources to ensure I am well-informed.	□	□	□	□	□	□
I know how to use the internet to answer my questions about health.	□	□	□	□	□	□
I have the skills I need to evaluate the health resources I find on the internet.	□	□	□	□	□	□
I use health information from the internet to make health decisions.	□	□	□	□	□	□
There is a lot of inaccurate information on the Internet about Alzheimer’s disease.	□	□	□	□	□	□
There is a lot of inaccurate information on the Internet about cancer.	□	□	□	□	□	□
There is a lot of inaccurate information on the Internet about heart disease.	□	□	□	□	□	□
There is a lot of inaccurate information on the Internet about COVID-19.	□	□	□	□	□	□

References

Artiga

Foutz

Cornachione

, et al. Key facts on health and health care by race and ethnicity [Internet]. Kaiser Family Foundation, June 2016, https://files.kff.org/attachment/Chartpack-Key-Facts-on-Health-and-Health-Care-by-Race-and-Ethnicity.

Radley

Baumgartner

Collins

, et al. Achieving racial and ethnic equity in U.S. health care: a scorecard of state performance – Digital Collections – National Library of Medicine [Internet]. New York, NY: The Commonwealth Fund, November 2021 (Health Policy and Services Research), http://resource.nlm.nih.gov/9918300980506676 (2021, accessed 24 October 2024).

Ka’apu

Burnette

. A culturally informed systematic review of mental health disparities among adult indigenous men and women of the USA: what is known? Br J Social Work 2019; 49: 880–898.

Subica

Link

. Cultural trauma as a fundamental cause of health disparities. Soc Sci Med 2022; 292: 114574.

Beans

Saunkeah

Woodbury

, et al. Community protections in American Indian and Alaska native participatory research—a scoping review. Soc Sci 2019; 8: 127.

Korostelina

Barrett

. Bridging the digital divide for native American tribes: roadblocks to broadband and community resilience. Policy Int 2023; 15: 306–326.

Hutchinson

Shin

. Systematic review of health disparities for cardiovascular diseases and associated factors among American Indian and Alaska Native populations. PLoS ONE 2014; 9: e80973.

Jacobs

Amuta

Jeon

. Health information seeking in the digital age: an analysis of health information seeking behavior among US adults. Cogent Soc. Sci 2017; 3: 1302785.

Geana

Greiner

Cully

, et al. Improving health promotion to American Indians in the Midwest United States: preferred sources of health information and its use for the medical encounter. J Community Health 2012; 37: 1253–1263.

10.

Geana

Daley

Nazir

, et al. Use of online health information resources by American Indians and Alaska natives. J Health Commun 2012; 17: 820–835.

11.

Wood

Sahali

Press

, et al. Tribal connections health information outreach: results, evaluation, and challenges. J Med Libr Assoc 2003; 91: 57–66.

12.

Kruse

Lopez-Carmen

Jensen

, et al. The Indian health service and American Indian/Alaska Native health outcomes. Annu Rev Public Health 2022; 43: 559–576.

13.

Allington

McAndrew

Moxham-Hall

, et al. Media usage predicts intention to be vaccinated against SARS-CoV-2 in the US and the UK. Vaccine 2021; 39: 2595–2603.

14.

Loomba

De Figueiredo

Piatek

, et al. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat Hum Behav 2021; 5: 337–348.

15.

Wang

McKee

Torbica

, et al. Systematic literature review on the spread of health-related misinformation on social Media. Soc Sci Med 2019; 240: 112552.

16.

Ali

. Combatting against COVID-19 & misinformation: a systematic review. Hu Arenas 2022; 5: 337–352.

17.

Aslett

Sanderson

Godel

, et al. Online searches to evaluate misinformation can increase its perceived veracity. Nature 2024; 625: 548–556.

18.

Liu

Huang

. Digital disinformation about COVID-19 and the third-person effect: examining the channel differences and negative emotional outcomes. Cyberpsychol Behav Soc Netw 2020; 23: 789–793.

19.

Chen

. Let’s fight the infodemic: the third-person effect process of misinformation during public health emergencies. Int Res 2022; 32: 1357–1377.

20.

The impact of historical trauma on American Indian health equity [Internet]. https://www.medicalnewstoday.com/articles/the-impact-of-historical-trauma-on-american-indian-health-equity (2020, accessed 28 October 2024).

21.

Inadequate Healthcare for American Indians in the United States [Internet]. Ballard Brief, https://ballardbrief.byu.edu/issue-briefs/inadequate-healthcare-for-american-indians-in-the-united-states (2024, accessed October 2024).

22.

“People die like that”: Native Americans face serious barriers in accessing care | USC Center for Health Journalism [Internet], https://centerforhealthjournalism.org/our-work/reporting/people-die-native-americans-face-serious-barriers-accessing-care (2024, accessed 28 October 2024).

23.

Boyd

Railey

Hsu

, et al. Social media use among American Indian and Alaska Native people: implications for health communication strategies. IJIH 2023;18:1–10.

24.

Denver March Powwow | Visit Denver [Internet], https://www.denver.org/things-to-do/spring-summer/festivals-events/march-powwow/ (accessed 31 October 2024).

25.

Seafair Indian Days | Powwow 19–21 July 2024 [Internet]. United Indians of All Tribes Foundation – Daybreak Star, https://unitedindians.org/seafairpowwow/ (2024, accessed 31 October 2024).

26.

Schweisberger

Billinson

Chock

. Facebook, the third-person effect, and the differential impact hypothesis. J Comput-Mediat Comm 2014; 19: 403–413.

27.

Hameleers

Brosius

De Vreese

. Where’s the fake news at? European news consumers’ perceptions of misinformation across information sources and topics. HKS Misinfo Review [Internet], 13 May 2021, https://misinforeview.hks.harvard.edu/?p=7212 (2021, accessed 11 March 2025).

28.

Estrela

Semedo

Roque

, et al. Sociodemographic determinants of digital health literacy: a systematic review and meta-analysis. Int J Med Inf 2023; 177: 105124.

29.

R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing, 2021, https://www.R-project.org/.

30.

Davison

. The third-person effect in communication. Public Opin Q 1983; 47: 1.

31.

Wexler

. Looking across three generations of Alaska natives to explore how culture fosters indigenous resilience. Transcult Psychiatry 2014; 51: 73–92.

32.

Lee

Moore

Hancock

. Designing misinformation interventions for all: Perspectives from AAPI, Black, Latino, and Native American community leaders on misinformation educational efforts. HKS Misinfo Review [Internet], 7 February 2023, https://misinforeview.hks.harvard.edu/article/designing-misinformation-interventions-for-all-perspectives-from-aapi-black-latino-and-native-american-community-leaders-on-misinformation-educational-efforts/ (2023, accessed 24 October 2024).

33.

Howard-Bobiwash

Joe

Lobo

. Concrete lessons: policies and practices affecting the impact of COVID-19 for urban indigenous communities in the United States and Canada. Front Sociol 2021; 6: 612029.

34.

Heizomi

Asgharijafarabadi

Allahverdipour

. Enabling mothers through improving mental health literacy and parenting skills. BioSocial Health J 2024; 1: 26–32.

35.

Lee

Chen

Hewitt

. Age differences in constraints encountered by seniors in their use of computers and the internet. Comput Human Behav 2011; 27: 1231–1237.

36.

Pourrazavi

Bazargan-Hejazi

. The model of online health information-seeking behavior among older adults. BioSocial Health J 2024; 1: 33–40.

37.

Moore

Hancock

. A digital media literacy intervention for older adults improves resilience to fake news. Sci Rep 2022; 12: 6008.

38.

Sillence

Blythe

Briggs

, et al. A revised model of trust in internet-based health information and advice: cross-sectional questionnaire study. J Med Int Res 2019; 21: e11125.

39.

Behbahani

Smith

Carvalho

, et al. Vulnerable immigrant populations in the New York metropolitan area and COVID-19: lessons learned in the epicenter of the crisis. Acad Med 2020; 95: 1827–1830.

40.

Nezafat Maldonado

Collins

Blundell

, et al. Engaging the vulnerable: a rapid review of public health communication aimed at migrants during the COVID-19 pandemic in Europe. J Migr Health 2020; 1–2: 100004.

41.

Advancing Public Health in Indian Country [Internet]. Tribal Epidemiology Centers, https://tribalepicenters.org/ (accessed 22 November 2024).

42.

Heaton

Gebel

Crawford

, et al. Using storytelling to address oral health knowledge in American Indian and Alaska native communities. Prev Chronic Dis 2018; 15: 170305.

43.

Cassidy

Kenyon

Ritchey

, et al. Tribal epidemiology centers: at the forefront of American Indian/Alaska native public health. Public Health Rep 2023; 138: 3S–6S.

44.

Zarei

Adivi

. Beyond information literacy: why metaliteracy is crucial for modern health education programs. BioSocial Health J 2024; 1: 113–114.

45.

Sequist

Cullen

Acton

. Indian Health service innovations have helped reduce health disparities affecting American Indian and Alaska native people. Health Aff 2011; 30: 1965–1973.

46.

Skewes

Gonzalez

Gameon

, et al. Health disparities research with American Indian communities: the importance of trust and transparency. Am J Comm Psychol 2020; 66: 302–313.

Misinformation and digital health literacy among American Indian and Alaska Native people

Abstract

Background

Objective

Methods

Results

Conclusions

Keywords

Introduction

Methods

Setting and participants

Measures

Statistical analysis

Ethics review

Results

Respondent demographics

People and health conditions most impacted by misinformation

Digital health literacy

Discussion

Limitations and future research

Conclusions

Footnotes

Acknowledgements

ORCID iDs

Ethical considerations

Informed Consent

Author contributions

Funding

Declaration of conflicting interests

Data availability

Appendix 1

References