COVID-19 vaccination in urban American Indian and Alaska Native children: Parental characteristics, beliefs and attitudes associated with vaccine acceptance.

Background: Little is known about vaccination rates for American Indian and Alaska Native (AI/AN) parents and their children, or parental decisions in this regard. Improving vaccination rates is a serious concern due to the disproportionate incidence and morbidity of COVID-19 in AI/AN people. Purpose: Our goal was to describe urban AI/AN parental attributes associated with COVID-19 vaccination of their children. Methods: Survey participants (n = 572) were ≥18 years of age, had children ≥5 years of age, AI/AN, and seen at one of six urban health organizations serving primarily AI/AN people within the prior year. They were asked about gender, age, education, marital status, perceived stress, trauma history, whether they had received the COVID-19 vaccine, tested positive for COVID-19 in the past, and if their child was vaccinated. They were also asked about 16 vaccine hesitancy reasons. Results: Parental vaccination rate was 82%, with 59% of their children vaccinated. Parents who vaccinated their children were older, had higher education, lower stress and trauma, and were more likely to be vaccinated compared to parents who did not vaccinate their children. Forty-two percent of parents indicated they would likely vaccinate their unvaccinated child in the future. Sixteen vaccine hesitancy reasons revealed four factors: distrust, inconvenience, lack of concern about the pandemic, and AI/AN concerns. Parents who had no plans to vaccinate their children had the highest vaccine distrust and lack of concern about the pandemic. Parents with greater vaccine distrust and AI/AN specific concern reported significantly greater trauma history and higher levels of education. Conclusion: Even though vaccination rates for AI/AN parents and children are high, the consequences of COVID-19 for AI/AN people are more severe than for other US populations. Providers should use trauma-informed, trust-building and culturally competent communication when discussing choices about vaccination with AI/AN parents.

Evaluating COVID-19 Response Within a Tribal Health Care Organization.

INTRODUCTION: Health care organizations of all types have adapted in response to COVID-19. Responding to the pandemic has varied across organizations and there are few standardized frameworks from which to evaluate an organizational response. This article reports COVID-19 responses by a tribal health care organization using a synthesized evaluation framework that integrated the organizational values and allowed for timely implementation of responses during this COVID-19 public health emergency. METHODS: The authors categorized each COVID-19 response into six domains, including leadership practices, patient engagement, operations management, teamwork and communication, public health, and external partnerships. Responses were analyzed by domain and across time. RESULTS: COVID-19 responses included medical alerts, modifying delivery of care, establishing testing and vaccination sites, additional benefits for employees, as well as new processes for communication between employees and senior leadership. COVID-19 responses were most frequently categorized into the operation management domain (80%). Many responses, especially those that considered patients, employees, and the community, also fit other domains. DISCUSSION: Operation management was the most frequent domain because the pandemic emerged rapidly and required swift, agile, action-oriented responses from the tribal health care organization. The majority of COVID-19 responses involved multiple, not single, domains. CONCLUSION: Our evaluation approach captured areas of importance to the tribal health organization and may be pertinent for other indigenous organizations and rural communities. It is especially important to consider tribal perspectives in response to COVID-19 because of the prior history with Western medicine and infectious diseases, and health care limitations experienced due to rurality.

Human-to-Computer Interactivity Features Incorporated Into Behavioral Health mHealth Apps: Systematic Search.

BACKGROUND: While there are thousands of behavioral health apps available to consumers, users often quickly discontinue their use, which limits their therapeutic value. By varying the types and number of ways that users can interact with behavioral health mobile health apps, developers may be able to support greater therapeutic engagement and increase app stickiness. OBJECTIVE: The main objective of this analysis was to systematically characterize the types of user interactions that are available in behavioral health apps and then examine if greater interactivity was associated with greater user satisfaction, as measured by app metrics. METHODS: Using a modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology, we searched several different app clearinghouse websites and identified 76 behavioral health apps that included some type of interactivity. We then filtered the results to ensure we were examining behavioral health apps and further refined our search to include apps that identified one or more of the following terms: peer or therapist forum, discussion, feedback, professional, licensed, buddy, friend, artificial intelligence, chatbot, counselor, therapist, provider, mentor, bot, coach, message, comment, chat room, community, games, care team, connect, share, and support in the app descriptions. In the final group of 34 apps, we examined the presence of 6 types of human-machine interactivities: human-to-human with peers, human-to-human with providers, human-to-artificial intelligence, human-to-algorithms, human-to-data, and novel interactive smartphone modalities. We also downloaded information on app user ratings and visibility, as well as reviewed other key app features. RESULTS: We found that on average, the 34 apps reviewed included 2.53 (SD 1.05; range 1-5) features of interactivity. The most common types of interactivities were human-to-data (n=34, 100%), followed by human-to-algorithm (n=15, 44.2%). The least common type of interactivity was human-artificial intelligence (n=7, 20.5%). There were no significant associations between the total number of app interactivity features and user ratings or app visibility. We found that a full range of therapeutic interactivity features were not used in behavioral health apps. CONCLUSIONS: Ideally, app developers would do well to include more interactivity features in behavioral health apps in order to fully use the capabilities of smartphone technologies and increase app stickiness. Theoretically, increased user engagement would occur by using multiple types of user interactivity, thereby maximizing the benefits that a person would receive when using a mobile health app.

Integrating Cultural Relevance into a Behavioral mHealth Intervention for Native American Youth.

Native American communities are disproportionately affected by a number of behavioral health disparities, including higher rates of depression, substance abuse, and suicide. As mobile health (mHealth) interventions gain traction as methods for addressing these disparities, they continue to lack relevance to Native American youth. In an effort to explore the design of relevant behavioral mHealth intervention for Native American communities, we have developed ARORA (Amplifying Resilience Over Restricted Internet Access), a prototype behavioral mHealth intervention that has been co-designed with Native American youth, a community advisory board, and a clinical psychologist. In this paper, we qualitatively analyze our co-design and focus group sessions using a grounded theory approach and identify the key themes that Native American community members have identified as being critical components of relevant mHealth designs. Notably, we find that the Native American youth who participated in our focus groups desired a greater level of didactic interaction with cultural and behavioral health elements. We conclude with a discussion of the significant challenges we faced in our efforts to co-design software with Native American stakeholders and provide recommendations that might guide other HCI researchers and designers through challenges that arise during the process of cross-cultural design.

Developing an obesity intervention in Micronesia: From needs assessment to planning.

Evidence-based obesity reduction programs in the Pacific are scarce to nonexistent. Using a community-based participatory research model (CBPR) we developed a collaboration between our university and a small Pacific Island nation. We established an advisory council of local stakeholders and then conducted an extensive needs assessment with youth, parents, professionals, and lay public. Only 9% of participants had tried a weight loss program. There was a strong tendency to engage in binge-eating cycles; difficulty eating healthy during frequent community celebrations with few healthy food options available in general; and limited traditions that involved physical activity. Participants wanted to learn how to increase their physical activity, make healthier food choices, and learn to reward themselves for healthy behaviors. Diets were most frequently high in energy or protein foods, followed by starch, sugary and fried foods; they ate vegetables and fruits least often. Together, our team then created the culturally relevant Fit Kit Palau©. We highlight lessons learned including how to determine partnerships; manage multiple cultural and geographic barriers; build capacity; and balance program fidelity with responsivity. As the program moves forward, we hope to increase access, engagement, and adherence and provide a model for other PI countries.

Hmong mental health needs assessment: a community-based partnership in a small mid-Western community.

The overall goal of this assessment was to verify the mental health needs of Hmong living in a mid-west community in order clarify the format, content, and feasibility of providing mental health services for Hmong in the future. Using a Community-Based Participatory Research (CBPR) model, we held four focus groups with 36 men, women, adolescents and professionals, all of Hmong descent, as well as interviewed 28 individual medical, mental health, education, and social service providers in the Eau Claire community. Our Hmong sample was frequently unclear about what "mental" health meant, indicating a low level of mental health literacy. Results confirmed that there are significant mental health needs in this refugee and immigrant population. Participants described problems consistent with depression, post-traumatic stress disorder (PTSD), anxiety disorders, somatoform disorders, and severe social stress and acculturation difficulties in every generation. Elder people and male adolescents were described as the most disaffected and in need of immediate services. It will be critical to address mental health literacy before designing future interventions. Treatment suggestions were provided with the intention of removing barriers and incorporating culturally sensitive methodologies, while continuing to work closely with our local mental health providers and Hmong leadership.