The aging of the US population has brought an unprecedented increase in the prevalence of Alzheimer disease and related dementias (ADRD). More than 6 million individuals in the United States live with ADRD, and this number is expected to increase dramatically in the coming years [1-3]. ADRD is costly, with annual direct medical expenditures calculated to be US $14,508 per older adult with ADRD versus US $10,096 per older adult without ADRD [2]. Individuals with ADRD may exhibit disruptive cognitive and behavioral symptoms such as impaired memory and decision-making, confusion, and agitation several years before the disease is recognized and supports are put into place [4]. This is distressing for the individual facing these symptoms, as well as untrained caregivers who often serve as the primary source of daily support. Caregivers are at high risk for health problems such as depression, poor sleep quality, high blood pressure, and heart disease [5-8]. This situation is exacerbated for care dyads (ie, people with cognitive impairment and their care partners) living in rural social contexts who are isolated [9], have few resources [10], experience geographic barriers to care [11], and lack basic access to ADRD-related information and services [12,13]. Compared with urban areas, rural individuals with ADRD experience more days in nursing homes and significantly higher mortality [14,15]. The likelihood that cases of ADRD in the United States are underdiagnosed and undercounted complicates our understanding of this emerging public health problem and leads to additional challenges for families [9,16].

Lack of access to specialty care (ie, neuropsychological evaluations) and geographic or transportation barriers make timely detection and early screening of cognitive impairment an ongoing challenge [17,18]. Local community centers in Northern Arizona offer unique potential to compensate for the lack of formal services in memory care deserts [17] and rural areas [18], but need to be more intentionally integrated into community-based screening efforts to address this gap. Community-based participatory approaches in rural communities can foster relevant service delivery (eg, screening, referrals, and brain health education), strengthen care networks, and support sustainability [19]. Local community centers for older adults are ideally situated to support brain health in socioculturally diverse rural communities because they serve as trusted resource hubs for older adults and caregivers [20]. Further, older adults from historically minoritized communities may be distrustful of formal health care systems or researchers and place more trust in community-based organizations [21,22], reinforcing the value of these networks.

There are currently few resources and interventions specifically designed to identify and support socioculturally diverse rural ADRD care dyads, including protocols for the early detection of potential precursors to ADRD, such as mild cognitive impairment (MCI) or subjective cognitive decline (SCD), and validated strategies for community-based delivery of support [23,24]. Early detection of MCI and SCD is crucial for maximizing the effectiveness of interventions and clinical trials while efficiently allocating resources toward people at greater risk of transitioning to Alzheimer disease (AD) [25]. Without early detection and intervention, diverse families living in rural areas are left to cope in isolation, leading to devastating individual, interpersonal, and community-level consequences. Further, decreasing risk factors and increasing protective factors for ADRD, MCI, and SCD can prevent or delay up to 40% of ADRD cases [26].

The Northern Arizona Memory Study (NAZMS) applies a preventive digital health approach to develop technologies and resources necessary to identify and support rural dyads with MCI or SCD at risk for ADRD. Our work is situated within the cognitive decline and impairment stage of the typical disease progression of AD, beginning with preclinical AD (ie, no observable symptoms but potential for changes in the brain), followed by SCD (ie, self-reported difficulty and decline in memory and thinking domains) and MCI (ie, mild impairment in objective performance across multiple domains), and ending in mild, moderate, and severe stages of Dementia due to AD [1]. Specifically, NAZMS focuses on individuals with MCI or SCD and their caregivers because these individuals are at an elevated risk of transitioning to ADRD. Early detection of SCD and MCI is crucial for maximizing the effectiveness of interventions and clinical trials while efficiently allocating resources toward people at greater risk of transitioning to AD [27]. Addressing SCD is especially relevant for diverse communities in Northern Arizona, as one in six Indigenous adults reports SCD, with nearly two-thirds of these individuals reporting their SCD interferes with day-to-day activities [27]. Among the 22 federally recognized tribes in Arizona, many Tribal Nations have lands and strong cultural connections to Northern Arizona, including the Hopi Tribe, Navajo Nation, Hualapai Tribe, Havasupai Tribe, Kaibab-Paiute Tribe, and San Juan Southern Paiute Tribe [28]. Our community-based participatory approach to identifying and supporting care dyads emphasizes community strengths and self-determination, as well as care dyads’ ability to express agency in their health care and in life in general.

By directly collaborating with local community centers for our study’s recruitment and outreach, we align our study to the unique needs, cultural norms, and practical realities that will ensure culturally informed supports and resources that are relevant for communities across Northern Arizona [29]. Our digital health study design and focus for future intervention development were further informed by the technology acceptance model for older adults, which recognizes that social context, user intention, perceived and confirmed usefulness, and ease of learning and use impact older adults’ acceptance or rejection of technologies [30]. The dyadic nature of NAZMS was informed by the developmental-contextual model of coping [31], which posits that dyadic illness appraisal (eg, illness ownership—yours or mine vs ours) and dyadic coping (eg, problem- and emotion-focused support) are key drivers of adjustment for care dyads. In this way, NAZMS ensures that both care partner and care receiver share their lived experiences, preferences, and insights throughout the study protocol.

The primary objective of NAZMS is to develop culturally informed and scalable resources to identify and support rural families at risk for ADRD. The purpose of this study is to introduce the NAZMS protocol and discuss its role in addressing dementia risk and promoting cognitive health in rural communities. NAZMS uses a dyadic mixed methods, digital health approach to address the following two aims: (1) determine culturally appropriate care dyad preferences for identifying and monitoring cognitive and behavioral symptoms of MCI and SCD, and for engaging in remotely delivered supportive interventions to contend with or reduce symptoms and associated problems from MCI and SCD; and (2) examine the impact of modifiable multilevel (eg, individual, dyadic, family, and environmental) risk and protective factors on daily cognitive health among rural individuals with MCI or SCD.

NAZMS uses a convergent mixed methods design [32,33], in which qualitative and quantitative data are collected separately and then merged to develop a comprehensive understanding of rural care dyads at risk of ADRD.

We are recruiting an initial target of 30 rural care dyads with MCI or SCD, who are interviewed [33,34] and purposively recruited [35] through partnerships with community centers in the 4-county Northern Arizona region. Recruitment efforts include local newspaper and community center advertisements (eg, flyers) and announcements made at community centers by center staff and study team members. The physical addresses of the community centers and participant homes were entered into the Health Resources and Services Administration rural eligibility tool [36] to determine rurality.

Target individuals (“people with memory or thinking challenges”) are included if they (1) live in the community, not in a nursing home; (2) score below 19 on the Montreal Cognitive Assessment (MoCA) Blind or report presence of decline in memory or thinking or concern in these domains; and (3) can identify a primary caregiver who is able and willing to participate in the study (spouse, adult child, other family member or friend, or family of choice). Both dyad members must be (1) capable of using technology with coaching and support; and (2) able to communicate in English and provide informed consent. Dyads are excluded if either dyad member is (1) under the age of 18 years; (2) pregnant; (3) has untreated severe mental illness, alcohol or drug abuse, or suicidality; or (4) has another neurological condition that significantly impacts function. Participants were considered capable of using technology with coaching and support if they indicated their willingness and capacity to engage with study-provided technology (eg, tablet for baseline survey and smartphone for 14-day daily diary) alongside trained study team members (eg, coaching and support). Exclusion criteria were applied to reduce the risk of confounding variables and health statuses that may obscure the focus of cognitive functioning in SCD or MCI due to AD [37,38]. If both dyad members experience MCI or SCD, the individual with the lower MoCA Blind score or more SCD is considered the target individual for this study.

Upon completion of the semistructured interview, care partners and individuals with cognitive impairment complete a baseline questionnaire. The participants with cognitive impairment are then invited to participate in the quantitative 14-day daily diary component of the study. Recruitment and data collection began in fall 2024 and will continue throughout 2026. Figure 1 provides a visual timeline of the NAZMS protocol from community referral to in-person or remote participation in the mixed methods protocol.

The technology support provided by trained study team members and the mixed methods, multicomponent nature of our protocol support participation in NAZMS across a wide spectrum of technology proficiencies and cognitive statuses. We provide the cognitive screening as a free community service without a requirement to enroll in the NAZMS protocol. This screening does not require tablet or smartphone technology use, as it can be conducted in person with responses provided to a team member. The qualitative arm of the protocol does not require any technology use from the respondents who participate in person, and care partners can assist with technology connections for qualitative interviews conducted on Zoom (Zoom Video Communications, Inc). As long as the participant is open to receiving coaching and support from study team members (eg, hands-on training and troubleshooting phone calls and emails), they can participate in the quantitative arm of the protocol. NAZMS is a first step in developing supports and resources for rural care dyads at risk for dementia. This study can lay the foundation for future work that expands modalities of protocol administration (eg, mailed paper-and-pencil booklets and more frequent road trips to rural communities for in-person administration) and language accessibility (eg, Spanish, Native languages within nearby Tribal Nations) to better represent populations across Northern Arizona.

Data from qualitative interviews will provide dyad preferences for identifying and monitoring MCI and SCD symptoms and for engaging in remotely delivered supportive interventions to contend with or reduce symptoms and associated problems of cognitive impairment. These interviews will also reveal information on essential components of a culturally appropriate intervention for rural ADRD dyads for intervention development, including the types of technology and comfort level with technologies for the detection of cognitive impairment and receiving support. The interviews may further inform the roles of technology access (eg, internet and broadband availability), comfort level with using technologies, and preferences toward written materials, digital tools, or online supports across different rural communities in Northern Arizona. For example, we may learn preferences that help inform a future pilot intervention for brain health support that could include combinations of written and digital-based screening, interactive features via website or mobile apps, accessible training materials, as well as live support or coaching. This is an important consideration for rural communities in the mountainous region of Northern Arizona, where the signal may be comparatively weaker or less consistent than the signal in more densely populated cities. Data collection will continue until saturation [32,84]. Data analysis will be informed by the principles of interpretative description [85], which include an emphasis on practical application of findings for practice. Interviews are audio recorded, professionally transcribed, quality-checked for accuracy, and analyzed using Dedoose (version 10.0.25; SocioCultural Research Consultants, LLC [86]). Development of analytic codes will be based on the framework method, which is recommended for qualitative health research and can be adapted for both deductive and inductive coding [87]. We plan to use deductive coding based on established models of technology acceptance [30,33] and dyadic processes [31], as well as inductive coding to understand unique and emergent patterns in the data provided by our analytic sample of rural dyads in Northern Arizona. Study interviewers will maintain field notes to provide context for data analysis. We will develop and adhere to a protocol to promote the credibility, transferability, dependability, and confirmability of qualitative findings [88].

Data from the quantitative protocol will specify for whom (ie, between-person) and on which days (ie, within-person) modifiable factors are related to better daily cognitive health. Our 14-day digital health approach repeatedly measures cognitive health in people’s natural environments, improving ecological validity, reducing recall bias, and enabling examination of unique signatures of variability in cognitive performance not possible in conventional single-shot assessments completed in clinics [55]. We will use multilevel models (MLMs) with maximum likelihood estimation to accommodate the nested structure of the data (14 days nested within people) and intermittent missing data using SAS (version 9.4; SAS Institute [89]). Day-level within-person deviation scores for psychosocial, sociocultural, and contextual social determinants will be computed by subtracting an individual’s average amount of the variable from their daily score [90,91]. Within-person deviation scores facilitate examination of which days modifiable protective and risk factors are related to better (or worse) cognitive health. Models will be adjusted for systematic trends across days, as well as relevant covariates (eg, caregiver strain and MoCA Blind score) and adherence rate to the protocol (to adjust for potential missing data patterns). Separate MLMs will be estimated for each cognitive outcome. To examine relationships between caregiver factors, dyadic factors, and the care receiver’s cognitive health, we will regress each cognitive health outcome on the caregiver factors and dyadic factors assessed at baseline. To examine daily influences of modifiable protective and risk factors on cognitive health among individuals with MCI, the day-level cognitive health outcomes will be regressed on the day-level within-person deviation scores for psychosocial, sociocultural, and contextual factors.

Our initial target sample size is 30 rural care dyads. We determined 30 rural care dyads as a starting point for NAZMS data collection based on its mixed methods design components (qualitative and quantitative), the dyadic nature of interviews (30 caregivers and 30 participants with cognitive impairment), and statistical power analysis for quantitative analyses. For the qualitative arm, 30 dyads will be sufficient to adequately address our research questions and reach saturation based on our theoretical grounding and qualitative analytic framework [92,93]. For the quantitative arm, statistical power for quantitative protocol analyses among the target 30 participants with cognitive impairment was determined using techniques recommended by Cohen [94] for determining power to detect effects using repeated-measures ANOVA and NAZMS details. Our proposed MLM approach is more powerful than repeated-measures ANOVA and uses all available data from up to 14 measurements; therefore, this calculation provides a conservative estimate of power to detect effects. Power analysis was conducted using G*Power (Franz Faul, Edgar Erdfelder, Albert-Georg Lang, and Axel Buchner [95]), power=0.80, target (n=30), and α=.05. We have 80% power to detect small-to-medium effects of daily predictors for cognitive health outcomes (Cohen f=0.15).

Our team intends to expand NAZMS data collection capacity in subsequent years as we learn from this initial target sample. Specifically, our goal is to expand the NAZMS sample size beyond these initial 30 dyads to build our capacity to better characterize supports for people with cognitive impairment and their family members in rural communities across the Southwest.

All participants provided written or verbal (if administered remotely via Zoom) informed consent and received compensation for their participation. All procedures were approved by our university’s Institutional Review Board (IRB; approval no. 2090130). Participants were eligible to receive up to US $100 in gift cards for their participation (US $50 after completion of the qualitative interviews and surveys and an additional US $50 upon completion of the daily diary protocol).

Through the project activities and subsequent data analysis, there are multiple implications for care dyads at risk for ADRD living in memory care deserts and rural areas. First, we will determine culturally appropriate methods and technologies for identifying rural families facing cognitive impairment in community-based settings (Aim 1). Second, through the convergence of interviews and daily cognitive assessment over 14 days, we will be able to characterize rural caregiver-receiver dyads with information from not only intervention content and engagement preferences, but also the modifiable protective and risk factors that influence cognitive health in everyday life (Aim 2). Third, we will establish the content needed to develop an intervention for this population (Aim 1). Fourth, we will establish collaborative relationships with regional community centers and Tribal Nations that will be needed for future research and resource distribution with rural ADRD care dyads (Aim 1). Finally, we will share what we learn through community forum events and brain health workshops designed to educate rural communities by confronting stigma and anxiety associated with cognitive impairment and to promote cognitive screenings and the importance of early detection (Aims 1 and 2).

Digital health tools and interventions can be viable ways to support people with cognitive impairment [96] and caregivers [97]. However, the needs and preferences for different digital health technologies vary depending on cognitive status and caregiving demands [98], with recent systematic reviews and meta-analyses calling for future research to address issues around accessibility, acceptability, and sustainability of digital health interventions in these populations [97] and rural communities [99]. For example, a mixed methods evaluation of a web-based platform designed to improve the quality of life for people with cognitive impairment and reduce burden for caregivers recognized the need for end users to be involved in the development of the digital health tools [98]. Our dyadic data collection, intentionally connecting with both members of the care dyad, will address the gaps outlined by this previous research and situate NAZMS in a broader literature of digital health technologies that can support both people with cognitive impairment and their family members living in rural communities.

We are in the process of developing partnerships with Tribal Nations to provide the NAZMS protocol to Indigenous communities residing on Tribal lands. In spring 2025, we received conditional approvals from our university’s Office of Native American and Indigenous Advancement’s Tribal Consultation process and IRB to seek formal Tribal IRB approval that can expand recruitment to Tribal lands in the future. Building on culturally informed frameworks [100] and community-engaged approaches with Tribal Nations [101], we look forward to examining unique protective and risk factors for ADRD in Indigenous communities [102] and supporting Indigenous care dyads in the Southwest.

The NAZMS dyadic mixed methods digital health protocol aims to support rural families at risk for ADRD by understanding intervention preferences and identifying the modifiable protective and risk factors that influence cognitive health in everyday life. The findings will respond to national priorities in ADRD research [27] for the development of community-based education programs and the use of digital assessments of cognitive health and well-being [1].