Understanding How Digital Dementia Risk-Reduction Education Works: A Realist-Informed Mixed-Methods Study of Engagement, Interpretation and Behavioural Readiness in Midlife Adults

Introduction

Dementia is one of the most significant and rapidly expanding global public health challenges of the twenty-first century. Rising prevalence rates, increasing longevity, and substantial social and economic consequences continue to place growing pressure on healthcare systems worldwide [1]. In Australia and internationally, dementia prevention has become an increasingly important component of public health policy, driven by evidence indicating that a substantial proportion of dementia cases may be attributable to potentially modifiable risk factors across the life course [2].

In response, a substantial amount of funding and resources have been committed to dementia risk-reduction education [3]. Educational initiatives have diversified substantially over the past decade and now include digital platforms, online learning modules, mobile applications, community programmes, caregiver education, and public awareness campaigns [4-6]. These interventions aim to improve public understanding of dementia risk factors, promote healthy lifestyle behaviours, reduce stigma, and support informed decision-making regarding cognitive health.

The increasing adoption of digital technologies has further accelerated the reach and accessibility of dementia education. Digital delivery offers several advantages, including scalability, personalisation, flexibility, and the potential to engage large populations at relatively low cost. Such approaches align with broader trends in public health promotion and health literacy development, where digital technologies are increasingly used to support prevention and behaviour change initiatives [7].

This emphasis on the life-course approach is reinforced by contemporary clinical epidemiology. The 2024 Lancet Commission report on dementia prevention estimates around 45% of cases could potentially be prevented or delayed if the risk factors were eliminated across fourteen specific lifestyle and health risks [8].

Crucially, the peak period for preventative optimization occurs between ages 40 and 65, where the onset of core modifiable factors, namely midlife hypertension, obesity, high LDL cholesterol, alcohol misuse, and untreated hearing loss—exerts a cumulative, damaging effect on cognitive reserve and neurological aging [9]. Consequently, the midlife cohort represents an irreplaceable window for targeted public health containment. Digital interventions deployed within this demographic do not merely expand reach; rather, they intercept lifestyle risks at the precise chronological intersection where behavior modification yields the highest lifetime yield in preventing clinical cognitive decline [10]. Despite these developments, research continuously shows that having access to knowledge by itself does not ensure significant behavioural results [11,12]. While many dementia education interventions successfully increase knowledge and awareness, improvements in knowledge frequently fail to translate into sustained behavioural intentions or behaviour change [13,14]. This knowledge–behaviour gap remains one of the most persistent challenges within health education and health promotion research.

Contemporary behavioural science increasingly recognises that individuals do not simply absorb information and subsequently modify their behaviour. Instead, educational information is actively interpreted, evaluated and integrated within existing beliefs, experiences, emotional responses and social contexts [15-17]. Consequently, educational outcomes are influenced not only by information quality but also by how individuals engage with, interpret, and apply information in real-world settings.

The literature further suggests that engagement with dementia education is influenced by multiple contextual factors, including health literacy, digital literacy, perceived susceptibility, social support, competing life priorities, emotional readiness, and environmental opportunities [18]. These factors contribute to substantial variation in intervention effectiveness across individuals and populations. Nevertheless, many evaluations continue to focus primarily on knowledge acquisition and short-term outcomes, with limited exploration of the mechanisms through which educational resources influence behavioural readiness and action [19,20].

To address these limitations, recent scholarship has called for a shift from evaluating information availability towards understanding effectiveness, usability, engagement, interpretation, and real-world impact [21,22]. Such approaches align with developments in implementation science and complex intervention research, which emphasise context, mechanisms, and practical outcomes rather than linear assumptions of information transfer.

This study responds to these calls by adopting a realist-informed perspective to examine how digital dementia risk-reduction education functions in practice [23]. Rather than asking whether digital education works, the study seeks to understand how it works, for whom, under what circumstances, and through which mechanisms.

The study is guided by a conceptual framework integrating health literacy theory, the Capability–Opportunity–Motivation Behaviour (COM-B) model, and implementation science [24]. Central to this framework is the proposition that engagement and interpretative engagement function as key mechanisms through which educational resources influence behavioural readiness and subsequent outcomes [25]. Accordingly, the study aims to evaluate the real-world effectiveness, usability, and impact of digital dementia risk-reduction education among cognitively unimpaired midlife adults aged 40–65 years. Through a realist-informed sequential mixed-methods design, the study seeks to generate Context–Mechanism–Outcome explanations that advance understanding of how digital dementia education may support behavioural readiness and contribute to dementia risk-reduction efforts [26].

Conceptual Framework and Theoretical Foundations

Moving Beyond Information Delivery

Traditional approaches to dementia education have been largely predicated on the assumption that increased knowledge will naturally result in behaviour change [27,28]. Within this perspective, educational interventions are designed to provide accurate information about dementia risk factors, prevention strategies, and health-promoting behaviours, with success typically measured through improvements in knowledge and awareness. These approaches have certainly helped the public to understand dementia better. However, evidence is growing that knowledge acquisition alone is not enough to lead to lasting behaviour change [29,30].

This limitation reflects a broader challenge within health education and public health intervention research. Individuals frequently demonstrate increased knowledge following educational interventions but fail to translate this knowledge into meaningful behavioural action [31-32]. This phenomenon, commonly described as the knowledge–behaviour gap, highlights the need for more comprehensive explanatory frameworks capable of accounting for the complex processes through which health information is interpreted, contextualised, and applied in everyday life.

Contemporary research increasingly recognises that health education functions as a dynamic and context-dependent process rather than a simple transfer of information. Educational outcomes are shaped not only by content quality but also by user engagement, interpretation, motivation, contextual influences, emotional responses, and environmental opportunities [33,34]. Consequently, understanding how educational interventions work requires examination of both observable outcomes and the mechanisms through which those outcomes are produced. To address these limitations, the present study adopts a conceptual framework integrating health literacy theory, behaviour change theory and implementation science.

The framework conceptualises digital dementia risk reduction education as a dynamic process where resource availability triggers engagement, engagement activates interpretative mechanisms and behavioural readiness is influenced by individual and contextual interactions [35,36].

Health Literacy as a Foundation for Engagement and Application

Health literacy provides an important theoretical foundation for understanding how individuals interact with dementia education resources [37]. Contemporary definitions conceptualise health literacy as the capacity to access, understand, evaluate, and apply health information to make informed decisions regarding health and wellbeing.

Importantly, health literacy extends beyond the acquisition of factual knowledge. It encompasses the ability to critically evaluate information, determine its personal relevance, and apply it within specific social and environmental contexts [38,39]. This broader conceptualisation is particularly relevant to dementia risk reduction, where individuals must interpret complex information regarding lifestyle behaviours, long-term risk factors, and preventative health strategies.

Research demonstrates considerable variation in health literacy across populations, influencing how individuals engage with educational resources and their capacity to translate information into meaningful action [40,41]. Educational interventions that fail to account for differences in literacy levels risk limiting accessibility and effectiveness. Consequently, health literacy is conceptualised within the present framework as a cross-cutting influence affecting engagement, interpretation, behavioural readiness, and outcomes.

Behaviour Change and the COM-B Model

The Capability–Opportunity–Motivation Behaviour (COM-B) model provides a complementary theoretical perspective for understanding behavioural outcomes. The COM-B framework proposes that behaviour emerges through interactions among three essential conditions:

• Capability – psychological and physical capacity to perform behaviour;

• Opportunity – social and environmental conditions that enable behaviour;

• Motivation – reflective and automatic processes that energise behaviour.

Within this framework, knowledge contributes primarily to psychological capability but does not independently determine behavioural outcomes.

Behavioural change also depends upon individuals having appropriate opportunities and sufficient motivation to act. This insight helps explain why educational interventions frequently improve knowledge without producing sustained behavioural change [42,43]. The COM-B model is particularly relevant to dementia risk-reduction education because many recommended behaviours involve long-term lifestyle modifications influenced by environmental constraints, competing priorities, social support systems, and personal motivation. Consequently, behavioural readiness is conceptualised within the present framework as an intermediate process through which engagement and interpretation begin to influence potential behavioural outcomes.

Implementation Science and Real-World Effectiveness

Implementation science contributes a further dimension by emphasising the importance of context, scalability, usability, and real-world effectiveness. While educational interventions may demonstrate efficacy under controlled conditions, successful implementation requires consideration of the contexts in which interventions are accessed, interpreted, and used. Implementation science recognises that interventions are embedded within complex systems such as social structures, organisational contexts, cultural influences and individual situations. In this view, dementia education assessment must take into account not only the quality of content, but also the experience of users with resources and how they are integrated into their everyday lives [45,46].

The present study therefore moves beyond traditional outcome-focused evaluation by examining the processes through which digital dementia education is utilised and applied within real-world settings. This perspective aligns with contemporary calls for greater emphasis on implementation, usability, and contextual adaptation within public health intervention research [47].

Interpretative Engagement as a Core Mechanism

As summarised in Table 1, traditional measures of engagement have primarily focused on observable behaviours such as participation, exposure, duration of use, or interaction with educational materials [48,49]. While these behavioural indicators provide valuable information regarding resource utilisation, they offer limited insight into the cognitive and emotional processes through which individuals construct meaning from educational content.

The present study conceptualises interpretative engagement as the active cognitive and emotional process through which individuals evaluate, contextualise, and assign personal relevance to health information [50,51]. Through interpretative engagement, individuals determine whether information is credible, applicable, meaningful, and worthy of behavioural consideration. Within the proposed conceptual framework, interpretative engagement functions as the key explanatory mechanism linking information exposure to behavioural readiness.

By positioning interpretative engagement as a mechanism rather than simply another dimension of engagement or an educational outcome, the framework extends existing approaches to digital health engagement and provides a more nuanced explanation of how educational interventions may influence behaviour.

Traditional Engagement	Interpretative Engagement
Attention to information	Construction of personal meaning
Interaction with educational resources	Cognitive appraisal of information
Navigation and resource use	Perceived personal relevance
Time spent engaging	Reflection on implications
Completion of educational activities	Integration of information into existing beliefs and behavioural intentions
Note. Traditional engagement primarily describes behavioural interaction with educational resources, whereas interpretative engagement refers to the cognitive and emotional processes through which individuals make sense of information, evaluate its personal relevance, and integrate it into existing beliefs and behavioural intentions.

                                      Table 1: Conceptual Comparison of Traditional Engagement and Interpretative Engagement

The distinction presented in Table 1 provides the conceptual foundation for the present study. By conceptualising interpretative engagement as the explanatory mechanism linking information exposure with behavioural readiness, the framework addresses an important limitation within existing dementia education research, where the processes of meaning-making and personal interpretation remain underexplored despite their likely influence on educational outcomes. This conceptualisation provides a more comprehensive explanation of how digital educational interventions may influence behavioural readiness beyond the acquisition of knowledge alone.

Operationalisation of Engagement, Interpretative Engagement and Behavioural Readiness

To enhance conceptual clarity and facilitate empirical measurement, the principal constructs within the conceptual framework are operationalised using observable indicators derived from both quantitative and qualitative data sources. While engagement has traditionally been measured through behavioural indicators such as duration of use, frequency of interaction, and completion rates (Perski et al., 2017; Yardley et al., 2016), the present study distinguishes between engagement and interpretative engagement as related but conceptually distinct processes [52,53].

Engagement refers to active interaction with educational content and reflects the extent to which users attend to, explore, and interact with digital dementia risk-reduction resources [54]. Interpretative engagement extends beyond interaction to encompass the cognitive and emotional processes through which individuals evaluate information, determine its personal relevance, integrate it with existing beliefs and experiences, and consider its implications for future behaviour. Behavioural readiness is conceptualised as an intermediate outcome representing an individual's willingness, motivation, and perceived capability to initiate health-related behaviour change [55]. Consistent with the COM-B framework (Michie et al., 2011), behavioural readiness is viewed as a precursor to behavioural action rather than evidence of behaviour change itself. To facilitate consistent measurement and analysis across the mixed-methods design, Table 2 presents the operational definitions and observable indicators that will guide data collection and interpretation throughout the study.

Construct	Conceptual Definition	Indicators
Engagement	Active interaction with educational content and resources	Navigation patterns, time spent on resources, completion of modules, observed attention, think-aloud commentary indicating active participation
Interpretative Engagement	Cognitive and emotional meaning-making processes through which information is evaluated and contextualised	Self-referencing statements, perceived relevance, reflection, risk appraisal, emotional responses, critical evaluation, meaning-making narratives
Behavioural Readiness	Motivation and preparedness to initiate behaviour change	Behavioural intention scores, readiness measures, planning statements, expressed willingness to modify lifestyle behaviours

                                                                    Table 2: Operationalisation of Core Study Constructs

Interpretative engagement is therefore operationalised as observable evidence that participants actively evaluate, contextualise, and assign personal meaning to dementia risk-reduction information. The identification of interpretative engagement will be guided by qualitative indicators emerging from think-aloud protocols and semi-structured interviews.

Conceptual Framework Overview

Figure 1 presents the study's conceptual framework. The framework conceptualises digital dementia education as a sequential yet dynamic process comprising six interconnected components:

1. Resource Availability;

2. User Engagement;

3. Interpretative Engagement;

4. Behavioural Readiness;

5. Contextual Influences; and

6. Real-World Outcomes.

                                                   Figure 1: Conceptual Framework

Note. This conceptual framework operationalizes a realist Context-Mechanism-Outcome (CMO) configuration (Pawson & Tilley, 1997) by synthesizing the COM-B model of behavior change (Michie et al., 2011) with health literacy theory and user engagement models. Active user engagement (attention, interaction, and perceived relevance) serves as the primary psychological mechanism (M) triggered by the digital intervention to drive educational outcomes (O). These causal pathways are moderated by pre-existing structural access conditions (C), emotional factors, and social environments [56,57].

The framework proposes that access to educational resources alone is insufficient to generate meaningful outcomes. Instead, outcomes emerge through interactions between engagement processes, interpretative mechanisms, behavioural readiness, and contextual influences. Contextual factors operate throughout the process by enabling or constraining the activation of mechanisms and the achievement of outcomes [58]. Importantly, the framework moves beyond linear assumptions of knowledge transfer by recognising the dynamic interplay between context, mechanisms, and outcomes. This perspective aligns with contemporary understandings of complex interventions and provides a foundation for the realist-informed methodological approach adopted in the present study.

By integrating health literacy theory, the COM-B model, and implementation science, the framework offers a theoretically grounded and practically relevant structure for investigating how digital dementia risk-reduction education functions in real-world contexts. It also provides the basis for the study's subsequent philosophical and methodological foundations.

Philosophical Foundations and Realist Methodology

Introduction

The literature reviewed in Chapter 2 demonstrates that dementia education has evolved substantially over recent decades, moving from traditional information-based approaches towards more complex, digitally enabled interventions designed to promote risk awareness, behavioural readiness, and preventative action. However, despite this consistent evidence, changes in knowledge do not necessarily translate into sustained behavioural intentions or behavioural change [59].

Contemporary research increasingly recognises that educational interventions operate within complex systems influenced by cognitive, emotional, social, and environmental factors. Individuals do not passively receive information; rather, they actively engage with, interpret, evaluate, and apply educational content according to their own experiences, motivations, beliefs, and contextual circumstances. Consequently, identical educational resources may generate markedly different outcomes across participants and settings [60].

These observations reveal important limitations within existing dementia education research. Although considerable attention has been paid to resource development, accessibility and knowledge acquisition, comparatively little research has been done on how people interact with dementia education, how they interpret information and how contextual conditions influence behavioural readiness and real-world outcomes [61,62]. Addressing these questions requires an approach capable of moving beyond simple outcome measurement to investigate the mechanisms through which outcomes are generated.

Accordingly, this study adopts a realist-informed methodological approach underpinned by a critical realist ontology and a pragmatic methodological orientation [63]. This combination provides a coherent philosophical foundation for examining how digital dementia risk-reduction education influences engagement, interpretation, behavioural readiness, and outcomes among midlife adults.

Critical Realism

Critical realism provides the ontological foundation for this study. Originally formulated by Bhaskar (1975/2008), this philosophical perspective asserts that an objective reality exists independently of human observation, while simultaneously acknowledging that human knowledge of this reality is inherently partial, socially situated, and epistemologically mediated [64,65]. To bridge the gap between abstract philosophy and applied clinical research, scholars frequently draw upon Alderson (2013), who operationalised critical realism within the contexts of health, illness, and healthcare practices [66,67]. A foundational tenet of critical realism is that empirical outcomes are not merely linear products of interventions, but are generated by deep-seated, often unobservable causal mechanisms triggered within specific contextual boundaries.

This approach explicitly departs from positivist paradigms that focus narrowly on statistical associations between independent variables and outcomes. Instead, a critical realist framework shifts the explanatory focus toward uncovering why outcomes occur, how they are generated, and why they systematically vary across different conditions. This perspective is particularly relevant to dementia education research. The literature demonstrates considerable variability in educational outcomes despite the widespread availability of educational resources [68,69]. Although many interventions successfully improve knowledge and awareness, behavioural outcomes remain inconsistent and difficult to predict. Such findings suggest that knowledge acquisition alone cannot adequately explain educational effectiveness. Critical realism provides an explanatory framework capable of addressing this complexity.

Rather than assuming that educational interventions directly produce outcomes, critical realism proposes that interventions create opportunities for mechanisms to be activated. Whether these mechanisms generate outcomes depends on the contextual conditions within which they operate [70]. In the context of digital dementia education, mechanisms may include engagement, interpretative engagement, reflection, perceived relevance, motivation, and behavioural readiness. Contextual conditions may include health literacy, digital literacy, social influences, prior experiences, perceived susceptibility, and competing life demands.

This approach aligns closely with the study's overarching objective of understanding not simply whether digital dementia education works, but how it works, for whom, under what circumstances, and why.

Pragmatic Methodological Orientation

While critical realism provides an explanatory ontological perspective, pragmatism informs the methodological orientation of the study [71]. Pragmatism prioritises the practical value of knowledge and advocates the use of methods most appropriate for addressing the research problem rather than adherence to a single methodological tradition.

The complexity identified within the dementia education literature supports the adoption of a pragmatic approach. Research examining health literacy, behaviour change, and implementation processes indicates that educational outcomes involve both measurable and experiential dimensions [72]. Quantitative methods are valuable for examining changes in knowledge, awareness, risk perception, and behavioural intentions, while qualitative methods provide insight into engagement experiences, interpretation processes, meaning-making, and contextual influences.

The integration of these forms of evidence is particularly important when examining complex educational interventions. Craig et al. (2018) and Skivington et al. (2021) argue that understanding complex interventions requires attention not only to outcomes but also to implementation processes, contextual influences, and mechanisms of change [73]. Similarly, implementation science emphasises the importance of examining how interventions function within real-world environments rather than relying solely on controlled evaluations. The pragmatic orientation adopted in this study therefore supports the use of a mixed-methods design capable of generating both explanatory and practical insights regarding digital dementia education.

Realist Evaluation

The methodological approach is further informed by the principles of realist evaluation [74]. Realist evaluation was developed to address the limitations of conventional outcome-focused approaches by recognising that interventions function differently across contexts and populations. This perspective aligns closely with contemporary evidence relating to dementia education. Studies increasingly demonstrate that engagement, emotional responses, perceived relevance, social influences, and contextual factors influence whether educational interventions produce meaningful outcomes. Yet many evaluations continue to focus primarily on knowledge acquisition while neglecting the mechanisms through which users interpret information and determine its personal relevance.

Realist evaluation addresses this limitation by focusing on the relationships between Contexts, Mechanisms, and Outcomes (CMO). Rather than treating interventions as universally effective or ineffective, realist approaches recognise that outcomes emerge through interactions between intervention resources, participant responses, and contextual conditions [75]. This point of view is particularly appropriate for digital dementia education because educational resources are experienced differently depending on individual circumstances, literacy levels, motivations, and environmental opportunities. Consequently, understanding effectiveness requires examination of the processes through which users engage with information and how these processes influence behavioural readiness.

Context–Mechanism–Outcome Logic

The conceptual framework developed for this study is closely aligned with Context–Mechanism–Outcome (CMO) logic. Within this framework, digital dementia education resources represent intervention inputs that provide opportunities for engagement and learning. However, access alone is insufficient to generate meaningful outcomes [76].

Context refers to the conditions within which educational resources are encountered and utilised. Consistent with health literacy theory and implementation science, relevant contextual factors include health literacy, digital literacy, prior knowledge, perceived susceptibility, social influences, cultural beliefs, life stage, and competing priorities [77].

Mechanisms represent the processes through which participants respond to educational resources. Drawing upon the literature reviewed in Chapter 2, engagement and interpretative engagement are conceptualised as central mechanisms. Engagement reflects active interaction with educational materials, while interpretative engagement refers to the cognitive and emotional processes through which individuals construct meaning, evaluate relevance, and integrate information into existing knowledge structures.

Outcomes include changes in knowledge, awareness, risk perception, behavioural intentions, and behavioural readiness. Consistent with behaviour change theory, these outcomes are understood as emergent products of interactions between contextual conditions and activated mechanisms rather than direct consequences of information exposure alone [78].

Methodological Implications

The philosophical foundations described above directly inform the methodological design adopted in this study. First, they support the use of a sequential mixed-methods design capable of capturing both measurable outcomes and explanatory processes [79]. Second, they justify the integration of quantitative and qualitative evidence to develop richer understandings of educational effectiveness. Third, they position engagement and interpretative engagement as central analytical constructs rather than secondary considerations.

The alignment between the study's philosophical foundation and its practical execution is operationalised through a structured cascade from ontology to empirical logic. The critical realist ontology and pragmatic orientation dictate a sequential multi-phase mixed-methods design where quantitative and qualitative components serve distinct, complementary explanatory purposes:

• Quantitative components capture measurable metrics such as knowledge change, risk perception, behavioural readiness, and early intention formation.

• Qualitative components capture real-time engagement experiences, interpretative processing, meaning-making, and emotional or contextual influences.

• These dual datasets are integrated through systematic triangulation and complementarity to generate realist-informed meta-inferences. This empirical architecture is structured using Context–Mechanism–Outcome (CMO) configurations, which are operationalised as follows:

• Context (C): The baseline conditions that trigger or inhibit causal pathways, defined here as participant health literacy, digital literacy, life stage, prior health beliefs, and wider social or environmental influences.

• Mechanism (M): The latent cognitive or emotional responses triggered within participants when interacting with the resource, including their interpretative engagement, reflection, perceived personal relevance, and cognitive processing.

• Outcomes (O): The observable results generated by the interaction of mechanisms and contexts, measured through knowledge development, heightened awareness, behavioural readiness, and early lifestyle adaptation.

Most importantly, these foundations support a shift from evaluating educational availability to understanding educational impact. Consistent with the conceptual framework developed in Chapter 2, digital dementia education is treated not as a static information delivery system, but as a dynamic, context-dependent process.

However, resource access alone is not enough; it is meaningful engagement that activates internal mechanisms that interact with baseline contextual conditions to produce real-world outcomes that are highly variable. This approach aligns with current guidance in health literacy, behaviour change, implementation science, and complex intervention evaluation [80]. It provides insights that are both theoretically sound and practically useful for designing, implementing, and evaluating future digital dementia risk-reduction education.

Methods

Study Design

This study adopts a sequential multi-phase mixed-methods design informed by critical realism and realist evaluation principles. This specific architecture was selected to address the inherent complexity of digital dementia risk-reduction education and to facilitate a structured examination of both measurable outcomes and the underlying causal mechanisms through which those outcomes are generated [81,82]. To bridge the conceptual framework detailed in Chapter 3 with empirical data collection, the study maps its theoretical tenets directly onto operational research tools. Figure 2 provides a structural blueprint of this realist-informed mixed-methods research design, detailing how the study’s philosophical foundations translate into distinct qualitative and quantitative parameters.

Figure 2: The Alignment of Philosophical Stance, Methodology & Mixed-Methods Data Collection Tools.

Note. The design sequences a critical realist ontology and a pragmatic methodological orientation through a proposed multi-phase, sequential mixed-methods approach. Data collection methods map directly onto Context–Mechanism–Outcome (CMO) configurations.

The methodological approach aligns with contemporary recommendations for evaluating complex health interventions, which emphasize the importance of examining implementation processes, contextual influences, and mechanisms of change in addition to outcomes. Consistent with the study's realist framework, the design seeks to explain how engagement, interpretation, and contextual backgrounds contribute to behavioural readiness among midlife adults. The study integrates quantitative and qualitative approaches sequentially, enabling the collection of complementary forms of evidence. Quantitative methods provide insight into changes in knowledge, risk perception, and behavioural intentions, while qualitative methods explore participant experiences, interpretative processes, and contextual influences shaping engagement with digital dementia education. The operational execution of this design, spanning participant recruitment, purposive sampling, specific study phases, data analysis, and integrated mixed-methods interpretation is visually mapped as a sequential workflow in Figure 3.

Figure 3: Recruitment, Sampling, Methodology, Phases, Data Analysis & Integrated Interpretation Flowchart.

Note. Progression path from initial participant recruitment through the four core empirical phases to final mixed-methods qualitative and quantitative data synthesis.

Study Population

The study focuses on cognitively unimpaired midlife adults aged 40–65 years. Midlife represents a particularly important period for dementia prevention because many modifiable risk factors associated with later cognitive decline emerge or become established during this stage of life [83,84]. Furthermore, contemporary dementia prevention strategies increasingly emphasise intervention during midlife rather than later stages when neuropathological changes may already be established. Participants will be recruited from community settings using purposive sampling strategies designed to capture diversity in:

• Age

• Gender

• Educational background

• Health literacy

• Digital literacy

• Prior exposure to dementia information

• Socioeconomic circumstances

This approach enables exploration of variation in context in line with realist evaluation principles. Sample size is consistent with a mixed-methods research approach which privileges depth, variation and explanatory power over statistical representativeness [85,86]. This sample is adequate for mixed-methods integration, enabling thematic depth, subgroup comparison, and explanatory analysis across CMO configurations. The participant group, sample size, and rationale for the sample size are displayed in Table 3.

Participant Group	Sample Size	Justification
Midlife adults (40–65 years)	30–45	Primary population for dementia risk reduction. Both qualitative breadth and quantitative comparison are supported by sample size in integrated mixed-methods study
Sub-group variation (embedded within sample)	Within total sample	Ensures diversity across health literacy, digital literacy, perceived risk, and engagement profiles to support realist and comparative analysis [87].
Total sample	~30–45 participants	Adequate for mixed-methods integration, enabling thematic depth, subgroup comparison, and explanatory analysis across CMO configurations.

                                                                 Table 3: Participants and Sample Size

Sample Size Justification

The proposed sample size of approximately 30–45 participants is consistent with recommendations for explanatory mixed-methods and realist-informed research where the primary objective is theory development and mechanism exploration rather than statistical generalisation [88].

From a quantitative perspective, the study is designed to identify patterns, trends, and preliminary changes in knowledge, risk perception, behavioural readiness, and educational engagement rather than estimate population-level effect sizes. Consequently, the quantitative component is exploratory and hypothesis-generating rather than hypothesis-testing and is therefore not powered to detect small statistical effects. Instead, it is intended to estimate trends that will be interpreted alongside qualitative findings to develop explanatory Context–Mechanism–Outcome (CMO) configurations [89,90].

From a qualitative point of view, the proposed sample size is expected to provide sufficient diversity across key contextual variables, including health literacy, digital literacy, perceived dementia risk, educational background, and prior exposure to dementia information [91-98]. Such variation is essential for realist analysis because it enables examination of how different contextual conditions influence the activation of mechanisms and the generation of outcomes.

The sample size is therefore considered adequate to support thematic depth, comparative subgroup analysis, integration, and the development of Context–Mechanism–Outcome (CMO) explanations while remaining feasible within the scope of the study.

Statistical representativeness or hypothesis testing are not prioritised within a realist-informed mixed-methods design, but instead explanatory depth, contextual variation and integration across quantitative and qualitative datasets.

Participant Recruitment

To recruit the target sample of 30-45 participants outlined in Table 2, the study utilises a multi-channel outreach strategy. In strict alignment with the recruitment pathways mapped in Figure 2, two primary channels are deployed concurrently:

• Community Groups (Physical Outreach): Local community hubs, public libraries, and non-clinical midlife social organisations are utilised to display ethics-approved physical flyers and brief informational notices in shared common areas. This reaches individuals interested in general wellness and brain health within the metropolitan area.

• Social Media and Online Platforms (Digital Outreach): Standardised recruitment notices are systematically distributed across targeted online environments. This includes localised community noticeboards (e.g., regional Facebook groups, Next-door), professional networks (e.g., LinkedIn), and digital health forums focused on wellness or cognitive aging.

All physical and digital recruitment materials direct prospective candidates via a secure hyperlink or QR code to a university-hosted landing page. This page hosts a brief, automated digital screening tool to verify inclusion criteria (age 40–65, cognitively unimpaired, internet-enabled device access) before giving access to the Phase 1 digital consent protocols.

Sampling Strategy

Purposive sampling will be employed to ensure inclusion of participants representing diverse experiences and characteristics relevant to engagement with digital health information. Realist-informed research prioritises explanatory depth over statistical representativeness. Consequently, sampling seeks to maximise variation in contextual conditions that may influence engagement, interpretation, and outcomes.

• Particular attention will be given to variations in:

• Health literacy

• Digital literacy

• Dementia knowledge

• Perceived dementia risk

• Family experience with dementia

• Work and family commitments

• Technology use. Such variation supports examination of how differing contexts influence the activation of mechanisms and the production of outcomes.

Data Collection Procedures: Study Phases

The empirical execution of this study is structured into a sequential four-phase mixed-methods workflow, moving from quantitative baseline profiling to qualitative and evaluative integration, as structurally outlined in Figure 3. This section details the operational procedures, data collection instruments, and realist parameters for Phase 1 and Phase 2.

Digital Dementia Risk-Reduction Educational Intervention

The educational intervention utilised in this study consists of standardised digital dementia risk-reduction education resources selected from evidence-based public health materials aligned with contemporary dementia prevention recommendations [99,100]. The intervention is designed to expose participants to information regarding modifiable dementia risk factors and preventative lifestyle behaviours commonly targeted within dementia prevention initiatives.

The educational materials incorporate a range of digital learning formats, including interactive web-based modules, visual infographics, personalised risk-reflection activities, and simulated mobile health (mHealth) features. Core educational content focuses on established modifiable risk factors, including cardiovascular health, physical activity, hearing health, cognitive stimulation, social engagement, sleep, smoking, alcohol consumption, and management of chronic health conditions.

To ensure consistency across participants, all individuals will engage with the same standardised educational materials within a structured 30-minute interaction session. Participants will be encouraged to explore the resources freely while verbalising their thoughts through a concurrent think-aloud protocol [101-105]. This approach permits examination of both engagement behaviours and interpretative processes while maintaining consistency in intervention exposure.

The intervention is not intended to evaluate the effectiveness of a specific commercial product or digital platform. Rather, the educational materials function as a representative example of contemporary digital dementia risk-reduction education and serve as a vehicle for exploring engagement, interpretation, and behavioural readiness.

Phase 1: Baseline Contextual Profiling

Phase 1 establishes a comprehensive quantitative baseline of participants' individual backgrounds before any exposure to the educational materials occurs. This phase focuses explicitly on capturing the Context (C) dimensions of the Context–Mechanism–Outcome (CMO) configurations.

• Administration and Logistics: Participants are automatically granted access to a secure, web-based questionnaire administered via an online 365 Microsoft platform Forms. The instrument requires approximately 15 minutes to complete.

• Target Variables and Measures: To map the personal and structural parameters that influence subsequent cognitive processing, the questionnaire includes standardised and custom modules and questions:

o Sociodemographic Profile: Captures baseline age, gender identification, educational attainment, occupational status, and subjective socioeconomic standing.

o Health Literacy: Measured using the Health Literacy Questionnaire (HLQ), specifically focusing on scales relating to having sufficient information to manage health and the ability to actively find good health information.

o Digital Health Literacy: Measured via the eHealth Literacy Scale (eHEALS) to evaluate participants' combined knowledge, comfort, and perceived skills at finding, evaluating, and applying electronic health information to health problems.

o Prior Beliefs and Exposure: Assesses pre-existing familiarity with neurological health concepts, personal family histories of cognitive decline or dementia, and pre-intervention perceptions of personal susceptibility to modifiable cardiovascular and lifestyle risk factors [106-110].

The structured data gathered during Phase 1 generates a multidimensional contextual baseline. This allows for subsequent subgroup comparisons and the identification of how specific baseline traits trigger or inhibit behavioural changes.

Phase 2: Digital Resource Engagement

Phase 2 shifts the focus from baseline context to exploring the activation of Mechanisms (M). This phase evaluates how participants interact with digital dementia risk-reduction tools in real time, capturing both objective usage metrics and immediate cognitive and emotional responses.

• Intervention Environment and Stimuli: Participants interact with selected digital dementia risk-reduction platforms deployed across two distinct health delivery environments:

o Interactive Web Modules: Structured online learning portals featuring personal lifestyle risk calculators, modular micro-learning units, and visual infographics tracking modifiable midlife risks e.g., hypertension, midlife cholesterol, social isolation (see Dementia Training Australia, 2026).

o Mobile Health (mHealth) App Frameworks: Dedicated smartphone interfaces providing habit-formation trackers, bite-sized health literacy tips, and simulated daily push notifications designed to reinforce long-term behavioural compliance (e.g., Brain Track App).

• Data Collection and Think-Aloud Approach: Interaction sessions are conducted individually within a controlled environment (either an on-campus research lab or a secure virtual breakout room) and are capped at 30 minutes. To capture implicit cognitive processing and real-time meaning-making, the session employs a concurrent think-aloud protocol. Participants are explicitly instructed to continuously verbalise their spontaneous thoughts, impressions, confusion, or emotional reactions while navigating the digital resources [111-120]. A think-aloud approach is a user study technique in which participants work on an interface or product while continuously expressing their thoughts, feelings, and observations out loud. It emphasises particular challenges and offers objective, real-time data on user cognition.

• System Capture: Screen movements, cursor navigation tracks, and participant audio streams are recorded using secure screen-capture software. This dual tracking allows for the objective evaluation of behavioural engagement (e.g., duration of stay on specific tabs, feature usage) alongside the concurrent mapping of experiential engagement, which is characterised by immediate attention, interest, and affective processing.

• The qualitative audio data from the think-aloud transcriptions is directly paired with the quantitative interaction data, providing an empirical record of how specific educational designs prompt or hinder participant interpretation.

Phase 3: Qualitative Interpretative Exploration

Phase 3 transitions the inquiry from immediate, real-time reactions to an in-depth, reflective exploration of participant meaning-making. This phase is designed to surface the cognitive, emotional, and social realities that drive the inner Mechanisms (M) of the study's CMO configurations.

• Administration and Context: Within 48 hours of completing the Phase 2 digital interaction session, participants engage in a one-on-one, semi-structured qualitative interview. Interviews are conducted either in person within a quiet university research room or via a secure online video conferencing protocol like Zoom or Office 365 Microsoft Teams, requiring approximately 35 to 40 minutes [121-129].

• Thematic Interview Architecture: The interview protocol uses open-ended prompts specifically designed to explore how personal contexts interact with digital health information to shape reasoning:

o Perceived Personal Relevance: Participants reflect on which specific modifiable risk factors (e.g., cardiovascular health, cognitive habits) resonated with their personal lifestyle, family histories, or perceived age-related susceptibility.

o Cognitive and Emotional Processing: Probes investigate deeper cognitive and affective responses that may not have surfaced during the think-aloud protocol, such as underlying anxieties regarding cognitive decline or feelings of self-efficacy in managing long-term health.

o Socio-Environmental Interactions: Explores how external contextual factors, including peer support networks, domestic responsibilities, and cultural beliefs, act as facilitators or structural barriers to applying digital health information [130].

• Audio Recording and Transcription Hygiene: All interviews are audio-recorded using dual hardware devices or encrypted cloud-based pathways. Verbatim audio files are subsequently fully transcribed and stripped of any identifying information, preparing the qualitative datasets for formal realist thematic synthesis.

Phase 4: Follow-Up Evaluative Integration

Phase 4 provides the final, post-intervention measurement layer. This phase documents the Outcomes (O) block of the CMO framework, evaluating shifts in participant orientation and behavioural intention after interacting with the digital materials.

• Administration and Timeline: Two weeks after the completion of Phase 3, participants receive an automated email invitation containing a secure link to the final, web-based questionnaires module hosted on a digital platform such as, Microsoft Forms or Qualtrics [131-136]. This short follow-up instrument requires approximately 10 minutes to complete.

• Evaluative Metrics and Target Variables: The quantitative instrument captures post-engagement metrics designed to match the Phase 1 baseline surveys, enabling a rigorous assessment of individual shifts:

o Dementia Risk Knowledge Change: Measures post-intervention knowledge acquisition and the retention of information regarding modifiable midlife risk profiles.

o Risk Perception and Susceptibility Calibration: Tracks changes in how participants view their personal long-term cognitive health trajectory and their vulnerability to dementia.

o Behavioural Intention and Readiness: Evaluates short-term intention formation and motivation to implement specific, modifiable lifestyle adjustments using standardised behavioural readiness indices.

o Digital Resource Acceptability and Usability: Utilises usability scales to capture participant satisfaction regarding the design, navigation, and practical real-world utility of the digital health tools.

It should be noted that both Microsoft Forms and Qualtrics can collect the required phase 4 data, but Qualtrics has native, advanced capabilities for longitudinal pre/post-intervention analysis, whereas Microsoft Forms requires external software to merge data and perform advanced statistical testing. Both tools are able to collect the required metrics through web-based questionnaires; however, Qualtrics can directly calculate knowledge shifts and behavior changes, whereas Microsoft Forms can only provide basic data visualizations and requires manual exporting [137-145].

The completion of Phase 4 generates the final dataset required to perform integrated mixed-methods synthesis. This allows the study to move beyond measuring plain intervention exposure to fully trace how distinct participant groups interact with digital resources to produce highly variable behavioural intentions.

Data Analysis and Synthesis

In strict accordance with the analytical workflows mapped in Figure 2, data analysis is executed through two parallel strands, quantitative statistical analysis and qualitative thematic analysis before converging into an integrated realist synthesis. This dual-strand approach ensures that objective outcome measurements are directly explained by participant-derived contextual and cognitive data.

Quantitative Data Analysis (Strand 1)

Quantitative questionnaire data collected during Phase 1 (Baseline Contextual Profiling) and Phase 4 (Follow-Up Evaluative Integration) are exported from Qualtrics into IBM SPSS Statistics software for numerical processing. The analysis evaluates sample traits, changes over time, and variations between different participant groups:

• Descriptive Statistics: Frequencies, percentages, means, and standard deviations are computed to construct the sociodemographic, health literacy, and digital health literacy profiles of the sample.

• Inferential Statistics: To assess longitudinal shifts in dementia risk knowledge, risk perception, and behavioural intentions from baseline to the two-week follow-up, paired-samples t-tests or Wilcoxon signed-rank tests are applied depending on data distribution normality.

• Subgroup Comparative Analysis: Independent-samples t-tests or Analysis of Variance (ANOVA) models are executed to explore whether post-intervention behavioural readiness scores vary significantly based on key contextual demographics, such as low versus high health literacy cohorts.

Qualitative Data Analysis (Strand 2)

Qualitative data derived from the Phase 2 concurrent think-aloud protocols and Phase 3 semi-structured interviews are processed using NVivo qualitative data analysis software. The transcripts are analysed using a hybrid inductive and deductive thematic approach structured around realist concepts:

• Transcription and Familiarisation: Audio recordings are transcribed verbatim, fully de-identified, and cross-verified against audio playbacks to ensure accuracy.

• Realist-Informed Coding Framework: Data coding departs from open thematic grouping to focus on identifying specific realist parameters:

o Context Codes: Prior beliefs, health literacy constraints, digital vulnerabilities, and environmental facilitators.

o Mechanism Codes: Moments of cognitive engagement, emotional friction, perceived personal relevance, and interpretative meaning-making.

o Outcome Codes: Declarations of behavioural intention, conceptual breakthroughs, or technology-induced frustration.

• Trustworthiness Guardrails: To ensure the validity and trustworthiness of the qualitative findings, thematic patterns derived from the think-aloud protocols and interviews are systematically cross-checked against participant transcripts. This rigorous process of investigator triangulation and constant comparative analysis ensures that the final thematic framework accurately reflects the participants' authentic lived experiences and cognitive processes.

Mixed-Methods Realist Integration and Synthesis

The ultimate analytical phase involves the configuration and synthesis of both computational strands to answer the overarching "how, for whom, and why" research questions. Rather than presenting quantitative outcomes and qualitative experiences in isolation, the datasets are integrated using Context–Mechanism– Outcome (CMO) configurations. Drawing upon core realist evaluation principles (Pawson & Tilley, 1997; van Belle et al., 2024), findings from across all four study phases are synthesised to systematically identify and link the following parameters:

• Contexts (C): Health literacy, digital literacy, prior beliefs, life stage, social influences, and environmental circumstances.

• Mechanisms (M): Active resource engagement, interpretative engagement, reflection, perceived personal relevance, motivation, and meaning-making.

• Outcomes (O): Knowledge development, risk awareness, behavioural readiness, behavioural intentions, and potential early behaviour adaptation.

To operationalise this analytical convergence, the study employs a structured visual layout known as a joint display, which represents the gold standard for achieving integration at the reporting and interpretive level [146-152].

Table 4 provides the operational blueprint for this mixed-methods synthesis, illustrating how the quantitative baseline context profiles and longitudinal outcome metrics are cross-examined against qualitative theme segments to construct explicit, multi-layered Context–Mechanism–Outcome (CMO) configurations.

Quantitative Cohort Profile (Context - C)	Quantitative Outcome Trend (Outcome - O)	Qualitative Evidence / Think-Aloud Themes (Mechanism - M)	Resulting Realist Explanatory Statement (CMO Configuration)
High Digital Literacy / Low Baseline Health Literacy   eHEALS Score: High (≥ 32) HLQ Score: Low (Scale 1 & 2) N = ~12–15	Significant Knowledge Gain & Sharp Intention Spikes   Significant post-intervention increases in dementia modifiable risk knowledge and behavioural readiness indices.	Theme: High Usability Lowering Cognitive Load   Participants would effortlessly bypass technical navigation issues. High digital self-efficacy triggered immediate interpretative engagement with the risk calculators, translating abstract stats into immediate personal relevance.	CMO 1: In midlife adults with robust digital skills but low health literacy (C), deployment of interactive, low-jargon web calculators (Intervention Input) triggers immediate personal relevance and reduces cognitive load (M), resulting in swift knowledge acquisition and strong early behavioural intention formation (O).
Low Digital Literacy / High Baseline Health Literacy   eHEALS Score: Low (< 26) HLQ Score: High (Scale 8 & 9) N = ~8–10	Stagnant Intention Scores / High Knowledge Baseline   No significant shift in risk perception or behavioural readiness from baseline to the two-week follow-up questionnaires.	Theme: Interface Friction Overriding Health Interest   Think-aloud data is expected to reveal high frustration with the mHealth app's swipe gestures and push settings. Technical friction may induce cognitive fatigue and anxiety, suppressing their high intrinsic motivation to learn.	CMO 2: When complex smartphone interfaces (Intervention Input) are introduced to highly health-literate adults who possess low digital technical skills (C), interface navigation friction triggers technical anxiety and cognitive exhaustion (M), which actively blocks health literacy advantages and leads to flat or stagnant behavioural intentions (O).
Varying Demographics / High Prior Exposure   Age Range: 55–65 years Context: Personal/family history of dementia N = ~10–12	Polarised Risk Calibration Outcomes   Pre-to-post questionnaires show highly variable shifts in personal susceptibility and polarised behavioural readiness metrics.	Theme: Emotional Shielding vs. Adaptive Motivation   Interviews surfaced deep emotional processing. For some, explicit risk information is expected to trigger defensive avoidance (fear/fatalism). For others, it catalysed adaptive reflection and high self-efficacy, viewing midlife as an actionable window.	CMO 3: For midlife adults approaching older age who have witnessed dementia in their family (C), explicit modifiable risk information (Intervention Input) triggers deep emotional processing (M); this manifests either as defensive fatalism which stalls behavior change, or proactive adaptation which dramatically boosts behavioural readiness (O).
Note. This integrated matrix maps Phase 1 and Phase 4 numerical parameters concurrently against Phase 2 and Phase 3 qualitative thematic frameworks to generate structured Context–Mechanism–Outcome explanatory statements.

                                                Table 4: Illustrative examples of potential CMO Configurations

Following the mapping of these data components within the display, the combined profiles are structured into formal explanatory accounts. These configurations support a granular explanation of how digital dementia education functions, for whom it is most effective, under what circumstances meaningful outcomes are generated, and why specific variations occur across the cohort.

Rigour and Quality Assurance

Several strategies will be implemented to enhance methodological rigour, credibility, and trustworthiness throughout the study. For the quantitative component, data will be screened for completeness, distributional characteristics, and outliers prior to analysis. Internal consistency of multi-item scales will be evaluated using Cronbach's alpha coefficients where appropriate. Statistical assumptions underpinning inferential analyses will be assessed before selecting parametric or non-parametric analytical procedures [153-155].

For the qualitative component, rigour will be supported through systematic data management, reflexive memoing, investigator triangulation, and maintenance of a comprehensive audit trail documenting coding decisions, theme development, and Context–Mechanism–Outcome (CMO) refinement. Coding frameworks will be iteratively reviewed throughout analysis to ensure alignment between participant accounts and emerging explanatory interpretations [156-158]. Methodological triangulation will be achieved through integration of quantitative findings, think-aloud observations, interview data, and digital interaction records.

This process allows convergence, complementarity, and expansion of findings across multiple sources of evidence. The mixed-methods design itself further contributes to rigour by enabling corroboration of findings across methodological traditions and supporting the development of richer explanatory accounts than would be achievable using either quantitative or qualitative methods alone [159,160].

Summary and Methodological Integration

This paper has detailed a systematic, sequential multi-phase mixed-methods research design explicitly mapped to a critical realist ontology and realist evaluation principles. By structuring empirical data collection across four distinct phases, the methodology moves beyond simple outcome evaluation. Instead, as operationally demonstrated by the analytical synthesis in Section 4.5.3 and the integration framework in Table 3, it establishes a robust framework capable of explaining how individual participant profiles interact with digital resources to activate specific cognitive and emotional mechanisms [161-163]. The structural and analytic workflow detailed throughout this paper is summarised as a cohesive operational pathway. To ensure rigorous integration of diverse data sources, the research design utilises a structured, seven-stage mixed-methods workflow. As illustrated in Figure 4, the process establishes an ontological and epistemological foundation before executing a multi-phase data collection strategy. This structured approach culminates in concurrent quantitative and qualitative analyses that are integrated into a final joint display for comprehensive theory building [164-166].

                                          Figure 4: Structural and Analytical Workflow

Ultimately, this integrated approach provides the rigorous empirical foundation required to trace how intervention inputs yield highly variable real-world outcomes. The structured datasets generated through this methodology form the analytical basis for the subsequent chapters. Chapter 5 presents the empirical findings, starting with the baseline quantitative profiling of the participant cohort before presenting the synthesised realist explanations derived directly from the rows of the joint display matrix.

Ethical Considerations

Formal ethical approval for this multi-phase mixed-methods study will be obtained from the relevant Human Research Ethics Committee (HREC) prior to initiating participant recruitment or data collection. All research procedures will be conducted in strict accordance with institutional research governance requirements and national ethical standards for human research. All data management processes will fully comply with a formal institutional Data Management Plan [167].

Participation in this study is entirely voluntary. The primary investigator will obtain written informed consent from each participant before administering the Phase 1 baseline questionnaire. The consent process will explicitly inform participants of the study's exact procedures, potential risks, and their unconditional right to withdraw from the study at any stage prior to data analysis without negative consequences or penalty [168].

To safeguard participant privacy and confidentiality, the researcher will strip all direct identifiers from both the quantitative datasets and qualitative interview transcripts immediately following data collection. De-identified files will be assigned a unique alphanumeric participant code and stored securely on password-protected institutional servers in full compliance with university research data management policies [169].

All digital research data, including questionnaire responses, audio recordings, screen-capture files, transcripts, and analytic outputs, will be stored on secure password-protected institutional servers accessible only to authorised members of the research team. Audio recordings and screen-capture files collected during think-aloud sessions and interviews will be encrypted during storage and transfer. Data will be retained in accordance with institutional research governance requirements for a minimum period of five years following publication or project completion, after which all identifiable materials will be permanently destroyed using secure deletion procedures [170-176].

Because reflecting on neurological health and future dementia risks may cause mild emotional discomfort or anxiety, the researcher will actively provide all participants with a written resource sheet. This sheet will outline qualified community support networks and professional psychological health hotlines, ensuring participants have immediate access to care if needed.

Discussion

Addressing the Knowledge–Behaviour Gap in Dementia Education

The present study addresses the persistent knowledge–behaviour gap in dementia prevention by examining not only whether digital educational interventions improve knowledge, but also how individuals engage with, interpret, and apply dementia risk-reduction information. By adopting a realist-informed mixed-methods approach, the study seeks to identify the mechanisms through which educational resources influence behavioural readiness within different contextual conditions. In doing so, it responds to growing calls for explanatory approaches that move beyond simple evaluations of educational outcomes [177-182]. 6.2 Advancing Understanding of Engagement in Digital Dementia Education

A central contribution of this study is its reconceptualisation of engagement as an explanatory process rather than simply a behavioural indicator. Whereas previous evaluations have frequently measured engagement using metrics such as completion rates, duration of use, or frequency of interaction, the present study is designed to examine how individuals experience, interpret, and assign meaning to digital educational content. This user-centred perspective aligns with contemporary conceptualisations of digital engagement that emphasize attention, involvement, and perceived relevance [183-190]. This approach has the potential to provide a richer understanding of why educational interventions are effective for some individuals but not others.

Interpretative Engagement as a Core Mechanism

A key conceptual contribution of this study is the conceptualisation of interpretative engagement as a distinct mechanism linking educational exposure and behavioural readiness. By positioning interpretation as the mechanism linking educational exposure with behavioural readiness, the framework extends existing models of health literacy and behaviour change [191-200]. Rather than assuming that interaction with educational resources is sufficient to influence behaviour, the proposed framework recognises that educational outcomes are likely to depend on how individuals construct meaning from information and integrate it into their existing beliefs, experiences, and life circumstances.

Contributions to Health Literacy and Behaviour Change Research

The study aims to theoretically advance both health literacy and behaviour change frameworks by mapping their intersection [201-207]. It seeks to extend dynamic health literacy models by tracking exactly how critical evaluation and contextual influences shape the translation of raw comprehension into behavioural readiness. Simultaneously, the study builds upon the Capability–Opportunity–Motivation Behaviour (COM-B) framework. While COM-B effectively categorises the determinants of behaviour, this study is positioned to isolate the real-time process through which educational engagement alters a participant's internal capability and motivation, providing a more comprehensive, dual-lens explanation of the cognitive pathways driving risk reduction [208-215].

Contributions to Implementation Science

By adopting a realist-informed approach, this study proposes to contribute to implementation science by evaluating how digital interventions function within uncontrolled, real-world environments [216-223]. This project directly addresses a recurring limitation in dementia literature, the over-reliance on short-term knowledge outcomes measured under ideal research conditions by shifting focus to implementation processes, contextual influences, and practical applicability [224-230]. The study is designed to systematically map how external variables enable or constrain educational effectiveness. The anticipated Context–Mechanism–Outcome (CMO) configurations aim to offer a clear blueprint detailing which specific intervention components work, for whom, and under what exact circumstances.

Implications for Dementia Prevention Policy and Practice

The anticipated findings are expected to offer a pragmatic pivot for future public health policy away from raw information dissemination and toward engagement optimization [231-233]. This study seeks to provide public health planners with evidence-based strategies to transition from generic mass-media campaigns to person-centred educational resources tailored to diverse literacy levels, motivations, and life circumstances. Practically, these insights are intended to guide public health designers to look beyond digital access metrics, offering concrete parameters to optimize user interpretation and enhance the long-term effectiveness of digital dementia prevention initiatives.

Strengths and Limitations

Strengths

This study has several notable strengths. First, it is grounded in a robust interdisciplinary framework integrating health literacy theory, behaviour change theory, implementation science, critical realism, and realist evaluation. This theoretical integration provides a comprehensive foundation for examining the complex processes through which digital dementia risk-reduction education influences behavioural readiness [234-236].

Second, the sequential mixed-methods design enables examination of both measurable outcomes and the underlying mechanisms that explain those outcomes. By integrating quantitative measures with think-aloud protocols and semi-structured interviews, the study provides a richer understanding of engagement, interpretation, and behavioural readiness than would be achievable through either quantitative or qualitative methods alone [237-240].

Third, the study adopts a realist-informed approach that explicitly considers contextual influences, allowing investigation of how individual characteristics, such as health literacy, digital literacy, and prior experience with dementia, shape engagement with digital educational resources. This emphasis on Context–Mechanism–Outcome (CMO) explanations extends beyond conventional evaluations focused solely on knowledge acquisition [241-243].

Finally, the study addresses an important gap within the dementia prevention literature by examining not only whether digital education is effective, but how it works, for whom, and under what circumstances, thereby generating findings with potential implications for both research and practice [244-246].

Limitations

Several limitations should also be acknowledged. First, the use of purposive sampling and a relatively small sample of cognitively unimpaired midlife adults limits the transferability of findings to other populations, age groups, and healthcare settings. As with most realist-informed mixed-methods studies, the objective is explanatory understanding rather than statistical generalisation [247-250].

Second, participation is voluntary and may therefore be subject to volunteer bias, whereby individuals with greater interest in dementia prevention, digital technologies, or personal health may be more likely to participate than the broader population.

Third, behavioural intentions and self-reported behavioural readiness constitute the primary outcome measures. Consequently, the study cannot determine whether reported intentions translate into sustained behavioural change or reductions in long-term dementia risk. Furthermore, the two-week follow-up period limits assessment of longer-term maintenance of behavioural readiness [251-253].

Fourth, the concurrent think-aloud protocol, while providing valuable insight into participants' cognitive and interpretative processes, may influence how participants interact with the educational resources. Verbalising thoughts during resource use may alter natural patterns of engagement compared with routine use outside the research setting.

Fifth, the qualitative findings rely on participant self-report and researcher interpretation. Although methodological rigour is enhanced through triangulation, reflexive memoing, and systematic coding procedures, some degree of interpretative subjectivity is unavoidable [254-256].

Finally, the study evaluates engagement using a single standardised digital dementia risk-reduction educational intervention. Although this approach enhances consistency across participants, findings may not be directly transferable to other digital platforms or educational designs with different features, levels of interactivity, or implementation contexts.

Despite these limitations, the study provides a rigorous and theoretically informed examination of how digital dementia risk-reduction education operates in practice and offers valuable explanatory insights to inform future intervention design, implementation, and evaluation [257,258].

Conclusion

Dementia prevention has emerged as a major public health priority, yet significant challenges remain in understanding how educational interventions contribute to meaningful behavioural outcomes. Existing research demonstrates that improvements in knowledge and awareness do not consistently translate into behavioural change, highlighting the need for more sophisticated approaches to evaluation.

This study addresses these challenges through a realist-informed mixed-methods investigation of how midlife adults engage with, interpret, and apply digital dementia risk-reduction education. By integrating health literacy theory, behaviour change theory, implementation science, and realist evaluation principles, the study moves beyond traditional outcome-focused approaches to examine the mechanisms through which educational interventions generate impact. Central to the study is the proposition that interpretative engagement represents a critical mechanism linking information exposure and behavioural readiness. Through development of Context–Mechanism–Outcome explanations, the study seeks to identify how digital dementia education works, for whom it is most effective, and under what circumstances meaningful outcomes emerge. The study contributes to contemporary debates regarding health literacy, behaviour change, implementation science, and dementia prevention while providing practical insights for the future design, implementation, and evaluation of digital health education interventions. Ultimately, it supports a conceptual shift from evaluating the availability of educational resources towards understanding their real-world effectiveness and impact.

Ethical Statement

Ethical approval for this multi-phase mixed-methods study will be obtained from the relevant Human Research Ethics Committee (HREC) prior to participant recruitment and data collection. All procedures will be conducted in strict accordance with institutional research governance requirements and national ethical standards for human research.

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