Evaluating Accessibility in Sequential Onboarding Flows: A Conceptual Framework and Scoring Rubric

Evaluating Accessibility in Sequential Onboarding Flows: A Conceptual Framework and Scoring Rubric | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Accessibility in Sequential Onboarding Flows: A Conceptual Framework and Scoring Rubric Israel Umola Akeji, Daniel Eleojo Okwoli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8795738/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Inclusive onboarding is a prerequisite for equitable access to digital services, particularly in domains such as digital health where early interaction barriers can lead to abandonment and exclusion. However, prevailing accessibility standards and evaluation frameworks remain predominantly compliance oriented and provide limited guidance for assessing sequential and time constrained onboarding flows. This paper introduces a conceptual evaluation model that operationalises accessibility assessment for onboarding interfaces with adaptation goals. Building on the established POUR principles, the model replaces Robustness with Personalisation and defines four evaluation dimensions: Perceivability, Operability, Understandability, and Personalisation. To support reproducibility and practical adoption, the approach integrates a structured scoring rubric, radar chart visualisation, and remediation mapping. The model is demonstrated through two expert evaluations and a formative user study (n = 4) using a digital health onboarding prototype. Expert assessment yielded an overall accessibility score of 2.9 out of 5, with strongest performance in Operability (4.0 out of 5) and weakest performance in Personalisation (2.0 out of 5). Participant ratings indicated higher Perceivability (3.25 out of 5) but lower and more variable Operability (2.75 out of 5), suggesting that onboarding accessibility can vary under assistive technology mediation and individual interaction strategies. Findings indicate that dimension-level accessibility scores can be translated into actionable design requirements that guide targeted onboarding improvements. Within this structure, Personalisation reflects the system’s readiness to support tailoring through adaptive or configurable onboarding pathways. The paper contributes a practical framework for evaluating onboarding accessibility beyond static compliance checking and for supporting adaptation-informed onboarding design. accessibility evaluation onboarding flows inclusive design personalisation user modelling digital health Figures Figure 1 Figure 2 1 INTRODUCTION The first moments a user spends with a digital product, commonly described as the onboarding experience, often influence long-term engagement and continued use. Human–computer interaction (HCI) research increasingly characterises onboarding as a critical first-use phase in which users form impressions of credibility, evaluate perceived risks, and decide whether to continue interacting with a system [ 1 , 2 ]. For users with disabilities, barriers during onboarding may undermine trust and prevent meaningful participation from the outset. Onboarding is designed to support new users by introducing core functionality, guiding initial interactions, enabling preference setting, and reducing uncertainty during early use. However, onboarding can also introduce accessibility challenges, including unlabelled interface controls, gesture-dependent navigation without accessible alternatives, cognitive overload caused by unfamiliar workflows, and limited support for adjustable pacing [ 3 – 5 ]. Evidence further suggests that inaccessible or confusing onboarding contributes to abandonment of digital services, with disproportionate impacts on users who experience interaction barriers. For example, Signicat’s Battle to Onboard report found that 63% of consumers abandoned digital application processes in 2020, and a follow-up report indicated an increase to 68% in 2022 [ 6 , 7 ]. In high-stakes domains such as digital health and finance, onboarding barriers may also reduce willingness to share essential information, thereby reinforcing unequal access to critical services [ 5 , 8 ]. Inaccessible onboarding is not merely an inconvenience but a structural barrier to digital inclusion. Because onboarding represents the user’s first sustained interaction with a product, its evaluation requires approaches capable of capturing more than static compliance status and isolated page-level issues. The Web Content Accessibility Guidelines (WCAG) provide foundational technical benchmarks for accessible interface design [ 9 – 13 ]. However, WCAG was primarily developed for relatively stable interface environments and may not fully represent the sequential and transitional nature of onboarding experiences. In practice, onboarding involves progressive interaction with user-dependent progression and is shaped by timing constraints, input modality, and dynamic content presentation. These characteristics can expose context-specific accessibility barriers that are insufficiently represented through static evaluation alone [ 11 , 14 , 15 ]. While heuristic audits and automated accessibility checkers can identify common violations, they often struggle to account for temporal dynamics and evolving interaction demands across onboarding stages [ 16 – 19 ]. This motivates evaluation approaches that conceptualise onboarding accessibility as an interaction process unfolding over time, rather than as isolated interface states. Recent HCI research therefore calls for accessibility evaluation frameworks that are more holistic, user-centred, and context-sensitive [ 12 , 17 , 20 ]. Within this agenda, onboarding remains underrepresented in both academic literature and design practice. Studies have investigated onboarding in contexts such as virtual reality [ 1 ], open-source software contribution environments [ 21 ], and civic technology platforms [ 22 ]. However, relatively few contributions provide structured and reproducible methods for evaluating accessibility during onboarding as a sequential experience that involves staged decision points and context-sensitive interaction demands [ 23 ]. As a result, designers, researchers, and QA practitioners may lack practical mechanisms for diagnosing accessibility breakdowns during onboarding and translating evaluation results into targeted design improvements. To address this gap, this paper proposes a four-dimensional framework that operationalises accessibility evaluation for onboarding experiences with adaptation goals. The framework extends WCAG’s POUR principles by replacing Robustness with Personalisation, reflecting the role of onboarding in determining whether systems can support diverse user abilities, contexts, and preferences [ 11 , 24 ]. The model defines four evaluative dimensions: Perceivability, Operability, Understandability, and Personalisation. A reproducible scoring rubric, supported by radar chart visualisation and remediation mapping, enables evaluators to generate structured outputs that guide targeted onboarding improvements and support systematic evaluation reporting. The framework’s applicability is demonstrated through a digital health onboarding scenario using two expert evaluations and a formative user evaluation involving participants with diverse access needs. Beyond the immediate case study, the framework provides a domain-agnostic approach for linking accessibility assessment with adaptation-informed onboarding design. Dimension-level scores are treated as evaluative signals that identify accessibility breakdowns and guide targeted adjustments across Perceivability, Operability, and Understandability. In this framework, Personalisation captures the extent to which onboarding supports tailoring through configurable or adaptation-ready interaction pathways. Collectively, these signals can support user modelling by enabling systems and designers to translate accessibility insights into actionable onboarding refinements [ 25 , 26 ]. The remainder of this paper is organised as follows. Section 2 reviews related work on accessibility evaluation, onboarding, and personalisation. Section 3 presents the proposed framework and scoring strategy. Section 4 describes the study methods. Section 5 reports findings from the expert evaluation, and Section 6 reports findings from the user evaluation and expert–participant comparison. Section 7 discusses implications for onboarding accessibility and adaptation, outlines limitations, and identifies future research directions. 2 RELATED WORK AND THEORETICAL POSITIONING Digital accessibility research has traditionally focused on evaluating fully deployed systems such as websites and mobile applications. Foundational frameworks, most notably the Web Content Accessibility Guidelines (WCAG), define the widely used POUR principles of Perceivability, Operability, Understandability, and Robustness [ 10 , 11 ]. These principles have shaped both academic research and industry practice, informing automated tools and accessibility audit workflows, while continuing to raise questions regarding the validity, reliability, and coverage of automated approaches [ 12 , 13 , 18 ]. WCAG and comparable standards provide essential technical baselines; however, they were primarily developed for relatively stable interface environments and generalised interaction patterns rather than transitional, sequential experiences such as onboarding. As a result, onboarding introduces accessibility demands that static, checklist-based evaluations may fail to capture, particularly where accessibility issues emerge through the progression of steps rather than within a single screen [ 11 , 14 , 15 ]. 2.1 Onboarding in HCI and accessibility research Onboarding has been examined across several HCI domains, including organisational and community orientation, product adoption, and participation in open-source ecosystems [ 21 , 27 , 28 ]. These contributions commonly address learning curves, cognitive load, and motivational strategies that support sustained engagement [ 27 , 29 ]. However, onboarding is rarely foregrounded as an accessibility evaluation target, and structured methods for assessing accessibility during first-use flows remain limited. Existing empirical work describes common onboarding barriers, such as unlabelled controls, low contrast, and limited screen reader compatibility, but these findings are often reported as isolated usability problems rather than integrated evaluation models that support systematic diagnosis and comparison [ 1 , 3 ]. Onboarding is inherently sequential and time constrained and frequently incorporates progressive disclosure, stepwise interactions, and animated transitions. Such features can produce compounding accessibility effects across steps, where an early barrier may lead to cascading failures later in the flow. Consequently, automated checkers and single-screen heuristic inspections are often insufficient for identifying temporal and interactional breakdowns that arise only through sustained progression [ 16 – 19 ]. Research on onboarding in contexts such as virtual reality, civic technology, and finance further highlights the contextual variability of onboarding requirements; nevertheless, it rarely provides domain-agnostic and operational accessibility evaluation methods applicable across first-use flows [ 1 , 22 , 30 ]. 2.2 Personalisation and inclusive design frameworks Recent research and design practice increasingly position personalisation as a core mechanism for inclusive interaction, particularly for users whose needs are not adequately supported by one-size-fits-all interface designs. Personalisation may enable users to tailor content presentation, interaction modalities, and pacing to their abilities and preferences [ 26 , 32 ]. Early adaptive systems demonstrated benefits for users with motor impairments by modifying interface parameters such as target size and layout [ 31 ]. More recent work suggests that tuned and adaptation-aware onboarding interfaces can support older adults and other user groups by adjusting language complexity, pacing, and interaction demands [ 33 ]. Despite these developments, prominent normative standards such as WCAG and ISO 9241 do not yet treat personalisation as a first-class evaluation criterion [ 11 , 34 ]. Recent W3C initiatives, including WAI-Adapt and ongoing work towards WCAG 3.0, signal increasing attention to personalisation semantics and outcome-based evaluation, although formal integration remains emergent [ 35 – 38 ]. In practice, industry toolkits and inclusive design guidance promote personalisation patterns; however, these resources rarely provide evaluation procedures that quantify personalisation as an accessibility outcome or link it explicitly to onboarding assessment [39– 41 ]. From a user modelling perspective, personalisation can be operationalised through data-driven and rule-based adaptation approaches. Work on SUPPLE and related systems demonstrates how interface configurations may be generated from modelled user capabilities, producing measurable improvements in task performance and satisfaction [ 26 , 31 ]. More broadly, user modelling research shows that observable interaction features and structured metrics can inform predictive models and adaptation policies [ 25 ]. This body of work suggests that accessibility evaluations producing structured quantitative outputs are candidates for integration with adaptation-oriented design decisions. While the present study does not implement an adaptive onboarding system, it demonstrates how evaluation outputs may be translated into adaptation parameters and user modelling features. 2.3 Compliance, usability, and real-world accessibility outcomes A recurring theme in accessibility research concerns the relationship between standard conformance and experienced accessibility in real use. Although conformance provides an essential baseline, it does not always predict usability or task success, particularly in complex interactive scenarios where accessibility barriers are shaped by context, cognitive demand, and interaction sequencing. Empirical work demonstrates that accessibility and usability are related but non-identical constructs, and that systems may pass technical checks yet remain difficult to use for disabled users during task completion [ 19 ]. This distinction is particularly relevant for onboarding, where first-use experiences require rapid comprehension, navigation confidence, and error recovery. Further evidence suggests that accessibility outcomes vary depending on interaction context and user characteristics, underscoring the need for evaluation approaches that incorporate user experience signals rather than relying solely on static compliance indicators. For example, studies examining the effects of accessibility compliance on user experience show measurable differences in performance and satisfaction when conformance levels differ, but also highlight that compliance alone does not guarantee positive user experience outcomes [ 42 ]. These findings reinforce the value of evaluation models that can capture multi-dimensional accessibility outcomes across both technical and experiential dimensions. 2.4 Reliability challenges in expert accessibility evaluation In addition, reliability and reproducibility challenges have been documented in expert-led accessibility evaluation methods. Usability and accessibility evaluation literature has long acknowledged evaluator-driven variability, including differences in judgement, interpretation, and severity ratings. The evaluator effect, originally documented in usability evaluation, indicates that different evaluators may identify different sets of issues and may vary in how they rate severity even when examining the same interface [ 43 ]. Accessibility auditing is similarly vulnerable to interpretive inconsistency, particularly when criteria require judgement about interaction flow, cognitive demand, and perceived control rather than direct technical checks [ 12 , 13 ]. These reliability challenges motivate structured rubrics and anchored rating scales to reduce ambiguity and improve comparability across evaluators and contexts. Rubric-based evaluation can provide clearer interpretation of mid-range performance, where systems exhibit partial support rather than binary compliance. Such approaches are particularly relevant for onboarding, where accessibility barriers often manifest through cumulative friction rather than discrete, easily classifiable failures. 2.5 Evaluation models in accessibility Accessibility evaluation methods include expert heuristic inspections, walkthroughs, participatory approaches, and hybrid methods combining automated scans with expert review [ 18 , 44 – 46 ]. Although such approaches are valuable for compliance and usability, they are typically applied to full systems and rarely isolate onboarding as a distinct evaluation target. Recent reviews and meta-analyses show steady progress in evaluation methods and tool ecosystems, while also identifying persistent gaps in assessing time-dependent interaction demands, sequential flows, and personalised or adaptive user experiences [ 47 , 48 ]. Visualisation techniques for multi-criteria accessibility assessment remain underused despite their communicative value. Compact visual summaries, including radar charts, can support multidimensional reporting and facilitate stakeholder decision-making, particularly where trade-offs across dimensions must be communicated clearly [ 44 , 49 , 50 ]. For onboarding, which involves progression across steps, visual summaries may further support diagnosis by enabling evaluators to communicate where accessibility breakdowns occur and which dimensions are most affected. Bridging evaluation outcomes with adaptation-oriented design requires interpretable outputs and reproducible scoring procedures so that evaluators, designers, and system developers can operate on shared constructs. 2.6 Identified gap and conceptual positioning The reviewed literature indicates a clear gap: limited work proposes structured and reproducible frameworks for evaluating onboarding accessibility as a multidimensional first-use flow. Existing standards, audits, and toolkits primarily emphasise static compliance checking and do not routinely generate structured outputs suitable for supporting systematic onboarding improvement. Furthermore, prior work rarely integrates assessment of Perceivability, Operability, Understandability, and Personalisation as interrelated dimensions of onboarding accessibility. The present study addresses this gap by reinterpreting WCAG-derived POUR principles for sequential onboarding contexts and by introducing Personalisation as an explicit evaluative dimension. The framework operationalises evaluation using a reproducible scoring rubric supported by checklist guidance and defined score interpretations, enabling comparable 0 to 5 dimension-level ratings across evaluators and onboarding contexts. By combining rubric-based scoring with compact visualisation, the framework supports clear communication of accessibility performance and guides prioritisation of remediation. Dimension-level outputs are further conceptualised as evaluative signals that may serve as features, triggers, or indicators for adaptation-informed onboarding design. The framework is demonstrated through expert and participant evaluations of a digital health onboarding prototype, illustrating how scores can inform remediation priorities and potential adaptation parameters. 3 THE PROPOSED FRAMEWORK: EVALUATING ACCESSIBLE ONBOARDING EXPERIENCES Digital onboarding experiences differ from conventional web and app interactions because they are transitional, time constrained, and dependent on progressive user participation. Rather than presenting information in full, onboarding flows typically reveal content sequentially and may incorporate animations, micro-interactions, and staged input requests. These characteristics introduce accessibility challenges that are not always captured by checklist-based standards and conventional audits, particularly when barriers emerge through temporal progression or cumulative interaction demands [ 11 , 18 , 51 , 52 ]. The proposed framework addresses this gap by providing a structured and reproducible method for evaluating accessibility in onboarding contexts and by formalising how evaluation outputs can be translated into practical remediation priorities and adaptation-informed design considerations. 3.1 Framework overview The framework is grounded in WCAG 2.2’s POUR principles and introduces a conceptual modification in which Robustness is replaced with Personalisation. In WCAG, Robustness concerns the technical reliability of content and its consistent interpretation by user agents and assistive technologies. Robustness remains fundamental for accessibility [ 11 ]. However, for onboarding-focused evaluation, robustness is treated as a prerequisite that is typically verified through code-level checks and automated technical audits prior to experiential assessment [ 18 , 44 , 51 ]. By assuming robustness as a baseline, the proposed framework emphasises interaction and experience factors that directly shape first-use success. Replacing Robustness with Personalisation aligns with inclusive design perspectives that emphasise user control, contextual responsiveness, and the capacity of interfaces to accommodate diverse needs and preferences [ 15 , 26 , 32 ]. In the context of onboarding, Personalisation refers to the extent to which onboarding interactions support tailoring of parameters such as pace, modality, and information density. This does not imply that systems must implement automatic adaptation; instead, the framework treats personalisation capacity as an accessibility-relevant property that affects whether onboarding can accommodate variation across users and contexts. The framework evaluates onboarding experiences using four interrelated dimensions: Perceivability : whether users with diverse sensory abilities can perceive onboarding content. Operability : whether users can navigate and interact with onboarding steps regardless of device and input method. Understandability : whether instructions, terminology, and feedback support comprehension across cognitive differences and first-use uncertainty. Personalisation : whether onboarding supports tailoring through mechanisms such as adjustable pace, alternative modalities, and preference control. Together, these dimensions surface accessibility barriers specific to onboarding as a staged first-use flow and provide a basis for prioritising remediation. 3.2 Perceivability Perceivability assesses whether onboarding content accommodates diverse sensory needs. While aligned with WCAG’s perceivability principle, this dimension emphasises temporal and contextual characteristics common in onboarding, including animated transitions, time-limited prompts, and progressive disclosure. Checklist indicators Text elements provide sufficient contrast and support scalable fonts. Images and icons include descriptive alternatives. Multimedia content is captioned, transcribed, or skippable. Screen readers announce content in a meaningful and correct sequence. Mobile onboarding frequently relies on animations and gesture-based navigation patterns that can obscure critical information for users with low vision or for those using assistive technologies [ 3 , 52 ]. Evidence from digital health contexts further indicates that missing captions and limited motion controls in tutorial content can reduce comprehension for users with sensory differences [ 8 ]. 3.3 Operability Operability measures whether users can progress through onboarding steps regardless of input method. In onboarding, operability is particularly critical because early interaction failures can prevent users from completing entry tasks entirely. Checklist indicators Keyboard, touch, and voice-based interaction options are supported where relevant. Interactive targets meet minimum size and spacing requirements. Focus order is logical, and navigation supports Back and Skip options. Gesture-only steps offer accessible alternatives. Common barriers include swipe-only navigation, tightly spaced targets, and inaccessible focus behaviour, which disproportionately affect users with motor impairments and those interacting on smaller mobile screens. Prior work shows that flexibility across input modalities can improve accuracy and reduce frustration, particularly for users relying on assistive interaction methods [ 31 , 53 ]. 3.4 Understandability Understandability concerns the clarity of instructions, terminology, and feedback. This dimension captures whether onboarding supports comprehension for first-time users who may be unfamiliar with a system’s goals, language, or interaction expectations. Checklist indicators Language is simple, consistent, and non-technical where possible. Steps follow a logical and progressive structure. Tooltips, examples, or visual cues clarify complex tasks. Feedback is timely, contextual, and actionable. Cognitive load theory suggests that unexplained terminology and fragmented information increase mental effort, particularly during first-use interaction [ 54 ]. In onboarding contexts, ambiguous phrasing and complex consent flows can reduce comprehension and trust [ 2 , 33 , 55 ]. Effective onboarding therefore requires transparent language and progression strategies that sustain comprehension. 3.5 Personalisation (new dimension) Personalisation assesses the extent to which onboarding supports tailoring to sensory, cognitive, or situational preferences. This dimension focuses on whether onboarding interactions provide mechanisms that accommodate diverse needs during initial engagement. Checklist indicators Steps can be skipped, paused, or repeated. Language, text size, and pacing are adjustable. Alternative formats (e.g., audio or text) are available when appropriate. Preferences persist across onboarding steps. This dimension is informed by ability-based design, which emphasises supporting users through flexible interaction pathways rather than enforcing a single interaction style [ 56 ]. It also relates to W3C work such as WAI-Adapt, which defines semantics that may support adaptation of content to user needs [ 10 , 24 ]. Evidence from ageing and learning research further suggests that providing control over complexity and pace can improve comprehension and engagement during early interaction [ 57 , 58 ]. In onboarding, personalisation therefore functions as an accessibility-enabling capacity that supports more inclusive first-use experiences. 3.6 Scoring and visualisation strategy Each dimension is rated using a 0 to 5 rubric. Rubric anchors define interpretation at each score level, from 0 (no support) to 5 (fully accessible and inclusive), with intermediate values reflecting increasing degrees of support. Scores can be visualised using a radar chart to highlight relative strengths and deficits across dimensions. This supports structured reporting and shared interpretation among evaluators, designers, and developers. Visualisation also enables rapid identification of priority barriers and can support translation of evaluation outcomes into remediation actions and design adjustments [ 44 , 49 , 50 ]. The next section describes the methodological procedures for applying the framework and demonstrates how these quantitative outputs can inform remediation priorities and adaptation-informed onboarding decisions. 4 METHODS: OPERATIONALIZING THE FRAMEWORK To support adoption in applied accessibility reviews and formative evaluation contexts, the proposed framework was operationalised into a practical evaluation procedure. This procedure guided both the expert evaluation (Section 5 ) and the formative user evaluation (Section 6). The section describes the workflow used in the present study, including preparation, baseline checks, scoring procedure, user triangulation, and reporting. 4.1 Preparation and evaluation scope Prior to evaluation, the onboarding scenario was documented in detail, including entry points, sequential screens, decision gates, and required permissions. Key first-use tasks were identified, such as account creation, consent processes, preference set-up, and completion of an initial core task. For each task, expected input methods and relevant time constraints were recorded. This scoping ensured that the evaluation remained focused on onboarding as a transitional and time constrained first-use flow rather than general product use. 4.2 Assumptions and baseline checks Because the framework treats Robustness as a prerequisite rather than an evaluative dimension, a lightweight technical screening was conducted before the experiential review. Screening focused on the presence of semantic roles for interactive elements, programmatic labelling of form inputs, detectable error states, and focus management during modal and screen transitions. Where development-level access was unavailable, proxy checks were performed by inspecting accessibility trees using platform tools and confirming that screen readers could traverse interactive elements in the expected order. 4.3 Walkthrough procedure and scoring Each onboarding flow was reviewed twice. The first pass was conducted without assistive technology simulation to establish a baseline view of onboarding progression and interaction demands. The second pass simulated one or more accessibility contexts relevant to the target domain, such as screen reader navigation, keyboard-only interaction, and reduced-motion settings. At each onboarding step, evaluators assigned provisional 0 to 5 scores for Perceivability, Operability, Understandability, and Personalisation. Each score was accompanied by a concise rationale anchored to observable evidence from the interaction. 4.4 Inter-rater consistency and rubric refinement To assess scoring consistency, two independent expert evaluators applied the rubric to the same onboarding flow and compared outputs across dimensions. Agreement was high for most dimensions, with score differences typically not exceeding one point on the 0 to 5 scale. Discrepancies were discussed and resolved through consensus. This process led to minor refinements to rubric anchors and clearer descriptions for mid-range score levels representing partial support. The cross-checking procedure strengthened shared interpretation of evaluation criteria and improved rubric reproducibility. Future studies involving multiple evaluators may report statistical inter-rater reliability measures to increase methodological transparency [ 59 ]. 4.5 User triangulation The expert walkthrough was complemented by moderated user sessions involving participants with diverse accessibility needs. Using the same onboarding prototype and scoring rubric, participants rated the onboarding experience across the four dimensions and provided qualitative feedback. Participant feedback was compared with expert rationales to identify convergence and divergence across accessibility concerns. Where discrepancies emerged, evaluators revisited affected onboarding steps to determine whether issues were attributable to content clarity, interaction pacing, navigation structure, or assistive technology mediation. This triangulation ensured that both expert assessment and lived interaction experiences informed interpretation and prioritisation of remediation. 4.6 Mapping findings to remediation priorities Each identified accessibility issue was mapped to a remediable design unit to support prioritised improvement. Mapping was organised by dimension: Perceivability : contrast, motion controls, alternative text coverage, and announcement order. Operability : navigation controls, target size and spacing, and input method alternatives. Understandability : content clarity, progressive disclosure, and contextual help. Personalisation : adjustable pacing, repetition controls, and preference persistence. This mapping supported prioritisation of improvements based on the risk posed to first-use onboarding success and enabled identification of recurring weaknesses that may inform adaptation-informed design. 4.7 Illustrative mapping of rubric outputs to adaptation parameters To illustrate how evaluation outputs may support adaptation-informed onboarding design, an example mapping from rubric scores to adaptation parameters is provided (Table 1 ). In this mapping, dimension scores are treated as interpretable triggers for interface-level adjustments. This mapping was not implemented as an adaptive system in the present study. It is included to demonstrate how structured evaluation outputs may be translated into adaptation parameters or user modelling features [ 25 , 26 ]. The mappings in Tables 1 and 2 are presented as illustrative interpretation aids. They define how rubric outputs may be translated into remediation priorities and potential adaptation-oriented parameters. These mappings are not derived from the evaluation results, but are included to support reproducibility and clarity of interpretation. Table 1 Illustrative mapping of rubric outputs to adaptation parameters Dimension (threshold) Score interpretation Example adaptation parameter(s) Illustrative action Perceivability ≤ 2 Low visual accessibility enable_high_contrast = true; captions = on Apply high-contrast theme and activate captions Operability ≤ 2 Low interaction accessibility navigation_mode = "explicit"; target_size = "large" Replace gestures with explicit buttons and enlarge touch targets Understandability ≤ 2 Low comprehension support simplify_language = true; show_examples = true Simplify phrasing and display contextual examples Personalisation ≤ 2 Low tailoring support quick_preferences = true; allow_replay = true Provide quick preference controls and replay functionality Table 2 Illustrative mapping of rubric outputs to remediation patterns Dimension (low score indicator) Common onboarding accessibility breakdowns Example remediation patterns (actionable fixes) Perceivability (≤ 2) Low contrast text; missing labels/alt text; critical information hidden by animations; screen reader announcements unclear or out of order Increase contrast and scalable text; add labels and alt text; provide captions/transcripts; add “Skip animation” or “Reduce motion” option; ensure screen reader order matches visual order Operability (≤ 2) Gesture-only navigation; small touch targets; missing Back/Skip controls; poor focus management; inability to navigate using keyboard or assistive interaction Add explicit buttons (Next/Back/Skip); increase target size and spacing; provide keyboard navigation support; correct focus order; add alternative interactions for gestures; ensure consistent navigation controls across steps Understandability (≤ 2) Technical language; unclear instructions; lack of examples; confusing consent/privacy prompts; unclear error feedback Simplify language; break steps into smaller tasks; use progressive disclosure; add examples or previews; provide contextual tooltips; rewrite error messages to be specific and actionable; summarise key points in consent steps Personalisation (≤ 2) No control of pace; no repetition; no preference capture; same onboarding path for all users; settings not persistent across steps Add pause/replay controls; allow skipping non-essential steps; provide quick preference toggles (text size, motion, modality); support alternative formats (audio/text); persist preferences across steps; provide selectable onboarding modes (guided vs quick setup) 4.8 Reporting strategy Results were reported using per-dimension scores, concise rationales, remediation recommendations, and radar chart visualisations summarising accessibility profiles across the four dimensions (Sections 5 and 6). For future applications, structured reporting formats may support communication with product teams. One practical option is to use one standardised page per dimension, containing: (1) the problem pattern, (2) supporting evidence such as an annotated screenshot or description of the interaction barrier, and (3) a recommended change with expected impact on first-use success. 4.9 Session logistics In the present study, expert walkthroughs required approximately 30 to 45 minutes per onboarding flow, followed by 15 to 20 minutes for score consolidation and documentation. For user sessions, a realistic duration for a moderated walkthrough is 25 to 40 minutes depending on participant needs and assistive technology context. A representative structure is as follows: 3 to 5 minutes: orientation and consent 15 to 25 minutes: think-aloud walkthrough of the onboarding flow 5 to 7 minutes: rubric scoring across four dimensions 3 to 5 minutes: open reflection questions, including: “What would make this easier the first time?” “Where would more control be helpful?” These prompts support actionable feedback while maintaining consistent data collection across participants. 5 CASE STUDY: EXPERT EVALUATION OF A DIGITAL HEALTH ONBOARDING FLOW To demonstrate application of the proposed framework, an expert-led evaluation was conducted using a digital health onboarding prototype focused on cognitive wellness. The prototype supported first-time users in exploring key features, granting consent for data usage, and setting initial lifestyle preferences. The onboarding flow combined animated feature tours, consent dialogues, and progressive set-up screens typical of contemporary health applications, where early interaction quality shapes trust and continued engagement [ 2 , 14 , 15 ]. Two accessibility specialists independently applied the four-dimensional framework (Perceivability, Operability, Understandability, and Personalisation) using an extended evaluator checklist. The checklist operationalises rubric scoring through concrete inspection items and evaluator guidance, supporting consistent assessment across interaction contexts. 5.1 Extended framework checklist Table 3 Extended framework checklist used for expert onboarding evaluation Dimension Checklist item Evaluator guidance Score (0–5) Perceivability Images have descriptive alternative text Use a screen reader to verify text equivalents 0 Perceivability Text has sufficient contrast Verify using a WCAG contrast checker 4 Perceivability Audio or video content has captions or transcripts Required for onboarding media 3 Operability All functionality is accessible via keyboard Test tab order and focus behaviour 4 Operability Buttons and inputs are sufficiently large and selectable Consider touch-target size and spacing 3 Operability Interaction is not hindered by time limits Warn users or allow extension if required 5 Understandability Language is simple and clear Avoid jargon and use plain instructions 3 Understandability Onboarding steps are logically ordered Evaluate cognitive load and transitions 3 Understandability Progress indicators are present Provide visual or textual cues 4 Personalisation Users can skip, repeat, or extend steps Flexibility supports inclusive use 4 Personalisation Content can be tailored to user role or needs Evaluate tailoring based on context 0 Personalisation Accessibility preferences are respected Verify system-level settings (e.g., reduced motion) 2 The checklist guided evaluators through each onboarding component and supported structured observation across the four dimensions. Scores were then aggregated at dimension level to produce an overall accessibility profile. 5.2 Scoring summary and observed patterns Aggregated results from the expert evaluation are summarised in Table 4 . Table 4 Expert evaluation scores for the digital health onboarding flow (0 = very poor accessibility, 5 = excellent accessibility) Dimension Score (0–5) Rationale Perceivability 2.3 Contrast failures, missing alternative text, and partially inaccessible animations Operability 4.0 Minor spacing issues; generally operable through keyboard and touch Understandability 3.3 Supportive tone, with occasional technical phrasing and limited scaffolding Personalisation 2.0 Limited tailoring support and largely fixed onboarding sequence Overall accessibility (mean) 2.9 Moderate accessibility, with major gaps in perceivability and personalisation To visualise performance across the four dimensions, a radar chart was generated. The radar chart indicates an uneven accessibility profile. Operability scored strongly, while Perceivability and Personalisation remained comparatively weak, suggesting priority areas for improving first-use accessibility. 5.3 Findings and remediation priorities by dimension Perceivability (2.3). Although contrast met WCAG requirements on several screens, failures were observed in gradient-based header designs. Missing alternative text and auto-playing animated content without captions reduced accessibility for screen reader users and users with low vision [ 3 , 52 , 60 – 62 ]. Remediation priorities include systematic coverage of alternative text, provision of captions or descriptive transcripts for onboarding media, and motion controls such as pause, replay, and reduced-motion support. Operability (4.0). The onboarding flow was largely operable using keyboard and touch navigation, with logical focus order and no restrictive time limits. However, closely spaced touch targets may reduce accuracy for users with limited dexterity and can increase effort on small mobile screens [ 31 , 53 , 63 – 65 ]. Remediation should prioritise improved spacing and target sizing while maintaining multi-input compatibility. Understandability (3.3). The flow used a generally supportive tone; however, occasional technical wording, especially in consent and privacy explanations, increased cognitive demand. Limited scaffolding, such as tooltips or concrete examples, may further reduce comprehension for first-time users unfamiliar with the domain [ 2 , 33 ]. Remediation should prioritise plain-language consent explanations, progressive disclosure, and contextual help at points of uncertainty. Personalisation (2.0). Basic flexibility was provided through the ability to skip or repeat certain steps. Nevertheless, onboarding lacked meaningful tailoring mechanisms such as pacing adjustment, text-size control, and persistence of user preferences across steps. Tailoring by user role, access need, or context was not supported, limiting inclusive autonomy and reducing the capacity of onboarding to accommodate variability in first-use interaction [ 32 , 66 , 67 ]. Remediation priorities include adjustable pacing, text and contrast controls, replay mechanisms, and explicit preference persistence during onboarding. 6 USER EVALUATION OF THE DIGITAL HEALTH ONBOARDING FLOW 6.1 Study overview A formative user evaluation was conducted to complement the expert review and examine how participants experienced onboarding accessibility using the same four-dimensional framework. This phase assessed whether the framework captured participant-reported accessibility barriers and enabled comparison between participant ratings and expert evaluation outcomes. Particular attention was given to convergence and divergence across dimensions, especially where operability assessments differed between expert evaluators and participants. Such differences may arise due to variation in assistive technology use, interaction strategies, and device contexts during first-use interaction. 6.2 Methodology Participants and materials Four participants (P1–P4) were recruited through accessibility-oriented communities and screened for familiarity with assistive tools or accessibility settings. Participants represented a range of access needs and used their own devices throughout the sessions, supporting ecological validity. Following completion of the onboarding flow, participants completed a user-facing self-evaluation form structured around the four framework dimensions: Perceivability, Operability, Understandability, and Personalisation. Each dimension included short guidance prompts and a standardised 0 to 5 rating scale (0 = not supported at all; 5 = fully accessible and inclusive), with optional open-text observations. Table 5 Participant accessibility evaluation form (summary; full version provided in Supplementary Material, Table S1 ) Framework dimension Participant guidance (summary) Perceivability Visual clarity and perceivable content (contrast, text alternatives, captions or transcripts, screen reader interpretation) Operability Ease of navigation and interaction (focus behaviour, gestures versus alternatives, error recovery) Understandability Clarity of flow and messaging (instructions, comprehension without overload, confirmation and error messages) Personalisation User control and adjustability (ability to skip or revisit steps, adjustable presentation such as text size and formats) 6.3 Quantitative results De-identified participant ratings across the four dimensions are presented in Table 6 . Table 6 De-identified user evaluation scores across onboarding accessibility dimensions (0 = very poor accessibility, 5 = excellent accessibility) Participant Perceivability Operability Understandability Personalisation Notes (selected anonymised rationale) P1 4 1 4 4 Onboarding video had no captions P2 3 4 3 3 Navigation controls were unclear P3 3 2 1 2 Limited visual scaffolding P4 3 4 3 2 Flow remained fixed throughout Average 3.25 2.75 2.75 2.75 To illustrate the average profile, a radar chart was generated. Participant ratings indicate Perceivability as the strongest dimension (mean = 3.25), suggesting that core content perception requirements were largely satisfied for most participants. Overall onboarding accessibility remained moderate due to lower ratings in Operability, Understandability, and Personalisation (mean = 2.75 in each dimension). Operability demonstrated the greatest variability across participants, including one participant reporting substantial difficulty (P1 = 1) while others reported relatively strong interaction accessibility (P2 and P4 = 4). This variability indicates that operability in onboarding may depend strongly on individual interaction strategies and assistive technology mediation. 6.4 Qualitative findings Participant observations were analysed using structured thematic coding aligned with the four framework dimensions. Table 6 summarises the coding structure and includes selected anonymised quotes. Table 7 Coding framework for qualitative analysis with selected anonymised quotes Dimension Thematic codes Example quotes (selected anonymised) Perceivability Contrast issues; missing text alternatives “Text blended into the background; hard to see.” Operability Gesture reliance; navigation difficulty; small targets “Swipe-only was exhausting.” Understandability Return or back uncertainty; unclear flow structure “Was not sure how to return back.” Personalisation Limited customisation; assistive-technology configuration constraints “No adjustable text size, and my accessibility settings were not supported.” Qualitative accounts contextualised numeric ratings, particularly for dimensions where participant scores varied. Operability concerns were primarily associated with navigation clarity, gesture reliance, and difficulty reversing actions or recovering from mistakes. Understandability barriers emerged where flow structure lacked scaffolding or where navigation behaviour (e.g., Back) was unclear. Personalisation concerns related to fixed progression, limited user control, and absence of adjustable presentation options such as text size. 6.5 Expert–participant comparison Comparison of expert and participant evaluations revealed both convergence and divergence. Both groups identified limited Personalisation, reinforcing the need for greater user control, adjustable settings, and preference persistence during onboarding [ 14 , 15 , 32 , 60 ]. Operability showed the largest discrepancy. Expert evaluators rated operability strongly (4.0), while participants reported a lower mean operability score (2.75) with notable variability. Participant feedback suggests that navigation clarity, gesture reliance, and uncertainty about returning to earlier steps contributed to reduced perceived operability for some users. This divergence highlights the value of triangulating expert judgement with user experience accounts, particularly in first-use flows where assistive technology mediation can shape interaction outcomes. 6.6 Implications for adaptation-informed onboarding design Participant ratings indicate that operability and personalisation needs can vary substantially across individuals, particularly when onboarding is mediated by assistive technologies and differing interaction strategies. Dimension-level scores can therefore function as evaluative signals for prioritising onboarding adjustments, such as providing explicit navigation controls, improving contextual scaffolding, and supporting early preference capture. These implications are further developed in the Discussion (Section 7) within an adaptation-informed framing [ 25 , 26 ]. 6.7 Summary of user evaluation findings The formative user evaluation indicates moderate onboarding accessibility overall. Participants reported relatively strong perceivability but continued barriers across operability, understandability, and personalisation. Differences between expert and participant operability ratings further emphasise the importance of combining expert evaluation with user participation. Assistive technology mediation and individual interaction strategies can influence how onboarding controls, navigation clarity, and recoverability are experienced. Overall, these findings support the framework’s utility as a diagnostic instrument and as a structured approach for translating accessibility assessment into practical design priorities for onboarding experiences. 7 DISCUSSION AND CONCLUSION This section synthesises findings from the expert evaluation (Section 5 ) and formative user evaluation (Section 6) to discuss broader implications for operationalising accessibility evaluation in onboarding flows. The combined evidence indicates that onboarding accessibility cannot be fully characterised through static compliance criteria alone. Accessibility outcomes were shaped by interaction sequencing, user control, navigation structure, and assistive technology mediation. By generating structured dimension-level scores alongside qualitative observations, the proposed framework supports both diagnostic accessibility evaluation and translation of findings into concrete improvement priorities that may inform adaptation-oriented onboarding design. The section concludes with key contributions, limitations, and directions for future work. 7.1 Confirming the framework’s relevance for onboarding accessibility Results from both evaluations support the framework’s relevance for analysing onboarding accessibility. The four dimensions, Perceivability, Operability, Understandability, and Personalisation, were sufficient for capturing the most salient accessibility barriers in the evaluated first-use flow. Across expert and participant perspectives, the framework highlighted that onboarding accessibility extends beyond technical compliance and requires attention to sequential interaction properties, navigational clarity, recoverability, and user control. A core contribution of the framework is the formal inclusion of Personalisation as an accessibility dimension. Both expert and participant evaluations showed that limited personalisation constrained user autonomy during onboarding. This supports the view that inclusive onboarding requires adjustable pacing, adaptable presentation, and preference persistence rather than fixed sequencing and uniform interaction demands [ 12 , 14 , 20 , 32 , 33 , 60 ]. In this positioning, personalisation functions as both an accessibility objective and an indicator of whether onboarding is capable of accommodating diverse needs through configurable or adaptation-ready mechanisms. 7.2 Accessibility barriers beyond compliance Conventional accessibility audits typically prioritise static interface attributes, including alternative text, semantic structure, and colour contrast. While these criteria remain essential, the results indicate that several critical onboarding barriers stem from interaction design constraints that manifest across steps. Issues including unclear navigation, uncertain return behaviour, gesture reliance, and rigid progression structures influenced accessibility outcomes across both evaluations. Such barriers can be difficult to detect using evaluations that do not explicitly represent onboarding as a sequential and transitional process [ 11 , 13 ]. Participant results indicated higher overall Perceivability than expert assessment, suggesting that perception-related concerns may not manifest uniformly across evaluators or contexts. However, perceivability limitations still emerged in participant accounts, such as missing captions for onboarding media. This indicates that onboarding accessibility is often configuration dependent and influenced by user preferences, assistive tools, and device-specific rendering. Accordingly, evaluations benefit from triangulation of expert judgement with participant experience, not because expert evaluation lacks validity, but because each perspective foregrounds different access risks. 7.3 Interpreting expert–participant divergence in operability The most notable divergence across evaluations occurred in Operability. Expert evaluators rated Operability strongly (4.0), while participants reported a lower mean score (2.75) with substantial variability. This discrepancy does not imply inconsistency in the rubric itself, since both groups assessed the same onboarding flow using the same scoring structure. Rather, it indicates that operability outcomes are sensitive to interaction strategies, navigation expectations, and assistive technology mediation. Participant feedback suggests that operability challenges did not primarily arise from non-functional interface controls, but from friction related to navigation clarity, gesture reliance, and uncertainty about returning to previous steps. These findings suggest that onboarding operability should be treated as both a functional and experiential accessibility property. Accessibility is not only whether interaction is technically possible, but whether it is manageable, recoverable, and cognitively navigable for diverse users during first use. 7.4 Implications for adaptation-informed onboarding design A key implication of this work is that the framework produces structured outputs that extend beyond reporting. The 0 to 5 dimension ratings and qualitative observations can support adaptation-informed onboarding design in three practical ways: Trigger-oriented adjustments : low scores in Operability or Personalisation may indicate the need for explicit navigation controls, simplified interaction patterns, or guided step-by-step alternatives. Preference capture and persistence : recurring personalisation limitations highlight the importance of lightweight preference set-up early in onboarding (e.g., text size, contrast, reduced motion, pacing) and persistence across steps. User modelling features : in systems that support adaptation, repeated accessibility-related scores can function as interpretable signals for ability-related needs, preferred interaction modes, or environmental constraints [ 25 , 26 ]. This framing positions accessibility evaluation not only as diagnostic, but also as a source of structured signals that may inform adaptation-oriented decision-making for inclusive first-use experiences. 7.5 Implications for practice For design and QA teams, the framework provides a structured accessibility evaluation procedure that is lightweight yet multidimensional. Radar chart visualisation supports communication of accessibility performance across dimensions, enabling stakeholders to identify strengths and weaknesses efficiently. In practical settings such as digital health, where first-use trust influences retention and disclosure behaviour, the framework supports proactive accessibility planning and targeted improvement rather than post-launch remediation [ 14 , 15 ]. 7.6 Limitations First, the participant sample was small, limiting generalisability and preventing statistical comparisons across user subgroups. The user evaluation was formative in nature and was not intended to support statistical inference. Second, the onboarding prototype was simplified for controlled evaluation and may not reflect the complexity of real-world onboarding environments, including multi-path flows, dynamic content, and layered permissions. Third, rubric consistency was supported through independent scoring by two expert evaluators followed by discrepancy resolution through consensus discussion. However, formal inter-rater reliability statistics, such as the intraclass correlation coefficient or Cohen’s kappa, were not computed due to the small scale of the study. Future work should report reliability coefficients across multiple raters to strengthen reproducibility and methodological transparency [ 59 ]. Finally, cultural and linguistic contexts were not varied, and accessibility expectations may differ across regions, languages, and infrastructures. 7.7 Validity considerations Construct validity may be affected by midpoint anchoring in the 0 to 5 scoring system, particularly in ambiguous cases. Internal validity was strengthened through consistent evaluation procedures and aligned rubrics; however, participant ratings may be influenced by device constraints and individual expectations. External validity remains limited due to the single prototype and domain context. Reliability should be strengthened through repeated applications with multiple evaluators, larger participant samples, agreement metrics, and rubric calibration procedures. 7.8 Future directions Future work should test the framework across diverse real-world onboarding systems, including multi-path flows, dynamic content, and embedded personalisation mechanisms. Larger and more heterogeneous participant samples would support stronger evidence for generalisability and more robust reliability reporting [ 12 ]. Beyond evaluation, further research should examine how accessibility-derived dimension ratings can inform adaptation-informed onboarding architectures, including preference learning, adaptive scaffolding, and context-aware interface selection. This direction aligns with ongoing shifts towards outcome-based accessibility evaluation, including emerging standards development such as WCAG 3.0 [ 35 ]. More broadly, the framework supports a shift in onboarding accessibility from compliance checking towards measurable inclusion capabilities informed by structured evaluation signals. 7.9 Conclusion Onboarding is a decisive phase of user experience because it shapes trust, comprehension, and inclusion during the first sustained interaction with a digital product. Although WCAG and related standards remain foundational, they do not fully capture the sequential, time constrained, and user-specific characteristics of onboarding flows. This paper introduced and applied a four-dimensional framework for onboarding accessibility evaluation, comprising Perceivability, Operability, Understandability, and Personalisation, derived from POUR but extended by formalising personalisation as an accessibility-relevant dimension. Evidence from expert and user evaluations demonstrated that the framework identifies accessibility barriers beyond static compliance and produces structured outputs that can guide targeted onboarding improvements. Findings further suggest that onboarding inclusivity depends not only on perceiving content, but also on navigating a first-use sequence with clarity, recoverability, and control, including under assistive technology mediation. Radar chart visualisation supports practical reporting by making accessibility profiles interpretable across dimensions. Most importantly, positioning Personalisation as a core dimension reframes onboarding accessibility from a static compliance outcome towards an adjustable and user-sensitive interaction capability. Future work should extend empirical validation across domains and further investigate how rubric outputs may support rule-based and learning-based approaches to personalisation. By linking accessibility evaluation outputs with adaptation-informed design objectives, the proposed framework contributes a pathway towards onboarding systems where inclusive first-use experiences become a default design expectation rather than an exception. Declarations Funding The authors received no specific funding for this work. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript. Ethics Approval Formal institutional ethics approval was not required for this study because it involved minimal risk, voluntary participation, and no collection of personally identifiable or sensitive personal data. No personal health information was collected, and the evaluated prototype did not require participants to provide identifiable personal data. Consent to Participate Informed consent was obtained from all participants prior to participation. Participants were informed of the study purpose, procedures, and their right to withdraw at any time without consequence. Consent for Publication Informed consent included permission for the use of anonymised participant feedback and de-identified quotes in academic publications. Data Availability All data supporting the findings of this study are included within the article and its Supplementary Material. The evaluation instruments (participant evaluation form, expert checklist, and scoring rubric), de-identified participant score summaries, and qualitative coding framework are provided in Supplementary Tables S1–S5. No personally identifiable participant data are included. The evaluated onboarding prototype is not publicly available due to licensing and business confidentiality constraints; therefore, a public system link is not provided. Author Contributions Israel Umola Akeji: Conceptualisation; Methodology; Framework development; Rubric design; Expert evaluation; Data analysis; Visualisation; Writing (original draft); Writing (review and editing). Daniel Eleojo Okwoli: Methodology support; Expert evaluation; Participant session planning and execution; Data collection support; Writing (review and editing). Acknowledgements The authors thank the study participants for contributing their time and feedback during the onboarding evaluation sessions. References Chauvergne, R., Santos, P., Le Bigot, L. and Argelaguet, F., 2023. User Onboarding in Virtual Reality: An Investigation of Current Practices. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). New York: ACM. https://doi.org/10.1145/3544548.3581211 Nouwens, M., Liccardi, I., Veale, M., Karger, D. and Kagal, L., 2020. Dark patterns after the GDPR: Scraping consent pop-ups and demonstrating their influence. 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International Journal of Human-Computer Studies, 66(12), pp.889–901. doi:10.1016/j.ijhcs.2008.03.002. Wobbrock, J.O. and Kientz, J.A., 2016 . Research contributions in human–computer interaction. Interactions, 23(3), pp.38–44. doi:10.1145/2907069. Clarkson, J., Coleman, R., Keates, S. and Lebbon, C. (eds), 2003. Inclusive Design: Design for the Whole Population. London: Springer. https://doi.org/10.1007/978-1-4471-0001-0 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8795738","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587263300,"identity":"0d5c6a83-0cda-487a-97dd-3eb4e7c9faea","order_by":0,"name":"Israel Umola Akeji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACHgYGgwQDEIu58QGIz0eCFsZmEMXDRowWKGBskwBRBLXw9xw+UPCgoC6fv72xrfJrjp0MGwPzw0c38GiRONuWAHTYYcsZZw623Zbdlgx0GJuxcQ4+a87zGAC1HDBguJHYdltyGzNQCw+bND4t8uf5PwC11BnI33/YViy5rZ6wFoOzPaAQYzYwuMHYxvhx22HCWgzPHAM57LCB4ZnEZmnGbcd52JgJ+EXuTPIzwx9/6gzkjh8++PHntmp7fvbmh4/xeh8YEQYwFjM4kpjxKwcreQBjMf4grHoUjIJRMApGIAAAF7VG3WSOqwoAAAAASUVORK5CYII=","orcid":"","institution":"Miva Open University","correspondingAuthor":true,"prefix":"","firstName":"Israel","middleName":"Umola","lastName":"Akeji","suffix":""},{"id":587263301,"identity":"d6ab97ca-8a02-4ee7-901f-99689f2bc46a","order_by":1,"name":"Daniel Eleojo Okwoli","email":"","orcid":"","institution":"University of the People","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Eleojo","lastName":"Okwoli","suffix":""}],"badges":[],"createdAt":"2026-02-05 10:39:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8795738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8795738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102909787,"identity":"82466795-c88b-4412-8c86-0007605e46d2","added_by":"auto","created_at":"2026-02-18 09:56:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91936,"visible":true,"origin":"","legend":"\u003cp\u003eRadar chart showing expert evaluation scores (0–5 scale) across onboarding accessibility dimensions (Perceivability, Operability, Understandability, and Personalisation)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8795738/v1/c70e88ebe94e7a7b3656659c.png"},{"id":102909788,"identity":"1f62718f-141a-4bbf-bd16-7e213f8109c4","added_by":"auto","created_at":"2026-02-18 09:56:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91624,"visible":true,"origin":"","legend":"\u003cp\u003eRadar chart showing average user evaluation scores (0 to 5 scale) across onboarding accessibility dimensions (Perceivability, Operability, Understandability, and Personalisation)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8795738/v1/5c458016d72f2ef64b2c28b0.png"},{"id":102909928,"identity":"aee3aff4-a0f5-4a99-8da3-40db599f806d","added_by":"auto","created_at":"2026-02-18 09:57:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1940122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8795738/v1/913eb4e2-6c14-4a53-bff0-8cad633dec23.pdf"},{"id":102909661,"identity":"ca259450-910f-48cd-863f-847a25544869","added_by":"auto","created_at":"2026-02-18 09:56:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":202946,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8795738/v1/dff2679954360e203622617a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Accessibility in Sequential Onboarding Flows: A Conceptual Framework and Scoring Rubric","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eThe first moments a user spends with a digital product, commonly described as the onboarding experience, often influence long-term engagement and continued use. Human\u0026ndash;computer interaction (HCI) research increasingly characterises onboarding as a critical first-use phase in which users form impressions of credibility, evaluate perceived risks, and decide whether to continue interacting with a system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For users with disabilities, barriers during onboarding may undermine trust and prevent meaningful participation from the outset. Onboarding is designed to support new users by introducing core functionality, guiding initial interactions, enabling preference setting, and reducing uncertainty during early use. However, onboarding can also introduce accessibility challenges, including unlabelled interface controls, gesture-dependent navigation without accessible alternatives, cognitive overload caused by unfamiliar workflows, and limited support for adjustable pacing [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Evidence further suggests that inaccessible or confusing onboarding contributes to abandonment of digital services, with disproportionate impacts on users who experience interaction barriers. For example, Signicat\u0026rsquo;s Battle to Onboard report found that 63% of consumers abandoned digital application processes in 2020, and a follow-up report indicated an increase to 68% in 2022 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In high-stakes domains such as digital health and finance, onboarding barriers may also reduce willingness to share essential information, thereby reinforcing unequal access to critical services [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInaccessible onboarding is not merely an inconvenience but a structural barrier to digital inclusion. Because onboarding represents the user\u0026rsquo;s first sustained interaction with a product, its evaluation requires approaches capable of capturing more than static compliance status and isolated page-level issues. The Web Content Accessibility Guidelines (WCAG) provide foundational technical benchmarks for accessible interface design [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, WCAG was primarily developed for relatively stable interface environments and may not fully represent the sequential and transitional nature of onboarding experiences. In practice, onboarding involves progressive interaction with user-dependent progression and is shaped by timing constraints, input modality, and dynamic content presentation. These characteristics can expose context-specific accessibility barriers that are insufficiently represented through static evaluation alone [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While heuristic audits and automated accessibility checkers can identify common violations, they often struggle to account for temporal dynamics and evolving interaction demands across onboarding stages [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This motivates evaluation approaches that conceptualise onboarding accessibility as an interaction process unfolding over time, rather than as isolated interface states.\u003c/p\u003e \u003cp\u003eRecent HCI research therefore calls for accessibility evaluation frameworks that are more holistic, user-centred, and context-sensitive [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Within this agenda, onboarding remains underrepresented in both academic literature and design practice. Studies have investigated onboarding in contexts such as virtual reality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], open-source software contribution environments [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and civic technology platforms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, relatively few contributions provide structured and reproducible methods for evaluating accessibility during onboarding as a sequential experience that involves staged decision points and context-sensitive interaction demands [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As a result, designers, researchers, and QA practitioners may lack practical mechanisms for diagnosing accessibility breakdowns during onboarding and translating evaluation results into targeted design improvements.\u003c/p\u003e \u003cp\u003eTo address this gap, this paper proposes a four-dimensional framework that operationalises accessibility evaluation for onboarding experiences with adaptation goals. The framework extends WCAG\u0026rsquo;s POUR principles by replacing Robustness with Personalisation, reflecting the role of onboarding in determining whether systems can support diverse user abilities, contexts, and preferences [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The model defines four evaluative dimensions: Perceivability, Operability, Understandability, and Personalisation. A reproducible scoring rubric, supported by radar chart visualisation and remediation mapping, enables evaluators to generate structured outputs that guide targeted onboarding improvements and support systematic evaluation reporting.\u003c/p\u003e \u003cp\u003eThe framework\u0026rsquo;s applicability is demonstrated through a digital health onboarding scenario using two expert evaluations and a formative user evaluation involving participants with diverse access needs. Beyond the immediate case study, the framework provides a domain-agnostic approach for linking accessibility assessment with adaptation-informed onboarding design. Dimension-level scores are treated as evaluative signals that identify accessibility breakdowns and guide targeted adjustments across Perceivability, Operability, and Understandability. In this framework, Personalisation captures the extent to which onboarding supports tailoring through configurable or adaptation-ready interaction pathways. Collectively, these signals can support user modelling by enabling systems and designers to translate accessibility insights into actionable onboarding refinements [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organised as follows. Section 2 reviews related work on accessibility evaluation, onboarding, and personalisation. Section 3 presents the proposed framework and scoring strategy. Section 4 describes the study methods. Section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports findings from the expert evaluation, and Section 6 reports findings from the user evaluation and expert\u0026ndash;participant comparison. Section 7 discusses implications for onboarding accessibility and adaptation, outlines limitations, and identifies future research directions.\u003c/p\u003e"},{"header":"2 RELATED WORK AND THEORETICAL POSITIONING","content":"\u003cp\u003eDigital accessibility research has traditionally focused on evaluating fully deployed systems such as websites and mobile applications. Foundational frameworks, most notably the Web Content Accessibility Guidelines (WCAG), define the widely used POUR principles of Perceivability, Operability, Understandability, and Robustness [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These principles have shaped both academic research and industry practice, informing automated tools and accessibility audit workflows, while continuing to raise questions regarding the validity, reliability, and coverage of automated approaches [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. WCAG and comparable standards provide essential technical baselines; however, they were primarily developed for relatively stable interface environments and generalised interaction patterns rather than transitional, sequential experiences such as onboarding. As a result, onboarding introduces accessibility demands that static, checklist-based evaluations may fail to capture, particularly where accessibility issues emerge through the progression of steps rather than within a single screen [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Onboarding in HCI and accessibility research\u003c/h2\u003e \u003cp\u003eOnboarding has been examined across several HCI domains, including organisational and community orientation, product adoption, and participation in open-source ecosystems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These contributions commonly address learning curves, cognitive load, and motivational strategies that support sustained engagement [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, onboarding is rarely foregrounded as an accessibility evaluation target, and structured methods for assessing accessibility during first-use flows remain limited. Existing empirical work describes common onboarding barriers, such as unlabelled controls, low contrast, and limited screen reader compatibility, but these findings are often reported as isolated usability problems rather than integrated evaluation models that support systematic diagnosis and comparison [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOnboarding is inherently sequential and time constrained and frequently incorporates progressive disclosure, stepwise interactions, and animated transitions. Such features can produce compounding accessibility effects across steps, where an early barrier may lead to cascading failures later in the flow. Consequently, automated checkers and single-screen heuristic inspections are often insufficient for identifying temporal and interactional breakdowns that arise only through sustained progression [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Research on onboarding in contexts such as virtual reality, civic technology, and finance further highlights the contextual variability of onboarding requirements; nevertheless, it rarely provides domain-agnostic and operational accessibility evaluation methods applicable across first-use flows [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Personalisation and inclusive design frameworks\u003c/h2\u003e \u003cp\u003eRecent research and design practice increasingly position personalisation as a core mechanism for inclusive interaction, particularly for users whose needs are not adequately supported by one-size-fits-all interface designs. Personalisation may enable users to tailor content presentation, interaction modalities, and pacing to their abilities and preferences [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Early adaptive systems demonstrated benefits for users with motor impairments by modifying interface parameters such as target size and layout [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. More recent work suggests that tuned and adaptation-aware onboarding interfaces can support older adults and other user groups by adjusting language complexity, pacing, and interaction demands [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these developments, prominent normative standards such as WCAG and ISO 9241 do not yet treat personalisation as a first-class evaluation criterion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Recent W3C initiatives, including WAI-Adapt and ongoing work towards WCAG 3.0, signal increasing attention to personalisation semantics and outcome-based evaluation, although formal integration remains emergent [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In practice, industry toolkits and inclusive design guidance promote personalisation patterns; however, these resources rarely provide evaluation procedures that quantify personalisation as an accessibility outcome or link it explicitly to onboarding assessment [39\u0026ndash; \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a user modelling perspective, personalisation can be operationalised through data-driven and rule-based adaptation approaches. Work on SUPPLE and related systems demonstrates how interface configurations may be generated from modelled user capabilities, producing measurable improvements in task performance and satisfaction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. More broadly, user modelling research shows that observable interaction features and structured metrics can inform predictive models and adaptation policies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This body of work suggests that accessibility evaluations producing structured quantitative outputs are candidates for integration with adaptation-oriented design decisions. While the present study does not implement an adaptive onboarding system, it demonstrates how evaluation outputs may be translated into adaptation parameters and user modelling features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Compliance, usability, and real-world accessibility outcomes\u003c/h2\u003e \u003cp\u003eA recurring theme in accessibility research concerns the relationship between standard conformance and experienced accessibility in real use. Although conformance provides an essential baseline, it does not always predict usability or task success, particularly in complex interactive scenarios where accessibility barriers are shaped by context, cognitive demand, and interaction sequencing. Empirical work demonstrates that accessibility and usability are related but non-identical constructs, and that systems may pass technical checks yet remain difficult to use for disabled users during task completion [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This distinction is particularly relevant for onboarding, where first-use experiences require rapid comprehension, navigation confidence, and error recovery.\u003c/p\u003e \u003cp\u003eFurther evidence suggests that accessibility outcomes vary depending on interaction context and user characteristics, underscoring the need for evaluation approaches that incorporate user experience signals rather than relying solely on static compliance indicators. For example, studies examining the effects of accessibility compliance on user experience show measurable differences in performance and satisfaction when conformance levels differ, but also highlight that compliance alone does not guarantee positive user experience outcomes [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These findings reinforce the value of evaluation models that can capture multi-dimensional accessibility outcomes across both technical and experiential dimensions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Reliability challenges in expert accessibility evaluation\u003c/h2\u003e \u003cp\u003eIn addition, reliability and reproducibility challenges have been documented in expert-led accessibility evaluation methods. Usability and accessibility evaluation literature has long acknowledged evaluator-driven variability, including differences in judgement, interpretation, and severity ratings. The evaluator effect, originally documented in usability evaluation, indicates that different evaluators may identify different sets of issues and may vary in how they rate severity even when examining the same interface [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Accessibility auditing is similarly vulnerable to interpretive inconsistency, particularly when criteria require judgement about interaction flow, cognitive demand, and perceived control rather than direct technical checks [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese reliability challenges motivate structured rubrics and anchored rating scales to reduce ambiguity and improve comparability across evaluators and contexts. Rubric-based evaluation can provide clearer interpretation of mid-range performance, where systems exhibit partial support rather than binary compliance. Such approaches are particularly relevant for onboarding, where accessibility barriers often manifest through cumulative friction rather than discrete, easily classifiable failures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Evaluation models in accessibility\u003c/h2\u003e \u003cp\u003eAccessibility evaluation methods include expert heuristic inspections, walkthroughs, participatory approaches, and hybrid methods combining automated scans with expert review [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Although such approaches are valuable for compliance and usability, they are typically applied to full systems and rarely isolate onboarding as a distinct evaluation target. Recent reviews and meta-analyses show steady progress in evaluation methods and tool ecosystems, while also identifying persistent gaps in assessing time-dependent interaction demands, sequential flows, and personalised or adaptive user experiences [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVisualisation techniques for multi-criteria accessibility assessment remain underused despite their communicative value. Compact visual summaries, including radar charts, can support multidimensional reporting and facilitate stakeholder decision-making, particularly where trade-offs across dimensions must be communicated clearly [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For onboarding, which involves progression across steps, visual summaries may further support diagnosis by enabling evaluators to communicate where accessibility breakdowns occur and which dimensions are most affected. Bridging evaluation outcomes with adaptation-oriented design requires interpretable outputs and reproducible scoring procedures so that evaluators, designers, and system developers can operate on shared constructs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Identified gap and conceptual positioning\u003c/h2\u003e \u003cp\u003eThe reviewed literature indicates a clear gap: limited work proposes structured and reproducible frameworks for evaluating onboarding accessibility as a multidimensional first-use flow. Existing standards, audits, and toolkits primarily emphasise static compliance checking and do not routinely generate structured outputs suitable for supporting systematic onboarding improvement. Furthermore, prior work rarely integrates assessment of Perceivability, Operability, Understandability, and Personalisation as interrelated dimensions of onboarding accessibility.\u003c/p\u003e \u003cp\u003eThe present study addresses this gap by reinterpreting WCAG-derived POUR principles for sequential onboarding contexts and by introducing Personalisation as an explicit evaluative dimension. The framework operationalises evaluation using a reproducible scoring rubric supported by checklist guidance and defined score interpretations, enabling comparable 0 to 5 dimension-level ratings across evaluators and onboarding contexts. By combining rubric-based scoring with compact visualisation, the framework supports clear communication of accessibility performance and guides prioritisation of remediation. Dimension-level outputs are further conceptualised as evaluative signals that may serve as features, triggers, or indicators for adaptation-informed onboarding design. The framework is demonstrated through expert and participant evaluations of a digital health onboarding prototype, illustrating how scores can inform remediation priorities and potential adaptation parameters.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 THE PROPOSED FRAMEWORK: EVALUATING ACCESSIBLE ONBOARDING EXPERIENCES","content":"\u003cp\u003eDigital onboarding experiences differ from conventional web and app interactions because they are transitional, time constrained, and dependent on progressive user participation. Rather than presenting information in full, onboarding flows typically reveal content sequentially and may incorporate animations, micro-interactions, and staged input requests. These characteristics introduce accessibility challenges that are not always captured by checklist-based standards and conventional audits, particularly when barriers emerge through temporal progression or cumulative interaction demands [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The proposed framework addresses this gap by providing a structured and reproducible method for evaluating accessibility in onboarding contexts and by formalising how evaluation outputs can be translated into practical remediation priorities and adaptation-informed design considerations.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Framework overview\u003c/h2\u003e \u003cp\u003eThe framework is grounded in WCAG 2.2\u0026rsquo;s POUR principles and introduces a conceptual modification in which Robustness is replaced with \u003cem\u003ePersonalisation.\u003c/em\u003e In WCAG, Robustness concerns the technical reliability of content and its consistent interpretation by user agents and assistive technologies. Robustness remains fundamental for accessibility [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, for onboarding-focused evaluation, robustness is treated as a prerequisite that is typically verified through code-level checks and automated technical audits prior to experiential assessment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. By assuming robustness as a baseline, the proposed framework emphasises interaction and experience factors that directly shape first-use success.\u003c/p\u003e \u003cp\u003eReplacing Robustness with Personalisation aligns with inclusive design perspectives that emphasise user control, contextual responsiveness, and the capacity of interfaces to accommodate diverse needs and preferences [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the context of onboarding, Personalisation refers to the extent to which onboarding interactions support tailoring of parameters such as pace, modality, and information density. This does not imply that systems must implement automatic adaptation; instead, the framework treats personalisation capacity as an accessibility-relevant property that affects whether onboarding can accommodate variation across users and contexts.\u003c/p\u003e \u003cp\u003eThe framework evaluates onboarding experiences using four interrelated dimensions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePerceivability\u003c/b\u003e: whether users with diverse sensory abilities can perceive onboarding content.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOperability\u003c/b\u003e: whether users can navigate and interact with onboarding steps regardless of device and input method.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eUnderstandability\u003c/b\u003e: whether instructions, terminology, and feedback support comprehension across cognitive differences and first-use uncertainty.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePersonalisation\u003c/b\u003e: whether onboarding supports tailoring through mechanisms such as adjustable pace, alternative modalities, and preference control.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTogether, these dimensions surface accessibility barriers specific to onboarding as a staged first-use flow and provide a basis for prioritising remediation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Perceivability\u003c/h2\u003e \u003cp\u003ePerceivability assesses whether onboarding content accommodates diverse sensory needs. While aligned with WCAG\u0026rsquo;s perceivability principle, this dimension emphasises temporal and contextual characteristics common in onboarding, including animated transitions, time-limited prompts, and progressive disclosure.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChecklist indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eText elements provide sufficient contrast and support scalable fonts.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImages and icons include descriptive alternatives.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMultimedia content is captioned, transcribed, or skippable.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScreen readers announce content in a meaningful and correct sequence.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eMobile onboarding frequently relies on animations and gesture-based navigation patterns that can obscure critical information for users with low vision or for those using assistive technologies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Evidence from digital health contexts further indicates that missing captions and limited motion controls in tutorial content can reduce comprehension for users with sensory differences [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Operability\u003c/h2\u003e \u003cp\u003eOperability measures whether users can progress through onboarding steps regardless of input method. In onboarding, operability is particularly critical because early interaction failures can prevent users from completing entry tasks entirely.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChecklist indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eKeyboard, touch, and voice-based interaction options are supported where relevant.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInteractive targets meet minimum size and spacing requirements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFocus order is logical, and navigation supports Back and Skip options.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGesture-only steps offer accessible alternatives.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCommon barriers include swipe-only navigation, tightly spaced targets, and inaccessible focus behaviour, which disproportionately affect users with motor impairments and those interacting on smaller mobile screens. Prior work shows that flexibility across input modalities can improve accuracy and reduce frustration, particularly for users relying on assistive interaction methods [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Understandability\u003c/h2\u003e \u003cp\u003eUnderstandability concerns the clarity of instructions, terminology, and feedback. This dimension captures whether onboarding supports comprehension for first-time users who may be unfamiliar with a system\u0026rsquo;s goals, language, or interaction expectations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChecklist indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLanguage is simple, consistent, and non-technical where possible.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSteps follow a logical and progressive structure.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTooltips, examples, or visual cues clarify complex tasks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFeedback is timely, contextual, and actionable.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCognitive load theory suggests that unexplained terminology and fragmented information increase mental effort, particularly during first-use interaction [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In onboarding contexts, ambiguous phrasing and complex consent flows can reduce comprehension and trust [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Effective onboarding therefore requires transparent language and progression strategies that sustain comprehension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Personalisation (new dimension)\u003c/h2\u003e \u003cp\u003ePersonalisation assesses the extent to which onboarding supports tailoring to sensory, cognitive, or situational preferences. This dimension focuses on whether onboarding interactions provide mechanisms that accommodate diverse needs during initial engagement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChecklist indicators\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSteps can be skipped, paused, or repeated.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLanguage, text size, and pacing are adjustable.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlternative formats (e.g., audio or text) are available when appropriate.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePreferences persist across onboarding steps.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis dimension is informed by ability-based design, which emphasises supporting users through flexible interaction pathways rather than enforcing a single interaction style [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. It also relates to W3C work such as WAI-Adapt, which defines semantics that may support adaptation of content to user needs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Evidence from ageing and learning research further suggests that providing control over complexity and pace can improve comprehension and engagement during early interaction [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In onboarding, personalisation therefore functions as an accessibility-enabling capacity that supports more inclusive first-use experiences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Scoring and visualisation strategy\u003c/h2\u003e \u003cp\u003eEach dimension is rated using a 0 to 5 rubric. Rubric anchors define interpretation at each score level, from 0 (no support) to 5 (fully accessible and inclusive), with intermediate values reflecting increasing degrees of support. Scores can be visualised using a radar chart to highlight relative strengths and deficits across dimensions. This supports structured reporting and shared interpretation among evaluators, designers, and developers. Visualisation also enables rapid identification of priority barriers and can support translation of evaluation outcomes into remediation actions and design adjustments [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The next section describes the methodological procedures for applying the framework and demonstrates how these quantitative outputs can inform remediation priorities and adaptation-informed onboarding decisions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 METHODS: OPERATIONALIZING THE FRAMEWORK","content":"\u003cp\u003eTo support adoption in applied accessibility reviews and formative evaluation contexts, the proposed framework was operationalised into a practical evaluation procedure. This procedure guided both the expert evaluation (Section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and the formative user evaluation (Section 6). The section describes the workflow used in the present study, including preparation, baseline checks, scoring procedure, user triangulation, and reporting.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Preparation and evaluation scope\u003c/h2\u003e \u003cp\u003ePrior to evaluation, the onboarding scenario was documented in detail, including entry points, sequential screens, decision gates, and required permissions. Key first-use tasks were identified, such as account creation, consent processes, preference set-up, and completion of an initial core task. For each task, expected input methods and relevant time constraints were recorded. This scoping ensured that the evaluation remained focused on onboarding as a transitional and time constrained first-use flow rather than general product use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Assumptions and baseline checks\u003c/h2\u003e \u003cp\u003eBecause the framework treats Robustness as a prerequisite rather than an evaluative dimension, a lightweight technical screening was conducted before the experiential review. Screening focused on the presence of semantic roles for interactive elements, programmatic labelling of form inputs, detectable error states, and focus management during modal and screen transitions. Where development-level access was unavailable, proxy checks were performed by inspecting accessibility trees using platform tools and confirming that screen readers could traverse interactive elements in the expected order.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Walkthrough procedure and scoring\u003c/h2\u003e \u003cp\u003eEach onboarding flow was reviewed twice. The first pass was conducted without assistive technology simulation to establish a baseline view of onboarding progression and interaction demands. The second pass simulated one or more accessibility contexts relevant to the target domain, such as screen reader navigation, keyboard-only interaction, and reduced-motion settings. At each onboarding step, evaluators assigned provisional 0 to 5 scores for Perceivability, Operability, Understandability, and Personalisation. Each score was accompanied by a concise rationale anchored to observable evidence from the interaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Inter-rater consistency and rubric refinement\u003c/h2\u003e \u003cp\u003eTo assess scoring consistency, two independent expert evaluators applied the rubric to the same onboarding flow and compared outputs across dimensions. Agreement was high for most dimensions, with score differences typically not exceeding one point on the 0 to 5 scale. Discrepancies were discussed and resolved through consensus. This process led to minor refinements to rubric anchors and clearer descriptions for mid-range score levels representing partial support. The cross-checking procedure strengthened shared interpretation of evaluation criteria and improved rubric reproducibility. Future studies involving multiple evaluators may report statistical inter-rater reliability measures to increase methodological transparency [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5 User triangulation\u003c/h2\u003e \u003cp\u003eThe expert walkthrough was complemented by moderated user sessions involving participants with diverse accessibility needs. Using the same onboarding prototype and scoring rubric, participants rated the onboarding experience across the four dimensions and provided qualitative feedback. Participant feedback was compared with expert rationales to identify convergence and divergence across accessibility concerns. Where discrepancies emerged, evaluators revisited affected onboarding steps to determine whether issues were attributable to content clarity, interaction pacing, navigation structure, or assistive technology mediation. This triangulation ensured that both expert assessment and lived interaction experiences informed interpretation and prioritisation of remediation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Mapping findings to remediation priorities\u003c/h2\u003e \u003cp\u003eEach identified accessibility issue was mapped to a remediable design unit to support prioritised improvement. Mapping was organised by dimension:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePerceivability\u003c/b\u003e: contrast, motion controls, alternative text coverage, and announcement order.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOperability\u003c/b\u003e: navigation controls, target size and spacing, and input method alternatives.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eUnderstandability\u003c/b\u003e: content clarity, progressive disclosure, and contextual help.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePersonalisation\u003c/b\u003e: adjustable pacing, repetition controls, and preference persistence.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis mapping supported prioritisation of improvements based on the risk posed to first-use onboarding success and enabled identification of recurring weaknesses that may inform adaptation-informed design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Illustrative mapping of rubric outputs to adaptation parameters\u003c/h2\u003e \u003cp\u003eTo illustrate how evaluation outputs may support adaptation-informed onboarding design, an example mapping from rubric scores to adaptation parameters is provided (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this mapping, dimension scores are treated as interpretable triggers for interface-level adjustments. This mapping was not implemented as an adaptive system in the present study. It is included to demonstrate how structured evaluation outputs may be translated into adaptation parameters or user modelling features [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mappings in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are presented as illustrative interpretation aids. They define how rubric outputs may be translated into remediation priorities and potential adaptation-oriented parameters. These mappings are not derived from the evaluation results, but are included to support reproducibility and clarity of interpretation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIllustrative mapping of rubric outputs to adaptation parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension (threshold)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore interpretation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample adaptation parameter(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllustrative action\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability\u0026thinsp;\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow visual accessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eenable_high_contrast\u0026thinsp;=\u0026thinsp;true; captions\u0026thinsp;=\u0026thinsp;on\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApply high-contrast theme and activate captions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability\u0026thinsp;\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow interaction accessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enavigation_mode = \"explicit\"; target_size = \"large\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReplace gestures with explicit buttons and enlarge touch targets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability\u0026thinsp;\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow comprehension support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esimplify_language\u0026thinsp;=\u0026thinsp;true; show_examples\u0026thinsp;=\u0026thinsp;true\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimplify phrasing and display contextual examples\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation\u0026thinsp;\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow tailoring support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003equick_preferences\u0026thinsp;=\u0026thinsp;true; allow_replay\u0026thinsp;=\u0026thinsp;true\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProvide quick preference controls and replay functionality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIllustrative mapping of rubric outputs to remediation patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension (low score indicator)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon onboarding accessibility breakdowns\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample remediation patterns (actionable fixes)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability (\u0026le;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow contrast text; missing labels/alt text; critical information hidden by animations; screen reader announcements unclear or out of order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncrease contrast and scalable text; add labels and alt text; provide captions/transcripts; add \u0026ldquo;Skip animation\u0026rdquo; or \u0026ldquo;Reduce motion\u0026rdquo; option; ensure screen reader order matches visual order\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability (\u0026le;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGesture-only navigation; small touch targets; missing Back/Skip controls; poor focus management; inability to navigate using keyboard or assistive interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdd explicit buttons (Next/Back/Skip); increase target size and spacing; provide keyboard navigation support; correct focus order; add alternative interactions for gestures; ensure consistent navigation controls across steps\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability (\u0026le;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical language; unclear instructions; lack of examples; confusing consent/privacy prompts; unclear error feedback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimplify language; break steps into smaller tasks; use progressive disclosure; add examples or previews; provide contextual tooltips; rewrite error messages to be specific and actionable; summarise key points in consent steps\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation (\u0026le;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo control of pace; no repetition; no preference capture; same onboarding path for all users; settings not persistent across steps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdd pause/replay controls; allow skipping non-essential steps; provide quick preference toggles (text size, motion, modality); support alternative formats (audio/text); persist preferences across steps; provide selectable onboarding modes (guided vs quick setup)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Reporting strategy\u003c/h2\u003e \u003cp\u003eResults were reported using per-dimension scores, concise rationales, remediation recommendations, and radar chart visualisations summarising accessibility profiles across the four dimensions (Sections \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e5\u003c/span\u003e and 6). For future applications, structured reporting formats may support communication with product teams. One practical option is to use one standardised page per dimension, containing: (1) the problem pattern, (2) supporting evidence such as an annotated screenshot or description of the interaction barrier, and (3) a recommended change with expected impact on first-use success.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.9 Session logistics\u003c/h2\u003e \u003cp\u003eIn the present study, expert walkthroughs required approximately 30 to 45 minutes per onboarding flow, followed by 15 to 20 minutes for score consolidation and documentation. For user sessions, a realistic duration for a moderated walkthrough is 25 to 40 minutes depending on participant needs and assistive technology context. A representative structure is as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e3 to 5 minutes: orientation and consent\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e15 to 25 minutes: think-aloud walkthrough of the onboarding flow\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e5 to 7 minutes: rubric scoring across four dimensions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3 to 5 minutes: open reflection questions, including:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e\u0026ldquo;What would make this easier the first time?\u0026rdquo;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e\u0026ldquo;Where would more control be helpful?\u0026rdquo;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese prompts support actionable feedback while maintaining consistent data collection across participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 CASE STUDY: EXPERT EVALUATION OF A DIGITAL HEALTH ONBOARDING FLOW","content":"\u003cp\u003eTo demonstrate application of the proposed framework, an expert-led evaluation was conducted using a digital health onboarding prototype focused on cognitive wellness. The prototype supported first-time users in exploring key features, granting consent for data usage, and setting initial lifestyle preferences. The onboarding flow combined animated feature tours, consent dialogues, and progressive set-up screens typical of contemporary health applications, where early interaction quality shapes trust and continued engagement [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo accessibility specialists independently applied the four-dimensional framework (Perceivability, Operability, Understandability, and Personalisation) using an extended evaluator checklist. The checklist operationalises rubric scoring through concrete inspection items and evaluator guidance, supporting consistent assessment across interaction contexts.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Extended framework checklist\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExtended framework checklist used for expert onboarding evaluation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChecklist item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaluator guidance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore (0\u0026ndash;5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImages have descriptive alternative text\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUse a screen reader to verify text equivalents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eText has sufficient contrast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerify using a WCAG contrast checker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAudio or video content has captions or transcripts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRequired for onboarding media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll functionality is accessible via keyboard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest tab order and focus behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eButtons and inputs are sufficiently large and selectable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsider touch-target size and spacing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction is not hindered by time limits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWarn users or allow extension if required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanguage is simple and clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvoid jargon and use plain instructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnboarding steps are logically ordered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaluate cognitive load and transitions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProgress indicators are present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvide visual or textual cues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsers can skip, repeat, or extend steps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlexibility supports inclusive use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContent can be tailored to user role or needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaluate tailoring based on context\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccessibility preferences are respected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerify system-level settings (e.g., reduced motion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe checklist guided evaluators through each onboarding component and supported structured observation across the four dimensions. Scores were then aggregated at dimension level to produce an overall accessibility profile.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Scoring summary and observed patterns\u003c/h2\u003e \u003cp\u003eAggregated results from the expert evaluation are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExpert evaluation scores for the digital health onboarding flow (0\u0026thinsp;=\u0026thinsp;very poor accessibility, 5\u0026thinsp;=\u0026thinsp;excellent accessibility)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore (0\u0026ndash;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRationale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContrast failures, missing alternative text, and partially inaccessible animations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinor spacing issues; generally operable through keyboard and touch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupportive tone, with occasional technical phrasing and limited scaffolding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited tailoring support and largely fixed onboarding sequence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall accessibility (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate accessibility, with major gaps in perceivability and personalisation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo visualise performance across the four dimensions, a radar chart was generated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe radar chart indicates an uneven accessibility profile. Operability scored strongly, while Perceivability and Personalisation remained comparatively weak, suggesting priority areas for improving first-use accessibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Findings and remediation priorities by dimension\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePerceivability (2.3).\u003c/b\u003e Although contrast met WCAG requirements on several screens, failures were observed in gradient-based header designs. Missing alternative text and auto-playing animated content without captions reduced accessibility for screen reader users and users with low vision [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Remediation priorities include systematic coverage of alternative text, provision of captions or descriptive transcripts for onboarding media, and motion controls such as pause, replay, and reduced-motion support.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOperability (4.0).\u003c/b\u003e The onboarding flow was largely operable using keyboard and touch navigation, with logical focus order and no restrictive time limits. However, closely spaced touch targets may reduce accuracy for users with limited dexterity and can increase effort on small mobile screens [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Remediation should prioritise improved spacing and target sizing while maintaining multi-input compatibility.\u003c/p\u003e \u003cp\u003e\u003cb\u003eUnderstandability (3.3).\u003c/b\u003e The flow used a generally supportive tone; however, occasional technical wording, especially in consent and privacy explanations, increased cognitive demand. Limited scaffolding, such as tooltips or concrete examples, may further reduce comprehension for first-time users unfamiliar with the domain [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Remediation should prioritise plain-language consent explanations, progressive disclosure, and contextual help at points of uncertainty.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePersonalisation (2.0).\u003c/b\u003e Basic flexibility was provided through the ability to skip or repeat certain steps. Nevertheless, onboarding lacked meaningful tailoring mechanisms such as pacing adjustment, text-size control, and persistence of user preferences across steps. Tailoring by user role, access need, or context was not supported, limiting inclusive autonomy and reducing the capacity of onboarding to accommodate variability in first-use interaction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Remediation priorities include adjustable pacing, text and contrast controls, replay mechanisms, and explicit preference persistence during onboarding.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 USER EVALUATION OF THE DIGITAL HEALTH ONBOARDING FLOW","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Study overview\u003c/h2\u003e \u003cp\u003eA formative user evaluation was conducted to complement the expert review and examine how participants experienced onboarding accessibility using the same four-dimensional framework. This phase assessed whether the framework captured participant-reported accessibility barriers and enabled comparison between participant ratings and expert evaluation outcomes. Particular attention was given to convergence and divergence across dimensions, especially where operability assessments differed between expert evaluators and participants. Such differences may arise due to variation in assistive technology use, interaction strategies, and device contexts during first-use interaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Methodology\u003c/h2\u003e \u003cp\u003e \u003cb\u003eParticipants and materials\u003c/b\u003e \u003c/p\u003e \u003cp\u003e Four participants (P1\u0026ndash;P4) were recruited through accessibility-oriented communities and screened for familiarity with assistive tools or accessibility settings. Participants represented a range of access needs and used their own devices throughout the sessions, supporting ecological validity.\u003c/p\u003e \u003cp\u003eFollowing completion of the onboarding flow, participants completed a user-facing self-evaluation form structured around the four framework dimensions: Perceivability, Operability, Understandability, and Personalisation. Each dimension included short guidance prompts and a standardised 0 to 5 rating scale (0\u0026thinsp;=\u0026thinsp;not supported at all; 5\u0026thinsp;=\u0026thinsp;fully accessible and inclusive), with optional open-text observations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant accessibility evaluation form (summary; full version provided in Supplementary Material, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramework dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipant guidance (summary)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual clarity and perceivable content (contrast, text alternatives, captions or transcripts, screen reader interpretation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEase of navigation and interaction (focus behaviour, gestures versus alternatives, error recovery)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClarity of flow and messaging (instructions, comprehension without overload, confirmation and error messages)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser control and adjustability (ability to skip or revisit steps, adjustable presentation such as text size and formats)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Quantitative results\u003c/h2\u003e \u003cp\u003eDe-identified participant ratings across the four dimensions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDe-identified user evaluation scores across onboarding accessibility dimensions (0\u0026thinsp;=\u0026thinsp;very poor accessibility, 5\u0026thinsp;=\u0026thinsp;excellent accessibility)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceivability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnderstandability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePersonalisation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNotes (selected anonymised rationale)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOnboarding video had no captions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNavigation controls were unclear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLimited visual scaffolding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFlow remained fixed throughout\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo illustrate the average profile, a radar chart was generated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParticipant ratings indicate Perceivability as the strongest dimension (mean\u0026thinsp;=\u0026thinsp;3.25), suggesting that core content perception requirements were largely satisfied for most participants. Overall onboarding accessibility remained moderate due to lower ratings in Operability, Understandability, and Personalisation (mean\u0026thinsp;=\u0026thinsp;2.75 in each dimension). Operability demonstrated the greatest variability across participants, including one participant reporting substantial difficulty (P1\u0026thinsp;=\u0026thinsp;1) while others reported relatively strong interaction accessibility (P2 and P4\u0026thinsp;=\u0026thinsp;4). This variability indicates that operability in onboarding may depend strongly on individual interaction strategies and assistive technology mediation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Qualitative findings\u003c/h2\u003e \u003cp\u003eParticipant observations were analysed using structured thematic coding aligned with the four framework dimensions. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarises the coding structure and includes selected anonymised quotes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoding framework for qualitative analysis with selected anonymised quotes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThematic codes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample quotes (selected anonymised)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceivability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContrast issues; missing text alternatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Text blended into the background; hard to see.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGesture reliance; navigation difficulty; small targets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Swipe-only was exhausting.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn or back uncertainty; unclear flow structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Was not sure how to return back.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimited customisation; assistive-technology configuration constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;No adjustable text size, and my accessibility settings were not supported.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e Qualitative accounts contextualised numeric ratings, particularly for dimensions where participant scores varied. Operability concerns were primarily associated with navigation clarity, gesture reliance, and difficulty reversing actions or recovering from mistakes. Understandability barriers emerged where flow structure lacked scaffolding or where navigation behaviour (e.g., Back) was unclear. Personalisation concerns related to fixed progression, limited user control, and absence of adjustable presentation options such as text size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e6.5 Expert\u0026ndash;participant comparison\u003c/h2\u003e \u003cp\u003eComparison of expert and participant evaluations revealed both convergence and divergence. Both groups identified limited Personalisation, reinforcing the need for greater user control, adjustable settings, and preference persistence during onboarding [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Operability showed the largest discrepancy. Expert evaluators rated operability strongly (4.0), while participants reported a lower mean operability score (2.75) with notable variability. Participant feedback suggests that navigation clarity, gesture reliance, and uncertainty about returning to earlier steps contributed to reduced perceived operability for some users. This divergence highlights the value of triangulating expert judgement with user experience accounts, particularly in first-use flows where assistive technology mediation can shape interaction outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e6.6 Implications for adaptation-informed onboarding design\u003c/h2\u003e \u003cp\u003eParticipant ratings indicate that operability and personalisation needs can vary substantially across individuals, particularly when onboarding is mediated by assistive technologies and differing interaction strategies. Dimension-level scores can therefore function as evaluative signals for prioritising onboarding adjustments, such as providing explicit navigation controls, improving contextual scaffolding, and supporting early preference capture. These implications are further developed in the Discussion (Section 7) within an adaptation-informed framing [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e6.7 Summary of user evaluation findings\u003c/h2\u003e \u003cp\u003eThe formative user evaluation indicates moderate onboarding accessibility overall. Participants reported relatively strong perceivability but continued barriers across operability, understandability, and personalisation. Differences between expert and participant operability ratings further emphasise the importance of combining expert evaluation with user participation. Assistive technology mediation and individual interaction strategies can influence how onboarding controls, navigation clarity, and recoverability are experienced. Overall, these findings support the framework\u0026rsquo;s utility as a diagnostic instrument and as a structured approach for translating accessibility assessment into practical design priorities for onboarding experiences.\u003c/p\u003e \u003c/div\u003e"},{"header":"7 DISCUSSION AND CONCLUSION","content":"\u003cp\u003eThis section synthesises findings from the expert evaluation (Section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and formative user evaluation (Section 6) to discuss broader implications for operationalising accessibility evaluation in onboarding flows. The combined evidence indicates that onboarding accessibility cannot be fully characterised through static compliance criteria alone. Accessibility outcomes were shaped by interaction sequencing, user control, navigation structure, and assistive technology mediation. By generating structured dimension-level scores alongside qualitative observations, the proposed framework supports both diagnostic accessibility evaluation and translation of findings into concrete improvement priorities that may inform adaptation-oriented onboarding design. The section concludes with key contributions, limitations, and directions for future work.\u003c/p\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Confirming the framework\u0026rsquo;s relevance for onboarding accessibility\u003c/h2\u003e \u003cp\u003eResults from both evaluations support the framework\u0026rsquo;s relevance for analysing onboarding accessibility. The four dimensions, Perceivability, Operability, Understandability, and Personalisation, were sufficient for capturing the most salient accessibility barriers in the evaluated first-use flow. Across expert and participant perspectives, the framework highlighted that onboarding accessibility extends beyond technical compliance and requires attention to sequential interaction properties, navigational clarity, recoverability, and user control.\u003c/p\u003e \u003cp\u003eA core contribution of the framework is the formal inclusion of Personalisation as an accessibility dimension. Both expert and participant evaluations showed that limited personalisation constrained user autonomy during onboarding. This supports the view that inclusive onboarding requires adjustable pacing, adaptable presentation, and preference persistence rather than fixed sequencing and uniform interaction demands [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In this positioning, personalisation functions as both an accessibility objective and an indicator of whether onboarding is capable of accommodating diverse needs through configurable or adaptation-ready mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Accessibility barriers beyond compliance\u003c/h2\u003e \u003cp\u003eConventional accessibility audits typically prioritise static interface attributes, including alternative text, semantic structure, and colour contrast. While these criteria remain essential, the results indicate that several critical onboarding barriers stem from interaction design constraints that manifest across steps. Issues including unclear navigation, uncertain return behaviour, gesture reliance, and rigid progression structures influenced accessibility outcomes across both evaluations. Such barriers can be difficult to detect using evaluations that do not explicitly represent onboarding as a sequential and transitional process [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticipant results indicated higher overall Perceivability than expert assessment, suggesting that perception-related concerns may not manifest uniformly across evaluators or contexts. However, perceivability limitations still emerged in participant accounts, such as missing captions for onboarding media. This indicates that onboarding accessibility is often configuration dependent and influenced by user preferences, assistive tools, and device-specific rendering. Accordingly, evaluations benefit from triangulation of expert judgement with participant experience, not because expert evaluation lacks validity, but because each perspective foregrounds different access risks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Interpreting expert\u0026ndash;participant divergence in operability\u003c/h2\u003e \u003cp\u003eThe most notable divergence across evaluations occurred in Operability. Expert evaluators rated Operability strongly (4.0), while participants reported a lower mean score (2.75) with substantial variability. This discrepancy does not imply inconsistency in the rubric itself, since both groups assessed the same onboarding flow using the same scoring structure. Rather, it indicates that operability outcomes are sensitive to interaction strategies, navigation expectations, and assistive technology mediation.\u003c/p\u003e \u003cp\u003eParticipant feedback suggests that operability challenges did not primarily arise from non-functional interface controls, but from friction related to navigation clarity, gesture reliance, and uncertainty about returning to previous steps. These findings suggest that onboarding operability should be treated as both a functional and experiential accessibility property. Accessibility is not only whether interaction is technically possible, but whether it is manageable, recoverable, and cognitively navigable for diverse users during first use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Implications for adaptation-informed onboarding design\u003c/h2\u003e \u003cp\u003eA key implication of this work is that the framework produces structured outputs that extend beyond reporting. The 0 to 5 dimension ratings and qualitative observations can support adaptation-informed onboarding design in three practical ways:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrigger-oriented adjustments\u003c/b\u003e: low scores in Operability or Personalisation may indicate the need for explicit navigation controls, simplified interaction patterns, or guided step-by-step alternatives.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePreference capture and persistence\u003c/b\u003e: recurring personalisation limitations highlight the importance of lightweight preference set-up early in onboarding (e.g., text size, contrast, reduced motion, pacing) and persistence across steps.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eUser modelling features\u003c/b\u003e: in systems that support adaptation, repeated accessibility-related scores can function as interpretable signals for ability-related needs, preferred interaction modes, or environmental constraints [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis framing positions accessibility evaluation not only as diagnostic, but also as a source of structured signals that may inform adaptation-oriented decision-making for inclusive first-use experiences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section2\"\u003e \u003ch2\u003e7.5 Implications for practice\u003c/h2\u003e \u003cp\u003eFor design and QA teams, the framework provides a structured accessibility evaluation procedure that is lightweight yet multidimensional. Radar chart visualisation supports communication of accessibility performance across dimensions, enabling stakeholders to identify strengths and weaknesses efficiently. In practical settings such as digital health, where first-use trust influences retention and disclosure behaviour, the framework supports proactive accessibility planning and targeted improvement rather than post-launch remediation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec44\" class=\"Section2\"\u003e \u003ch2\u003e7.6 Limitations\u003c/h2\u003e \u003cp\u003eFirst, the participant sample was small, limiting generalisability and preventing statistical comparisons across user subgroups. The user evaluation was formative in nature and was not intended to support statistical inference. Second, the onboarding prototype was simplified for controlled evaluation and may not reflect the complexity of real-world onboarding environments, including multi-path flows, dynamic content, and layered permissions. Third, rubric consistency was supported through independent scoring by two expert evaluators followed by discrepancy resolution through consensus discussion. However, formal inter-rater reliability statistics, such as the intraclass correlation coefficient or Cohen\u0026rsquo;s kappa, were not computed due to the small scale of the study. Future work should report reliability coefficients across multiple raters to strengthen reproducibility and methodological transparency [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Finally, cultural and linguistic contexts were not varied, and accessibility expectations may differ across regions, languages, and infrastructures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section2\"\u003e \u003ch2\u003e7.7 Validity considerations\u003c/h2\u003e \u003cp\u003eConstruct validity may be affected by midpoint anchoring in the 0 to 5 scoring system, particularly in ambiguous cases. Internal validity was strengthened through consistent evaluation procedures and aligned rubrics; however, participant ratings may be influenced by device constraints and individual expectations. External validity remains limited due to the single prototype and domain context. Reliability should be strengthened through repeated applications with multiple evaluators, larger participant samples, agreement metrics, and rubric calibration procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec46\" class=\"Section2\"\u003e \u003ch2\u003e7.8 Future directions\u003c/h2\u003e \u003cp\u003eFuture work should test the framework across diverse real-world onboarding systems, including multi-path flows, dynamic content, and embedded personalisation mechanisms. Larger and more heterogeneous participant samples would support stronger evidence for generalisability and more robust reliability reporting [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Beyond evaluation, further research should examine how accessibility-derived dimension ratings can inform adaptation-informed onboarding architectures, including preference learning, adaptive scaffolding, and context-aware interface selection.\u003c/p\u003e \u003cp\u003eThis direction aligns with ongoing shifts towards outcome-based accessibility evaluation, including emerging standards development such as WCAG 3.0 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. More broadly, the framework supports a shift in onboarding accessibility from compliance checking towards measurable inclusion capabilities informed by structured evaluation signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section2\"\u003e \u003ch2\u003e7.9 Conclusion\u003c/h2\u003e \u003cp\u003eOnboarding is a decisive phase of user experience because it shapes trust, comprehension, and inclusion during the first sustained interaction with a digital product. Although WCAG and related standards remain foundational, they do not fully capture the sequential, time constrained, and user-specific characteristics of onboarding flows. This paper introduced and applied a four-dimensional framework for onboarding accessibility evaluation, comprising Perceivability, Operability, Understandability, and Personalisation, derived from POUR but extended by formalising personalisation as an accessibility-relevant dimension.\u003c/p\u003e \u003cp\u003eEvidence from expert and user evaluations demonstrated that the framework identifies accessibility barriers beyond static compliance and produces structured outputs that can guide targeted onboarding improvements. Findings further suggest that onboarding inclusivity depends not only on perceiving content, but also on navigating a first-use sequence with clarity, recoverability, and control, including under assistive technology mediation. Radar chart visualisation supports practical reporting by making accessibility profiles interpretable across dimensions. Most importantly, positioning Personalisation as a core dimension reframes onboarding accessibility from a static compliance outcome towards an adjustable and user-sensitive interaction capability.\u003c/p\u003e \u003cp\u003eFuture work should extend empirical validation across domains and further investigate how rubric outputs may support rule-based and learning-based approaches to personalisation. By linking accessibility evaluation outputs with adaptation-informed design objectives, the proposed framework contributes a pathway towards onboarding systems where inclusive first-use experiences become a default design expectation rather than an exception.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormal institutional ethics approval was not required for this study because it involved minimal risk, voluntary participation, and no collection of personally identifiable or sensitive personal data. No personal health information was collected, and the evaluated prototype did not require participants to provide identifiable personal data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants prior to participation. Participants were informed of the study purpose, procedures, and their right to withdraw at any time without consequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent included permission for the use of anonymised participant feedback and de-identified quotes in academic publications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are included within the article and its Supplementary Material. The evaluation instruments (participant evaluation form, expert checklist, and scoring rubric), de-identified participant score summaries, and qualitative coding framework are provided in Supplementary Tables S1\u0026ndash;S5. No personally identifiable participant data are included. The evaluated onboarding prototype is not publicly available due to licensing and business confidentiality constraints; therefore, a public system link is not provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIsrael Umola Akeji: Conceptualisation; Methodology; Framework development; Rubric design; Expert evaluation; Data analysis; Visualisation; Writing (original draft); Writing (review and editing).\u003cbr\u003e\u0026nbsp;Daniel Eleojo Okwoli: Methodology support; Expert evaluation; Participant session planning and execution; Data collection support; Writing (review and editing).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the study participants for contributing their time and feedback during the onboarding evaluation sessions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChauvergne, R., Santos, P., Le Bigot, L. and Argelaguet, F., 2023. 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Available at: https://5310879.fs1.hubspotusercontent-na1.net/hubfs/5310879/battle-to-onboard-2022.pdf\u003c/li\u003e\n\u003cli\u003eSignicat, 2020. The Battle to Onboard: The growing power of consumer demand. Trondheim: Signicat. Available at: https://d2qcmozihn2auk.cloudfront.net/whitepapers/SG-Battle-to-onboard.pdf\u003c/li\u003e\n\u003cli\u003eWang, T., Wang, W., Liang, J., Nuo, M., Wen, Q., Wei, W., Han, H., and Lei, J., 2022. Identifying major impact factors affecting the continuance intention of mHealth: a systematic review and multi-subgroup meta-analysis. npj Digital Medicine, 5, 145. https://doi.org/10.1038/s41746-022-00692-9\u003c/li\u003e\n\u003cli\u003eSeixas Pereira, L., Duarte, C., 2025. Evaluating and monitoring digital accessibility: practitioners\u0026rsquo; perspectives on challenges and opportunities. 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Social Sciences, 13(10), 522. https://doi.org/10.3390/socsci13100522\u003c/li\u003e\n\u003cli\u003eISO/IEC, 2011. ISO/IEC 25010:2011 Systems and software engineering \u0026mdash; Systems and software Quality Requirements and Evaluation (SQuaRE) \u0026mdash; System and software quality models. Geneva: International Organization for Standardization. Available at: https://www.iso.org/standard/35733.html\u003c/li\u003e\n\u003cli\u003eETSI, 2021. EN 301 549 V3.2.1 (2021-03): Accessibility requirements for ICT products and services. Sophia Antipolis: European Telecommunications Standards Institute. Available at: https://www.etsi.org/standards?search=EN%20301%20549%20V3.2.1\u003c/li\u003e\n\u003cli\u003eGernsbacher, M.A., 2015. Video captions benefit everyone. Policy Insights from the Behavioral and Brain Sciences, 2(1), pp.195\u0026ndash;202. doi:10.1177/2372732215602130.\u003c/li\u003e\n\u003cli\u003eCrossland, M.D. and Rubin, G.S., 2012. Text accessibility by people with reduced contrast sensitivity. Optometry and Vision Science, 89(9), pp.1276\u0026ndash;1281. doi:10.1097/OPX.0b013e318264cc62.\u003c/li\u003e\n\u003cli\u003eW3C WAI (Web Accessibility Initiative) (2025) Guidance on Applying WCAG 2.2 to Mobile Applications (WCAG2Mobile), Draft Note, 6 May. Available at: https://www.w3.org/TR/wcag2mobile-22/\u003c/li\u003e\n\u003cli\u003eHenni, S. H., Maurud, S., Fuglerud, K. S. et al., 2022. The experiences, needs and barriers of people with impairments related to usability and accessibility of digital health solutions, levels of involvement in the design process and strategies for participatory and universal design: a scoping review. BMC Public Health, 22, 35. DOI: 10.1186/s12889-021-12393-1.\u003c/li\u003e\n\u003cli\u003eKurniawan, S., 2008. Older people and mobile phones: A multi-method investigation. International Journal of Human-Computer Studies, 66(12), pp.889\u0026ndash;901. doi:10.1016/j.ijhcs.2008.03.002.\u003c/li\u003e\n\u003cli\u003eWobbrock, J.O. and Kientz, J.A., 2016\u003cstrong\u003e.\u003c/strong\u003e Research contributions in human\u0026ndash;computer interaction. Interactions, 23(3), pp.38\u0026ndash;44. doi:10.1145/2907069.\u003c/li\u003e\n\u003cli\u003eClarkson, J., Coleman, R., Keates, S. and Lebbon, C. (eds), 2003. Inclusive Design: Design for the Whole Population. London: Springer. https://doi.org/10.1007/978-1-4471-0001-0\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"universal-access-in-the-information-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"uais","sideBox":"Learn more about [Universal Access in the Information Society](http://link.springer.com/journal/10209)","snPcode":"10209","submissionUrl":"https://submission.nature.com/new-submission/10209/3","title":"Universal Access in the Information Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"accessibility evaluation, onboarding flows, inclusive design, personalisation, user modelling, digital health","lastPublishedDoi":"10.21203/rs.3.rs-8795738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8795738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInclusive onboarding is a prerequisite for equitable access to digital services, particularly in domains such as digital health where early interaction barriers can lead to abandonment and exclusion. However, prevailing accessibility standards and evaluation frameworks remain predominantly compliance oriented and provide limited guidance for assessing sequential and time constrained onboarding flows. This paper introduces a conceptual evaluation model that operationalises accessibility assessment for onboarding interfaces with adaptation goals. Building on the established POUR principles, the model replaces Robustness with \u003cem\u003ePersonalisation\u003c/em\u003e and defines four evaluation dimensions: Perceivability, Operability, Understandability, and Personalisation. To support reproducibility and practical adoption, the approach integrates a structured scoring rubric, radar chart visualisation, and remediation mapping. The model is demonstrated through two expert evaluations and a formative user study (n = 4) using a digital health onboarding prototype. Expert assessment yielded an overall accessibility score of 2.9 out of 5, with strongest performance in Operability (4.0 out of 5) and weakest performance in Personalisation (2.0 out of 5). Participant ratings indicated higher Perceivability (3.25 out of 5) but lower and more variable Operability (2.75 out of 5), suggesting that onboarding accessibility can vary under assistive technology mediation and individual interaction strategies. Findings indicate that dimension-level accessibility scores can be translated into actionable design requirements that guide targeted onboarding improvements. Within this structure, Personalisation reflects the system’s readiness to support tailoring through adaptive or configurable onboarding pathways. The paper contributes a practical framework for evaluating onboarding accessibility beyond static compliance checking and for supporting adaptation-informed onboarding design.\u003c/p\u003e","manuscriptTitle":"Evaluating Accessibility in Sequential Onboarding Flows: A Conceptual Framework and Scoring Rubric","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 09:53:45","doi":"10.21203/rs.3.rs-8795738/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"103399956791937438481577024685157784133","date":"2026-04-13T13:28:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T14:52:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T14:36:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-07T02:15:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Universal Access in the Information Society","date":"2026-02-05T09:57:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"universal-access-in-the-information-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"uais","sideBox":"Learn more about [Universal Access in the Information Society](http://link.springer.com/journal/10209)","snPcode":"10209","submissionUrl":"https://submission.nature.com/new-submission/10209/3","title":"Universal Access in the Information Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7c1fb90b-ff3b-4c3f-9d4f-d0f9531c45e7","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T15:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 09:53:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8795738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8795738","identity":"rs-8795738","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}