Digital Health Readiness Questionnaire (DHRQ)

Abstract

The Digital Health Readiness Questionnaire (DHRQ) is a standardized, psychometrically validated patient-reported outcome measure developed by Martijn Scherrenberg and colleagues (2023) at the Heart Centre Hasselt, Jessa Hospital, and Hasselt University in Belgium. Engineered specifically to address the accelerating deployment of telemedicine, mobile health (mHealth), wearable biosensors, and remote patient monitoring, the DHRQ evaluates an individual’s operational preparedness, technological competence, and psychological receptivity to engage with digital healthcare interventions. Comprising 20 items categorized across five operational subdomains—Digital Access, Digital Technology Usage/Skills, Digital Literacy, Digital Health Literacy, and Digital Learnability—the instrument yields two overarching composite scores: a comprehensive Digital Health Readiness metric and an independent Digital Learnability index. Originally developed and validated simultaneously in Dutch and English among a diverse cohort of adult cardiology outpatients aged 21 to 97 years, the DHRQ demonstrates robust psychometric properties. Content and face validity were rigorously confirmed through iterative expert panel consensus and cognitive patient debriefing. Confirmatory factor analysis (CFA) substantiated a coherent multi-dimensional structure with acceptable goodness-of-fit indices (χ²₁₆₀ = 559.0, p < .001; Standardized Root Mean Square Residual [SRMR] = 0.065; Root Mean Square Error of Approximation [RMSEA] = 0.098, 90% CI [0.090, 0.106]; Tucker-Lewis Index [TLI] = 0.895; Comparative Fit Index [CFI] = 0.912). Internal consistency across all constituent domains is high, with Cronbach’s alpha coefficients ranging from 0.735 for digital usage to 0.937 for digital literacy. By discriminating between fixed structural barriers (such as device non-ownership) and modifiable psychological constructs (such as motivation and instructional learnability), the DHRQ provides clinicians and behavioral researchers with an actionable screening tool to mitigate digital health disparities and tailor clinical onboarding pathways.

Keywords

Digital Health Readiness Questionnaire, DHRQ, Telemedicine, Digital Health Literacy, Digital Divide, eHealth Literacy, Remote Patient Monitoring, Digital Learnability, Psychometrics, Telecardiology, Patient-Reported Outcome Measures, Health Equity

Authors

The Digital Health Readiness Questionnaire was designed, validated, and published by an interdisciplinary team of clinical cardiologists, digital health researchers, and behavioral scientists across Belgium and the Netherlands:

• Martijn Scherrenberg, MD, PhD (ORCID: 0000-0001-9483-3828) — Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium. (Corresponding Author: [email protected]; Address: Jessa Hospital, Heart Centre Hasselt, Stadsomvaart 11, 3500 Hasselt, Belgium)
• Maarten Falter, MD, PhD (ORCID: 0000-0001-7407-115X) — Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
• Toshiki Kaihara, MD (ORCID: 0000-0003-0941-8035) — Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium.
• Linqi Xu, MSc (ORCID: 0000-0002-4346-0547) — Data Science and Biostatistics, Hasselt University, Hasselt, Belgium.
• Mayke van Leunen, MSc (ORCID: 0000-0002-5048-3747) — Department of Cardiology, Máxima Medical Centre, Veldhoven, The Netherlands.
• Hareld Kemps, MD, PhD — Department of Cardiology, Máxima Medical Centre, Veldhoven, The Netherlands; Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands.
• Hanne Kindermans, PhD (ORCID: 0000-0003-2723-1645) — Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium.
• Paul Dendale, MD, PhD — Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium.

Purpose

The contemporary healthcare paradigm is undergoing a profound structural transition, marked by the rapid decentralization of clinical services from hospital wards to home-based environments. This shift is driven by advances in teleconsultation platforms, cloud-connected wearable biosensors, tele-rehabilitation programs, and smartphone-guided therapeutic applications. Although these digital health innovations offer scalable solutions for chronic disease management, equitable access remains a critical challenge. The non-critical deployment of digital interventions risks exacerbating structural inequalities, generating an unintended "digital divide" or "inverse care law," wherein socially disadvantaged, elderly, or lower-literacy patients who carry the highest burden of chronic disease are systematically disenfranchised from digital healthcare delivery.

Before the development of the DHRQ, clinicians lacked a pragmatic, brief, and empirically validated instrument suited for routine clinical workflows that could simultaneously evaluate material digital access, functional competencies, and behavioral receptivity. Historic instruments—such as the eHealth Literacy Scale (eHEALS) developed by Norman and Skinner in 2006—predominantly concentrated on static Web 1.0 information retrieval skills (e.g., searching for medical knowledge on web browsers). Consequently, eHEALS does not capture the interactive, hardware-dependent, and multi-modal ecosystem characterizing contemporary digital care, which includes Bluetooth-paired blood pressure cuffs, continuous glucose monitors, step tracking, asynchronous tele-messaging, and interactive mobile apps.

The primary purpose of the Digital Health Readiness Questionnaire (DHRQ) is to fulfill this diagnostic gap by providing an efficient, multi-dimensional assessment tool designed for clinical practice and translational trials. Specifically, the DHRQ serves three foundational functions:

• Point-of-Care Clinical Triaging: The questionnaire identifies patients who possess the technological access and self-efficacy required to immediately commence decentralized tele-interventions (e.g., home telerehabilitation or digital remote heart failure monitoring), versus those requiring structured pre-intervention support or traditional in-person clinical care pathways.
• Stratification of Support Needs via Learnability: Uniquely, the DHRQ dissociates a patient’s current operational competence from their potential to acquire skills. The dedicated Learnability subscale isolates cognitive openness, intrinsic motivation, and instructional preferences (e.g., preference for written guides versus synchronous human coaching). This informs targeted digital onboarding protocols rather than excluding patients based solely on baseline deficits.
• Standardized Research Phenotyping: In clinical trials evaluating digital therapeutics (DTx) or remote patient management systems, the DHRQ provides a standardized psychometric covariate. This enables researchers to control for baseline digital literacy, analyze heterogeneous treatment effects across literacy strata, and evaluate changes in digital self-efficacy longitudinally.

Psychological Construct

Digital health readiness is conceptualized within modern psychometrics not as an innate, static personal trait, but as a dynamic, context-dependent socio-technical construct. The DHRQ models digital health readiness through five interconnected operational subdomains that reflect an individual’s technical infrastructure, procedural knowledge, navigational autonomy, critical appraisal, and cognitive-behavioral plasticity:

1. Digital Access

Digital access captures the physical, infrastructural, and economic availability of digital hardware and network connectivity. Anchored in the first-level digital divide theory, readiness is impossible without consistent access to telecommunication tools. The DHRQ evaluates access across four distinct modalities: general internet connectivity, desktop/laptop computers, mobile smart devices (smartphones/tablets), and biometric wearable tracking sensors (such as smartwatches or dedicated accelerometers). This differentiation is vital, as a patient may have high smartphone penetration yet lack the peripheral wearable tracking technology or high-speed broadband necessary for intensive video-based telerehabilitation.

2. Usage of Digital Technology (Digital Skills)

This dimension evaluates functional, instrumental, and procedural self-efficacy—the practical capability to execute basic, unassisted technical operations. Moving beyond passive consumption, this construct assesses whether a patient can compose and transmit an electronic mail independently, engage in interactive video conferencing (a prerequisite for real-time synchronous telemedicine), capture and transmit diagnostic digital imagery (essential for teledermatology and remote wound tracking), utilize social media platforms, and record and review biometric tracking indicators (such as daily step counts). These behaviors represent foundational digital actions required to navigate modern clinical applications.

3. Digital Literacy

Digital literacy within the DHRQ context is operationalized as the navigational agency, perceptual security, and cognitive control an individual experiences when interacting with generic digital information systems. Grounded in psychological control theory and digital security paradigms, this subscale captures whether a user feels safe from algorithmic exploitation or data breaches, retains an internal locus of control during technological navigation, and knows how to systematically retrieve helpful online resources without experiencing paralyzing cognitive load.

4. Digital Health Literacy

Extending beyond generic digital literacy, digital health literacy reflects the sophisticated synthesis of health literacy, media literacy, and information literacy applied to biomedical contexts. It evaluates the patient’s critical appraisal skills—the capacity to distinguish between evidence-based health guidance and internet-circulated misinformation, evaluate source credibility, and actively integrate online information into their personal disease self-management routines. Furthermore, it measures the operational habit of utilizing dedicated health applications to monitor chronic symptomatology, vital signs, or medication adherence.

5. Digital Learnability

The most theoretically innovative domain of the DHRQ is digital learnability. Drawing from Carol Dweck’s growth mindset framework and Albert Bandura’s self-efficacy theory, learnability assesses an individual’s forward-looking orientation toward technical acquisition. It assesses the patient’s intrinsic motivation to learn new technologies, perceived confidence in self-guided learning, responsiveness to varied pedagogical formats (written manuals versus individualized interpersonal coaching), and outcome expectancies regarding whether mastering digital skills will tangibly benefit their clinical prognosis and personal wellbeing.

Theoretical Framework

The architecture of the Digital Health Readiness Questionnaire is built upon several converging theoretical models from behavioral psychology, human-computer interaction, and medical sociology:

The Technology Acceptance Model (TAM) and UTAUT

The foundational baseline of the DHRQ is informed by the Technology Acceptance Model (TAM) (Davis, 1989) and its successor, the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). TAM posits that the adoption of any technological system is predominantly governed by two core behavioral perceptions: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Within the DHRQ, PEOU is directly operationalized through the Digital Skills and Digital Literacy domains, which quantify the baseline procedural friction a patient encounters when operating digital devices. Perceived Usefulness is embodied within the Learnability subscale through items assessing the patient’s expectation that digital skills will directly improve their health status. Furthermore, UTAUT’s concept of Facilitating Conditions—the degree to which an individual believes that an organizational and technical infrastructure exists to support system use—is reflected in DHRQ items exploring instructional formats (such as personal guidance and written documentation).

Social Cognitive Theory and Self-Efficacy

Albert Bandura’s Social Cognitive Theory (1977, 1997) provides the psychological engine for the DHRQ’s assessment of digital mastery. Bandura defined self-efficacy as an individual’s belief in their capability to execute the behaviors necessary to produce specific performance attainments. In the DHRQ, behavioral tasks are scaled according to task-specific self-efficacy (e.g., "I am able to write and send an email independently," "I am able to perform videocalling"). The Learnability domain directly captures Bandura’s efficacy expectations and outcome expectancies: patients with high readiness express high confidence in their mastery potential and anticipate positive health-related outcomes from engaging with digital care.

The eHealth Literacy Framework (eHLF)

Rather than relying on early, one-dimensional literacy conceptualizations, the DHRQ incorporates principles from the contemporary eHealth Literacy Framework (eHLF) formulated by Kayser, Karnoe, and colleagues (2018). The eHLF articulates digital health engagement across seven domains, spanning personal knowledge, system access, individual-system interaction, and critical evaluation. The DHRQ adapts this socio-technical taxonomy into clinical language, synthesizing the eHLF’s emphasis on user safety, operational autonomy, and critical appraisal of therapeutic data into an efficient questionnaire for clinical environments.

Validity

The psychometric validation of the DHRQ followed rigorous international measurement standards, establishing robust validity across multiple complementary domains:

Face and Content Validity

Content validity was established through an iterative, multi-stage consensus process led by an expert multidisciplinary panel comprising digital health researchers, clinical cardiologists, nursing specialists, and health sociologists. The panel systematically reviewed existing validated questionnaires—notably the Danish eHealth Literacy Questionnaire (eHLQ) and the eHealth Literacy Assessment Toolkit (Kayser et al., 2018; Karnoe et al., 2018). Items were designed to balance psychometric granularity against clinical brevity, ensuring the instrument could be completed by compromised, acutely ill, or elderly patients without inducing test fatigue. Face validity was qualitatively established via cognitive debriefing interviews with hospitalized and ambulatory adult cardiology patients (spanning young adults to geriatric cohorts over 85 years of age). Feedback confirmed that instructions, item phrasings, and Likert scales were unambiguous, culturally neutral, and easily understood.

Construct and Factorial Validity

Construct validity was evaluated empirically using Confirmatory Factor Analysis (CFA) conducted on empirical data from a prospective cross-sectional cohort of adult cardiology patients recruited at Jessa Hospital in Hasselt, Belgium. The sample exhibited wide demographic breadth, with respondent ages ranging from 21 to 97 years, encompassing a spectrum of cardiovascular etiologies (ischemic heart disease, heart failure, cardiac arrhythmias, and post-cardiac surgery recovery). Factor loadings across the five designated dimensions were statistically significant (p < .001) and substantial, confirming that items effectively converged onto their theoretical latent factors.

Discriminant and Clinical Validity

The DHRQ demonstrated strong discriminant capacity, accurately distinguishing between patient subgroups across different socio-demographic strata. The analysis revealed pronounced, statistically significant gradients in Digital Access, Digital Skills, and Digital Health Literacy as a function of chronological age: older cardiovascular patients (>75 years) scored significantly lower in basic digital skills and wearable utilization than younger cohorts (18–45 years). However, the Learnability subscale exhibited unique discriminant properties: several geriatric patients with low baseline technical usage demonstrated moderate-to-high learnability and intrinsic motivation, provided that personalized clinical guidance was guaranteed. This dissociation between baseline operational skills and cognitive learnability demonstrates that the DHRQ does not merely mirror chronological age, but measures a nuanced behavioral construct.

Reliability

The reliability of the Digital Health Readiness Questionnaire has been demonstrated across multiple psychometric analyses, establishing high measurement stability and internal consistency:

Internal Consistency

Internal consistency was examined across each individual subscale using Cronbach’s alpha (α) coefficients. All five theoretical domains surpassed the conventional psychometric threshold of α ≥ 0.70, indicating reliable scale construction without excessive item redundancy:

• Digital Technology Usage / Skills: α = 0.735, reflecting acceptable-to-good internal consistency across diverse procedural actions (email, video communication, social networking, image capture, and step monitoring).
• Digital Access: Demonstrates high inter-item correlation across hardware modalities and telecommunication infrastructure.
• Digital Literacy: α = 0.937, indicating outstanding internal consistency and strong unidimensionality among items measuring safety, navigation, and perceived control online.
• Digital Health Literacy: Exhibits high internal consistency (> 0.80), demonstrating coherent measurement of health-specific web usage, app-based monitoring, and critical appraisal.
• Digital Learnability: High internal consistency (> 0.85), confirming that motivation, confidence, self-efficacy, and instructional modality preferences form a unified psychometric metric.

Scale Sensitivity and Floor/Ceiling Dynamics

Psychometric sensitivity analysis demonstrated wide dispersion of total and subscale scores, confirming the absence of substantial ceiling effects within unselected cardiac outpatients. While young adult respondents exhibited minor ceiling trends on baseline smartphone access (Item 3), geriatric cohorts displayed wide item variance, enabling the instrument to detect fine-grained gradations of readiness in populations with low-to-moderate digital literacy.

Factor Analysis

The structural integrity and latent dimensionality of the DHRQ were confirmed through rigorous factor analytic methods. The underlying factor structure was verified through Confirmatory Factor Analysis (CFA) using robust maximum likelihood estimation procedures to accommodate ordinal response parameters:

Confirmatory Factor Analysis (CFA) Results

The hypothesized five-factor correlated structure—hierarchically organized into a composite general readiness profile and a distinct learnability index—was evaluated against the sample data (χ²₁₆₀ = 559.0, p < .001). Given that the chi-square test of exact fit is notoriously sensitive to sample size and frequently yields significant p-values in clinical cohorts, multi-faceted approximate goodness-of-fit indices were evaluated:

• Standardized Root Mean Square Residual (SRMR): 0.065. An SRMR value below 0.08 indicates good absolute model fit, demonstrating that the residual differences between the observed and model-implied covariance matrices are small.
• Root Mean Square Error of Approximation (RMSEA): 0.098 (90% Confidence Interval: [0.090, 0.106]). While an RMSEA below 0.06 is classically considered indicative of close fit, an RMSEA under 0.10 is widely accepted in complex socio-behavioral and clinical measurement models as demonstrating acceptable fit.
• Comparative Fit Index (CFI): 0.912. The CFI exceeds the recognized psychometric benchmark of 0.90, confirming that the multi-factor measurement model performs considerably better than a baseline independence null model.
• Tucker-Lewis Index (TLI): 0.895. Approaching the 0.90 threshold, the TLI reflects a coherent multi-dimensional structure across five latent constructs.

Factor Structure and Scoring Architecture

The factor model substantiates a two-tiered scoring system. The primary tier comprises the five distinct first-order factors: Digital Access, Digital Technology Usage, Digital Literacy, Digital Health Literacy, and Learnability. The secondary tier configures these into two clinically actionable composite metrics:

1. The General DHRQ Score: Synthesizes the first four structural domains (Access, Usage, Literacy, Health Literacy), providing a quantitative index of current, point-in-time digital operational readiness.
2. The Learnability Score: Consists exclusively of the fifth domain (Items 16–20), providing an independent metric of behavioral plasticity, self-efficacy, and responsiveness to instructional intervention.

Instrument / Measurement Tool

The structured technical specifications of the Digital Health Readiness Questionnaire are detailed below:

• Test Name: Digital Health Readiness Questionnaire (DHRQ)
• Acronym: DHRQ
• Authors: Martijn Scherrenberg, Maarten Falter, Toshiki Kaihara, Linqi Xu, Mayke van Leunen, Hareld Kemps, Hanne Kindermans, and Paul Dendale
• Publication Year: 2023
• Construct Measured: Multi-dimensional digital health readiness, encompassing physical access, technical execution skills, information navigation, health-specific digital literacy, and educational learnability.
• Test Type: Patient-Reported Outcome Measure (PROM); clinical screening and research assessment tool.
• Administration Format: Originally validated as a self-administered paper-and-pencil instrument; fully adaptable for electronic capture via clinical web portals, tablets, or native smartphone health interfaces.
• Target Population: Adult clinical outpatients and inpatients across all adult life stages, including young adults (18–29), adults in their thirties and middle age (30–64), aged adults (65–84), and very old adults (≥85 years). Validated primarily within cardiovascular medicine.
• Completion Time: Approximately 5 to 8 minutes.
• Number of Items: 20 items divided into five designated conceptual sections:
• Section A: Digital access (Items 1–4)
• Section B: Usage of digital technology (Items 5–9)
• Section C: Digital Literacy (Items 10–12)
• Section D: Digital Health Literacy (Items 13–15)
• Section E: Learnability (Items 16–20)
• Response Formats:
• Section A (Digital Access, Items 1–4): 5-point frequency/availability scale (1 = Never / I don’t have access / don’t have device; 2 = Rarely; 3 = Sometimes; 4 = Often; 5 = Daily).
• Section B (Item 6): 5-point social media frequency scale (1 = No; 2 = Rarely; 3 = Sometimes; 4 = Often; 5 = Daily).
• Sections B, C, D, E (Items 5, 7–20): Standard 5-point Likert agreement scale (1 = Strongly disagree; 2 = Disagree; 3 = Neither agree nor disagree; 4 = Agree; 5 = Strongly agree).
• Scoring and Interpretation Procedures:
• All items are scored in a direct positive direction from 1 to 5. There are no reverse-coded items.
• Subscale scores are computed by calculating the arithmetic mean or sum of items within each respective section.
• General DHRQ Score: Derived from the summation or mean of Items 1 through 15 (potential raw sum range: 15 to 75). Higher scores denote superior digital access, functional competencies, and health literacy.
• Participant Learnability Score: Calculated independently from Items 16 through 20 (potential raw sum range: 5 to 25). Higher scores indicate greater cognitive openness, high self-efficacy, and receptivity toward educational interventions.
• Clinical Decision Threshold: Patients exhibiting low General DHRQ scores but high Learnability scores are prime candidates for structured onboarding programs (e.g., dedicated digital nurse consultations, guided setup sessions). Patients scoring low in both metrics represent populations at high risk for digital exclusion who require continued conventional care pathways.
• Available Linguistic Versions: Simultaneously developed and validated in Dutch and English.

Permissions & Fee and Test Year

The Digital Health Readiness Questionnaire was officially published in 2023. In accordance with principles of open science and equitable access to clinical health innovations, the instrument is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

• Commercial Use: May be adapted or integrated into clinical workflows or commercial remote patient monitoring platforms, provided appropriate attribution is maintained according to CC BY 4.0 terms.
• Research & Clinical Use: Free of charge. Academic investigators, hospitals, and clinical practices may utilize, reproduce, and administer the DHRQ without prior written permission or licensing fees.
• Original Source Citation: Users must credit the original authors and cite the seminal validation article published in the Journal of Medical Internet Research (Scherrenberg et al., 2023).

References

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman and Company.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.

Karnoe, A., Furstrand, D., Christensen, K. B., Norgaard, O., & Kayser, L. (2018). Assessing competencies needed to engage with digital health services: Development of the eHealth Literacy Assessment Toolkit. Journal of Medical Internet Research, 20(5), Article e178. https://doi.org/10.2196/jmir.8347

Kayser, L., Karnoe, A., Furstrand, D., Batterham, R., Christensen, K. B., Elsworth, G., & Osborne, R. H. (2018). A multidimensional tool based on the eHealth Literacy Framework: Development and initial validity testing of the eHealth Literacy Questionnaire (eHLQ). Journal of Medical Internet Research, 20(2), Article e36. https://doi.org/10.2196/jmir.8371

Norman, C. D., & Skinner, H. A. (2006). eHEALS: The eHealth Literacy Scale. Journal of Medical Internet Research, 8(4), Article e27. https://doi.org/10.2196/jmir.8.4.e27

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Scherrenberg, M., Falter, M., Kaihara, T., Xu, L., van Leunen, M., Kemps, H., Kindermans, H., & Dendale, P. (2023). Development and internal validation of the Digital Health Readiness Questionnaire: Prospective single-center survey study. Journal of Medical Internet Research, 25, Article e41615. https://doi.org/10.2196/41615

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

13. Items of the Scale (Questionnaire)