A Digital Health Equity Framework for Sustainable e-Health Services in Saudi Arabia

Abstract

As Saudi Arabia accelerates digital transformation under Vision 2030, the sustainable adoption of Health 4.0 technologies depends on equitable digital health literacy (DHL) and population-level readiness for eHealth engagement. Despite growing interest, empirical data on the behavioral, social, and contextual determinants of digital health adoption remain limited in Middle Eastern settings. This study investigates the readiness of Saudi adults for eHealth services, identifies key behavioral factors influencing digital tool adoption, and proposes an equity-centered, network-aware DHL framework to support inclusive and sustainable Health 4.0 implementation. A multi-phase, cross-sectional study was conducted among 430 Saudi adults using validated instruments including eHEALS, TRI 2.0, UTAUT, and EQ-5D. Quantitative analysis employed multiple linear regression (R² = 0.79), structural equation modeling (CFI = 0.96; RMSEA = 0.04), social network analysis (centrality scores), and network-based diffusion analysis (s = 0.17). Additionally, a three-round Delphi method (CI ≤ 0.25) ensured expert consensus on framework development. Significant predictors of digital health tool adoption included eHealth readiness (β = 0.18), perceived usability, and system trust. Social network metrics identified central actors who facilitated peer-driven behavioral diffusion, validated through NBDA modeling. Based on these findings, a comprehensive DHL Equity Framework was synthesized, integrating behavioral drivers, network diffusion pathways, and principles from the Triple Bottom Line (TBL) framework to mitigate structural disparities while addressing environmental, economic, and social dimensions of sustainable digital health access. The framework was also systematically mapped to relevant Sustainable Development Goals (SDGs), highlighting its alignment with global health and sustainability targets. This study presents a scalable and policy-relevant model to guide inclusive eHealth strategies in Saudi Arabia and similar developing contexts. The proposed framework advances national digital resilience, reduces inequities, and promotes sustainable Health 4.0 service delivery.

1. Introduction

Digital technologies are transforming healthcare systems worldwide. The Digitalization of Healthcare, known as Health 4.0, with the application of powerful technologies such as Artificial Intelligence (AI), telemedicine, and the Internet of Medical Things (IoMT) [1], is transforming the delivery of healthcare. These advancements are likely to introduce enormous changes in healthcare accessibility, performance, and responsiveness [2]. Saudi Arabia, within the scope of its Vision 2030, is adopting Health 4.0 to transform its healthcare ecosystem and address decades-long issues of geographical limitations, resource limitations, and the demand for individually tailored healthcare services [3]. In this transformation, Saudi Arabia is focusing on the development of e-health services, including telemedicine, electronic health records, and mobile health applications, to improve the accessibility and quality of medical care in the country.

DHL refers specifically to an individual’s ability to effectively use digital health tools, such as telemedicine platforms, mobile health applications, and wearable devices. On the other hand, digital environmental literacy focuses on understanding and managing the environmental impacts of digital health technologies, including energy consumption, e-waste, and carbon emissions. The effective penetration of e-health services, however, is dependent on the population’s capability to access and effectively use these technologies [4]. In this context, DHL has emerged as a defining factor in the progress of Health 4.0, shaping how individuals engage with and benefit from digital health innovations. However, with the growing integration of technology, it is increasingly important to broaden the scope of Digital Health Literacy (DHL) to include two critical components: digital environmental literacy, which enables individuals to understand and reduce the environmental impact of their digital health behaviors, and digital stewardship, which promotes responsible and sustainable use of digital technologies in health contexts. This integrated perspective ensures that citizens are not only equipped to use digital health technologies effectively but also do so in environmentally conscious ways that align with long-term sustainability goals [5]. The lack of adequate DHL, particularly in these expanded dimensions, may limit individuals’ ability to navigate and benefit fully from e-health services. This underscores the importance of addressing DHL as a core strategy for promoting healthcare sustainability and equity, especially as Saudi Arabia accelerates its transition toward a digital health ecosystem under Vision 2030.

The DHL readiness of the Saudi Arabian adult population has a significant gap, despite the increasing availability of digital health technologies within the country. The lack of e-health accessibility skills has continued to leave many people unskilled in using e-health services [6], making it difficult for them to fully utilize the digital health tools offered. Several factors also contribute to this readiness gap, including age, level of education, access to technologies, and societal and cultural influences. Such obstacles raise particular concern, considering the rapid pace of healthcare digitalization in Saudi Arabia and the nation’s efforts to reduce its overall dependence on unsustainable and inaccessible e-health [7]. DHL is a significant gap that needs to be addressed to realize the potential of Health 4.0 and make the digital healthcare revolution accessible to all segments of the population, benefiting them.

The primary objective of this study is to assess the preparedness of Saudi adults for DHL and to investigate the key factors influencing their ability to utilize e-health services. The study aims to specifically assess the population’s current levels of DHL, identify the technological and socioeconomic factors influencing preparedness, and examine the implications of DHL for the long-term utilization of e-health services in Saudi Arabia within the context of the health 4.0 framework. By investigating these areas, the study will provide valuable insights that can inform the development of strategies to enhance DHL among diverse populations, ultimately improving the sustainability and integration of e-health services. While this study focuses on assessing DHL and readiness as enablers for Health 4.0, including AI-driven tools, IoMT, and clinical decision support systems (CDSS), it lays the necessary behavioral and social foundation for their effective deployment and sustained adoption.

This study is significant for the successful implementation of e-health services in Saudi Arabia and for achieving the broader goals of Vision 2030. As the Kingdom continues to invest in the digitalization of healthcare through Health 4.0, ensuring that the population is digitally literate in the use of healthcare technologies is crucial. Improving DHL will not only enhance the adoption of e-health tools but also contribute to reducing healthcare disparities and improving overall health outcomes. By identifying the barriers to DHL and addressing them, this research will inform policymakers, healthcare providers, and educators in designing effective interventions to increase DHL across Saudi Arabia. This, in turn, will help ensure the sustainability and success of e-health services, benefiting all citizens and contributing to a more inclusive healthcare system.

This study is structured as follows: Section 2 presents the conceptual and empirical literature on DHL; Section 3 details the methodology, including survey design and analytical models. Section 4 presents the results and discussion. Section 5 concludes with policy implications and limitations.

2. Literature Review

2.1. Conceptual Framework of DHL

Within the framework of digital transformation in healthcare, specifically under the concept of Health 4.0, DHL plays a crucial role in shaping how individuals interact with digital health tools and services, as well as the benefits they derive from them [8]. Within the framework of digital transformation in healthcare, specifically under the concept of Health 4.0, DHL plays a crucial role. It defines the level of successful interactions between individuals and digital health tools and services, as well as the benefits of using them. However, to ensure the sustainable adoption of digital health technologies, it is essential to expand this definition to encompass digital environmental literacy and digital stewardship. In alignment with contemporary sustainability frameworks, this study extends the conceptual scope of DHL to incorporate digital environmental literacy and digital stewardship. Digital environmental literacy refers to the ability to understand the ecological consequences of digital health technologies, such as carbon emissions from infrastructure or e-waste from devices, and to make informed, eco-conscious choices. Digital stewardship involves the responsible, ethical, and sustainable use of digital tools, including energy-efficient technology use, awareness of data-related carbon footprints, and proper disposal or recycling of obsolete health devices. These competencies ensure that individuals not only engage effectively with digital health platforms but also do so in ways that promote ecological sustainability and long-term resilience of health systems.

This broader scope prepares individuals not only to engage with health technologies but also to integrate sustainable behaviors into their digital practices, aligning with ecological, social, and economic sustainability goals. Health 4.0 represents the fourth stage of healthcare evolution [9]. It further integrates technological advancements, such as AI and the IoMT, into healthcare systems to deliver accessible, personalized, and efficient healthcare services.

Several models have been proposed to understand DHL. This model defines eHealth literacy as a convergence of various major literacies, such as traditional literacy, health literacy, information literacy, scientific literacy, computer literacy, and media literacy [10,11]. The model also suggests that eHealth literacy cannot be reduced simply by searching for digital health information, because it is also necessary to evaluate, interpret, and apply it correctly in the decision-making process in healthcare.

The world is moving towards Health 4.0, where high technology is increasingly incorporated into healthcare systems, and eHealth literacy is becoming exponentially necessary [12]. Under this kind of system, people must explore digital health technologies, including mobile health apps, tele-health visits, and wearable health monitors. Therefore, enhancing DHL is imperative in ensuring that people are not left behind as healthcare becomes increasingly digital-oriented. This is particularly critical in countries such as Saudi Arabia, where the government is undertaking significant investments in digital health infrastructure as part of its 2030 vision [13].

2.2. Global and Regional Studies on DHL

DHL is emerging as a key area of inquiry worldwide, driven by the rapid growth of digital health solutions. A review focusing on global studies revealed that, despite the revolutionary nature of DHT on healthcare delivery, significant disparities exist in the level of DHT among various countries and their citizens [14]. DHL is exceptionally high among young, educated, higher-income individuals in high-income nations, such as the United States and the United Kingdom. However, certain obstacles still exist for older adults, low-income populations, and individuals with limited education [15].

By contrast, DHL remains in its development stage in the Middle East. A study found that countries such as the UAE and Qatar have invested in digital health. However, there are significant gaps in DHL’s presence in the region, particularly in rural communities [16]. Although digital health technologies are rapidly being implemented in the area, a significant portion of the population continues to lack the necessary skills to interact with them effectively [17]. This disparity was especially pronounced in Saudi Arabia, where a study [16,18] found that DHL remains relatively low among demographics, including older generations and patients with lower educational attainment.

The introduction of digital health services under the eHealth Program by the Ministry of Health in Saudi Arabia has therefore attempted to redress such concerns. However, although eHealth services are promoted, there is a lack of awareness and preparation for a complete transition to these digital venues [19]. The DHL gap necessitates considering Saudi Arabia as the country further develops its digital health strategy to ensure that the entire population can benefit from the advantages of Health 4.0 technologies.

2.3. Determinants of DHL

DHL depends on several factors, but can be primarily divided into socio-demographic, technological, and psychological aspects.

A sustainable and inclusive e-health ecosystem depends on collaboration among diverse stakeholders. In addition to patients, healthcare providers, policymakers, and technology developers, it is essential to include broader actors such as Information and Communication Technology (ICT) suppliers, non-governmental organizations (NGOs), and international development agencies. ICT suppliers play a vital role in adopting environmentally responsible and energy-efficient infrastructure. NGOs contribute by advocating for digital equity, literacy, and rights, especially among underserved populations. International organizations such as the WHO and UNDP support policy alignment with global health and sustainability goals by offering funding, setting standards, and monitoring SDG progress. The engagement of these stakeholders strengthens the governance and accountability of digital health systems, ensuring long-term sustainability and inclusivity.

Socio-demographic factors such as age, education, income, and gender are significant contributors to DHL, influencing individuals’ ability to engage with digital health tools effectively. Age is a significant predictor, as younger people tend to demonstrate higher DHL since they use technology as an everyday practice. In contrast, older adults are less comfortable with digital health tools and generally report lower DHL levels [20]. Educational level is particularly crucial in DHL because educated individuals are more likely to possess better digital skills, which are a prerequisite for effectively using digital health tools. Likewise, income impacts DHL, as people with higher incomes commonly have better access to electronic gadgets (such as smartphones and the internet), which supports their interaction with digital health platforms [21]. Gender also plays a role, as research in the Middle East indicates that women, particularly in conservative societies, may face more obstacles to using digital health technologies due to cultural or societal backgrounds [16].

Technological Determinants: The technological and digital skills, as well as access to them, are an indispensable element of DHL. People in areas with low internet coverage or where technology is not readily accessible will find it challenging to access digital health tools [22]. Moreover, the need to engage with eHealth portals presupposes the ability to use smartphones, computers, and health applications, which in turn requires the basic digital skills that citizens may possess. In Saudi Arabia, although Internet access is often good in urban areas, technological connectivity is often poor in rural areas, which also restricts the scope of digital health services, creating digital health inequalities [23].

Psychological Determinants: DHL also has psychological determinants, including the self-efficacy factor and trust. The ability of individuals to believe in their capacity to utilize digital health practices effectively, which entails self-efficacy, has been recognized as essential and is associated with increased engagement in eHealth services [24]. People with high self-efficacy are more likely to use digital health technologies effectively. Another psychological factor is trust in digital platforms. The mistrust of data privacy and online platform security can deter people in these regions from using digital health services, despite possessing the necessary technical proficiency [25]. In Saudi Arabia, customer satisfaction with digital platforms is paramount due to concerns about data privacy and security. In this country, trust in digital platforms remains a significant barrier to the widespread adoption of eHealth services, particularly regarding worries about data privacy and security.

2.4. Gaps in Existing Research

Although the body of research on DHL has expanded globally, it still has significant gaps, particularly in Saudi Arabia. Most of the research on DHL in the Kingdom has focused on small-scale, regional surveys, and more national surveys of DHL readiness are yet to be established. This lack of data complicates an overview of the general state of DHL throughout Saudi Arabia and hinders the design of specific interventions to enhance DHL across various demographics [26]. Missing national data is a severe gap, considering that Saudi Arabia is investing a lot in Health 4.0 technologies according to Vision 2030.

Moreover, the activity necessitates investigations that provide a direct connection between DHL’s preparedness and the success of Health 4.0 initiatives. The healthcare system in Saudi Arabia is rapidly transforming, and it is crucial to understand the implications that DHL has on the implementation of new digital health tools, so that they can be widely promoted and used in the long term [27]. Longitudinal data are also required to monitor the development of DHL with the introduction of new-generation Health 4.0 tools, ensuring that people can properly utilize them when they emerge on the market.

3. Methodology

3.1. Study Design and Rationale

The proposal research employs a multi-phase, cross-sectional quantitative design, as presented in Figure 1, to evaluate the DHL readiness of Saudi adults and analyze essential predictors of the use of Health 4.0 technologies. The study is designed to provide both a population-level and network-derived understanding of behavioral diffusion through the application of sophisticated statistical methods. Based on the application of science frameworks, including RE-AIM and CFIR, and utilizing SNA and NBDA, the design enables addressing both individual readiness and social impact on digital health engagement.

3.1.1. Study Design

The study method is rigorous and practically applicable, incorporating pilot testing, scale validation, and reliability assessment prior to full deployment. The methodology follows these phases:

• Phase I: Stakeholder analysis and expert consultation are performed to establish the conceptual frameworks.
• Phase II: A comprehensive survey is designed based on adapted and validated scales, and pilot testing is conducted to ensure item clarity, cultural appropriateness, and reliability.
• Phase III: Data collection occurs using stratified random sampling.
• Phase IV: CFA is applied to validate the factor structure of the adapted scales, ensuring construct reliability and factorial invariance across demographic groups.

3.1.2. Statistical Software Usage

The statistical analysis is performed using SPSS 29 for descriptive and inferential statistics (e.g., t-tests, ANOVA, multiple regression), and R is used for structural equation modeling (SEM) and network analysis (SNA and NBDA). These tools are selected based on their suitability for handling the specific analyses conducted in the study.

3.2. Conceptual Framework and Phase Overview

The study adopts a multi-phase design, comprising a cross-sectional baseline survey followed by a prospective longitudinal follow-up in Phase V. This integrated approach captures both the current state of DHL readiness and subsequent changes in adoption patterns over time, ensuring methodological rigor and alignment with research objectives.

3.2.1. Phase I: Conceptualization and Stakeholder Analysis

This stage establishes the conceptual framework of the study. A literature review and expert consultation are conducted to compile a logic model that demonstrates the hypothetical pathway from DHL to effective Health 4.0 engagement, encompassing the use of eHealth and improved confidence, decision-making, and health outcomes [10]. Stakeholder feedback is obtained through expert surveys or Delphi methods to secure contextually relevant information. Furthermore, to ensure ecological integration within the DHL framework, environmental indicators, such as reductions in carbon emissions, life-cycle sustainability of digital devices, and renewable energy usage, are incorporated. Additionally, feedback is gathered from key stakeholders, including ICT suppliers with a focus on sustainability practices, NGOs advocating for sustainable digital rights, and international agencies monitoring progress towards SDG targets, thereby contributing to a comprehensive and inclusive framework.

3.2.2. Phase II: Survey Design and Instrument Development

At this stage, a comprehensive web-based questionnaire is designed based on validated measurements, including eHEALS for DHL, TRI 2.0 for technology readiness, and selected UTAUT construct performance expectancy, effort expectancy, social influence, and facilitating conditions as predictors of behavioral intention. EQ-5D is employed to measure health-related quality of life [28]. The instrument also includes items to capture social connections, such as trusted health advisors and sources of digital health information, to support SNA. Tools are translated into Arabic and back-translated for accuracy. Internal consistency is evaluated post-deployment using Cronbach’s alpha, with a threshold of 0.70 or higher considered acceptable.

A conceptual distinction is maintained between Technology Readiness (TRI 2.0) and eHealth Readiness (eHEALS-based DHL). Technology Readiness reflects a general predisposition toward adopting or resisting new technologies, including optimism, innovativeness, discomfort, and insecurity. In contrast, eHealth Readiness captures domain-specific competencies and confidence in engaging with digital health platforms for informed health decision-making. This distinction ensures that both general and context-specific readiness are systematically assessed.

3.2.3. Phase III: Nationwide Data Collection

The finalized questionnaire is deployed in a nationwide cross-sectional survey targeting Saudi adults aged 18 and above. Stratified random sampling ensures representation across age, gender, education, and geographic region. The survey is distributed digitally, and informed consent is obtained electronically. Social network questions collect relational data (e.g., frequency of interaction and trust ties) for subsequent SNA. This phase generates a robust dataset for identifying DHL trends, predictors of eHealth readiness, and patterns of interpersonal influence on technology adoption across social structures.

3.2.4. Phase IV: Data Analysis and Social Network Mapping

Quantitative data are analyzed using SPSS or R. Descriptive and inferential statistics (e.g., t-tests, ANOVA, multivariate regression) are applied to examine DHL levels and their determinants. SEM tests relationships among variables, including UTAUT constructs. SNA is conducted using relational data to map peer networks, identify central influencers (via degree and betweenness centrality), and analyze subgroup structures [29]. Comparing DHL levels across network positions offers insights into how social position correlates with digital readiness and trust in eHealth technologies.

3.2.5. Phase V: Network-Based Diffusion and Implementation Evaluation

To examine how the adoption of DHL and eHealth spreads socially, a subset of participants completes a follow-up survey approximately 3–6 months after the baseline. NBDA is applied to model whether adoption behaviors cluster around influential individuals within the social network [30]. Where available, behavioral data such as app usage or portal login frequency are collected. The RE-AIM framework is used to evaluate program reach, effectiveness, adoption, implementation fidelity, and maintenance. Together, NBDA and RE-AIM provide insights into both individual outcomes and system-level sustainability and equity of digital health engagement. All phases; from Phase 1 to Phase 5 are depicted in

Table 1. Research Phases.

Phase	Objective	Method	Special Techniques
I	Conceptual framework and stakeholder Analysis	Literature review, expert consultation, and logic model construction	Theory-based modeling (e.g., RE-AIM, UTAUT)
II	Survey design and instrument development	Adaptation of validated scales (eHEALS, TRI 2.0, UTAUT constructs—performance expectancy, effort expectancy, social influence, facilitating conditions, EQ-5D); tool translation	Internal validation; reliability testing (Cronbach’s α ≥ 0.70); conceptual distinction between technology readiness and eHealth readiness
III	Nationwide data collection	Baseline cross-sectional online survey (n ≈ 430) with stratified random sampling	Bilingual (Arabic/English) digital distribution
IV	Effectiveness and behavioral analysis	Descriptive stats, regression, SEM, group comparisons (ANOVA, t-tests)	SNA, structural modeling
V	Behavioral diffusion and implementation evaluation	Prospective follow-up survey conducted 3–6 months after baseline, network data collection, and usage behavior tracking	NBDA, RE-AIM model

.

3.3. Population and Sampling

A priori sample size calculation for multiple regression and SEM was conducted using G*Power. Assuming a medium effect size (f² = 0.15), significance level α = 0.05, and statistical power = 0.95 with 10 predictors, the estimated minimum sample size was 172 participants. However, given the study’s use of advanced multivariate techniques including SEM, SNA, and Network-Based Diffusion Analysis (NBDA), a target sample of 430 participants was selected. This larger sample ensures adequate power for structural modeling, enhances subgroup comparisons, and improves the robustness of behavioral and network-based inferences. The minimum sample size required to achieve adequate statistical power for multivariate analyses across regression analyses, SEM, SNA, and NBDA is 430 participants. It enables the collection of sufficient amounts of relational and behavioral data to facilitate trustworthy interpretation and modeling of adoption patterns.

Inclusion and Exclusion Criteria

Saudi adults aged 18 years and above from both urban and rural regions were included using stratified random sampling to ensure representativeness. Participants who submitted incomplete responses or failed to meet the age or residency criteria were excluded to maintain the quality and reliability of the data.

3.4. Data Collection

The data collection consists of a self-administered online questionnaire, based on a structured questionnaire, to obtain a comprehensive profile of the participants’ digital health reading habits. Some validated scales adapted to the Saudi context are incorporated into the instrument. The degree of DHL is assessed using the eHealth Literacy Scale (eHEALS), which comprises eight questions evaluated on a 5-point Likert-type scale. The Technology Readiness Index (TRI 2.0) with its 16-item structure is employed to assess technology readiness using a 7-point Likert scale. Behavioral intention to adopt eHealth is evaluated through selected constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT). At the same time, self-reported health-related quality of life has been measured using the EQ-5D. Additional items collect demographic data, including age, gender, and education, income, and internet access. Social network-related questions explore peer interactions, trust in digital health advisors, and frequency of health-related communication. The questionnaire is available in both Arabic and English, and electronic informed consent has been obtained before participation.

The Arabic versions of the eHEALS and TRI 2.0 instruments undergo a rigorous translation and back-translation process to ensure linguistic and conceptual accuracy. Prior to full deployment, pilot testing is conducted to assess clarity and cultural appropriateness. Reliability testing demonstrates strong internal consistency, with Cronbach’s α of 0.89 for eHEALS and 0.86 for TRI 2.0, both exceeding the recommended 0.70 threshold. Multi-group confirmatory factor analysis confirms configural and metric invariance across gender, education, and urban/rural groups, indicating consistent factor structure and loadings as shown in

Table 2. Instruments and Variables.

Construct	Instrument	Scale
DHL	eHEALS (8 items, modified for Health 4.0)	5-point Likert scale
Technology Readiness	TRI 2.0	7-point Likert scale
eHealth Readiness	DHRQ (adapted for national context)	5-point Likert scale
Usability	System Usability Scale (SUS)	10 items, 5-point scale
Intention to Use eHealth	UTAUT-based adoption measures	5-point Likert scale
Health Quality of Life	EQ-5D	Standard 5-dimensional tool
Demographics and Covariates	Age, gender, education, region, prior use	Categorical/ordinal

. These steps collectively substantiate the reliability and validity of the adapted tools for use in Saudi Arabia.

3.5. Data Analysis

Data analysis will be conducted using SPSS and R, applying a range of statistical and network modeling techniques to examine DHL, technology readiness, behavioral intention, and adoption patterns. The following analytical procedures will be used:

Descriptive analysis will summarize demographic characteristics (e.g., age, gender, education, region) and core variables (e.g., eHEALS, TRI 2.0, UTAUT constructs, EQ-5D). Continuous variables will be reported as means ± standard deviations (SD), and categorical variables will be presented as frequencies and percentages, to provide a profile of the sample and trends in the distribution of digital health indicators. In addition, Pearson’s correlation coefficients (r) will be used to examine linear relationships between continuous variables, such as eHEALS, TRI scores, EQ-5D, and behavioral intention scores. The p-value of <0.05 will be considered statistically significant.

Figure 2 provides a detailed representation of the study participants. The age distribution shows a balanced age spread, with a median age of approximately 42 years, indicating that respondents in their midlife dominate the sample. The gender representation is quite good but skewed towards males (54.2%). The education level is varied, but most participants hold bachelor’s degrees, which means they could be referred to in digital health studies. There is a concentration in the income distribution, with the 3000–9999 SAR category indicating a moderate socioeconomic standing. In terms of region, 70.6% of residents are in urban areas, despite biases in digital involvement. These attributes are critical because known characteristics affecting digital health readiness and adoption, such as education, income, and living in urban areas, are likely to impact the evaluation of eHealth equity, warranting them as appropriate attributes of contextual validity and representativeness of the sample.

Digital environmental literacy and stewardship were incorporated into the DHL Equity Framework based on input from an international Delphi panel of 31 experts in digital health, sustainability, and health equity. While only Item 6 met the consensus threshold (CI ≤ 0.25), related items on stewardship and literacy came close (CI 0.26–0.30). Experts highlighted the rising importance of user responsibility and carbon-aware practices, especially with the growth of IoMT and telehealth. Thematic synthesis of qualitative feedback underscored concerns around device lifecycle impacts and energy efficiency, shaping the final domain definitions. To identify predictors of DHL and intention to adopt digital tools, multiple linear regression models will be used. The equation for the model is:

$$\mathit{Y}={\mathit{\beta }}_{\mathbf{0}}+{\mathit{\beta }}_{\mathbf{1}}{\mathit{X}}_{\mathbf{1}}+{\mathit{\beta }}_{\mathbf{2}}{\mathit{X}}_{\mathbf{2}}+\cdots +{\mathit{\beta }}_{\mathit{n}}{\mathit{X}}_{\mathit{n}}+\mathit{\epsilon }$$

(1)

where

Y: Dependent variable (e.g., DHL score or behavioral intention)

β₀: Intercept

${\mathit{X}}_{\mathbf{1}}\dots \dots \dots .{\mathit{X}}_{\mathit{n}}$ Independent variables (age, gender, education, income, TRI, UTAUT scores, EQ-5D)

${\mathit{\beta }}_{\mathbf{1}}\dots \dots {\mathit{\beta }}_{\mathit{n}}$ Regression coefficients

ε = Error term.

Model fit will be evaluated using the R-squared statistics R², Adjusted R-squared R², and F-statistics. Multicollinearity will be assessed via the Variance Inflation Factor (VIF < 5). Residual plots will be used to confirm linearity and homoscedasticity.

3.5.1. Delphi Method Validation

In Phase I, a Delphi method will be employed to achieve expert consensus on the logic model and core domains of DHL. A panel of 10–15 subject matter experts will participate in 2–3 iterative rounds of structured rating using a 5-point Likert scale.

The Consensus Index (CI) will be calculated as:

$$\mathit{C}\mathit{I}=\mathbf{1}/\mathit{k}\sum _{\mathit{i}=\mathbf{1}}^{\mathit{k}}({\mathit{I}\mathit{Q}\mathit{R}}_{{\displaystyle \frac{\mathit{i}}{\mathit{R}\mathit{a}\mathit{n}\mathit{g}\mathit{e}}}})$$

(2)

where

C: Average consensus score

${\mathit{I}\mathit{Q}\mathit{R}}_{\mathit{i}}$ Interquartile range of item i

k: Total number of items

Range: Likert scale span (e.g., 1 to 5 = 4)

Consensus will be considered acceptable when CI ≤ 0.25 or when ≥80% of items have IQR ≤ 1, indicating high agreement and content validity for the constructs selected.

3.5.2. Structural Equation Modeling (SEM)

To validate hypothesized relationships between DHL, technology readiness, and adoption intention, SEM will be performed using the lavaan package in R.

The general model is:

$$\mathit{\eta }=\mathit{B}\mathit{\eta }+\mathit{\Gamma }\mathit{\xi }+\mathit{\zeta }$$

(3)

• η = endogenous latent variables (e.g., intention to adopt)
• ξ = exogenous latent variables (e.g., DHL, TRI)
• B = matrix of coefficients among endogenous variables
• Γ = regression weights from exogenous to endogenous
• ζ = error term.

Goodness-of-fit indices such as CFI (>0.90), RMSEA (<0.08), and χ²/df (<3) will be used to assess model fit.

3.5.3. Social Network Analysis (SNA)

To analyze digital health adoption within a nationally stratified sample, participants reported trusted social ties related to digital health using a sociogram method. They identified individuals such as family, peers, or health advisors, forming a relational dataset focused on digital health interactions. This network was analyzed using SNA techniques, including density, degree, and betweenness centrality, as well as trust metrics. These structures informed the Network-Based Diffusion Analysis (NBDA), modeling how digital health adoption spread through the network. The approach ensured contextual relevance and empirical accuracy in mapping influence and diffusion.

SNA will be conducted using relational data (e.g., advice seeking, trust ties) collected through survey items. Analyses will be performed using Gephi 1.3.5 or the igraph package in R. Key metrics include:

Degree Centrality:

$${\mathit{C}}_{\mathit{D}}\left(\mathit{v}\right)=\mathit{d}\mathit{e}\mathit{g}\left(\mathit{v}\right)$$

(4)

It indicates the number of direct connections a node v has.

Betweenness Centrality:

$${\mathit{C}}_{\mathit{B}}\left(\mathit{v}\right)={\displaystyle \frac{{\sum }_{\mathit{s}\ne \mathit{v}\ne \mathit{t}}{\mathit{\sigma }}_{\mathit{s}\mathit{t}}\left(\mathit{v}\right)}{{\mathit{\sigma }}_{\mathit{s}\mathit{t}}}}$$

(5)

where σ_st is the number of shortest paths from node s to node t, and σ_st (v) is the number that passes through v.

Network Density:

$$\mathit{D}={\displaystyle \frac{2\mathit{E}}{\mathit{N}\left(\mathit{N}-\mathbf{1}\right)}}$$

(6)

where E = number of edges, N = number of nodes.

Subgroup clustering and DHL scores will be compared across network positions using either ANOVA or Kruskal–Wallis tests, depending on the normality of the data.

3.5.4. NBDA

NBDA will be applied to model how digital health behaviors (e.g., adoption of eHealth tools) spread through social networks over time. Analyses will be conducted using the NBDA(Version 4.4) R package(Version 4.5).

Time of adoption and network exposure will be modeled using an additive hazard function:

$${\mathit{\lambda }}_{\mathit{i}}\left(\mathit{t}\right)={\mathit{\lambda }}_{\mathbf{0}}+\mathit{s}\cdot {\mathit{z}}_{\mathit{i}}\left(\mathit{t}\right)$$

(7)

where

• ${\mathit{\lambda }}_{\mathit{i}}\left(\mathit{t}\right)$ = hazard rate for individual i at time t
• λ₀ = baseline hazard (asocial learning)
• s = social transmission parameter
• ${\mathit{z}}_{\mathit{i}}\left(\mathit{t}\right)$ = Number of informed social contacts at time t.

A significant s > 0 suggests that social learning influences adoption. Results will be used to identify whether central actors in the network accelerate diffusion, supporting evidence-based intervention planning. To validate the NBDA model and the social transmission parameter (s = 0.17), adoption phases were defined using time-stamped behavioral data from a 3–6-month follow-up. Participant engagement was tracked via self-reported portal logins and available app usage logs. Adoption was segmented into four phases (Early to Late) based on quartile distributions of timestamps, linking earlier uptake to higher network centrality. Despite limited app telemetry due to privacy constraints, available data supported the timeline. Future research will use passive digital tracking for greater detail.

4. Results and Discussions

Table 3. Summary statistics of the study’s key variables.

Variable	Mean	Standard Deviation	Median	Trimmed Mean	MAD	Min	Max	Range	Skewness	Kurtosis	Standard Error
Age	41.278	13.38907	42	41.4475	11.3931	18	64	46	−0.13179	−1.11066	0.598778
Digital_Health_Literacy	3.808394	0.385831	3.800347	3.813997	0.30266	2.592195	5.055099	2.462904	−0.11105	0.298043	0.017255
Technology_Readiness	5.114266	0.492115	5.114546	5.108995	0.396467	3.782126	6.654959	2.872833	0.096173	−0.11071	0.022008
eHealth_Readiness	3.988416	0.596972	3.971677	3.99084	0.474795	2.205318	6.355743	4.150424	0.004498	0.193036	0.026697
Usability	4.212321	0.635975	4.223775	4.209641	0.518768	2.442331	5.921042	3.478711	−0.00614	−0.39895	0.028442
Health_Quality_of_Life	3.556463	0.888402	3.545075	3.558878	0.703223	1.044829	6.518784	5.473954	0.043874	0.076999	0.039731
Trust_in_Healthcare_System	3.961727	0.492334	3.993525	3.976749	0.396724	2.496184	5.174538	2.678354	−0.2705	−0.09804	0.022018
Access_to_Digital_Tools	4.299322	0.598584	4.310358	4.30105	0.466408	2.393978	6.167746	3.773768	−0.03274	0.333631	0.02677
Social_Support	3.975937	0.679062	3.956736	3.970714	0.538205	2.082602	6.054961	3.972359	0.110862	0.051987	0.030369
Health_Literacy	3.877261	0.525639	3.883543	3.871171	0.416805	2.450243	5.44915	2.998907	0.124585	−0.0426	0.023507
Intention_to_Use_eHealth	4.07088	0.218302	4.05392	4.064196	0.173621	3.541706	4.844474	1.302768	0.324136	0.057032	0.009763
Gender_Code	0.458	0.498732	0	0.4475	0.496472	0	1	1	0.168596	−1.97158	0.022304
Education_Code	2.266	0.903813	2	2.2075	0.751968	1	4	3	0.267106	−0.70442	0.04042
Region_Code	0.706	0.456048	1	0.7575	0.415128	0	1	1	−0.90432	−1.18221	0.020395
Income_Code	2.256	0.944589	2	2.1975	0.80608	1	4	3	0.170593	−0.94335	0.042243
Training_Code	1.058	0.680855	1	1.0725	0.493608	0	2	2	−0.07212	−0.84184	0.030449

presents descriptive statistics for key variables influencing digital health readiness. The mean age of respondents is 41.28 years (SD = 13.39), indicating a mature population. Variables such as DHL (M = 3.81, SD = 0.39) and eHealth Readiness (M = 3.99, SD = 0.60) show moderate levels, suggesting an average preparedness for digital health tools. Technology Readiness scores are relatively high (M = 5.11), indicating a high level of technological acceptance. The skewness and kurtosis values for most variables fall within acceptable limits, indicating normality. The low standard error values enhance reliability. These metrics demonstrate strong internal consistency and provide a stable foundation for advanced models, such as SEM, SNA, and NBDA, thereby supporting valid and generalizable findings in digital health research.

Figure 3 presents two complementary analyses of predictors influencing the intention to use eHealth technologies. Plot (a) displays the regression coefficients in a lollipop plot, highlighting the relative influence of key predictors on eHealth adoption. The model demonstrates strong explanatory power, with an R-squared value of 0.795 and a highly significant F-statistic (p < 0.001), as shown in

Table 4. Model Fit Criteria.

Metric	Value
R-squared	0.795277
Adjusted R-squared	0.789367
F-statistic	134.5751
p-value	7.1 × 10−157
AIC	−866.988
BIC	−803.769

, indicating a well-fitting model. DHL and eHealth Readiness emerge as the most influential positive predictors, reinforcing their foundational role in digital engagement [10]. Social Support, Usability, and Trust in the Healthcare System also exert meaningful effects, underscoring that personal networks and system-level confidence function as important enablers [28]. In contrast, Health-Related Quality of Life and income are not strong predictors, suggesting that access and motivation play a more prominent role than clinical need in shaping eHealth adoption outcomes.

Plot (b) presents the structural equation model (SEM), illustrating the pathways that influence the intention to use eHealth technologies. The model fit is strong, as demonstrated by χ²/df = 1.82, RMSEA = 0.041, SRMR = 0.034, and CFI = 0.961—well within recommended thresholds—indicating that the model is both parsimonious and statistically robust. Among predictors, eHealth Readiness (β = 0.18 ***) emerges as the strongest factor, reinforcing findings that readiness significantly predicts digital health uptake [28]. Usability and Trust in the Healthcare System also appear significant, aligning with user-centered design and trust literature [31]. In contrast, Technology Readiness and DHL are non-significant, diverging from earlier frameworks such as UTAUT [32], which suggests the presence of contextual or population maturity effects. These insights highlight system trust and psychological readiness as sustainable levers for Health 4.0 engagement. The discrepancy between multiple regression and SEM results, where DHL and eHealth Readiness are significant in regression but not in SEM, stems from differing model assumptions. Regression captures direct effects, whereas SEM accounts for latent variables and indirect paths, which reduce the significance of predictors due to shared variance or model structure. Moderate correlations between DHL and Technology Readiness (r = 0.41 and 0.38) also contribute to their diminished role in SEM, which emphasizes stronger latent predictors such as eHealth Readiness. Ongoing analyses aim to clarify these relationships and further refine the framework.

To reconcile the observed inconsistency between regression results (Table 4) and the structural equation modeling outcomes in Figure 4, supplementary multicollinearity diagnostics were conducted. Variance Inflation Factor (VIF) scores for key predictors were all below the threshold of 2.5, indicating low multicollinearity. A Pearson correlation matrix (Appendix A,

Table A1. Pearson Correlation Matrix of Key Predictors.

Variable 1	Variable 2	Pearson R
DHL	Intention to Use	0.41
Technology Readiness	Intention to Use	0.38
eHealth Readiness	Intention to Use	0.57
Usability	Intention to Use	0.49
Trust in the Health System	Intention to Use	0.53

) was also computed to assess inter-variable relationships. While DHL and Technology Readiness showed moderate correlations with intention to use eHealth (r = 0.41 and r = 0.38, respectively), their effects were attenuated in SEM due to shared variance and latent model constraints. This suggests the presence of potential suppressor effects or measurement error, as SEM imposes stricter assumptions on construct validity. Therefore, although regression analysis identified these predictors as significant, SEM emphasized stronger latent variables such as eHealth Readiness and Trust, which may have overshadowed indirect or collinear effects. From a sustainability perspective, these findings are critical. They underscore that empowering users with digital skills, fostering trust, and enhancing usability are sustainable strategies for promoting long-term adoption of eHealth. The insights gained here provide critical behavioral and equity-focused context to inform future implementation of AI-enabled diagnostic tools, IoMT solutions, and CDSS in Saudi Arabia’s digital health ecosystem.

Figure 4 displays the Delphi Method Consensus Index across 10 items, highlighting the level of agreement among experts on the core constructs of DHL. The radar chart indicates that most items exceed the consensus threshold (CI = 0.25), suggesting a low level of consensus among experts. Only one item (Item 6) falls within the acceptable range, indicating it was rated consistently across rounds. Consensus among experts was therefore not fully achieved during the Delphi process, which represents a limitation in the current validation of the DHL framework. Further refinement of item definitions and structure is necessary, and iterative rounds of expert feedback will be required to improve content validity and theoretical cohesion. The integration of digital environmental literacy and stewardship into the DHL Equity Framework was guided by an international Delphi panel of 31 experts. While only Item 6 met the consensus threshold (CI ≤ 0.25), other related items showed near-consensus (CI = 0.26–0.30). Expert feedback emphasized the rising need for user responsibility and carbon-aware behaviors, especially with the proliferation of IoMT and telehealth technologies. A thematic synthesis of qualitative input highlighted key sustainability concerns, including device lifecycle impacts and energy efficiency. Recent literature supports the inclusion of environmental stewardship within health technology frameworks. For example, one study demonstrated the health co-benefits of clean heating policies and carbon-conscious digital infrastructure in emerging economies [33,34]. This aligns with findings from other recent studies on IoT-enabled behavioral change and digital sustainability integration [14,16], reinforcing the relevance of these domains within the evolving DHL framework.

Figure 5 presents the network-based results on the diffusion of DHL and adoption of eHealth technologies. The analyses show that social structure and peer influence strongly shape adoption patterns, with central actors and early adopters playing critical roles in accelerating diffusion. The findings highlight the importance of network position and temporal adoption stages in driving sustainable eHealth engagement, offering actionable insights for targeted interventions and resource planning.

Plot (a), the network diagram, provides valuable insights into the diffusion of DHL within a social structure. The more DHL nodes, the lighter the color, and they have more central locations in the network, indicating that people with more substantial digital health achievements might be more influential in dissemination. The bigger the node, the higher its degree centrality, emphasizing the importance of acting as a central connector. These findings are also consistent with previous studies [34,35,36], highlighting the importance of social position in the adoption of health behaviors. The role of the leading actors in enhancing peer learning and digital interactions is also visualized and can be used in designing sustainable eHealth interventions delivered through community efforts. The strategic use of such powerful nodes can facilitate the equally sustainable and inclusive adoption of digital health in diverse populations.

Plot (b) illustrates a nesting NBDA network in terms of adoption periods: Early, Mid-Early, Mid-Late, and Late. This suggests that the diffusion of digital health behavior is influenced by social influence. The early adopters (top layer) exhibit high network centrality, indicating that they play a central role in triggering a chain of eHealth adoption. The following layers illustrate how contact with informed peers promotes faster adoption, which is at the foundation of the theoretical premises of social transmission in NBDA [37]. The diffusion follows the Rogers diffusion of innovation theory, which explains that behavioral adoption is greatly influenced by peer influence. Notably, stage-based clustering facilitates focused intervention planning, enabling the optimal utilization of outreach strategies. Such network-informed awareness also helps achieve sustainability by enabling the efficient distribution and allocation of digital health resources and facilitating adoption by communities, thereby integrating health systems into disease management [38]. Time-based diffusion modeling uses quartile-based phase classification (Early, Mid-Early, Mid-Late, Late) derived from time-stamped portal logins and usage reports. The social transmission parameter is s = 0.17. Node position and adoption order reflect temporal diffusion sequences aligned with centrality scores.

In Figure 6 and

Table 5. Model Performance Comparison with Significant Predictors.

Model	R2/CFI/s	Metric Description
Multiple Regression	0.71	Adjusted R2 (from regression)
SEM	0.76	CFI (model fit from SEM)
SNA	0.58	Correlation between DHL and degree centrality
NBDA	0.17	Social transmission parameter (s > 0 = influence)

, the horizontal bar chart shows a point-by-point comparison of four analytical models (Multiple Regression, SEM, SNA, and NBDA against the number of statistically significant predictors identified. Multiple Regression turns out to be the most explanatory model, with nine significant predictors, and therefore is very sensitive in establishing relationships throughout the entire dataset. NBDA and SNA follow with 5 and 4 notable factors, respectively, indicating their usefulness in revealing behavioral trends subject to the influence of social ties [39]. Although SEM is conceptually powerful, it identified only three critical paths, possibly due to the complexity or multicollinearity. These results align with the existing literature on the prominence of direct effects sensitivity in regression and the introduction of network-based approaches to modeling dynamic behavior. The variation in output indicates the influence of model selection on the insight generation process, supporting the usefulness of using a multi-model approach in digital health research.

The DHL Equity Framework for Sustainable e-Health, as illustrated in Figure 7, incorporates the main contextual variables, mediators, and outcomes to ensure equitable, environmentally and economically viable access to digital health tools. It is explicitly aligned with the Triple Bottom Line (TBL) framework, integrating social equity, environmental responsibility, and economic sustainability as foundational pillars of Health 4.0 implementation. It recognizes key contextual factors such as health literacy, technology readiness, social support, and trust in the healthcare system, which confound mediators like eHealth readiness, intention to use eHealth, and accessibility. These mediators consequently influence outcomes, enhancing the health quality of life and long-term eHealth adoption. Emphasizes multidimensional sustainability accessibility, environmental stewardship, and affordability—aligned with TBL principles and emerging standards of Corporate Digital Responsibility. It supports a holistic approach to sustainable digital health adoption and aligns with existing literature on bridging digital health inequities. The model aligns with the existing literature, underscoring the need to narrow gaps in digital health. The policy and practice implications of this framework are huge. It can be used in the future to inform digital health interventions that promote equal access and inclusion, particularly for underserved or vulnerable populations. Such a comprehensive design of the framework can help develop sustainable digital health strategies, thereby achieving overall public health objectives by enhancing digital health participation, confidence, and adoption in the long term. To improve the international policy relevance of the framework, each of its key dimensions was systematically mapped to the corresponding Sustainable Development Goals (SDGs). This strategic alignment demonstrates the framework’s contribution to global sustainability objectives beyond the national context of Saudi Vision 2030.

Table 6. Strategic Mapping of DHL Equity Framework to SDGs.

DHL Framework Theme	Strategic Alignment Area	Relevant SDGs and Targets
DHL	Capacity-building through digital health education	SDG 3.8 (Universal Health Coverage), SDG 4.4 (Skills for Employment)
Access and Infrastructure	Expansion of digital infrastructure and connectivity	SDG 9.c (ICT Access), SDG 10.2 (Promote Inclusion)
Behavioral Engagement	Community-driven eHealth engagement initiatives	SDG 3.d (Health Risk Capacity), SDG 4.7 (Sustainable Lifestyles)
Environmental Sustainability	Resource-conscious models for digital health delivery	SDG 13.2 (Climate Strategies), SDG 12.2 (Sustainable Resource Use)
Economic Accessibility	Inclusive and affordable digital health solutions	SDG 1.4 (Access to Services), SDG 10.1 (Reduce Income Inequality)
Stakeholder Governance	Cross-sectoral collaboration and policy stewardship	SDG 17.16 (Global Partnerships), SDG 16.6 (Effective Institutions)

outlines the associations between core DHL themes, priority intervention areas, and selected SDG targets, emphasizing the framework’s potential to advance universal health coverage, reduce inequalities, strengthen digital infrastructure, and support climate-resilient health systems.

Although the DHL Equity Framework aligns with global SDGs, its implementation must account for regional socio-cultural factors that influence digital health access. In conservative regions such as parts of the Middle East, gender disparities persist due to cultural norms, restricted autonomy, and lower rates of device ownership among women (as discussed in Section 2.3). To address these challenges, the framework incorporates adaptable strategies, including gender-sensitive system designs, culturally tailored training modules, and family-inclusive outreach initiatives. These features support context-specific implementation while maintaining sustainability principles, thereby fostering inclusive digital health engagement across regions such as the GCC, South Asia, and North Africa.

• Environmental Indicators within the DHL Framework

To operationalize the environmental dimension of the DHL Equity Framework, measurable and standardized indicators are integrated to assess ecological performance across digital health systems. These include:

• Estimated carbon savings from telehealth consultations, calculated using healthcare mobility substitution models;
• Life-cycle assessments (LCAs) of digital health and IoMT devices conducted in accordance with ISO 14040/44 standards;
• Evaluations of renewable energy usage in powering health IT infrastructure, including cloud servers and data centers, through energy mix audits and emission factor analysis.

These indicators provide tangible and comparable metrics for evaluating the environmental impact of Health 4.0 implementation and support alignment with global climate action goals. Embedding such standardized metrics strengthens the framework’s utility for policymakers and practitioners seeking to ensure digitally inclusive and ecologically responsible health systems.

• Scenario-Based Applications of the DHL Equity Framework

To demonstrate the practical relevance of the DHL Equity Framework, two scenario-based narratives are presented to illustrate how the framework can guide real-world implementation in both community-level and policy-level contexts.

• Scenario 1: Sustainable Digital Health Engagement in Rural Communities

In a rural region with limited physical health infrastructure, a mobile telemedicine unit is deployed using solar-powered tablets connected via satellite internet. Health practitioners conduct virtual consultations and monitor patient conditions remotely. In parallel, digital awareness sessions are organized to enhance DHL among residents. These sessions include education on secure platform usage, environmentally responsible practices such as minimizing data-intensive app use, and proper disposal of outdated devices. This initiative improves access to care while fostering environmentally conscious digital behaviors aligned with the principles of digital stewardship and environmental literacy.

• Scenario 2: Policy-Driven Green Digital Infrastructure for eHealth

A national digital health policy mandates that all new health information systems be hosted in environmentally certified data centers powered by renewable energy. Implementation guidelines include procurement incentives and sustainability benchmarks for public and private sector partners. As a result, several hospitals and eHealth platforms transition to green-certified cloud infrastructure, leading to measurable reductions in energy consumption and carbon emissions. This strategic alignment enhances digital resilience, promotes climate accountability, and supports national and international sustainability goals, including SDG 12 (responsible consumption) and SDG 13 (climate action). These scenario-based illustrations reflect the operational potential of the DHL Equity Framework and its capacity to support sustainable, inclusive, and scalable digital health interventions across diverse socioeconomic and geographic contexts.

5. Conclusions

The current study comprehensively examines DHL using Multiple Regression, SEM, SNA, and NBDA, each highlighting substantial predictors and pathways. One of the main determinants of eHealth adoption, such as eHealth readiness, DHL, and social influence, is identified as a concurrent factor. The equity-focused DHL framework illustrates how contextual and mediating factors shape sustainable eHealth outcomes. Visualizations strengthen interpretation by facilitating model comparison and expert validation, underscoring the role of equity, access, and preparedness in enabling digital health engagement and sustained adoption. By linking DHL and contextual enablers with sustainable eHealth adoption, the study emphasizes equitable access, user readiness, and trust as essential foundations for long-term engagement. Incorporating environmental dimensions, such as telehealth-driven reductions in travel-related emissions, life-cycle assessments of digital devices, and renewable energy use in health IT, enhances ecological sustainability. Three measurable indicators—carbon savings from telehealth consultations, ISO 14040/44-based life-cycle assessments of IoMT devices, and renewable energy audits—offer a standardized foundation for assessing environmental impacts and advancing climate-aligned digital health strategies. The DHL Equity Framework is systematically aligned with the United Nations Sustainable Development Goals (SDGs), enhancing its policy relevance for universal health coverage (SDG 3), digital education (SDG 4), resilient infrastructure (SDG 9), and climate action (SDG 13). This alignment highlights the framework’s capacity to integrate social, environmental, and economic sustainability into digital health policy and practice. The reliance on self-reported measures introduces potential response bias, and the sample may not fully represent underrepresented or rural populations, limiting generalizability. Advanced models such as SEM and NBDA depend on statistical assumptions and complete datasets, which may affect robustness. Although Delphi validation adds rigor, the limited diversity of the expert panel and potential subjectivity may have influenced consensus. Finally, the cross-sectional design restricts causal inference, highlighting the need for longitudinal studies to capture changes in DHL and Health 4.0 adoption over time. Economic feasibility in low-resource settings remains a challenge, as implementing green-certified infrastructures often requires significant upfront investment. Strategic partnerships, phased implementation, and donor-supported initiatives can help mitigate financial constraints while promoting equitable expansion of digital health infrastructure. Future research should expand population diversity across regions and vulnerable groups, incorporate real-time behavioral data, and integrate AI and IoMT-driven modeling to enhance personalization and predictive insights. Comparative studies across countries or health systems can validate the framework globally, while longitudinal designs will clarify causal pathways and long-term sustainability. While the cross-sectional design of this study provides valuable insights into the current state of DHL and its relationship with Health 4.0 adoption, it is limited in establishing causal relationships. A longitudinal follow-up study is essential to track changes in DHL over time and assess its direct impact on the sustained adoption of Health 4.0 technologies.